The hSSB1 orthologue Obfc2b is essential for skeletogenesis but dispensable for the DNA damage response in vivo

We recently became aware of panel duplications in Supplementary Figures S6 and S7, due to pasting errors of similar flow cytometry images during figure preparation. This concerned the first two panels in the top row of Suppl. Fig S6A; second and third panel in the bottom row of Suppl. Fig S7B; and third and fourth panel in the bottom row of Suppl. Fig S7C.Furthermore, we noted a typographical error in Suppl. Fig S7B (top row, sixth plot), where the indicated percentage was wrongly given as 1.4%, instead of 1.1%. These errors did not change the results or the interpretation of the data. We deeply apologize to the scientific community for any confusion these errors may have caused. The updated appendix is published with this corrigendum.The original FlowJo analysis plots related to the affected figures are published as source data with this corrigendum. Please note that initial labelling of the experiments in these files referred to the official gene name Obfc2b informally as hSSB1, and Obfc2a‐shRNAs as ‘sh1’ and sh4’.Open in a separate windowFigure S6AOriginalOpen in a separate windowFigure S6ACorrectedOpen in a separate windowFigure S7BOriginalOpen in a separate windowFigure S7BCorrectedOpen in a separate windowFigure S7COriginalOpen in a separate windowFigure S7CCorrected     

A Novel Improved Gram Staining Method Based on the Capillary Tube

In this work, an exploratory study was conducted to examine Gram staining based on the capillary tube. Each Gram staining step for all bacterial strains tested was completed in capillary tubes. The results showed that different Gram staining morphologies were clearly visible in the capillary tubes. The results presented here demonstrated that the improved method could effectively distinguish between Gram-positive and Gram-negative bacteria, and only small volumes of reagents were required in this method. Collectively, this efficient method could rapidly and accurately identify the types of bacteria. Therefore, our findings could be used as a useful reference study for other staining methods.Key words: Gram staining, capillary tube, bacteria, and glass slide

Since Hans Christian Joachim Gram reported a staining method in 1884 (Gram 1884), such a technique has experienced more than a century of development and has become frequently used in bacteriology. From 1940 to 1960, Gram staining’s clinical application reaches its peak (Kass 1987). In recent years, several automated instruments for Gram staining have also been applied for microbiological analysis (Baron et al. 2010; Li et al. 2020). With the development of modern science and technology, some new technologies are expected to replace Gram staining. For example, Sizemore et al. (1990) have developed an alternative Gram staining technique using a fluorescent lectin. Later on, several fluorescent Gram staining methods have been established, and some Gram staining techniques suitable for live bacterial suspension have been described (Mason et al. 1998; Fife et al. 2000; Forster et al. 2002; Kwon et al. 2019). Sharma et al. (2020) have found that acridine orange fluorescent staining is more sensitive than the Gram staining. Besides, Berezin et al. (2017) have established a method for detecting Gram-negative bacteria based on enhanced Raman spectroscopy. Lemozerskii et al. (2020) have also reported a method of bacterial discrimination using an acoustic resonator. However, Gram staining is still an vital detection method in practical application for many microbiologists and clinicians due to its rapidity and simplicity (Thompson et al. 2017; Jahangiri et al. 2018; Li et al. 2018a).Over the years, Gram staining has been modified for many times, such as the Brown-Hopps method, Brown-Brenn method, and Gram-Twort method (Brown and Brenn 1931; Brown and Hopps 1973; Peck and Badrick 2017), and these approaches as mentioned earlier are widely used in anatomical pathology laboratories. Through the comparison of various improved methods, it is found that Gram’s original four-step method is still used, and some researchers have adopted the three-step method, while its basic principle has not been changed. As reported by Huang and Cui (1996), the three-step Gram staining method combines the two steps of alcohol decolorization and re-staining procedure in one step. Although Gram staining is one of the most commonly used detection methods in clinical microbiology laboratories, many clinicians are skeptical of its results due to differences in operators, low control, and standardization (Samuel et al. 2016; Thomson 2016). Researchers have made efforts to improve the Gram staining’s accuracy and reliability over the past few years, such as repeated training and standardization of the staining procedure (Thomson 2016; Siguenza et al. 2019). In this study, we developed a standardized Gram staining procedure for bacterial identification using a capillary tube. A modified Gram staining method based on the capillary tube has not yet been reported to the best of our knowledge. Therefore, we proposed a novel improved Gram staining method to improve the accuracy of the detection results and Gram staining efficiency.Eight bacterial strains, Staphylococcus aureus, Escherichia coli, Bacillus subtilis, Bacillus Licheniformis, Serratia marcescens, Vibrio parahaemolyticus, Lactobacillus delbrueckii ssp. bulgaricus, and Streptococcus thermophilus were provided by the Laboratory of Microbial Engineering, College of Life Science, Luoyang Normal University. L. bulgaricus and S. thermophilus were inoculated into skim milk culture medium and maintained at 37°C for 12 h. S. marcescens, B. Licheniformis, E. coli, B. subtilis, V. parahaemolyticus, and S. aureus were inoculated into beef peptone agar slants and maintained at 37°C for 16 h.Capillary tubes with an internal diameter of 0.5 mm and a length of 100 mm were purchased from the Instrument Factory of West China University of Medical Sciences. Gram staining reagent was obtained from the Anhui Chaohuhongci Medical Equipment Co., Ltd.Procedure: (1) One or two drops of sterile water were placed in the center of a clean glass slide. An inoculating loop was hold in a flame until it was red-hot and then allowed to cool approximately 30 seconds. Subsequently, a loop of culture was transferred to the center of the slide. The sample was spread onto the slide using the inoculating loop, and a small volume of bacterial suspension was automatically transferred into the capillary tube.(2) The capillary tube was then heated by passing over a flame for several times until the liquid was completely evaporated. The capillary tube was naturally cooled in the air for several seconds.(3) One end of the capillary tube was hold upward, and the crystal violet solution was automatically transferred to the capillary tube, followed by standing for 1 minute. The remaining crystal violet solution of the capillary tube was then transferred to absorbent paper. The capillary tube was washed in a gentle and indirect stream of tap water for a few seconds, and samples were dried on absorbent paper.(4) One end of the capillary tube was hold upward, and Gram’s iodine solution was automatically transferred to the capillary tube, followed by standing for 1 minute. Subsequently, the capillary tube was washed using the same procedure as described above.(5) One end of the capillary tube was hold upward, and 95% ethanol was automatically transferred to the capillary tube, followed by standing for 30 seconds. Subsequently, the capillary tube was washed using the same procedure as described above.(6) One end of the capillary tube was hold upward, and the Safranin solution was automatically transferred to the capillary tube, followed by standing for 30 seconds to 1 minute. The subsequent procedure was the same as described above. Besides, conventional Gram staining was carried out according to the instructions from the reagent kit. According to the instructions, Gram-negative cells are in pink to red, and Gram-positive cells show a purple or blue color when observed under a microscope.The Gram staining is always the “first-stage criteria” in the preliminary identification of bacterial species according to their cell walls (Li et al. 2018b). Eight different bacterial species were examined to investigate our approach, and the strains were selected according to the Gram staining pattern. Gram-negative bacteria E. coli, V. parahaemolyticus, and S. marcescens were examined. Gram-positive bacteria S. thermophilus, L. bulgaricus, S. aureus, B. licheniformis and B. subtilis were also assessed. Fig. ​Fig.1,1, ​,2,2, and ​and33 illustrate the results of Gram staining of E. coli, V. parahaemolyticus, and S. marcescens, respectively. Fig. ​Fig.4,4, ​,5,5, ​,6,6, ​,7,7, and ​and88 show the Gram staining results of S. thermophilus, L. bulgaricus, S. aureus, B. subtilis, and B. licheniformis, respectively. These results were compared with those obtained using a glass slide for Gram staining. No matter spherical or rod-shaped or not, all bacterial strains could be differentiated into two classifications: Gram-positive and Gram-negative. Comparing these results, we found that the results obtained by the capillary tube method were consistent with the conventional Gram staining approach. It was worth mentioning that in contrast to direct heat fixation of bacteria on glass slides, heat fixation by passing the capillary tube over a flame should be carried out quickly and carefully. If the capillary tube was overheated, it might cause the capillary tube to rupture, and it is easy to blur the field of vision, making it challenging to observe the staining result (Fig. ​(Fig.9).9). Therefore, before the experiments, it is better to conduct a preliminary experiment and achieve the desired results.Open in a separate windowFig. 1.The Gram staining results of E. coli. A – Capillary sample, B – Glass slide sample.Open in a separate windowFig. 2.The Gram staining results of V. parahaemolyticus. A – Capillary sample, B – Glass slide sample.Open in a separate windowFig. 3.The Gram staining results of S. marcescens. A – Capillary sample, B – Glass slide sample.Open in a separate windowFig. 4.The Gram staining results of S. thermophiles. A – Capillary sample, B – Glass slide sample.Open in a separate windowFig. 5.The Gram staining results of L. bulgaricus. A – Capillary sample, B – Glass slide sample.Open in a separate windowFig. 6.The Gram staining results of S. aureus. A – Capillary sample, B – Glass slide sample.Open in a separate windowFig. 7.The Gram staining results of B. subtilis. A – Capillary sample, B – Glass slide sample.Open in a separate windowFig. 8.The Gram staining results of B. Licheniformis. A – Capillary sample, B – Glass slide sample.Open in a separate windowFig. 9.The microstructure of the overheated capillary tube.Several studies (Chimento et al. 1996; Wada et al. 2012; Li Zhu 2018b) have already pointed out that the property of the bacterial cell wall determines whether the organism will be Gram-positive or Gram-negative, and it plays a role in the choice of antibiotics when infection occurs. Since it has frequently been observed that not all bacteria react in the same manner to such staining procedure (Hale and Bisset 1956), it is necessary to make more tests upon a representative selection of Gram-positive and Gram-negative bacteria in future studies.Molecular biology techniques and high-precision measurement systems have been successfully developed, and they can distinguish bacterial types in clinical samples and improve microbial detection (Klaschik et al. 2002; Dolch et al. 2016; Kim et al. 2018). However, it is still urgently needed to develop a rapid and straightforward Gram staining approach to detect bacteria, especially for those who have only primary experimental conditions. Our results indicated a promising method for bacterial differentiation using the capillary tube as a carrier. Successful differentiation required only small volumes of reagents, and the results were achieved within a few minutes by applying an optical microscope. In addition, the method proposed in this paper had reference value to other staining methods requiring expensive reagents.In the present study, the improved Gram staining method was developed based on the pure cultures, and it was only a comparison of the staining results between known Gram-negative and Gram-positive bacteria in a glass slide and capillary tube. In order to improve the accuracy and stability of the results, future study is necessary to detect more bacterial species. In addition, the modified method was not applicable for direct Gram staining of clinical samples. In the future, it may have a positive effect by developing a special method for processing clinical samples.The experimental results demonstrated that an improved Gram staining method was suitable for differentiating the strains tested in our laboratory. The method could rapidly discriminate Gram-positive and Gram-negative bacteria. Besides, the method only required small volumes of reagents. A much more comfortable and reproducible Gram staining approach can be developed for microbiology research based on our studies.     

Healthy herds in the phytoplankton: the benefit of selective parasitism

The impact of selective predation of weaker individuals on the general health of prey populations is well-established in animal ecology. Analogous processes have not been considered at microbial scales despite the ubiquity of microbe-microbe interactions, such as parasitism. Here we present insights into the biotic interactions between a widespread marine thraustochytrid and a diatom from the ecologically important genus Chaetoceros. Physiological experiments show the thraustochytrid targets senescent diatom cells in a similar way to selective animal predation on weaker prey individuals. This physiology-selective targeting of ‘unhealthy’ cells appears to improve the overall health (i.e., increased photosynthetic quantum yield) of the diatom population without impacting density, providing support for ‘healthy herd’ dynamics in a protist–protist interaction, a phenomenon typically associated with animal predators and their prey. Thus, our study suggests caution against the assumption that protist–protist parasitism is always detrimental to the host population and highlights the complexity of microbial interactions.Subject terms: Microbial ecology, Water microbiology

Animal predators can exert overall positive effects on the health of prey populations by removing individuals with suboptimal health [1, 2] in a manner that has been termed ‘healthy herd’ dynamics [3]. While such top-down processes are well-established in animal ecology [1–3], they have largely been unconsidered in microbe-microbe interactions.Protist–protist parasitism is widespread in the marine environment [4] and is generally considered to be detrimental to host populations [5, 6]. However, despite their ubiquity, the ecophysiological impact of protist–protist parasitism remains poorly understood. An important case that necessitates investigation is protist parasitism of diatoms, which have limited representation with culture-dependent model systems despite the significance of diatoms in marine ecosystem functioning and global primary production [7].We observed and isolated a heterotrophic protist growing epibiotically on moribund and dead Chaetoceros sp. diatoms from a summer bloom at Station L4 in the Western English Channel off Plymouth (UK) (Fig. 1A, B; Supplementary Figs. 1 and 2; Supplementary Methods). Single-cell picking achieved diatom and parasite co-cultures and uninfected host diatoms. The 18 S rRNA gene V4 region of the protist (termed ‘ThrauL4’) identified the epibiont as a novel thraustochytrid (Stramenopila; Labyrinthulomycota; Thraustochytrida) (Supplementary Fig. 3). Searching for ThrauL4 18 S rRNA gene homologues in the Ocean Sampling Day dataset revealed that the parasite has a wide distribution in coastal temperate regions (Supplementary Fig. 4).Open in a separate windowFig. 1Growth experiments demonstrate that thraustochytrids preferentially target and grow on unhealthy diatom cells.A Differential interference contrast (DIC) image of Chaetoceros chain exhibiting different degrees of infection by ThrauL4. Uninfected cell (un), a lightly infected cell (li), heavily infected cells (hi) and a dead, empty frustule (d). Scale bar = 20 µm. B Scanning Electron Micrograph (SEM) of a Chaetoceros diatom swarmed by ThrauL4. Scale bar = 5 µm. C ThrauL4 growth dynamics on a selected range of diatoms and dinoflagellates (Alexandrium minutum and Prorocentrum minimum) (±SEM, n = 3). D Chaetoceros growth with ThrauL4 (±SEM, n = 5). Dashed lines demarcate the lag (1), exponential (2) and stationary (3) phases of Chaetoceros growth. E Time-lapse of Chaetoceros-ThrauL4 showing ThrauL4 colonising unhealthy cells. Asterisk = cytoplasmic bleb from unhealthy diatom. Arrowhead = initial thraustochytrid colonisation. Timestamp = HH:MM:SS. Difference in the abundance (F) and prevalence (G) of parasites in healthy (control), stressed and dead Chaetoceros populations (n = 5) inoculated with ThrauL4 following heat stress exposure. ANOVA Tukey’s HSD n.s p > 0.05 (not significant), *p < 0.05, **p < 0.01, ***p < 0.001. H Example diatom exposed to different laser powers used to generate individual Chaetoceros cells of varying health. Red channel overlay demarks chlorophyll autofluorescence. Scale bar = 5 µm. I Time taken for individual diatom cells (n = 15) exposed to varying laser treatments to be colonised by ThrauL4. J Diagrammatic representation of the proposed diatom-thraustochytrid interaction cycle based on time-lapse microscopy observations (see Supplementary Videos).Stable Chaetoceros-ThrauL4 co-cultures permitted the characterisation of ThrauL4 internal structures (Supplementary Figs. 5 and 6), epibiotic growth (Fig. 1A, B; Supplementary Figs. 7 and 8) and infection dynamics (Fig. 1C, D). ThrauL4 also attached to other diatoms (Odontella sinensis, Ditylum brightwellii and Coscindodiscus sp.) in a similar manner to Chaetoceros sp. but not dinoflagellates (Fig. 1C; Supplementary Fig. 9).The proportion of diatom cells with ThrauL4 attached increased when Chaetoceros sp. cells entered the stationary growth phase (Fig. 1D). Time-lapse microscopy revealed the dynamic nature of the ThrauL4-diatom interaction (Fig. 1E, Supplementary Movies 1–6), with the motile ThrauL4 apparently targeting physiologically ‘unhealthy’ cells identified by cytoplasmic blebbing prior to colonisation (Fig. 1E).We set out to test the hypothesis that ThrauL4 targeted unhealthy diatoms using population-level ecophysiology experiments. When introduced to heat-stressed diatom populations, ThrauL4 had a higher fitness (i.e. became more abundant) and infected more Chaetoceros sp. cells than when exposed to healthy un-stressed diatoms (Fig. 1F, G), confirming more optimal growth of the parasite amongst unhealthy diatom populations. Furthermore, selective targeting was also demonstrated at the single-cell level using laser-damaged individual cells and time-lapse microscopy (Fig. 1H, I). 80% of stressed cells and 60% of dead cells were colonised by ThrauL4 during the 30 min experimental period, whereas diatoms in healthy control populations were un-colonised.These results led us to investigate the physiological impact of thraustochytrid parasitism on host diatom populations by comparing the dynamics and health of parasite exposed and non-exposed Chaetoceros sp. populations (Fig. 2A–C). Based on the previous growth experiments showing ThrauL4 proliferation during the diatom stationary phase (Fig. 1D), Chaetoceros sp. cultures grown to their stationary phase after 7 d were chosen to mimic environmental bloom decline. Using the photosynthetic quantum yield (Fv/Fm) as a proxy for overall diatom health [8], after 8 d, the parasitized Chaetoceros sp. populations were consistently healthier than those in the control non-exposed populations (Fig. 2A). Diatom population density was similar in both treatments (Fig. 2B) and parasite prevalence peaked after 8 days (Fig. 2C). In a separate experiment to investigate the role of genotype specificity in ThrauL4 parasitism, we generated a clonal Chaetoceros sp. population by single-cell picking and exposed the population to ThrauL4 cultures growing independently from diatoms. Although the clonal population declined in health more rapidly overall, ThrauL4 parasitism also resulted in healthier populations (Fig. 2D–F) suggesting that these results are a not an artefact of genotype specificity and succession.Open in a separate windowFig. 2Selective targeting of unhealthy diatom cells by thraustochytrids improves the overall health of the diatom population.A–C Population dynamics of the Fv/Fm (A) and total number (B) of stationary Chaetoceros diatoms for control and parasitized diatom populations over the experimental period (±SEM, n = 5). Welch’s t-test *p < 0.05, **p < 0.01, ***p < 0.001. The parasite prevalence did not exceed about a third of the total population (C) (±SEM, n = 5). Parasites added at 0 d. In a separate experiment (D–F), a clonal Chaetoceros population was generated. Population dynamics of the Fv/Fm (D), total number (E) and infection prevalence (F) of stationary Chaetoceros diatoms for control and parasitized populations made clonal by single-cell picking (±SEM, n = 5). Significance values as above. Parasites added at 0 day. Taken together these results indicate that preferential thraustochytrid parasitism of unhealthy diatoms strengthens the overall health of the population therefore providing evidence for the ‘healthy herd’ hypothesis in a phytoplankton population, which is summarised diagrammatically in (G).By removing physiologically weaker individuals from the population, the remaining cells will constitute an overall healthier population. However, other mechanisms may also promote an overall healthier diatom population. It may be that selective parasitism relieves nutrient competition between unhealthy and healthy individuals. In the natural environment, diatom-diatom competition is a major growth limiting factor [9, 10] and removing the pressure exerted by weaker cells may allow the population to be more robust. It is also possible that the thraustochytrid could be ‘cleaning’ the population by preventing the build-up of toxic waste products or the proliferation of detrimental co-culture bacteria in an analogous way to how carrion removal by vultures prevents the spread of diseases to mammals [11]. In addition, thraustochytid parasitism could accelerate nutrient recycling by releasing nutrients from dying cells. The consequences of physiology-selective diatom parasitism should be assessed in the marine environment, including impacts at the community scale and in the context of ecosystem functioning.The proposed influence of thraustochytrid parasitism on diatom population health is summarised in Fig. 2G. We suggest that this thraustochytrid-diatom interaction provides evidence of ‘healthy herd’ dynamics in a protist–protist interaction, an ecological phenomenon typically associated with animal predator-prey interactions [3]. As we show here with ThrauL4, animal predators such as lions [12], cougars [13], African wild dogs [14], and wolves [15] have been shown to target prey with suboptimal health. The ‘healthy herd’ hypothesis states that by selective predation on unhealthy prey, predators increase the overall health of the prey population by increasing resource availability or by removing potential carriers of disease [3]. Evidence for ‘healthy herd’ dynamics where predation generates healthier prey populations has also been demonstrated in lobster-sea urchin [16], fish-Daphnia [17], and fox-grouse [18] predator–prey systems. Here, we provide analogous supportive evidence from a marine protist–protist system.‘Heathy herd’ dynamics between protists challenges the assumption that protist–protist parasitism is always detrimental to the host population and raises caution in this assumption in ecosystem modelling or inference from molecular ecology surveys (e.g., metabarcoding). Our results have demonstrated the potential complexity of protist–protist symbioses, highlighting the value of culture-based experimentation and the importance of developing model co-culture systems in resolving complex ecological interactions. The underpinning biology and ecological importance in natura of such interactions now require further investigation.     

Loss of symbiont infectivity following thermal stress can be a factor limiting recovery from bleaching in cnidarians

Increases in seawater temperature can cause coral bleaching through loss of symbiotic algae (dinoflagellates of the family Symbiodiniaceae). Corals can recover from bleaching by recruiting algae into host cells from the residual symbiont population or from the external environment. However, the high coral mortality that often follows mass-bleaching events suggests that recovery is often limited in the wild. Here, we examine the effect of pre-exposure to heat stress on the capacity of symbiotic algae to infect cnidarian hosts using the Aiptasia (sea-anemone)-Symbiodiniaceae model system. We found that the symbiont strain Breviolum sp. CS-164 (ITS2 type B1), both free-living and in symbiosis, loses the capacity to infect the host following exposure to heat stress. This loss of infectivity is reversible, however, a longer exposure to heat stress increases the time taken for reversal. Under the same experimental conditions, the loss of infectivity was not observed in another strain Breviolum psygmophilum CCMP2459 (ITS2 type B2). Our results suggest that recovery from bleaching can be limited by the loss of symbiont infectivity following exposure to heat stress.Subject terms: Microbial ecology, Biodiversity

Cnidarians including reef-building corals harbor endosymbiotic dinoflagellates of the family Symbiodiniaceae, from which they derive the majority of their energy. Therefore, the breakdown of the symbiotic relationship, a process known as bleaching, can result in the host starving. However, bleaching is not always lethal because symbiont densities can recover [1, 2]. Recovery from bleaching is driven mainly by symbiotic algae that remain within the bleached corals (the residual population) dividing and spreading throughout the colony [3], and also possibly through the recruitment of free-living symbiotic algae from the external environment [4]. In the last few decades, coral cover has drastically decreased in many regions, due to frequent mass coral bleaching events caused by global warming [5], implying that recovery from bleaching is often limited by unknown factors. In the present study, we demonstrate that both free-living and residual symbiont cells lose their capacity to infect cnidarian host cells once they are exposed to high temperature stress, and present this mechanism as a limiting factor for the host’s recovery from bleaching.We first examined the effect of pre-exposure to high temperature on infectivity using aposymbiotic Exaiptasia pallida (or “Aiptasia”) polyps (Supplementary Fig. 1) and cultured strains of Breviolum sp. CS-164 (ITS2 type B1). Symbiotic algae and polyps were separately incubated at either 25 or 32 °C for 3 days. Following this initial treatment, polyps were inoculated with symbiotic algae at 25 °C for 3 days. Infectivity was then determined by counting the number of algae in the tentacles where algal colonization occurs quickly and individual symbiont cells are easily visualized [6]. When both symbiotic algae and hosts were pre-exposed to 25 °C, significant numbers of algae were seen in tentacles (Fig. 1a). In contrast, the number (Fig. 1a) and density (Fig. 1b) of algae in the tentacles were significantly lower when both the algae and the host were exposed simultaneously to 32 °C, and when the algae alone were exposed to 32 °C. Thus, symbiotic algae, but not host polyps, lose their capacity to form a symbiotic relationship once they are exposed to high temperature. Neither cell viability measured by Evans blue staining (Supplementary Figs. 2a, b and 3) nor cell density (Supplementary Fig. 2c) differed between CS-164 cells exposed to 25 and 32 °C, indicating that infectivity was not lost by the lethal damage to cells. We repeated this experiment with another strain, B. psygmophilum CCMP2459 (ITS2 type B2). In contrast to the results with CS-164, high temperature had no effect on infectivity of CCMP2459 (Supplementary Fig. 4a). These results demonstrate that symbiotic algae can lose their capacity to infect host cells following exposure to high temperature and that thermal sensitivity differs between these two algal strains (Fig. 1b and Supplementary Fig. 4a).Open in a separate windowFig. 1Loss of infectivity in Breviolum sp. CS-164 following exposure to elevated temperature.a Fluorescent photographs of Aiptasia polyps 3 days after culturing with symbiont cells in four different treatments (i) neither symbionts nor polyps exposed to high temperature (32 °C) for 3 days, (ii) only symbionts exposed to high temperature, (iii) only polyps exposed to high temperature, (iv) both symbionts and polyps exposed to high temperature. Red dots show chlorophyll fluorescence from algal symbionts. b The density of symbionts in tentacles was measured 3 days after culturing Aiptasia polyps (H) with symbiont cells (S) in four different treatments, as indicated below the panel and outlined in the text. c The density of symbionts was measured 3 days after culturing Aiptasia polyps with symbiotic algae in different treatments. In this experiment, symbiotic algae that had been expelled from Aiptasia polyps cultured at 25 or 32 °C for 3 days were used to infect Aiptasia. b, c Values are log₂ fold changes with respect to the samples without any temperature treatment. Each point represents an independent experiment. ns, not significant (with p > 0.05); **, p < 0.01.We next examined whether the loss of infectivity following pre-exposure to high temperature also occurs when symbiotic algae are in symbiosis with the host rather than free living (Fig. 1c and Supplementary Fig. 4b). We prepared symbiotic Aiptasia polyps with either CCMP2459 or CS-164 by separately inoculating them in aposymbiotic polyps, and then exposed each group to either 25 or 32 °C for 3 days. Algae expelled from the polyps during this treatment were collected and then used to inoculate aposymbiotic Aiptasia at 25 °C. In CS-164, after 3 days of inoculation, symbiont density in Aiptasia became lower with algae collected at 32 °C than 25 °C (Fig. 1c). However, in CCMP2459, there was no difference in the infectivity between algae collected at 25 and 32 °C (Supplementary Fig. 4b). Our results demonstrate that symbiont cells, both free-living and in symbiosis, can lose infectivity following exposure to high temperature and that thermal sensitivity differs between these two algal strains.We then tested whether or not the temperature-induced loss of infectivity was reversible (Fig. 2). Free-living CS-164 cells were pre-exposed to 25 or 32 °C for either 2 or 3 days after which they were allowed to recover for a maximum of 10 days at 25 °C. After these treatments, symbiotic algae were used to inoculate aposymbiotic Aiptasia polyps at 25 °C for 3 days. Cells with pre-exposure to 32 °C for 2 days had lower infectivity but regained the capacity to infect host cells after a 5-day recovery period (Fig. 2). However, after 3 days exposure, infectivity gradually recovered but remained lower than the controls even after 10 days (Fig. 2). Our results demonstrate that the loss of infectivity following temperature stress is reversible in algal cells but a longer exposure to heat stress increases the time taken to reverse the loss of infectivity.Open in a separate windowFig. 2Reversibility of the lost infectivity upon the exposure to elevated temperature in Breviolum sp. CS-164.The density of symbionts in tentacles was measured following treatments as described in the text and shown by the relative to control. Values are log₂ fold changes with respect to the control. The box and line represent the quartiles and median, respectively. Each point represents an independent experiment. *, p < 0.05.In the present study, infectivity was tested by introducing symbiotic algae directly into the host’s body cavity, suggesting that the loss of infectivity seen in our experiments is likely due to a failure of the host to take up the algal cells via phagocytosis or for symbiont cells to persist within host cells. Factors such as symbiont cell size [7] and the symbiont surface glycome [8, 9] are potentially important determinants of symbiont uptake and persistence, though we still know little about this topic (see review [10]). Furthermore, it is unknown how these various discriminatory factors are influenced by thermal stress.In coral larvae and juveniles, the initiation of symbiosis with algae is reduced at high temperatures, suggesting that global warming will complicate the relationship between host and symbiont [11–13]. However, the mechanism for this reduction in the rate of symbiosis establishment is not clear. Our results suggest that the loss of symbiont infectivity is one possible cause of this phenomenon.Recovery of symbiont densities following coral bleaching relies on a supply of symbiont cells either from within the host or from the external environment. Our results show that symbiotic algae, both free-living and symbiotic, lose the capacity to infect the host following exposure to high temperature stress (Fig. 1). This loss of infectivity is reversible but dependent on the duration of the thermal stress (Fig. 2). Thus, following coral bleaching events, especially those induced by thermal anomalies that can last for weeks, symbiont densities within the host are unlikely to recover in time to avoid the host starving due to physiological compromise of the symbionts, rather than the host. Nonetheless, given the differences in sensitivity between two strains tested (Fig. 1b and Supplementary Fig. 4a), if heat tolerant symbionts are available in the environment, this might provide a chance for recovery.     

Membrane protein biogenesis by the EMC

Recent cryo‐EM‐based models reveal how the ER membrane protein complex may accomplish insertion of protein transmembrane domains with limited hydrophobicity.

Insertion of strongly hydrophobic TMDs into the ER membrane is mediated by the Sec61 complex for co‐translational insertion and the GET complex for post‐translational insertion of tail‐anchors (Volkmar & Christianson, 2020). By contrast, the EMC inserts TMDs of limited hydrophobicity, frequently located at the N‐ or C‐termini of proteins, and is involved in biogenesis of multi‐spanning membrane proteins (Volkmar & Christianson, 2020).The EMC is highly conserved (Wideman, 2015). In vertebrates, ten subunits have been identified (EMC1‐10), two of which, EMC8 and EMC9, are homologous and the result of a vertebrate‐specific gene duplication (Wideman, 2015). In Saccharomyces cerevisiae, EMC8 has been lost (Wideman, 2015). Only EMC3 displays clear homology to other membrane protein insertases, the Oxa1 family (Wideman, 2015; Volkmar & Christianson, 2020). This family includes YidC, which inserts TMDs into the bacterial cytoplasmic membrane, usually in cooperation with the Sec61‐homologous SecYEG channel (Volkmar & Christianson, 2020). Their association, along with the SecDF ancillary complex, forms a holo‐translocon capable of protein secretion and TMD insertion, with striking similarities to the EMC complex (Martin et al, 2019).Recent work by Pleiner et al (2020) presented a 3.4 Å cryo‐EM structure of the human EMC purified via a GFP‐tag on EMC2 and incorporated into a phospholipid nanodisc. The complex is formed by nine proteins (EMC1‐8, EMC10) (Pleiner et al, 2020). EMC8 and EMC9 are structurally similar, and their association with EMC2 is mutually exclusive (O''Donnell et al, 2020). Of the 12 TMDs, nine constitute the pseudosymmetric central ordered core, with a basket‐shaped cytosolic vestibule formed primarily by alpha‐helices of the EMC3 and EMC6 TMDs and cytosolic EMC2 (Fig 1A; Pleiner et al, 2020). The L‐shaped lumenal domain of the EMC consists mostly of beta‐sheets (Fig 1A; Pleiner et al, 2020), flanked by a conspicuous and conserved amphipathic alpha‐helix of EMC1 sealing the vestibule at the interface between the membrane and the ER lumen, together with another smaller amphipathic helix contributed by EMC3 (Fig 1A; Pleiner et al, 2020). In the ER lumen, the two 8‐bladed propellers of EMC1 contact six of the eight other subunits and stabilize the entire complex (Fig 1A; Pleiner et al, 2020). Beta‐sandwiches of EMC7 and EMC10 are anchored to the EMC1 lumenal domain (Fig 1A; Pleiner et al, 2020). In the cytosol, the tetratricopeptide repeat (TPR) spiral of EMC2 forms a cup underneath the partially hydrophilic vestibule in the membrane between the TMDs of EMC3 and EMC6, bridging the cytosolic ends of TMDs of EMC1, 3 and 5 (Fig 1A; Pleiner et al, 2020). Cytosolic EMC8 is bound to the opposite face of EMC2 (Fig 1A).Open in a separate windowFigure 1Comparison of the structures of human and yeast EMC(A) Cryo‐EM 3D map of the human (emdb‐21929) and yeast (emdb‐21587) EMC, showing front and back views with individual subunits coloured. Membrane position, obtained from the OPM database, is shown by grey discs. (B) Close‐up view of the EMC cavity formed by EMC3 and EMC6. Left, shown in a hydrophobicity surface pattern. Right, surface representation overlapped with the TMDs of EMC3 and EMC6. EMC4, flexible and with a gate function at the substrate‐binding place, is shown in pink in the yeast representation. EMC4 is not visible at the atomic EMC human structure, although is observed as a weak density at the human model, accompanied by TMs of EMC7 and EMC10 (Pleiner et al, 2020). (C) The yeast EMC following > 5 µs of CG‐MD simulation. The protein is shown as surface and coloured as per Pleiner et al (2020). The computed densities of waters and phospholipid tails and phosphates are shown as blue, yellow and lime green densities, sliced to bisect the cavity for clarity. Right, inset of the EMC cavity. Methods: CG‐MD simulations were built using PDB 6WB9 in a solvated symmetric POPC/POPE/cholesterol membrane and run in the Martini forcefield as described in Martin et al (2019). 3 µs unrestrained simulations were run, followed by 2.5 µs backbone restrained simulation for density calculation, done using VolMap in VMD (Humphrey et al, 1996).The 3.0 Å cryo‐EM structure of the yeast EMC presented by Bai and colleagues shows a very similar overall organization (Bai et al, 2020). Here, purification was via a 3xFLAG‐tag on EMC5, and the structure of the 8‐subunit complex (without EMC8/9) was visualized in detergent solution (Bai et al, 2020). The yeast complex has twelve TMDs like the human EMC, but unlike the human structure, EMC4 in yeast has three TMDs that are clearly visible (Bai et al, 2020). They are angled in the membrane pointing away from the complex at the cytosolic end (Fig 1A), and Bai et al (2020) propose that TMDs of EMC4, EMC3 and EMC6 form a substrate‐binding pocket similar to that of YidC. As in the human EMC, there are two amphipathic helices (EMC1 and EMC3) at the membrane/lumen interface (Fig 1A; Bai et al, 2020). In the ER lumen, yeast EMC1 only has one 8‐bladed beta‐propeller, to which the beta‐sandwiches of EMC7 and EMC10 are anchored (Fig 1A; Bai et al, 2020). In the cytosol, EMC2 bridges EMC3, 4 and 5, and its TPR repeats form a cup underneath the vestibule similar to human EMC2 (Fig 1A; Bai et al, 2020).The authors propose that insertion of a partially hydrophilic TMD by the yeast EMC is mechanistically similar to insertion by bacterial YidC (Bai et al, 2020). Yeast EMC is proposed to bind substrate between TMD2 of EMC3 and TMD2 of EMC4 in a pocket with polar and positively charged amino acids at either end and hydrophobic amino acids in the centre (Fig 1B; Bai et al, 2020). Much has been made of a conserved positive region within the EMC complex here, present in an equivalent position also in YidC (Kumazaki et al, 2014): It is claimed to be important for the incorporation of more‐hydrophilic TMDs and perhaps responsible for the “positive‐inside” orientation rule (von Heijne, 1992). Yeast and human EMC3 contain a specific R31 and R26 residue, respectively, conserved also in YidC and important for function of the EMC, as well as for YidC in Gram‐positive, but interestingly not Gram‐negative, bacteria (Chen et al, 2014; Pleiner et al, 2020; Bai et al, 2020). Another interesting feature, also conserved with YidC, is the flexibility of the TMDs flanking the substrate‐binding pocket, critical for EMC entry of substrates (Bai et al, 2020).In the human EMC, methionine residues in a cytosolic loop of EMC3 act as a substrate bait (Pleiner et al, 2020). Polar and charged residues within the substrate‐binding groove guide the lumenal domain across the membrane, facilitated by local membrane thinning (Pleiner et al, 2020; Fig 1B). The positive charges within the substrate‐binding site exclude signal peptides and enforce the “positive‐inside rule” (von Heijne, 1992; Pleiner et al, 2020). Flexible TMDs of EMC4, EMC7 and EMC10 forming a “lateral gate” of the substrate‐binding groove allow sampling of the bilayer by the substrate TMD (Pleiner et al, 2020). As the shortened TMDs of EMC3 and EMC6 cannot stably bind the substrate TMD, they favour its release into the bilayer (Pleiner et al, 2020). The EMC1 beta‐propeller(s) may recruit additional protein maturation factors in the ER lumen (Pleiner et al, 2020; Bai et al, 2020) or bind the Sec61 channel to allow cooperation between the two insertases (Bai et al, 2020).Arguably, the most interesting feature of the EMC complex is the location of a large interior cavity with distinctive hydrophilic character, which likely aids TMD insertion (Fig 1B). We ran a coarse‐grained molecular dynamics (CG‐MD) simulation of the yeast EMC structure, which highlights a profound perturbation of the phospholipid bilayer in the EMC interior cavity (Fig 1C). Here, a deep gorge forms in the cytoplasmic leaflet of the bilayer, allowing the cavity to become flooded with water (Fig 1C). Note the location of the lipid head groups here (lime green), which presumably define the site of amphipathic TMD insertion. The incursion of phospholipids into the centre of the EMC complex is a feature shared by the bacterial holo‐translocon (Martin et al, 2019) and perhaps by all membrane protein insertases. The shape and character of the EMC cavity presumably dictate its predisposition for less hydrophobic TMDs; it would be interesting to see whether the cavities of different insertases are similarly tailored to suit their substrates.     

From the freezer to the clinic: Antifreeze proteins in the preservation of cells,tissues, and organs

Understanding the mechanisms by which natural anti‐freeze proteins protect cells and tissues from cold could help to improve the availability of donor organs for transplantation.

The first successful organ transplant in humans was performed in 1954 by Joseph Murray, who used a patient’s twin as a kidney donor. Murrays’ breakthrough paved the way for organ transplantation and the number of transplanted organs has grown ever since. For example, in 2017, a total of 139.024 solid organs—mostly kidney, liver, heart, lung, pancreas, and small bowel—were transplanted (Fig 1A). But this number only reflects 10% of the worldwide need; many patients still die of end‐stage organ failure while on a waiting list. The limited number of donor organs contributes only partially to this shortage. Many donor organs are not transplanted eventually owing to inefficient preservation techniques that shorten their extracorporeal lifetime. In fact, the percentage of donor organs that are unused is estimated to range from around 25% for kidneys and livers up to 70–80% for hearts and lungs (Giwa et al, 2017); Fig 1B).Open in a separate windowFigure 1Organ transplantation and preservability statusStatistics show a positive correlation between the duration of ex vivo preservation and the number of organ transplants. Number of solid organs transplanted in 2017 (A). Percentage of organs failed to be transplanted (B). Duration of solid organ ex vivo preservation in static cold storage (C). Sources: Data from the Global Observatory on Donation and Transplantation and (Parsons et al, 2014), (Guibert et al, 2011) and (Editorial: Buying time for transplants (2017))

Many donor organs are not transplanted eventually owing to inefficient preservation techniques that shorten their extracorporeal lifetime.

To address the shortage of donor organs and decrease the number of organs that go to waste, biobanks could efficiently store viable tissues and organs until transplantation. Yet, the current standard for ex vivo preservation of donor organs is static cold storage (4–8°C) which, depending on the organ, ensures viable conservation for only some hours; hearts are typically viable for a maximum of only 4 h (Fig 1C). In addition, this approach leads to hypothermic damage and to ischemia/reperfusion injury.Hence, there is an urgent need for strategies that prolong the viable preservation of donor organs. Two main strategies have emerged for cryopreservation and subzero storage, both of which cool tissues below the freezing point. While subzero storage just below 0°C may suffice for short‐term preservation, cryopreservation at −80°C or even lower temperatures is required for long‐term storage in biobanks. A proof‐of‐principle study already demonstrated that subzero preservation extended the preservation of rat hearts up to 24 h after collection (Amir et al, 2004); cryopreservation of whole hearts is currently not possible. The main reason is that lowering the temperature below the freezing point of water leads to ice formation, which causes cell damage and destroys tissues. One of the main challenges in biomedical research for organ transplantation is therefore finding non‐toxic and biocompatible antifreeze compounds that enable subzero storage and cryopreservation without causing tissue damage. An additional benefit is a larger time window to perform evaluation in terms of organ size and human leukocyte antigens matching and preparing the recipient patient to increase the chance of a successful transplantation.     

Splenogonadal Fusion: An Unusual Case of an Acute Scrotum

We highlight a case on a normal left testicle with a fibrovascular cord with three nodules consistent with splenic tissue. The torsed splenule demonstrated hemorrhage with neutrophilic infiltrate and thrombus consistent with chronic infarction and torsion. Splenogonadal fusion (SGF) is a rather rare entity, with approximately 184 cases reported in the literature. The most comprehensive review was that of 123 cases completed by Carragher in 1990. Since then, an additional 61 cases have been reported in the scientific literature. We have studied these 61 cases in detail and have included a summary of that information here.Key words: Splenogonadal fusion, Acute scrotumA 10-year-old boy presented with worsening left-sided scrotal pain of 12 hours’ duration. The patient reported similar previous episodes occurring intermittently over the past several months. His past medical history was significant for left hip dysplasia, requiring multiple hip surgeries. On examination, he was found to have an edematous left hemiscrotum with a left testicle that was rigid, tender, and noted to be in a transverse lie. The ultrasound revealed possible polyorchism, with two testicles on the left and one on the right (Figure 1), and left epididymitis. One of the left testicles demonstrated a loss of blood flow consistent with testicular torsion (Figure 2).Open in a separate windowFigure 1Ultrasound of the left hemiscrotum reveals two spherical structures; the one on the left is heterogeneous and hyperdense in comparison to the right.Open in a separate windowFigure 2Doppler ultrasound of left hemiscrotum. No evidence of blood flow to left spherical structure.The patient was taken to the operating room for immediate scrotal exploration. A normalappearing left testicle with a normal epididymis was noted. However, two accessory structures were noted, one of which was torsed 720°; (Figure 3). An inguinal incision was then made and a third accessory structure was noted. All three structures were connected with fibrous tissue, giving a “rosary bead” appearance. The left accessory structures were removed, a left testicular biopsy was taken, and bilateral scrotal orchipexies were performed.Open in a separate windowFigure 3Torsed accessory spleen with splenogonadal fusion.Pathology revealed a normal left testicle with a fibrovascular cord with three nodules consistent with splenic tissue. The torsed splenule demonstrated hemorrhage with neutrophillic infiltrate and thrombus consistent with chronic infarction and torsion (Figure 4).Open in a separate windowFigure 4Splenogonadal fusion, continuous type with three accessory structures.     

Correlation between serum renin-angiotensin system (RAS) level and depression and anxiety symptoms in patients with Parkinson's disease

To investigate the correlation between serum renin-angiotensin system (RAS) level and Symptoms of anxiety and depression in Parkinson disease patients (PD). A number of 90 PD patients (47 males and 43 females) were collected on an empty stomach 12 h after stopping taking anti-PD medicines. ELISA has been found in Serum RAS ((Ang) I, Ang II, Ang (1–7), Angiotensin converting enzyme (ACE), ACE2). Depression scale (HAMD) and Anxiety scale (HAMA) in Hamilton are used for the assessment of signs of depression and anxiety. The 90 patients were diagnosed with moderate depression (HAMD score 8 ~ 19); in 32 of those (35.56 percent), and 12 (13.33%) were diagnosed as moderate and severe depression (HAMD score ≥ 20). 20 cases (22.22%) were diagnosed as possible anxiety disorder (HAMA score 7 ~ 13) and 16 cases (17.78%) as definite anxiety disorder (HAMA score ≥ 14). The association of serum Ang I, Ang II and Ang (1–7) with HAMD (r= − 0.820, P < 0.001; r = −0.846, P < 0.001) showed negative linkage with HAMD (r = −0.887, P < 0.003; P < 0.001; Negative correlation of the settings with HAMA (r = −0.850, P < 0.001; r = −0.887, P < 0.001; r = 0.003; r = 0.001, P < 0.001, Fig. 2, Fig. 3); The HAMD score and the HAMA score (all P > 0.05) were not associated to the serum ACE and ACE2. The serum Ang I, Ang II, and Ang (1–7) were found to be adversely associated with HAMD score (r = 0.826, P < 0,001; r = −0.818, p> >0,001; r = −0.876, P < 0,001; P = 0,001) P < 0,001; And have been negatively correlated (r = 0.870, Fig. 1, Fig. 2, Fig. 3) with AMA-scores (r = −0.876, P < 0.001, Table 1, Fig. 3), R = −0.862, P > 0.001; The HAMD score and the HAMA score (all P > 0.05) were not correlated to the serum ACE and ACE2. Finally, in PD patients, non-engine signs, including depression and anxiety, are normal. Thus, Serum levels Ang I, Ang II and Ang (1–7) were substantially decreased in female and male patients and associated with symptoms of depression and anxiety, ACE and ACE2 levels have not been attributed to signs of depression and anxiety. Serum Ang I, Ang II, and Ang (1–7) are important markers of depression and anxiety prevention and diagnosis in patients with DP.Table 1Comparison of serum ACE, ACE2, Ang I, Ang II, Ang (1–7) levels and HAMD and HAMA scores between male and female patients with PD.

Straightening up is required to nucleate new microtubules

In this issue, Ayukawa, Iwata, Imai, and colleagues (2021. J. Cell Biol. https://doi.org/10.1083/jcb.202007033) use rapid temporal and high-spatial-resolution electron microscopy imaging to examine the earliest stages of new microtubule nucleation. They discover that straightening of curved tubulin oligomers increases the efficiency of microtubule nucleation.

Microtubules are long cytoskeletal filaments that provide structure to cells, allow for transport within the cells, and participate in cell division by acting together with molecular motors to build a mitotic spindle. While microtubules act as the stiff “bones” of the cell, they also have a unique and important ability to rapidly restructure their length and organization in response to cellular cues. Therefore, large arrays of microtubules, such as in a mitotic spindle, can rapidly depolymerize and disappear as needed. However, in order to rebuild these microtubule networks, the nucleation of new microtubules is required. This nucleation of new microtubules, whether from existing templates such as centrosomes or spindle poles, or via the de novo organization of the tubulin subunits that make up microtubules, remains a poorly understood process.In this issue, Ayukawa, Iwata, Imai, and colleagues used rapid temporal and high-spatial-resolution imaging to study the earliest stages of microtubule nucleation (1). First, the authors purified αβ-tubulin heterodimers with a Y222F mutation in the β-tubulin subunit. This Y222F β-tubulin mutation increased the rate of microtubule assembly and, importantly, greatly accelerated the nucleation rate of new microtubules. Thus, a comparison between wild-type and mutant tubulin allowed the authors to dissect differences in the early nucleation process that could explain the increased nucleation rate for the mutant tubulin. Importantly, a “rapid flush method” was used to capture high-resolution transmission electron microscopy images of tubulin subunits very early in the nucleation process. The rapid flush method revealed “oligomers” of tubulin subunits: chains of tubulin subunits linked together along their long axis.The authors reasoned that these oligomers are likely on-pathway intermediates that are crucial for new microtubule nucleation. Examination of the oligomers revealed differences in curvature between wild-type and mutant tubulin: the mutant tubulin that nucleated more readily had oligomers that were straighter and less curled than the wild-type tubulin. Further, a comparison of GTP tubulin and GDP tubulin early nucleation intermediates revealed that, for both wild-type and mutant tubulin, the GDP tubulin oligomers, which did not nucleate efficiently, were more curved than the GTP tubulin oligomers that nucleated more efficiently (Fig. 1 A, left, red versus blue). Importantly, the fraction of nearly straight oligomers in each sample directly corresponded to the respective nucleation rate for each tubulin type. Thus, the degree of curvature of the oligomers predicted the overall nucleation rate, such that an increase in the fraction of straight oligomers was directly correlated to an increase in the microtubule nucleation rate (Fig. 1 A).Open in a separate windowFigure 1.Long, straight oligomers promote microtubule nucleation. (A) The nucleation of new microtubules is limited by the availability of critical-length, straight GTP tubulin (blue) oligomers. GDP tubulin oligomers (red) are more curved, with reduced nucleation efficiency. (B) Straight GTP tubulin protofilaments (blue) that are attached to templates (gray) could also be required to facilitate the nucleation of new microtubules from templates such as in centrosomes. Curved GDP tubulin protofilaments (red) attached to templates (gray) would not efficiently facilitate nucleation of new microtubules.The authors then examined the role of oligomer length in the nucleation rate, to determine whether there was a critical minimum oligomer length that could predict efficient microtubule nucleation. They fit their bulk microtubule growth curves (turbidity) to a standard nucleation-and-growth model (2) to estimate the minimum size of the oligomers that were likely to grow into microtubules, i.e., the critical length. For wild-type tubulin, ∼4 tubulin dimers were required in order for oligomers to grow into microtubules. However, this critical length was common, and indeed prevalent, within the early nucleation mixtures. Therefore, it seems likely that oligomer curvature, rather than length, is the limiting factor that slows the microtubule nucleation rate (Fig. 1 A).Why would oligomer curvature limit microtubule nucleation rate? While oligomers of sufficient length were readily observed in early nucleation mixtures, lateral association of new oligomers with existing oligomers was rare. Further, when multiple oligomers had indeed associated laterally to form a new, multi-protofilament assembly, the length of the longest strand greatly exceeded the maximum size of the single-stranded oligomers. Thus, it is likely that straight oligomers facilitate the lateral association of new tubulin subunits or oligomers along their length, stabilizing the nascent microtubules and allowing for their stable growth as a multistranded filament.While this work sheds light on the nucleation of new microtubules using purified tubulin, the described results provide interesting insights into potential nucleation mechanisms inside of cells. In cells, microtubules predominately grow from templates, such as centrosomes, and various microtubule-associated proteins may also influence the nucleation process. A recent study of microtubule nucleation from templates found that there was a significant time lag between the arrival of new tubulin subunits to a template and the growth of a new microtubule from the template (3). That study concluded that GTP hydrolysis inhibits microtubule nucleation by destabilizing the nascent microtubule. These results are consistent with the conclusion from Ayukawa, Iwata, Imai, and colleagues that GDP tubulin oligomers are curved and therefore unable to efficiently make lateral associations with other oligomers to stabilize the new microtubule and allow for growth (Fig. 1 B; 1). However, recent electron microscopy studies have also revealed that the GTP tubulin–containing ends of growing microtubules show extended protofilaments with a gentle curvature (4, 5, 6, 7, 8). Thus, one additional barrier to the nucleation of new microtubules on templates may be the straightening of GTP tubulin oligomers. Here, the straightening of template-attached, gently curved GTP tubulin oligomers would then allow for lateral binding of new tubulin dimers and oligomers (Fig. 1 B). The transient straightening of gently curved GTP tubulin oligomers could potentially be accomplished via thermal forces and the resulting protofilament curvature fluctuations. However, this straightening process could also be facilitated by microtubule-associated proteins that promote nucleation, such as TPX2 and XMAP215 (9, 10). In support of this idea, Ayukawa, Iwata, Imai, and colleagues found that the distribution of oligomer curvatures appeared similar to what has been reported for the protofilaments at the growing ends of microtubules (5).In light of these results, interesting future work could explore whether microtubule-associated proteins act to regulate the curvature of the oligomers involved in microtubule nucleation. While many aspects of the mechanisms of microtubule nucleation remain unknown, this new work sheds light on the very earliest stages of microtubule nucleation, which may have wide-ranging implications in future studies of microtubule nucleation and growth in cells.     

Focus Issue on Plant Cell Walls: Sequencing around 5-Hydroxyconiferyl Alcohol-Derived Units in Caffeic Acid O-Methyltransferase-Deficient Poplar Lignins

Caffeic acid O-methyltransferase (COMT) is a bifunctional enzyme that methylates the 5- and 3-hydroxyl positions on the aromatic ring of monolignol precursors, with a preference for 5-hydroxyconiferaldehyde, on the way to producing sinapyl alcohol. Lignins in COMT-deficient plants contain benzodioxane substructures due to the incorporation of 5-hydroxyconiferyl alcohol (5-OH-CA), as a monomer, into the lignin polymer. The derivatization followed by reductive cleavage method can be used to detect and determine benzodioxane structures because of their total survival under this degradation method. Moreover, partial sequencing information for 5-OH-CA incorporation into lignin can be derived from detection or isolation and structural analysis of the resulting benzodioxane products. Results from a modified derivatization followed by reductive cleavage analysis of COMT-deficient lignins provide evidence that 5-OH-CA cross couples (at its β-position) with syringyl and guaiacyl units (at their O-4-positions) in the growing lignin polymer and then either coniferyl or sinapyl alcohol, or another 5-hydroxyconiferyl monomer, adds to the resulting 5-hydroxyguaiacyl terminus, producing the benzodioxane. This new terminus may also become etherified by coupling with further monolignols, incorporating the 5-OH-CA integrally into the lignin structure.Lignins are polymeric aromatic constituents of plant cell walls, constituting about 15% to 35% of the dry mass (Freudenberg and Neish, 1968; Adler, 1977). Unlike other natural polymers such as cellulose or proteins, which have labile linkages (glycosides and peptides) between their building units, lignins’ building units are combinatorially linked with strong ether and carbon-carbon bonds (Sarkanen and Ludwig, 1971; Harkin, 1973). It is difficult to completely degrade lignins. Lignins are traditionally considered to be dehydrogenative polymers derived from three monolignols, p-coumaryl alcohol 1h (which is typically minor), coniferyl alcohol 1g, and sinapyl alcohol 1s (Fig. 1; Sarkanen, 1971). They can vary greatly in their composition in terms of their plant and tissue origins (Campbell and Sederoff, 1996). This variability is probably determined and regulated by different activities and substrate specificities of the monolignol biosynthetic enzymes from different sources, and by the carefully controlled supply of monomers to the lignifying zone (Sederoff and Chang, 1991).Open in a separate windowFigure 1.The monolignols 1, and marker compounds 2 to 4 resulting from incorporation of novel monomer 15h into lignins: thioacidolysis monomeric marker 2, dimers 3, and DFRC dimeric markers 4.Recently there has been considerable interest in genetic modification of lignins with the goal of improving the utilization of lignocellulosics in various agricultural and industrial processes (Baucher et al., 2003; Boerjan et al., 2003a, 2003b). Studies on mutant and transgenic plants with altered monolignol biosynthesis have suggested that plants have a high level of metabolic plasticity in the formation of their lignins (Sederoff et al., 1999; Ralph et al., 2004). Lignins in angiosperm plants with depressed caffeic acid O-methyltransferase (COMT) were found to derive from significant amounts of 5-hydroxyconiferyl alcohol (5-OH-CA) monomers 15h (Fig. 1) substituting for the traditional monomer, sinapyl alcohol 1s (Marita et al., 2001; Ralph et al., 2001a, 2001b; Jouanin et al., 2004; Morreel et al., 2004b). NMR analysis of a ligqnin from COMT-deficient poplar (Populus spp.) has revealed that novel benzodioxane structures are formed through β-O-4 coupling of a monolignol with 5-hydroxyguaiacyl units (resulting from coupling of 5-OH-CA), followed by internal trapping of the resultant quinone methide by the phenolic 5-hydroxyl (Ralph et al., 2001a). When the lignin was subjected to thioacidolysis, a novel 5-hydroxyguaiacyl monomer 2 (Fig. 1) was found in addition to the normal guaiacyl and syringyl thioacidolysis monomers (Jouanin et al., 2000). Also, a new compound 3g (Fig. 1) was found in the dimeric products from thioacidolysis followed by Raney nickel desulfurization (Lapierre et al., 2001; Goujon et al., 2003).Further study with the lignin using the derivatization followed by reductive cleavage (DFRC) method also confirmed the existence of benzodioxane structures, with compounds 4 (Fig. 1) being identified following synthesis of the authentic parent compounds 9 (Fig. 2). However, no 5-hydroxyguaiacyl monomer could be detected in the DFRC products. These facts imply that the DFRC method leaves the benzodioxane structures fully intact, suggesting that the method might therefore be useful as an analytical tool for determining benzodioxane structures that are linked by β-O-4 ethers. Using a modified DFRC procedure, we report here on results that provide further evidence for the existence of benzodioxane structures in lignins from COMT-deficient plants, that 5-OH-CA is behaving as a rather ideal monolignol that can be integrated into plant lignins, and demonstrate the usefulness of the DFRC method for determining these benzodioxane structures.Open in a separate windowFigure 2.Synthesis of benzodioxane DFRC products 12 (see later in Fig. 6 for their structures). i, NaH, THF. ii, Pyrrolidine. iii, 1g or 1s, benzene/acetone (4/1, v/v). iv, DIBAL-H, toluene. v, Iodomethane-K₂CO₃, acetone. vi, Ac₂O pyridine.     

Chromosome segregation during female meiosis in C. elegans: A tale of pushing and pulling

The role of the kinetochore during meiotic chromosome segregation in C. elegans oocytes has been a matter of controversy. Danlasky et al. (2020. J. Cell. Biol. https://doi.org/10.1083/jcb.202005179) show that kinetochore proteins KNL-1 and KNL-3 are required for early stages of anaphase during female meiosis, suggesting a new kinetochore-based model of chromosome segregation.

Meiosis consists of two consecutive chromosome segregation events preceded by a single round of DNA replication. Homologous chromosomes are separated in meiosis I, which is followed by sister chromatid separation in meiosis II to produce haploid gametes. Both of these stages require chromosomes/chromatids to align during metaphase before separating to opposite poles during anaphase. During mitosis, microtubules emanating from centrosomes at opposite poles of the cell bind chromosomes through a multiprotein complex called the kinetochore, allowing chromosomes to be pulled apart (1, 2). This segregation event takes place in two stages: anaphase A, where chromosomes are pulled toward spindle poles due to microtubule depolymerization, and anaphase B, where spindle poles themselves move farther apart, taking the attached chromosomes with them (3, 4). In many organisms, including mammals, oocytes lack centrosomes, and it has been of great interest to clarify the mechanisms used to ensure chromosomes are properly segregated during female meiosis (5, 6). Caenorhabditis elegans has served as a model for studying both mitosis and meiosis, but the mechanisms operating during female meiosis have been a matter of debate and controversy.In 2010, Dumont et al. showed that the kinetochore is required for chromosome alignment and congression during metaphase (7). However, they suggested that chromosome segregation was the result of microtubule polymerization between the segregating chromosomes (Fig. 1), resulting in a pushing force exerted onto chromosomes toward the spindle poles in a largely kinetochore-independent manner (7). This mechanism was also supported by the finding that CLIP-associated protein (CLASP)–dependent microtubule polymerization between the segregating chromosomes is essential for chromosome separation (8). An alternative model suggested that chromosomes are transported through microtubule-free channels toward the spindle poles by the action of dynein (9). Later evidence put in doubt a role for dynein and favored a model in which chromosomes initially separate when the spindle shortens and the poles overlap with chromosomes in an anaphase A–like mechanism. This is then followed by separation of chromosome-bound poles by outward microtubule sliding in an anaphase B–like fashion (10). However, because microtubules emanating from the spindle poles are not required to separate the homologous chromosomes but microtubules between the separating chromosomes are (8), this model is unlikely, at least as an explanation for mid-/late-anaphase movement. Furthermore, although lateral microtubule interactions with chromosomes predominate during metaphase of C. elegans oocyte meiosis, cryo-electron tomography data described end-on attachments between the separating chromosomes as anaphase progresses (11). This led to the suggestion that lateral microtubule interactions with chromosomes are responsible for the initial separation, but microtubule polymerization between the separating chromosomes is required for the later stages of segregation (11). The mechanisms involved in this initial separation have remained obscure. In this issue, Danlasky et al. show that the kinetochore is in fact required for the initial stages of chromosome segregation during female meiosis—an important step forward in our understanding of the mechanisms governing acentrosomal chromosome segregation (12).Open in a separate windowFigure 1.Some of the key findings in Danlasky et al. Kinetochore proteins surround the outer surface of the chromosomes, resulting in a characteristic cup shape. As anaphase progresses, chromosomes come into close contact to the spindle poles (anaphase A). Chromosome stretching is provided by KNL-1, MIS-12 (KNL-3), and NDC-80 (KMN)–dependent forces. Once the spindle starts elongating (anaphase B), central spindle microtubules provide the pushing forces for chromosome segregation. At this stage, kinetochore proteins also occupy the inward face of separating chromosomes. Upon KMN network depletion, bivalents flatten, and chromosome congression and alignment are defective. Anaphase A chromosome movement is almost absent, which leads to error-prone anaphase B.By simultaneously depleting kinetochore proteins KNL-1 and KNL-3 in C. elegans, Danlasky et al. observed the meiotic chromosome congression and alignment defects described in previous studies (7). However, this double-depletion phenotype displayed three key characteristics that suggested a role for kinetochores in chromosome segregation, which are discussed below.The kinetochore is required for bivalent stretching. It was previously shown that the bivalent chromosomes stretch before the initiation of segregation (10). Danlasky et. al found that this stretching of the chromosomes did not occur when KNL-1,3 were depleted, indicating that the kinetochore is required for this process (Fig. 1). Together with the observation that kinetochore proteins appear to extend toward the spindle poles, this finding suggested that pulling forces resulting from the interaction between the kinetochore and spindle microtubules are occurring during metaphase/preanaphase (Fig. 1).The kinetochore is required for anaphase A. In C. elegans female meiosis, anaphase A occurs when homologous chromosomes begin to separate during spindle shortening, and anaphase B when the chromosomes separate alongside the spindle poles (10). Danlasky et al. observed that KNL-1,3 depletion drastically reduced the velocity of anaphase A, as chromosomes only separated when spindle poles began to move apart. This indicated that pulling forces caused by the interaction between the kinetochore and spindle microtubules are also important for the initial separation of homologous chromosomes in anaphase A.The kinetochore is required for proper separation of homologous chromosomes. In KNL-1,3 depletion strains, 60% of bivalents failed to separate before segregation began, resulting in intact bivalents being pulled to the same spindle pole (Fig. 1). This failure of homologous chromosomes to separate was not thought to be a result of KNL-1,3 depletion interfering with the cleavage of cohesin that holds the two homologous chromosomes together because (a) separase and AIR-2^AuroraB, both of which are required for cohesin cleavage, localized normally during metaphase and anaphase, and (b) bivalents separated by metaphase II. This leaves the possibility open that the failure of bivalents to separate was due to the disrupted pulling forces thought to be important in bivalent stretching and anaphase A.Altogether, these data strongly indicate that the kinetochore is required not only for chromosome congression and alignment but also for the early stages of homologue separation. Anaphase B occurred successfully in the absence of KNL-1,3 but was more error prone, likely as a result of the earlier congression and anaphase A defects. While it is clear that chromosome masses do segregate in the absence of the kinetochore, this segregation is highly erroneous as a result of defects during the earlier stages of segregation in anaphase A (Fig. 1).The findings of Danlasky et al. raise testable hypotheses that could significantly enhance our understanding of acentrosomal chromosome segregation. Further investigation of the proposed pulling forces required during metaphase and early anaphase will be of great interest. Additionally, a more detailed analysis of the dynamic localization of separase and Securin, as well as assessing successful cohesin cleavage when KNL-1,3 are depleted, would back up the assertion that the failure of homologous chromosomes to separate was not due to the kinetochore impacting cohesin cleavage. It has previously been shown that the CLASP orthologue CLS-2 in C. elegans localizes to the kinetochore surrounding the bivalent chromosomes during metaphase before relocalizing to the central spindle during anaphase (7, 8, 13). It will be interesting to examine whether this key microtubule-stabilizing protein contributes to anaphase A pulling forces alongside its essential role in microtubule polymerization between chromosomes in anaphase B (8).While the regulation of proper chromosome segregation during acentrosomal meiosis in C. elegans is not yet fully understood, Danlasky et al.’s results represent a significant step forward in this endeavor by showing that the kinetochore is in fact required for the early stages of chromosome segregation.     

Big steps toward understanding dynein

Dynein is a microtubule-based molecular motor that is involved in various biological functions, such as axonal transport, mitosis, and cilia/flagella movement. Although dynein was discovered 50 years ago, the progress of dynein research has been slow due to its large size and flexible structure. Recent progress in understanding the force-generating mechanism of dynein using x-ray crystallography, cryo-electron microscopy, and single molecule studies has provided key insight into the structure and mechanism of action of this complex motor protein.It has been 50 years since dynein was discovered and named by Ian Gibbons as a motor protein that drives cilia/flagella bending (Gibbons, 1963; Gibbons and Rowe, 1965). In the mid-1980s, dynein was also found to power retrograde transport in neurons (Paschal and Vallee, 1987). Subsequently, the primary amino acid sequence of the cytoplasmic dynein heavy chain, which contains the motor domain, was determined from the cDNA sequence (Mikami et al., 1993; Zhang et al., 1993). Like other biological motors, such as kinesins and myosins, the amino acid sequence of the dynein motor domain is well conserved. There are 16 putative genes that encode dynein heavy chains in the human genome (Yagi, 2009). Among these is one gene encoding cytoplasmic dynein heavy chain and one encoding retrograde intraflagellar transport dynein heavy chain, while the rest encode for heavy chains of axonemal dyneins. Most of the genes encoding the human dynein heavy chain have a counterpart in Chlamydomonas reinhardtii, which suggests that their functions are conserved from algae to humans.Dynein is unique compared with kinesin and myosin because dynein molecules form large molecular complexes. For example, one axonemal outer arm dynein molecule of C. reinhardtii is composed of three dynein heavy chains, two intermediate chains, and more than ten light chains (King, 2012). Mammalian cytoplasmic dynein consists of two heavy chains and several smaller subunits (Fig. 1 A; Vallee et al., 1988; Allan, 2011). The cargoes of cytoplasmic dynein are various membranous organelles, including lysosomes, endosomes, phagosomes, and the Golgi complex (Hirokawa, 1998). It is likely that one cytoplasmic dynein heavy chain can adapt to diverse cargos and functions by changing its composition.Open in a separate windowFigure 1.Atomic structures of cytoplasmic dynein. (A) Schematic structure of cytoplasmic dynein complex, adapted from Allan (2011). (B) The primary structure of cytoplasmic dynein. (C and D) The atomic model of D. discoideum cytoplasmic dynein motor domain (PDB accession no. 3VKG) overlaid on a microtubule (EMDB-5193; Sui and Downing, 2010) according to the orientation determined by Mizuno et al. (2007) (C) Side view. (D) View from the plus end of microtubule. (E) Schematic domain structure of dynein.Dynein must have a distinct motor mechanism from kinesin and myosin, because it belongs to the AAA+ family of proteins and does not have the conserved amino acid motifs, called the switch regions, present in kinesins, myosins, and guanine nucleotide-binding proteins (Vale, 1996). Therefore, studying dynein is of great interest because it will reveal new design principles of motor proteins. This review will focus on the mechanism of force generation by cytoplasmic and axonemal dynein heavy chains revealed by recent structural and biophysical studies.

Anatomy of dynein

To understand the chemomechanical cycle of dynein based on its molecular structure, it is important to obtain well-diffracting crystals and build accurate atomic models. Recently, Kon and colleagues determined the crystal structures of Dictyostelium discoideum cytoplasmic dynein motor domain, first at 4.5-Å resolution (Kon et al., 2011), and subsequently at 2.8 Å (without the microtubule binding domain) and 3.8-Å (wild type) resolution (Kon et al., 2012). Carter and colleagues also determined the crystal structures of the Saccharomyces cerevisiae (yeast) cytoplasmic dynein motor domain, first at 6-Å resolution (Carter et al., 2011), and later at 3.3–3.7-Å resolution (Schmidt et al., 2012). According to these crystal structures as well as previous EM studies, the overall structure of the dynein heavy chain is divided into four domains: tail, linker, head, and stalk (Fig. 1, B–E). Simply put, each domain carries out one essential function of a motor protein: the tail is the cargo binding domain, the head is the site of ATP hydrolysis, the linker is the mechanical amplifier, and the stalk is the track-binding domain.The tail, which is not part of the motor domain and is absent from crystal structures, is located at the N-terminal ∼1,400 amino acid residues and involved in cargo binding (gray in Fig. 1, B and E). The next ∼550 residues comprise the “linker” (pink in Fig. 1, B–E), which changes its conformation depending on the nucleotide state (Burgess et al., 2003; Kon et al., 2005). This linker domain was first observed by negative staining EM in combination with single particle analysis of dynein c, an isoform of inner arm dynein from C. reinhardtii flagella (Burgess et al., 2003). According to the crystal structures, the linker is made of bundles of α-helices and lies across the AAA+ head domain, forming a 10-nm-long rod-like structure (Fig. 1, C and D). Recent class averaged images of D. discoideum cytoplasmic dynein show that the linker domain is stiff along its entire length when undocked from the head (Roberts et al., 2012). The head (motor) domain of dynein is composed of six AAA+ (ATPase associated with diverse cellular activities) modules (Neuwald et al., 1999; color-coded in Fig. 1, B–E). Although many AAA+ family proteins are a symmetric homohexamer (Ammelburg et al., 2006), the AAA+ domains of dynein are encoded by a single heavy chain gene and form an asymmetric heterohexamer. Among the six AAA+ domains, hydrolysis at the first AAA domain mainly provides the energy for dynein motility (Imamula et al., 2007; Kon et al., 2012). The hexameric ring has two distinct faces: the linker face and the C-terminal face. The linker face is slightly convex and the linker domain lies across this side (Fig. 1 D, left side). The other side of the ring has the C-terminal domain (Fig. 1 D, right side).The stalk domain of dynein was identified as the microtubule-binding domain (MTBD; Gee et al., 1997). It emanates from the C-terminal face of AAA4 and is composed of antiparallel α-helical coiled-coil domain (yellow in Fig. 1, B–E). The tip of the stalk is the actual MTBD. Interestingly, the crystal structures revealed another antiparallel α-helical coiled coil that emerges from AAA5 (orange in Fig. 1, B–E), and this region is called the buttress (Carter et al., 2011) or strut (Kon et al., 2011), which was also observed as the bifurcation of the stalk by negative-staining EM (Burgess et al., 2003; Roberts et al., 2009). The tip of the buttress/strut is in contact with the middle of the stalk and probably works as a mechanical reinforcement of the stalk.

The chemomechanical cycle of dynein

Based on structural and biochemical data, a putative chemomechanical cycle of dynein is outlined in Fig. 2 (A–E). In the no-nucleotide state, dynein is bound to a microtubule through its stalk domain, and its tail region is bound to cargoes (Fig. 2 A). The crystal structures of yeast dynein are considered to be in this no-nucleotide state. When ATP is bound to the AAA+ head, the MTBD quickly detaches from the microtubule (Fig. 2 B; Porter and Johnson, 1983). The ATP binding also induces “hinging” of the linker from the head (Fig. 2 C). According to the biochemical analysis of recombinant D. discoideum dynein (Imamula et al., 2007), the detachment from the microtubule (Fig. 2, A and B) is faster than the later hinging (Fig. 2, B and C). As a result of these two reactions, the head rotates or shifts toward the minus end of the microtubule (for more discussion about “rotate” versus “shift” see the “Dyneins in the axoneme” section) and the MTBD steps forward. The directionality of stepping seems to be mainly determined by the MTBD, because the direction of dynein movement does not change even if the head domain is rotated relative to the microtubule by insertion or deletion of the stalk (Carter et al., 2008). In the presence of ADP and vanadate, dynein is considered to be in this state (Fig. 2 C).Open in a separate windowFigure 2.Presumed chemomechanical cycle and stepping of dynein. (A–E) Chemomechanical cycle of dynein. The pre- and post-power stroke states are also called the primed and unprimed states, respectively. The registries of the stalk coiled coil are denoted as α and β according to Gibbons et al. (2005). (F and G) Processive movement of kinesin (F) and dynein (G). (F) Hand-over-hand movement of kinesin. A step by one head (red) is always followed by the step of another head (green). The stepping of kinesin is on one protofilament of microtubule. (G) Presumed stepping of dynein. The step size varies and the interhead separation can be large. A step by one head (red) is not always flowed by the step of another head (green). (H) A model of strain-based dynein ATPase activation. (G, top) Without strain, the gap between the AAA1 and AAA2 is open and the motor domain cannot hydrolyze ATP. (G, bottom) Under a strain imposed between MTBD and tail (thin black arrows), the gap becomes smaller (thick black arrows) and turns on ATP hydrolysis by dynein.After the MTBD rebinds to the microtubule at the forward site (Fig. 2 D), release of hydrolysis products from the AAA+ head is activated (Holzbaur and Johnson, 1989) and the hinged linker goes back to the straight conformation (Fig. 2 E; Kon et al., 2005). The crystal structure of D. discoideum dynein is considered to be in the state after phosphate release and before ADP release. This straightening of the linker is considered to be the power-generating step and brings the cargo forward relative to the microtubule.

The MTBD of dynein

As outlined in Fig. 2, the nucleotide state of the head domain may control the affinity of the MTBD to the microtubule. Conversely, the binding of the MTBD to the microtubule should activate the ATPase activity of the head domain. This two-way communication is transmitted through the simple ∼17-nm-long α-helical coiled-coil stalk and the buttress/strut, and its structural basis has been a puzzling question.Currently there are three independent MTBD atomic structures in the Protein Data Bank (PDB): One of the crystal structures of the D. discoideum dynein motor domain contains the MTBD (Fig. 3 A), and Carter et al. (2008) crystallized the MTBD of mouse cytoplasmic dynein fused with a seryl tRNA-synthetase domain (Fig. 3 C). The MTBD structure of C. reinhardtii axonemal dynein was solved using nuclear magnetic resonance (PDB accession no. 2RR7; Fig. 3 B). The MTBD is mostly composed of α-helices and the three structures are quite similar to each other within the globular MTBD (Fig. 3). Note that dynein c has an additional insert at the MTBD–microtubule interface (Fig. 3 B, inset), whose function is not yet clear. The three structures start to deviate from the junction between the MTBD and the coiled-coil region of the stalk (Fig. 3, A–C, blue arrowheads). Particularly, one of the stalk α-helix (CC2) in D. discoideum dynein motor domain appears to melt at the junction with the MTBD (Fig. 3 A, red arrowhead). This structural deviation suggests that the stalk coiled coil at the junction is flexible, which is consistent with the observation by EM (Roberts et al., 2009).Open in a separate windowFigure 3.Atomic models of the MTBD of dynein. (A) D. discoideum cytoplasmic dynein (PDB accession no. 3VKH). (B) C. reinhardtii dynein c (PDB accession no. 2RR7). The inset shows the side view, highlighting the dynein c–specific insert. (C) Mouse cytoplasmic dynein (PDB accession no. 3ERR). (D) Mouse cytoplasmic dynein fit to the MTBD–microtubule complex derived from cryo-EM (PDB accession no. 3J1T). All the MTBD structures were aligned using least square fits and color-coded with a gradient from the N to C terminus. CC1, coiled coil helix 1; CC2, coiled coil helix 2. The blue arrowheads points to the junction between MTBD and the stalk, where a well-conserved proline residue (colored pink) is located. In C and D, two residues (isoleucine 3269 and leucine 3417) are shown as spheres. The two residues form hydrophobic contacts in the β-registry (C), whereas they are separated in the α-registry (D) because of the sliding between the two α-helices (blue and red arrows). Conformational changes observed in the mouse dynein MTBD in complex with a microtubule by cryo-EM are shown by black arrows. Note that the cryo-EM density map does not have enough resolution to observe sliding between CC1 and CC2. The sliding was modeled based on targeted molecular dynamics (Redwine et al., 2012).Various mechanisms have been proposed to explain how the affinity between the MTBD and a microtubule is controlled. Gibbons et al. (2005) proposed “the helix-sliding hypothesis” (for review see Cho and Vale, 2012). In brief, this hypothesis proposes that the sliding between two α-helices CC1 and CC2 (Fig. 3, C and D; blue and red arrows) may control the affinity of this domain to a microtubule. When Gibbons’s classification (Gibbons et al., 2005) of the sliding state is applied to the three MTBD structures, the stalk in the D. discoideum dynein motor domain is in the “α-registry” state (not visible in Fig. 3 A because of the melting of CC2), which corresponds to the strong binding state. However, the mouse cytoplasmic and C. reinhardtii axonemal MTBDs have the “β-registry” stalk (Fig. 3 C), which corresponds to the weak binding state.To observe conformational changes induced by the α-registry and/or microtubule binding, Redwine et al. (2012) solved the structure of mouse dynein MTBD in complex with a microtubule at 9.7-Å resolution using cryo-EM and single particle analysis. The MTBD was coupled with seryl tRNA-synthetase to fix the stalk helix in the α-registry. At this resolution, α-helices are visible, and they used molecular dynamics to fit the crystal structure of mouse MTBD (β-registry) to the cryo-EM density map. According to this result, the first helix H1 moves ∼10 Å to a position that avoids a clash with the microtubule (Fig. 3 D, black arrows). This also induces opening of the stalk helix (CC1). Together with mutagenesis and single-molecule motility assays, Redwine et al. (2012) proposed that this new structure represents the strong binding state. Currently, it is not clear why the MTBD structure of D. discoideum dynein motor domain (α-registry, Fig. 3 A) is not similar to the new α-registry mouse dynein MTBD, and this problem needs to be addressed by further studies.

Structures around the first ATP binding site

Another central question about motor proteins is how Ångstrom-scale changes around the nucleotide are amplified to generate steps >8 nm. For dynein, the interface between the first nucleotide-binding pocket and the linker seem to be the key force-generating element (Fig. 4). The crystal structures of dynein give us clues about how nucleotide-induced conformational changes may be transmitted to and amplified by the linker domain.Open in a separate windowFigure 4.Structures around the first ATP binding site. (A) Schematic domain structure of the head domain. Regions contacting the linker domain are colored purple. (B) AAA submodules surrounding the first nucleotide-binding pocket (PDB accession no. 3VKG, chain A). The linker is connected to AAA1 domain by the “N-loop.” To highlight that the two finger-like structures are protruding, the shadow of the atomic structure has been cast on the plane parallel to the head domain. (C) Interaction between the linker and the two finger-like structures. The pink arrowhead points to the hinge-like structure of the linker. The pink numbers indicates the subdomain of the linker.The main ATP catalytic site is located between AAA1 and AAA2 (Fig. 4, A and B). There are four ADP molecules in the D. discoideum dynein crystal structures, but the first ATP binding site alone drives the microtubule-activated ATPase activity, based on biochemical experiments on dyneins whose ATP binding sites were mutated (Kon et al., 2012).One AAA+ module is composed of a large submodule and a small α submodule (Fig. 4 B). The large α/β submodule is located inside of the ring and the small α submodule is located outside. The large submodule bulges toward the linker face, and the overall ring forms a dome-like shape (Fig. 1 D).The main ATP catalytic site is surrounded by three submodules: AAA1 large α/β, AAA1 small α, and AAA2 large α/β (Fig. 4, A and B). Based on the structural changes of other AAA+ proteins (Gai et al., 2004; Suno et al., 2006; Wendler et al., 2012), the gap between AAA1 and AAA2 modules is expected to open and close during the ATPase cycle.In fact, the size of the gap varies among the dynein crystal structures. The crystal structures of yeast dyneins show a larger gap between AAA1 and AAA2, which might be the reason why no nucleotide was found in the binding pocket. Although Schmidt et al. (2012) soaked the crystals in a high concentration of various nucleotides (up to 25 mM of ATP), no electron densities corresponding to the nucleotide were observed at the first ATP binding site. Among dynein crystal structures, one of D. discoideum dynein (PDB accession no. 3VKH, chain A) has the smallest gap, but it is still considered to be in an “open state” because the arginine finger in the AAA2 module (Fig. 4 B, red) is far from the phosphates of ADP. Because the arginine finger is essential for ATP hydrolysis in other AAA+ proteins (Ogura et al., 2004), the gap is expected to close and the arginine finger would stabilize the negative charge during the transition state of ATP hydrolysis.The presumed open/close conformational change between AAA1 and AAA2 would result in the movement of two “finger-like” structures protruding from the AAA2 large α/β submodule (Fig. 4 B). The two finger-like structures are composed of the H2 insert β-hairpin and preSensor I (PS-I) insert. In D. discoideum dynein crystal structure, the two finger-like structures are in contact with the “hinge-like cleft” of the linker (Fig. 4 C, pink arrowhead). The hinge-like cleft is one of the thinnest parts of the linker, where only one α-helix is connecting between the linker subdomains 2 and 3.In the yeast crystal structures, which have wider gaps between AAA1 and AAA2, the two finger-like structures are not in direct contact with the linker and separated by 18 Å. Instead, the N-terminal region of the linker is in contact with the AAA5 domain (Fig. 4 A). To test the functional role of the linker–AAA5 interaction, Schmidt et al. (2012) mutated a residue involved in the interaction (Phe3446) and found that the mutation resulted in severe motility defects, showing strong microtubule binding and impaired ATPase activities. In D. discoideum dynein crystals, there is no direct interaction between AAA5 and the linker, which suggests that the gap between AAA1 and AAA2 may influence the interaction between the head and linker domain. The contact between the linker and AAA5 may also influence the gap around AAA5, because the gap between AAA5 and AAA6 is large in yeast dynein crystal, whereas the one between AAA4 and AAA5 is large in D. discoideum dynein.The movement of two finger-like structures would induce remodeling of the linker. According to the recent cryo-EM 3D reconstructions of cytoplasmic dynein and axonemal dynein c (Roberts et al., 2012), the linker is visible across the head and there is a large gap between AAA1 and AAA2 in the no-nucleotide state. This linker structure is considered to be the “straight” state (Fig. 2, A and E). In the presence of ADP vanadate, the gap between AAA1 and AAA2 is closed and the N-terminal region of linker is near AAA3, which corresponds to the pre-power stroke “hinged” state (Fig. 2, C and D). The transition from the hinged state to the straight state of the linker is considered to be the force-generating step of dynein.

Processivity of dynein

As the structure of dynein is different from other motor proteins, dynein’s stepping mechanism is also distinct. Both dynein and kinesin are microtubule-based motors and move processively. Based on the single molecule tracking experiment with nanometer accuracy (Yildiz et al., 2004), it is widely accepted that kinesin moves processively by using its two motor domain alternately, called the “hand-over-hand” mechanism. To test whether dynein uses a similar mechanism to kinesin or not, recently Qiu et al. (2012) and DeWitt et al. (2012) applied similar single-molecule approaches to dynein.To observe the stepping, the two head domains of yeast recombinant cytoplasmic dynein were labeled with different colors and the movement of two head domains was tracked simultaneously. If dynein walks by the hand-over-hand mechanism, the step size would be 16 nm and the stepping of one head domain would always be followed by the stepping of another head domain (alternating pattern), and the trailing head would always take a step (Fig. 2 F). Contrary to this prediction, both groups found that the stepping of the head domains is not coordinated when the two head domains are close together. These observations indicated that the chances of a leading or trailing head domain stepping are not significantly different (Fig. 2 G; DeWitt et al., 2012; Qiu et al., 2012).This stepping pattern predicts that the distance between the head domains can be long. In fact, the distance between the two head domains is on average ∼18 ± 11 nm (Qiu et al., 2012) or 28.4 ± 10.7 nm (DeWitt et al., 2012), and as large as ∼50 nm (DeWitt et al., 2012). When the two head domains are separated, there is a tendency where stepping of the trailing head is preferred over that of the forward head.In addition, even though the recombinant cytoplasmic dynein is a homodimer, the two heavy chains do not function equally. While walking along the microtubule, the leading head tends to walk on the right side, whereas the trailing head walks on the left side (DeWitt et al., 2012; Qiu et al., 2012). This arrangement suggests that the stepping mechanism is different between the two heads. In fact, when one of the two dynein heavy chains is mutated to abolish the ATPase activity at AAA1, the heterodimeric dynein still moves processively (DeWitt et al., 2012), with the AAA1-mutated dynein heavy chain remaining mostly in the trailing position. This result clearly demonstrates that allosteric communication between the two AAA1 domains is not required for processivity of dynein. It is likely that the mutated head acts as a tether to the microtubule, as it is known that wild-type dynein can step processively along microtubules under external load even in the absence of ATP (Gennerich et al., 2007).These results collectively show that dynein moves by a different mechanism from kinesin. It is likely that the long stalk and tail allow dynein to move in a more flexible manner.

Dyneins in the axoneme

As mentioned in the introduction, >10 dyneins work in motile flagella and cilia. The core of flagella and cilia is the axoneme, which is typically made of nine outer doublet microtubules and two central pair microtubules (“9 + 2,” Fig. 5 A). The axonemes are found in various eukaryotic cells ranging from the single-cell algae C. reinhardtii to human. Recent extensive cryo-electron tomography (cryo-ET) in combination with genetics revealed the highly organized and complex structures of axonemes that are potentially important for regulating dynein activities (Fig. 5, C and D; Nicastro et al., 2006; Bui et al., 2008, 2009, 2012; Heuser et al., 2009, 2012; Movassagh et al., 2010; Lin et al., 2012; Carbajal-González et al., 2013; Yamamoto et al., 2013).Open in a separate windowFigure 5.Arrangement of axonemal dyneins. (A) The schematic structure of the motile 9 + 2 axoneme, viewed from the base of flagella. (B) Quasi-planar asymmetric movement of the 9 + 2 axoneme typically observed in trachea cilia or in C. reinhardtii flagella. (C and D) 3D structure of a 96-nm repeat of doublet microtubules in the distal/central region of C. reinhardtii flagella (EMDB-2132; Bui et al., 2012). N-DRC, the nexin-dynein regulatory complex; ICLC, intermediate chain/light chain complex. Inner arm dynein subspecies are labeled according to Bui et al. (2012) and Lin et al. (2012). To avoid the confusion with the linker domain of dynein, the structures connecting between outer and inner arm dyneins are labeled as “connecters,” which are normally called “linkers.” Putative ATP binding sites of outer arm dynein determined by biotin-ADP (Oda et al., 2013) are indicated by orange circles. The atomic structure of cytoplasmic dynein is placed into the β-heavy chain of outer arm dynein and its enlarged view is shown in the inset. (D) Two doublet microtubules, viewed from the base of flagella.The basic mechanochemical cycles of axonemal dyneins are believed to be shared with cytoplasmic dynein. Dynein c is an inner arm dynein of C. reinhardtii and used extensively to investigate the conformational changes of dynein, as shown in Fig. 2 (A–E), by combining EM and single-particle analysis (Burgess et al., 2003; Roberts et al., 2012). Structural changes of axonemal dyneins complexed with microtubules are also observed by quick-freeze and deep-etch EM (Goodenough and Heuser, 1982; Burgess, 1995), cryo-EM (Oda et al., 2007), negative-staining EM (Ueno et al., 2008), and cryo-ET (Movassagh et al., 2010). According to these studies, the AAA+ head domains are constrained near the A-tubule in the no-nucleotide state. In the presence of nucleotide, the head domains move closer to the B-tubule and/or the minus end of microtubule, and their appearance becomes heterogeneous, which is consistent with the observation of isolated dynein c that shows greater flexibility between tail and stalk in the ADP/vanadate state (Burgess et al., 2003).One of the controversies about the structural changes of axonemal dyneins is whether their stepping involves “rotation” or “shift” of the head (Fig. 2, B to D). The stalk angle relative to the microtubule seems to be a constant ∼60° irrespective of the nucleotide state (Ueno et al., 2008; Movassagh et al., 2010). This angle is similar to the angle obtained from cryo-EM study of the MTBD–microtubule complex (Redwine et al., 2012). Based on these observations, Ueno et al. (2008) and Movassagh et al. (2010) hypothesize that the “shift” of the head pulls the B-microtubule toward the distal end. However, Roberts et al. (2012) propose that the “rotation” of head and stalk is involved in the stepping based on the docking of dynein c head into an averaged flagella tomogram obtained by Movassagh et al. (2010). This issue needs to be resolved by more reliable and high-resolution data, but these two models may not be mutually exclusive. For example, averaged tomograms may be biased toward the microtubule-bound stalk because tomograms are aligned using microtubules.To interpret these structural changes of axonemal dyneins, docking atomic models of dynein is necessary. According to Roberts et al. (2012), the linker face of inner arm dynein c is oriented outside of axoneme (Fig. 5 D). For outer arm dyneins, we used cryo-EM in combination with biotin-ADP-streptavidin labeling and showed that the ATP binding site, most likely AAA1, is on the left side of the AAA+ head (Fig. 5 C; Oda et al. (2013)). Assuming that the stalks extend out of the plane toward the viewer, the linker face of outer arm dynein is oriented outside of axoneme (Fig. 5 C, inset; and Fig. 5 D). If it were the opposite, the AAA1 would be located on the right side of the AAA+ head. In summary, both inner and outer arm dynein seem to have the same arrangement, with their linker face oriented outside of the axoneme (Fig. 5 D).A unique characteristic of axonemal dyneins is that these dyneins are under precise temporal and spatial control. To generate a planer beating motion (Fig. 5 B), dyneins should be asymmetrically controlled, because the dyneins located on doublets 2–4 drive the effective stroke, whereas the ones on doublets 6–8 drive the recovery stroke (Fig. 5 A). Based on the cryo-ET observation of axonemes, Nicastro et al. (2006) proposed that “linkers” between dyneins provide hard-wiring to coordinate motor activities. Because the linkers in axonemes are distinct structures from the linker domain of dynein, for clarity, here we call them “connecters.” According to the recent cryo-ET of proximal region of C. reinhardtii flagella (Bui et al., 2012), there are in fact asymmetries among nine doublets that are localized to the connecters between outer and inner arm dynein, called the outer-inner dynein (OID) connecters (Fig. 5, A and C). Recently we identified that the intermediate chain 2 (IC2) of outer arm dynein is a part of the OID connecters, and a mutation of the N-terminal region of IC2 affects functions of both outer and inner arm dyneins (Oda et al., 2013), which supports the idea that the connecters between dyneins are involved in axonemal dynein regulation.

Closing remarks

Thanks to the crystal structures, we can now design and interpret experiments such as single molecule assays and EM based on the atomic models of dynein. Our understanding of the molecular mechanism and cellular functions of dyneins will be significantly advanced by these experiments in the near future.One important direction of dynein research is to understand the motor mechanisms closer to the in vivo state. For example, the step sizes of cytoplasmic dynein purified from porcine brain is ∼8 nm independent of load (Toba et al., 2006). This result suggests that intermediate and light chain bound to the dynein heavy chain may modulate the motor activity of dynein. To address such questions, Trokter et al. (2012) reconstituted human cytoplasmic dynein complex from recombinant proteins, although the reconstituted dynein did not show robust processive movement. Further studies are required to understand the movement of cytoplasmic dynein. Similarly, axonemal dyneins should also be studied using mutations in a specific gene that does not affect the overall flagella structure, rather than depending on null mutants that cause the loss of large protein complexes.Detailed full chemomechanical cycle of dynein and its regulation are of great importance. Currently, open/closed states of the gap between AAA1 and AAA2 are not clearly correlated with the chemomechanical cycle of dynein. Soaking dynein crystal with nucleotides showed that the presence of ATP alone is not sufficient to close the gap, at least in the crystal (Schmidt et al., 2012). This result suggests that other factors such as a conformational change of the linker are required. For other motors, ATP hydrolysis is an irreversible chemical step, which is often “gated” by strain. In the case of kinesin, ATP is hydrolyzed by a motor domain only when a forward strain is applied by the other motor domain through the neck linker (Cross, 2004; Kikkawa, 2008). A similar strain-based gating mechanism may play important roles in controlling the dynein ATPase. Upon MTBD binding to the forward binding site, a strain between MTBD and tail would be applied to the dynein molecule. The Y-shaped stalk and strut/buttress under the strain would force the head domain to close the gap between AAA4 and AAA5 (Fig. 2 H). Similarly, the linker under the strain would be hooked onto the two finger-like structures and close the gap between AAA1 and AAA2 (Fig. 2 H). The gap closure then triggers ATP hydrolysis by dynein. This strain-based gating of dynein is consistent with the observation that the rate of nonadvancing backward steps, which would depend on ATP hydrolysis, is increased by load applied to dynein (Gennerich et al., 2007). To explain cilia and flagella movement, the geometric clutch hypothesis has been proposed (Lindemann, 2007), which contends that the forces transverse (t-force) to the axonemal axis act on the dynein to regulate dynein activities. In the axoneme, dynein itself can be the sensor of the t-force by the strain-based gating mechanism. Further experiments are required to test this idea, but the strain-based gating could be a shared property of biological motors.     

Novel Growth Regime of MDCK II Model Tissues on Soft Substrates

It is well established that MDCK II cells grow in circular colonies that densify until contact inhibition takes place. Here, we show that this behavior is only typical for colonies developing on hard substrates and report a new growth phase of MDCK II cells on soft gels. At the onset, the new phase is characterized by small, three-dimensional droplets of cells attached to the substrate. When the contact area between the agglomerate and the substrate becomes sufficiently large, a very dense monolayer nucleates in the center of the colony. This monolayer, surrounded by a belt of three-dimensionally packed cells, has a well-defined structure, independent of time and cluster size, as well as a density that is twice the steady-state density found on hard substrates. To release stress in such dense packing, extrusions of viable cells take place several days after seeding. The extruded cells create second-generation clusters, as evidenced by an archipelago of aggregates found in a vicinity of mother colonies, which points to a mechanically regulated migratory behavior.Studying the growth of cell colonies is an important step in the understanding of processes involving coordinated cell behavior such as tissue development, wound healing, and cancer progression. Apart from extremely challenging in vivo studies, artificial tissue models are proven to be very useful in determining the main physical factors that affect the cooperativity of cells, simply because the conditions of growth can be very well controlled. One of the most established cell types in this field of research is the Madin-Darby canine kidney epithelial cell (MDCK), originating from the kidney distal tube (1). A great advantage of this polarized epithelial cell line is that it retained the ability for contact inhibition (2), which makes it a perfect model system for studies of epithelial morphogenesis.Organization of MDCK cells in colonies have been studied in a number of circumstances. For example, it was shown that in three-dimensional soft Matrigel, MDCK cells form a spherical enclosure of a lumen that is enfolded by one layer of polarized cells with an apical membrane exposed to the lumen side (3). These structures can be altered by introducing the hepatocyte growth factor, which induces the formation of linear tubes (4). However, the best-studied regime of growth is performed on two-dimensional surfaces where MDCK II cells form sheets and exhibit contact inhibition. Consequently, the obtained monolayers are well characterized in context of development (5), mechanical properties (6), and obstructed cell migration (7–9).Surprisingly, in the context of mechanics, several studies of monolayer formation showed that different rigidities of polydimethylsiloxane gels (5) and polyacrylamide (PA) gels (9) do not influence the nature of monolayer formation nor the attainable steady-state density. This is supposedly due to long-range forces between cells transmitted by the underlying elastic substrate (9). These results were found to agree well with earlier works on bovine aortic endothelial cells (10) and vascular smooth muscle cells (11), both reporting a lack of sensitivity of monolayers to substrate elasticity. Yet, these results are in stark contrast with single-cell experiments (12–15) that show a clear response of cell morphology, focal adhesions, and cytoskeleton organization to substrate elasticity. Furthermore, sensitivity to the presence of growth factors that are dependent on the elasticity of the substrate in two (16) and three dimensions (4) makes this result even more astonishing. Therefore, we readdress the issue of sensitivity of tissues to the elasticity of the underlying substrate and show that sufficiently soft gels induce a clearly different tissue organization.We plated MDCK II cells on soft PA gels (Young’s modulus E = 0.6 ± 0.2 kPa), harder PA gels (E = 5, 11, 20, 34 kPa), and glass, all coated with Collagen-I. Gels were prepared following the procedure described in Rehfeldt et al. (17); rigidity and homogeneity of the gels was confirmed by bulk and microrheology (see the Supporting Material for comparison). Seeding of MDCK II cells involved a highly concentrated solution dropped in the middle of a hydrated gel or glass sample. For single-cell experiments, cells were dispersed over the entire dish. Samples were periodically fixed up to Day 12, stained for nuclei and actin, and imaged with an epifluorescence microscope. Details are described in the Supporting Material.On hard substrates and glass it was found previously that the area of small clusters expands exponentially until the movement of the edge cannot keep up with the proliferation in the bulk (5). Consequently, the bulk density increases toward the steady state, whereas the density of the edge remains low. At the same time, the colony size grows subexponentially (5). This is what we denote “the classical regime of growth”. Our experiments support these observations for substrates with E ≥ 5 kPa. Specifically, on glass, colonies start as small clusters of very low density of 700 ± 200 cells/mm² (Fig. 1, A and B), typically surrounded by a strong actin cable (Fig. 1, B and C). Interestingly, the spreading area of single cells (Fig. 1 A) on glass was found to be significantly larger, i.e., (2.0 ± 0.9) × 10⁻³ mm². After Day 4 (corresponding cluster area of 600 ± 100 mm²), the density in the center of the colony reached the steady state with 6,800 ± 500 cells/mm², whereas the mean density of the edge profile grew to 4,000 ± 500 cells/mm². This density was retained until Day 12 (cluster area 1800 ± 100 mm²), which is in agreement with previous work (9).Open in a separate windowFigure 1Early phase of cluster growth on hard substrates. (A) Well-spread single cells, and small clusters with a visible actin cable 6 h after seeding. (B) Within one day, clusters densify and merge, making small colonies. (C) Edge of clusters from panel B.In colonies grown on 0.6 kPa gels, however, we encounter a very different growth scenario. The average spreading area of single cells is (0.34 ± 0.3) × 10⁻³ mm², which is six times smaller than on glass substrates (Fig. 2 A). Clusters of only few cells show that cells have a preference for cell-cell contacts (a well-established flat contact zone can be seen at the cell-cell interface in Fig. 2 A) rather than for cell-substrate contacts (contact zone is diffusive and the shape of the cells appears curved). The same conclusion emerges from the fact that dropletlike agglomerates, resting on the substrate, form spontaneously (Fig. 2 A), and that attempts to seed one single cluster of 90,000 cells fail, resulting in a number of three-dimensional colonies (Fig. 2 A). When the contact area with the substrate exceeds 4.7 × 10⁻³ mm², a monolayer appears in the center of such colonies (Fig. 2 B). The colonies can merge, and if individual colonies are small, the collapse into a single domain is associated with the formation of transient irregular structures (Fig. 2 B). Ultimately, large elliptical colonies (average major/minor axis of e = 1.8 ± 0.6) with a smooth edge are formed (Fig. 2 C), unlike on hard substrates where circular clusters (e = 1.06 ± 0.06) with a ragged edge comprise the characteristic phenotype.Open in a separate windowFigure 2Early phase of cluster growth on soft substrates. (A) Twelve hours after seeding, single cells remain mostly round and small. They are found as individual, or within small, three-dimensional structures (top). The latter nucleate a monolayer in their center (bottom), if the contact area with the substrate exceeds ∼5 × 10⁻³ mm². (B) Irregularly-shaped clusters appear due to merging of smaller droplets. A stable monolayer surrounded by a three-dimensional belt of densely packed cells is clearly visible, even in larger structures. (C) All colonies are recorded on Day 4.Irrespective of cluster size, in the new regime of growth, the internal structure is built of two compartments (Fig. 2 B):

• 1.The first is the edge (0.019 ± 0.05-mm wide), a three-dimensional structure of densely packed cells. This belt is a signature of the new regime because on hard substrates the edge is strictly two-dimensional (Fig. 1 C).
• 2.The other is the centrally placed monolayer with a spatially constant density that is very weakly dependent on cluster size and age (Fig. 3). The mean monolayer density is 13,000 ± 2,000 cells/mm², which is an average over 130 clusters that are up to 12 days old and have a size in the range of 10⁻³ to 10 mm², each shown by a data point in Fig. 3. This density is twice the steady-state density of the bulk tissue in the classical regime of growth.Open in a separate windowFigure 3Monolayer densities in colonies grown on 0.6 kPa substrates, as a function of the cluster size and age. Each cluster is represented by a single data point signifying its mean monolayer density. (Black lines) Bulk and (red dashed lines) edge of steady-state densities from monolayers grown on glass substrates. Error bars are omitted for clarity, but are discussed in the Supporting Material.

Until Day 4, the monolayer is very homogeneous, showing a nearly hexagonal arrangement of cells. From Day 4, however, defects start to appear in the form of small holes (typical size of (0.3 ± 0.1) × 10⁻³ mm²). These could be attributed to the extrusions of viable cells, from either the belt or areas of increased local density in the monolayer (inset in Fig. 4). This suggests that extrusions serve to release stress built in the tissue, and, as a consequence, the overall density is decreased.Open in a separate windowFigure 4Cell nuclei within the mother colony and in the neighboring archipelago of second-generation clusters grown on 0.6 kPa gels at Day 12. (Inset; scale bar = 10 μm) Scar in the tissue, a result of a cell-extrusion event. (Main image; scale bar = 100 μm) From the image of cell nuclei (left), it is clear that there are no cells within the scar, whereas the image of actin (right) shows that the cytoplasm of the cells at the edge has closed the hole.Previous reports suggest that isolated MDCK cells undergo anoikis 8 h after losing contact with their neighbors (18). However, in this case, it appears that instead of dying, the extruded cells create new colonies, which can be seen as an archipelago surrounding the mother cluster (Fig. 4). The viability of off-cast cells is further evidenced by the appearance of single cells and second-generation colonies with sizes varying over five orders of magnitude, from Day 4 until the end of the experiment, Day 12. Importantly, no morphological differences were found in the first- and second-generation colonies.In conclusion, we show what we believe to be a novel phase of growth of MDCK model tissue on soft PA gels (E = 0.6 kPa) that, to our knowledge, despite previous similar efforts (9), has not been observed before. This finding is especially interesting in the context of elasticity of real kidneys, for which a Young’s modulus has been found to be between 0.05 and 5 kPa (19,20). This coincides with the elasticity of substrates studied herein, and opens the possibility that the newly found phase of growth has a particular biological relevance. Likewise, the ability to extrude viable cells may point to a new migratory pathway regulated mechanically by the stresses in the tissue, the implication of which we hope to investigate in the future.     

Bringing Rad52 foci into focus

In 2007, we published the results of a genome-wide screen for ORFs that affect the frequency of Rad52 foci in yeast. That paper was published within the constraints of conventional online publishing tools, and it provided only a glimpse into the actual screen data. New tools in the JCB DataViewer now show how these data can—and should—be shared.

Complete screen data

https://doi.org/10.1083/jcb.201108095.dv The Rad52 protein has pivotal functions in double strand break repair and homologous recombination. The activity of Rad52 is often monitored by the subnuclear foci that it forms spontaneously in S phase or after DNA damage (Lisby et al., 2001). In mammals, the functions of yeast Rad52 may be divided between human RAD52 and the tumor suppressor BRCA2 (Feng et al., 2011). The full host of molecular players that govern Rad52 focus formation and maintenance was not well known when we initiated our screen. Using a high-content, image-based assay, we assessed the proportion of cells containing spontaneous Rad52-YFP foci in 4,805 viable Saccharomyces cerevisiae deletion strains (Alvaro et al., 2007). Starting with 96-well arrays of a deletion strain library, we created hybrid diploid strains (homozygous for the deletions) using systematic hybrid loss of heterozygosity (SHyLOH; Alvaro et al., 2006). We then manually and sequentially examined each strain using epifluorescence microscopy for the presence of Rad52-YFP foci. All of our image analysis was performed manually.As is often the case, our screen was published showing only a couple of representative images and providing data tables to summarize the findings. Tomes of data that could not be included in the published paper were relegated to supplemental Excel tables, typical of genome-wide screens. Also, the raw image data were sequestered in the laboratory on DVDs. With considerable help from JCB and Glencoe Software, we are delighted that the raw data from our Rad52 screen are now freely available online through the JCB DataViewer. A new interface within the JCB DataViewer brings presentation and preservation of high-content, multidimensional image-based screening data to a whole new level. To facilitate the development of this new interface, JCB required a dataset that was not time sensitive, and we were happy to provide our previously published Rad52 data. In the future, this new interface will be used to present high-content screening (HCS) datasets linked to published JCB papers. Indeed, the first publication of this sort appears in this issue of JCB (Rohn et al., 2011).The presentation of our data in the JCB DataViewer clearly shows the many benefits of this new publishing resource for the scientific community. Users now can view the complete collection of 3D image data across the entire screen, not just the two images in our original publication (Alvaro et al., 2007). Additionally, detailed information on image acquisition parameters, locus identities, and more is easily accessible (Fig. 1). Phenotypic scoring results can be visualized in interactive chart formats (Fig. 1), and search (Fig. 2) and database-linking tools (Fig. 1) allow extensive mining of the data for genes and phenotypes of interest. These tools provide an unprecedented view into HCS data in their entirety, as well as a means for authors to share and archive their data. This kind of accessibility to the direct visualization of the entire set of original screening data, on a scale previously only available to the scientists performing the screen, allows users to understand the full context of the image data analyzed in a screen. Furthermore, it is only through full access to the raw images and associated metadata that this information can be of maximum use to the community for large-scale data mining.Open in a separate windowFigure 1.The HCS interface of the JCB DataViewer provides interactive tools for the analysis of complete datasets from image-based screens. The miniviewer (top left) provides information for each gene in the screen through a zoomable and scrollable display of original multidimensional image data. It contains detailed metadata and a gene ontology (GO) summary, a link to a relevant external database (e.g., the Saccharomyces Genome Database [SGD]; top right), and a link to phenotypic scoring data for the complete screen in the chart view (bottom right). Within the chart view, hits designated by the screen authors are shown in blue, and the strain currently on display in the miniviewer is shown in red. The plate view (bottom left) shows the position of the strain of interest (red box) relative to other strains screened.Open in a separate windowFigure 2.The HCS interface of the JCB DataViewer provides search tools for the mining of complete datasets from image-based screens. (A) Users can search screen data by gene name or keywords (e.g., DNA repair). (B) Users can pick candidates for further analysis from the phenotypic scoring information in the chart view.As in all large-scale screens, the real data are variable; e.g., some strains provide a clear Rad52 focus phenotype, whereas others are more ambiguous. For our particular screen, images were not collected using automated technology but were acquired manually, strain by strain, over a period of months, leading to different levels of fluorescence intensity of Rad52-YFP as a result of, for example, changes in the intensity of our mercury arc lamp. Differences also exist in the number of fields and z stacks captured for each strain. In the absence of automated image collection, images from the primary screen in a few cases were not archived with the others and thus for all intents and purposes have been lost. In addition, our Rad52 screen only assayed nonessential genes, and some mutants are refractory to the SHyLOH methodology. Knowing all of this information allows users to view the data in a realistic manner and further highlights the importance of providing a central repository to archive HCS data.When published through conventional publication media, many important imaging details are known only to the original screeners. The new HCS interface of the JCB DataViewer shines a light on screening data as metadata become freely accessible, allowing any user to ask novel questions of the dataset. For example, the plate view for images (Fig. 1) allows users to assess whether neighboring colonies played any role in determining the phenotype and to delve deeper into why that might be. For example, are any “hits” a result of contamination from adjacent strains, resulting in clusters of positives? In the context of an automated screen, how were control and experimental samples arrayed across a plate during data collection? Did the controls on a particular plate behave as expected? Because our screen used a novel chromosome-specific loss of the heterozygosity method, users can ask whether mutations on specific chromosomes share features of Rad52 foci levels. The global resolution of the dataset provided through this new interface puts users of the dataset as close to the seat of the original screening scientist as possible, allowing them to ask, “what did the authors really see?”Presenting HCS data in the JCB DataViewer holds immense potential value to the scientific community. Through this new interface, users can access powerful interactive tools for analyzing scored phenotypes across the entire dataset (Fig. 1). Each gene ID can be charted against the phenotypic parameters scored in the original screen (e.g., the percentage of cells with Rad52 foci) and compared with all other loci (Fig. 1). Users can take our data and create their own list of hits based on their criteria, create a gallery of thumbnails for their selections (Fig. 2), and seamlessly move between their list of hits and the original data in the plate display format (Fig. 1). Users can also compare their candidates with our list (Fig. 2). The ability to visualize these data for comparative analyses creates a whole new perspective. The HCS interface of the JCB DataViewer allows users to look for their favorite gene, compare related genes, and discover new genes they never anticipated were involved in a given process.In summary, these new features of the JCB DataViewer will allow users to access the primary data from large-scale screens and to look at the full dataset to see what all of the images really look like. The ability to mine these data opens up whole new dimensions in data sharing and transparency. In the future, we anticipate that it will be possible to search many genome-wide screens, such as our Rad52 dataset, to identify commonalities in protein localization, concentration, cell morphology, etc. However, this will only occur if image data are archived and made freely available to the scientific community. We wholeheartedly support the efforts of JCB and hope that groups that use image-based HCS will increasingly make their images available using tools such as the JCB DataViewer.     

Triple X Egyptian woman and a Down's syndrome offspring

The 47, XXX karyotype (triple X) has a frequency of 1 in 1000 female newborns. However, this karyotype is not usually suspected at birth or childhood. Female patients with a sex chromosome abnormality may be fertile. In patients with a 47, XXX cell line there appears to be an increased risk of a cytogenetically abnormal child but the extent of this risk cannot yet be determined; it is probably lower in the non-mosaic 47, XXX patient than the mosaic 46, XX/47, XXX one. We describe a new rare case of triple X woman and a Down''s syndrome offspring. The patient is 26 years of age. She is a housewife, her height is 160 cm and weight is 68 kg and her physical features and mentality are normal. She has had one pregnancy at the age of 25 years resulted in a girl with Down''s syndrome. The child had 47 chromosomes with trisomy 21 (47, XX, +21) Figure 1. The patient also has 47 chromosomes with a triple X karyotype (47, XX, +X) Figure 2. The patient''s husband (27 years old) is physically and mentally normal. He has 46 chromosomes with a normal XY karyotype (46, XY). There are neither Consanguinity between her parent''s nor she and her husband.Open in a separate windowFigure 1Karyotype 47, XX + 21 of the daughter of Triple X syndromeOpen in a separate windowFigure 2Karyptype 47, XX + X of the Down syndrome''s mother     

The evolution of genomic imprinting: theories,predictions and empirical tests

The epigenetic phenomenon of genomic imprinting has motivated the development of numerous theories for its evolutionary origins and genomic distribution. In this review, we examine the three theories that have best withstood theoretical and empirical scrutiny. These are: Haig and colleagues'' kinship theory; Day and Bonduriansky''s sexual antagonism theory; and Wolf and Hager''s maternal–offspring coadaptation theory. These theories have fundamentally different perspectives on the adaptive significance of imprinting. The kinship theory views imprinting as a mechanism to change gene dosage, with imprinting evolving because of the differential effect that gene dosage has on the fitness of matrilineal and patrilineal relatives. The sexual antagonism and maternal–offspring coadaptation theories view genomic imprinting as a mechanism to modify the resemblance of an individual to its two parents, with imprinting evolving to increase the probability of expressing the fitter of the two alleles at a locus. In an effort to stimulate further empirical work on the topic, we carefully detail the logic and assumptions of all three theories, clarify the specific predictions of each and suggest tests to discriminate between these alternative theories for why particular genes are imprinted.The discovery of genomic imprinting, where the expression of an allele depends on its parental origin, motivated a diversity of theories attempting to explain its existence (Spencer and Clark, 2014). Three main theories have withstood scrutiny and are the focus of this review: Haig and colleagues'' kinship theory (Haig and Westoby, 1989; Haig, 2000a, 2004); Day and Bonduriansky''s (2004) sexual antagonism theory (see also Bonduriansky, 2007); and Wolf and Hager''s (2006) maternal–offspring coadaptation theory (see also Wolf and Hager, 2009; Wolf, 2013). Although these theories rest on different logic and fundamental assumptions, they share a critical common feature: some process creates a selective asymmetry between the maternally and paternally inherited allelic copies at a locus that causes selection to favor differential expression of the alleles (typically silencing of one of the copies) (Figures 1, ​,2,2, ​,33).Open in a separate windowFigure 1The kinship theory of genomic imprinting has two prerequisites: first, epigenetic marks that differentiate matrigenes from patrigenes; second, a difference in the relatedness of matrigenes and patrigenes to the social group. (a) The social group in the example depicted is a single litter of offspring, and multiple mating produces a relatedness asymmetry between half-siblings. The relatedness for matrigenes is ½ and the relatedness for patrigenes is 0. (Other sources of relatedness asymmetry are possible—e.g., sex-biased dispersal or high fitness variance in one sex—and social interactions are not limited to the juvenile period only). (b) The kinship theory envisions kin selection acting independently on genes of maternal and paternal origin and solves for the evolutionarily stable gene expression strategy for matrigenes and patrigenes. (c) For genes where the matrigenic allele''s optimum expression level is higher than that of the patrigene''s (e.g., a fetal growth inhibitor), the kinship theory predicts silencing of the patrigenic allele; for genes with the opposite effect (e.g., a fetal growth enhancer), the prediction is for patrigenic expression.Open in a separate windowFigure 2(a, b) The sexual antagonism theory of genomic imprinting starts with sexually antagonistic selection, which produces different allele frequencies, shown as pie charts, for genes of maternal and paternal origin. (c, d) Natural selection favors individuals that are able to express the fitter of the two alleles at a locus, which for males will be the patrigenic allele and for females will be the matrigenic allele. (In addition, the sexual antagonism theory may predict matrigenic or patrigenic expression in both sexes, such that the expressed allele derives from the parental sex that experiences stronger selection pressure. This scenario is not depicted).Open in a separate windowFigure 3(a) The maternal–offspring coadaptation theory of genomic imprinting relies on the correlation of genes in the mother and genes of maternal origin in the offspring (shown in light blue). (b) Fitness of offspring is determined by the interaction (shown in dark purple) between the phenotypes of mothers and offspring. (c) Imprinted silencing of the patrigenic allele can be favored for either of two reasons, depending on the genetic architecture of the interacting phenotypes. First, when a single gene governs the interaction and phenotypic matching between mothers and their offspring produces high fitness, then silencing of the patrigenic allele is beneficial to offspring because it raises the probability of producing a match. Second, if different loci are involved in the phenotypic interaction, past correlational selection will have produced a covariance between them, generating haplotypes with combinations of alleles that interact well together. (N.B. This multi-locus interaction is not depicted in the figure.) The offspring is more likely to inherit from its mother an allele that interacts well with the alleles in the mother''s genotype. This also favors the imprinted silencing of the patrigenic allele because it raises the probability that the offspring expresses an allele that makes for a good interaction with the maternal phenotype.Here we provide an overview of the fundamental logic and critical assumptions of these models. We then derive predictions that can be used to distinguish between theories. In doing so, we also highlight ambiguities in and overlap between the predictions they make, with a goal of motivating further research. In addition, we suggest some areas for future work that will test some of these predictions.     

3D-SIM Super-resolution of FtsZ and Its Membrane Tethers in Escherichia coli Cells

FtsZ, a bacterial homolog of eukaryotic tubulin, assembles into the Z ring required for cytokinesis. In Escherichia coli, FtsZ interacts directly with FtsA and ZipA, which tether the Z ring to the membrane. We used three-dimensional structured illumination microscopy to compare the localization patterns of FtsZ, FtsA, and ZipA at high resolution in Escherichia coli cells. We found that FtsZ localizes in patches within a ring structure, similar to the pattern observed in other species, and discovered that FtsA and ZipA mostly colocalize in similar patches. Finally, we observed similar punctate and short polymeric structures of FtsZ distributed throughout the cell after Z rings were disassembled, either as a consequence of normal cytokinesis or upon induction of an endogenous cell division inhibitor.The assembly of the bacterial tubulin FtsZ has been well studied in vitro, but the fine structure of the cytokinetic Z ring it forms in vivo is not well defined. Super-resolution microscopy methods including photoactivated localization microscopy (PALM) and three-dimensional-structured illumination microscopy (3D-SIM) have recently provided a more detailed view of Z-ring structures. Two-dimensional PALM showed that Z rings in Escherichia coli are likely composed of loosely-bundled dynamic protofilaments (1,2). Three-dimensional PALM studies of Caulobacter crescentus initially showed that Z rings were comprised of loosely bundled protofilaments forming a continuous but dynamic ring (1–3). However, a more recent high-throughput study showed that the Z rings of this bacterium are patchy or discontinuous (4), similar to Z rings of Bacillus subtilis and Staphylococcus aureus using 3D-SIM (5). Strauss et al. (5) also demonstrated that the patches in B. subtilis Z rings are highly dynamic.Assembly of the Z ring is modulated by several proteins that interact directly with FtsZ and enhance assembly or disassembly (6). For example, FtsA and ZipA promote ring assembly in E. coli by tethering it to the cytoplasmic membrane (7,8). SulA is an inhibitor of FtsZ assembly, induced only after DNA damage, which sequesters monomers of FtsZ to prevent its assembly into a Z ring (9). Our initial goals were to visualize Z rings in E. coli using 3D-SIM, and then examine whether any FtsZ polymeric structures remain after SulA induction. We also asked whether FtsA and ZipA localized in patchy patterns similar to those of FtsZ.We used a DeltaVision OMX V4 Blaze microscope (Applied Precision, GE Healthcare, Issaquah, WA) to view the high-resolution localization patterns of FtsZ in E. coli cells producing FtsZ-GFP (Fig. 1). Three-dimensional views were reconstructed using softWoRx software (Applied Precision). To rule out GFP artifacts, we also visualized native FtsZ from a wild-type strain (WM1074) by immunofluorescence (IF).Open in a separate windowFigure 1Localization of FtsZ in E. coli. (A) Cell with a Z ring labeled with FtsZ-GFP. (B) Rotated view of Z ring in panel A. (C) Cell with a Z ring labeled with DyLight 550 (Thermo Fisher Scientific, Waltham, MA). (D) Rotated view of Z ring in panel C. (B1 and D1) Three-dimensional surface intensity plots of Z rings in panels B and D, respectively. (E) A dividing cell producing FtsZ-GFP. The cell outline is shown in the schematic. (Asterisk) Focus of FtsZ localization; (open dashed ovals) filamentous structures of FtsZ. Three-dimensional surface intensity plots were created using the software ImageJ (19). Scale bars, 1 μm.Both FtsZ-GFP (Fig. 1, A, B, and B1) and IF staining for FtsZ (Fig. 1, C, D, and D1) consistently localized to patches around the ring circumference, similar to the B. subtilis and C. crescentus FtsZ patterns (4,5). Analysis of fluorescence intensities (see Fig. S1, A and B, in the Supporting Material) revealed that the majority of Z rings contain one or more gaps in which intensity decreases to background levels (82% for FtsZ-GFP and 69% for IF). Most rings had 3–5 areas of lower intensity, but only a small percentage of these areas had fluorescence below background intensity (34% for FtsZ-GFP and 21% for IF), indicating that the majority of areas with lower intensity contain at least some FtsZ.To elucidate how FtsZ transitions from a disassembled ring to a new ring, we imaged a few dividing daughter cells before they were able to form new Z rings (Fig. 1 E). Previous conventional microscopy had revealed dynamic FtsZ helical structures (10), but the resolution had been insufficient to see further details. Here, FtsZ visualized in dividing cells by 3D-SIM localized throughout as a mixture of patches and randomly-oriented short filaments (asterisk and dashed oval in Fig. 1, respectively). These structures may represent oligomeric precursors of Z ring assembly.To visualize FtsZ after Z-ring disassembly another way, we overproduced SulA, a protein that blocks FtsZ assembly. We examined E. coli cells producing FtsZ-GFP after induction of sulA expression from a pBAD33-sulA plasmid (pWM1736) with 0.2% arabinose. After 30 min of sulA induction, Z rings remained intact in most cells (Fig. 2 A and data not shown). The proportion of cellular FtsZ-GFP in the ring before and after induction of sulA was consistent with previous data (data not shown) (1,11).Open in a separate windowFigure 2Localization of FtsZ after overproduction of SulA. (A) Cell producing FtsZ-GFP after 0.2% arabinose induction of SulA for 30 min. (B) After 45 min. (B1) Magnified cell shown in panel B. (C) Cell producing native FtsZ labeled with AlexaFluor 488 (Life Technologies, Carlsbad, CA) 30 min after induction; (D) 45 min after induction. (D1) Magnified cell shown in panel D. Scale bars, 1 μm. (Asterisk) Focus of FtsZ localization; (open dashed ovals) filamentous structures of FtsZ.Notably, after 45 min of sulA induction, Z rings were gone (Fig. 2, B and B1), replaced by numerous patches and randomly-oriented short filaments (asterisk and dashed ovals in Fig. 2), similar to those observed in a dividing cell. FtsZ normally rapidly recycles from free monomers to ring-bound polymers (11), but a critical concentration of SulA reduces the pool of available FtsZ monomers, resulting in breakdown of the Z ring (9). The observed FtsZ-GFP patches and filaments are likely FtsZ polymers that disassemble before they can organize into a ring.We confirmed this result by overproducing SulA in wild-type cells and detecting FtsZ localization by IF (Fig. 2, C, D, and D1). The overall fluorescence patterns in cells producing FtsZ-GFP versus cells producing only native FtsZ were similar (Fig. 2, B1 and D1), although we observed fewer filaments with IF, perhaps because FtsZ-GFP confers slight resistance to SulA, or because the increased amount of FtsZ in FtsZ-GFP producing cells might titrate the SulA more effectively.Additionally, we wanted to observe the localization patterns of the membrane tethers FtsA and ZipA. Inasmuch as both proteins bind to the same C-terminal conserved tail of FtsZ (12–14), they would be expected to colocalize with the circumferential FtsZ patches in the Z ring. We visualized FtsA using protein fusions to mCherry and GFP (data not shown) as well as IF using a wild-type strain (WM1074) (Fig. 3 A). We found that the patchy ring pattern of FtsA localization was similar to the FtsZ pattern. ZipA also displayed a similar patchy localization in WM1074 by IF (Fig. 3 B).Open in a separate windowFigure 3Localization of FtsA (A) and ZipA (B) by IF using AlexaFluor 488. (C) FtsA-GFP ring. (D) Same cell shown in panel C with ZipA labeled with DyLight 550. (C1 and D1) Three-dimensional surface intensity plots of FtsA ring from panel C or ZipA ring from panel D, respectively. (E) Merged image of FtsA (green) and ZipA (red) from the ring shown in panels C and D. (F) Intensity plot of FtsA (green) and ZipA (red) of ring shown in panel E. The plot represents intensity across a line drawn counterclockwise from the top of the ring around the circumference, then into its lumen. Red/green intensity plot and three-dimensional surface intensity plots were created using the software ImageJ (19). Scale bar, 1 μm.To determine whether FtsA and ZipA colocalized to these patches, we used a strain producing FtsA-GFP (WM4679) for IF staining of ZipA using a red secondary antibody. FtsA-GFP (Fig. 3 C) and ZipA (Fig. 3 D) had similar patterns of fluorescence, although the three-dimensional intensity profiles (Fig. 3, C1 and D1) reveal slight differences in intensity that are also visible in a merged image (Fig. 3 E). Quantitation of fluorescence intensities around the circumference of the rings revealed that FtsA and ZipA colocalized almost completely in approximately half of the rings analyzed (Fig. 3 F, and see Fig. S2 A), whereas in the other rings there were significant differences in localization in one or more areas (see Fig. S2 B). FtsA and ZipA bind to the same C-terminal peptide of FtsZ and may compete for binding. Cooperative self-assembly of FtsA or ZipA might result in large-scale differential localization visible by 3D-SIM.In conclusion, our 3D-SIM analysis shows that the patchy localization of FtsZ is conserved in E. coli and suggests that it may be widespread among bacteria. After disassembly of the Z ring either in dividing cells or by excess levels of the cell division inhibitor SulA, FtsZ persisted as patches and short filamentous structures. This is consistent with a highly dynamic population of FtsZ monomers and oligomers outside the ring, originally observed as mobile helices in E. coli by conventional fluorescence microscopy (10) and by photoactivation single-molecule tracking (15). FtsA and ZipA, which bind to the same segment of FtsZ and tether it to the cytoplasmic membrane, usually display a similar localization pattern to FtsZ and each other, although in addition to the differences we detect by 3D-SIM, there are also likely differences that are beyond its ∼100-nm resolution limit in the X,Y plane.As proposed previously (16), gaps between FtsZ patches may be needed to accommodate a switch from a sparse Z ring to a more condensed ring, which would provide force to drive ring constriction (17). If this model is correct, the gaps should close upon ring constriction, although this may be beyond the resolution of 3D-SIM in constricted rings. Another role for patches could be to force molecular crowding of low-abundance septum synthesis proteins such as FtsI, which depend on FtsZ/FtsA/ZipA for their recruitment, into a few mobile supercomplexes.How are FtsZ polymers organized within the Z-ring patches? Recent polarized fluorescence data suggest that FtsZ polymers are oriented both axially and circumferentially within the Z ring in E. coli (18). The seemingly random orientation of the non-ring FtsZ polymeric structures we observe here supports the idea that there is no strong constraint requiring FtsZ oligomers to follow a circumferential path around the cell cylinder. The patches of FtsZ in the unperturbed E. coli Z ring likely represent randomly oriented clusters of FtsZ filaments that are associated with ZipA, FtsA, and essential septum synthesis proteins. New super-resolution microscopy methods should continue to shed light on the in vivo organization of these protein assemblies.     

Specific inactivation of an antifungal bacterial siderophore by a fungal plant pathogen

Bacteria and fungi secrete many natural products that inhibit each other’s growth and development. The dynamic changes in secreted metabolites that occur during interactions between bacteria and fungi are complicated. Pyochelin is a siderophore produced by many Pseudomonas and Burkholderia species that induces systemic resistance in plants and has been identified as an antifungal agent. Through imaging mass spectrometry and metabolomics analysis, we found that Phellinus noxius, a plant pathogen, can modify pyochelin and ent-pyochelin to an esterification product, resulting in reduced iron-chelation and loss of antifungal activity. We also observed that dehydroergosterol peroxide, the fungal metabolite, is only accumulated in the presence of pyochelin produced through bacteria–fungi interactions. For the first time, we show the fungal transformation of pyochelin in the microbial interaction. Our findings highlight the importance of understanding the dynamic changes of metabolites in microbial interactions and their influences on microbial communities.Subject terms: Microbial ecology, Metabolomics

Microorganisms use various strategies to establish themselves within an ecological niche while facing keen competition in the environment. Natural products such as antibiotics, quorum sensing molecules, and siderophores are crucial in microbial interactions [1–3]. Certain microorganisms are equipped with uptake systems that enable them to acquire siderophores, even by those that may not produce them [4]. For example, pyochelin is a siderophore produced by many Pseudomonas and Burkholderia strains. Such bacterial strains are commonly found in soils, as endophytes, and from the rhizosphere where they may inhibit plant pathogens [5, 6].Burkholderia cenocepacia 869T2 was isolated as an endophyte and showed beneficial abilities to control banana Fusarium wilt [7]. It harbors many biosynthetic gene clusters of secondary metabolites, such as pyochelin, pyrrolnitrin, and pyrroloquinoline quinone [8]. Recently, we found that this strain could temporarily inhibit the growth of P. noxius, a fungal pathogen of brown root rot disease, which is prevalent in tropical and subtropical regions and has a wide host range covering over 200 plant species [9]. However, in the competition between fungi and bacteria, P. noxius can resist this inhibition and overwhelm bacterial colonies after 1–2 weeks under dual-culture conditions (Fig. S1). These results imply that fungi might have resistance responses and undergo metabolic changes in bacteria–fungi interactions [10]. Here we unveiled metabolic changes in the competitive interaction between B. cenocepacia 869T2 and P. noxius 2252 using the matrix-assisted laser desorption ionization-time of flight imaging mass spectrometry (MALDI-TOF IMS) [11, 12].We specifically monitored the metabolites in the inhibition region of B. cenocepacia 869T2 and P. noxius 2252 dual-culture using MALDI-TOF IMS. Several induced or enzymatically modified metabolites were detected, including m/z 275, 362, 383, and 427 (Fig. 1A). In particular, pyochelin (m/z 325), surrounding the B. cenocepacia 869T2 colony, showed asymmetric distribution in dual-culture samples. Near the P. noxius 2252 mycelia, a new metabolite with m/z 383 was detected with a complementary distribution to pyochelin (Fig. 1A). In LC-MS/MS-based molecular networking analysis [13], we found that this new metabolite structure is an esterification product of pyochelin and glycolic acid, which we named pyochelin-GA (Fig. 1B). We then constructed a pchF-null mutant strain, ΔpchF, which cannot produce pyochelin, and then dual cultured it with P. noxius. Pyochelin and pyochelin-GA were not observed in the MALDI-TOF IMS and LC-MS analysis of dual-culture samples (Fig. 1A and Fig. S2). We further inoculated P. noxius 2252 with pyochelin-GA-free extract harvested from B. cenocepacia 869T2 single culture, and the complementary distribution of pyochelin and pyochelin-GA was observed by MALDI-TOF IMS again (Fig. S3). These results demonstrated that pyochelin-GA was transformed from pyochelin by P. noxius 2252, rather than produced by B. cenocepacia 869T2 under dual-culture conditions.Open in a separate windowFig. 1Metabolic changes in the bacteria–fungi interaction.A Spatial distribution of selected mass signals (m/z) in MALDI-TOF IMS analysis of Phellinus noxius 2252 (Pn2252) dual-cultured with Burkholderia cenocepacia 869T2 (869T2) and a pchF-null mutant strain (Δ pchF). B Molecular networking analysis of pyochelin and analogs from the dual-culture sample. The red node is pyochelin, and the green node is pyochelin-GA. The structures of pyochelin, pyochelin-GA, and dehydroergosterol peroxide (DHEP), together with their mass signals in MALDI-TOF IMS, are shown. C Iron-chelating abilities of pyochelin and pyochelin-GA were evaluated by Chrome Azurol S liquid assay using different concentrations (2.5, 1.25, 0.63, 0.31, and 0.16 mM, n = 3). Proportions of siderophore units are shown in Fig. S14. D Fungal transformation of pyochelin and ent-pyochelin by treating P. noxius 2252 with ethyl acetate crude extracts of B. cenocepacia 869T2, Pseudomonas aeruginosa PAO1, and P. protegens Pf-5 for 8 h. LC-MS was used to monitor the signals of pyochelin (red), ent-pyochelin (blue), and transformation product 383 (black).The chemical structure of pyochelin-GA was further confirmed via total synthesis, NMR, and LC-MS/MS analysis (Supplementary Material and Methods, and Figs. S4–7). The purified pyochelin and pyochelin-GA were also evaluated for their iron-chelating ability. Chrome Azurol S assay indicated that pyochelin had the dose-dependent iron-chelating ability, but pyochelin-GA had lower iron-binding efficiency (Fig. 1C, Fig. S8). Pyochelin chelates iron in the extracellular medium and transports it into cells via the specific outer membrane transporter FptA. The X-ray structure of FptA-pyochelin-Fe indicated that the terminal carboxylic acid of pyochelin plays an essential role in the iron uptake ability [14, 15]. Our docking analysis suggested that the glycolic ester moiety of pyochelin-GA would affect the binding pocket shape of FptA and result in different binding properties compared to FptA-pyochelin (Fig. S9).Pyochelin and ent-pyochelin are produced independently by different biosynthetic gene clusters in Pseudomonas species [16]. To determine whether P. noxius 2252 can transform both enantiomers via this esterification process, we treated P. noxius 2252 with the extracts of pyochelin producers (P. aeruginosa PAO1 and B. cenocepacia 869T2) and an ent-pyochelin producer (P. protegens Pf-5). After 8 h of treatment, both pyochelin and ent-pyochelin were converted to pyochelin-GA (or ent-pyochelin-GA) (Fig. 1D), demonstrating this is a non-stereospecific transformation.To better understand the iron-chelating ability of pyochelin, we used pyochelin and pyochelin-GA to treat P. noxius 2252 under iron-deficiency conditions, by adding the iron chelator deferoxamine, and iron-rich conditions by adding FeCl₃ (Fig. 2). Pyochelin-GA did not affect the growth of P. noxius 2252 under all conditions. However, P. noxius 2252 was more sensitive to pyochelin in iron-deficient conditions and more resistant to pyochelin in iron-rich conditions, demonstrating that iron availability directly affected the tolerance of P. noxius 2252 to pyochelin. A similar phenomenon was reported previously for Aspergillus fumigatus [17].Open in a separate windowFig. 2Pyochelin inhibition of mycelial growth of Phellinus noxius 2252 is inversely associated with iron concentration.Pyochelin-GA did not have an inhibition effect on P. noxius 2252. Potato dextrose agar (PDA) with deferoxamine (DFO; 200 and 400 µM) was used to mimic iron-deficiency conditions. Iron-rich conditions was prepared by adding FeCl₃ (200 and 400 µM) in PDA. P. noxius 2252 was treated with 0.03, 0.06, 0.12, and 0.24 µmol of pyochelin or pyochelin-GA at 30 °C for 24 h. The antifungal assay was performed in two biological replicates.Using MALDI-TOF IMS analysis of the dual-culture of B. cenocepacia 869T2 and P. noxius 2252, we observed that several metabolites (e.g., m/z 275, 362, and 427) were only observed in the boundary of fungal mycelia (Fig. 1A). Although those metabolites were not detected in the dual-culture of ΔpchF and P. noxius 2252 (Fig. 1A), they were present when we treated P. noxius 2252 with pyochelin (Fig. S10). We identified the metabolite associated with m/z 427 as dehydroergosterol peroxide (DHEP) (Fig. S11), which was initially oxidized from ergosterol and dehydroergosterol [18]. Pyochelin can enhance intercellular reactive oxygen species (ROS) and ultimately disrupts membrane integrity, leading to cell death [17, 19, 20]. To clarify whether ROS induced the accumulation of DHEP, we treated P. noxius 2252 with pyochelin, pyochelin-GA, and 2,2′-bipyridyl (an iron chelator). Pyochelin and 2,2′-bipyridyl showed antifungal effects on P. noxius 2252 and induced ROS production (Fig. S12). However, the accumulation of DHEP in P. noxius 2252 was only associated with pyochelin treatment (Fig. S13). The induction of ROS in P. noxius 2252 by pyochelin and pyochelin-GA was not significantly different (Fig. S14). Therefore, we predict that pyochelin-induced accumulation of DHEP in P. noxius 2252 is independent of ROS production and iron-deficiency.Overall, we demonstrate that pyochelin transformation by fungi, in the interaction between pyochelin-producing bacteria and the plant pathogen P. noxius transforms pyochelin and ent-pyochelin into pyochelin-GA (and ent-pyochelin-GA). This product no longer functions as an iron chelator and no longer shows antifungal activity. The production of a fungal metabolite, dehydroergosterol peroxide, was induced explicitly by pyochelin through an unknown mechanism. These results highlight the importance of monitoring dynamic changes of metabolites in situ to better understand the functions and influences of metabolites on microbial community interactions.     

Conformational States of Melittin at a Bilayer Interface

The distribution of peptide conformations in the membrane interface is central to partitioning energetics. Molecular-dynamics simulations enable characterization of in-membrane structural dynamics. Here, we describe melittin partitioning into dioleoylphosphatidylcholine lipids using CHARMM and OPLS force fields. Although the OPLS simulation failed to reproduce experimental results, the CHARMM simulation reported was consistent with experiments. The CHARMM simulation showed melittin to be represented by a narrow distribution of folding states in the membrane interface.Unstructured peptides fold into the membrane interface because partitioned hydrogen-bonded peptide bonds are energetically favorable compared to free peptide bonds (1–3). This folding process is central to the mechanisms of antimicrobial and cell-penetrating peptides, as well as to lipid interactions and stabilities of larger membrane proteins (4). The energetics of peptide partitioning into membrane interfaces can be described by a thermodynamic cycle (Fig. 1). State A is a theoretical state representing the fully unfolded peptide in water, B is the unfolded peptide in the membrane interface, C is the peptide in water, and D is the folded peptide in the membrane. The population of peptides in solution (State C) is best described as an ensemble of folded and unfolded conformations, whereas the population of peptides in State D generally is assumed to have a single, well-defined helicity, as shown in Fig. 1 A (5). Given that, in principle, folding in solution and in the membrane interface should follow the same basic rules, peptides in state D could reasonably be assumed to also be an ensemble. A fundamental question (5) is therefore whether peptides in state D can be correctly described as having a single helicity. Because differentiating an ensemble of conformations and a single conformation may be an impossible experimental task (5), molecular-dynamics (MD) simulations provide a unique high-resolution view of the phenomenon.Open in a separate windowFigure 1Thermodynamic cycles for peptide partitioning into a membrane interface. States A and B correspond to the fully unfolded peptide in solution and membrane interface, respectively. The folded peptide in solution is best described as an ensemble of unfolded and folded conformations (State C). State D is generally assumed to be one of peptides with a narrow range of conformations, but the state could actually be an ensemble of states as in the case of State C.Melittin is a 26-residue, amphipathic peptide that partitions strongly into membrane interfaces and therefore has become a model system for describing folding energetics (3,6–8). Here, we describe the structural dynamics of melittin in a dioleoylphosphatidylcholine (DOPC) bilayer by means of two extensive MD simulations using two different force fields.We extended a 12-ns equilibrated melittin-DOPC system (9) by 17 μs using the Anton specialized hardware (10) with the CHARMM22/36 protein/lipid force field and CMAP correction (11,12) (see Fig. S1 and Fig. S2 in the Supporting Material). To explore force-field effects, a similar system was simulated for 2 μs using the OPLS force field (13) (see Methods in the Supporting Material). In agreement with x-ray diffraction measurements on melittin in DOPC multilayers (14), melittin partitioned spontaneously into the lipid headgroups at a position below the phosphate groups at similar depth as glycerol/carbonyl groups (Fig. 2).Open in a separate windowFigure 2Melittin partitioned into the polar headgroup region of the lipid bilayer. (A) Snapshot of the simulation cell showing two melittin molecules (MLT1 and MLT2, in yellow) at the lipid-water interface. (B) Density cross-section of the simulation cell extracted from the 17-μs simulation. The peptides are typically located below the lipid phosphate (PO4) groups, in a similar depth as the glycerol/carbonyl (G/C) groups.To describe the secondary structure for each residue, we defined helicity by backbone dihedral angles (φ, ψ) within 30° from the ideal α-helical values (–57°, –47°). The per-residue helicity in the CHARMM simulation displays excellent agreement with amide exchange rates from NMR measurements that show a proline residue to separate two helical segments, which are unfolded below Ala⁵ and above Arg²² (15) (Fig. 3 A). In contrast, the OPLS simulation failed to reproduce the per-residue helicity except for a short central segment (see Fig. S3).Open in a separate windowFigure 3Helicity and conformational distribution of melittin as determined via MD simulation. (A) Helicity per residue for MLT1 and MLT2. (B) Corresponding evolution of the helicity. (C) Conformational distributions over the entire 17-μs simulation.Circular dichroism experiments typically report an average helicity of ∼70% for melittin at membrane interfaces (3,6,16,17), but other methods yield average helicities as high as 85% (15,18). Our CHARMM simulations are generally consistent with the experimental results, especially amide-exchange measurements (15); melittin helicity averaged to 78% for MLT1, whereas MLT2 transitioned from 75% to 89% helicity at t ≈ 8 μs, with an overall average helicity of 82% (Fig. 3 B). However, in the OPLS simulation, melittin steadily unfolds over the first 1.3 μs, after which the peptide remains only partly folded, with an average helicity of 33% (see Fig. S3). Similar force-field-related differences in peptide helicity were recently reported, albeit at shorter timescales (19). Although suitable NMR data are not presently available, we have computed NMR quadrupolar splittings for future reference (see Fig. S4).To answer the question asked in this article—whether the conformational space of folded melittin in the membrane interface can be described by a narrow distribution—the helicity distributions for the equilibrated trajectories are shown in Fig. 3 C. Whereas MLT1 in the CHARMM simulation produces a single, narrow distribution of the helicity, MLT2 has a bimodal distribution as a consequence of the folding event at t ≈ 8 μs (Fig. 3 C). We note that CHARMM force fields have a propensity for helix-formation and this transition might therefore be an artifact. We performed a cluster analysis to describe the structure of the peptide in the membrane interface. The four most populated conformations in the CHARMM simulation are shown in Fig. 4. The dominant conformation for both peptides was a helix kinked at G12 and unfolded at the last 5–6 residues of the C-terminus. The folding transition of MLT2 into a complete helix is visible by the 48% occupancy of a fully folded helix.Open in a separate windowFigure 4Conformational clusters of the two melittin peptides (MLT1 and MLT2) from the 17-μs CHARMM simulation in DOPC. Clustering is based on Cα-RMSD with a cutoff criterion of 2 Å.We conclude that the general assumption when calculating folding energetics holds: Folded melittin partitioned into membrane interfaces can be described by a narrow distribution of conformations. Furthermore, extended (several microsecond) simulations are needed to differentiate force-field effects. Although the CHARMM and OPLS simulations would seem to agree for the first few hundred nanoseconds, the structural conclusions differ drastically with longer trajectories, with CHARMM parameters being more consistent with experiments. However, as implied by the difference in substate distributions between MLT1 and MLT2, 17 μs might not be sufficient to observe the fully equilibrated partitioning process. The abrupt change in MLT2 might indicate that the helicity will increase to greater than experimentally observed in a sufficiently long simulation. On the other hand, it could be nothing more than a transient fluctuation. Increased sampling will provide further indicators of convergence of the helix partitioning process.