Transcriptome Research Research Articles

Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics.

The latest breakthroughs in spatially resolved transcriptomics technology offer comprehensive opportunities to delve into gene expression patterns within the tissue microenvironment. However, the precise identification of spatial domains within tissues remains challenging. In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current graph neural networks have not extensively addressed. Benchmark testing demonstrates its significant efficacy in elucidating tissue anatomy and interpreting tumor heterogeneity, indicating its potential in advancing spatial transcriptomics research and understanding complex biological phenomena.

Abstract 7431: IAMSAM: Image-based analysis of molecular signatures using the Segment-anything model - Integrative analysis tool for tumor microenvironment

Abstract Introduction. Spatial transcriptomics (ST) is a powerful approach for investigating gene expression patterns in tissues while preserving their spatial context. However, working with ST data presents challenges due to its complexity, high dimensionality, and the absence of user-friendly tools. IAMSAM (Image-based Analysis of Molecular signatures using the Segment-Anything Model) leverages the Segment-anything model (SAM), a cutting-edge deep learning model developed by Meta, which is specifically designed for image segmentation. We demonstrated IAMSAM's capabilities with publicly available ST datasets, showcasing its user-friendly interface, featuring its user-friendly interface for exploring pathophysiology and potential biomarkers. Methods. IAMSAM is a web-based tool designed for analyzing ST data, utilizing the SAM for image segmentation. It offers two modes: everything-mode and prompt-mode. In everything-mode, the model automatically generates segmentation masks for the entire histologic image. In prompt-mode, users can draw rectangle boxes to provide input prompts for SAM. IAMSAM's downstream analysis includes the identification of DEGs, enrichment analysis, and cell type prediction of selected regions of interest. DEGs are represented in volcano plots and box plots, aiding in the identification of genes associated with image features of tumors such as cancer-enriched areas or stromal patterns. Enrichment analysis assesses gene sets for over-representation, providing insights into pathways and biological processes relevant to cancer. Cell type prediction utilizes the CellTypist API, offering valuable information about the cellular composition and heterogeneity within the tumor microenvironment. Results. IAMSAM has been applied to publicly available ST datasets to demonstrate its effectiveness in cancer research. It accurately identified cancer markers in breast cancer samples and aligned with spot clustering results in various cancer samples, all of which have significant implications for understanding cancer biology. Prompt-mode effectively identified stromal regions in tumor sections, and with magnification, it uncovered microvessels in prostate cancer samples, providing valuable insights for cancer pathophysiology research. IAMSAM was also applied to fluorescence imaging, demonstrating its versatility and potential in various cancer-related studies. Conclusions. IAMSAM is a valuable web-based tool for cancer research using spatial transcriptomics data particularly integratively analyzing tumor microenvironments with a user-friendly interface. IAMSAM opens up new avenues for spatial transcriptomics research in the context of cancer, making it a promising resource for cancer researchers. Citation Format: Dongjoo Lee, Jeongbin Park, Seungho Cook, Seongjin Yoo, Daeseung Lee, Hongyoon Choi. IAMSAM: Image-based analysis of molecular signatures using the Segment-anything model - Integrative analysis tool for tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7431.

Mammalian Diaphanous1 signalling in neurovascular complications of diabetes.

Over the past few decades, diabetes gradually has become one of the top non-communicable disorders, affecting 476.0 million in 2017 and is predicted to reach 570.9 million people in 2025. It is estimated that 70 to 100% of all diabetic patients will develop some if not all, diabetic complications over the course of the disease. Despite different symptoms, mechanisms underlying the development of diabetic complications are similar, likely stemming from deficits in both neuronal and vascular components supplying hyperglycaemia-susceptible tissues and organs. Diaph1, protein diaphanous homolog 1, although mainly known for its regulatory role in structural modification of actin and related cytoskeleton proteins, in recent years attracted research attention as a cytoplasmic partner of the receptor of advanced glycation end-products (RAGE) a signal transduction receptor, whose activation triggers an increase in proinflammatory molecules, oxidative stressors and cytokines in diabetes and its related complications. Both Diaph1 and RAGE are also a part of the RhoA signalling cascade, playing a significant role in the development of neurovascular disturbances underlying diabetes-related complications. In this review, based on the existing knowledge as well as compelling findings from our past and present studies, we address the role of Diaph1 signalling in metabolic stress and neurovascular degeneration in diabetic complications. In light of the most recent developments in biochemical, genomic and transcriptomic research, we describe current theories on the aetiology of diabetes complications, highlighting the function of the Diaph1 signalling system and its role in diabetes pathophysiology.

De novo transcriptome for Chiloscyllium griseum, a long-tail carpet shark of the Indian waters

Sharks have thrived in the oceans for 400 million years, experienced five extinctions and evolved into today’s apex predators. However, enormous genome size, poor karyotyping and limited tissue sampling options are the bottlenecks in shark research. Sharks of the family Orectolobiformes act as model species in transcriptome research with exceptionally high reproductive fecundity, catch prominence and oviparity. The present study illustrates a de novo transcriptome for an adult grey bamboo shark, Chiloscyllium griseum (Chondrichthyes; Hemiscyllidae) using paired-end RNA sequencing. Around 150 million short Illumina reads were obtained from five different tissues and assembled using the Trinity assembler. 70,647 hits on Uniprot by BLASTX was obtained after the transcriptome annotation. The data generated serve as a basis for transcriptome-based population genetic studies and open up new avenues in the field of comparative transcriptomics and conservation biology.

Flu-CED: A comparative transcriptomics database of influenza virus-infected human and animal models.

The continuing emergence of influenza virus has highlighted the value of public databases and related bioinformatic analysis tools in investigating transcriptomic change caused by different influenza virus infections in human and animal models. We collected a large amount of transcriptome research data related to influenza virus-infected human and animal models in public databases (GEO and ArrayExpress), and extracted and integrated array and metadata. The gene expression matrix was generated through strictly quality control, balance, standardization, batch correction, and gene annotation. We then analyzed gene expression in different species, virus, cells/tissues or after antibody/vaccine treatment and imported sample metadata and gene expression datasets into the database. Overall, maintaining careful processing and quality control, we collected 8064 samples from 103 independent datasets, and constructed a comparative transcriptomics database of influenza virus named the Flu-CED database (Influenza comparative expression database, https://flu.com-med.org.cn/). Using integrated and processed transcriptomic data, we established a user-friendly website for realizing the integration, online retrieval, visualization, and exploration of gene expression of influenza virus infection in different species and the biological functions involved in differential genes. Flu-CED can quickly query single and multi-gene expression profiles, combining different experimental conditions for comparative transcriptome analysis, identifying differentially expressed genes (DEGs) between comparison groups, and conveniently finding DEGs. Flu-CED provides data resources and tools for analyzing gene expression in human and animal models infected with influenza virus that can deepen our understanding of the mechanisms underlying disease occurrence and development, and enable prediction of key genes or therapeutic targets that can be used for medical research.

When Livestock Genomes Meet Third-Generation Sequencing Technology: From Opportunities to Applications.

Third-generation sequencing technology has found widespread application in the genomic, transcriptomic, and epigenetic research of both human and livestock genetics. This technology offers significant advantages in the sequencing of complex genomic regions, the identification of intricate structural variations, and the production of high-quality genomes. Its attributes, including long sequencing reads, obviation of PCR amplification, and direct determination of DNA/RNA, contribute to its efficacy. This review presents a comprehensive overview of third-generation sequencing technologies, exemplified by single-molecule real-time sequencing (SMRT) and Oxford Nanopore Technology (ONT). Emphasizing the research advancements in livestock genomics, the review delves into genome assembly, structural variation detection, transcriptome sequencing, and epigenetic investigations enabled by third-generation sequencing. A comprehensive analysis is conducted on the application and potential challenges of third-generation sequencing technology for genome detection in livestock. Beyond providing valuable insights into genome structure analysis and the identification of rare genes in livestock, the review ventures into an exploration of the genetic mechanisms underpinning exemplary traits. This review not only contributes to our understanding of the genomic landscape in livestock but also provides fresh perspectives for the advancement of research in this domain.

Enhancing transcriptome analysis in medicinal plants: multiple unigene sets in Astragalus membranaceus.

Astragalus membranaceus is a medicinal plant mainly used in East Asia and contains abundant secondary metabolites. Despite the importance of this plant, the available genomic and genetic information is still limited. De novo transcriptome construction is recognized as an essential method for transcriptome research when reference genome information is incomplete. In this study, we constructed three individual transcriptome sets (unigene sets) for detailed analysis of the phenylpropanoid biosynthesis pathway, a major metabolite of A. membranaceus. Set-1 was a circular consensus sequence (CCS) generated using PacBio sequencing (PacBio-seq). Set-2 consisted of hybridized assembled unigenes with Illumina sequencing (Illumina-seq) reads and PacBio CCS using rnaSPAdes. Set-3 unigenes were assembled from Illumina-seq reads using the Trinity software. Construction of multiple unigene sets provides several advantages for transcriptome analysis. First, it provides an appropriate expression filtering threshold for assembly-based unigenes: a threshold transcripts per million (TPM) ≥ 5 removed more than 88% of assembly-based unigenes, which were mostly short and low-expressing unigenes. Second, assembly-based unigenes compensated for the incomplete length of PacBio CCSs: the ends of the 5`/3` untranslated regions of phenylpropanoid-related unigenes derived from set-1 were incomplete, which suggests that PacBio CCSs are unlikely to be full-length transcripts. Third, more isoform unigenes could be obtained from multiple unigene sets; isoform unigenes missing in Set-1 were detected in set-2 and set-3. Finally, gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that phenylpropanoid biosynthesis and carbohydrate metabolism were highly activated in A. membranaceus roots. Various sequencing technologies and assemblers have been developed for de novo transcriptome analysis. However, no technique is perfect for de novo transcriptome analysis, suggesting the need to construct multiple unigene sets. This method enables efficient transcript filtering and detection of longer and more diverse transcripts.

A time-course transcriptome analysis revealing the potential molecular mechanism of early gonadal differentiation in the Chinese giant salamander

The Chinese giant salamander (CGS) Andrias davidianus is the largest extant amphibian and has recently become an important species for aquaculture with high economic value. Meanwhile, its wild populations and diversity are in urgent need of protection. Exploring the mechanism of its early gonadal differentiation will contribute to the development of CGS aquaculture and the recovery of its wild population. In this study, transcriptomic and phenotypic research was conducted on the critical time points of early gonadal differentiation of CGS. The results indicate that around 210 days post-hatching (dph) is the critical window for female CGS's gonadal differentiation, while 270 dph is that of male CGS. Besides, the TRPM1 gene may be the crucial gene among many candidates determining the sex of CGS. More importantly, in our study, key genes involved in CGS's gonadal differentiation and development are identified and their potential pathways and regulatory models at early stage are outlined. This is an initial exploration of the molecular mechanisms of CGS's early gonadal differentiation at multiple time points, providing essential theoretical foundations for its captive breeding and offering unique insights into the conservation of genetic diversity in wild populations from the perspective of sex development.

Mitochondria-Associated Transcriptome Profiling via Localizable Aggregation-Induced Emission Photosensitizers in Live Cells.

In recent decades, there has been increasing interest in studying mitochondria through transcriptomic research. Various exogenous fusion protein-based proximity labeling methods have been reported that focus on the site of one particular protein/peptide and might also influence the corresponding localization or interactome. To enable unbiased and high spatial-resolution profiling of mitochondria-associated transcriptomes in live cells, a flexible RNA proximity labeling approach was developed using aggregation-induced emission (AIE) type photosensitizers (PSs) that possess great mitochondria-targeting capabilities. Their accumulation in an enclosed mitochondrial environment tends to enhance the fluorescence emission and reactive oxygen species generation. By comparing the in vitro optical properties, photosensitization processes, as well as the in cellulo mitochondrial specificity and RNA labeling performance of four AIE PSs, high-throughput sequencing analysis was conducted using TFPy-mediated RNA proximity labeling in live HeLa cells. This approach successfully captured a comprehensive list of transcripts, including mitochondria-encoded RNAs, as well as some nuclear-derived RNAs located at the outer mitochondrial membrane and interacting organelles. This small molecule-based proximity labeling method bypasses complex genetic manipulation and transfection steps, making it readily applicable for diverse research purposes.

A low-cost, low-input method establishment for m6A MeRIP-seq.

N6-methyladenosine (m6A) is a highly prevalent modification found in mammal mRNA molecules that plays a crucial role in the regulation of cellular function. m6A RNA immunoprecipitation sequencing (MeRIP-seq) has been frequently used in transcriptomics research to identify the location of m6A. MABE572 (Millipore) is the most widely utilized and efficient anti-m6A antibody for MeRIP-seq. However, due to the high dose and price of this antibody, which has also been taken off the market, we discovered that CST's anti-m6A antibody can be used instead of MABE572 to map the m6A transcriptome. In the present study, we performed different concentrations of the CST anti-m6A antibodies with the corresponding initiation RNA of HEK293T cells, 2.5 μg antibody with 1 μg total RNA, 1.25 μg antibody with 0.5 μg total RNA, and 1.25 μg antibody with 0.1 μg total RNA. By comparing the m6A peak calling, enriched motifs, alternative splicing events, and nuclear transcripts modified by m6A between the CST and Millipore libraries, it was found that the CST library presented similar data to Millipore, even at incredibly low doses. The volume and cost of antibodies are significantly reduced by this refined MeRIP-seq using CST antibody, making it convenient to map future large-scale sample m6A methylation.

Comparative transcriptome analysis between rhesus macaques ( Macaca mulatta) and crab-eating macaques ( M. fascicularis).

Understanding gene expression variations between species is pivotal for deciphering the evolutionary diversity in phenotypes. Rhesus macaques ( Macaca mulatta, MMU) and crab-eating macaques ( M. fascicularis, MFA) serve as crucial nonhuman primate biomedical models with different phenotypes. To date, however, large-scale comparative transcriptome research between these two species has not yet been fully explored. Here, we conducted systematic comparisons utilizing newly sequenced RNA-seq data from 84 samples (41 MFA samples and 43 MMU samples) encompassing 14 common tissues. Our findings revealed a small fraction of genes (3.7%) with differential expression between the two species, as well as 36.5% of genes with tissue-specific expression in both macaques. Comparison of gene expression between macaques and humans indicated that 22.6% of orthologous genes displayed differential expression in at least two tissues. Moreover, 19.41% of genes that overlapped with macaque-specific structural variants showed differential expression between humans and macaques. Of these, the FAM220A gene exhibited elevated expression in humans compared to macaques due to lineage-specific duplication. In summary, this study presents a large-scale transcriptomic comparison between MMU and MFA and between macaques and humans. The discovery of gene expression variations not only enhances the biomedical utility of macaque models but also contributes to the wider field of primate genomics.

The forgotten variable? Does the euthanasia method and sample storage condition influence an organisms transcriptome – a gene expression analysis on multiple tissues in pigs

BackgroundTranscriptomic studies often require collection of fresh tissues post euthanasia. The chosen euthanasia method might have the potential to induce variations in gene expressions that are unlinked with the experimental design. The present study compared the suitability of ‘nitrogen gas in foam’ (ANOXIA) in comparison to a non-barbiturate anaesthetic, T-61® (T61), for euthanizing piglets used in transcriptome research. Further, the effect of common tissue storage conditions, RNAlater™ (RL) and snap freezing in liquid nitrogen (LN2), on gene expression profiles were also analysed.ResultsOn comparison of the 3’mRNA-Seq data generated from pituitary, hypothalamus, liver and lung tissues, no significant differential expression in the protein coding genes were detected between the euthanasia methods. This implies that the nitrogen anoxia method could be a suitable alternative for euthanasia of piglets used in transcriptomic research. However, small nuclear RNAs (snRNAs) that constitute the eukaryotic spliceosomal machinery were found to be significantly higher (log2fold change ≥ 2.0, and adjusted p value ≤ 0.1) in pituitary samples collected using ANOXIA. Non-protein coding genes like snRNAs that play an important role in pre-mRNA splicing can subsequently modify gene expression. Storage in RL was found to be superior in preserving RNA compared to LN2 storage, as evidenced by the significantly higher RIN values in representative samples. However, storage in RL as opposed to LN2, also influenced differential gene expression in multiple tissues, perhaps as a result of its inability to inhibit biological activity during storage. Hence such external sources of variations should be carefully considered before arriving at research conclusions.ConclusionsSource of biological variations like euthanasia method and storage condition can confound research findings. Even if we are unable to prevent the effect of these external factors, it will be useful to identify the impact of these variables on the parameter under observation and thereby prevent misinterpretation of our results.

Transcriptome research of human amniocytes identifies hub genes associated with developmental dysplasia in down syndrome.

Trisomy 21, or Down syndrome (DS), is the most frequent human autosomal chromosome aneuploidy, which leads to multiple developmental disorders, especially mental retardation in individuals. The presence of an additional human chromosome 21 (HSA21) could account for the pathological manifestations in DS. In this study, we analyzed the mRNA gene expression profile of DS-derived amniocytes compared with normal amniocytes, aiming to evaluate the relationship between candidate dysregulated HSA21 genes and DS developmental phenotypes. Differentially expressed genes (DEGs) included 1794 upregulated genes and 1411 downregulated genes, which are mainly involved in cell adhesion, inflammation, cell proliferation and thus may play an important role in inducing multiple dysplasia during DS fetal development. Furthermore, STRING protein network studies demonstrated 7 candidate HSA21 genes participated Gene Ontology (GO) terms: cell adhesion and extracellular matrix remodeling (COL6A1, COL6A2, COL18A1, ADAMTS5, JAM2, and POFUT2), inflammation and virus infection response (MX1 and MX2), histone modification and chromatin remodeling (NRIP1), glycerolipid and glycerophospholipid metabolism (AGPAT3), mitochondrial function (ATP5PF and ATP5PO), synaptic vesicle endocytosis (ITSN1 and SYNJ1) and amyloid metabolism (APP). Meanwhile, GSEA enrichment identified several transcription factors and miRNAs, which may target gene expression in the DS group. Our study established connections between dysregulated genes, especially HSA21 genes, and DS-associated phenotypes. The alteration of multiple pathways and biological processes may contribute to DS developmental disorders, providing potential pathogenesis and therapeutic targets for DS.

A Simple, Rapid, and Effective Heparinase Protocol to Enable Nucleic Acid Study from Frozen Heparinized Plasma.

Cell-free RNAs (cfRNAs) are promising analytes as non-invasive biomarkers and have even greater potential if tied in with metabolomics. Plasma is an optimal source for cfRNAs but is often derived from a variety of anticoagulants. Plasma obtained in heparin is suitable for metabolomics but is difficult to utilize for qPCR-based downstream analysis. In the present study, we aimed to develop a simple, time-efficient, and cost-effective heparinase protocol, followed by library preparation and sequencing of human plasma cfRNAs drawn and stored in heparin at -80 °C for several years. Blood was collected in CPT™ sodium heparin tubes from patients with chronic HCV infection (NCT02400216) at the National Institutes of Health (NIH) Clinical Center. Plasma cfRNAs were treated with heparinase I and used for library preparation and next-generation sequencing (NGS). Heparinase treatment maintained RNA integrity and allowed for successful library preparation for all the study subjects even with 7 ng of cfRNAs as starting material. The classification report derived from Pavian R package v1.2.0 showed no artificial reads. The abundance of chordate over microbial reads suggests no addition of experimental error through heparinase I treatment. We report a novel and practical approach to heparinase treatment for human plasma collected and frozen in sodium heparin for several years. This is an effective demonstration of utilizing heparin plasma for NGS and downstream transcriptomic research, which could then be integrated with metabolomics from the same samples, maximizing efficiency and minimizing blood draws.

Herbgenomics meets Papaveraceae: a promising -omics perspective on medicinal plant research.

Herbal medicines were widely used in ancient and modern societies as remedies for human ailments. Notably, the Papaveraceae family includes well-known species, such as Papaver somniferum and Chelidonium majus, which possess medicinal properties due to their latex content. Latex-bearing plants are a rich source of diverse bioactive compounds, with applications ranging from narcotics to analgesics and relaxants. With the advent of high-throughput technologies and advancements in sequencing tools, an opportunity exists to bridge the knowledge gap between the genetic information of herbs and the regulatory networks underlying their medicinal activities. This emerging discipline, known as herbgenomics, combines genomic information with other -omics studies to unravel the genetic foundations, including essential gene functions and secondary metabolite biosynthesis pathways. Furthermore, exploring the genomes of various medicinal plants enables the utilization of modern genetic manipulation techniques, such as Clustered Regularly-Interspaced Short Palindromic Repeats (CRISPR/Cas9) or RNA interference. This technological revolution has facilitated systematic studies of model herbs, targeted breeding of medicinal plants, the establishment of gene banks and the adoption of synthetic biology approaches. In this article, we provide a comprehensive overview of the recent advances in genomic, transcriptomic, proteomic and metabolomic research on species within the Papaveraceae family. Additionally, it briefly explores the potential applications and key opportunities offered by the -omics perspective in the pharmaceutical industry and the agrobiotechnology field.

Automated Cut&run Brings Scalable Epigenomic Profiling to Hematology

Genome-wide association studies (GWAS) and corresponding transcriptomics research have been leveraged to identify disease risk variants in hematological disorders with the hopes of developing more personalized treatment regimens. Interestingly, >90% of GWAS-identified variants are found in non-coding regions of the genome, indicating they likely affect the regulatory machinery that governs chromatin structure. Non-coding elements, such as enhancers, play important roles in supporting chromatin mechanisms, including cell-type specific gene expression, cell fates and disease states. However, these variants are difficult to study compared to protein-coding mutations and have been challenging to directly associate with phenotypic readouts. To date, efforts to understand the effects of non-coding variants on chromatin dynamics have been focused on chromatin accessibility (ATAC-seq) and DNA methylation profiling. Notably, these assays can only provide a binary, “open-or-closed,” view of chromatin and often fail to provide mechanistic insight into disease etiology. Epigenomic features - such as histone post-translational modifications (PTMs) and chromatin-associated proteins - mark distinct genomic compartments (e.g., promoters, enhancers) and regulate chromatin structure, gene expression, and cell function. Mapping these features provides a rich context to study cell fate and has great potential for discovering new biomarkers and drug targets. Despite this, efforts to integrate epigenomics into large scale lymphatic and myeloid research have been hampered by the poor sensitivity, high background, and low throughput of traditional chromatin mapping technology (i.e., ChIP-seq). Here, we present autoCUT&RUN, a high throughput assay for rapid, ultra-sensitive profiling of epigenomic features from FACS-isolated primary cells or tissues. This workflow generates reliable profiles from <10,000 cells per reaction, and is supported by a rigorous optimization strategy, high-quality antibodies, and quantitative spike-in controls. As part of a multi-site collaboration with the Immunological Genome Consortium, we used our autoCUT&RUN platform to build a comprehensive epigenomic database of mouse immune cells - composed of >1,500 epigenomic profiles from >100 different FACS-isolated primary immune cell types. These studies set the stage to leverage high-throughput epigenomics for multi-site clinical studies to identify and/or characterize novel biomarkers for precision medicine applications. Further, we will discuss our integration of CUT&RUN and Enzymatic Methylation-sequencing (CUT&RUN-EM) to deliver a true multiomic view of the co-occurrence of epigenomic features and DNA methylation, which can reveal multivalent cellular mechanisms and deconvolute sample heterogeneity.

Genome-wide identification and expression profiling of WRKY family suggest their potential role in cannabinoid regulation in Cannabis sativa L

The WRKY transcription factors are unique and essential for plant development and various external stresses. The recent advancements in the genome and transcriptome research in Cannabis pave the path to discovering the transcription factors. However, minimal and non-reductant studies have been done to identify the complete WRKY transcription factor family based on the high-quality whole genome available in Cannabis sativa. With the recent surge in the therapeutic potential of cannabinoids, there is increased interest in the research on the developmental and adaptive potential of Cannabis sativa. The present study revealed a complete chromosomal-based identification of 48 WRKYs in Cannabis sativa and differentiated them into three main groups and one sub-group based on the physiochemical and phylogeny analysis. Furthermore, based on the composition of gene structure, motif analysis, and gene duplication analysis, the CsWRKY genes have remained conserved during evolution. Our study revealed that the upstream sequences of all the CsWRKYs had abiotic and biotic stress-related elements present that might display the role of the specific CsWRKY gene. Furthermore, to predict the potential regulatory network of CsWRKYs, functional analysis and protein-protein interaction were carried out and revealed their close networking and signalling. Recently, it has been shown that the CsWRKY might regulate the respective cannabinoid enzymes. Therefore, the expression patterns of CsWRKY genes in two accessions from different altitudes of Cannabis sativa -were evaluated. Most of the CsWRKY genes were significantly (p ≤ 0.05) highly expressed in the inflorescence of CAN2 (high altitude) accession, suggesting their possible role in inflorescence development or cannabinoid accumulation. CsWRKY18, CsWRKY27 and CsWRKY37 have shown similarities with the AtWRKY75, AaGSW1, and AaWRKY9, respectively and might be the candidate genes to explore further in determining the functions during inflorescence development and cannabinoid regulation. The present study lays the foundation to explore the biological processes and regulatory mechanism of CsWRKY genes during the growth, development, various stresses, and accumulation of secondary metabolites in Cannabis sativa.

Returning incidentally discovered Hepatitis C RNA-seq results to COPDGene study participants

The consequences of returning infectious pathogen test results identified incidentally in research studies have not been well-studied. Concerns include identification of an important health issue for individuals, accuracy of research test results, public health impact, potential emotional distress for participants, and need for IRB permissions. Blood RNA-sequencing analysis for non-human RNA in 3984 participants from the COPDGene study identified 228 participants with evidence suggestive for hepatitis C virus (HCV) infection. We hypothesized that incidentally discovered HCV results could be effectively returned to COPDGene participants with attention to the identified concerns. In conjunction with a COPDGene Participant Advisory Panel, we developed and obtained IRB approval for a process of returning HCV research results and an HCV Follow-Up Study questionnaire to capture information about previous HCV diagnosis and treatment information and participant reactions to return of HCV results. During phone calls following the initial HCV notification letter, 84 of 124 participants who could be contacted (67.7%) volunteered that they had been previously diagnosed with HCV infection. Thirty-one of these 124 COPDGene participants were enrolled in the HCV Follow-Up Study. Five of the 31 HCV Follow-Up Study participants did not report a previous diagnosis of HCV. For four of these participants, subsequent clinical HCV testing confirmed HCV infection. Thus, 30/31 Follow-Up Study participants had confirmed HCV diagnoses, supporting the accuracy of the HCV research test results. However, the limited number of participants in the Follow-Up Study precludes an accurate assessment of the false-positive and false-negative rates of the research RNA sequencing evidence for HCV. Most HCV Follow-Up Study participants (29/31) were supportive of returning HCV research results, and most participants found the process for returning HCV results to be informative and not upsetting. Newly diagnosed participants were more likely to be pleased to learn about a potentially curable infection (p = 0.027) and showed a trend toward being more frightened by the potential health risks of HCV (p = 0.11). We conclude that HCV results identified incidentally during transcriptomic research studies can be successfully returned to research study participants with a carefully designed process.

Stress combinations and their interactions in plants database: a one-stop resource on combined stress responses in plants.

We have developed a compendium and interactive platform, named Stress Combinations and their Interactions in Plants Database (SCIPDb; http://www.nipgr.ac.in/scipdb.php), which offers information on morpho-physio-biochemical (phenome) and molecular (transcriptome and metabolome) responses of plants to different stress combinations. SCIPDb is a plant stress informatics hub for data mining on phenome, transcriptome, trait-gene ontology, and data-driven research for advancing mechanistic understanding of combined stress biology. We analyzed global phenome data from 939 studies to delineate the effects of various stress combinations on yield in major crops and found that yield was substantially affected under abiotic-abiotic stresses. Transcriptome datasets from 36 studies hosted in SCIPDb identified novel genes, whose roles have not been earlier established in combined stress. Integretome analysis under combined drought-heat stress pinpointed carbohydrate, amino acid, and energy metabolism pathways as the crucial metabolic, proteomic, and transcriptional components in plant tolerance to combined stress. These examples illustrate the application of SCIPDb in identifying novel genes and pathways involved in combined stress tolerance. Further, we showed the application of this database in identifying novel candidate genes and pathways for combined drought and pathogen stress tolerance. To our knowledge, SCIPDb is the only publicly available platform offering combined stress-specific omics big data visualization tools, such as an interactive scrollbar, stress matrix, radial tree, global distribution map, meta-phenome analysis, search, BLAST, transcript expression pattern table, Manhattan plot, and co-expression network. These tools facilitate a better understanding of the mechanisms underlying plant responses to combined stresses.

TransIntegrator: capture nearly full protein-coding transcript variants via integrating Illumina and PacBio transcriptomes.

Genes have the ability to produce transcript variants that perform specific cellular functions. However, accurately detecting all transcript variants remains a long-standing challenge, especially when working with poorly annotated genomes or without a known genome. To address this issue, we have developed a new computational method, TransIntegrator, which enables transcriptome-wide detection of novel transcript variants. For this, we determined 10 Illumina sequencing transcriptomes and a PacBio full-length transcriptome for consecutive embryo development stages of amphioxus, a species of great evolutionary importance. Based on the transcriptomes, we employed TransIntegrator to create a comprehensive transcript variant library, namely iTranscriptome. The resulting iTrancriptome contained 91915 distinct transcript variants, with an average of 2.4 variants per gene. This substantially improved current amphioxus genome annotation by expanding the number of genes from 21954 to 38777. Further analysis manifested that the gene expansion was largely ascribed to integration of multiple Illumina datasets instead of involving the PacBio data. Moreover, we demonstrated an example application of TransIntegrator, via generating iTrancriptome, in aiding accurate transcriptome assembly, which significantly outperformed other hybrid methods such as IDP-denovo and Trinity. For user convenience, we have deposited the source codes of TransIntegrator on GitHub as well as a conda package in Anaconda. In summary, this study proposes an affordable but efficient method for reliable transcriptomic research in most species.