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Bioinformatics has evolved from providing basic solutions, such as sequence alignment, structure predictions, and phylogenetic analysis to an independent data-driven field. The unprecedented growth of genomic technologies and the enormous data have opened an avenue for bioinformaticians (Bioinformatics professionals) never been seen before in the history of mankind. The novel opportunity also requires creative solutions that need skills to deal with noisy, unstructured information to offer valuable biological insights. Currently, we are seeing only the tip of an iceberg and the future will revolve around big data sets in all forms of biological research. The emerging challenge is to unfold the hidden iceberg of data.

NK cells are the predominant innate lymphocyte subset that contribute to anti-tumor immunity. The molecular profile of human tumor-infiltrating NK cells, and particularly, their characteristics in relation to CD8+ cytotoxic T lymphocyte (CTL) density within tumors is poorly understood. Here, we performed single-cell and bulk transcriptomic analysis of NK cells within tumor and adjacent normal lung tissue from patients with treatment-naïve lung cancer. We found divergent NK cell subsets with distinct functional features that were associated with the magnitude of CD8+ CTL response in tumors. CD16pos NK cells present in tumors with high density of CD8+ CTLs were enriched for transcripts linked to cytotoxicity, pro-inflammatory chemokines and effector function. CD16neg NK cells predominantly infiltrated tumors relative to lung tissue and harbored sub-populations with unique gene expression programs. Notably, tumors with high density of CD8+ CTLs were enriched for a CD16neg NK cell subset with features of tissue residency and NK cell memory as well as a CD16neg ILC3-like subset that displayed transcriptional features linked to activation, NFκB signaling, dendritic cell recruitment and lymphoid-tissue inducing properties. These data reveal that a potentially synergistic NK cell innate immune program may co-operate with CD8+ CTLs to mediate tumor clearance in tumors with a high tumor-infiltrating lymphocyte (TIL) density.

Clarke et al. interrogate human TRM cells from cancer and nonmalignant tissue. These analyses highlight that PD-1 expression in tumor-infiltrating TRM cells was positively correlated with features suggestive of active proliferation and superior functionality rather than dysfunction., High numbers of tissue-resident memory T (TRM) cells are associated with better clinical outcomes in cancer patients. However, the molecular characteristics that drive their efficient immune response to tumors are poorly understood. Here, single-cell and bulk transcriptomic analysis of TRM and non-TRM cells present in tumor and normal lung tissue from patients with lung cancer revealed that PD-1–expressing TRM cells in tumors were clonally expanded and enriched for transcripts linked to cell proliferation and cytotoxicity when compared with PD-1–expressing non-TRM cells. This feature was more prominent in the TRM cell subset coexpressing PD-1 and TIM-3, and it was validated by functional assays ex vivo and also reflected in their chromatin accessibility profile. This PD-1+TIM-3+ TRM cell subset was enriched in responders to PD-1 inhibitors and in tumors with a greater magnitude of CTL responses. These data highlight that not all CTLs expressing PD-1 are dysfunctional; on the contrary, TRM cells with PD-1 expression were enriched for features suggestive of superior functionality., Graphical Abstract

Systemic lupus erythematosus (SLE) is an incurable autoimmune disease disproportionately affecting women. A major obstacle in finding targeted therapies for SLE is its remarkable heterogeneity in clinical manifestations as well as in the involvement of distinct cell types. To identify cell-specific targets as well as cross-correlation relationships among expression programs of different cell types, we here analyze six major circulating immune cell types from SLE patient blood. Our results show that presence of an interferon response signature stratifies patients into two distinct groups (IFNneg vs. IFNpos). Comparing these two groups using differential gene expression and differential gene coexpression analysis, we prioritize a relatively small list of genes from classical monocytes including two known immune modulators:TNFSF13B/BAFF(target ofbelimumab, an approved therapeutic for SLE) andIL1RN(the basis ofanakinra,a therapeutic for rheumatoid arthritis). We then develop a multi–cell type extension of the weighted gene coexpression network analysis (WGCNA) framework, termed mWGCNA. Applying mWGCNA to RNA-seq data from six sorted immune cell populations (15 SLE, 10 healthy donors), we identify a coexpression module with interferon-stimulated genes (ISGs) among all cell types and a cross–cell type correlation linking expression of specific T helper cell markers to B cell response as well as toTNFSF13Bexpression from myeloid cells, all of which in turn correlates with disease severity of IFNpos patients. Our results demonstrate the power of a hypothesis-free and data-driven approach to discover drug targets and to reveal novel cross-correlation across cell types in SLE with implications for other autoimmune diseases.

The systemic lupus erythematosus (SLE) is an incurable autoimmune disease disproportionately affecting women and may lead to damage in multiple different organs. The marked heterogeneity in its clinical manifestations is a major obstacle in finding targeted treatments and involvement of multiple immune cell types further increases this complexity. Thus, identifying molecular subtypes that best correlate with disease heterogeneity and severity as well as deducing molecular cross-talk among major immune cell types that lead to disease progression are critical steps in the development of more informed therapies for SLE. Here we profile and analyze gene expression of six major circulating immune cell types from patients with well-characterized SLE (classical monocytes (n=64), T cells (n=24), neutrophils (n=24), B cells (n=20), conventional (n=20) and plasmacytoid (n=22) dendritic cells) and from healthy control subjects. Our results show that the interferon (IFN) response signature was the major molecular feature that classified SLE patients into two distinct groups: IFN-signature negative (IFNneg) and positive (IFNpos). We show that the gene expression signature of IFN response was consistent (i) across all immune cell types, (ii) all single cells profiled from three IFNpos donors using single-cell RNA-seq, and (iii) longitudinal samples of the same patient. For a better understanding of molecular differences of IFNpos versus IFNneg patients, we combined differential gene expression analysis with differential Weighted Gene Co-expression Network Analysis (WGCNA), which revealed a relatively small list of genes from classical monocytes including two known immune modulators, one the target of an approved therapeutic for SLE (TNFSF13B/BAFF: belimumab) and one itself a therapeutic for Rheumatoid Arthritis (IL1RN: anakinra). For a more integrative understanding of the cross-talk among different cell types and to identify potentially novel gene or pathway connections, we also developed a novel gene co-expression analysis method for joint analysis of multiple cell types named integrated WGNCA (iWGCNA). This method revealed an interesting cross-talk between T and B cells highlighted by a significant enrichment in the expression of known markers of T follicular helper cells (Tfh), which also correlate with disease severity in the context of IFNpos patients. Interestingly, higher expression of BAFF from all myeloid cells also shows a strong correlation with enrichment in the expression of genes in T cells that may mark circulating Tfh cells or related memory cell populations. These cell types have been shown to promote B cell class-switching and antibody production, which are well-characterized in SLE patients. In summary, we generated a large-scale gene expression dataset from sorted immune cell populations and present a novel computational approach to analyze such data in an integrative fashion in the context of an autoimmune disease. Our results reveal the power of a hypothesis-free and data-driven approach to discover drug targets and reveal novel cross-talk among multiple immune cell types specific to a subset of SLE patients. This approach is immediately useful for studying autoimmune diseases and is applicable in other contexts where gene expression profiling is possible from multiple cell types within the same tissue compartment.

To determine the nature of CD4+ T cells that provide ‘help’ for generating robust anti-tumor CD8+ cytotoxic T cell (CTL) responses, we profiled the transcriptomes of patient-matched CD4+ and CD8+ T cells present in the tumor micro-environment (TME) and analyzed them jointly using integrated weighted gene correlation network analysis. We found the follicular helper T cell (TFH) program in CD4+ T cells was strongly associated with proliferation and tissue-residency in CD8+ CTLs. Single-cell analysis demonstrated the presence of TFH-like cells and features linked to cytotoxic function and their provision of CD8+ T cell ‘help’. Tumor-infiltrating TFH-like cells expressed PD-1 and were enriched in tumors following checkpoint blockade, suggesting that they may respond to anti-PD-1 therapy. Adoptive transfer or induction of TFH cells in mouse models resulted in augmented CD8+ CTL responses and impairment of tumor growth, indicating an important role of TFH-like CD4+ T cells in anti-tumor immunity.

Immune checkpoint blockade (ICB) has shown remarkable clinical success in boosting anti-tumor immunity. However, the breadth of its cellular targets and specific mode of action remain elusive. We find that T follicular regulatory (TFR) cells are present in high numbers in multiple tumors, inhibit anti-tumor immunity and are responsive to ICB. TCR-seq data, trajectory analyses and adoptive transfer studies indicate intratumoral TREG to TFR cell conversion. When compared to TREG cells, TFR cells exhibited enhanced suppressive capacity. In syngeneic tumor models, anti-PD-1 treatment increased the number of tumor-infiltrating TFR cells. Conditional knockout of TFR cells or depletion of TFR cells with anti-CTLA-4 antibody prior to anti-PD-1 treatment, improved tumor control in mice. Notably, in a cohort of melanoma patients, treatment with anti-CTLA-4 followed by anti-PD-1 at progression was associated with better long-term survival outcomes than anti-PD-1 or anti-CTLA-4 monotherapy, anti-PD-1 followed by anti CTLA-4 at progression or concomitant combination therapy.

Background Blinatumomab is a BiTE ® (bispecific T cell engager) molecule that engages patients' T cells to the CD19 antigen on lymphoid tumor cells. Blinatumomab administered as a 28-day continuous intravenous infusion (cIV) is approved in multiple regions for the treatment of R/R B-ALL in adults and children. Subcutaneous delivery may improve the convenience and satisfaction of patients with R/R B-ALL who are candidates for blinatumomab therapy. Here we report the results from the first cohort of adults with R/R B-ALL receiving SC blinatumomab. Methods In this ongoing multicenter, single arm, open-label, phase 1b dose-finding study (NCT04521231), patients received multiple cycles of SC blinatumomab. Each cycle included a treatment period and a treatment-free interval. In cohort 1, cycle 1, patients received a lower first dose of SC blinatumomab for several days followed by a higher dose multiple times weekly; in subsequent cycles, patients received the higher dose several times weekly during the treatment period. Bone marrow (BM) evaluation was performed on day 27 of each cycle. Results Six patients from cohort 1 were included in this June 22, 2021 data cutoff. Median age was 64 (range 38-83) years. The number of prior therapies ranged from 2-4. Two patients had disease refractory to primary therapy or salvage therapy, 2 patients relapsed after chemotherapy, and 2 patients relapsed after prior allogeneic hematopoietic stem cell transplant. Median BM blast count at study start was 85% (range 28%-95%). Only 1 patient had Preliminary pharmacokinetic results support the SC dosing intervals used in this study and potentially longer intervals. Exposures for SC doses were similar to the efficacious exposures of the approved cIV regimen: mean average concentrations at steady state of 215 and 853 pg/mL for the lower and higher SC dosing regimens of cohort 1, respectively, vs mean steady state concentrations of 228 and 616 pg/mL for 9 and 28 µg/day cIV dosing, respectively. The pharmacodynamic profile following SC blinatumomab of peripheral immune cell redistribution (circulating CD3+ and CD8+ CD69+ T cells), transient cytokine elevation (IL-6, IL-10, IFN-γ) and CD19+ B cell counts declining below the detection limit was consistent with the historical pharmacodynamic profile following cIV blinatumomab. No grade ≥3 cytokine release syndrome events were reported (Table). One patient developed herpes encephalitis and experienced a grade 5 neurological event unrelated to blinatumomab; no other neurological events were reported. Two patients discontinued treatment because of adverse events (injection site reaction in patient with no response, hyperleukocytosis due to disease progression). Three patients had complete hematological response (CR) with no measurable residual disease (MRD) ( Conclusions In this phase 1b dose-finding study, SC blinatumomab has demonstrated encouraging anti-leukemia activity in heavily pretreated patients with R/R B-ALL. Pharmacokinetic and pharmacodynamic results support the use of SC dosing in this population. The safety profile was manageable and consistent with that reported for cIV blinatumomab. Figure 1 Figure 1. Disclosures Gordon: Amgen: Current Employment, Current equity holder in publicly-traded company. Schwartz: Morphosys: Research Funding; Pfizer: Honoraria, Speakers Bureau; Gilead: Other: Travel grants, Speakers Bureau; Jazz Pharmaceuticals: Other: Travel grants, Speakers Bureau; Novartis: Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees, Other: Travel grants, Speakers Bureau; BTG International Inc: Membership on an entity's Board of Directors or advisory committees; MSD Sharp & Dohme: Membership on an entity's Board of Directors or advisory committees; Basilea: Other: Travel grants. Rossi: Celgene: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Jazz: Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo: Consultancy, Honoraria; Abbvie: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria; Alexion: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Huguet: Novartis: Other: Advisor; Jazz Pharmaceuticals: Other: Advisor; Celgene: Other: Advisor; BMS: Other: Advisor; Amgen: Other: Advisor; Pfizer: Other: Advisor. Hernández-Rivas: Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene/BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Wong: Amgen: Current equity holder in publicly-traded company; Amgen: Current Employment. Markovic: Amgen: Current Employment, Current equity holder in publicly-traded company. Katlinskaya: Amgen: Current Employment, Current equity holder in publicly-traded company. Panwar: Amgen: Current Employment, Current equity holder in publicly-traded company. Zugmaier: Micromet/Amgen: Patents & Royalties: Patents 20190300609 and 20130323247 licensed; receives royalties of family members of international applications published as WO2010/052014; WO2010/052013; WO2011/051307; WO2012/055961; WO 2012/062596; WO2014/122251; and WO2015/181683; Amgen: Current Employment; Amgen: Current equity holder in publicly-traded company.

Background The olfactory stimulus-percept problem has been studied for more than a century, yet it is still hard to precisely predict the odor given the large-scale chemoinformatic features of an odorant molecule. A major challenge is that the perceived qualities vary greatly among individuals due to different genetic and cultural backgrounds. Moreover, the combinatorial interactions between multiple odorant receptors and diverse molecules significantly complicate the olfaction prediction. Many attempts have been made to establish structure-odor relationships for intensity and pleasantness, but no models are available to predict the personalized multi-odor attributes of molecules. In this study, we describe our winning algorithm for predicting individual and population perceptual responses to various odorants in the DREAM Olfaction Prediction Challenge. Results We find that random forest model consisting of multiple decision trees is well suited to this prediction problem, given the large feature spaces and high variability of perceptual ratings among individuals. Integrating both population and individual perceptions into our model effectively reduces the influence of noise and outliers. By analyzing the importance of each chemical feature, we find that a small set of low- and nondegenerative features is sufficient for accurate prediction. Conclusions Our random forest model successfully predicts personalized odor attributes of structurally diverse molecules. This model together with the top discriminative features has the potential to extend our understanding of olfactory perception mechanisms and provide an alternative for rational odorant design.

Despite recent advances in asthma management with anti–IL-5 therapies, many patients have eosinophilic asthma that remains poorly controlled. IL-3 shares a common β subunit receptor with both IL-5 and GM-CSF but, through α-subunit–specific properties, uniquely influences eosinophil biology and may serve as a potential therapeutic target. We aimed to globally characterize the transcriptomic profiles of GM-CSF, IL-3, and IL-5 stimulation on human circulating eosinophils and identify differences in gene expression using advanced statistical modeling. Human eosinophils were isolated from the peripheral blood of healthy volunteers and stimulated with either GM-CSF, IL-3, or IL-5 for 48 h. RNA was then extracted and bulk sequencing performed. DESeq analysis identified differentially expressed genes and weighted gene coexpression network analysis independently defined modules of genes that are highly coexpressed. GM-CSF, IL-3, and IL-5 commonly upregulated 252 genes and downregulated 553 genes, producing a proinflammatory and survival phenotype that was predominantly mediated through TWEAK signaling. IL-3 stimulation yielded the most numbers of differentially expressed genes that were also highly coexpressed (n = 119). These genes were enriched in pathways involving JAK/STAT signaling. GM-CSF and IL-5 stimulation demonstrated redundancy in eosinophil gene expression. In conclusion, IL-3 produces a distinct eosinophil gene expression program among the β-chain receptor cytokines. IL-3–upregulated genes may provide a foundation for research into therapeutics for patients with eosinophilic asthma who do not respond to anti–IL-5 therapies.

Genetic variants at the PTPN2 locus, which encodes the tyrosine phosphatase PTPN2, cause reduced gene expression and are linked to rheumatoid arthritis (RA) and other autoimmune diseases. PTPN2 inhibits signaling through the T cell and cytokine receptors, and loss of PTPN2 promotes T cell expansion and CD4- and CD8-driven autoimmunity. However, it remains unknown whether loss of PTPN2 in FoxP3+ regulatory T cells (Tregs) plays a role in autoimmunity. Here we aimed to model human autoimmune-predisposing PTPN2 variants, the presence of which results in a partial loss of PTPN2 expression, in mouse models of RA. We identified that reduced expression of Ptpn2 enhanced the severity of autoimmune arthritis in the T cell-dependent SKG mouse model and demonstrated that this phenotype was mediated through a Treg-intrinsic mechanism. Mechanistically, we found that through dephosphorylation of STAT3, PTPN2 inhibits IL-6-driven pathogenic loss of FoxP3 after Tregs have acquired RORγt expression, at a stage when chromatin accessibility for STAT3-targeted IL-17-associated transcription factors is maximized. We conclude that PTPN2 promotes FoxP3 stability in mouse RORγt+ Tregs and that loss of function of PTPN2 in Tregs contributes to the association between PTPN2 and autoimmunity.

The vast majority of human multiexon genes undergo alternative splicing and produce a variety of splice variant transcripts and proteins, which can perform different functions. These protein-coding splice variants (PCSVs) greatly increase the functional diversity of proteins. Most functional annotation algorithms have been developed at the gene level; the lack of isoform-level gold standards is an important intellectual limitation for currently available machine learning algorithms. The accumulation of a large amount of RNA-seq data in the public domain greatly increases our ability to examine the functional annotation of genes at isoform level. In the present study, we used a multiple instance learning (MIL)-based approach for predicting the function of PCSVs. We used transcript-level expression values and gene-level functional associations from the Gene Ontology database. A support vector machine (SVM)-based 5-fold cross-validation technique was applied. Comparatively, genes with multiple PCSVs performed better than single PCSV genes, and performance also improved when more examples were available to train the models. We demonstrated our predictions using literature evidence of ADAM15, LMNA/C, and DMXL2 genes. All predictions have been implemented in a web resource called "IsoFunc", which is freely available for the global scientific community through http://guanlab.ccmb.med.umich.edu/isofunc .

Alternative splicing allows a single gene to produce multiple transcript-level splice isoforms from which the translated proteins may show differences in their expression and function. Identifying the major functional or canonical isoform is important for understanding gene and protein functions. Identification and characterization of splice isoforms is a stated goal of the HUPO Human Proteome Project and of neXtProt. Multiple efforts have catalogued splice isoforms as "dominant", "principal", or "major" isoforms based on expression or evolutionary traits. In contrast, we recently proposed highest connected isoforms (HCIs) as a new class of canonical isoforms that have the strongest interactions in a functional network and revealed their significantly higher (differential) transcript-level expression compared to nonhighest connected isoforms (NCIs) regardless of tissues/cell lines in the mouse. HCIs and their expression behavior in the human remain unexplored. Here we identified HCIs for 6157 multi-isoform genes using a human isoform network that we constructed by integrating a large compendium of heterogeneous genomic data. We present examples for pairs of transcript isoforms of ABCC3, RBM34, ERBB2, and ANXA7. We found that functional networks of isoforms of the same gene can show large differences. Interestingly, differential expression between HCIs and NCIs was also observed in the human on an independent set of 940 RNA-seq samples across multiple tissues, including heart, kidney, and liver. Using proteomic data from normal human retina and placenta, we showed that HCIs are a promising indicator of expressed protein isoforms exemplified by NUDFB6 and M6PR. Furthermore, we found that a significant percentage (20%, p = 0.0003) of human and mouse HCIs are homologues, suggesting their conservation between species. Our identified HCIs expand the repertoire of canonical isoforms and are expected to facilitate studying main protein products, understanding gene regulation, and possibly evolution. The network is available through our web server as a rich resource for investigating isoform functional relationships (http://guanlab.ccmb.med.umich.edu/hisonet). All MS/MS data were available at ProteomeXchange Web site (http://www.proteomexchange.org) through their identifiers (retina: PXD001242, placenta: PXD000754).

We have developed the web-based Michigan Proteome Visualization Tool (MI-PVT) to visualize and compare protein expression and isoform-level function across human chromosomes and tissues (http://guanlab.ccmb.med.umich.edu/mipvt). As proof of principle, we have populated the tool with Human Proteome Map (HPM) data. We were able to observe many biologically interesting features. From the vantage point of our chromosome 17 team, for example, we found more than 300 proteins from chromosome 17 expressed in each of the 30 tissues and cell types studied, with the highest number of expressed proteins being 685 in testis. Comparisons of expression levels across tissues showed low numbers of proteins expressed in esophagus, but esophagus had 12 cytoskeletal proteins coded on chromosome 17 with very high expression (>1000 spectral counts). This customized MI-PVT should be helpful for biologists to browse and study specific proteins and protein data sets across tissues and chromosomes. Users can upload any data of interest in MI-PVT for visualization. Our aim is to integrate extensive mass-spectrometric proteomic data into the tool to facilitate finding chromosome-centric protein expression and correlation across tissues.

This study was conducted as a part of the Chromosome-Centric Human Proteome Project (C-HPP) of the Human Proteome Organization. The main objective is to identify and evaluate functionality of a set of specific noncanonical isoforms expressed in HER2-neu positive, estrogen receptor negative (ER−), and progesterone receptor negative (PR−) breast cancers (HER2+/ER−/PR− BC), an aggressive subtype of breast cancers that cause significant morbidity and mortality. We identified 11 alternative splice isoforms that were differentially expressed in HER2+/ER−/PR− BC compared to normal mammary, triple negative breast cancer and triple positive breast cancer tissues (HER2+/ER+/PR+). We used a stringent criterion that differentially expressed noncanonical isoforms (adjusted p value < 0.05) and have to be expressed in all replicates of HER2+/ER−/PR− BC samples, and the trend in differential expression (up or down) is the same in all comparisons. Of the 11 protein isoforms, six were overexpressed in HER2+/ER−/PR− BC. We explored possible functional roles of these six proteins using several complementary computational tools. Biological processes including cell cycle events and glycolysis were linked to four of these proteins. For example, glycolysis was the top ranking functional process for DMXL2 isoform 3, with a fold change of 27 compared to just two for the canonical protein. No previous reports link DMXL2 with any metabolic processes; the canonical protein is known to participate in signaling pathways. Our results clearly indicate distinct functions for the six overexpressed alternative splice isoforms, and these functions could be specific to HER2+/ER−/PR− tumor progression. Further detailed analysis is warranted as these proteins could be explored as potential biomarkers and therapeutic targets for HER2+/ER−/PR− BC patients.

Spinocerebellar ataxia type 3 (SCA3) is a neurodegenerative disorder caused by a polyglutamine-encoding CAG repeat expansion in the ATXN3 gene. This expansion leads to misfolding and aggregation of mutant ataxin-3 (ATXN3) and degeneration of select brain regions. A key unanswered question in SCA3 and other polyglutamine diseases is the extent to which neurodegeneration is mediated through gain-of-function versus loss-of-function. To address this question in SCA3, we performed transcriptional profiling on the brainstem, a highly vulnerable brain region in SCA3, in a series of mouse models with varying degrees of ATXN3 expression and aggregation. We include two SCA3 knock-in mouse models: our previously published model that erroneously harbors a tandem duplicate of the CAG repeat-containing exon, and a corrected model, introduced here. Both models exhibit dose-dependent neuronal accumulation and aggregation of mutant ATXN3, but do not exhibit a behavioral phenotype. We identified a molecular signature that correlates with ATXN3 neuronal aggregation yet is primarily linked to oligodendrocytes, highlighting early white matter dysfunction in SCA3. Two robustly elevated oligodendrocyte transcripts, Acy3 and Tnfrsf13c, were confirmed as elevated at the protein level in SCA3 human disease brainstem. To determine if mutant ATXN3 acts on oligodendrocytes cell-autonomously, we manipulated the repeat expansion in the variant SCA3 knock-in mouse by cell-type specific Cre/LoxP recombination. Changes in oligodendrocyte transcripts are driven cell-autonomously and occur independent of neuronal ATXN3 aggregation. Our findings support a primary toxic gain of function mechanism and highlight a previously unrecognized role for oligodendrocyte dysfunction in SCA3 disease pathogenesis.

Allelic exclusion describes the essential immunological process by which feedback repression of sequential DNA rearrangements ensures that only one autosome expresses a functional T or B cell receptor. In wild-type mammals, approximately 60% of cells have recombined the DNA of one T cell receptor β (TCRβ) V-to-DJ-joined allele in a functional configuration, while the second allele has recombined only the DJ sequences; the other 40% of cells have recombined the V to the DJ segments on both alleles, with only one of the two alleles predicting a functional TCRβ protein. Here we report that the transgenic overexpression of GATA3 leads predominantly to biallelic TCRβ gene (Tcrb) recombination. We also found that wild-type immature thymocytes can be separated into distinct populations based on intracellular GATA3 expression and that GATA3LO cells had almost exclusively recombined only one Tcrb locus (that predicted a functional receptor sequence), while GATA3HI cells had uniformly recombined both Tcrb alleles (one predicting a functional and the other predicting a nonfunctional rearrangement). These data show that GATA3 abundance regulates the recombination propensity at the Tcrb locus and provide new mechanistic insight into the historic immunological conundrum for how Tcrb allelic exclusion is mediated.

Motivation MicroRNAs (miRNAs) are small non-coding RNAs that are involved in post-transcriptional regulation of gene expression. In this high-throughput sequencing era, a tremendous amount of RNA-seq data is accumulating, and full utilization of publicly available miRNA data is an important challenge. These data are useful to determine expression values for each miRNA, but quantification pipelines are in a primitive stage and still evolving; there are many factors that affect expression values significantly. Results We used 304 high-quality microRNA sequencing (miRNA-seq) datasets from NCBI-SRA and calculated expression profiles for different tissues and cell-lines. In each miRNA-seq dataset, we found an average of more than 500 miRNAs with higher than 5x coverage, and we explored the top five highly expressed miRNAs in each tissue and cell-line. This user-friendly miRmine database has options to retrieve expression profiles of single or multiple miRNAs for a specific tissue or cell-line, either normal or with disease information. Results can be displayed in multiple interactive, graphical and downloadable formats. Availability and Implementation http://guanlab.ccmb.med.umich.edu/mirmine Supplementary information Supplementary data are available at Bioinformatics online.

Despite the proven critical role of the aero-allergen house dust mite (HDM) in the pathogenesis of airway allergic diseases and asthma, the molecular mechanisms leading to allergen-specific T cell activation, as well as the molecular differences driving to disease between different type of HDM-sensitized patients are unknown. The recent development of robust allergen activation assays combined with single-cell RNA-Seq technologies made us able to perform the first large single-cell RNA-Seq study for around 50,000 cells. We collected blood from 12 asthmatic patients with or without HDM sensitization, 6 patients with Allergic Rhinitis HDM-sensitized and 6 healthy subjects. We stimulated PBMCs with a pool HDM peptides for a period of 6 hours and sorted activated cells based on the expression of 2 surface markers: CD154+ (effector T cells) and CD137high/CD154low (regulatory T cells). We performed standard bulk RNA-Seq and single-cell RNA-Seq. Differential gene expression analysis showed important changes in gene expression upon TCR engagement in both populations. Comparison of disease groups identified several known but also new genes with relevant functional significance in allergy and asthma. Clustering analysis of Effector T cells single-cell RNA-Seq revealed an unexpected level of heterogeneity of gene expression among T cells. Major changes were observed between disease groups, revealing important pathological consequences to the understanding of immunity in allergy. Our study was the first attempt to generate a large-scale of single-cell RNA-Seq datasets from activated HDM-specific T cell. We showed presence of new T-cell subsets with unknown function as well as new molecules of therapeutic interest in allergy and asthma.

We present COMPASS, a COmputational Model to Predict the development of Alzheimer’s diSease Spectrum, to model Alzheimer’s disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer’s Disease Big Data challenge to predict changes in Mini-Mental State Examination (MMSE) scores over 24-months using standardized data. In the present study, we conducted three additional analyses beyond the DREAM challenge question to improve the clinical contribution of our approach, including: (1) adding pre-validated baseline cognitive composite scores of ADNI-MEM and ADNI-EF, (2) identifying subjects with significant declines in MMSE scores and (3) incorporating SNPs of top 10 genes connected to APOE identified from functional-relationship network. For (1) above, we significantly improved predictive accuracy, especially for the Mild Cognitive Impairment (MCI) group. For (2), we achieved an area under ROC of 0.814 in predicting significant MMSE decline: our model has 100% precision at 5% recall and 91% accuracy at 10% recall. For (3), “genetic only” model has Pearson’s correlation of 0.15 to predict progression in the MCI group. Even though addition of this limited genetic model to COMPASS did not improve prediction of progression of MCI group, the predictive ability of SNP information extended beyond well-known APOE allele.

Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling functional relationship networks, which summarize the probability of gene co-functionality relationships. Such modeling can be based on either expression data only or heterogeneous data integration. Numerous methods have been deployed to infer the functional relationship networks, while most of them target the global (non-context-specific) functional relationship networks. However, it is expected that functional relationships consistently reprogram under different tissues or biological processes. Thus, advanced methods have been developed targeting tissue-specific or developmental stage-specific networks. This article brings together the state-of-the-art functional relationship network modeling methods, emphasizes the need for heterogeneous genomic data integration and context-specific network modeling and outlines future directions for functional relationship networks.

The RNA-protein interactions play a diverse role in the cells, thus identification of RNA-protein interface is essential for the biologist to understand their function. In the past, several methods have been developed for predicting RNA interacting residues in proteins, but limited efforts have been made for the identification of protein-interacting nucleotides in RNAs. In order to discriminate protein-interacting and non-interacting nucleotides, we used various classifiers (NaiveBayes, NaiveBayesMultinomial, BayesNet, ComplementNaiveBayes, MultilayerPerceptron, J48, SMO, RandomForest, SMO and SVM(light)) for prediction model development using various features and achieved highest 83.92% sensitivity, 84.82 specificity, 84.62% accuracy and 0.62 Matthew's correlation coefficient by SVM(light) based models. We observed that certain tri-nucleotides like ACA, ACC, AGA, CAC, CCA, GAG, UGA, and UUU preferred in protein-interaction. All the models have been developed using a non-redundant dataset and are evaluated using five-fold cross validation technique. A web-server called RNApin has been developed for the scientific community (http://crdd.osdd.net/raghava/rnapin/).

Background The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure. Results In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL). It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i) vitamin interacting residues (VIRs), (ii) vitamin-A interacting residues (VAIRs), (iii) vitamin-B interacting residues (VBIRs) and (iv) pyridoxal-5-phosphate (vitamin B6) interacting residues (PLPIRs) have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM) features of protein sequences. Finally, we selected best performing SVM modules and obtained highest MCC of 0.53, 0.48, 0.61, 0.81 for VIRs, VAIRs, VBIRs, PLPIRs respectively, using PSSM-based evolutionary information. All the modules developed in this study have been trained and tested on non-redundant datasets and evaluated using five-fold cross-validation technique. The performances were also evaluated on the balanced and different independent datasets. Conclusions This study demonstrates that it is possible to predict VIRs, VAIRs, VBIRs and PLPIRs from evolutionary information of protein sequence. In order to provide service to the scientific community, we have developed web-server and standalone software VitaPred (http://crdd.osdd.net/raghava/vitapred/).

Background Evidence is accumulating that non-coding transcripts, previously thought to be functionally inert, play important roles in various cellular activities. High throughput techniques like next generation sequencing have resulted in the generation of vast amounts of sequence data. It is therefore desirable, not only to discriminate coding and non-coding transcripts, but also to assign the noncoding RNA (ncRNA) transcripts into respective classes (families). Although there are several algorithms available for this task, their classification performance remains a major concern. Acknowledging the crucial role that non-coding transcripts play in cellular processes, it is required to develop algorithms that are able to precisely classify ncRNA transcripts. Results In this study, we initially develop prediction tools to discriminate coding or non-coding transcripts and thereafter classify ncRNAs into respective classes. In comparison to the existing methods that employed multiple features, our SVM-based method by using a single feature (tri-nucleotide composition), achieved MCC of 0.98. Knowing that the structure of a ncRNA transcript could provide insights into its biological function, we use graph properties of predicted ncRNA structures to classify the transcripts into 18 different non-coding RNA classes. We developed classification models using a variety of algorithms (BayeNet, NaiveBayes, MultilayerPerceptron, IBk, libSVM, SMO and RandomForest) and observed that model based on RandomForest performed better than other models. As compared to the GraPPLE study, the sensitivity (of 13 classes) and specificity (of 14 classes) was higher. Moreover, the overall sensitivity of 0.43 outperforms the sensitivity of GraPPLE (0.33) whereas the overall MCC measure of 0.40 (in contrast to MCC of 0.29 of GraPPLE) was significantly higher for our method. This clearly demonstrates that our models are more accurate than existing models. Conclusions This work conclusively demonstrates that a simple feature, tri-nucleotide composition, is sufficient to discriminate between coding and non-coding RNA sequences. Similarly, graph properties based feature set along with RandomForest algorithm are most suitable to classify different ncRNA classes. We have also developed an online and standalone tool-- RNAcon (http://crdd.osdd.net/raghava/rnacon).

The advent of high-throughput genome scale technologies has enabled us to unravel a large amount of the previously unknown transcriptionally active regions of the genome. Recent genome-wide studies have provided annotations of a large repertoire of various classes of noncoding transcripts. Long noncoding RNAs (lncRNAs) form a major proportion of these novel annotated noncoding transcripts, and presently known to be involved in a number of functionally distinct biological processes. Over 18 000 transcripts are presently annotated as lncRNA, and encompass previously annotated classes of noncoding transcripts including large intergenic noncoding RNA, antisense RNA and processed pseudogenes. There is a significant gap in the resources providing a stable annotation, cross-referencing and biologically relevant information. lncRNome has been envisioned with the aim of filling this gap by integrating annotations on a wide variety of biologically significant information into a comprehensive knowledgebase. To the best of our knowledge, lncRNome is one of the largest and most comprehensive resources for lncRNAs. Database URL: http://genome.igib.res.in/lncRNome

Since endo-symbiotic events occur, all genes of mitochondrial aminoacyl tRNA synthetase (AARS) were lost or transferred from ancestral mitochondrial genome into the nucleus. The canonical pattern is that both cyto- solic and mitochondrial AARSs coexist in the nuclear genome. In the present scenario all mitochondrial AARSs are nucleus-encoded, synthesized on cytosolic ribosomes and post-translationally imported from the cytosol into the mitochondria in eukaryotic cell. The site-based discrimi- nation between similar types of enzymes is very chal- lenging because they have almost same physico-chemical properties. It is very important to predict the sub-cellular location of AARSs, to understand the mitochondrial pro- tein synthesis. We have analyzed and optimized the dis- tinguishable patterns between cytosolic and mitochondrial AARSs. Firstly, support vector machines (SVM)-based modules have been developed using amino acid and dipeptide compositions and achieved Mathews correlation coefficient (MCC) of 0.82 and 0.73, respectively. Sec- ondly, we have developed SVM modules using position- specific scoring matrix and achieved the maximum MCC of 0.78. Thirdly, we developed SVM modules using N-terminal, intermediate residues, C-terminal and split amino acid composition (SAAC) and achieved MCC of 0.82, 0.70, 0.39 and 0.86, respectively. Finally, a SVM module was developed using selected attributes of split amino acid composition (SA-SAAC) approach and achieved MCC of 0.92 with an accuracy of 96.00%. All modules were trained and tested on a non-redundant data set and evaluated using fivefold cross-validation technique. On the independent data sets, SA-SAAC based prediction model achieved MCC of 0.95 with an accuracy of 97.77%. The web-server 'MARSpred' based on above study is available at http://www.imtech.res.in/raghava/marspred/.

Background Benzylisoquinoline is the structural backbone of many alkaloids with a wide variety of structures including papaverine, noscapine, codeine, morphine, apomorphine, berberine, protopine and tubocurarine. Many benzylisoquinoline alkaloids have been reported to show therapeutic properties and to act as novel medicines. Thus it is important to collect and compile benzylisoquinoline alkaloids in order to explore their usage in medicine. Description We extract information about benzylisoquinoline alkaloids from various sources like PubChem, KEGG, KNApSAcK and manual curation from literature. This information was processed and compiled in order to create a comprehensive database of benzylisoquinoline alkaloids, called BIAdb. The current version of BIAdb contains information about 846 unique benzylisoquinoline alkaloids, with multiple entries in term of source, function leads to total number of 2504 records. One of the major features of this database is that it provides data about 627 different plant species as a source of benzylisoquinoline and 114 different types of function performed by these compounds. A large number of online tools have been integrated, which facilitate user in exploring full potential of BIAdb. In order to provide additional information, we give external links to other resources/databases. One of the important features of this database is that it is tightly integrated with Drugpedia, which allows managing data in fixed/flexible format. Conclusions A database of benzylisoquinoline compounds has been created, which provides comprehensive information about benzylisoquinoline alkaloids. This database will be very useful for those who are working in the field of drug discovery based on natural products. This database will also serve researchers working in the field of synthetic biology, as developing medicinally important alkaloids using synthetic process are one of important challenges. This database is available from http://crdd.osdd.net/raghava/biadb/.

Background Aminoacyl tRNA synthetases (aaRSs) catalyse the first step of protein synthesis in all organisms. They are responsible for the precise attachment of amino acids to their cognate transfer RNAs. There are twenty different types of aaRSs, unique for each amino acid. These aaRSs have been divided into two classes, each comprising ten enzymes. It is important to predict and classify aaRSs in order to understand protein synthesis. Results In this study, all models were developed on a non-redundant dataset containing 117 aaRSs and an equal number of non-aaRSs, in which no two sequences have more than 30% similarity. First, we applied the similarity search technique, BLAST, and achieved a maximum accuracy of 67.52%. We observed that 62% of tRNA synthetases contain one or more domains from amongst the following four PROSITE domains: PS50862, PS00178, PS50860 and PS50861. An SVM-based model was developed to discriminate between aaRSs, and non-aaRSs, and achieved a maximum MCC of 0.68 with accuracy of 83.73%, using selective dipeptide composition. We developed a hybrid approach and achieved a maximum MCC of 0.72 with accuracy of 85.49%, where SVM model developed using selected dipeptide composition and information of four PROSITE domains. We further developed an SVM-based model for classifying the aaRSs into class-1 and class-2, using selective dipeptide composition and achieved an MCC of 0.79. We also observed that two domains (PS00178, PS50889) in class-1 and three domains (PS50862, PS50860, PS50861) in class-2 were preferred. A hybrid method was developed using these domains as descriptor, along with selected dipeptide composition, and achieved an MCC of 0.87 with a sensitivity of 94.55% and an accuracy of 93.19%. All models were evaluated using a five-fold cross-validation technique. Conclusions We have analyzed protein sequences of aaRSs (class-1 and class-2) and non-aaRSs and identified interesting patterns. The high accuracy achieved by our SVM models using selected dipeptide composition demonstrates that certain types of dipeptide are preferred in aaRSs. We were able to identify PROSITE domains that are preferred in aaRSs and their classes, providing interesting insights into tRNA synthetases. The method developed in this study will be useful for researchers studying aaRS enzymes and tRNA biology. The web-server based on the above study, is available at http://www.imtech.res.in/raghava/icaars/.

Background Predicting the function of a protein is one of the major challenges in the post-genomic era where a large number of protein sequences of unknown function are accumulating rapidly. Lectins are the proteins that specifically recognize and bind to carbohydrate moieties present on either proteins or lipids. Cancerlectins are those lectins that play various important roles in tumor cell differentiation and metastasis. Although the two types of proteins are linked, still there is no computational method available that can distinguish cancerlectins from the large pool of non-cancerlectins. Hence, it is imperative to develop a method that can distinguish between cancer and non-cancerlectins. Results All the models developed in this study are based on a non-redundant dataset containing 178 cancerlectins and 226 non-cancerlectins in which no two sequences have more than 50% sequence similarity. We have applied the similarity search based technique, i.e. BLAST, and achieved a maximum accuracy of 43.25%. The amino acids compositional analysis have shown that certain residues (e.g. Leucine, Proline) were preferred in cancerlectins whereas some other (e.g. Asparatic acid, Asparagine) were preferred in non-cancerlectins. It has been found that the PROSITE domain "Crystalline beta gamma" was abundant in cancerlectins whereas domains like "SUEL-type lectin domain" were found mainly in non-cancerlectins. An SVM-based model has been developed to differentiate between the cancer and non-cancerlectins which achieved a maximum Matthew's correlation coefficient (MCC) value of 0.32 with an accuracy of 64.84%, using amino acid compositions. We have developed a model based on dipeptide compositions which achieved an MCC value of 0.30 with an accuracy of 64.84%. Thereafter, we have developed models based on split compositions (2 and 4 parts) and achieved an MCC value of 0.31, 0.32 with accuracies of 65.10% and 66.09%, respectively. An SVM model based on Position Specific Scoring Matrix (PSSM), generated by PSI-BLAST, was developed and achieved an MCC value of 0.36 with an accuracy of 68.34%. Finally, we have integrated the PROSITE domain information with PSSM and developed an SVM model that has achieved an MCC value of 0.38 with 69.09% accuracy. Conclusion BLAST has been found inefficient to distinguish between cancer and non-cancerlectins. We analyzed the protein sequences of cancer and non-cancerlectins and identified interesting patterns. We have been able to identify PROSITE domains that are preferred in cancer and non-cancerlectins and thus provided interesting insights into the two types of proteins. The method developed in this study will be useful for researchers studying cancerlectins, lectins and cancer biology. The web-server based on the above study, is available at http://www.imtech.res.in/raghava/cancer_pred/