The iPlant Collaborative: Cyberinfrastructure for Plant Biology

G2PDeep-v2: a web-based deep-learning framework for phenotype prediction and biomarker discovery using multi-omics data

The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases and other biological processes. The G2PDeep-v2 server is publicly available at https://g2pdeep.org/.

Gene function annotations for the maize NAM founder lines

OBJECTIVES

We annotated the latest published sequences of the 26 Zea mays Nested Association Mapping (NAM) founder lines using GOMAP, the Gene Ontology Meta Annotator for Plants. The maize NAM panel enables researchers to understand and identify the genetic basis of complex traits. Annotations of predicted functions for genes can help researchers investigate gene-phenotype associations, prioritize candidate genes for phenotypes of interest, and formulate testable hypotheses about gene function/phenotype associations. The creation and release of high-confidence, high-coverage gene function annotation sets for the NAM founder lines is critical to accelerate the generation of knowledge in maize genetics research. GOMAP is a high-throughput computational pipeline that annotates gene functions genome-wide in plant genomes using Gene Ontology functional class terms. Here we report and share GOMAP-generated functional annotations for the NAM founder lines.

DATA DESCRIPTION

Datasets include the protein sequences used as input, GOMAP-generated annotation files, scripts used to update obsolete terms, and GAF-formatted tab-delimited text files of gene function annotations along with README files that describe formatting, content, and how files relate to each other.

Cotton Meristem Transcriptomes: Constructing an RNA-Seq Pipeline to Explore Crop Architecture Regulation

Plants stem cells, known as meristems, specify all patterns of growth and organ size. Differences in meristem activities contribute to diverse shoot architectures. As many architectural traits, such as branching patterns, flowering time, and fruit size, are yield determinants, meristem regulation is of fundamental importance to crop productivity. Cotton (Gossypium spp.) produces our most prevalent natural fiber that finds its way into products ranging from industrial cellulose, medical supplies, and paper currency, to a broad diversity of textiles, not least of which is our clothing. However, the cotton plant has growth habits that challenge management practices and limit harvest yield and quality. Unraveling and leveraging the genetic networks regulating meristem activities offers the potential to overcome these limitations. We use virus-based technologies in cotton to perturb signals regulating meristem fate and size. In this chapter, we describe our pipeline for altering cotton meristem dynamics and preparing, analyzing, and exploring the transcriptomes from isolated meristems.

A Dual Transcriptomic Approach Reveals Contrasting Patterns of Differential Gene Expression During Drought in Arbuscular Mycorrhizal Fungus and Carrot

While arbuscular mycorrhizal (AM) fungi are known for providing host plants with improved drought tolerance, we know very little about the fungal response to drought in the context of the fungal-plant relationship. In this study, we evaluated the drought responses of the host and symbiont, using the fungus Rhizophagus irregularis with carrot (Daucus carota) as a plant model. Carrots inoculated with spores of R. irregularis DAOM 197198 were grown in a greenhouse. During taproot development, carrots were exposed to a 10-day water restriction. Compared with well-watered conditions, drought caused diminished photosynthetic activity and reduced plant growth in carrot with and without AM fungi. Droughted carrots had lower root colonization. For R. irregularis, 93% of 826 differentially expressed genes (DEGs) were upregulated during drought, including phosphate transporters, several predicted transport proteins of potassium, and the aquaporin RiAQPF2. In contrast, 78% of 2,486 DEGs in AM carrot were downregulated during drought, including the symbiosis-specific genes FatM, RAM2, and STR, which are implicated in lipid transfer from the host to the fungus and were upregulated exclusively in AM carrot during well-watered conditions. Overall, this study provides insight into the drought response of an AM fungus in relation to its host; the expression of genes related to symbiosis and nutrient exchange were downregulated in carrot but upregulated in the fungus. This study reveals that carrot and R. irregularis exhibit contrast in their regulation of gene expression during drought, with carrot reducing its apparent investment in symbiosis and the fungus increasing its apparent symbiotic efforts. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Depth-driven patterns in lytic viral diversity, auxiliary metabolic gene content, and productivity in offshore oligotrophic waters

Introduction

Marine viruses regulate microbial population dynamics and biogeochemical cycling in the oceans. The ability of viruses to manipulate hosts' metabolism through the expression of viral auxiliary metabolic genes (AMGs) was recently highlighted, having important implications in energy production and flow in various aquatic environments. Up to now, the presence and diversity of viral AMGs is studied using -omics data, and rarely using quantitative measures of viral activity alongside.

Methods

In the present study, four depth layers (5, 50, 75, and 1,000 m) with discrete hydrographic features were sampled in the Eastern Mediterranean Sea; we studied lytic viral community composition and AMG content through metagenomics, and lytic production rates through the viral reduction approach in the ultra-oligotrophic Levantine basin where knowledge regarding viral actions is rather limited.

Results and Discussion

Our results demonstrate depth-dependent patterns in viral diversity and AMG content, related to differences in temperature, nutrients availability, and host bacterial productivity and abundance. Although lytic viral production rates were similar along the water column, the virus-to-bacteria ratio was higher and the particular set of AMGs was more diverse in the bathypelagic (1,000 m) than the shallow epipelagic (5, 50, and 75 m) layers, revealing that the quantitative effect of viruses on their hosts may be the same along the water column through the intervention of different AMGs. In the resource- and energy-limited bathypelagic waters of the Eastern Mediterranean, the detected AMGs could divert hosts' metabolism toward energy production, through a boost in gluconeogenesis, fatty-acid and glycan biosynthesis and metabolism, and sulfur relay. Near the deep-chlorophyll maximum depth, an exceptionally high percentage of AMGs related to photosynthesis was noticed. Taken together our findings suggest that the roles of viruses in the deep sea might be even more important than previously thought as they seem to orchestrate energy acquisition and microbial community dynamics, and thus, biogeochemical turnover in the oceans.

Genomic Variations Explorer (GenVarX): a toolset for annotating promoter and CNV regions using genotypic and phenotypic differences

The rapid growth of sequencing technology and its increasing popularity in biology-related research over the years has made whole genome re-sequencing (WGRS) data become widely available. A large amount of WGRS data can unlock the knowledge gap between genomics and phenomics through gaining an understanding of the genomic variations that can lead to phenotype changes. These genomic variations are usually comprised of allele and structural changes in DNA, and these changes can affect the regulatory mechanisms causing changes in gene expression and altering the phenotypes of organisms. In this research work, we created the GenVarX toolset, that is backed by transcription factor binding sequence data in promoter regions, the copy number variations data, SNPs and Indels data, and phenotypes data which can potentially provide insights about phenotypic differences and solve compelling questions in plant research. Analytics-wise, we have developed strategies to better utilize the WGRS data and mine the data using efficient data processing scripts, libraries, tools, and frameworks to create the interactive and visualization-enhanced GenVarX toolset that encompasses both promoter regions and copy number variation analysis components. The main capabilities of the GenVarX toolset are to provide easy-to-use interfaces for users to perform queries, visualize data, and interact with the data. Based on different input windows on the user interface, users can provide inputs corresponding to each field and submit the information as a query. The data returned on the results page is usually displayed in a tabular fashion. In addition, interactive figures are also included in the toolset to facilitate the visualization of statistical results or tool outputs. Currently, the GenVarX toolset supports soybean, rice, and Arabidopsis. The researchers can access the soybean GenVarX toolset from SoyKB via https://soykb.org/SoybeanGenVarX/, rice GenVarX toolset, and Arabidopsis GenVarX toolset from KBCommons web portal with links https://kbcommons.org/system/tools/GenVarX/Osativa and https://kbcommons.org/system/tools/GenVarX/Athaliana, respectively.

Regulation of plant epigenetic memory in response to cold and heat stress: towards climate resilient agriculture

Plants have evolved to adapt and grow in hot and cold climatic conditions. Some also adapt to daily and seasonal temperature changes. Epigenetic modifications play an important role in regulating plant tolerance under such conditions. DNA methylation and post-translational modifications of histone proteins influence gene expression during plant developmental stages and under stress conditions, including cold and heat stress. While short-term modifications are common, some modifications may persist and result in stress memory that can be inherited by subsequent generations. Understanding the mechanisms of epigenomes responding to stress and the factors that trigger stress memory is crucial for developing climate-resilient agriculture, but such an integrated view is currently limited. This review focuses on the plant epigenetic stress memory during cold and heat stress. It also discusses the potential of machine learning to modify stress memory through epigenetics to develop climate-resilient crops.

Microbial Characterization of Arctic Glacial Ice Cores with a Semiautomated Life Detection System

The search for extant microbial life will be a major focus of future astrobiology missions; however, no direct extant life detection instrumentation is included in current missions to Mars. In this study, we developed the semiautomated MicroLife detection platform that collects and processes environmental samples, detects biosignatures, and characterizes microbial activity. This platform is composed of a drill for sample collection, a redox dye colorimetric system for microbial metabolic activity detection and assessment (μMAMA [microfluidics Microbial Activity MicroAssay]), and a MinION sequencer for biosignature detection and characterization of microbial communities. The MicroLife platform was field-tested on White Glacier on Axel Heiberg Island in the Canadian high Arctic, with two extracted ice cores. The μMAMA successfully detected microbial metabolism from the ice cores within 1 day of incubation. The MinION sequencing of the ice cores and the positive μMAMA card identified a microbial community consistent with cold and oligotrophic environments. Furthermore, isolation and identification of microbial isolates from the μMAMA card corroborated the MinION sequencing. Together, these analyses support the MicroLife platform's efficacy in identifying microbes natively present in cryoenvironments and detecting their metabolic activity. Given our MicroLife platform's size and low energy requirements, it could be incorporated into a future landed platform or rovers for life detection.

Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding

Given the challenges of population growth and climate change, there is an urgent need to expedite the development of high-yielding stress-tolerant crop cultivars. While traditional breeding methods have been instrumental in ensuring global food security, their efficiency, precision, and labour intensiveness have become increasingly inadequate to address present and future challenges. Fortunately, recent advances in high-throughput phenomics and genomics-assisted breeding (GAB) provide a promising platform for enhancing crop cultivars with greater efficiency. However, several obstacles must be overcome to optimize the use of these techniques in crop improvement, such as the complexity of phenotypic analysis of big image data. In addition, the prevalent use of linear models in genome-wide association studies (GWAS) and genomic selection (GS) fails to capture the nonlinear interactions of complex traits, limiting their applicability for GAB and impeding crop improvement. Recent advances in artificial intelligence (AI) techniques have opened doors to nonlinear modelling approaches in crop breeding, enabling the capture of nonlinear and epistatic interactions in GWAS and GS and thus making this variation available for GAB. While statistical and software challenges persist in AI-based models, they are expected to be resolved soon. Furthermore, recent advances in speed breeding have significantly reduced the time (3-5-fold) required for conventional breeding. Thus, integrating speed breeding with AI and GAB could improve crop cultivar development within a considerably shorter timeframe while ensuring greater accuracy and efficiency. In conclusion, this integrated approach could revolutionize crop breeding paradigms and safeguard food production in the face of population growth and climate change.

Horn fly transcriptome data of ten populations from the southern United States with varying degrees and molecular mechanisms of pesticide resistance

Haematobia irritans irritans (Linnaeus, 1758: Diptera: Muscidae), the horn fly, is an external parasite of penned and pastured livestock that causes a major economic impact on cattle production worldwide. Pesticides such as synthetic pyrethroids and organophosphates are routinely used to control horn flies; however, resistance to these chemicals has become a concern in several countries. To further elucidate the molecular mechanisms of resistance in horn fly populations, we sequenced the transcriptomes of ten populations of horn flies from the southern US possessing varying degrees of pesticide resistance levels to pyrethroids, organophosphates, and endosulfans. We employed an Illumina paired end HiSeq approach, followed by de novo assembly of the transcriptomes using CLC Genomics Workbench 8.0.1 De Novo Assembler using multiple kmers, and annotation using Blast2GO PRO version 5.2.5. The Gene Ontology biological process term Response to Insecticide was found in all the populations, but at an increased frequency in the populations with higher levels of insecticide resistance. The raw sequence reads are archived in the Sequence Read Archive (SRA) and assembled population transcriptomes in the Transcriptome Shotgun Assembly (TSA) at the National Center for Biotechnology Information (NCBI).

Altered expression of SELF-PRUNING disrupts homeostasis and facilitates signal delivery to meristems

Meristem maintenance, achieved through the highly conserved CLAVATA-WUSCHEL (CLV-WUS) regulatory circuit, is fundamental in balancing stem cell proliferation with cellular differentiation. Disruptions to meristem homeostasis can alter meristem size, leading to enlarged organs. Cotton (Gossypium spp.), the world's most important fiber crop, shows inherent variation in fruit size, presenting opportunities to explore the networks regulating meristem homeostasis and to impact fruit size and crop value. We identified and characterized the cotton orthologs of genes functioning in the CLV-WUS circuit. Using virus-based gene manipulation in cotton, we altered the expression of each gene to perturb meristem regulation and increase fruit size. Targeted alteration of individual components of the CLV-WUS circuit modestly fasciated flowers and fruits. Unexpectedly, controlled expression of meristem regulator SELF-PRUNING (SP) increased the impacts of altered CLV-WUS expression on flower and fruit fasciation. Meristem transcriptomics showed SP and genes of the CLV-WUS circuit are expressed independently from each other, suggesting these gene products are not acting in the same path. Virus-induced silencing of GhSP facilitated the delivery of other signals to the meristem to alter organ specification. SP has a role in cotton meristem homeostasis, and changes in GhSP expression increased access of virus-derived signals to the meristem.

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

Concerning the Etiology of Syrah Decline: A Fresh Perspective on an Old and Complex Issue Facing the Global Grape and Wine Industry

Syrah decline, first identified in Southern France in the 1990s, has become a major concern in the global grape and wine industry. This disease mainly affects Syrah (Shiraz) grapevines. Characteristic symptoms include the bright and uniform reddening of leaves throughout the canopy in late summer or early fall; the appearance of abnormalities on the trunk, mainly at the graft union (swelling, pits, grooves, and necrosis); and a reduction in vine vigor, yield and berry quality. Diseased vines may die a few years after disease onset. Damages to the vine are even more pronounced in cool climate regions such as Ontario (Canada), where the affected vines are subjected to very cold and prolonged winters, leading to large numbers of vine deaths. Despite the extensive efforts of the global grape research community over the past few decades, the etiology of this disease remains unclear. In this study, we conducted extensive analyses of viruses in declining Syrah vines identified in commercial vineyards in the Niagara region (Ontario, Canada) through high-throughput sequencing, PCR, RT-PCR and the profiling of genetic variants of select viruses. Multiple viruses and viral strains, as well as three viroids, were identified. However, an unequivocal causal relationship cannot be established between Syrah decline and any of these viruses, although the possibility that certain virus or genetic variants, or both in combination, may contribute to the disease cannot be excluded. Gleaning all information that is available to date, we feel that the traditional approach and an insistence on finding a single cause for such a complex disorder in a woody perennial fruit crop involving grafting will prove to be futile. We hope that this study offers new conceptual perspectives on the etiology of this economically important but enigmatic disease complex that affects the global grape and wine industry.

Using the Collaborative Cross and Diversity Outbred Mice in Immunology

The Collaborative Cross (CC) and the Diversity Outbred (DO) stock mouse panels are the most powerful murine genetics tools available to the genetics community. Together, they combine the strength of inbred animal models with the diversity of outbred populations. Using the 63 CC strains or a panel of DO mice, each derived from the same 8 parental mouse strains, researchers can map genetic contributions to exceptionally complex immunological and infectious disease traits that would require far greater powering if performed by genome-wide association studies (GWAS) in human populations. These tools allow genes to be studied in heterozygous and homozygous states and provide a platform to study epistasis between interacting loci. Most importantly, once a quantitative phenotype is investigated and quantitative trait loci are identified, confirmatory genetic studies can be performed, which is often problematic using the GWAS approach. In addition, novel stable mouse models for immune phenotypes are often derived from studies utilizing the DO and CC mice that can serve as stronger model systems than existing ones in the field. The CC/DO systems have contributed to the fields of cancer immunology, autoimmunity, vaccinology, infectious disease, allergy, tissue rejection, and tolerance but have thus far been greatly underutilized. In this article, we present a recent review of the field and point out key areas of immunology that are ripe for further investigation and awaiting new CC/DO research projects. We also highlight some of the strong computational tools that have been developed for analyzing CC/DO genetic and phenotypic data. Additionally, we have formed a centralized community on the CyVerse infrastructure where immunogeneticists can utilize those software tools, collaborate with groups across the world, and expand the use of the CC and DO systems for investigating immunogenetic phenomena. © 2022 Wiley Periodicals LLC.

Leaf cell-specific and single-cell transcriptional profiling reveals a role for the palisade layer in UV light protection

Like other complex multicellular organisms, plants are composed of different cell types with specialized shapes and functions. For example, most laminar leaves consist of multiple photosynthetic cell types. These cell types include the palisade mesophyll, which typically forms one or more cell layers on the adaxial side of the leaf. Despite their importance for photosynthesis, we know little about how palisade cells differ at the molecular level from other photosynthetic cell types. To this end, we have used a combination of cell-specific profiling using fluorescence-activated cell sorting and single-cell RNA-sequencing methods to generate a transcriptional blueprint of the palisade mesophyll in Arabidopsis thaliana leaves. We find that despite their unique morphology, palisade cells are otherwise transcriptionally similar to other photosynthetic cell types. Nevertheless, we show that some genes in the phenylpropanoid biosynthesis pathway have both palisade-enriched expression and are light-regulated. Phenylpropanoid gene activity in the palisade was required for production of the ultraviolet (UV)-B protectant sinapoylmalate, which may protect the palisade and/or other leaf cells against damaging UV light. These findings improve our understanding of how different photosynthetic cell types in the leaf can function uniquely to optimize leaf performance, despite their transcriptional similarities.

Freshwater macrophytes harbor viruses representing all five major phyla of the RNA viral kingdom Orthornavirae

Research on aquatic plant viruses is lagging behind that of their terrestrial counterparts. To address this knowledge gap, here we identified viruses associated with freshwater macrophytes, a taxonomically diverse group of aquatic phototrophs that are visible with the naked eye. We surveyed pooled macrophyte samples collected at four spring sites in Florida, USA through next generation sequencing of RNA extracted from purified viral particles. Sequencing efforts resulted in the detection of 156 freshwater macrophyte associated (FMA) viral contigs, 37 of which approximate complete genomes or segments. FMA viral contigs represent putative members from all five major phyla of the RNA viral kingdom Orthornavirae. Similar to viral types found in land plants, viral sequences identified in macrophytes were dominated by positive-sense RNA viruses. Over half of the FMA viral contigs were most similar to viruses reported from diverse hosts in aquatic environments, including phototrophs, invertebrates, and fungi. The detection of FMA viruses from orders dominated by plant viruses, namely Patatavirales and Tymovirales, indicate that members of these orders may thrive in aquatic hosts. PCR assays confirmed the presence of putative FMA plant viruses in asymptomatic vascular plants, indicating that viruses with persistent lifestyles are widespread in macrophytes. The detection of potato virus Y and oat blue dwarf virus in submerged macrophytes suggests that terrestrial plant viruses infect underwater plants and highlights a potential terrestrial-freshwater plant virus continuum. Defining the virome of unexplored macrophytes will improve our understanding of virus evolution in terrestrial and aquatic primary producers and reveal the potential ecological impacts of viral infection in macrophytes.

Diversifying the genomic data science research community

Over the past 20 years, the explosion of genomic data collection and the cloud computing revolution have made computational and data science research accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support these programs in local education and research at underserved institutions (UIs). These include community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities that support ethnically, racially, and socioeconomically underrepresented students in the United States. We have formed the Genomic Data Science Community Network to support students, faculty, and their networks to identify opportunities and broaden access to genomic data science. These opportunities include expanding access to infrastructure and data, providing UI faculty development opportunities, strengthening collaborations among faculty, recognizing UI teaching and research excellence, fostering student awareness, developing modular and open-source resources, expanding course-based undergraduate research experiences (CUREs), building curriculum, supporting student professional development and research, and removing financial barriers through funding programs and collaborator support.

Interpreting phylogenetic conflict: Hybridization in the most speciose genus of lichen-forming fungi

While advances in sequencing technologies have been invaluable for understanding evolutionary relationships, increasingly large genomic data sets may result in conflicting evolutionary signals that are often caused by biological processes, including hybridization. Hybridization has been detected in a variety of organisms, influencing evolutionary processes such as generating reproductive barriers and mixing standing genetic variation. Here, we investigate the potential role of hybridization in the diversification of the most speciose genus of lichen-forming fungi, Xanthoparmelia. As Xanthoparmelia is projected to have gone through recent, rapid diversification, this genus is particularly suitable for investigating and interpreting the origins of phylogenomic conflict. Focusing on a clade of Xanthoparmelia largely restricted to the Holarctic region, we used a genome skimming approach to generate 962 single-copy gene regions representing over 2 Mbp of the mycobiont genome. From this genome-scale dataset, we inferred evolutionary relationships using both concatenation and coalescent-based species tree approaches. We also used three independent tests for hybridization. Although different species tree reconstruction methods recovered largely consistent and well-supported trees, there was widespread incongruence among individual gene trees. Despite challenges in differentiating hybridization from ILS in situations of recent rapid radiations, our genome-wide analyses detected multiple potential hybridization events in the Holarctic clade, suggesting one possible source of trait variability in this hyperdiverse genus. This study highlights the value in using a pluralistic approach for characterizing genome-scale conflict, even in groups with well-resolved phylogenies, while highlighting current challenges in detecting the specific impacts of hybridization.

Deconvoluting signals downstream of growth and immune receptor kinases by phosphocodes of the BSU1 family phosphatases

Hundreds of leucine-rich repeat receptor kinases (LRR-RKs) have evolved to control diverse processes of growth, development and immunity in plants, but the mechanisms that link LRR-RKs to distinct cellular responses are not understood. Here we show that two LRR-RKs, the brassinosteroid hormone receptor BRASSINOSTEROID INSENSITIVE 1 (BRI1) and the flagellin receptor FLAGELLIN SENSING 2 (FLS2), regulate downstream glycogen synthase kinase 3 (GSK3) and mitogen-activated protein (MAP) kinases, respectively, through phosphocoding of the BRI1-SUPPRESSOR1 (BSU1) phosphatase. BSU1 was previously identified as a component that inactivates GSK3s in the BRI1 pathway. We surprisingly found that the loss of the BSU1 family phosphatases activates effector-triggered immunity and impairs flagellin-triggered MAP kinase activation and immunity. The flagellin-activated BOTRYTIS-INDUCED KINASE 1 (BIK1) phosphorylates BSU1 at serine 251. Mutation of serine 251 reduces BSU1's ability to mediate flagellin-induced MAP kinase activation and immunity, but not its abilities to suppress effector-triggered immunity and interact with GSK3, which is enhanced through the phosphorylation of BSU1 at serine 764 upon brassinosteroid signalling. These results demonstrate that BSU1 plays an essential role in immunity and transduces brassinosteroid-BRI1 and flagellin-FLS2 signals using different phosphorylation sites. Our study illustrates that phosphocoding in shared downstream components provides signalling specificities for diverse plant receptor kinases.

BioViz Connect: Web Application Linking CyVerse Cloud Resources to Genomic Visualization in the Integrated Genome Browser

Genomics researchers do better work when they can interactively explore and visualize data. Due to the vast size of experimental datasets, researchers are increasingly using powerful, cloud-based systems to process and analyze data. These remote systems, called science gateways, offer user-friendly, Web-based access to high performance computing and storage resources, but typically lack interactive visualization capability. In this paper, we present BioViz Connect, a middleware Web application that links CyVerse science gateway resources to the Integrated Genome Browser (IGB), a highly interactive native application implemented in Java that runs on the user's personal computer. Using BioViz Connect, users can 1) stream data from the CyVerse data store into IGB for visualization, 2) improve the IGB user experience for themselves and others by adding IGB specific metadata to CyVerse data files, including genome version and track appearance, and 3) run compute-intensive visual analytics functions on CyVerse infrastructure to create new datasets for visualization in IGB or other applications. To demonstrate how BioViz Connect facilitates interactive data visualization, we describe an example RNA-Seq data analysis investigating how heat and desiccation stresses affect gene expression in the model plant Arabidopsis thaliana. The RNA-Seq use case illustrates how interactive visualization with IGB can help a user identify problematic experimental samples, sanity-check results using a positive control, and create new data files for interactive visualization in IGB (or other tools) using a Docker image deployed to CyVerse via the Terrain API. Lastly, we discuss limitations of the technologies used and suggest opportunities for future work. BioViz Connect is available from https://bioviz.org.

Prevalence of an Insect-Associated Genomic Region in Environmentally Acquired Burkholderiaceae Symbionts

Microbial symbionts are critical for the development and survival of many eukaryotes. Recent research suggests that the genes enabling these relationships can be localized in horizontally transferred regions of microbial genomes termed "symbiotic islands." Recently, a putative symbiotic island was found that may facilitate symbioses between true bugs and numerous Burkholderia species, based on analysis of five Burkholderia symbionts. We expanded on this work by exploring the putative island's prevalence, origin, and association with colonization across the bacterial family Burkholderiaceae. We performed a broad comparative analysis of 229 Burkholderiaceae genomes, including 8 new genomes of insect- or soil-associated Burkholderia sequenced for this study. We detected the region in 23% of the genomes; these were located solely within two Burkholderia clades. Our analyses suggested that the contiguous region arose at the common ancestor of plant- and insect-associated Burkholderia clades, but the genes themselves are ancestral. Although the region was initially discovered on plasmids and we did detect two likely instances of horizontal transfer within Burkholderia, we found that the region is almost always localized to a chromosome and does not possess any of the mobility elements that typify genomic islands. Finally, to attempt to deduce the region's function, we combined our data with information on several strains' abilities to colonize the insect's symbiotic organ. Although the region was associated with improved colonization of the host, this relationship was confounded with, and likely driven by, Burkholderia clade membership. These findings advance our understanding of the genomic underpinnings of a widespread insect-microbe symbiosis. IMPORTANCE Many plants and animals form intricate associations with bacteria. These pairings can be mediated by genomic islands, contiguous regions containing numerous genes with cohesive functionality. Pathogen-associated islands are well described, but recent evidence suggests that mutualistic islands, which benefit both host and symbiont, may also be common. Recently, a putative symbiosis island was found in Burkholderia symbionts of insects. We determined that this genomic region is located in only two clades of Burkholderia (the plant- and insect-associated species) and that although it has undergone horizontal transfer, it is most likely a symbiosis-associated region rather than a true island. This region is associated with improved host colonization, although this is may be due to specific Burkholderia clades' abilities to colonize rather than presence of the region. By studying the genomic basis of the insect-Burkholderia symbiosis, we can better understand how mutualisms evolve in animals.

Providing Scale to a Known Taxonomic Unknown—At Least a 70-Fold Increase in Species Diversity in a Cosmopolitan Nominal Taxon of Lichen-Forming Fungi

Robust species delimitations provide a foundation for investigating speciation, phylogeography, and conservation. Here we attempted to elucidate species boundaries in the cosmopolitan lichen-forming fungal taxon Lecanora polytropa. This nominal taxon is morphologically variable, with distinct populations occurring on all seven continents. To delimit candidate species, we compiled ITS sequence data from populations worldwide. For a subset of the samples, we also generated alignments for 1209 single-copy nuclear genes and an alignment spanning most of the mitochondrial genome to assess concordance among the ITS, nuclear, and mitochondrial inferences. Species partitions were empirically delimited from the ITS alignment using ASAP and bPTP. We also inferred a phylogeny for the L. polytropa clade using a four-marker dataset. ASAP species delimitations revealed up to 103 species in the L. polytropa clade, with 75 corresponding to the nominal taxon L. polytropa. Inferences from phylogenomic alignments generally supported that these represent evolutionarily independent lineages or species. Less than 10% of the candidate species were comprised of specimens from multiple continents. High levels of candidate species were recovered at local scales but generally with limited overlap across regions. Lecanora polytropa likely ranks as one of the largest species complexes of lichen-forming fungi known to date.

Genomics and Informatics, Conjoined Tools Vital for Understanding and Protecting Plant Health

Genomics' impact on crop production continuously expands. The number of sequenced plant and microbial species and strains representing diverse populations of individual species rapidly increases thanks to the advent of next-generation sequencing technologies. Their genomic blueprints revealed candidate genes involved in various functions and processes crucial for crop health and helped in understanding how the sequenced organisms have evolved at the genome level. Functional genomics quickly translates these blueprints into a detailed mechanistic understanding of how such functions and processes work and are regulated; this understanding guides and empowers efforts to protect crops from diverse biotic and abiotic threats. Metagenome analyses help identify candidate microbes crucial for crop health and uncover how microbial communities associated with crop production respond to environmental conditions and cultural practices, presenting opportunities to enhance crop health by judiciously configuring microbial communities. Efficient conversion of disparate types of massive genomics data into actionable knowledge requires a robust informatics infrastructure supporting data preservation, analysis, and sharing. This review starts with an overview of how genomics came about and has quickly transformed life science. We illuminate how genomics and informatics can be applied to investigate various crop health-related problems using selected studies. We end the review by noting why community empowerment via crowdsourcing is crucial to harnessing genomics to protect global food and nutrition security without continuously expanding the environmental footprint of crop production.

CyVerse for Reproducible Research: RNA-Seq Analysis

Posing complex research questions poses complex reproducibility challenges. Datasets may need to be managed over long periods of time. Reliable and secure repositories are needed for data storage. Sharing big data requires advance planning and becomes complex when collaborators are spread across institutions and countries. Many complex analyses require the larger compute resources only provided by cloud and high-performance computing infrastructure. Finally at publication, funder and publisher requirements must be met for data availability and accessibility and computational reproducibility. For all of these reasons, cloud-based cyberinfrastructures are an important component for satisfying the needs of data-intensive research. Learning how to incorporate these technologies into your research skill set will allow you to work with data analysis challenges that are often beyond the resources of individual research institutions. One of the advantages of CyVerse is that there are many solutions for high-powered analyses that do not require knowledge of command line (i.e., Linux) computing. In this chapter we will highlight CyVerse capabilities by analyzing RNA-Seq data. The lessons learned will translate to doing RNA-Seq in other computing environments and will focus on how CyVerse infrastructure supports reproducibility goals (e.g., metadata management, containers), team science (e.g., data sharing features), and flexible computing environments (e.g., interactive computing, scaling).

Effects of 'Candidatus Liberibacter solanacearum' haplotypes A and B on tomato gene expression and geotropism

BACKGROUND

The tomato psyllid, Bactericera cockerelli Šulc (Hemiptera: Triozidae), is a pest of solanaceous crops such as tomato (Solanum lycopersicum L.) in the U.S. and vectors the disease-causing pathogen 'Candidatus Liberibacter solanacearum' (or Lso). Disease symptom severity is dependent on Lso haplotype: tomato plants infected with Lso haplotype B experience more severe symptoms and higher mortality compared to plants infected with Lso haplotype A. By characterizing the molecular differences in the tomato plant's responses to Lso haplotypes, the key components of LsoB virulence can be identified and, thus, targeted for disease mitigation strategies.

RESULTS

To characterize the tomato plant genes putatively involved in the differential immune responses to Lso haplotypes A and B, RNA was extracted from tomato 'Moneymaker' leaves 3 weeks after psyllid infestation. Gene expression levels were compared between uninfected tomato plants (i.e., controls and plants infested with Lso-free psyllids) and infected plants (i.e., plants infested with psyllids infected with either Lso haplotype A or Lso haplotype B). Furthermore, expression levels were compared between plants infected with Lso haplotype A and plants infected with Lso haplotype B. A whole transcriptome analysis identified 578 differentially expressed genes (DEGs) between uninfected and infected plants as well as 451 DEGs between LsoA- and LsoB-infected plants. These DEGs were primarily associated with plant defense against abiotic and biotic stressors, growth/development, plant primary metabolism, transport and signaling, and transcription/translation. These gene expression changes suggested that tomato plants traded off plant growth and homeostasis for improved defense against pathogens, especially when infected with LsoB. Consistent with these results, tomato plant growth experiments determined that LsoB-infected plants were significantly stunted and had impaired negative geotropism. However, it appeared that the defense responses mounted by tomatoes were insufficient for overcoming the disease symptoms and mortality caused by LsoB infection, while these defenses could compensate for LsoA infection.

CONCLUSION

The transcriptomic analysis and growth experiments demonstrated that Lso-infected tomato plants underwent gene expression changes related to abiotic and biotic stressors, impaired growth/development, impaired plant primary metabolism, impaired transport and signaling transduction, and impaired transcription/translation. Furthermore, the transcriptomic analysis also showed that LsoB-infected plants, relative to LsoA-infected, experienced more severe stunting, had improved responses to some stressors and impaired responses to others, had poorer transport and signaling transduction, and had impaired carbohydrate synthesis and photosynthesis.

Leaf form diversification in an ornamental heirloom tomato results from alterations in two different HOMEOBOX genes

Domesticated plants display diverse phenotypic traits. However, the influence of breeding effort on this phenotypic diversity remains unknown. Here, we demonstrate that a single nucleotide deletion in the homeobox motif of BIPINNATA, a BEL-LIKE HOMEODOMAIN gene, led to a highly complex leaf phenotype in an heirloom tomato (Solanum lycopersicum), Silvery Fir Tree (SiFT), which is used as a landscaping and ornamental plant. A comparative gene network analysis revealed that repression of SOLANIFOLIA, the ortholog of WUSCHEL RELATED HOMEOBOX 1, caused the narrow leaflet phenotype seen in SiFT. Comparative genomics indicated that the bip mutation in SiFT likely arose de novo and is unique to SiFT and not introgressed from other tomato genomes. These results provide new insights into the natural variation in phenotypic traits introduced into crops during improvement processes after domestication and establish homeobox genes as evolutionary hotspots.

Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions

Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.

Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions

Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.

DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays)

The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.

A Molecular Analysis of Microalgae from Around the Globe to Revise Raphidonema (Trebouxiophyceae, Chlorophyta)

We isolated five microalgal strains from alpine snow near Vancouver, Canada, which display morphological features suggestive of the genera Koliella and Raphidonema. Due to variations in cell size and shape, we could not make a clear delimitation based on morphology. We proceeded to a molecular analysis and included 22 strains from the CCCryo culture collection, previously identified as members of four closely related genera: Raphidonema, Koliella, Stichococcus, and Pseudochlorella. For greater taxonomic context in our phylogenetic analysis, we also obtained authentic strains for the type species of Koliella and Pseudochlorella, but were unable to find one for Raphidonema. To examine generic boundaries, we did a phylogenetic analysis on the rbcL gene for all strains, establishing distinct lineages. Our novel isolates fell within Raphidonema, and so we analyzed the ITS2 gene of all Raphidonema strains to delimit species. To support species delimitations, we did a Compensatory Base Change analysis using the secondary structure of the ITS2 gene to assist in aligning the sequence. We also computed a maximum likelihood phylogenetic tree to examine species clades of Raphidonema. We assigned epitypes for two Raphidonema species based on the best morphological match to strains in the ITS2 clades. We then amended their diagnoses so they can be more reliably identified using DNA sequence data. We also propose two new species, R. catena and R. monicae, that formed their own species clades according to our ITS2 analysis.

Phylogeography of the smooth greensnake, Opheodrys vernalis (Squamata: Colubridae): divergent lineages and variable demographics in a widely distributed yet enigmatic species

Phylogeographic studies can uncover robust details about the population structure, demographics, and diversity of species. The smooth greensnake, Opheodrys vernalis, is a small, cryptic snake occupying mesic grassland and sparsely wooded habitats. Although O. vernalis has a wide geographical range, many metapopulations are patchy and some are declining. We used mitochondrial DNA and double digest restriction-site associated DNA sequencing to construct the first phylogeographic assessment of O. vernalis. Genomic analysis of 119 individuals (mitochondrial DNA) and a subset of another 45 smooth greensnakes (nuclear DNA; N = 3031 single nucleotide polymorphisms) strongly supports two longitudinally separated lineages, with admixture in the Great Lakes region. Post-Pleistocene secondary contact best explains admixture from populations advancing northwards. Overall, populations expressed low heterozygosity, variable inbreeding rates, and moderate to high differentiation. Disjunct populations in the Rocky Mountains and central Great Plains regions might be contracting relicts, whereas northerly populations in more continuous mesic habitats (e.g., Prairie Pothole region, southern Canada) had signals of population expansion. Broadly, conservation management efforts should be focused on local populations, because habitat connectivity may facilitate gene flow and genetic diversity.

HTSQualC is a flexible and one-step quality control software for high-throughput sequencing data analysis

Use of high-throughput sequencing (HTS) has become indispensable in life science research. Raw HTS data contains several sequencing artifacts, and as a first step it is imperative to remove the artifacts for reliable downstream bioinformatics analysis. Although there are multiple stand-alone tools available that can perform the various quality control steps separately, availability of an integrated tool that can allow one-step, automated quality control analysis of HTS datasets will significantly enhance handling large number of samples parallelly. Here, we developed HTSQualC, a stand-alone, flexible, and easy-to-use software for one-step quality control analysis of raw HTS data. HTSQualC can evaluate HTS data quality and perform filtering and trimming analysis in a single run. We evaluated the performance of HTSQualC for conducting batch analysis of HTS datasets with 322 samples with an average ~ 1 M (paired end) sequence reads per sample. HTSQualC accomplished the QC analysis in ~ 3 h in distributed mode and ~ 31 h in shared mode, thus underscoring its utility and robust performance. In addition to command-line execution, we integrated HTSQualC into the free, open-source, CyVerse cyberinfrastructure resource as a GUI interface, for wider access to experimental biologists who have limited computational resources and/or programming abilities.

Workflows for Rapid Functional Annotation of Diverse Arthropod Genomes

Genome sequencing of a diverse array of arthropod genomes is already underway, and these genomes will be used to study human health, agriculture, biodiversity, and ecology. These new genomes are intended to serve as community resources and provide the foundational information required to apply 'omics technologies to a more diverse set of species. However, biologists require genome annotation to use these genomes and derive a better understanding of complex biological systems. Genome annotation incorporates two related, but distinct, processes: Demarcating genes and other elements present in genome sequences (structural annotation); and associating a function with genetic elements (functional annotation). While there are well-established and freely available workflows for structural annotation of gene identification in newly assembled genomes, workflows for providing the functional annotation required to support functional genomics studies are less well understood. Genome-scale functional annotation is required for functional modeling (enrichment, networks, etc.). A first-pass genome-wide functional annotation effort can rapidly identify under-represented gene sets for focused community annotation efforts. We present an open-source, open access, and containerized pipeline for genome-scale functional annotation of insect proteomes and apply it to various arthropod species. We show that the performance of the predictions is consistent across a set of arthropod genomes with varying assembly and annotation quality.

Comparative genome analyses suggest a hemibiotrophic lifestyle and virulence differences for the beech bark disease fungal pathogens Neonectria faginata and Neonectria coccinea

Neonectria faginata and Neonectria coccinea are the causal agents of the insect-fungus disease complex known as beech bark disease (BBD), known to cause mortality in beech forest stands in North America and Europe. These fungal species have been the focus of extensive ecological and disease management studies, yet less progress has been made toward generating genomic resources for both micro- and macro-evolutionary studies. Here, we report a 42.1 and 42.7 mb highly contiguous genome assemblies of N. faginata and N. coccinea, respectively, obtained using Illumina technology. These species share similar gene number counts (12,941 and 12,991) and percentages of predicted genes with assigned functional categories (64 and 65%). Approximately 32% of the predicted proteomes of both species are homologous to proteins involved in pathogenicity, yet N. coccinea shows a higher number of predicted mitogen-activated protein kinase genes, virulence determinants possibly contributing to differences in disease severity between N. faginata and N. coccinea. A wide range of genes encoding for carbohydrate-active enzymes capable of degradation of complex plant polysaccharides and a small number of predicted secretory effector proteins, secondary metabolite biosynthesis clusters and cytochrome oxidase P450 genes were also found. This arsenal of enzymes and effectors correlates with, and reflects, the hemibiotrophic lifestyle of these two fungal pathogens. Phylogenomic analysis and timetree estimations indicated that the N. faginata and N. coccinea species divergence may have occurred at ∼4.1 million years ago. Differences were also observed in the annotated mitochondrial genomes as they were found to be 81.7 kb (N. faginata) and 43.2 kb (N. coccinea) in size. The mitochondrial DNA expansion observed in N. faginata is attributed to the invasion of introns into diverse intra- and intergenic locations. These first draft genomes of N. faginata and N. coccinea serve as valuable tools to increase our understanding of basic genetics, evolutionary mechanisms and molecular physiology of these two nectriaceous plant pathogenic species.

G2PDeep: a web-based deep-learning framework for quantitative phenotype prediction and discovery of genomic markers

G2PDeep is an open-access web server, which provides a deep-learning framework for quantitative phenotype prediction and discovery of genomics markers. It uses zygosity or single nucleotide polymorphism (SNP) information from plants and animals as the input to predict quantitative phenotype of interest and genomic markers associated with phenotype. It provides a one-stop-shop platform for researchers to create deep-learning models through an interactive web interface and train these models with uploaded data, using high-performance computing resources plugged at the backend. G2PDeep also provides a series of informative interfaces to monitor the training process and compare the performance among the trained models. The trained models can then be deployed automatically. The quantitative phenotype and genomic markers are predicted using a user-selected trained model and the results are visualized. Our state-of-the-art model has been benchmarked and demonstrated competitive performance in quantitative phenotype predictions by other researchers. In addition, the server integrates the soybean nested association mapping (SoyNAM) dataset with five phenotypes, including grain yield, height, moisture, oil, and protein. A publicly available dataset for seed protein and oil content has also been integrated into the server. The G2PDeep server is publicly available at http://g2pdeep.org. The Python-based deep-learning model is available at https://github.com/shuaizengMU/G2PDeep_model.

Knowledge and Attitudes Among Life Scientists Toward Reproducibility Within Journal Articles: A Research Survey

We constructed a survey to understand how authors and scientists view the issues around reproducibility, focusing on interactive elements such as interactive figures embedded within online publications, as a solution for enabling the reproducibility of experiments. We report the views of 251 researchers, comprising authors who have published in eLIFE Sciences, and those who work at the Norwich Biosciences Institutes (NBI). The survey also outlines to what extent researchers are occupied with reproducing experiments themselves. Currently, there is an increasing range of tools that attempt to address the production of reproducible research by making code, data, and analyses available to the community for reuse. We wanted to collect information about attitudes around the consumer end of the spectrum, where life scientists interact with research outputs to interpret scientific results. Static plots and figures within articles are a central part of this interpretation, and therefore we asked respondents to consider various features for an interactive figure within a research article that would allow them to better understand and reproduce a published analysis. The majority (91%) of respondents reported that when authors describe their research methodology (methods and analyses) in detail, published research can become more reproducible. The respondents believe that having interactive figures in published papers is a beneficial element to themselves, the papers they read as well as to their readers. Whilst interactive figures are one potential solution for consuming the results of research more effectively to enable reproducibility, we also review the equally pressing technical and cultural demands on researchers that need to be addressed to achieve greater success in reproducibility in the life sciences.

Review: Application of Artificial Intelligence in Phenomics

Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.

New Insights Into Lignification via Network and Multi-Omics Analyses of Arogenate Dehydratase Knock-Out Mutants in Arabidopsis thaliana

Multiple Arabidopsis arogenate dehydratase (ADT) knock-out (KO) mutants, with phenotypes having variable lignin levels (up to circa 70% reduction), were studied to investigate how differential reductions in ADTs perturb its overall plant systems biology. Integrated "omics" analyses (metabolome, transcriptome, and proteome) of wild type (WT), single and multiple ADT KO lines were conducted. Transcriptome and proteome data were collapsed into gene ortholog (GO) data, with this allowing for enzymatic reaction and metabolome cross-comparisons to uncover dominant or likely metabolic biosynthesis reactions affected. Network analysis of enzymes-highly correlated to stem lignin levels-deduced the involvement of novel putative lignin related proteins or processes. These included those associated with ribosomes, the spliceosome, mRNA transport, aminoacyl tRNA biosynthesis, and phosphorylation. While prior work helped explain lignin biosynthesis regulation at the transcriptional level, our data here provide support for a new hypothesis that there are additional post-transcriptional and translational level processes that need to be considered. These findings are anticipated to lead to development of more accurate depictions of lignin/phenylpropanoid biosynthesis models in situ, with new protein targets identified for further biochemical analysis and/or plant bioengineering. Additionally, using KEGG defined functional categorization of proteomics and transcriptomics analyses, we detected significant changes to glucosinolate, α-linolenic acid, nitrogen, carotenoid, aromatic amino acid, phenylpropanoid, and photosynthesis-related metabolic pathways in ADT KO mutants. Metabolomics results also revealed that putative carotenoid and galactolipid levels were generally increased in amount, whereas many glucosinolates and phenylpropanoids (including flavonoids and lignans) were decreased in the KO mutants.

BSAseq: an interactive and integrated web-based workflow for identification of causal mutations in bulked F2 populations

SUMMARY

With the advance of next-generation sequencing technologies and reductions in the costs of these techniques, bulked segregant analysis (BSA) has become not only a powerful tool for mapping quantitative trait loci but also a useful way to identify causal gene mutations underlying phenotypes of interest. However, due to the presence of background mutations and errors in sequencing, genotyping, and reference assembly, it is often difficult to distinguish true causal mutations from background mutations. In this study, we developed the BSAseq workflow, which includes an automated bioinformatics analysis pipeline with a probabilistic model for estimating the linked region (the region linked to the causal mutation) and an interactive Shiny web application for visualizing the results. We deeply sequenced a sorghum male-sterile parental line (ms8) to capture the majority of background mutations in our bulked F2 data. We applied the workflow to 11 bulked sorghum F2 populations and 1 rice F2 population and identified the true causal mutation in each population. The workflow is intuitive and straightforward, facilitating its adoption by users without bioinformatics analysis skills. We anticipate that the BSAseq workflow will be broadly applicable to the identification of causal mutations for many phenotypes of interest.

AVAILABILITY AND IMPLEMENTATION

BSAseq is freely available on https://www.sciapps.org/page/bsa.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Soybean transporter database: A comprehensive database for identification and exploration of natural variants in soybean transporter genes

Transporters, a class of membrane proteins that facilitate exchange of solutes including diverse molecules and ions across the cellular membrane, are vital component for the survival of all organisms. Understanding plant transporters is important to get insight of the basic cellular processes, physiology, and molecular mechanisms including nutrient uptake, signaling, response to external stress, and many more. In this regard, extensive analysis of transporters predicted in soybean and other plant species was performed. In addition, an integrated database for soybean transporter protein, SoyTD, was developed that will facilitate the identification, classification, and extensive characterization of transporter proteins by integrating expression, gene ontology, conserved domain and motifs, gene structure organization, and chromosomal distribution features. A comprehensive analysis was performed to identify highly confident transporters by integrating various prediction tools. Initially, 7541 transmembrane (TM) proteins were predicted in the soybean genome; out of these, 3306 non-redundant transporter genes carrying two or more transmembrane domains were selected for further analysis. The identified transporter genes were classified according to a standard transporter classification (TC) system. Comparative analysis of transporter genes among 47 plant genomes provided insights into expansion and duplication of transporter genes in land plants. The whole genome resequencing (WGRS) and tissue-specific transcriptome datasets of soybean were integrated to investigate the natural variants and expression profile associated with transporter(s) of interest. Overall, SoyTD provides a comprehensive interface to study genetic and molecular function of soybean transporters. SoyTD is publicly available at http://artemis.cyverse.org/soykb_dev/SoyTD/.

Stretch-activated ion channels identified in the touch-sensitive structures of carnivorous Droseraceae plants

In response to touch, some carnivorous plants such as the Venus flytrap have evolved spectacular movements to capture animals for nutrient acquisition. However, the molecules that confer this sensitivity remain unknown. We used comparative transcriptomics to show that expression of three genes encoding homologs of the MscS-Like (MSL) and OSCA/TMEM63 family of mechanosensitive ion channels are localized to touch-sensitive trigger hairs of Venus flytrap. We focus here on the candidate with the most enriched expression in trigger hairs, the MSL homolog FLYCATCHER1 (FLYC1). We show that FLYC1 transcripts are localized to mechanosensory cells within the trigger hair, transfecting FLYC1 induces chloride-permeable stretch-activated currents in naïve cells, and transcripts coding for FLYC1 homologs are expressed in touch-sensing cells of Cape sundew, a related carnivorous plant of the Droseraceae family. Our data suggest that the mechanism of prey recognition in carnivorous Droseraceae evolved by co-opting ancestral mechanosensitive ion channels to sense touch.

Lasting consequences of psyllid (Bactericera cockerelli L.) infestation on tomato defense, gene expression, and growth

BACKGROUND

The tomato psyllid, Bactericera cockerelli Šulc (Hemiptera: Triozidae), is a pest of solanaceous crops such as tomato (Solanum lycopersicum L.) in the U.S. and vectors the disease-causing pathogen 'Candidatus Liberibacter solanacearum'. Currently, the only effective strategies for controlling the diseases associated with this pathogen involve regular pesticide applications to manage psyllid population density. However, such practices are unsustainable and will eventually lead to widespread pesticide resistance in psyllids. Therefore, new control strategies must be developed to increase host-plant resistance to insect vectors. For example, expression of constitutive and inducible plant defenses can be improved through selection. Currently, it is still unknown whether psyllid infestation has any lasting consequences on tomato plant defense or tomato plant gene expression in general.

RESULTS

In order to characterize the genes putatively involved in tomato defense against psyllid infestation, RNA was extracted from psyllid-infested and uninfested tomato leaves (Moneymaker) 3 weeks post-infestation. Transcriptome analysis identified 362 differentially expressed genes. These differentially expressed genes were primarily associated with defense responses to abiotic/biotic stress, transcription/translation, cellular signaling/transport, and photosynthesis. These gene expression changes suggested that tomato plants underwent a reduction in plant growth/health in exchange for improved defense against stress that was observable 3 weeks after psyllid infestation. Consistent with these observations, tomato plant growth experiments determined that the plants were shorter 3 weeks after psyllid infestation. Furthermore, psyllid nymphs had lower survival rates on tomato plants that had been previously psyllid infested.

CONCLUSION

These results suggested that psyllid infestation has lasting consequences for tomato gene expression, defense, and growth.

Genetic variation among 481 diverse soybean accessions, inferred from genomic re-sequencing

We report characteristics of soybean genetic diversity and structure from the resequencing of 481 diverse soybean accessions, comprising 52 wild (Glycine soja) selections and 429 cultivated (Glycine max) varieties (landraces and elites). This data was used to identify 7.8 million SNPs, to predict SNP effects relative to genic regions, and to identify the genetic structure, relationships, and linkage disequilibrium. We found evidence of distinct, mostly independent selection of lineages by particular geographic location. Among cultivated varieties, we identified numerous highly conserved regions, suggesting selection during domestication. Comparisons of these accessions against the whole U.S. germplasm genotyped with the SoySNP50K iSelect BeadChip revealed that over 95% of the re-sequenced accessions have a high similarity to their SoySNP50K counterparts. Probable errors in seed source or genotype tracking were also identified in approximately 5% of the accessions.

Molecular characterization of western corn rootworm pyrethroid resistance

BACKGROUND

Western corn rootworm (WCR) pyrethroid resistance has been confirmed in the western US Corn Belt. Toxicological and biochemical studies indicated that multiple mechanisms of resistance might be involved in the resistance trait, such as enhanced metabolism and/or kdr target-site mutation(s) in the voltage-gated sodium channels. To characterize the mechanisms of WCR pyrethroid resistance at the molecular level, pairwise comparisons were made between RNA-Seq data collected from pyrethroid-resistant and -susceptible WCR populations. Gene expression levels and sodium channel sequences were evaluated.

RESULTS

Seven transcripts exhibited significantly different expression (q ≤ 0.05) when comparing field-collected pyrethroid-resistant (R-Field) and -susceptible (S-Field) WCR populations. Three of the differentially expressed transcripts were P450s overexpressed in R-Field (9.2-26.2-fold). A higher number (99) of differentially expressed transcripts was found when comparing laboratory-derived pyrethroid-resistant (R-Lab) and -susceptible (S-Lab) WCR populations. Eight of the significant transcripts were P450s overexpressed in R-Lab (2.7-39.8-fold). This study did not detect kdr mutations in pyrethroid-resistant WCR populations. Other differentially expressed transcripts that may play a role in WCR pyrethroid resistance are discussed.

CONCLUSION

This study revealed that P450-mediated metabolism is likely to be a major mechanism of WCR pyrethroid resistance, which could affect the efficacy of other insecticides sharing similar metabolic pathways. Additionally, results suggested that although laboratory selection of a pyrethroid-resistant WCR population may help to characterize resistance mechanisms, a field-selected population provided rare and perhaps major variants corresponding to the resistance trait.

Planet Microbe: a platform for marine microbiology to discover and analyze interconnected 'omics and environmental data

In recent years, large-scale oceanic sequencing efforts have provided a deeper understanding of marine microbial communities and their dynamics. These research endeavors require the acquisition of complex and varied datasets through large, interdisciplinary and collaborative efforts. However, no unifying framework currently exists for the marine science community to integrate sequencing data with physical, geological, and geochemical datasets. Planet Microbe is a web-based platform that enables data discovery from curated historical and on-going oceanographic sequencing efforts. In Planet Microbe, each ‘omics sample is linked with other biological and physiochemical measurements collected for the same water samples or during the same sample collection event, to provide a broader environmental context. This work highlights the need for curated aggregation efforts that can enable new insights into high-quality metagenomic datasets. Planet Microbe is freely accessible from https://www.planetmicrobe.org/.

Genome-enabled insights into the biology of thrips as crop pests

BACKGROUND

The western flower thrips, Frankliniella occidentalis (Pergande), is a globally invasive pest and plant virus vector on a wide array of food, fiber, and ornamental crops. The underlying genetic mechanisms of the processes governing thrips pest and vector biology, feeding behaviors, ecology, and insecticide resistance are largely unknown. To address this gap, we present the F. occidentalis draft genome assembly and official gene set.

RESULTS

We report on the first genome sequence for any member of the insect order Thysanoptera. Benchmarking Universal Single-Copy Ortholog (BUSCO) assessments of the genome assembly (size = 415.8 Mb, scaffold N50 = 948.9 kb) revealed a relatively complete and well-annotated assembly in comparison to other insect genomes. The genome is unusually GC-rich (50%) compared to other insect genomes to date. The official gene set (OGS v1.0) contains 16,859 genes, of which ~ 10% were manually verified and corrected by our consortium. We focused on manual annotation, phylogenetic, and expression evidence analyses for gene sets centered on primary themes in the life histories and activities of plant-colonizing insects. Highlights include the following: (1) divergent clades and large expansions in genes associated with environmental sensing (chemosensory receptors) and detoxification (CYP4, CYP6, and CCE enzymes) of substances encountered in agricultural environments; (2) a comprehensive set of salivary gland genes supported by enriched expression; (3) apparent absence of members of the IMD innate immune defense pathway; and (4) developmental- and sex-specific expression analyses of genes associated with progression from larvae to adulthood through neometaboly, a distinct form of maturation differing from either incomplete or complete metamorphosis in the Insecta.

CONCLUSIONS

Analysis of the F. occidentalis genome offers insights into the polyphagous behavior of this insect pest that finds, colonizes, and survives on a widely diverse array of plants. The genomic resources presented here enable a more complete analysis of insect evolution and biology, providing a missing taxon for contemporary insect genomics-based analyses. Our study also offers a genomic benchmark for molecular and evolutionary investigations of other Thysanoptera species.

SINGLE FLOWER TRUSS and SELF-PRUNING signal developmental and metabolic networks to guide cotton architectures

Patterns of indeterminate and determinate growth specify plant architecture and influence crop productivity. In cotton (Gossypium hirsutum), SINGLE FLOWER TRUSS (SFT) stimulates the transition to flowering and determinate growth, while its closely related antagonist SELF-PRUNING (SP) maintains meristems in indeterminate states to favor vegetative growth. Overexpressing GhSFT while simultaneously silencing GhSP produces highly determinate cotton with reduced foliage and synchronous fruiting. These findings suggest that GhSFT, GhSP, and genes in these signaling networks hold promise for enhancing 'annualized' growth patterns and improving cotton productivity and management. To identify the molecular programs underlying cotton growth habits, we used comparative co-expression networks, differential gene expression, and phenotypic analyses in cotton varieties expressing altered levels of GhSFT or GhSP. Using multiple cotton and tomato datasets, we identified diverse genetic modules highly correlated with SFT or SP orthologs which shared related Gene Ontologies in different crop species. Notably, altering GhSFT or GhSP levels in cotton affected the expression of genes regulating meristem fate and metabolic pathways. Further phenotypic analyses of gene products involved in photosynthesis, secondary metabolism, and cell wall biosynthesis showed that early changes in GhSFT and GhSP levels profoundly impacted later development in distal tissues. Identifying the molecular underpinnings of GhSFT and GhSP activities emphasizes their broad actions in regulating cotton architecture.

The Bean Beetle Microbiome Project: A Course-Based Undergraduate Research Experience in Microbiology

Course-based undergraduate research experiences (CUREs) are an effective means of transforming the learning and teaching of science by involving students in the scientific process. The potential importance of the microbiome in shaping both environmental health and disease makes investigations of microbiomes an excellent teaching tool for undergraduate microbiology. Here, we present a CURE based on the microbiome of the bean beetle (Callosobruchus maculatus), a model system for undergraduate laboratory education. Despite the extensive research literature on bean beetles, little is known about their microbiome, making them an ideal system for a discovery-based CURE. In the CURE, students acquire microbiological technical skills by characterizing both culturable and unculturable members of the beetle gut-microbial community. Students plate beetle gut homogenates on different media, describe the colonies that are formed to estimate taxonomic diversity, extract DNA from colonies of interest, PCR amplify the16S rRNA gene for Sanger sequencing, and use the NCBI-nBLAST database to taxonomically classify sequences. Additionally, students extract total DNA from beetle gut homogenates for high-throughput paired-end sequencing and perform bioinformatic and statistical analyses of bacterial communities using a combination of open-access data processing software. Each activity allows students to engage with studies of microbiomes in a real-world context, to apply concepts and laboratory techniques to investigate either student or faculty-driven research questions, and to gain valuable experiences working with large high-throughput datasets. The CURE is designed such that it can be implemented over either 6-weeks (half semester) or 12-weeks (full semester), allowing for flexibility within the curriculum. Furthermore, student-generated data from the CURE (including bacterial colony phenotypic data, full-length 16S rRNA gene sequences from cultured isolates, and bacterial community sequences from gut homogenates) has been compiled in a continuously curated open-access database on the Bean Beetle Microbiome Project website, facilitating the generation of broader research questions across laboratory classrooms.

The genome of Ectocarpus subulatus - A highly stress-tolerant brown alga

Brown algae are multicellular photosynthetic stramenopiles that colonize marine rocky shores worldwide. Ectocarpus sp. Ec32 has been established as a genomic model for brown algae. Here we present the genome and metabolic network of the closely related species, Ectocarpus subulatus Kützing, which is characterized by high abiotic stress tolerance. Since their separation, both strains show new traces of viral sequences and the activity of large retrotransposons, which may also be related to the expansion of a family of chlorophyll-binding proteins. Further features suspected to contribute to stress tolerance include an expanded family of heat shock proteins, the reduction of genes involved in the production of halogenated defence compounds, and the presence of fewer cell wall polysaccharide-modifying enzymes. Overall, E. subulatus has mainly lost members of gene families down-regulated in low salinities, and conserved those that were up-regulated in the same condition. However, 96% of genes that differed between the two examined Ectocarpus species, as well as all genes under positive selection, were found to encode proteins of unknown function. This underlines the uniqueness of brown algal stress tolerance mechanisms as well as the significance of establishing E. subulatus as a comparative model for future functional studies.

Plant science decadal vision 2020-2030: Reimagining the potential of plants for a healthy and sustainable future

Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020-2030 frames our ability to perform vital and far-reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1-4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant-based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid-response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non-invasive imaging, sensors, and plug-and-play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence-assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.