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Positive correlations have been reported between wastewater SARS-CoV-2 concentrations and a community's burden of infection, disease or both. However, previous studies mostly compared wastewater to clinical case counts or nonrepresentative convenience samples, limiting their quantitative potential.This study examined whether wastewater SARS-CoV-2 concentrations could provide better estimations for SARS-CoV-2 community prevalence than reported cases of COVID-19. In addition, this study tested whether wastewater-based epidemiology methods could identify neighborhood-level COVID-19 hotspots and SARS-CoV-2 variants.Community SARS-CoV-2 prevalence was estimated from eight randomized door-to-door nasal swab sampling events in six Oregon communities of disparate size, location, and demography over a 10-month period. Simultaneously, wastewater SARS-CoV-2 concentrations were quantified at each community's wastewater treatment plant and from 22 Newport, Oregon, neighborhoods. SARS-CoV-2 RNA was sequenced from all positive wastewater and nasal swab samples. Clinically reported case counts were obtained from the Oregon Health Authority.Estimated community SARS-CoV-2 prevalence ranged from 8 to 1,687/10,000 persons. Community wastewater SARS-CoV-2 concentrations ranged from 2.9 toThe greater reliability of wastewater SARS-CoV-2 concentrations over clinically reported case counts was likely due to systematic biases that affect reported case counts, including variations in access to testing and underreporting of asymptomatic cases. With these advantages, combined with scalability and low costs, wastewater-based epidemiology can be a key component in public health surveillance of COVID-19 and other communicable infections. https://doi.org/10.1289/EHP10289.

Background Long noncoding RNAs (lncRNAs) have roles in gene regulation, epigenetics, and molecular scaffolding and it is hypothesized that they underlie some mammalian evolutionary adaptations. However, for many mammalian species, the absence of a genome assembly precludes the comprehensive identification of lncRNAs. The genome of the American beaver (Castor canadensis) has recently been sequenced, setting the stage for the systematic identification of beaver lncRNAs and the characterization of their expression in various tissues. The objective of this study was to discover and profile polyadenylated lncRNAs in the beaver using high-throughput short-read sequencing of RNA from sixteen beaver tissues and to annotate the resulting lncRNAs based on their potential for orthology with known lncRNAs in other species. Results Using de novo transcriptome assembly, we found 9528 potential lncRNA contigs and 187 high-confidence lncRNA contigs. Of the high-confidence lncRNA contigs, 147 have no known orthologs (and thus are putative novel lncRNAs) and 40 have mammalian orthologs. The novel lncRNAs mapped to the Oregon State University (OSU) reference beaver genome with greater than 90% sequence identity. While the novel lncRNAs were on average shorter than their annotated counterparts, they were similar to the annotated lncRNAs in terms of the relationships between contig length and minimum free energy (MFE) and between coverage and contig length. We identified beaver orthologs of known lncRNAs such as XIST, MEG3, TINCR, and NIPBL-DT. We profiled the expression of the 187 high-confidence lncRNAs across 16 beaver tissues (whole blood, brain, lung, liver, heart, stomach, intestine, skeletal muscle, kidney, spleen, ovary, placenta, castor gland, tail, toe-webbing, and tongue) and identified both tissue-specific and ubiquitous lncRNAs. Conclusions To our knowledge this is the first report of systematic identification of lncRNAs and their expression atlas in beaver. LncRNAs—both novel and those with known orthologs—are expressed in each of the beaver tissues that we analyzed. For some beaver lncRNAs with known orthologs, the tissue-specific expression patterns were phylogenetically conserved. The lncRNA sequence data files and raw sequence files are available via the web supplement and the NCBI Sequence Read Archive, respectively.

Wastewater-based epidemiology uses pooled wastewater samples to monitor community health and has been used extensively during the COVID-19 pandemic to track SARS-CoV-2 RNA shed by infected individuals into wastewater. Wastewater concentrations of SARS-CoV-2 RNA have been positively correlated with contemporaneous counts of COVID-19 cases, making it useful for following relative disease burden trends within a community. However, the statistical associations are too weak for wastewater-based epidemiology to reliably predict reported case counts, limiting its potential. Here we show that wastewater SARS-CoV-2 concentrations are highly correlated with the community prevalence estimated from 8 randomized household community surveys in 6 Oregon communities over a 10-month period. We found that wastewater-based epidemiology is a significantly better predictor of COVID-19 community prevalence than reported case counts, which suffer from systematic biases including variations in access to testing and underreporting of asymptomatic cases, even after accounting for uncertainty inherent in the wastewater and prevalence estimates by using Monte Carlo simulations. Additionally, our results show that wastewater-based epidemiology can identify the rise and fall of neighborhood-scale COVID-19 hot spots and provide rapid information about the presence of SARS-CoV-2 variants at the neighborhood- and city-scale through sequence analyses of the wastewater. These results validate the potential of wastewater-based epidemiology to be a quantitative method to predict the prevalence of SARS-CoV-2 and identify the presence of variants of concern in a given community or neighborhood, independent of availability and access to individual-level testing. These advantages in combination with its scalability, relatively modest cost and low labor requirements, makes integrating permanent wastewater-based epidemiology infrastructure into public health systems a key component in creating pandemic-resilient cities in the future.

Chia (Salvia hispanica L.), now a popular superfood and a pseudocereal, is one of the richest sources of dietary nutrients such as protein, fiber, and polyunsaturated fatty acids (PUFAs). At present, the genomic and genetic information available in the public domain for this crop are scanty, which hinders an understanding of its growth and development and genetic improvement. We report an RNA-sequencing (RNA-Seq)-based comprehensive transcriptome atlas of Chia sampled from 13 tissue types covering vegetative and reproductive growth stages. We used ~355 million high-quality reads of total ~394 million raw reads from transcriptome sequencing to generate de novo reference transcriptome assembly and the tissue-specific transcript assemblies. After the quality assessment of the merged assemblies and implementing redundancy reduction methods, 82,663 reference transcripts were identified. About 65,587 of 82,663 transcripts were translated into 99,307 peptides, and we were successful in assigning InterPro annotations to 45,209 peptides and gene ontology (GO) terms to 32,638 peptides. The assembled transcriptome is estimated to have the complete sequence information for ~86% of the genes found in the Chia genome. Furthermore, the analysis of 53,200 differentially expressed transcripts (DETs) revealed their distinct expression patterns in Chia's vegetative and reproductive tissues; tissue-specific networks and developmental stage-specific networks of transcription factors (TFs); and the regulation of the expression of enzyme-coding genes associated with important metabolic pathways. In addition, we identified 2,411 simple sequence repeats (SSRs) as potential genetic markers from the transcripts. Overall, this study provides a comprehensive transcriptome atlas, and SSRs, contributing to building essential genomic resources to support basic research, genome annotation, functional genomics, and molecular breeding of Chia.

Summary With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility, and reusability of pipeline data; to provide a template for data processing of future spaceflight-relevant datasets; and to encourage cross-analysis of data from other databases with the data available in GeneLab., Graphical abstract, Highlights • Analysis of omics data from different spaceflight studies presents unique challenges • A standardized pipeline for RNA-seq analysis eliminates data processing variation • The GeneLab RNA-seq pipeline includes QC, trimming, mapping, quantification, and DGE • Space-relevant data processed with this pipeline are available at genelab.nasa.gov, Omics; Space Sciences

The availability of RNA-Seq method allows researchers to capture the spatial or temporal profile of transcriptomes from various types of biological samples. The transcriptome data from a species can be analyzed in the context of its sequenced genomes or closely related genome to score biological sample-specific transcript isoforms, novel transcribed regions and to refine gene models including identification of new genes, in addition to the differential gene expression analysis. However, many plant species of importance currently lack a sequenced genome or a closely related reference genome and thus, rely on the de novo methods for generating transcript models and transcriptome assemblies. Here we describe various tools used for de novo transcriptome assembly and discuss the data management practices and standards.

The genetic arms race between pathogen and host plant is a tug of war that has been ongoing for millennia. The “battles” are those of disruption, restoration of signaling and information transmission on a subcellular level. One such battle occurs between rice an important crop that feeds 50% of the world population and the sheath blight disease (SB) caused by the fungusRhizoctonia solani. It results in 10□30% global yield loss annually and can reach 50% under severe outbreak. Many Receptor□like kinases (RLKs) are recruited as soldiers in these battles. Wall Associated Receptor Kinases (WAKs) a subfamily of receptor-like kinases have been shown to play a role in fungal defense. Here we show that rice geneOsWAK91, present in the major SB resistance QTL region on Chromosome□9 is a key component in defense against rice sheath blight. An SNP mutation C/T separates susceptible variety, Cocodrie (CCDR) from the resistant line MCR010277 (MCR). The resistant allele C results in the stop codon loss that results in 68 amino acids longer C□terminus carrying longer protein kinase domain and phosphorylation sites. Our genotype and phenotype analysis of the top 20 individuals of the double haploid SB population shows a strong correlation with the SNP. The susceptible allele appears as a recent introduction found in the japonica subspecies reference genome and a majority of the tropical and temperate japonica lines sequenced by the 3000 rice genome project. Multiple US commercial varieties with japonica background carry the susceptible allele and are known for SB susceptibility. This discovery opens the possibility of introducing resistance alleles into high yielding commercial varieties to reduce yield losses incurred by the disease.

Expression Atlas (http://www.ebi.ac.uk/gxa) is an added value database that provides information about gene and protein expression in different species and contexts, such as tissue, developmental stage, disease or cell type. The available public and controlled access data sets from different sources are curated and re-analysed using standardized, open source pipelines and made available for queries, download and visualization. As of August 2017, Expression Atlas holds data from 3,126 studies across 33 different species, including 731 from plants. Data from large-scale RNA sequencing studies including Blueprint, PCAWG, ENCODE, GTEx and HipSci can be visualized next to each other. In Expression Atlas, users can query genes or gene-sets of interest and explore their expression across or within species, tissues, developmental stages in a constitutive or differential context, representing the effects of diseases, conditions or experimental interventions. All processed data matrices are available for direct download in tab-delimited format or as R-data. In addition to the web interface, data sets can now be searched and downloaded through the Expression Atlas R package. Novel features and visualizations include the on-the-fly analysis of gene set overlaps and the option to view gene co-expression in experiments investigating constitutive gene expression across tissues or other conditions.

Gramene (http://www.gramene.org) is a knowledgebase for comparative functional analysis in major crops and model plant species. The current release, #54, includes over 1.7 million genes from 44 reference genomes, most of which were organized into 62,367 gene families through orthologous and paralogous gene classification, whole-genome alignments, and synteny. Additional gene annotations include ontology-based protein structure and function; genetic, epigenetic, and phenotypic diversity; and pathway associations. Gramene's Plant Reactome provides a knowledgebase of cellular-level plant pathway networks. Specifically, it uses curated rice reference pathways to derive pathway projections for an additional 66 species based on gene orthology, and facilitates display of gene expression, gene–gene interactions, and user-defined omics data in the context of these pathways. As a community portal, Gramene integrates best-of-class software and infrastructure components including the Ensembl genome browser, Reactome pathway browser, and Expression Atlas widgets, and undergoes periodic data and software upgrades. Via powerful, intuitive search interfaces, users can easily query across various portals and interactively analyze search results by clicking on diverse features such as genomic context, highly augmented gene trees, gene expression anatomograms, associated pathways, and external informatics resources. All data in Gramene are accessible through both visual and programmatic interfaces.

The goal of Gramene database ( www.gramene.org ) is to empower the plant research community in conducting comparative genomics studies across model plants and crops by employing a phylogenetic framework and orthology-based projections. Gramene database (release #49) provides resources for comparative plant genomics including well-annotated plant genomes (39 complete reference genomes and six partial genomes), genetic or structural variation data for 14 plant species, pathways for 58 plant species, and gene expression data for 14 species including Arabidopsis, rice, maize, soybean, wheat, etc. (fetched from EBI-EMBL Gene Expression Atlas database). Gramene also facilitates visualization and analysis of user-defined data in the context of species-specific Genome Browsers or pathways. This chapter describes basic navigation for Gramene users and illustrates how they can use the genome section to analyze the gene expression and nucleotide variation data generated in their labs. This includes (1) upload and display of genomic data onto a Genome Browser track, (2) analysis of variation data using online Variant Effect Predictor (VEP) tool for smaller data sets, and (3) the use of the stand-alone Perl scripts and command line protocols for variant effect prediction on larger data sets.

The goal of Gramene database ( www.gramene.org ) is to empower the plant research community in conducting comparative genomics studies across model plants and crops by employing a phylogenetic framework and orthology-based projections. Gramene database (release #49) provides resources for comparative plant genomics including well-annotated plant genomes (39 complete reference genomes and six partial genomes), genetic or structural variation data for 14 plant species, pathways for 58 plant species, and gene expression data for 14 species including Arabidopsis, rice, maize, soybean, wheat, etc. (fetched from EBI-EMBL Gene Expression Atlas database). Gramene also facilitates visualization and analysis of user-defined data in the context of species-specific Genome Browsers or pathways. This chapter describes basic navigation for Gramene users and illustrates how they can use the genome section to analyze the gene expression and nucleotide variation data generated in their labs. This includes (1) upload and display of genomic data onto a Genome Browser track, (2) analysis of variation data using online Variant Effect Predictor (VEP) tool for smaller data sets, and (3) the use of the stand-alone Perl scripts and command line protocols for variant effect prediction on larger data sets.

Outside the protection of Earth's atmosphere, astronauts are exposed to low doses of high linear energy transfer (LET) radiation. Future NASA plans for deep space missions or a permanent settlement on the moon are limited by the health risks associated with space radiation exposures. There is a paucity of direct epidemiological data for low dose exposures to space radiation-relevant high LET ions. Health risk models are used to estimate the risk for such exposures, though these models are based on high dose experiments. There is increasing evidence, however, that low and high dose exposures result in different signaling events at the molecular level, and may involve different response mechanisms. Further, despite their low abundance, high LET particles have been identified as the major contributor to health risk during manned space flight. The human skin is exposed in every external radiation scenario, making it an ideal epithelial tissue model in which to study radiation induced effects. Here, we exposed an in vitro three dimensional (3-D) human organotypic skin tissue model to low doses of high LET oxygen (O), silicon (Si) and iron (Fe) ions. We measured proliferation and differentiation profiles in the skin tissue and examined the integrity of the skin's barrier function. We discuss the role of secondary particles in changing the proportion of cells receiving a radiation dose, emphasizing the possible impact on radiation-induced health issues in astronauts.

Background: Triticum monococcum (2n) is a close ancestor of T. urartu, the A-genome progenitor of cultivated hexaploid wheat, and is therefore a useful model for the study of components regulating photomorphogenesis in diploid wheat. In order to develop genetic and genomic resources for such a study, we constructed genome-wide transcriptomes of two Triticum monococcum subspecies, the wild winter wheat T. monococcum ssp. aegilopoides (accession G3116) and the domesticated spring wheat T. monococcum ssp. monococcum (accession DV92) by generating de novo assemblies of RNASeq data derived from both etiolated and green seedlings. Principal Findings: The de novo transcriptome assemblies of DV92 and G3116 represent 120,911 and 117,969 transcripts, respectively. We successfully mapped ,90% of these transcripts from each accession to barley and ,95% of the transcripts to T. urartu genomes. However, only ,77% transcripts mapped to the annotated barley genes and ,85% transcripts mapped to the annotated T. urartu genes. Differential gene expression analyses revealed 22% more light up-regulated and 35% more light down-regulated transcripts in the G3116 transcriptome compared to DV92. The DV92 and G3116 mRNA sequence reads aligned against the reference barley genome led to the identification of ,500,000 single nucleotide polymorphism (SNP) and ,22,000 simple sequence repeat (SSR) sites. Conclusions: De novo transcriptome assemblies of two accessions of the diploid wheat T. monococcum provide new empirical transcriptome references for improving Triticeae genome annotations, and insights into transcriptional programming during photomorphogenesis. The SNP and SSR sites identified in our analysis provide additional resources for the development of molecular markers.

The effects of low dose high linear energy transfer (LET) radiation on human health are of concern for space, occupational, and clinical exposures. As epidemiological data for such radiation exposures are scarce for making relevant predictions, we need to understand the mechanism of response especially in normal tissues. Our objective here is to understand the effects of heavy ion radiation on tissue homeostasis in a realistic model system. Towards this end, we exposed an in vitro three dimensional skin equivalent to low fluences of neon (Ne) ions (300 MeV u(-1)), and determined the differentiation profile as a function of time following exposure using immunohistochemistry. We found that Ne ion exposures resulted in transient increases in the tissue regions expressing the differentiation markers keratin 10, and filaggrin, and more subtle time-dependent effects on the number of basal cells in the epidermis. We analyzed the data using a mathematical model of the skin equivalent, to quantify the effect of radiation on cell proliferation and differentiation. The agent-based mathematical model for the epidermal layer treats the epidermis as a collection of heterogeneous cell types with different proliferation-differentiation properties. We obtained model parameters from the literature where available, and calibrated the unknown parameters to match the observed properties in unirradiated skin. We then used the model to rigorously examine alternate hypotheses regarding the effects of high LET radiation on the tissue. Our analysis indicates that Ne ion exposures induce rapid, but transient, changes in cell division, differentiation and proliferation. We have validated the modeling results by histology and quantitative reverse transcription polymerase chain reaction (qRT-PCR). The integrated approach presented here can be used as a general framework to understand the responses of multicellular systems, and can be adapted to other epithelial tissues.

The Genome-Wide Association Studies approach was used to detect Quantitative Trait Loci associated with tocochromanol concentrations using a panel of 1,466 barley accessions. All major tocochromanol types- α-, β-, δ-, γ-tocopherol and tocotrienol- were assayed. We found 13 single nucleotide polymorphisms associated with the concentration of one or more of these tocochromanol forms in barley, seven of which were within 2 cM of sequences homologous to cloned genes associated with tocochromanol production in barley and/or other plants. These associations confirmed a prior report based on bi-parental QTL mapping. This knowledge will aid future efforts to better understand the role of tocochromanols in barley, with specific reference to abiotic stress resistance. It will also be useful in developing barley varieties with higher tocochromanol concentrations, although at current recommended daily consumption amounts, barley would not be an effective sole source of vitamin E. However, it could be an important contributor in the context of whole grains in a balanced diet.