A protein kinase C α and ß inhibitor blunts hyperphagia to halt renal function decline and reduces adiposity in a rat model of obesity-driven type 2 diabetes.

Type 2 diabetes (T2D) and its complications can have debilitating, sometimes fatal consequences for afflicted individuals. The disease can be difficult to control, and therapeutic strategies to prevent T2D-induced tissue and organ damage are needed. Here we describe the results of administering a potent and selective inhibitor of Protein Kinase C (PKC) family members PKCα and PKCß, Cmpd 1, in the ZSF1 obese rat model of hyperphagia-induced, obesity-driven T2D. Although our initial intent was to evaluate the effect of PKCα/ß inhibition on renal damage in this model setting, Cmpd 1 unexpectedly caused a marked reduction in the hyperphagic response of ZSF1 obese animals. This halted renal function decline but did so indirectly and indistinguishably from a pair feeding comparator group. However, above and beyond this food intake effect, Cmpd 1 lowered overall animal body weights, reduced liver vacuolation, and reduced inguinal adipose tissue (iWAT) mass, inflammation, and adipocyte size. Taken together, Cmpd 1 had strong effects on multiple disease parameters in this obesity-driven rodent model of T2D. Further evaluation for potential translation of PKCα/ß inhibition to T2D and obesity in humans is warranted.

The Type I Interferon Signature Reflects Multiple Phenotypic and Activity Measures in Dermatomyositis.

OBJECTIVE: The type 1 interferon (IFN) pathway is up-regulated in dermatomyositis (DM). We sought to define how organ-specific disease activity as well as autoantibodies and other clinical factors are independently associated with systemic type I IFN activity in adult patients with DM. METHODS: RNA sequencing was performed on 355 whole blood samples collected from 202 well-phenotyped DM patients followed up during the course of their clinical care. A previously defined 13-gene type I IFN score was modeled as a function of demographic, serologic, and clinical variables using both cross-sectional and longitudinal data. RESULTS: The pattern of type I IFN-driven transcriptional response was stereotyped across samples with a sequential modular activation pattern strikingly similar to systemic lupus erythematosus. The median type I IFN score was higher or lower in patients with anti-melanoma differentiation-associated protein 5 (anti-MDA-5) or anti-Mi-2 antibodies, respectively, compared to patients without these antibodies. Absolute type I IFN score was independently associated with muscle and skin disease activity, interstitial lung disease, and anti-MDA-5 antibodies. Changes in the type I IFN score over time were significantly associated with changes in skin or muscle disease activity. Stratified analysis accounting for heterogeneity in organ involvement and antibody class revealed high correlation between changes in the type I IFN score and skin disease activity (Spearman's ρ = 0.84-0.95). CONCLUSION: The type I IFN score is independently associated with skin and muscle disease activity as well as certain clinical and serologic features in DM. Accounting for the effect of muscle disease and anti-MDA-5 status revealed that the type I IFN score is strongly correlated with skin disease activity, providing support for type I IFN blockade as a therapeutic strategy for DM.

QuickIsoSeq for Isoform Quantification in Large-Scale RNA Sequencing.

RNA-sequencing (RNA-seq) is a powerful technology for transcriptome profiling. While most RNA-seq projects focus on gene-level quantification and analysis, there is growing evidence that most mammalian genes are alternatively spliced to generate different isoforms that can be subsequently translated to protein molecules with diverse or even opposing biological functions. Quantifying the expression levels of these isoforms is key to understanding the genes biological functions in healthy tissues and the progression of diseases. Among open source tools developed for isoform quantification, Salmon, Kallisto, and RSEM are recommended based upon previous systematic evaluation of these tools using both experimental and simulated RNA-seq datasets. However, isoform quantification in practical RNA-seq data analysis needs to deal with many QC issues, such as the abundance of rRNAs in mRNA-seq, the efficiency of globin RNA depletion in whole blood samples, and potential sample swapping. To overcome these practical challenges, QuickIsoSeq was developed for large-scale RNA-seq isoform quantification along with QC. In this chapter, we describe the pipeline and detailed the steps required to deploy and use it to analyze RNA-seq datasets in practice. The QuickIsoSeq package can be downloaded from https://github.com/shanrongzhao/QuickIsoSeq.

Single-cell analyses of Crohn's disease tissues reveal intestinal intraepithelial T cells heterogeneity and altered subset distributions.

Crohn's disease (CD) is a chronic transmural inflammation of intestinal segments caused by dysregulated interaction between microbiome and gut immune system. Here, we profile, via multiple single-cell technologies, T cells purified from the intestinal epithelium and lamina propria (LP) from terminal ileum resections of adult severe CD cases. We find that intraepithelial lymphocytes (IEL) contain several unique T cell subsets, including NKp30+γδT cells expressing RORγt and producing IL-26 upon NKp30 engagement. Further analyses comparing tissues from non-inflamed and inflamed regions of patients with CD versus healthy controls show increased activated TH17 but decreased CD8+T, γδT, TFH and Treg cells in inflamed tissues. Similar analyses of LP find increased CD8+, as well as reduced CD4+T cells with an elevated TH17 over Treg/TFH ratio. Our analyses of CD tissues thus suggest a potential link, pending additional validations, between transmural inflammation, reduced IEL γδT cells and altered spatial distribution of IEL and LP T cell subsets.

Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols.

In recent years, RNA-sequencing (RNA-seq) has emerged as a powerful technology for transcriptome profiling. For a given gene, the number of mapped reads is not only dependent on its expression level and gene length, but also the sequencing depth. To normalize these dependencies, RPKM (reads per kilobase of transcript per million reads mapped) and TPM (transcripts per million) are used to measure gene or transcript expression levels. A common misconception is that RPKM and TPM values are already normalized, and thus should be comparable across samples or RNA-seq projects. However, RPKM and TPM represent the relative abundance of a transcript among a population of sequenced transcripts, and therefore depend on the composition of the RNA population in a sample. Quite often, it is reasonable to assume that total RNA concentration and distributions are very close across compared samples. Nevertheless, the sequenced RNA repertoires may differ significantly under different experimental conditions and/or across sequencing protocols; thus, the proportion of gene expression is not directly comparable in such cases. In this review, we illustrate typical scenarios in which RPKM and TPM are misused, unintentionally, and hope to raise scientists' awareness of this issue when comparing them across samples or different sequencing protocols.

Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.

BACKGROUND: The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as clinical outcome has not been attained in almost any disease area. Here, we report a comprehensive analysis spanning prediction tasks from ulcerative colitis, atopic dermatitis, diabetes, to many cancer subtypes for a total of 24 binary and multiclass prediction problems and 26 survival analysis tasks. We systematically investigate the influence of gene subsets, normalization methods and prediction algorithms. Crucially, we also explore the novel use of deep representation learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boost the performance of state-of-the-art methods. The resources and findings in this work should serve as both an up-to-date reference on attainable performance, and as a benchmarking resource for further research. RESULTS: Approaches that combine large numbers of genes outperformed single gene methods consistently and with a significant margin, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses overall. CONCLUSIONS: Transcriptomics-based phenotype prediction benefits from proper normalization techniques and state-of-the-art regularized regression approaches. In our view, breakthrough performance is likely contingent on factors which are independent of normalization and general modeling techniques; these factors might include reduction of systematic errors in sequencing data, incorporation of other data types such as single-cell sequencing and proteomics, and improved use of prior knowledge.

Publisher Correction: Subsets of ILC3-ILC1-like cells generate a diversity spectrum of innate lymphoid cells in human mucosal tissues.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Subsets of ILC3-ILC1-like cells generate a diversity spectrum of innate lymphoid cells in human mucosal tissues.

Innate lymphoid cells (ILCs) are tissue-resident lymphocytes categorized on the basis of their core regulatory programs and the expression of signature cytokines. Human ILC3s that produce the cytokine interleukin-22 convert into ILC1-like cells that produce interferon-γ in vitro, but whether this conversion occurs in vivo remains unclear. In the present study we found that ILC3s and ILC1s in human tonsils represented the ends of a spectrum that included additional discrete subsets. RNA velocity analysis identified an intermediate ILC3-ILC1 cluster, which had strong directionality toward ILC1s. In humanized mice, the acquisition of ILC1 features by ILC3s showed tissue dependency. Chromatin studies indicated that the transcription factors Aiolos and T-bet cooperated to repress regulatory elements active in ILC3s. A transitional ILC3-ILC1 population was also detected in the human intestine. We conclude that ILC3s undergo conversion into ILC1-like cells in human tissues in vivo, and that tissue factors and Aiolos were required for this process.

Applications of machine learning in drug discovery and development.

Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.

Alternative splicing, RNA-seq and drug discovery.

Alternative splicing, hereafter referred to as AS, is an essential component of gene expression regulation that contributes to the diversity of proteomes. Recent developments in RNA sequencing (RNA-seq) technologies, combined with the advent of computational tools, have enabled transcriptome-wide studies of AS at an unprecedented scale and resolution. RNA mis-splicing can cause human disease, and to target alternative splicing has led to the development of novel therapeutics. Splice variants diversify the repertoire of biomarkers and functionally contribute to drug resistance. Our expanding knowledge of AS variation in human populations holds great promise for improving disease diagnoses and ultimately patient care in the era of sequencing and precision medicine.

Molecular Profiling of Ulcerative Colitis Subjects from the TURANDOT Trial Reveals Novel Pharmacodynamic/Efficacy Biomarkers.

BACKGROUND AND AIMS: To define pharmacodynamic and efficacy biomarkers in ulcerative colitis [UC] patients treated with PF-00547659, an anti-human mucosal addressin cell adhesion molecule-1 [MAdCAM-1] monoclonal antibody, in the TURANDOT study. METHODS: Transcriptome, proteome and immunohistochemistry data were generated in peripheral blood and intestinal biopsies from 357 subjects in the TURANDOT study. RESULTS: In peripheral blood, C-C motif chemokine receptor 9 [CCR9] gene expression demonstrated a dose-dependent increase relative to placebo, but in inflamed intestinal biopsies CCR9 gene expression decreased with increasing PF-00547659 dose. Statistical models incorporating the full RNA transcriptome in inflamed intestinal biopsies showed significant ability to assess response and remission status. Oncostatin M [OSM] gene expression in inflamed intestinal biopsies demonstrated significant associations with, and good accuracy for, efficacy, and this observation was confirmed in independent published studies in which UC patients were treated with infliximab or vedolizumab. Compared with the placebo group, intestinal T-regulatory cells demonstrated a significant increase in the intermediate 22.5-mg dose cohort, but not in the 225-mg cohort. CONCLUSIONS: CCR9 and OSM are implicated as novel pharmacodynamic and efficacy biomarkers. These findings occur amid coordinated transcriptional changes that enable the definition of surrogate efficacy biomarkers based on inflamed biopsy or blood transcriptomics data.ClinicalTrials.gov identifierNCT01620255.

Computational identification and validation of alternative splicing in ZSF1 rat RNA-seq data, a preclinical model for type 2 diabetic nephropathy.

Obese ZSF1 rats exhibit spontaneous time-dependent diabetic nephropathy and are considered to be a highly relevant animal model of progressive human diabetic kidney disease. We previously identified gene expression changes between disease and control animals across six time points from 12 to 41 weeks. In this study, the same data were analysed at the isoform and exon levels to reveal additional disease mechanisms that may be governed by alternative splicing. Our analyses identified alternative splicing patterns in genes that may be implicated in disease pathogenesis (such as Shc1, Serpinc1, Epb4.1l5, and Il-33), which would have been overlooked in standard gene-level analysis. The alternatively spliced genes were enriched in pathways related to cell adhesion, cell-cell interactions/junctions, and cytoskeleton signalling, whereas the differentially expressed genes were enriched in pathways related to immune response, G protein-coupled receptor, and cAMP signalling. Our findings indicate that additional mechanistic insights can be gained from exon- and isoform-level data analyses over standard gene-level analysis. Considering alternative splicing is poorly conserved between rodents and humans, it is noted that this work is not translational, but the point holds true that additional insights can be gained from alternative splicing analysis of RNA-seq data.

canvasDesigner: a versatile interactive high-resolution scientific multi-panel visualization toolkit.

Summary: We present a bioinformatics and systems biology visualization toolkit harmonizing real time interactive exploring and analyzing of big data, full-fledged customizing of look-n-feel and producing multi-panel publication-ready figures in PDF format simultaneously. Availability and implementation: Source code and detailed user guides are available at http://canvasxpress.org, https://baohongz.github.io/canvasDesigner and https://baohongz.github.io/canvasDesigner/demo_video.html. Supplementary information: Supplementary data are available at Bioinformatics online.

QuickRNASeq: Guide for Pipeline Implementation and for Interactive Results Visualization.

Sequencing of transcribed RNA molecules (RNA-Seq) has been used wildly for studying cell transcriptomes in bulk or at the single-cell level (Wang et al., Nat Rev Genet, 10:57-63, 2009; Ozsolak and Milos, Nat Rev Genet, 12:87-98, 2011; Sandberg, Nat Methods, 11:22-24, 2014) and is becoming the de facto technology for investigating gene expression level changes in various biological conditions, on the time course, and under drug treatments. Furthermore, RNA-Seq data helped identify fusion genes that are related to certain cancers (Maher et al., Nature, 458:97-101, 2009). Differential gene expression before and after drug treatments provides insights to mechanism of action, pharmacodynamics of the drugs, and safety concerns (Dixit et al., Genomics, 107:178-188, 2016). Because each RNA-Seq run generates tens to hundreds of millions of short reads with size ranging from 50 to 200 bp, a tool that deciphers these short reads to an integrated and digestible analysis report is in high demand. QuickRNASeq (Zhao et al., BMC Genomics, 17:39-53, 2016) is an application for large-scale RNA-Seq data analysis and real-time interactive visualization of complex data sets. This application automates the use of several of the best open-source tools to efficiently generate user friendly, easy to share, and ready to publish report. Figures in this protocol illustrate some of the interactive plots produced by QuickRNASeq. The visualization features of the application have been further improved since its first publication in early 2016. The original QuickRNASeq publication (Zhao et al., BMC Genomics, 17:39-53, 2016) provided details of background, software selection, and implementation. Here, we outline the steps required to implement QuickRNASeq in user's own environment, as well as demonstrate some basic yet powerful utilities of the advanced interactive visualization modules in the report.

Evaluation of two main RNA-seq approaches for gene quantification in clinical RNA sequencing: polyA+ selection versus rRNA depletion.

To allow efficient transcript/gene detection, highly abundant ribosomal RNAs (rRNA) are generally removed from total RNA either by positive polyA+ selection or by rRNA depletion (negative selection) before sequencing. Comparisons between the two methods have been carried out by various groups, but the assessments have relied largely on non-clinical samples. In this study, we evaluated these two RNA sequencing approaches using human blood and colon tissue samples. Our analyses showed that rRNA depletion captured more unique transcriptome features, whereas polyA+ selection outperformed rRNA depletion with higher exonic coverage and better accuracy of gene quantification. For blood- and colon-derived RNAs, we found that 220% and 50% more reads, respectively, would have to be sequenced to achieve the same level of exonic coverage in the rRNA depletion method compared with the polyA+ selection method. Therefore, in most cases we strongly recommend polyA+ selection over rRNA depletion for gene quantification in clinical RNA sequencing. Our evaluation revealed that a small number of lncRNAs and small RNAs made up a large fraction of the reads in the rRNA depletion RNA sequencing data. Thus, we recommend that these RNAs are specifically depleted to improve the sequencing depth of the remaining RNAs.

RORγt and RORα signature genes in human Th17 cells.

RORγt and RORα are transcription factors of the RAR-related orphan nuclear receptor (ROR) family. They are expressed in Th17 cells and have been suggested to play a role in Th17 differentiation. Although RORγt signature genes have been characterized in mouse Th17 cells, detailed information on its transcriptional control in human Th17 cells is limited and even less is known about RORα signature genes which have not been reported in either human or mouse T cells. In this study, global gene expression of human CD4 T cells activated under Th17 skewing conditions was profiled by RNA sequencing. RORγt and RORα signature genes were identified in these Th17 cells treated with specific siRNAs to knock down RORγt or RORα expression. We have generated selective small molecule RORγt modulators and they were also utilized as pharmacological tools in RORγt signature gene identification. Our results showed that RORγt controlled the expression of a very selective number of genes in Th17 cells and most of them were regulated by RORα as well albeit a weaker influence. Key Th17 genes including IL-17A, IL-17F, IL-23R, CCL20 and CCR6 were shown to be regulated by both RORγt and RORα. Our results demonstrated an overlapping role of RORγt and RORα in human Th17 cell differentiation through regulation of a defined common set of Th17 genes. RORγt as a drug target for treatment of Th17 mediated autoimmune diseases such as psoriasis has been demonstrated recently in clinical trials. Our results suggest that RORα could be involved in same disease mechanisms and gene signatures identified in this report could be valuable biomarkers for tracking the pharmacodynamic effects of compounds that modulate RORγt or RORα activities in patients.

Evaluation and comparison of computational tools for RNA-seq isoform quantification.

BACKGROUND: Alternatively spliced transcript isoforms are commonly observed in higher eukaryotes. The expression levels of these isoforms are key for understanding normal functions in healthy tissues and the progression of disease states. However, accurate quantification of expression at the transcript level is limited with current RNA-seq technologies because of, for example, limited read length and the cost of deep sequencing. RESULTS: A large number of tools have been developed to tackle this problem, and we performed a comprehensive evaluation of these tools using both experimental and simulated RNA-seq datasets. We found that recently developed alignment-free tools are both fast and accurate. The accuracy of all methods was mainly influenced by the complexity of gene structures and caution must be taken when interpreting quantification results for short transcripts. Using TP53 gene simulation, we discovered that both sequencing depth and the relative abundance of different isoforms affect quantification accuracy CONCLUSIONS: Our comprehensive evaluation helps data analysts to make informed choice when selecting computational tools for isoform quantification.

High resolution molecular and histological analysis of renal disease progression in ZSF1 fa/faCP rats, a model of type 2 diabetic nephropathy.

ZSF1 rats exhibit spontaneous nephropathy secondary to obesity, hypertension, and diabetes, and have gained interest as a model system with potentially high translational value to progressive human disease. To thoroughly characterize this model, and to better understand how closely it recapitulates human disease, we performed a high resolution longitudinal analysis of renal disease progression in ZSF1 rats spanning from early disease to end stage renal disease. Analyses included metabolic endpoints, renal histology and ultrastructure, evaluation of a urinary biomarker of fibrosis, and transcriptome analysis of glomerular-enriched tissue over the course of disease. Our findings support the translational value of the ZSF1 rat model, and are provided here to assist researchers in the determination of the model's suitability for testing a particular mechanism of interest, the design of therapeutic intervention studies, and the identification of new targets and biomarkers for type 2 diabetic nephropathy.

QuickMIRSeq: a pipeline for quick and accurate quantification of both known miRNAs and isomiRs by jointly processing multiple samples from microRNA sequencing.

BACKGROUND: Genome-wide miRNA expression data can be used to study miRNA dysregulation comprehensively. Although many open-source tools for microRNA (miRNA)-seq data analyses are available, challenges remain in accurate miRNA quantification from large-scale miRNA-seq dataset. We implemented a pipeline called QuickMIRSeq for accurate quantification of known miRNAs and miRNA isoforms (isomiRs) from multiple samples simultaneously. RESULTS: QuickMIRSeq considers the unique nature of miRNAs and combines many important features into its implementation. First, it takes advantage of high redundancy of miRNA reads and introduces joint mapping of multiple samples to reduce computational time. Second, it incorporates the strand information in the alignment step for more accurate quantification. Third, reads potentially arising from background noise are filtered out to improve the reliability of miRNA detection. Fourth, sequences aligned to miRNAs with mismatches are remapped to a reference genome to further reduce false positives. Finally, QuickMIRSeq generates a rich set of QC metrics and publication-ready plots. CONCLUSIONS: The rich visualization features implemented allow end users to interactively explore the results and gain more insights into miRNA-seq data analyses. The high degree of automation and interactivity in QuickMIRSeq leads to a substantial reduction in the time and effort required for miRNA-seq data analysis.

QuickRNASeq lifts large-scale RNA-seq data analyses to the next level of automation and interactive visualization.

BACKGROUND: RNA sequencing (RNA-seq), a next-generation sequencing technique for transcriptome profiling, is being increasingly used, in part driven by the decreasing cost of sequencing. Nevertheless, the analysis of the massive amounts of data generated by large-scale RNA-seq remains a challenge. Multiple algorithms pertinent to basic analyses have been developed, and there is an increasing need to automate the use of these tools so as to obtain results in an efficient and user friendly manner. Increased automation and improved visualization of the results will help make the results and findings of the analyses readily available to experimental scientists. RESULTS: By combing the best open source tools developed for RNA-seq data analyses and the most advanced web 2.0 technologies, we have implemented QuickRNASeq, a pipeline for large-scale RNA-seq data analyses and visualization. The QuickRNASeq workflow consists of three main steps. In Step #1, each individual sample is processed, including mapping RNA-seq reads to a reference genome, counting the numbers of mapped reads, quality control of the aligned reads, and SNP (single nucleotide polymorphism) calling. Step #1 is computationally intensive, and can be processed in parallel. In Step #2, the results from individual samples are merged, and an integrated and interactive project report is generated. All analyses results in the report are accessible via a single HTML entry webpage. Step #3 is the data interpretation and presentation step. The rich visualization features implemented here allow end users to interactively explore the results of RNA-seq data analyses, and to gain more insights into RNA-seq datasets. In addition, we used a real world dataset to demonstrate the simplicity and efficiency of QuickRNASeq in RNA-seq data analyses and interactive visualizations. The seamless integration of automated capabilites with interactive visualizations in QuickRNASeq is not available in other published RNA-seq pipelines. CONCLUSION: The high degree of automation and interactivity in QuickRNASeq leads to a substantial reduction in the time and effort required prior to further downstream analyses and interpretation of the analyses findings. QuickRNASeq advances primary RNA-seq data analyses to the next level of automation, and is mature for public release and adoption.