Uncovering the genetic diversity of yams (Dioscorea spp.) in China by combining phenotypic trait and molecular marker analyses

Yam is an important edible tuber and root plant worldwide; China as one of the native places of yams has many diverse local resources. The goal of this study was to clarify the genetic diversity of the commonly cultivated yam landraces and the genetic relationship between the main yam species in China. In this study, 26 phenotypic traits of 112 yam accessions from 21 provinces in China were evaluated, and 24 simple sequence repeat (SSR) and 29 sequence‐related amplified polymorphism (SRAP) markers were used for the genetic diversity analysis. Phenotypic traits revealed that Dioscorea opposita had the highest genetic diversity, followed by D. alata, D. persimilis, D. fordii, and D. esculenta. Among the 26 phenotypic traits, the Shannon diversity indexes of leaf shape, petiole color, and stem color were high, and the range in the variation of tuber‐related traits in the underground part was higher than that in the aboveground part. All accessions were divided into six groups by phenotypic trait clustering, which was also supported by principal component analysis (PCA). Molecular marker analysis showed that SSR and SRAP markers had good amplification effects and could effectively and accurately evaluate the genetic variation of yam. The unweighted pair‐group method with arithmetic means analysis based on SSR‐SRAP marker data showed that the 112 accessions were also divided into six groups, similar to the phenotypic trait results. The results of PCA and population structure analysis based on SSR‐SRAP data also produced similar results. In addition, the analysis of the origin and genetic relationship of yam indicated that the species D. opposita may have originated from China. These results demonstrate the genetic diversity and distinctness among the widely cultivated species of Chinese yam and provide a theoretical reference for the classification, breeding, germplasm innovation, utilization, and variety protection of Chinese yam resources.     

Intelligent high‐throughput intervention testing platform in Daphnia

We present a novel platform for testing the effects of interventions on the life‐ and healthspan of a short‐lived freshwater organism with complex behavior and physiology—the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioral features of both routine motion and response to stimuli are continuously quantified over large synchronized cohorts via an automated phenotyping pipeline. We build predictive machine‐learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life‐long and instant assays as illustrated using a long‐term dose‐response profile of metformin and a short‐term assay of well‐studied substances such as caffeine and alcohol.     

Bee Tracker—an open‐source machine learning‐based video analysis software for the assessment of nesting and foraging performance of cavity‐nesting solitary bees

The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radiofrequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honeybees, behavioral studies on solitary bees still largely depend on direct observations, which is very time‐consuming. Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity‐nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by machine learning‐based software. This Bee Tracker software consists of four trained deep learning networks to detect bees that enter or leave their nest and to recognize individual IDs on the bees’ thorax and the IDs of their nests according to their positions in the nesting unit. The software is able to identify each nest of each individual nesting bee, which permits to measure individual‐based measures of reproductive success. Moreover, the software quantifies the number of cavities a female enters until it finds its nest as a proxy of nest recognition, and it provides information on the number and duration of foraging trips. By training the software on 8 videos recording 24 nesting females per video, the software achieved a precision of 96% correct measurements of these parameters. The software could be adapted to various experimental setups by training it according to a set of videos. The presented method allows to efficiently collect large amounts of data on cavity‐nesting solitary bee species and represents a promising new tool for the monitoring and assessment of behavior and reproductive success under laboratory, semi‐field, and field conditions.     

Depicting the phenotypic space of the annual plant Diplotaxis acris in hyperarid deserts

The phenotypic space encompasses the assemblage of trait combinations yielding well‐suited integrated phenotypes. At the population level, understanding the phenotypic space structure requires the quantification of among‐ and within‐population variations in traits and the correlation pattern among them. Here, we studied the phenotypic space of the annual plant Diplotaxis acris occurring in hyperarid deserts. Given the advance of warming and aridity in vast regions occupied by drylands, D. acris can indicate the successful evolutionary trajectory that many other annual plant species may follow in expanding drylands. To this end, we conducted a greenhouse experiment with 176 D. acris individuals from five Saudi populations to quantify the genetic component of variation in architectural and life history traits. We found low among‐population divergence but high among‐individual variation in all traits. In addition, all traits showed a high degree of genetic determination in our study experimental conditions. We did not find significant effects of recruitment and fecundity on fitness. Finally, all architectural traits exhibited a strong correlation pattern among them, whereas for life history traits, only higher seed germination implied earlier flowering. Seed weight appeared to be an important trait in D. acris as individuals with heavier seeds tended to advance flowering and have a more vigorous branching pattern, which led to higher fecundity. Population divergence in D. acris might be constrained by the severity of the hyperarid environment, but populations maintain high among‐individual genetic variation in all traits. Furthermore, D. acris showed phenotypic integration for architectural traits and, to a lesser extent, for life history traits. Overall, we hypothesize that D. acris may be fine‐tuned to its demanding extreme environments. Evolutionary speaking, annual plants facing increasing warming, aridity, and environmental seasonality might modify their phenotypic spaces toward new phenotypic configurations strongly dominated by correlated architectural traits enhancing fecundity and seed‐related traits advancing flowering time.     

Looking for the needle in a downsized haystack: Whole‐exome sequencing unravels genomic signals of climatic adaptation in Douglas‐fir (Pseudotsuga menziesii)

1. Conifers often occur along steep gradients of diverse climates throughout their natural ranges, which is expected to result in spatially varying selection to local climate conditions. However, signals of climatic adaptation can often be confounded, because unraveled clines covary with signals caused by neutral evolutionary processes such as gene flow and genetic drift. Consequently, our understanding of how selection and gene flow have shaped phenotypic and genotypic differentiation in trees is still limited.
2. A 40‐year‐old common garden experiment comprising 16 Douglas‐fir (Pseudotsuga menziesii) provenances from a north‐to‐south gradient of approx. 1,000 km was analyzed, and genomic information was obtained from exome capture, which resulted in an initial genomic dataset of >90,000 single nucleotide polymorphisms. We used a restrictive and conservative filtering approach, which permitted us to include only SNPs and individuals in environmental association analysis (EAA) that were free of potentially confounding effects (LD, relatedness among trees, heterozygosity deficiency, and deviations from Hardy–Weinberg proportions). We used four conceptually different genome scan methods based on F_ST outlier detection and gene–environment association in order to disentangle truly adaptive SNPs from neutral SNPs.
3. We found that a relatively small proportion of the exome showed a truly adaptive signal (0.01%–0.17%) when population substructuring and multiple testing was accounted for. Nevertheless, the unraveled SNP candidates showed significant relationships with climate at provenance origins, which strongly suggests that they have featured adaptation in Douglas‐fir along a climatic gradient. Two SNPs were independently found by three of the employed algorithms, and one of them is in close proximity to an annotated gene involved in circadian clock control and photoperiodism as was similarly found in Populus balsamifera.

Synthesis. We conclude that despite neutral evolutionary processes, phenotypic and genomic signals of adaptation to climate are responsible for differentiation, which in particular explain disparity between the well‐known coastal and interior varieties of Douglas‐fir.     

Negative frequency‐dependent interactions can underlie phenotypic heterogeneity in a clonal microbial population

Genetically identical cells in microbial populations often exhibit a remarkable degree of phenotypic heterogeneity even in homogenous environments. Such heterogeneity is commonly thought to represent a bet‐hedging strategy against environmental uncertainty. However, evolutionary game theory predicts that phenotypic heterogeneity may also be a response to negative frequency‐dependent interactions that favor rare phenotypes over common ones. Here we provide experimental evidence for this alternative explanation in the context of the well‐studied yeast GAL network. In an environment containing the two sugars glucose and galactose, the yeast GAL network displays stochastic bimodal activation. We show that in this mixed sugar environment, GAL‐ON and GAL‐OFF phenotypes can each invade the opposite phenotype when rare and that there exists a resulting stable mix of phenotypes. Consistent with theoretical predictions, the resulting stable mix of phenotypes is not necessarily optimal for population growth. We find that the wild‐type mixed strategist GAL network can invade populations of both pure strategists while remaining uninvasible by either. Lastly, using laboratory evolution we show that this mixed resource environment can directly drive the de novo evolution of clonal phenotypic heterogeneity from a pure strategist population. Taken together, our results provide experimental evidence that negative frequency‐dependent interactions can underlie the phenotypic heterogeneity found in clonal microbial populations.     

Evaluating new technology for biodiversity monitoring: Are drone surveys biased?

Drones and machine learning‐based automated detection methods are being used by ecologists to conduct wildlife surveys with increasing frequency. When traditional survey methods have been evaluated, a range of factors have been found to influence detection probabilities, including individual differences among conspecific animals, which can thus introduce biases into survey counts. There has been no such evaluation of drone‐based surveys using automated detection in a natural setting. This is important to establish since any biases in counts made using these methods will need to be accounted for, to provide accurate data and improve decision‐making for threatened species. In this study, a rare opportunity to survey a ground‐truthed, individually marked population of 48 koalas in their natural habitat allowed for direct comparison of the factors impacting detection probability in both ground observation and drone surveys with manual and automated detection. We found that sex and host tree preferences impacted detection in ground surveys and in manual analysis of drone imagery with female koalas likely to be under‐represented, and koalas higher in taller trees detected less frequently when present. Tree species composition of a forest stand also impacted on detections. In contrast, none of these factors impacted on automated detection. This suggests that the combination of drone‐captured imagery and machine learning does not suffer from the same biases that affect conventional ground surveys. This provides further evidence that drones and machine learning are promising tools for gathering reliable detection data to better inform the management of threatened populations.     

Behavioral fingerprints predict insecticide and anthelmintic mode of action

Novel invertebrate‐killing compounds are required in agriculture and medicine to overcome resistance to existing treatments. Because insecticides and anthelmintics are discovered in phenotypic screens, a crucial step in the discovery process is determining the mode of action of hits. Visible whole‐organism symptoms are combined with molecular and physiological data to determine mode of action. However, manual symptomology is laborious and requires symptoms that are strong enough to see by eye. Here, we use high‐throughput imaging and quantitative phenotyping to measure Caenorhabditis elegans behavioral responses to compounds and train a classifier that predicts mode of action with an accuracy of 88% for a set of ten common modes of action. We also classify compounds within each mode of action to discover substructure that is not captured in broad mode‐of‐action labels. High‐throughput imaging and automated phenotyping could therefore accelerate mode‐of‐action discovery in invertebrate‐targeting compound development and help to refine mode‐of‐action categories.     

The effects of adaptation to urea on feeding rates and growth in Drosophila larvae

A collection of forty populations were used to study the phenotypic adaptation of Drosophila melanogaster larvae to urea‐laced food. A long‐term goal of this research is to map genes responsible for these phenotypes. This mapping requires large numbers of populations. Thus, we studied fifteen populations subjected to direct selection for urea tolerance and five controls. In addition, we studied another twenty populations which had not been exposed to urea but were subjected to stress or demographic selection. In this study, we describe the differentiation in these population for six phenotypes: (1) larval feeding rates, (2) larval viability in urea‐laced food, (3) larval development time in urea‐laced food, (4) adult starvation times, (5) adult desiccation times, and (6) larval growth rates. No significant differences were observed for desiccation resistance. The demographically/stress‐selected populations had longer times to starvation than urea‐selected populations. The urea‐adapted populations showed elevated survival and reduced development time in urea‐laced food relative to the control and nonadapted populations. The urea‐adapted populations also showed reduced larval feeding rates relative to controls. We show that there is a strong linear relationship between feeding rates and growth rates at the same larval ages feeding rates were measured. This suggests that feeding rates are correlated with food intake and growth. This relationship between larval feeding rates, food consumption, and efficiency has been postulated to involve important trade‐offs that govern larval evolution in stressful environments. Our results support the idea that energy allocation is a central organizing theme in adaptive evolution.     

Cyclic di‐GMP modulates sessile‐motile phenotypes and virulence in Dickeya oryzae via two PilZ domain receptors

Dickeya oryzae is a bacterial pathogen causing the severe rice stem rot disease in China and other rice‐growing countries. We showed recently that the universal bacterial second messenger c‐di‐GMP plays an important role in modulation of bacterial motility and pathogenicity, but the mechanism of regulation remains unknown. In this study, bioinformatics analysis of the D. oryzae EC1 genome led to the identification of two proteins, YcgR and BcsA, both of which contain a conserved c‐di‐GMP receptor domain, known as the PilZ‐domain. By deleting all the genes encoding c‐di‐GMP‐degrading enzymes in D. oryzae EC1, the resultant mutant 7ΔPDE with high c‐di‐GMP levels became nonmotile, formed hyperbiofilm, and lost the ability to colonize and invade rice seeds. These phenotypes were partially reversed by deletion of ycgR in the mutant 7ΔPDE, whereas deletion of bcsA only reversed the hyperbiofilm phenotype of mutant 7ΔPDE. Significantly, double deletion of ycgR and bcsA in mutant 7ΔPDE rescued its motility, biofilm formation, and virulence to levels of wild‐type EC1. In vitro biochemical experiments and in vivo phenotypic assays further validated that YcgR and BcsA proteins are the receptors for c‐di‐GMP, which together play a critical role in regulating the c‐di‐GMP‐associated functionality. The findings from this study fill a gap in our understanding of how c‐di‐GMP modulates bacterial motility and biofilm formation, and provide useful clues for further elucidation of sophisticated virulence regulatory mechanisms in this important plant pathogen.     

Altered gut microbiota and metabolites profile are associated with reduced bone metabolism in ethanol‐induced osteoporosis

ObjectiveChronic heavy drinking causes ethanol‐induced osteoporosis (EIO). The present study aimed to explore the role of GM in EIO.Material and MethodsA rat EIO model was established by chronic ethanol intake. Taking the antibiotic application as the matched group of dysbacteriosis, an integrated 16S rRNA sequencing and liquid chromatography–tandem mass spectrometry‐based metabolomics in serum and faeces were applied to explore the association of differential metabolic phenotypes and screen out the candidate metabolites detrimental to ossification. The colon organoids were used to track the source of 5‐HT and the effect of 5‐HT on bone formation was examined in vitro . ResultsCompared with antibiotics application, ethanol‐gavaged decreased the BMD in rats. We found that both ethanol and antibiotic intake affected the composition of GM, but ethanol intake increased the ratio of Firmicutes to Bacteroidetes. Elevated serotonin was proved to be positively correlated with the changes of the composition of GM and faecal metabolites and inhibited the proliferation and mineralization of osteogenesis‐related cells. However, the direct secretory promotion of serotonin was absent in the colon organoids exposed to ethanol.ConclusionThis study demonstrated that ethanol consumption led to osteoporosis and intestinal‐specific dysbacteriosis. Conjoint analysis of the genetic profiles of GM and metabolic phenotypes in serum and faeces allowed us to understand the endogenous metabolite, 5‐HT, as detrimental regulators in the gut‐bone axis to impair bone formation.

A rat ethanol‐induced osteoporosis model was established by long‐term ethanol intake. Taking the antibiotic application as the matched group of dysbacteriosis, an integrated 16S rRNA sequencing and liquid chromatography–tandem mass spectrometry‐based metabolomics in serum and faeces were applied to explore the association of differential metabolic phenotypes and screen out the candidate metabolites detrimental to ossification. The colon organoids were used to track the source of 5‐HT and the effect of 5‐HT on bone formation was examined in vitro .     

Exploratory analysis of multi‐trait coadaptations in light of population history

During the process of range expansion, populations encounter a variety of environments. They respond to the local environments by modifying their mutually interacting traits. Common approaches of landscape analysis include first focusing on the genes that undergo diversifying selection or directional selection in response to environmental variation. To understand the whole history of populations, it is ideal to capture the history of their range expansion with reference to the series of surrounding environments and to infer the multitrait coadaptation. To this end, we propose a complementary approach; it is an exploratory analysis using up‐to‐date methods that integrate population genetic features and features of selection on multiple traits. First, we conduct correspondence analysis of site frequency spectra, traits, and environments with auxiliary information of population‐specific fixation index (F _ST). This visualizes the structure and the ages of populations and helps infer the history of range expansion, encountered environmental changes, and selection on multiple traits. Next, we further investigate the inferred history using an admixture graph that describes the population split and admixture. Finally, principal component analysis of the selection on edge‐by‐trait (SET) matrix identifies multitrait coadaptation and the associated edges of the admixture graph. We introduce a newly defined factor loadings of environmental variables in order to identify the environmental factors that caused the coadaptation. A numerical simulation of one‐dimensional stepping‐stone population expansion showed that the exploratory analysis reconstructed the pattern of the environmental selection that was missed by analysis of individual traits. Analysis of a public dataset of natural populations of black cottonwood in northwestern America identified the first principal component (PC) coadaptation of photosynthesis‐ vs growth‐related traits responding to the geographical clines of temperature and day length. The second PC coadaptation of volume‐related traits suggested that soil condition was a limiting factor for aboveground environmental selection.     

Prevalent intron retention fine‐tunes gene expression and contributes to cellular senescence

Intron retention (IR) is the least well‐understood alternative splicing type in animals, and its prevalence and function in physiological and pathological processes have long been underestimated. Cellular senescence contributes to individual aging and age‐related diseases and can also serve as an important cancer prevention mechanism. Dynamic IR events have been observed in senescence models and aged tissues; however, whether and how IR impacts senescence remain unclear. Through analyzing polyA⁺ RNA‐seq data from human replicative senescence models, we found IR was prevalent and dynamically regulated during senescence and IR changes negatively correlated with expression alteration of corresponding genes. We discovered that knocking down (KD) splicing factor U2AF1, which showed higher binding density to retained introns and decreased expression during senescence, led to senescence‐associated phenotypes and global IR changes. Intriguingly, U2AF1‐KD‐induced IR changes also negatively correlated with gene expression. Furthermore, we demonstrated that U2AF1‐mediated IR of specific gene (CPNE1 as an example) contributed to cellular senescence. Decreased expression of U2AF1, higher IR of CPNE1, and reduced expression of CPNE1 were also discovered in dermal fibroblasts with age. We discovered prevalent IR could fine‐tune gene expression and contribute to senescence‐associated phenotypes, largely extending the biological significance of IR.     

Characterization of a novel microfluidic platform for the isolation of rare single cells to enable CTC analysis from head and neck squamous cell carcinoma patients

Detailed examination of tumor components is leading‐edge to establish personalized cancer therapy. Accompanying research on cell‐free DNA, the cell count of circulating tumor cells (CTCs) in patient blood is seen as a crucial prognostic factor. The potential of CTC analysis is further not limited to the determination of the overall survival rate but sheds light on understanding inter‐ and intratumoral heterogeneity. In this regard, commercial CTC isolation devices combining an efficient enrichment of rare cells with a droplet deposition of single cells for downstream analysis are highly appreciated. The Liquid biopsy platform CTCelect was developed to realize a fully‐automated enrichment and single cell dispensing of CTCs from whole blood without pre‐processing. We characterized each process step with two different carcinoma cell lines demonstrating up to 87 % enrichment (n = 10) with EpCAM coupled immunomagnetic beads, 73 % optical detection and dispensing efficiency (n = 5). 40 to 56.7 % of cells were recovered after complete isolation from 7.5 ml untreated whole blood (n = 6). In this study, CTCelect enabled automated dispensing of single circulating tumor cells from HNSCC patient samples, qPCR‐based confirmation of tumor‐related biomarkers and immunostaining. Finally, the platform was compared to commercial CTC isolation technologies to highlight advantages and limitations of CTCelect. This system offers new possibilities for single cell screening in cancer diagnostics, individual therapy approaches and real‐time monitoring.     

Reversion of a resistance‐breaking mutation shows reversion costs and high virus diversity at necrotic local lesions

An instance of host range evolution relevant to plant virus disease control is resistance breaking. Resistance breaking can be hindered by across‐host fitness trade‐offs generated by negative effects of resistance‐breaking mutations on the virus fitness in susceptible hosts. Different mutations in pepper mild mottle virus (PMMoV) coat protein result in the breaking in pepper plants of the resistance determined by the L ³ resistance allele. Of these, mutation M138N is widespread in PMMoV populations, despite associated fitness penalties in within‐host multiplication and survival. The stability of mutation M138N was analysed by serial passaging in L ³ resistant plants. Appearance on passaging of necrotic local lesions (NLL), indicating an effective L ³ resistance, showed reversion to nonresistance‐breaking phenotypes was common. Most revertant genotypes had the mutation N138K, which affects the properties of the virus particle, introducing a penalty of reversion. Hence, the costs of reversion may determine the evolution of resistance‐breaking in addition to resistance‐breaking costs. The genetic diversity of the virus population in NLL was much higher than in systemically infected tissues, and included mutations reported to break L ³ resistance other than M138N. Infectivity assays on pepper genotypes with different L alleles showed high phenotypic diversity in respect to L alleles in NLL, including phenotypes not reported in nature. Thus, high diversity at NLL may potentiate the appearance of genotypes that enable the colonization of new host genotypes or species. Collectively, the results of this study contribute to better understanding the evolutionary dynamics of resistance breaking and host‐range expansions.     

Parents exposed to warming produce offspring lower in weight and condition

The parental environment can alter offspring phenotypes via the transfer of non‐genetic information. Parental effects may be viewed as an extension of (within‐generation) phenotypic plasticity. Smaller size, poorer physical condition, and skewed sex ratios are common responses of organisms to global warming, yet whether parental effects alleviate, exacerbate, or have no impact on these responses has not been widely tested. Further, the relative non‐genetic influence of mothers and fathers and ontogenetic timing of parental exposure to warming on offspring phenotypes is poorly understood. Here, we tested how maternal, paternal, and biparental exposure of a coral reef fish (Acanthochromis polyacanthus) to elevated temperature (+1.5°C) at different ontogenetic stages (development vs reproduction) influences offspring length, weight, condition, and sex. Fish were reared across two generations in present‐day and projected ocean warming in a full factorial design. As expected, offspring of parents exposed to present‐day control temperature that were reared in warmer water were shorter than their siblings reared in control temperature; however, within‐generation plasticity allowed maintenance of weight, resulting in a higher body condition. Parental exposure to warming, irrespective of ontogenetic timing and sex, resulted in decreased weight and condition in all offspring rearing temperatures. By contrast, offspring sex ratios were not strongly influenced by their rearing temperature or that of their parents. Together, our results reveal that phenotypic plasticity may help coral reef fishes maintain performance in a warm ocean within a generation, but could exacerbate the negative effects of warming between generations, regardless of when mothers and fathers are exposed to warming. Alternatively, the multigenerational impact on offspring weight and condition may be a necessary cost to adapt metabolism to increasing temperatures. This research highlights the importance of examining phenotypic plasticity within and between generations across a range of traits to accurately predict how organisms will respond to climate change.     

Predicting insect outbreaks using machine learning: A mountain pine beetle case study

Planning forest management relies on predicting insect outbreaks such as mountain pine beetle, particularly in the intermediate‐term future, e.g., 5‐year. Machine‐learning algorithms are potential solutions to this challenging problem due to their many successes across a variety of prediction tasks. However, there are many subtle challenges in applying them: identifying the best learning models and the best subset of available covariates (including time lags) and properly evaluating the models to avoid misleading performance‐measures. We systematically address these issues in predicting the chance of a mountain pine beetle outbreak in the Cypress Hills area and seek models with the best performance at predicting future 1‐, 3‐, 5‐ and 7‐year infestations. We train nine machine‐learning models, including two generalized boosted regression trees (GBM) that predict future 1‐ and 3‐year infestations with 92% and 88% AUC, and two novel mixed models that predict future 5‐ and 7‐year infestations with 86% and 84% AUC, respectively. We also consider forming the train and test datasets by splitting the original dataset randomly rather than using the appropriate year‐based approach and show that this may obtain models that score high on the test dataset but low in practice, resulting in inaccurate performance evaluations. For example, a k‐nearest neighbor model with the actual performance of 68% AUC, scores the misleadingly high 78% on a test dataset obtained from a random split, but the more accurate 66% on a year‐based split. We then investigate how the prediction accuracy varies with respect to the provided history length of the covariates and find that neural network and naive Bayes, predict more accurately as history‐length increases, particularly for future 1‐ and 3‐year predictions, and roughly the same holds with GBM. Our approach is applicable to other invasive species. The resulting predictors can be used in planning forest and pest management and planning sampling locations in field studies.