DEvis: an R package for aggregation and visualization of differential expression data

Time-course transcriptome analysis of lungs from mice infected with inhaled aerosolized Stenotrophomonas maltophilia

Background

Stenotrophomonas maltophilia (SMA) has emerged as an important pathogen capable of causing an opportunistic and nosocomial infection. We performed RNA sequencing (RNA-seq) of lung tissues from mice with pulmonary SMA infection over time via aerosolized intratracheal inhalation to investigate transcription profile changes in SMA-infected lungs.

Methods

A mouse model of acute lethal SMA pneumonia was established in this study using aerosolized intratracheal inhalation, laying the groundwork for future SMA research. RNA-seq was then used to create a transcriptional profile of the lungs of the model mice at 0, 4, 12, 24, 48, and 72 hours post-infection (hpi). Mfuzz time clustering, weighted gene coexpression network analysis (WGCNA), and Immune Cell Abundance Identifier for mouse (ImmuCellAI-mouse) were used to analyze RNA-seq data.

Results

A gradual change in the lung transcriptional profile was observed, which was consistent with the expected disease progression. At 4 hpi, the expression of genes related to the acute phase inflammatory response increased, as predicted abundance of innate immune cells. At this stage, an increased demand for energy was also observed, including an increase in the expression of genes involved in circulation, muscle function and mitochondrial respiratory chain function. The expression of genes associated with endoplasmic reticulum stress (ERS) and autophagy increased at 24 hpi. Unlike the number of natural killer (NK) cells following most bacterial lung infections, the abundance of NK cells decreased following infection with SMA. The expression levels of Cxcl10, Cd14, Gbp5, Cxcr2, Tnip1, Zc3h12a, Egr1, Sell and Gbp2 were high and previously unreported in SMA pneumonia, and they may be important targets for future studies.

Conclusions

To our knowledge, this is the first study to investigate the pulmonary transcriptional response to SMA infection. The findings shed light on the molecular mechanisms underlying the pathogenesis of SMA pneumonia, which may aid in the development of therapies to reduce the occurrence of SMA pulmonary infection.

HIV infection drives pro-inflammatory immunothrombotic pathway activation and organ dysfunction among adults with sepsis in Uganda

BACKGROUND

The global burden of sepsis is concentrated in high HIV-burden settings in sub-Saharan Africa (SSA). Despite this, little is known about the immunopathology of sepsis in persons with HIV (PWH) in the region. We sought to determine the influence of HIV on host immune responses and organ dysfunction among adults hospitalized with suspected sepsis in Uganda.

DESIGN

Prospective cohort study.

METHODS

We compared organ dysfunction and 30-day outcome profiles of PWH and those without HIV. We quantified 14 soluble immune mediators, reflective of key domains of sepsis immunopathology, and performed whole-blood RNA-sequencing on samples from a subset of patients. We used propensity score methods to match PWH and those without HIV by demographics, illness duration, and clinical severity, and compared immune mediator concentrations and gene expression profiles across propensity score-matched groups.

RESULTS

Among 299 patients, 157 (52.5%) were PWH (clinical stage 3 or 4 in 80.3%, 67.7% with known HIV on antiretroviral therapy). PWH presented with more severe physiologic derangement and shock, and had higher 30-day mortality (34.5% vs. 10.2%; P  < 0.001). Across propensity score-matched groups, PWH exhibited greater pro-inflammatory immune activation, including upregulation of interleukin (IL)-6, IL-8, IL-15, IL-17 and HMGB1 signaling, with concomitant T-cell exhaustion, prothrombotic pathway activation, and angiopoeitin-2-related endothelial dysfunction.

CONCLUSIONS

Sepsis-related organ dysfunction and mortality in Uganda disproportionately affect PWH, who demonstrate exaggerated activation of multiple immunothrombotic and metabolic pathways implicated in sepsis pathogenesis. Further investigations are needed to refine understanding of sepsis immunopathology in PWH, particularly mechanisms amenable to therapeutic manipulation.

Multidimensional analysis of the host response reveals prognostic and pathogen-driven immune subtypes among adults with sepsis in Uganda

Background

The global burden of sepsis is concentrated in sub-Saharan Africa, where severe infections disproportionately affect young, HIV-infected adults and high-burden pathogens are unique. In this context, poor understanding of sepsis immunopathology represents a crucial barrier to development of locally-effective treatment strategies. We sought to determine inter-individual immunologic heterogeneity among adults hospitalized with sepsis in a sub-Saharan African setting, and characterize associations between immune subtypes, infecting pathogens, and clinical outcomes.

Methods

Among a prospective observational cohort of 288 adults hospitalized with suspected sepsis in Uganda, we applied machine learning methods to 14 soluble host immune mediators, reflective of key domains of sepsis immunopathology (innate and adaptive immune activation, endothelial dysfunction, fibrinolysis), to identify immune subtypes in randomly-split discovery (N = 201) and internal validation (N = 87) sub-cohorts. In parallel, we applied similar methods to whole-blood RNA-sequencing data from a consecutive subset of patients (N = 128) to identify transcriptional subtypes, which we characterized using biological pathway and immune cell-type deconvolution analyses.

Results

Unsupervised clustering consistently identified two immune subtypes defined by differential activation of pro-inflammatory innate and adaptive immune pathways, with transcriptional evidence of concomitant CD56(-)/CD16( +) NK-cell expansion, T-cell exhaustion, and oxidative-stress and hypoxia-induced metabolic and cell-cycle reprogramming in the hyperinflammatory subtype. Immune subtypes defined by greater pro-inflammatory immune activation, T-cell exhaustion, and metabolic reprogramming were consistently associated with a high-prevalence of severe and often disseminated HIV-associated tuberculosis, as well as more extensive organ dysfunction, worse functional outcomes, and higher 30-day mortality.

Conclusions

Our results highlight unique host- and pathogen-driven features of sepsis immunopathology in sub-Saharan Africa, including the importance of severe HIV-associated tuberculosis, and reinforce the need to develop more biologically-informed treatment strategies in the region, particularly those incorporating immunomodulation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-03907-3.

Dashboard-style interactive plots for RNA-seq analysis are R Markdown ready with Glimma 2.0

Glimma 1.0 introduced intuitive, point-and-click interactive graphics for differential gene expression analysis. Here, we present a major update to Glimma that brings improved interactivity and reproducibility using high-level visualization frameworks for R and JavaScript. Glimma 2.0 plots are now readily embeddable in R Markdown, thus allowing users to create reproducible reports containing interactive graphics. The revamped multidimensional scaling plot features dashboard-style controls allowing the user to dynamically change the colour, shape and size of sample points according to different experimental conditions. Interactivity was enhanced in the MA-style plot for comparing differences to average expression, which now supports selecting multiple genes, export options to PNG, SVG or CSV formats and includes a new volcano plot function. Feature-rich and user-friendly, Glimma makes exploring data for gene expression analysis more accessible and intuitive and is available on Bioconductor and GitHub.

qTeller: a tool for comparative multi-genomic gene expression analysis

MOTIVATION

Over the last decade, RNA-Seq whole-genome sequencing has become a widely used method for measuring and understanding transcriptome-level changes in gene expression. Since RNA-Seq is relatively inexpensive, it can be used on multiple genomes to evaluate gene expression across many different conditions, tissues and cell types. Although many tools exist to map and compare RNA-Seq at the genomics level, few web-based tools are dedicated to making data generated for individual genomic analysis accessible and reusable at a gene-level scale for comparative analysis between genes, across different genomes and meta-analyses.

RESULTS

To address this challenge, we revamped the comparative gene expression tool qTeller to take advantage of the growing number of public RNA-Seq datasets. qTeller allows users to evaluate gene expression data in a defined genomic interval and also perform two-gene comparisons across multiple user-chosen tissues. Though previously unpublished, qTeller has been cited extensively in the scientific literature, demonstrating its importance to researchers. Our new version of qTeller now supports multiple genomes for intergenomic comparisons, and includes capabilities for both mRNA and protein abundance datasets. Other new features include support for additional data formats, modernized interface and back-end database and an optimized framework for adoption by other organisms' databases.

AVAILABILITY AND IMPLEMENTATION

The source code for qTeller is open-source and available through GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/qTeller). A maize instance of qTeller is available at the Maize Genetics and Genomics database (MaizeGDB) (https://qteller.maizegdb.org/), where we have mapped over 200 unique datasets from GenBank across 27 maize genomes.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Role of the transcriptional regulator SP140 in resistance to bacterial infections via repression of type I interferons

Type I interferons (IFNs) are essential for anti-viral immunity, but often impair protective immune responses during bacterial infections. An important question is how type I IFNs are strongly induced during viral infections, and yet are appropriately restrained during bacterial infections. The Super susceptibility to tuberculosis 1 (Sst1) locus in mice confers resistance to diverse bacterial infections. Here we provide evidence that Sp140 is a gene encoded within the Sst1 locus that represses type I IFN transcription during bacterial infections. We generated Sp140^–/– mice and found that they are susceptible to infection by Legionella pneumophila and Mycobacterium tuberculosis. Susceptibility of Sp140^–/– mice to bacterial infection was rescued by crosses to mice lacking the type I IFN receptor (Ifnar^–/–). Our results implicate Sp140 as an important negative regulator of type I IFNs that is essential for resistance to bacterial infections.

Dynamic Polarization of Rab11a Modulates Crb2a Localization and Impacts Signaling to Regulate Retinal Neurogenesis

Interkinetic nuclear migration (IKNM) is the process in which pseudostratified epithelial nuclei oscillate from the apical to basal surface and in phase with the mitotic cycle. In the zebrafish retina, neuroepithelial retinal progenitor cells (RPCs) increase Notch activity with apical movement of the nuclei, and the depth of nuclear migration correlates with the probability that the next cell division will be neurogenic. This study focuses on the mechanisms underlying the relationships between IKNM, cell signaling, and neurogenesis. In particular, we have explored the role IKNM has on endosome biology within RPCs. Through genetic manipulation and live imaging in zebrafish, we find that early (Rab5-positive) and recycling (Rab11a-positive) endosomes polarize in a dynamic fashion within RPCs and with reference to nuclear position. Functional analyses suggest that dynamic polarization of recycling endosomes and their activity within the neuroepithelia modulates the subcellular localization of Crb2a, consequently affecting multiple signaling pathways that impact neurogenesis including Notch, Hippo, and Wnt activities. As nuclear migration is heterogenous and asynchronous among RPCs, Rab11a-affected signaling within the neuroepithelia is modulated in a differential manner, providing mechanistic insight to the correlation of IKNM and selection of RPCs to undergo neurogenesis.

ideal: an R/Bioconductor package for interactive differential expression analysis

BACKGROUND

RNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking.

RESULTS

We developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility.

CONCLUSION

ideal is distributed as an R package in the Bioconductor project ( http://bioconductor.org/packages/ideal/ ), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

The diverse and growing omics data in public domains provide researchers with tremendous opportunity to extract hidden, yet undiscovered, knowledge. However, the vast majority of archived data remain unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory analysis of massive datasets. Researchers, without coding, can interactively visualize and evaluate data in the context of its metadata, honing-in on groups of samples or genes based on attributes such as expression values, statistical associations, metadata terms and ontology annotations. Interaction with data is easy via interactive visualizations such as line charts, box plots, scatter plots, histograms and volcano plots. Statistical analyses include co-expression analysis, differential expression analysis and differential correlation analysis, with significance tests. Researchers can send data subsets to R for additional analyses. Multithreading and indexing enable efficient big data analysis. A researcher can create new MOG projects from any numerical data; or explore an existing MOG project. MOG projects, with history of explorations, can be saved and shared. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, where we identify novel putative biomarker genes in different tumors, and microarray and metabolomics data from Arabidopsis thaliana. MOG executable and code: http://metnetweb.gdcb.iastate.edu/ and https://github.com/urmi-21/MetaOmGraph/.

Transcriptional Correlates of Tolerance and Lethality in Mice Predict Ebola Virus Disease Patient Outcomes

Host response to infection is a major determinant of disease severity in Ebola virus disease (EVD), but gene expression programs associated with outcome are poorly characterized. Collaborative Cross (CC) mice develop strain-dependent EVD phenotypes of differential severity, ranging from tolerance to lethality. We screen 10 CC lines and identify clinical, virologic, and transcriptomic features that distinguish tolerant from lethal outcomes. Tolerance is associated with tightly regulated induction of immune and inflammatory responses shortly following infection, as well as reduced inflammatory macrophages and increased antigen-presenting cells, B-1 cells, and γδ T cells. Lethal disease is characterized by suppressed early gene expression and reduced lymphocytes, followed by uncontrolled inflammatory signaling, leading to death. We apply machine learning to predict outcomes with 99% accuracy in mice using transcriptomic profiles. This signature predicts outcomes in a cohort of EVD patients from western Africa with 75% accuracy, demonstrating potential clinical utility.