Annotated Gene Expression Research Articles

Epigenome-wide association studies identify novel DNA methylation sites associated with PTSD: A meta-analysis of 23 military and civilian cohorts.

The occurrence of post-traumatic stress disorder (PTSD) following a traumatic event is associated with biological differences that can represent the susceptibility to PTSD, the impact of trauma, or the sequelae of PTSD itself. These effects include differences in DNA methylation (DNAm), an important form of epigenetic gene regulation, at multiple CpG loci across the genome. Moreover, these effects can be shared or specific to both central and peripheral tissues. Here, we aim to identify blood DNAm differences associated with PTSD and characterize the underlying biological mechanisms by examining the extent to which they mirror associations across multiple brain regions. As the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup, we conducted the largest cross-sectional meta-analysis of epigenome-wide association studies (EWASs) of PTSD to date, involving 5077 participants (2156 PTSD cases and 2921 trauma-exposed controls) from 23 civilian and military studies. PTSD diagnosis assessments were harmonized following the standardized guidelines established by the PGC-PTSD Workgroup. DNAm was assayed from blood using either Illumina HumanMethylation450 or MethylationEPIC (850K) BeadChips. A common QC pipeline was applied. Within each cohort, DNA methylation was regressed on PTSD, sex (if applicable), age, blood cell proportions, and ancestry. An inverse variance-weighted meta-analysis was performed. We conducted replication analyses in tissue from multiple brain regions, neuronal nuclei, and a cellular model of prolonged stress. We identified 11 CpG sites associated with PTSD in the overall meta-analysis (1.44e-09 < p < 5.30e-08), as well as 14 associated in analyses of specific strata (military vs civilian cohort, sex, and ancestry), including CpGs in AHRR and CDC42BPB. Many of these loci exhibit blood-brain correlation in methylation levels and cross-tissue associations with PTSD in multiple brain regions. Methylation at most CpGs correlated with their annotated gene expression levels. This study identifies 11 PTSD-associated CpGs, also leverages data from postmortem brain samples, GWAS, and genome-wide expression data to interpret the biology underlying these associations and prioritize genes whose regulation differs in those with PTSD.

A 50-gene biomarker identifies estrogen receptor-modulating chemicals in a microarray compendium

High throughput transcriptomics (HTTr) profiling has the potential to rapidly and comprehensively identify molecular targets of environmental chemicals that can be linked to adverse outcomes. We describe here the construction and characterization of a 50-gene expression biomarker designed to identify estrogen receptor (ER) active chemicals in HTTr datasets. Using microarray comparisons, the genes in the biomarker were identified as those that exhibited consistent directional changes when ER was activated (4 ER agonists; 4 ESR1 gene constitutively active mutants) and opposite directional changes when ER was suppressed (4 antagonist treatments; 4 ESR1 knockdown experiments). The biomarker was evaluated as a predictive tool using the Running Fisher algorithm by comparison to annotated gene expression microarray datasets including those evaluating the transcriptional effects of hormones and chemicals in MCF-7 cells. Depending on the reference dataset used, the biomarker had a predictive accuracy for activation of up to 96%. To demonstrate applicability for HTTr data analysis, the biomarker was used to identify ER activators in a set of 15 chemicals that are considered potential bisphenol A (BPA) alternatives examined at up to 10 concentrations in MCF-7 cells and analyzed by full-genome TempO-Seq. Using benchmark dose (BMD) modeling, the biomarker genes stratified the ER potency of BPA alternatives consistent with previous studies. These results demonstrate that the ER biomarker can be used to accurately identify ER activators in transcript profile data derived from MCF-7 cells.

Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles.

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge.

MCProj: metacell projection for interpretable and quantitative use of transcriptional atlases

We describe MCProj—an algorithm for analyzing query scRNA-seq data by projections over reference single-cell atlases. We represent the reference as a manifold of annotated metacell gene expression distributions. We then interpret query metacells as mixtures of atlas distributions while correcting for technology-specific gene biases. This approach distinguishes and tags query cells that are consistent with atlas states from unobserved (novel or artifactual) behaviors. It also identifies expression differences observed in successfully mapped query states. We showcase MCProj functionality by projecting scRNA-seq data on a blood cell atlas, deriving precise, quantitative, and interpretable results across technologies and datasets.

A pan-cancer clinical platform to predict immunotherapy outcomes and prioritize immuno-oncology combinations in early-phase trials

Immunotherapy is effective, but current biomarkers forpatient selection have proven modest sensitivity. Here, we developed VIGex, an optimized gene signature based on the expression level of 12 genes involved in immune response with RNA sequencing. We implemented VIGex using the nCounter platform (Nanostring) on a large clinical cohort encompassing 909 tumor samples across 45 tumor types. VIGex was developed as a continuous variable, with cutoffs selected to detect three main categories (hot, intermediate-cold and cold) based on the different inflammatory status of the tumor microenvironment. Hot tumors had the highest VIGex scores and exhibited an increased abundance of tumor-infiltrating lymphocytes as compared with the intermediate-cold and cold. VIGex scores varied depending on tumor origin and anatomic site of metastases, with liver metastases showing an immunosuppressive tumor microenvironment. The predictive power of VIGex-Hot was observed in a cohort of 98 refractory solid tumor from patients treated in early-phase immunotherapy trials and its clinical performance was confirmed through an extensive metanalysis across 13 clinically annotated gene expression datasets from 877 patients treated with immunotherapy agents. Last, we generated a pan-cancer biomarker platform that integrates VIGex categories with the expression levels of immunotherapy targets under development in early-phase clinical trials. Our results support the clinical utility of VIGex as a tool toaid clinicians for patient selection and personalized immunotherapy interventions. BBVA Foundation; 202-2021 Division of Medical Oncology and Hematology Fellowship award; Princess Margaret Cancer Center.

Redefining normal breast cell populations using long noncoding RNAs.

Single-cell RNAseq has allowed unprecedented insight into gene expression across different cell populations in normal tissue and disease states. However, almost all studies rely on annotated gene sets to capture gene expression levels and sequencing reads that do not align to known genes are discarded. Here, we discover thousands of long noncoding RNAs (lncRNAs) expressed in human mammary epithelial cells and analyze their expression in individual cells of the normal breast. We show that lncRNA expression alone can discriminate between luminal and basal cell types and define subpopulations of both compartments. Clustering cells based on lncRNA expression identified additional basal subpopulations, compared to clustering based on annotated gene expression, suggesting that lncRNAs can provide an additional layer of information to better distinguish breast cell subpopulations. In contrast, these breast-specific lncRNAs poorly distinguish brain cell populations, highlighting the need to annotate tissue-specific lncRNAs prior to expression analyses. We also identified a panel of 100 breast lncRNAs that could discern breast cancer subtypes better than protein-coding markers. Overall, our results suggest that lncRNAs are an unexplored resource for new biomarker and therapeutic target discovery in the normal breast and breast cancer subtypes.

Combined method of whole mount and block-face imaging: Acquisition of 3D data of gene expression pattern from conventional in situ hybridization.

Visualization of spatiotemporal expression of a gene of interest is a fundamental technique for analyzing the involvements of genes in organ development. In situ hybridization (ISH) is one of the most popular methods for visualizing gene expression. When conventional ISH is performed on sections or whole-mount specimens, the gene expression pattern is represented in 2-dimensional (2D) microscopic images or in the surface view of the specimen. To obtain 3-dimensional (3D) data of gene expression from conventional ISH, the "serial section method" has traditionally been employed. However, this method requires an extensive amount of time and labor because it requires researchers to collect a tremendous number of sections, label all sections by ISH, and image them before 3D reconstruction. Here, we proposed a rapid and low-cost 3D imaging method that can create 3D gene expression patterns from conventional ISH-labeled specimens. Our method consists of a combination of whole-mount ISH and Correlative Microscopy and Blockface imaging (CoMBI). The whole-mount ISH-labeled specimens were sliced using a microtome or cryostat, and all block-faces were imaged and used to reconstruct 3D images by CoMBI. The 3D data acquired using our method showed sufficient quality to analyze the morphology and gene expression patterns in the developing mouse heart. In addition, 2D microscopic images of the sections can be obtained when needed. Correlating 2D microscopic images and 3D data can help annotate gene expression patterns and understand the anatomy of developing organs. These results indicated that our method can be useful in the field of developmental biology.

Abstract 697: Partnering with patients to create a rare soft tissue sarcoma target discovery platform as a community resource

Abstract Precision cancer medicine is based on the ability to predict the dependencies of a given tumor from its molecular makeup. Despite successes in multiple common cancers, such prediction remains challenging for the majority of rare and understudied tumors, given the absence of laboratory model systems in which to discover and/or validate therapeutic hypotheses. Crucially, we lack a comprehensive knowledge of ex vivo growth requirements given the tumor’s molecular and cellular makeup. To address this challenge, we developed a low-input multiplexed sequencing protocol allowing the systematic tracking of changes to tumor cell fraction across hundreds of growth conditions. We coupled this approach with a patient-partnered pipeline for fresh sample sourcing to tackle the challenge of model generation in rare diseases including desmoid tumors, a rare soft-tissue tumor driven by activating beta-catenin mutations. We show that non-malignant cell outgrowth contributes to the failure of long-term model generation in over 70% of cases when a traditional single-media approach is used. By utilizing our systematic media screening strategy, we were able to identify several conditions that preserved the tumor component over at least 3 passages, in triplicate. Notably, there was a sample-to-sample variability in which media conditions preserved tumor composition, supporting our hypothesis that empirical screening of media conditions increases model generation success rate. We also aim to understand the relationship between tumor cell preservation in culture and their molecular makeup. However, while classic tissue markers or copy-number variation can be used to identify the tumor and/or stromal components in common epithelial cancers, no such reference exists for rare sarcomas with relatively quiet genomes. To create a reference of transcriptional patterns for these diseases, we are adapting Seq-Well, a low-cost single-cell RNA sequencing platform, to annotate gene expression with allelic information. In a proof-of-concept, we sequenced 552 cells from an admixed sample and we successfully resolved the genotype of 331 (60%) cells. Identification of differentially expressed genes (DEGs) between genotypes using the single cell data showed agreement with DEGs identified via bulk sequencing methods, demonstrating the feasibility of our approach. Looking ahead, we aim to predict ex vivo growth requirements for rare sarcomas based on technical, clinical, and genomic properties of the starting tumor material. We also aim to utilize our strategy to identify genetic or drug perturbations that specifically give the tumor cells a growth disadvantage, enabling the validation of putative targets in early patient samples. Moreover, we are making all expandable, long-term cell lines generated from our strategy publicly available to the scientific community. Citation Format: Kathryn Cebula, Grace Johnson, Mushriq Al-Jazrawe, Irene Lernman, Barbara Van Hare, Carmen Rios, Moony Tseng, Jesse Boehm. Partnering with patients to create a rare soft tissue sarcoma target discovery platform as a community resource [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 697.

Transcriptional Alterations of Mouse Trigeminal Ganglion Neurons Following Orofacial Inflammation Revealed by Single-Cell Analysis.

Orofacial inflammation leads to transcriptional alterations in trigeminal ganglion (TG) neurons. However, diverse alterations and regulatory mechanisms following orofacial inflammatory pain in different types of TG neurons remain unclear. Here, orofacial inflammation was induced by injection of complete Freund’s adjuvant (CFA) in mice. After 7 days, we performed single-cell RNA-sequencing on TG cells of mice from control and treatment groups. We identified primary sensory neurons, Schwann cells, satellite glial cells, oligodendrocyte-like cells, immune cells, fibroblasts, and endothelial cells in TG tissue. After principal component analysis and hierarchical clustering, we identified six TG neuronal subpopulations: peptidergic nociceptors (PEP1 and PEP2), non-peptidergic nociceptors (NP1 and NP2), C-fiber low-threshold mechanoreceptors (cLTMR) and myelinated neurons (Nefh-positive neurons, NF) based on annotated marker gene expression. We also performed differential gene expression analysis among TG neuronal subtypes, identifying several differential genes involved in the inflammatory response, neuronal excitability, neuroprotection, and metabolic processes. Notably, we identified several potential novel targets associated with pain modulation, including Arl6ip1, Gsk3b, Scn7a, and Zbtb20 in PEP1, Rgs7bp in PEP2, and Bhlha9 in cLTMR. The established protein–protein interaction network identified some hub genes, implying their critical involvement in regulating orofacial inflammatory pain. Our study revealed the heterogeneity of TG neurons and their diverse neuronal transcriptomic responses to orofacial inflammation, providing a basis for the development of therapeutic strategies for orofacial inflammatory pain.

Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology

Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we develop Bayesian cell proportion reconstruction inferred using statistical marginalization (BayesPrism), a Bayesian method to predict cellular composition and gene expression in individual cell types from bulk RNA-seq, using patient-derived, scRNA-seq as prior information. We conduct integrative analyses in primary glioblastoma, head and neck squamous cell carcinoma and skin cutaneous melanoma to correlate cell type composition with clinical outcomes across tumor types, and explore spatial heterogeneity in malignant and nonmalignant cell states. We refine current cancer subtypes using gene expression annotation after exclusion of confounding nonmalignant cells. Finally, we identify genes whose expression in malignant cells correlates with macrophage infiltration, T cells, fibroblasts and endothelial cells across multiple tumor types. Our work introduces a new lens to accurately infer cellular composition and expression in large cohorts of bulk RNA-seq data.

Identification of Genetic Variants Associated with Sex-Specific Lung-Cancer Risk.

Simple SummaryThe incidence of lung cancer differs between men and women, suggesting the potential role of sex-specific influences in susceptibility to this cancer. While behavioural differences, such as smoking rates, may account for much of the risk, another possibility is that X chromosome susceptibility genes may have an effect. Therefore, in this study, we tested specifically for the influence of X chromosome single-nucleotide polymorphisms (SNPs) in male lung cancer cases, and found 24 that were significantly associated with male, but not female, lung cancer cases. Examining these in detail, we observed these SNPs resided in blocks near the annotated genes DMD, PTCHD1-AS, and AL008633.1. We also observed that DMD was differentially expressed in lung cancer subtypes curated in the Cancer Genome Atlas database. Examining this gene further, we found that expression and mutation of DMD may have effects on immune function. This work defines potential targets for sex-specific lung cancer prevention.Background: The incidence of lung cancer differs between men and women, suggesting the potential role of sex-specific influences in susceptibility to this cancer. While behavioural differences may account for some of the risk, another possibility is that X chromosome susceptibility genes may have an effect. Little is known about genetic variants on the X chromosome that contribute to sex-specific lung-cancer risk, so we investigated this in a previously characterized cohort. Methods: We conducted a genetic association reanalysis of 518 lung cancer patients and 844 controls to test for lung cancer susceptibility variants on the X chromosome. Annotated gene expression, co-expression analysis, pathway, and immune infiltration analyses were also performed. Results: 24 SNPs were identified as significantly associated with male, but not female, lung cancer cases. These resided in blocks near the annotated genes DMD, PTCHD1-AS, and AL008633.1. Of these, DMD was differentially expressed in lung cancer cases curated in The Cancer Genome Atlas. A functional enrichment and a KEGG pathway analysis of co-expressed genes revealed that differences in immune function could play a role in sex-specific susceptibility. Conclusions: Our analyses identified potential genetic variants associated with sex-specific lung cancer risk. Integrating GWAS and RNA-sequencing data revealed potential targets for lung cancer prevention.

Inferred inactivation of the Cftr gene in the duodena of mice exposed to hexavalent chromium (Cr(VI)) in drinking water supports its tumor-suppressor status and implies its potential role in Cr(VI)-induced carcinogenesis of the small intestines

Carcinogenicity of hexavalent chromium [Cr (VI)] has been supported by a number of epidemiological and animal studies; however, its carcinogenic mode of action is still incompletely understood. To identify mechanisms involved in cancer development, we analyzed gene expression data from duodena of mice exposed to Cr(VI) in drinking water. This analysis included (i) identification of upstream regulatory molecules that are likely responsible for the observed gene expression changes, (ii) identification of annotated gene expression data from public repositories that correlate with gene expression changes in duodena of Cr(VI)-exposed mice, and (iii) identification of hallmark and oncogenic signature gene sets relevant to these data. We identified the inactivated CFTR gene among the top scoring upstream regulators, and found positive correlations between the expression data from duodena of Cr(VI)-exposed mice and other datasets in public repositories associated with the inactivation of the CFTR gene. In addition, we found enrichment of signatures for oncogenic signaling, sustained cell proliferation, impaired apoptosis and tissue remodeling. Results of our computational study support the tumor-suppressor role of the CFTR gene. Furthermore, our results support human relevance of the Cr(VI)-mediated carcinogenesis observed in the small intestines of exposed mice and suggest possible groups that may be more vulnerable to the adverse outcomes associated with the inactivation of CFTR by hexavalent chromium or other agents. Lastly, our findings predict, for the first time, the role of CFTR inactivation in chemical carcinogenesis and expand the range of plausible mechanisms that may be operative in Cr(VI)-mediated carcinogenesis of intestinal and possibly other tissues.

Abstract LB230: Inactivation of the CFTR gene in duodena of mice exposed to hexavalent chromium (Cr(VI)) in drinking water supports its tumor-suppressor status and implies its role in Cr(VI)-induced carcinogenesis of the small intestines

Abstract Hexavalent chromium [Cr (VI)] compounds are used in metallurgical, chemical and refractory industries. Their carcinogenicity has been supported by a number of epidemiological and animal studies; however, their carcinogenic mode of action is still incompletely understood. To identify mechanisms involved in cancer development from toxicologically relevant exposures to Cr(VI), we analyzed gene expression data from duodena of mice exposed for 7 or 90 days to Cr(VI) in drinking water at concentrations 0.3-520 mg/L. This analysis included identification of upstream regulatory molecules that are likely responsible for the observed gene expression changes, and identification of annotated gene expression data from public repositories that correlate with gene expression changes in duodena of Cr(VI)-exposed mice. We identified the CFTR gene among top scoring upstream regulators, including at low exposure levels that, for the given timepoints, did not induce observable tissue damage. Furthermore, the expression data from duodena of Cr(VI)-exposed mice displayed a positive correlation with other datasets in public repositories associated with the inactive CFTR gene, of which the top scoring were datasets for ilea and duodena of Cftr-knockout mice. Our results support the tumor-suppressor role of the CFTR gene, which was previously suggested for intestinal carcinogenesis in humans and mice. Furthermore, our findings indicate, for the first time, the role of CFTR inactivation in chemical carcinogenesis and expand the range of plausible mechanisms that may be operative in Cr(VI)-mediated carcinogenesis of intestinal and possibly other tissues. Disclaimer: The views expressed are those of the authors and do not necessarily represent the views or policies of the US EPA. Citation Format: Roman Mezencev, Scott S. Auerbach. Inactivation of the CFTR gene in duodena of mice exposed to hexavalent chromium (Cr(VI)) in drinking water supports its tumor-suppressor status and implies its role in Cr(VI)-induced carcinogenesis of the small intestines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB230.

Single-Cell Transcriptional Profiling of Acute Myeloid Leukemia Identifies Prognostic Transcriptional Profiles at Diagnosis

Acute myeloid leukemia (AML) is a lethal cancer with a survival of less than 50%. Standard cytotoxic therapies frequently induce complete remission, but patients frequently relapse and die of their disease. Leukemia stem cells (LSCs) are the leukemia cells with self-renewal potential and ability to recapitulate the disease. In hematopoietic stem cells, the mechanisms of proliferation are distinct from self-renewal (Li et al. Nature 2013). Consequently, targeting proliferation may explain the failure of traditional chemotherapy to target LSCs and eradicate AML. Our goal is define the molecular mechanisms that allow AML to relapse. We previously showed that activated NRAS (NRASG12V) facilitates self-renewal in the LSC-enriched subpopulation of a mouse model of AML (Mll-AF9 / NRASG12V, Sachs et al. Blood 2014, Kim et al. Blood 2009). We hypothesize that understanding the NRAS-activated pathways required for self-renewal at the single-cell level can be used to identify clinically relevant features of human AML.We used single-cell RNA sequencing to identify the self-renewing cells among the LSC-enriched subgroup in this model (Mac1LowKit+Sca1+, “MKS”). We identified three discrete transcriptional profiles among the LSC-enriched subpopulation and found that that two of these profiles (Profile 1 and Profile 2) are NRASG12V-dependent. These two profiles can be differentiated by CD36 and CD69 expression. We sorted the MKS LSCs based on CD36 and CD69 expression. Sorted LSC subsets were transplanted into recipient mice to compare their ability to transfer leukemia as a measure of their self-renewal capacity. We found that MKS-CD36-CD69+ cells (consistent with Profile 1) rapidly transferred leukemia with high penetrance in 20 of 22 mice. In contrast, MKS-CD36+CD69- cells (Profile 2) failed transfer leukemia in most mice; only 2 of 25 of these mice developed AML (p < 0.004).In our previous work, we demonstrated that the NRASG12V-activated self-renewal gene expression profile that we identified in our murine model was expressed in human AML, suggesting that the gene expression behavior of LSCs from this model may recapitulate the gene expression behavior of human LSCs (Sachs et al. Blood 2014). We used our murine single-cell self-renewal transcriptional profile to define a 96-gene panel consisting of 88 genes from this profile and 8 housekeeping genes. We sorted primary, diagnostic human AML cells for leukemia stem and progenitor cells (CD34+CD38-) and performed single-cell qPCR on these cells using our 96-gene panel. We found that a subset of these human LSCs preferentially expresses Profile 1, the self-renewal gene expression profile that we identified in our murine model, and another subset preferentially expresses Profile 2 (the profile associated with no leukemia-reconstituting capacity).Next, we investigated whether CD36 and CD69 delineate clinically relevant transcriptional subgroups in human AML. We used previously published, clinically annotated gene expression datasets of human AML that include associated survival outcomes data (TCGA, GSE12417 (Metzeler et al. Blood 2008), and GSE6891 (Verhaak et al. Haematologica 2009)). Gene Cluster Expression Summary Score (GCESS, Scott et al. under review) analysis identified gene clusters associated with CD69 and CD36 in our murine single-cell dataset. We found that the CD36 and CD69-associated clusters are conserved in the human transcriptional datasets. More importantly, patients that express either the CD69 or the CD36-associated gene expression profile suffer from shorter survival times in these three, independent human transcriptional datasets.In these experiments, we use a murine model of AML to define the LSC self-renewal gene expression profile at the single-cell level and functionally validate this profile in vivo . Analogous to the murine model, a subset of human AML stem and progenitor cells expresses this LSC self-renewal gene expression profile at the single-cell level. Finally, we found that the gene expression profiles we identified in our murine single-cell data are associated with poor outcomes in human AML indicating that our single-cell gene expression analyses can be used to identify prognostic patient subgroups. These findings suggest that these gene expression profiles can be used to identify functional transcriptional modules associated with poor outcome in human AML. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

LSTrAP-Kingdom: an automated pipeline to generate annotated gene expression atlases for kingdoms of life.

There are now more than two million RNA sequencing experiments for plants, animals, bacteria and fungi publicly available, allowing us to study gene expression within and across species and kingdoms. However, the tools allowing the download, quality control and annotation of this data for more than one species at a time are currently missing. To remedy this, we present the Large-Scale Transcriptomic Analysis Pipeline in Kingdom of Life (LSTrAP-Kingdom) pipeline, which we used to process 134521 RNA-seq samples, achieving ∼12000 processed samples per day. Our pipeline generated quality-controlled, annotated gene expression matrices that rival the manually curated gene expression data in identifying functionally related genes. LSTrAP-Kingdom is available from: https://github.com/wirriamm/plants-pipeline and is fully implemented in Python and Bash. Supplementary data are available at Bioinformatics online.

The ontology of the anatomy and development of the solitary ascidian Ciona: the swimming larva and its metamorphosis

Ciona robusta (Ciona intestinalis type A), a model organism for biological studies, belongs to ascidians, the main class of tunicates, which are the closest relatives of vertebrates. In Ciona, a project on the ontology of both development and anatomy is ongoing for several years. Its goal is to standardize a resource relating each anatomical structure to developmental stages. Today, the ontology is codified until the hatching larva stage. Here, we present its extension throughout the swimming larva stages, the metamorphosis, until the juvenile stages. For standardizing the developmental ontology, we acquired different time-lapse movies, confocal microscope images and histological serial section images for each developmental event from the hatching larva stage (17.5 h post fertilization) to the juvenile stage (7 days post fertilization). Combining these data, we defined 12 new distinct developmental stages (from Stage 26 to Stage 37), in addition to the previously defined 26 stages, referred to embryonic development. The new stages were grouped into four Periods named: Adhesion, Tail Absorption, Body Axis Rotation, and Juvenile. To build the anatomical ontology, 203 anatomical entities were identified, defined according to the literature, and annotated, taking advantage from the high resolution and the complementary information obtained from confocal microscopy and histology. The ontology describes the anatomical entities in hierarchical levels, from the cell level (cell lineage) to the tissue/organ level. Comparing the number of entities during development, we found two rounds on entity increase: in addition to the one occurring after fertilization, there is a second one during the Body Axis Rotation Period, when juvenile structures appear. Vice versa, one-third of anatomical entities associated with the embryo/larval life were significantly reduced at the beginning of metamorphosis. Data was finally integrated within the web-based resource "TunicAnatO", which includes a number of anatomical images and a dictionary with synonyms. This ontology will allow the standardization of data underpinning an accurate annotation of gene expression and the comprehension of mechanisms of differentiation. It will help in understanding the emergence of elaborated structures during both embryogenesis and metamorphosis, shedding light on tissue degeneration and differentiation occurring at metamorphosis.

The Association between Immune Infiltration and Clinical Phenotypes and Prognosis of Prostate Cancer.

Background: Some evidences show that immune infiltration is closely related to the clinical outcomes in cancers such as colorectal cancer. However, previous studies have not explained the diversity of cell types that make up the immune response. In particular, although some studies and reviews have shown that immunotherapy is important for cancer treatment, few studies have elucidated the relationship between prostate cancer (PCa) phenotype and immune infiltration.Objectives: In this study, we analyzed whether different types of tumor-infiltrating immune cells would affect the clinical phenotypes and survival of PCa based on a deconvolution algorithm and annotated gene expression profiles.Materials and Methods: The 22 subsets of immune cells inferred by CIBERSORT and the infiltration abundance of 6 immune cells calculated by TIMER were used to determine the associations between them and the PCa traits and survival response. In addition, the survival tree models were constructed to classify PCa patients into four subtypes, and the traits and prognosis were compared among these subtypes.Results: As a result, we found that some PCa patients with high death risk lacking immune infiltration were related to the poor prognosis. For the cell subsets studied and subtypes analysis, a low proportion of mast resting cells and T-cells follicular helper exhibited the obvious association with poor outcome.Conclusions: In summary, our study suggested the differences in the cellular composition of the immune infiltrate in PCa, and these differences might be important determinants for PCa traits and prognosis.

Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis.

A central theme in learning from image data is to develop appropriate representations for the specific task at hand. Thus, a practical challenge is to determine what features are appropriate for specific tasks. For example, in the study of gene expression patterns in Drosophila, texture features were particularly effective for determining the developmental stages from in situ hybridization images. Such image representation is however not suitable for controlled vocabulary term annotation. Here, we developed feature extraction methods to generate hierarchical representations for ISH images. Our approach is based on the deep convolutional neural networks that can act on image pixels directly. To make the extracted features generic, the models were trained using a natural image set with millions of labeled examples. These models were transferred to the ISH image domain. To account for the differences between the source and target domains, we proposed a partial transfer learning scheme in which only part of the source model is transferred. We employed multi-task learning method to fine-tune the pre-trained models with labeled ISH images. Results showed that feature representations computed by deep models based on transfer and multi-task learning significantly outperformed other methods for annotating gene expression patterns at different stage ranges.

DeepRibo: a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns.

Annotation of gene expression in prokaryotes often finds itself corrected due to small variations of the annotated gene regions observed between different (sub)-species. It has become apparent that traditional sequence alignment algorithms, used for the curation of genomes, are not able to map the full complexity of the genomic landscape. We present DeepRibo, a novel neural network utilizing features extracted from ribosome profiling information and binding site sequence patterns that shows to be a precise tool for the delineation and annotation of expressed genes in prokaryotes. The neural network combines recurrent memory cells and convolutional layers, adapting the information gained from both the high-throughput ribosome profiling data and ribosome binding translation initiation sequence region into one model. DeepRibo is designed as a single model trained on a variety of ribosome profiling experiments, used for the identification of open reading frames in prokaryotes without a priori knowledge of the translational landscape. Through extensive validation of the model trained on various sets of data, multiple species sequence similarity, mass spectrometry and Edman degradation verified proteins, the effectiveness of DeepRibo is highlighted.

Comprehensive analysis of long noncoding RNA expression in dorsal root ganglion reveals cell-type specificity and dysregulation after nerve injury.

Dorsal root ganglion (DRG) neurons provide connectivity between peripheral tissues and the spinal cord. Transcriptional plasticity within DRG sensory neurons after peripheral nerve injury contributes to nerve repair but also leads to maladaptive plasticity, including the development of neuropathic pain. This study presents tissue and neuron-specific expression profiling of both known and novel long noncoding RNAs (LncRNAs) in the rodent DRG after nerve injury. We have identified a large number of novel LncRNAs expressed within the rodent DRG, a minority of which were syntenically conserved between the mouse, rat, and human, and including, both intergenic and antisense LncRNAs. We have also identified neuron type-specific LncRNAs in the mouse DRG and LncRNAs that are expressed in human IPS cell-derived sensory neurons. We show significant plasticity in LncRNA expression after nerve injury, which in mice is strain and gender dependent. This resource is publicly available and will aid future studies of DRG neuron identity and the transcriptional landscape in both the naive and injured DRG. We present our work regarding novel antisense and intergenic LncRNAs as an online searchable database, accessible from PainNetworks (http://www.painnetworks.org/). We have also integrated all annotated gene expression data in PainNetworks, so they can be examined in the context of their protein interactions.