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We report a public resource for examining the spatiotemporal RNA expression of 54,893M. truncatulagenes during the first 72 hours of response to rhizobial inoculation. Using a methodology that allows synchronous inoculation and growth of over 100 plants in a single media container, we harvested the same segment of each root responding to rhizobia in the initial inoculation over a time course, collected individual tissues from these segments with laser capture microdissection, and created and sequenced RNA libraries generated from these tissues. We demonstrate the utility of the resource by examining the expression patterns of a set of genes induced very early in nodule signaling, as well as two gene families (CLE peptides and nodule specific PLAT-domain proteins) and show that despite similar whole root expression patterns, there are tissue differences in expression between the genes. Using a rhizobial response data set generated from transcriptomics on intact root segments, we also examined differential temporal expression patterns and determined that, after nodule tissue, the epidermis and cortical cells contained the most temporally patterned genes. We circumscribed gene lists for each time and tissue examined and developed an expression pattern visualization tool. Finally, we explored transcriptomic differences between the inner cortical cells that become nodules and those that do not, confirming that the expression of ACC synthases distinguishes inner cortical cells that become nodules and provide and describe potential downstream genes involved in early nodule cell division.

Nodule number regulation in legumes is controlled by a feedback loop that integrates nutrient and rhizobia symbiont status signals to regulate nodule development. Signals from the roots are perceived by shoot receptors, including a CLV1-like receptor-like kinase known as SUNN in the annual medicMedicago truncatula. In the absence of functional SUNN, the autoregulation feedback loop is disrupted, resulting in hypernodulation. To elucidate early autoregulation mechanisms disrupted inSUNNmutants, we searched for genes with altered expression in the loss-of-functionsunn-4mutant and included therdn1-2autoregulation mutant for comparison. We identified constitutively altered expression of small groups of genes insunn-4roots, including higher levels of transcription factorNF-YA2, and insunn-4shoots. All genes with verified roles in nodulation that were induced in wild type roots during the establishment of nodules were also induced insunn-4, including, surprisingly, autoregulation genesTML2andTML1. Among all genes with a differential response to rhizobia in wild type roots, only an isoflavone-7-O-methyltransferase gene (Medtr7g014510) was found to be unresponsive insunn-4. In shoot tissues of wild type, eight rhizobia-responsive genes were identified, including a MYB family transcription factor gene (Medtr3111880) which remained at a baseline level insunn-4; three genes were found to be induced by rhizobia in shoots ofsunn-4but not wild type. We also cataloged the temporal induction profiles of many small secreted peptide (MtSSP) genes in nodulating root tissues, encompassing members of twenty-four peptide families, including the CLE and IRON MAN families. The discovery that expression ofTMLgenes in roots, a key factor in inhibiting nodulation in response to autoregulation signals, is also triggered insunn-4in the section of roots analyzed suggests that the mechanism of TML regulation inM. truncatulamay be more complex than published models.

Summary Large-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc.). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed-up simulation by ∼4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Fully deterministic speed-up facilitates model initialization, parameter estimation and sensitivity analysis tasks. Availability and implementation Source code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.3.0 implemented in python, and supported on Linux, Windows and MacOS (via Docker).

SummaryLarge-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed up simulation by ~4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations.Availability and ImplementationSource code is freely available athttps://github.com/birtwistlelab/SPARCED/releases/tag/v1.1.0implemented in python, and supported on Linux, Windows, and MacOS (via Docker).ContactMarc Birtwistlembirtwi@clemson.eduSupplementary informationN/A

Background Thyroid cancer (THCA) is the most common endocrine malignancy and incidence is increasing. There is an urgent need to better understand the molecular differences between THCA tumors at different pathologic stages so appropriate diagnostic, prognostic, and treatment strategies can be applied. Transcriptome State Perturbation Generator (TSPG) is a tool created to identify the changes in gene expression necessary to transform the transcriptional state of a source sample to mimic that of a target. Methods We used TSPG to perturb the bulk RNA expression data from various THCA tumor samples at progressive stages towards the transcriptional pattern of normal thyroid tissue. The perturbations produced were analyzed to determine if there are consistently up- or down-regulated genes or functions in certain stages of tumors. Results Some genes of particular interest were investigated further in previous research. SLC6A15 was found to be down-regulated in all stage 1–3 samples. This gene has previously been identified as a tumor suppressor. The up-regulation of PLA2G12B in all samples was notable because the protein encoded by this gene belongs to the PLA2 superfamily, which is involved in metabolism, a major function of the thyroid gland. REN was up-regulated in all stage 3 and 4 samples. The enzyme renin encoded by this gene, has a role in the renin-angiotensin system; this system regulates angiogenesis and may have a role in cancer development and progression. This is supported by the consistent up-regulation of REN only in later stage tumor samples. Functional enrichment analysis showed that olfactory receptor activities and similar terms were enriched for the up-regulated genes which supports previous research concluding that abundance and stimulation of olfactory receptors is linked to cancer. Conclusions TSPG can be a useful tool in exploring large gene expression datasets and extracting the meaningful differences between distinct classes of data. We identified genes that were characteristically perturbed in certain sample types, including only late-stage THCA tumors. Additionally, we provided evidence for potential transcriptional signatures of each stage of thyroid cancer. These are potentially relevant targets for future investigation into THCA tumorigenesis.

Bigenic expression relationships are conventionally defined based on metrics such as Pearson or Spearman correlation that cannot typically detect latent, non-linear dependencies or require the relationship to be monotonic. Further, the combination of intrinsic and extrinsic noise as well as embedded relationships between sample sub-populations reduces the probability of extracting biologically relevant edges during the construction of gene co-expression networks (GCNs). In this report, we address these problems via our NetExtractor algorithm. NetExtractor examines all pairwise gene expression profiles first with Gaussian mixture models (GMMs) to identify sample sub-populations followed by mutual information (MI) analysis that is capable of detecting non-linear differential bigenic expression relationships. We applied NetExtractor to brain tissue RNA profiles from the Genotype-Tissue Expression (GTEx) project to obtain a brain tissue specific gene expression relationship network centered on cerebellar and cerebellar hemisphere enriched edges. We leveraged the PsychENCODE pre-frontal cortex (PFC) gene regulatory network (GRN) to construct a cerebellar cortex (cerebellar) GRN associated with transcriptionally active regions in cerebellar tissue. Thus, we demonstrate the utility of our NetExtractor approach to detect biologically relevant and novel non-linear binary gene relationships.

Gene co-expression networks (GCNs) provide multiple benefits to molecular research including hypothesis generation and biomarker discovery. Transcriptome profiles serve as input for GCN construction and are derived from increasingly larger studies with samples across multiple experimental conditions, treatments, time points, genotypes, etc. Such experiments with larger numbers of variables confound discovery of true network edges, exclude edges and inhibit discovery of context (or condition) specific network edges. To demonstrate this problem, a 475-sample dataset is used to show that up to 97% of GCN edges can be misleading because correlations are false or incorrect. False and incorrect correlations can occur when tests are applied without ensuring assumptions are met, and pairwise gene expression may not meet test assumptions if the expression of at least one gene in the pairwise comparison is a function of multiple confounding variables. The ‘one-size-fits-all’ approach to GCN construction is therefore problematic for large, multivariable datasets. Recently, the Knowledge Independent Network Construction toolkit has been used in multiple studies to provide a dynamic approach to GCN construction that ensures statistical tests meet assumptions and confounding variables are addressed. Additionally, it can associate experimental context for each edge of the network resulting in context-specific GCNs (csGCNs). To help researchers recognize such challenges in GCN construction, and the creation of csGCNs, we provide a review of the workflow.

Background Quantification of gene expression from RNA-seq data is a prerequisite for transcriptome analysis such as differential gene expression analysis and gene co-expression network construction. Individual RNA-seq experiments are larger and combining multiple experiments from sequence repositories can result in datasets with thousands of samples. Processing hundreds to thousands of RNA-seq data can result in challenges related to data management, access to sufficient computational resources, navigation of high-performance computing (HPC) systems, installation of required software dependencies, and reproducibility. Processing of larger and deeper RNA-seq experiments will become more common as sequencing technology matures. Results GEMmaker, is a nf-core compliant, Nextflow workflow, that quantifies gene expression from small to massive RNA-seq datasets. GEMmaker ensures results are highly reproducible through the use of versioned containerized software that can be executed on a single workstation, institutional compute cluster, Kubernetes platform or the cloud. GEMmaker supports popular alignment and quantification tools providing results in raw and normalized formats. GEMmaker is unique in that it can scale to process thousands of local or remote stored samples without exceeding available data storage. Conclusions Workflows that quantify gene expression are not new, and many already address issues of portability, reusability, and scale in terms of access to CPUs. GEMmaker provides these benefits and adds the ability to scale despite low data storage infrastructure. This allows users to process hundreds to thousands of RNA-seq samples even when data storage resources are limited. GEMmaker is freely available and fully documented with step-by-step setup and execution instructions.

38 p.-5 fig.-2 tab.-8 fig. supl., PREMISE:Plants synthesize information from multiple environmental stimuli when determining their direction of growth. Gravity, being ubiquitous on Earth, plays a major role in determining the direction of growth and overall architecture of the plant. Here, we utilized the microgravity environment on board the International Space Station (ISS) to identify genes involved influencing growth and development of phototropically stimulated seedlings of Arabidopsis thaliana., METHODS:Seedlings were grown on the ISS, and RNA was extracted from 7 samples (pools of 10-15 plants) grown in microgravity (μg) or Earth gravity conditions (1-g). Transcriptomic analyses via RNA sequencing (RNA-seq) of differential gene expression was performed using the HISAT2-Stringtie-DESeq2 RNASeq pipeline. Differentially expressed genes were further characterized by using Pathway Analysis and enrichment for Gene Ontology classifications., RESULTS:For 296 genes that were found significantly differentially expressed between plants in microgravity compared to 1-g controls, Pathway Analysis identified eight molecular pathways that were significantly affected by reduced gravity conditions. Specifically, light-associated pathways (e.g., photosynthesis-antenna proteins, photosynthesis, porphyrin, and chlorophyll metabolism) were significantly downregulated in microgravity., CONCLUSIONS:Gene expression in A. thaliana seedlings grown in microgravity was significantly altered compared to that of the 1-g control. Understanding how plants grow in conditions of microgravity not only aids in our understanding of how plants grow and respond to the environment but will also help to efficiently grow plants during long-range space missions., Funding was provided by grants NNX12A065G and 80NSSC17K0546 from NASA (PI = J. Z. Kiss).

The objective of this study was to assess how exposure to ergot alkaloids during 2 stages of gestation alters fetal growth, muscle fiber formation, and miRNA expression. Pregnant ewes (n = 36; BW = 83.26 ± 8.14 kg; 4/group; 9 groups) were used in a 2 × 2 factorial arrangement with 2 tall fescue seed treatments [endophyte-infected (E+) vs. endophyte-free (E−)] fed during 2 stages of gestation (MID, days 35 to 85 vs. LATE, days 86 to 133), which created 4 possible treatments (E−/E−, E+/E−, E−/E+, or E+/E+). Ewes were individually fed a total mixed ration containing E+ or E− fescue seed according to treatment assignment. Terminal surgeries were conducted on day 133 of gestation for the collection of fetal measurements and muscle samples. Data were analyzed as a 2 × 2 factorial with fescue treatment, stage of gestation, and 2-way interaction as fixed effects. Fetuses exposed to E+ seed during LATE gestation had reduced (P = 0.0020) fetal BW by 10% compared with E− fetuses; however, fetal body weight did not differ (P = 0.41) with E+ exposure during MID gestation. Fetuses from ewes fed E+ seed during MID and LATE gestation tended to have smaller (P = 0.058) kidney weights compared with E− fetuses. Liver weight was larger (P = 0.0069) in fetuses fed E− during LATE gestation compared with E+. Fetal brain weight did not differ by fescue treatment fed during MID (P = 0.36) or LATE (P = 0.40) gestation. The percentage of brain to empty body weight (EBW) was greater (P = 0.0048) in fetuses from ewes fed E+ fescue seed during LATE gestation, which is indicative of intrauterine growth restriction (IUGR). Primary muscle fiber number was lower (P = 0.0005) in semitendinosus (STN) of fetuses exposed to E+ during MID and/or LATE gestation compared with E−/E−. miRNA sequencing showed differential expression (P < 0.010) of 6 novel miRNAs including bta-miR-652_R+1, mdo-miR-22-3p, bta-miR-1277_R-1, ppy-miR-133a_L+1_1ss5TG, hsa-miR-129-1-3p, and ssc-miR-615 in fetal STN muscle. These miRNA are associated with glucose transport, insulin signaling, intracellular ATP, hypertension, or adipogenesis. This work supports the hypothesis that E+ tall fescue seed fed during late gestation reduces fetal weight and causes asymmetrical growth, which is indicative of IUGR. Changes in primary fiber number and miRNA of STN indicate that exposure to E+ fescue fed during MID and LATE gestation alters fetal muscle development that may affect postnatal muscle growth and meat quality.

Mechanistic models of how single cells respond to different perturbagens can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Our lab previously constructed one of the largest mechanistic models for single mammalian cell regulation of proliferation and death (774 species, 141 genes, 8 ligands, 2400 reactions). However, this, as many other large-scale models, was written using licensed software (MATLAB) with intricate programming structure, impeding alteration, expansion, and sharing. Here, we generated a new foundation for this model, which includes a python-based creation and simulation pipeline converting a few structured text files into an SBML-compatible format. This new open-source model (named SPARCED) is high-performance- and cloud-computing compatible and enables the study of virtual cell population responses at the single-cell level. We applied this new model to a subset of the LINCS MCF10A Data Cube, which observed that IFNγ acts as an anti-proliferative factor, but the reasons why were unknown. After expanding the SPARCED model with an IFNγ signaling module (to 950 species, 150 genes, 9 ligands, 2500 reactions), we ran stochastic single-cell simulations for two different putative crosstalk mechanisms and looked at the number of cycling cells in each case. Our model-based analysis suggested, and experiments support that these observations are better explained by IFNγ-induced SOCS1 expression sequestering activated EGF receptors, thereby downregulating AKT activity, as opposed to direct IFNγ-induced upregulation of p21 expression. This work forms a foundation for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically predictive mechanistic models.

Urgent responses to the COVID‐19 pandemic depend on increased collaboration and sharing of data, models, and resources among scientists and researchers. In many scientific fields and disciplines, institutional norms treat data, models, and resources as proprietary, emphasizing competition among scientists and researchers locally and internationally. Concurrently, long‐standing norms of open data and collaboration exist in some scientific fields and have accelerated within the last two decades. In both cases—where the institutional arrangements are ready to accelerate for the needed collaboration in a pandemic and where they run counter to what is needed—the rules of the game are “on the table” for institutional‐level renegotiation. These challenges to the negotiated order in science are important, difficult to study, and highly consequential. The COVID‐19 pandemic offers something of a natural experiment to study these dynamics. Preliminary findings highlight: the chilling effect of politics where open sharing could be expected to accelerate; the surprisingly conservative nature of contests and prizes; open questions around whether collaboration will persist following an inflection point in the pandemic; and the strong potential for launching and sustaining pre‐competitive initiatives.

Advanced imaging and DNA sequencing technologies now enable the diverse biology community to routinely generate and analyze terabytes of high resolution biological data. The community is rapidly heading toward the petascale in single investigator laboratory settings. As evidence, the single NCBI SRA central DNA sequence repository contains over 45 petabytes of biological data. Given the geometric growth of this and other genomics repositories, an exabyte of mineable biological data is imminent. The challenges of effectively utilizing these datasets are enormous as they are not only large in the size but also stored in geographically distributed repositories in various repositories such as National Center for Biotechnology Information (NCBI), DNA Data Bank of Japan (DDBJ), European Bioinformatics Institute (EBI), and NASA’s GeneLab. In this work, we first systematically point out the data-management challenges of the genomics community. We then introduce Named Data Networking (NDN), a novel but well-researched Internet architecture, is capable of solving these challenges at the network layer. NDN performs all operations such as forwarding requests to data sources, content discovery, access, and retrieval using content names (that are similar to traditional filenames or filepaths) and eliminates the need for a location layer (the IP address) for data management. Utilizing NDN for genomics workflows simplifies data discovery, speeds up data retrieval using in-network caching of popular datasets, and allows the community to create infrastructure that supports operations such as creating federation of content repositories, retrieval from multiple sources, remote data subsetting, and others. Named based operations also streamlines deployment and integration of workflows with various cloud platforms. Our contributions in this work are as follows 1) we enumerate the cyberinfrastructure challenges of the genomics community that NDN can alleviate, and 2) we describe our efforts in applying NDN for a contemporary genomics workflow (GEMmaker) and quantify the improvements. The preliminary evaluation shows a sixfold speed up in data insertion into the workflow. 3) As a pilot, we have used an NDN naming scheme (agreed upon by the community and discussed in Section 4) to publish data from broadly used data repositories including the NCBI SRA. We have loaded the NDN testbed with these pre-processed genomes that can be accessed over NDN and used by anyone interested in those datasets. Finally, we discuss our continued effort in integrating NDN with cloud computing platforms, such as the Pacific Research Platform (PRP). The reader should note that the goal of this paper is to introduce NDN to the genomics community and discuss NDN’s properties that can benefit the genomics community. We do not present an extensive performance evaluation of NDN—we are working on extending and evaluating our pilot deployment and will present systematic results in a future work.

Online biological databases housing genomics, genetic and breeding data can be constructed using the Tripal toolkit. Tripal is an open-source, internationally developed framework that implements FAIR data principles and is meant to ease the burden of constructing such websites for research communities. Use of a common, open framework improves the sustainability and manageability of such as site. Site developers can create extensions for their site and in turn share those extensions with others. One challenge that community databases often face is the need to provide tools for their users that analyze increasingly larger datasets using multiple software tools strung together in a scientific workflow on complicated computational resources. The Tripal Galaxy module, a ‘plug-in’ for Tripal, meets this need through integration of Tripal with the Galaxy Project workflow management system. Site developers can create workflows appropriate to the needs of their community using Galaxy and then share those for execution on their Tripal sites via automatically constructed, but configurable, web forms or using an application programming interface to power web-based analytical applications. The Tripal Galaxy module helps reduce duplication of effort by allowing site developers to spend time constructing workflows and building their applications rather than rebuilding infrastructure for job management of multi-step applications.

The human brain is a complex organ that consists of several regions each with a unique gene expression pattern. Our intent in this study was to construct a gene co-expression network (GCN) for the normal brain using RNA expression profiles from the Genotype-Tissue Expression (GTEx) project. The brain GCN contains gene correlation relationships that are broadly present in the brain or specific to thirteen brain regions, which we later combined into six overarching brain mini-GCNs based on the brain’s structure. Using the expression profiles of brain region-specific GCN edges, we determined how well the brain region samples could be discriminated from each other, visually with t-SNE plots or quantitatively with the Gene Oracle deep learning classifier. Next, we tested these gene sets on their relevance to human tumors of brain and non-brain origin. Interestingly, we found that genes in the six brain mini-GCNs showed markedly higher mutation rates in tumors relative to matched sets of random genes. Further, we found that cortex genes subdivided Head and Neck Squamous Cell Carcinoma (HNSC) tumors and Pheochromocytoma and Paraganglioma (PCPG) tumors into distinct groups. The brain GCN and mini-GCNs are useful resources for the classification of brain regions and identification of biomarker genes for brain related phenotypes.

Additional file 6:Reference sequences. Table showing reference sequences and coordinates for studied source genomes. (DOC 32 KB)

Authors’ original file for figure 6

Additional file 8:Arabidopsis genome pools' assembly scores. Table showing assembly scores for individual pseudomolecules of each 3Mbp pool for the Arabidopsis genome. (DOC 96 KB)

Additional file of Assessing pooled BAC and whole genome shotgun strategies for assembly of complex genomes

Additional file 9:Arabidopsis genome pools' repeat content vs. assembly scores. Figure showing repeat content and scores for Arabidopsis whole genome pool assemblies. (PPT 72 KB)

Additional file 2:Default vs. optimized assembly parameters. Figure comparing default and optimized assembly parameters for 3Mbp assemblies of five genomes. (PPT 742 KB)

The large amount of time spent transferring experimental data in fields such as genomics is hampering the ability of scientists to generate new knowledge. Often, computer hardware is capable of faster transfers but sub-optimal transfer software and configurations are limiting performance. This work seeks to serve as a guide to identifying the optimal configuration for performing genomics data transfers. A wide variety of tests narrow in on the optimal data transfer parameters for parallel data streaming across Internet2 and between two CloudLab clusters loading real genomics data onto a parallel file system. The best throughput was found to occur with a configuration using GridFTP with at least 5 parallel TCP streams with a 16 MiB TCP socket buffer size to transfer to/from 4–8 BeeGFS parallel file system nodes connected by InfiniBand.