Open Labware: 3-D Printing Your Own Lab Equipment

The introduction of affordable, consumer-oriented 3-D printers is a milestone in the current “maker movement,” which has been heralded as the next industrial revolution. Combined with free and open sharing of detailed design blueprints and accessible development tools, rapid prototypes of complex products can now be assembled in one’s own garage—a game-changer reminiscent of the early days of personal computing. At the same time, 3-D printing has also allowed the scientific and engineering community to build the “little things” that help a lab get up and running much faster and easier than ever before.Applications of 3-D printing technologies (Fig. 1A, Box 1) have become as diverse as the types of materials that can be used for printing. Replacement parts at the International Space Station may be printed in orbit from durable plastics or metals, while back on Earth the food industry is starting to explore the same basic technology to fold strings of chocolate into custom-shaped confectionary. Also, consumer-oriented laser-cutting technology makes it very easy to cut raw materials such as sheets of plywood, acrylic, or aluminum into complex shapes within seconds. The range of possibilities comes to light when those mechanical parts are combined with off-the-shelf electronics, low-cost microcontrollers like Arduino boards [1], and single-board computers such as a Beagleboard [2] or a Raspberry Pi [3]. After an initial investment of typically less than a thousand dollars (e.g., to set-up a 3-D printer), the only other materials needed to build virtually anything include a few hundred grams of plastic (approximately US$30/kg), cables, and basic electronic components [4,5].Open in a separate windowFig 1Examples of open 3-D printed laboratory tools. A ₁, Components for laboratory tools, such as the base for a micromanipulator [18] shown here, can be rapidly prototyped using 3-D printing. A _2, The printed parts can be easily combined with an off-the-shelf continuous rotation servo-motor (bottom) to motorize the main axis. B ₁, A 3-D printable micropipette [8], designed in OpenSCAD [19], shown in full (left) and cross-section (right). B ₂, The pipette consists of the printed parts (blue), two biro fillings with the spring, an off-the-shelf piece of tubing to fit the tip, and one screw used as a spacer. B ₃, Assembly is complete with a laboratory glove or balloon spanned between the two main printed parts and sealed with tape to create an airtight bottom chamber continuous with the pipette tip. Accuracy is ±2–10 μl depending on printer precision, and total capacity of the system is easily adjusted using two variables listed in the source code, or accessed via the “Customizer” plugin on the thingiverse link [8]. See also the first table.

Box 1. Glossary

Open source

A collective license that defines terms of free availability and redistribution of published source material. Terms include free and unrestricted distribution, as well as full access to source code/blueprints/circuit board designs and derived works. For details, see http://opensource.org.

Maker movement

Technology-oriented extension of the traditional “Do-it-Yourself (DIY)” movement, typically denoting specific pursuits in electronics, CNC (computer numerical control) tools such as mills and laser cutters, as well as 3-D printing and related technologies.

3-D printing

Technology to generate three-dimensional objects from raw materials based on computer models. Most consumer-oriented 3-D printers print in plastic by locally melting a strand of raw material at the tip (“hot-end”) and “drawing” a 3-D object in layers. Plastic materials include Acrylnitrile butadiene styrene (ABS) and Polylactic acid (PLA). Many variations of 3-D printers exist, including those based on laser-polymerization or fusion of resins or powdered raw materials (e.g., metal or ceramic printers).

Arduino boards

Inexpensive and consumer-oriented microcontroller boards built around simple processors. These boards offer a variety of interfaces (serial ports, I2C and CAN bus, etc.), μs-timers, and multiple general-purpose input-output (GPIO) pins suitable for running simple, time-precise programs to control custom-built electronics.

Single board computers

Inexpensive single-board computers capable of running a mature operating system with graphical-user interface, such as Linux. Like microcontroller boards, they offer a variety of hardware interfaces and GPIO pins to control custom-built electronics.It therefore comes as no surprise that these technologies are also routinely used by research scientists and, especially, educators aiming to customize existing lab equipment or even build sophisticated lab equipment from scratch for a mere fraction of what commercial alternatives cost [6]. Designs for such “Open Labware” include simple mechanical adaptors [7], micropipettes (Fig. 1B) [8], and an egg-whisk–based centrifuge [9] as well as more sophisticated equipment such as an extracellular amplifier for neurophysiological experiments [10], a thermocycler for PCR [11], or a two-photon microscope [12]. At the same time, conceptually related approaches are also being pursued in chemistry [13–15] and material sciences [16,17]. See also

Intrinsic Disorder in Pathogen Effectors: Protein Flexibility as an Evolutionary Hallmark in a Molecular Arms Race

Effector proteins represent a refined mechanism of bacterial pathogens to overcome plants’ innate immune systems. These modular proteins often manipulate host physiology by directly interfering with immune signaling of plant cells. Even if host cells have developed efficient strategies to perceive the presence of pathogenic microbes and to recognize intracellular effector activity, it remains an open question why only few effectors are recognized directly by plant resistance proteins. Based on in-silico genome-wide surveys and a reevaluation of published structural data, we estimated that bacterial effectors of phytopathogens are highly enriched in long-disordered regions (>50 residues). These structurally flexible segments have no secondary structure under physiological conditions but can fold in a stimulus-dependent manner (e.g., during protein–protein interactions). The high abundance of intrinsic disorder in effectors strongly suggests positive evolutionary selection of this structural feature and highlights the dynamic nature of these proteins. We postulate that such structural flexibility may be essential for (1) effector translocation, (2) evasion of the innate immune system, and (3) host function mimicry. The study of these dynamical regions will greatly complement current structural approaches to understand the molecular mechanisms of these proteins and may help in the prediction of new effectors.Plants and pathogens are entangled in a continual arms race. While host organisms have developed complex and dynamic immune systems able to recognize a wide range of pathogens and to discriminate them from beneficial microbes (Jones and Dangl, 2006; Medzhitov, 2007), bacterial pathogens have evolved refined adaptation strategies to overcome the plant’s innate immune system. Among these ingenious adaptations are effector proteins. Most of these proteins are secreted via the type III secretion system (TTSS) into the host cytoplasm, where they manipulate the immune signaling and the physiology of plant cells and thereby improve bacterial fitness within the host (Dean, 2011).Plant–pathogen interactions are highly dynamic processes, both from the evolutionary and the physiological point of view. Here, we postulate that they are equally dynamic at the protein-structure level. This is based on our finding that numerous effector proteins are predicted to be intrinsically disordered (ID) and that this feature may be essential for (1) effector translocation, (2) evasion of the innate immune system, and (3) host function mimicry. Intrinsic disorder has so far been postulated to preferentially occur in eukaryotic proteins. While on average ∼20% of the eukaryotic proteome harbors long (>50 residues) ID segments, these regions are only predicted at low abundance (8% on average) in bacterial proteomes (Dunker et al., 2000). The most likely reason for this discrepancy is the lack of efficient mechanisms to protect unfolded proteins from degradation (Ward et al., 2004). However, when surveying genomes of pathogenic bacteria with the widely used PONDR VL-XT program (Romero et al., 2001), we observed that not only the average percentage of sequence disorder, but most strikingly long (>50 residues) stretches of intrinsic disorder are highly overrepresented in secreted effectors, with especially high levels in phytopathogenic bacteria (Pseudomonas syringae, ∼39%; Ralstonia solanacearum, ∼70%; Xanthomonas spp, ∼77%) (Supplemental Table 1 online). This striking enrichment of unstructured regions strongly suggests positive evolutionary selection of intrinsic disorder in effector proteins and highlights their dynamic nature.

Table 1.

Predictions of Intrinsic Disorder in Effectors and Whole Proteomes of Different Bacterial Species

On the Extent of Tyrosine Phosphorylation in Chloroplasts

Reanalysis of published mass spectrometry data on Tyr-phosphorylated chloroplast proteins indicates that the majority of peptide spectrum matches reporting Tyr phosphorylation are ambiguous.Tyr phosphorylation is a controversial issue in plant phosphoproteomics, ever since early analyses reported up to 5% Tyr phosphorylation in Arabidopsis (Arabidopsis thaliana), despite the lack of a classical Tyr kinase in the Arabidopsis genome (Sugiyama et al., 2008; de la Fuente van Bentem and Hirt, 2009). The same controversy extends to the phosphorylation of chloroplast proteins. In the past 20 years, several indications for Tyr phosphorylation in chloroplasts were reported, and Rubisco is annotated as Tyr phosphorylated protein (www.arabidopsis.org). Initially, Tullberg et al. (1998) found the protein Tyr kinase inhibitor genistein to inhibit the phosphorylation of thylakoid membrane proteins. Supported by the observed stability of some thylakoid phosphoproteins against acid and base hydrolysis, a characteristic property of phospho-Tyr, the authors argue that Tyr phosphorylation of thylakoid membrane proteins is vital for short-term acclimation responses. Similar biochemical properties were observed for autophosphorylation of the chloroplast sensor kinase CSK (Puthiyaveetil et al., 2008). Support for Tyr phosphorylation came from the cross-reactivity of thylakoid membrane proteins and Calvin cycle enzymes (e.g. Rubisco) with phospho-Tyr-specific antibodies (Forsberg and Allen, 2001; Fedina et al., 2008; Ghelis et al., 2008). With the same set of methods, no Tyr phosphorylation was observed in mitochondrial proteins (Forsberg and Allen, 2001).The above reported data are indirect hints for Tyr phosphorylation, and none of the applied methods is sufficiently specific to serve as solid evidence. For example, all phospho-Tyr-specific antibodies have significant cross reactivity with phospho-Ser and phospho-Thr when these have an aromatic amino acid in the +1 position (Zerweck et al., 2009). Using phospho-Tyr-specific antibodies, Forsberg and Allen (2001) found genistein inhibition of light-harvesting complex II phosphorylation with a 50% inhibition of initial activity of around 15 µm. Surprisingly, the same inhibition kinetics were observed with phospho-Thr-specific antibodies, suggesting a lack of specificity of either genistein or the phospho-amino acid antibodies, or both. So far, direct proof for Tyr phosphorylation in chloroplasts by phospho-amino acid analyses is missing. However, mass spectrometry-based phosphoproteomics experiments with plant cell extracts reported phospho-Tyr-containing peptides in chloroplasts but surprisingly not in abundant thylakoid membrane proteins or Calvin cycle enzymes. A recent meta-analysis collated data from 27 published studies and several internal data sets, resulting in a cumulative data set with 5% Tyr-phosphorylated peptides in the entire data set and 12% to 19% in the mitochondria (van Wijk et al., 2014). In this data set, almost 30% of the plastid phosphoproteins are flagged as Tyr phosphorylated (90 proteins from around 300; see supplemental table 5B in van Wijk et al., 2014), standing in stark contrast to dedicated plastid phosphoproteome analyses that identified less than 1% Tyr phosphorylation in the cellular phosphoproteome and none in chloroplast proteins (Reiland et al., 2009).Many of the phospho-Tyr-containing peptides were identified in analyses that applied multistage activation to elevate fragment ion intensity in spectra dominated by the neutral loss of phosphoric acid from phospho-Ser and/or phospho-Thr, sometimes in combination with searches for the phospho-Tyr-specific immonium ion at mass-to-charge ratio 216.0426 (see table 1 in van Wijk et al., 2014). In one instance, phospho-Tyr-specific antibodies were used to enrich Tyr phosphorylated proteins from Arabidopsis full cell extracts (Mithoe et al., 2012). Remarkably, there is almost no overlap in phospho-Tyr peptide identification between the different studies (Mithoe et al., 2012; Wu et al., 2013; van Wijk et al., 2014). Although this could be the result of diverse acquisition methods, enrichment strategies, and data interpretation software in different analyses (Bodenmiller et al., 2007), the low reproducibility and the discrepancies in phospho-Tyr detection among different analyses require further attention, because both are characteristic for incorrect peptide spectrum matches. This is a specific problem here, because false discovery rates (FDRs) accumulate in cumulated data sets.Therefore, we decided to assess the quality of matches to Tyr phosphorylated peptides by a dedicated reanalysis of the original data and benchmarked the robustness of peptide identification by using different software tools for spectra interpretation. Different tools use different scoring schemes to calculate identification probabilities from the fragment ion spectrum; however, they all use basic rules for spectrum matching, such as consecutive b- or y-ion series, matching of the highest intensity peaks to peptide fragments, and identification of matches to plausible derivatives of the major fragments such as losses of ammonia or water. Because of the differences in scoring the identified fragment ions, software tools may interpret spectra differently. However, since the basic rules for peptide matching apply to all identifications, it is clear that robust and reliable identifications are made by different tools that agree on the same interpretation for a spectrum. A specific aspect in the interpretation of phosphopeptide spectra is the assignment of the exact modification site. Common database matching software is usually unsuitable to distinguish modifications at closely spaced amino acids, and it is insufficiently explicit when spectra do not allow distinguishing between alternatives. To circumvent this problem, specialized software tools were developed that score spectra for alternative phosphorylation sites within the peptide sequence by searching for specific fragment ions supporting one or another phosphorylation site (MacLean et al., 2008; Martin et al., 2010).We extracted from the different data sets Tyr-phosphorylated chloroplast proteins and extracted the spectrum information in the form of a MASCOT generic file (mgf) from either PhosphAT (van Wijk et al., 2014) or PRIDE (Mithoe et al., 2012). This resulted in 139 spectra identifying 53 unique peptides representing putative Tyr phosphorylation sites in 53 chloroplast proteins (Supplemental Table S1A; Supplemental Data Set S1). This set of spectra was reanalyzed with MASCOT to assess the significance of the identifications and two alternative software tools established for database searches: PEAKS, a database matching software with a de novo sequencing option (Ma et al., 2003); and Proteome Discoverer with the search engine SEQUEST (Thermo Scientific). With the original search parameters of dynamic phosphorylation of Ser, Thr, and Tyr, dynamic oxidation of Met, fixed carbamidomethylation of Cys, and maximum of two missed cleavages at mass tolerances for precursor and fragment ion matching of 20 ppm/0.5 D (Wu et al., 2013), 50 ppm/0.8 D (internal data sets in van Wijk et al., 2014), and 10 ppm/0.9 D (Mithoe et al., 2012), 11 out of 53 unique peptides were identified with the reported amino acid sequence above the MASCOT significance threshold of P < 0.05, while 42 mgf matchings were reported as insignificant or gave rise to an unrelated peptide identification (www.matrixscience.com; Supplemental Table S1B). The lack of significance correlates with the relaxed search parameters and the many degrees of freedom allowed for peptide matching. With a variation of the above mass tolerance settings, PEAKS identified five (9%) and Proteome Discoverer identified 11 (21%) out of 53 peptides from the data set at a fully relaxed FDR, of which three (6%) identifications by PEAKS and one (2%) by Proteome Discoverer were significant (van Wijk et al., 2014)Presented are those peptides that were identified at least once with one of the alternative tools at one of the indicated mass tolerance settings: precursor tolerance/tandem mass spectrometry/ion match tolerance 50 ppm/0.8 D, 20 ppm/0.5 D, or 10 ppm/0.9 D. We reported all identifications irrespective of the score. Identifications considered significant are labeled with asterisks. Provided is the FDR at which the identification was made. Proteome Discoverer has two FDR settings: below 1% (stringent) or below 5% (relaxed). All matches above a 5% FDR threshold are considered insignificant. The PEAKS FDR is calculated individually for every peptide. Dashes indicate that the peptide was not identified.

The Russian invasion of Ukraine: a humanitarian tragedy and a tragedy for science

The Invasion of Ukraine prompts us to support our Ukranian colleagues but also to keep open communication with the Russian scientists who oppose the war.

In the eyes of the civilized world, Russia has already lost the war: politically, it is becoming ever more isolated; economically as the sanctions take an enormous toll; militarily as the losses of the Russian army mount. In contrast, the courage of Ukrainian people fighting for their independence has united the Western world that is providing enormous support for those Ukrainians who fight the Russian invasion and those who have fled their war‐torn country. Once this war is over, Ukraine will have to heal the wounds of war, reunite families, restore its economy, reestablish infrastructure, and rebuild science and education. Russia will have to restore its dignity and overcome its self‐inflicted isolation.Europe’s unity in condemning Russia’s war of aggression and showing its solidarity with Ukraine has been impressive. This includes not the least welcoming and accommodating millions of refugees. We, the scientific community in Europe, have a moral obligation to help Ukrainian students and colleagues by providing safe space to study and to continue their research. First, European research organizations and funding agencies should develop strategies to support them in the years to come. Second, efforts by EMBO, research funders, universities, and research institutions to support Ukrainian students and scientists are necessary. As a first priority, dedicated and unbureaucratic short‐term scholarship and grant programs are required to accommodate Ukrainian scientists; such programs have been already initiated by many organizations, for example, by EMBO, Volkswagen Stiftung, Max Planck Society, and the ERC among others. These help Ukrainian scientists to stay connected to research and become integrated into the European research landscape. In the long‐term and after the war, this aid should be complemented by funding for research centers of excellence in Ukraine, to which scientists could then return.Even though the priority must be to help Ukrainians, we must also think of students and colleagues in Russia who oppose the war and are affected by the sanctions. As the Iron Curtain closes again, we have to think differently about our ongoing and future collaborations. Although freezing most, if not all, research collaborations with official Russian organizations is justified, it would be a mistake to extend these sanctions to all scientists and students. There is already an exodus of Russian and Belarusian scholars, which will only accelerate in the next months and years, and accepting scientists who ask for political asylum will be beneficial for Europe.The fraction of Russian society in open opposition to the war is, unfortunately, smaller than that officially in support of it. At the beginning of the war, a number of Russian scientists published an open letter on the internet, in which they condemn this war (https://t‐invariant.org/2022/02/we‐are‐against‐war/). They clearly state that "The responsibility for unleashing a new war in Europe lies entirely with Russia. There is no rational justification for this war”, and “demand an immediate halt to all military operations directed against Ukraine". At the same time, other prominent Russian science and education officials signed the “Statement of the Russian Union of University Rectors (Provosts)”, which expressed unwavering support for Russia, its president and its Army and their goal to “to achieve demilitarization and denazification of Ukraine and thus to defend ourselves from the ever‐growing military threat” (https://www.rsr‐online.ru/news/2022‐god/obrashchenie‐rossiyskogo‐soyuza‐rektorov1/).Inevitably, Russian scientists must decide themselves how to live and continue their scientific work under the increasingly tight surveillance of the Kremlin regime. History is repeating itself. Not long ago, during the Cold War, Soviet scientists were largely isolated from the international research community and worked in government‐controlled research. In some fields, no one knew what they were working on or where. However, even in those dark times, courageous individuals such as Andrei Sakharov spoke out against the regime and tried to educate the next generation about the importance of free will. Many Soviet geneticists had been arrested under Stalin’s regime of terror and as a result of Lysenkoism and were executed or sent to the Gulag or had to emigrate, such as Nikolaj Timofeev‐Resovskij, one of the great geneticists of his time and an opponent of communism. As a result of sending dissident scientists to Siberia, great educational institutions were created in the region, which trained many famous scientists. History tells us that it is impossible to kill free will and the search for truth.The Russian invasion of Ukraine is a major humanitarian tragedy and a tragedy for science at many levels. Our hope is that the European science community, policymakers, and funders will be prepared to continue and expand support for our colleagues from Ukraine and eventually help to rebuild the bridges with Russian science that have been torn down.This commentary has been endorsed and signed by the EMBO Young Investigators and former Young Investigators listed below.

All signatories are current and former EMBO Young Investigators and endorse the statements in this article.

Vast (but avoidable) underestimation of global biodiversity

The number of species on Earth is highly uncertain. A recent study has suggested that there are less than 2 million prokaryotic species on Earth; this Formal Comment suggests instead that there are more likely hundreds of millions or billions of species, and that the majority of these are bacteria associated with insects and other animals.

The number of species on Earth is a fundamental number in science. Yet, estimates of global biodiversity have been highly uncertain. There are presently approximately 1.9 million described species [1]. Estimates of the actual number (both described and undescribed) have ranged from the low millions into the trillions [2,3]. Furthermore, described species richness [1] is dominated by animals (1.3 million; 68%), not bacteria (approximately 10,000 species; 0.5%). Larsen and colleagues [2] summarized evidence suggesting that the majority of species on Earth may be bacteria associated with insect hosts and that bacterial richness may push global biodiversity into the hundreds of millions of species or even low billions.Louca and colleagues [4] (LEA hereafter) have claimed instead that there are only 40,100 host-associated bacterial species among all animal species and 0.8 to 1.6 million prokaryotic species overall (see their “Author summary”). Strangely, they excluded bacterial species associated with animal hosts from their estimates of total prokaryotic diversity and justified this by claiming that the estimates of Larsen and colleagues [2] were “mathematically flawed.” Here, I examine their claims and present new estimates of global biodiversity.Remarkably, all projections by LEA for host-associated bacterial richness were based on an estimate from one ant genus (Cephalotes), an estimate that is demonstrably incorrect by orders of magnitude (S1 Text). Without examining the underlying data [5], LEA estimated only 40 bacterial species among all 130 ant species in this genus. Yet, simply counting the bacterial species among the 25 sampled ant species in that genus reveals 616 unique bacterial species, of which 539 appear to be unique to the genus and 369 each unique to a single ant species (using the standard 97% cutoff for 16S divergence and data from [5]). Thus, there were >500 bacterial species among 25 ant species, not 40 bacterial species among 130 ant species. This mistake was further exacerbated by inexplicably ignoring data from the other 2 insect genera analyzed by Larsen and colleagues [2], thus maximizing the impact of their incorrect estimate for this genus.Their overall estimate of bacterial richness was also strongly influenced by their questionable assumption that all animal genera can share bacterial species (i.e., reducing their estimate of 3 million host-associated bacterial species to only 40,100). They assumed “a conservative overlap of only 0.1% between any two randomly chosen genera” for the number of bacterial species shared between animal genera. No justification was given for this value of 0.1%, nor were any alternative values explored. Furthermore, they implicitly assumed that any bacterial species can be shared between any pair of animal genera, regardless of their phylogeny, habitat, or geographic range. So, for example, a bacterial species that is a gut endosymbiont of a terrestrial herbivorous insect species endemic to Madagascar could somehow be shared with a deep-sea worm in the northern Pacific Ocean. This is ridiculous: there must be a reason why bacterial species are shared among host species and genera (e.g., shared phylogeny, location, diet). For example, broad-scale studies show that sharing of bacteria among insect hosts is associated with both host phylogeny and diet [6].LEA stated “it is known that substantial overlap exists between the microbiota of different host genera and even of distantly related animal taxa.” However, they provided no numbers to justify this “substantial overlap.” In fact, none of the papers they cited as supporting this assumption actually do (S2 Text). For example, one study [7] found 5 bacterial species shared among 5 insect genera utilizing the same type of host plant (cycads). However, LEA do not mention that this study found 1,789 unique bacterial species among just these 5 insect species (or 177 after filtering). This seems inconsistent with their estimate of only 40,100 bacterial species across all animals. In summary, rather than estimating the overlap of bacterial species among host genera, LEA simply made a number up and combined this with unrealistic, unsupported assumptions about overlap. If LEA had considered Cephalotes (which all their estimates were based on), a survey of this genus and related genera [5] found 1,019 bacterial species, with only 77 of the 616 bacterial species in Cephalotes shared with other sampled genera, and the sharing of bacterial species among hosts strongly related to host phylogeny.Numerous surveys of bacterial diversity in insects strongly suggest that there are far more than 40,100 bacterial species among all animals (8] found roughly twice as many bacterial species as those of approximately 30 insect species [5,9], and the study of 218 insect species [6] found >3.5 times as many as the study of 62 insect species. The simple fact that a study found 9,301 bacterial species among only 218 sampled insect species strongly suggests that there are more than 40,100 bacteria among all animals.Table 1Surveys of bacterial diversity among insect species.LEA incorrectly estimated that a genus of 130 ant species (Cephalotes) hosts only 40 bacterial species and subsequently assumed that all animal genera have the same low number of bacterial species. These broad surveys of bacterial species among insects suggest that many insects (including Cephalotes) host much larger numbers of bacterial species.

Big Data: Astronomical or Genomical?

Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a “four-headed beast”—it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the “genomical” challenges of the next decade.We compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Astronomy has faced the challenges of Big Data for over 20 years and continues with ever-more ambitious studies of the universe. YouTube burst on the scene in 2005 and has sparked extraordinary worldwide interest in creating and sharing huge numbers of videos. Twitter, created in 2006, has become the poster child of the burgeoning movement in computational social science [6], with unprecedented opportunities for new insights by mining the enormous and ever-growing amount of textual data [7]. Particle physics also produces massive quantities of raw data, although the footprint is surprisingly limited since the vast majority of data are discarded soon after acquisition using the processing power that is coupled to the sensors [8]. Consequently, we do not include the domain in full detail here, although that model of rapid filtering and analysis will surely play an increasingly important role in genomics as the field matures.To compare these four disparate domains, we considered the four components that comprise the “life cycle” of a dataset: acquisition, storage, distribution, and analysis ( Data Phase Astronomy Twitter YouTube Genomics Acquisition 25 zetta-bytes/year0.5–15 billion tweets/year500–900 million hours/year1 zetta-bases/year Storage 1 EB/year1–17 PB/year1–2 EB/year2–40 EB/year Analysis In situ data reductionTopic and sentiment miningLimited requirementsHeterogeneous data and analysisReal-time processingMetadata analysisVariant calling, ~2 trillion central processing unit (CPU) hoursMassive volumesAll-pairs genome alignments, ~10,000 trillion CPU hours Distribution Dedicated lines from antennae to server (600 TB/s)Small units of distributionMajor component of modern user’s bandwidth (10 MB/s)Many small (10 MB/s) and fewer massive (10 TB/s) data movementOpen in a separate window     

Does an Eye-Hand Coordination Test Have Added Value as Part of Talent Identification in Table Tennis? A Validity and Reproducibility Study

This study investigated the added value, i.e. discriminative and concurrent validity and reproducibility, of an eye-hand coordination test relevant to table tennis as part of talent identification. Forty-three table tennis players (7–12 years) from national (n = 13), regional (n = 11) and local training centres (n = 19) participated. During the eye-hand coordination test, children needed to throw a ball against a vertical positioned table tennis table with one hand and to catch the ball correctly with the other hand as frequently as possible in 30 seconds. Four different test versions were assessed varying the distance to the TotalNationalRegionalLocalTotal43131119Boys268810Girls17539Age (years)10.4±1.410.9±1.510.4±1.510.1±1.47 year olds1--18 year olds51139 year olds3-3-10 year olds1232711 year olds1151512 year olds11443Length (cm)149±11150±12150±12148±10Weight (kg )38±837±737±738±9Right-handed359917Left-handed8422Training (hours*week-1)6 (0–20)11 (7–20)7 (4–11)2 (0–3)Competition (points)173 (−52–430)297 (144–430)188 (72–317)36 (−52–130)Open in a separate windowData are frequencies, except for age, length and weight (years±SD), and training and competition (mean (range)).     

Hepatitis C Virus (HCV) Sequence Variation Induces an HCV-Specific T-Cell Phenotype Analogous to Spontaneous Resolution

Hepatitis C virus (HCV)-specific CD8⁺ T cells in persistent HCV infection are low in frequency and paradoxically show a phenotype associated with controlled infections, expressing the memory marker CD127. We addressed to what extent this phenotype is dependent on the presence of cognate antigen. We analyzed virus-specific responses in acute and chronic HCV infections and sequenced autologous virus. We show that CD127 expression is associated with decreased antigenic stimulation after either viral clearance or viral variation. Our data indicate that most CD8 T-cell responses in chronic HCV infection do not target the circulating virus and that the appearance of HCV-specific CD127⁺ T cells is driven by viral variation.Hepatitis C virus (HCV) persists in the majority of acutely infected individuals, potentially leading to chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma. The cellular immune response has been shown to play a significant role in viral control and protection from liver disease. Phenotypic and functional studies of virus-specific T cells have attempted to define the determinants of a successful versus an unsuccessful T-cell response in viral infections (10). So far these studies have failed to identify consistent distinguishing features between a T-cell response that results in self-limiting versus chronic HCV infection; similarly, the impact of viral persistence on HCV-specific memory T-cell formation is poorly understood.Interleukin-7 (IL-7) receptor alpha chain (CD127) is a key molecule associated with the maintenance of memory T-cell populations. Expression of CD127 on CD8 T cells is typically only observed when the respective antigen is controlled and in the presence of significant CD4⁺ T-cell help (9). Accordingly, cells specific for persistent viruses (e.g., HIV, cytomegalovirus [CMV], and Epstein-Barr virus [EBV]) have been shown to express low levels of CD127 (6, 12, 14) and to be dependent on antigen restimulation for their maintenance. In contrast, T cells specific for acute resolving virus infections, such as influenza virus, respiratory syncytial virus (RSV), hepatitis B virus (HBV), and vaccinia virus typically acquire expression of CD127 rapidly with the control of viremia (5, 12, 14). Results for HCV have been inconclusive. The expected increase in CD127 levels in acute resolving but not acute persisting infection has been found, while a substantial proportion of cells with high CD127 expression have been observed in long-established chronic infection (2). We tried to reconcile these observations by studying both subjects with acute and chronic HCV infection and identified the presence of antigen as the determinant of CD127 expression.Using HLA-peptide multimers we analyzed CD8⁺ HCV-specific T-cell responses and CD127 expression levels in acute and chronic HCV infection. We assessed a cohort of 18 chronically infected subjects as well as 9 individuals with previously resolved infection. In addition, we longitudinally studied 9 acutely infected subjects (5 individuals who resolved infection spontaneously and 4 individuals who remain chronically infected) (Tables ​(Tables11 and ​and2).2). Informed consent in writing was obtained from each patient, and the study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki, as reflected in a priori approval from the local institutional review boards. HLA-multimeric complexes were obtained commercially from Proimmune (Oxford, United Kingdom) and Beckman Coulter (CA). The staining and analysis procedure was as described previously (10). Peripheral blood mononuclear cells (PBMCs) were stained with the following antibodies: CD3 from Caltag; CD8, CD27, CCR7, CD127, and CD38 from BD Pharmingen; and PD-1 (kindly provided by Gordon Freeman). Primer sets were designed for different genotypes based on alignments of all available sequences from the public HCV database (http://hcvpub.ibcp.fr). Sequence analysis was performed as previously described (8).

TABLE 1.

Patient information and autologous sequence analysis for patients with chronic and resolved HCV infection

Single Nucleotide Polymorphism-Based Diagnostic System for Crop-Associated Sclerotinia Species

A molecular diagnostic system using single nucleotide polymorphisms (SNPs) was developed to identify four Sclerotinia species: S. sclerotiorum (Lib.) de Bary, S. minor Jagger, S. trifoliorum Erikss., and the undescribed species Sclerotinia species 1. DNAs of samples are hybridized with each of five 15-bp oligonucleotide probes containing an SNP site midsequence unique to each species. For additional verification, hybridizations were performed using diagnostic single nucleotide substitutions at a 17-bp sequence of the calmodulin locus. The accuracy of these procedures was compared to that of a restriction fragment length polymorphism (RFLP) method based on Southern hybridizations of EcoRI-digested genomic DNA probed with the ribosomal DNA-containing plasmid probe pMF2, previously shown to differentiate S. sclerotiorum, S. minor, and S. trifoliorum. The efficiency of the SNP-based assay as a diagnostic test was evaluated in a blind screening of 48 Sclerotinia isolates from agricultural and wild hosts. One isolate of Botrytis cinerea was used as a negative control. The SNP-based assay accurately identified 96% of Sclerotinia isolates and could be performed faster than RFLP profiling using pMF2. This method shows promise for accurate, high-throughput species identification.Sclerotinia is distinguished morphologically from other genera in the Sclerotiniaceae (Ascomycota, Pezizomycotina, Leotiomycetes) by the production of tuberoid sclerotia that do not incorporate host tissue, by the production of microconidia that function as spermatia but not as a disseminative asexual state, and by the development of a layer of textura globulosa composing the outer tissue of apothecia (8). Two hundred forty-six species of Sclerotinia have been reported, most distinguished morphotaxonomically (Index Fungorum [www.indexfungorum.org]). These include the four species of agricultural importance now recognized plus many that are imperfectly known, seldom collected, or apparently endemic to relatively small geographic areas (2, 5, 6, 7, 8, 9, 17).The main species of phythopathological interest in the genus Sclerotinia are S. sclerotiorum (Lib.) de Bary, S. minor Jagger, S. trifoliorum Erikss., and the undescribed species Sclerotinia species 1. Sclerotinia species 1 is an important cause of disease in vegetables in Alaska (16) and has been found in association with wild Taraxacum sp., Caltha palustris, and Aconitum septentrionalis in Norway (7). It is morphologically indistinguishable from S. sclerotiorum, but it was shown to be a distinct species based on distinctive polymorphisms in sequences from internal transcribed spacer 2 (ITS2) of the nuclear ribosomal repeat (7). The other three species have been delimited using morphological, cytological, biochemical, and molecular characters (3, 8, 9, 10, 12, 15). Interestingly, given that the ITS is sufficiently polymorphic in many fungal genera to resolve species, in Sclerotinia, only species 1 and S. trifoliorum are distinguished by characteristic ITS sequence polymorphisms; S. sclerotiorum and S. minor cannot be distinguished based on ITS sequence (2, 7).Sclerotinia sclerotiorum is a necrotrophic pathogen with a broad host range (1). S. minor has a more restricted host range but causes disease in a variety of important crops such as lettuce, peanut, and sunflower crops (11). S. trifoliorum has a much narrower host range, limited to the Fabaceae (3, 8, 9). Sclerotial and ascospore characteristics also serve to differentiate among the three species. Sclerotinia minor has small sclerotia that develop throughout the colony in vitro and aggregate to form crusts on the host, while the sclerotia of S. sclerotiorum and S. trifoliorum are large and form at the colony periphery in vitro, remaining separate on the host (8, 9). The failure of an isolate to produce sclerotia or apothecia in vitro is not unusual, especially after serial cultivation (8). The presence of dimorphic, tetranucleate ascospores characterizes S. trifoliorum, while S. sclerotiorum and S. minor both have uniformly sized ascospores that are binucleate and tetranucleate, respectively (9, 14).With the apparent exception of Sclerotinia species 1, morphological characteristics are sufficient to delimit Sclerotinia species given that workers have all manifestations of the life cycle in hand. In cultures freshly isolated from infected plants, investigators usually have mycelia and sclerotia but not apothecia. Restriction fragment length polymorphisms (RFLPs) in ribosomal DNA (rDNA) are diagnostic for Sclerotinia species (3, 10), but the assay requires cloned probes (usually accessed from other laboratories) hybridized to Southern blots from vertical gels, an impractical procedure for large samples. We have analyzed sequence data from previous phylogenetic studies (2) and have identified diagnostic variation for the rapid identification of the four Sclerotinia species. The single nucleotide polymorphism (SNP) assay that we report here is amenable to a high throughput of samples and requires only PCR amplification with a standard set of primers and oligonucleotide hybridizations to Southern blots in a dot format.The SNP assay was performed using two independent sets of species-specific oligonucleotide probes, all with SNP sites shown to differentiate the four Sclerotinia species (Fig. ​(Fig.1).1). A panel of 49 anonymously coded isolates (Table ​(Table1)1) was screened using these species-specific SNP probes, as outlined in Fig. ​Fig.1.1. The assay was validated by comparison to Southern hybridizations of EcoRI-digested genomic DNA hybridized with pMF2, a plasmid probe containing the portion of the rDNA repeat with the 18S, 5.8S, and 26S rRNA cistrons of Neurospora crassa (4, 10).Open in a separate windowFIG. 1.Protocol for the SNP-based identification of Sclerotinia species, with diagnostic SNP sites underlined and in boldface type for each hybridization probe.

TABLE 1.

Isolates and hybridization results for all SNP-based oligonucleotide probes^f

Focus Issue on Roots: The Optimal Lateral Root Branching Density for Maize Depends on Nitrogen and Phosphorus Availability

Observed phenotypic variation in the lateral root branching density (LRBD) in maize (Zea mays) is large (1–41 cm⁻¹ major axis [i.e. brace, crown, seminal, and primary roots]), suggesting that LRBD has varying utility and tradeoffs in specific environments. Using the functional-structural plant model SimRoot, we simulated the three-dimensional development of maize root architectures with varying LRBD and quantified nitrate and phosphorus uptake, root competition, and whole-plant carbon balances in soils varying in the availability of these nutrients. Sparsely spaced (less than 7 branches cm⁻¹), long laterals were optimal for nitrate acquisition, while densely spaced (more than 9 branches cm⁻¹), short laterals were optimal for phosphorus acquisition. The nitrate results are mostly explained by the strong competition between lateral roots for nitrate, which causes increasing LRBD to decrease the uptake per unit root length, while the carbon budgets of the plant do not permit greater total root length (i.e. individual roots in the high-LRBD plants stay shorter). Competition and carbon limitations for growth play less of a role for phosphorus uptake, and consequently increasing LRBD results in greater root length and uptake. We conclude that the optimal LRBD depends on the relative availability of nitrate (a mobile soil resource) and phosphorus (an immobile soil resource) and is greater in environments with greater carbon fixation. The median LRBD reported in several field screens was 6 branches cm⁻¹, suggesting that most genotypes have an LRBD that balances the acquisition of both nutrients. LRBD merits additional investigation as a potential breeding target for greater nutrient acquisition.At least four major classes of plant roots can be distinguished based on the organ from which they originate: namely the seed, the shoot, the hypocotyl/mesocotyl, and other roots (Zobel and Waisel, 2010). The last class is lateral roots, which form in most plants the majority of the root length, but not necessarily of the root weight, as lateral roots have smaller diameter. Lateral roots start with the formation of lateral root primordia, closely behind the root tip of the parent root. These primordia undergo nine distinguishable steps, of which the last step is the emergence from the cortex of the parent root just behind the zone of elongation, usually only a few days after the first cell divisions that lead to their formation (Malamy and Benfey, 1997). However, not all primordia develop into lateral roots; some stay dormant (Dubrovsky et al., 2006), although dormancy of primordia may not occur in maize (Zea mays; Jordan et al., 1993; Ploshchinskaia et al., 2002). The final number of lateral roots is thereby dependent on the rate of primordia formation as well as the percentage of primordia that develop into lateral roots. This process of primordia formation and lateral root emergence is being studied intensively, including the genes that are activated during the different steps and the hormones regulating the process (López-Bucio et al., 2003; Dubrovsky et al., 2006; Osmont et al., 2007; Péret et al., 2009; Lavenus et al., 2013). Significant genotypic variation in the density of lateral roots has been observed, ranging from no lateral roots to 41 roots cm⁻¹ in maize (Trachsel et al., 2010; Lynch, 2013). This suggests that clear tradeoffs exist for the development of lateral roots and that these genotypes have preprogrammed growth patterns that are adaptive to specific environments. While some of the variation for lateral root branching density (LRBD) that has been observed across environments, for example by Trachsel et al. (2010), is constitutive, many genotypes have strong plasticity responses of LRBD to variations in soil fertility (Zhu et al., 2005a; Osmont et al., 2007). Both the nutrient and carbon status of the plant and the local nutrient environment of the (parent) root tip influence LRBD. Many studies have documented these plasticity responses, and others have tried to unravel parts of the sensing and signaling pathways that regulate LRBD. The utility of root proliferation into a nutrient patch has been studied and debated (Robinson et al., 1999; Hodge, 2004), but much less so the utility of having fewer or more branches across the whole root system. Our understanding of the adaptive significance of variation in LRBD among genotypes is thereby limited, with many studies not accounting for relevant tradeoffs. In this study, we integrate several functional aspects of LRBD with respect to nutrient acquisition, root competition, and internal resource costs and quantify these functional aspects using the functional structural plant model SimRoot. SimRoot simulates plant growth with explicit representation of root architecture in three dimensions (Fig. 1; Supplemental Movie S1). The model focuses on the resource acquisition by the root system and carbon fixation by the shoot while estimating the resource utilization and requirements by all the different organs.

Table I.

Minimum, maximum, and median LRBD in different populations phenotyped by various researchers at several locations in the worldLocations are as follows: D, Juelich, Germany; PA, State College, PA; and SA, Alma, South Africa. Some of the experiments included nutrient treatments: LN, low nitrogen availability; and LP, low phosphorus availability. Data were collected by counting the number of roots along a nodal root segment. Data were supplied by the person indicated under source: H.S., H. Schneider; L.Y., L. York; A.Z., A. Zhan; and J.P., J.A. Postma. WiDiv, Wisconsin Diversity panel; IBM, intermated B73 × Mo17; NAM, nested association mapping.

A conifer genome spruces up plant phylogenomics

The Norway spruce genome provides key insights into the evolution of plant genomes, leading to testable new hypotheses about conifer, gymnosperm, and vascular plant evolution.In the past year a burst of plant genome sequences have been published, providing enhanced phylogenetic coverage of green plants (Figure ​(Figure1)1) and inclusion of new agricultural, ecological, and evolutionary models. Collectively, these sequences are revealing some extraordinary structural and evolutionary attributes in plant genomes. Perhaps most surprising is the exceptionally high frequency of whole-genome duplication (WGD): nearly every genome that has been analyzed has borne the signature of one or more WGDs, with particularly notable events having occurred in the common ancestors of seed plants, of angiosperms, and of core eudicots (the latter ''WGD'' represents two WGDs in close succession) [1,2]. Given this tendency for plant genomes to duplicate and then return to an essentially diploid genetic system (an example is the cotton genomes, which have accumulated the effects of perhaps 15 WGDs [3]), the conservation of genomes in terms of gene number, chromosomal organization, and gene content is astonishing. From the publication of the first plant genome, Arabidopsis thaliana [4], the number of inferred genes has been between 25,000 and 30,000, with many gene families shared across all land plants, although the number of members and patterns of expansion and contraction vary. Furthermore, conserved synteny has been detected across the genomes of diverse angiosperms, despite WGDs, diploidization, and millions of years of evolution.Open in a separate windowFigure 1Simplified phylogeny of land plants, showing major clades and their component lineages. Asterisks indicate species (or lineage) for which whole-genome sequence (or sequences) is (are) available. Increases and decreases in genome size are shown by arrows.Despite the proliferation of genome sequences available for angiosperms, genome-level data for both ferns (and their relatives, collectively termed monilophytes; Figure ​Figure1)1) and gymnosperms have been conspicuously lacking - until recently, with the publication of the genome sequence of the gymnosperm Norway spruce (Picea abies) [5]. The large genome sizes for both monilophytes and gymnosperms have discouraged attempts at genome sequencing and assembly, whereas the smaller genome size of angiosperms has resulted in more genome sequences being available (Table ​(Table1)1) [6]. Because of this limited phylogenetic sample, our understanding of the timing and phylogenetic positions of WGDs, the core number of plant genes, possible conserved syntenic regions, and patterns of expansion and contraction of gene families across both tracheophytes (vascular plants) and across all land plants is imperfect. This sampling problem is particularly acute in analyses of the genes and genomes of seed plants; many hundreds of genes are present in angiosperms that are not present in mosses or lycophytes, but whether these genes arose in the common ancestor of seed plants or of angiosperms cannot be determined without a gymnosperm genome sequence. The Norway spruce genome therefore offers tremendous power, not only for understanding the structure and evolution of conifer genomes, but also as a reference for interpreting gene and genome evolution in angiosperms.

Table 1

Genome sizes in land plants