GSK-3 Inhibitor Elraglusib Enhances Tumor-Infiltrating Immune Cell Activation in Tumor Biopsies and Synergizes with Anti-PD-L1 in a Murine Model of Colorectal Cancer.

Glycogen synthase kinase-3 (GSK-3) is a serine/threonine kinase that has been implicated in numerous oncogenic processes. GSK-3 inhibitor elraglusib (9-ING-41) has shown promising preclinical and clinical antitumor activity across multiple tumor types. Despite promising early-phase clinical trial results, there have been limited efforts to characterize the potential immunomodulatory properties of elraglusib. We report that elraglusib promotes immune cell-mediated tumor cell killing of microsatellite stable colorectal cancer (CRC) cells. Mechanistically, elraglusib sensitized CRC cells to immune-mediated cytotoxicity and enhanced immune cell effector function. Using western blots, we found that elraglusib decreased CRC cell expression of NF-κB p65 and several survival proteins. Using microarrays, we discovered that elraglusib upregulated the expression of proapoptotic and antiproliferative genes and downregulated the expression of cell proliferation, cell cycle progression, metastasis, TGFß signaling, and anti-apoptotic genes in CRC cells. Elraglusib reduced CRC cell production of immunosuppressive molecules such as VEGF, GDF-15, and sPD-L1. Elraglusib increased immune cell IFN-γ secretion, which upregulated CRC cell gasdermin B expression to potentially enhance pyroptosis. Elraglusib enhanced immune effector function resulting in augmented granzyme B, IFN-γ, TNF-α, and TRAIL production. Using a syngeneic, immunocompetent murine model of microsatellite stable CRC, we evaluated elraglusib as a single agent or combined with immune checkpoint blockade (anti-PD-1/L1) and observed improved survival in the elraglusib and anti-PD-L1 group. Murine responders had increased tumor-infiltrating T cells, augmented granzyme B expression, and fewer regulatory T cells. Murine responders had reduced immunosuppressive (VEGF, VEGFR2) and elevated immunostimulatory (GM-CSF, IL-12p70) cytokine plasma concentrations. To determine the clinical significance, we then utilized elraglusib-treated patient plasma samples and found that reduced VEGF and BAFF and elevated IL-1 beta, CCL22, and CCL4 concentrations correlated with improved survival. Using paired tumor biopsies, we found that tumor-infiltrating immune cells had a reduced expression of inhibitory immune checkpoints (VISTA, PD-1, PD-L2) and an elevated expression of T-cell activation markers (CTLA-4, OX40L) after elraglusib treatment. These results address a significant gap in knowledge concerning the immunomodulatory mechanisms of GSK-3 inhibitor elraglusib, provide a rationale for the clinical evaluation of elraglusib in combination with immune checkpoint blockade, and are expected to have an impact on additional tumor types, besides CRC.

GSK-3 inhibitor elraglusib enhances tumor-infiltrating immune cell activation in tumor biopsies and synergizes with anti-PD-L1 in a murine model of colorectal cancer.

Inhibition of GSK-3 using small-molecule elraglusib has shown promising preclinical antitumor activity. Using in vitro systems, we found that elraglusib promotes immune cell-mediated tumor cell killing, enhances tumor cell pyroptosis, decreases tumor cell NF-κB-regulated survival protein expression, and increases immune cell effector molecule secretion. Using in vivo systems, we observed synergy between elraglusib and anti-PD-L1 in an immunocompetent murine model of colorectal cancer. Murine responders had more tumor-infiltrating T-cells, fewer tumor-infiltrating Tregs, lower tumorigenic circulating cytokine concentrations, and higher immunostimulatory circulating cytokine concentrations. To determine the clinical significance, we utilized human plasma samples from patients treated with elraglusib and correlated cytokine profiles with survival. Using paired tumor biopsies, we found that CD45+ tumor-infiltrating immune cells had lower expression of inhibitory immune checkpoints and higher expression of T-cell activation markers in post-elraglusib patient biopsies. These results introduce several immunomodulatory mechanisms of GSK-3 inhibition using elraglusib, providing a rationale for the clinical evaluation of elraglusib in combination with immunotherapy. Statement of significance: Pharmacologic inhibition of GSK-3 using elraglusib sensitizes tumor cells, activates immune cells for increased anti-tumor immunity, and synergizes with anti-PD-L1 immune checkpoint blockade. These results introduce novel biomarkers for correlations with response to therapy which could provide significant clinical utility and suggest that elraglusib, and other GSK-3 inhibitors, should be evaluated in combination with immune checkpoint blockade.

Analyzing mRNA Epigenetic Sequencing Data with TRESS.

RNA epigenetics has emerged as an active topic to study gene regulation mechanisms. In this regard, the MeRIP-seq technology allows profiling transcriptome-wide mRNA modifications, in particular m6A. The primary goals for the analysis of MeRIP-seq data are the identification of m6A-methylated regions under each condition and across different biological conditions. Here we describe detailed procedures to guide researchers in MeRIP-seq data analyses by providing step-by-step instructions of the dedicated bioconductor package TRESS.

Overexpression of the WWE domain of RNF146 modulates poly-(ADP)-ribose dynamics at sites of DNA damage.

Protein poly-ADP-ribosylation (PARylation) is a post-translational modification formed by transfer of successive units of ADP-ribose to target proteins to form poly-ADP-ribose (PAR) chains. PAR plays a critical role in the DNA damage response (DDR) by acting as a signaling platform to promote the recruitment of DNA repair factors to the sites of DNA damage that bind via their PAR-binding domains (PBDs). Several classes of PBD families have been recognized, which identify distinct parts of the PAR chain. Proteins encoding PBDs play an essential role in conveying the PAR-mediated signal through their interaction with PAR chains, which mediates many cellular functions, including the DDR. The WWE domain identifies the iso-ADP-ribose moiety of the PAR chain. We recently described the WWE domain of RNF146 as a robust genetically encoded probe, when fused to EGFP, for detection of PAR in live cells. Here, we evaluated other PBD candidates as molecular PAR probes in live cells, including several other WWE domains and an engineered macrodomain. In addition, we demonstrate unique PAR dynamics when tracked by different PAR binding domains, a finding that that can be exploited for modulation of the PAR-dependent DNA damage response.

Liparismacrosepala (Orchidaceae), a new species from southwest China with its phylogenetic position.

A new orchid species, Liparismacrosepala, is illustrated and described from Yunnan Province, China, based on morphological and molecular analyses. This plant is characterised by the ovoid-fusiform, slightly compressed pseudobulbs with 4 or 5 leaves with slightly crisped margins on their apical half, dorsal sepal heart-shaped, lip with a bituberculate basal callus and a thickened folded lateral lobe on each side, centrally with one cavity with slightly raised margins, the column with a single pair of broadly triangular, obtuse wings. Maximum Likelihood and Bayesian Inference analyses of combined nrITS and plastid matK DNA sequences place this species in section Cestichis.

Non-linear Normalization for Non-UMI Single Cell RNA-Seq.

Single cell RNA-seq data, like data from other sequencing technology, contain systematic technical noise. Such noise results from a combined effect of unequal efficiencies in the capturing and counting of mRNA molecules, such as extraction/amplification efficiency and sequencing depth. We show that such technical effects are not only cell-specific, but also affect genes differently, thus a simple cell-wise size factor adjustment may not be sufficient. We present a non-linear normalization approach that provides a cell- and gene-specific normalization factor for each gene in each cell. We show that the proposed normalization method (implemented in "SC2P" package) reduces more technical variation than competing methods, without reducing biological variation. When technical effects such as sequencing depths are not balanced between cell populations, SC2P normalization also removes the bias due to uneven technical noise. This method is applicable to scRNA-seq experiments that do not use unique molecular identifier (UMI) thus retain amplification biases.

Detecting m6A methylation regions from Methylated RNA Immunoprecipitation Sequencing.

MOTIVATION: The post-transcriptional epigenetic modification on mRNA is an emerging field to study the gene regulatory mechanism and their association with diseases. Recently developed high-throughput sequencing technology named Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq) enables one to profile mRNA epigenetic modification transcriptome wide. A few computational methods are available to identify transcriptome-wide mRNA modification, but they are either limited by over-simplified model ignoring the biological variance across replicates or suffer from low accuracy and efficiency. RESULTS: In this work, we develop a novel statistical method, based on an empirical Bayesian hierarchical model, to identify mRNA epigenetic modification regions from MeRIP-seq data. Our method accounts for various sources of variations in the data through rigorous modeling and applies shrinkage estimation by borrowing information from transcriptome-wide data to stabilize the parameter estimation. Simulation and real data analyses demonstrate that our method is more accurate, robust and efficient than the existing peak calling methods. AVAILABILITY AND IMPLEMENTATION: Our method TRES is implemented as an R package and is freely available on Github at https://github.com/ZhenxingGuo0015/TRES. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Simulation, power evaluation and sample size recommendation for single-cell RNA-seq.

MOTIVATION: Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. RESULTS: We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA-sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship. The data simulator in POWSC outperforms two other state-of-art simulators in capturing key characteristics of real datasets. The power assessor in POWSC provides a variety of power evaluations including stratified and marginal power analyses for DEs characterized by two forms (phase transition or magnitude tuning), under different comparison scenarios. In addition, POWSC offers information for optimizing the tradeoffs between sample size and sequencing depth with the same total reads. AVAILABILITY AND IMPLEMENTATION: POWSC is an open-source R package available online at https://github.com/suke18/POWSC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering.

Cell clustering is one of the most common routines in single cell RNA-seq data analyses, for which a number of specialized methods are available. The evaluation of these methods ignores an important biological characteristic that the structure for a population of cells is hierarchical, which could result in misleading evaluation results. In this work, we develop two new metrics that take into account the hierarchical structure of cell types. We illustrate the application of the new metrics in constructed examples as well as several real single cell datasets and show that they provide more biologically plausible results.

Dissecting differential signals in high-throughput data from complex tissues.

MOTIVATION: Samples from clinical practices are often mixtures of different cell types. The high-throughput data obtained from these samples are thus mixed signals. The cell mixture brings complications to data analysis, and will lead to biased results if not properly accounted for. RESULTS: We develop a method to model the high-throughput data from mixed, heterogeneous samples, and to detect differential signals. Our method allows flexible statistical inference for detecting a variety of cell-type specific changes. Extensive simulation studies and analyses of two real datasets demonstrate the favorable performance of our proposed method compared with existing ones serving similar purpose. AVAILABILITY AND IMPLEMENTATION: The proposed method is implemented as an R package and is freely available on GitHub (https://github.com/ziyili20/TOAST). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: genomics meets medicine.

During June 10-12, 2018, the International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held in Los Angeles, California, USA. The conference included 11 scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics ranging from 3D genome structure analysis and visualization, next generation sequencing analysis, computational drug discovery, medical informatics, cancer genomics to systems biology. While medical genomics has always been a main theme in ICIBM, this year we for the first time organized the BMC Medical Genomics Supplement for ICIBM. Here, we describe 15 ICIBM papers selected for publishing in BMC Medical Genomics.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: genomics with bigger data and wider applications.

The sixth International Conference on Intelligent Biology and Medicine (ICIBM) took place in Los Angeles, California, USA on June 10-12, 2018. This conference featured eleven regular scientific sessions, four tutorials, one poster session, four keynote talks, and four eminent scholar talks. The scientific program covered a wide range of topics from bench to bedside, including 3D Genome Organization, reconstruction of large scale evolution of genomes and gene functions, artificial intelligence in biological and biomedical fields, and precision medicine. Both method development and application in genomic research continued to be a main component in the conference, including studies on genetic variants, regulation of transcription, genetic-epigenetic interaction at both single cell and tissue level and artificial intelligence. Here, we write a summary of the conference and also briefly introduce the four high quality papers selected to be published in BMC Genomics that cover novel methodology development or innovative data analysis.

Meta-analysis of Genetic Modifiers Reveals Candidate Dysregulated Pathways in Amyotrophic Lateral Sclerosis.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that has significant overlap with frontotemporal dementia (FTD). Mutations in specific genes have been identified that can cause and/or predispose patients to ALS. However, the clinical variability seen in ALS patients suggests that additional genes impact pathology, susceptibility, severity, and/or progression of the disease. To identify molecular pathways involved in ALS, we undertook a meta-analysis of published genetic modifiers both in patients and in model organisms, and undertook bioinformatic pathway analysis. From 72 published studies, we generated a list of 946 genes whose perturbation (1) impacted ALS in patient populations, (2) altered defects in laboratory models, or (3) modified defects caused by ALS gene ortholog loss of function. Herein, these are all called modifier genes. We found 727 modifier genes that encode proteins with human orthologs. Of these, 43 modifier genes were identified as modifiers of more than one ALS gene/model, consistent with the hypothesis that shared genes and pathways may underlie ALS. Further, we used a gene ontology-based bioinformatic analysis to identify pathways and associated genes that may be important in ALS. To our knowledge this is the first comprehensive survey of ALS modifier genes. This work suggests that shared molecular mechanisms may underlie pathology caused by different ALS disease genes. Surprisingly, few ALS modifier genes have been tested in more than one disease model. Understanding genes that modify ALS-associated defects will help to elucidate the molecular pathways that underlie ALS and provide additional targets for therapeutic intervention.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: bioinformatics towards translational applications.

The 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held on June 10-12, 2018, in Los Angeles, California, USA. The conference consisted of a total of eleven scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks, which covered a wild range of aspects of bioinformatics, medical informatics, systems biology and intelligent computing. Here, we summarize nine research articles selected for publishing in BMC Bioinformatics.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: systems biology on diverse data types.

Between June 10-12, 2018, the International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held in Los Angeles, California, USA. The conference included 11 scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics in 3D genome structure analysis and visualization, next generation sequencing analysis, computational drug discovery, medical informatics, cancer genomics and systems biology. Systems biology has been a main theme in ICIBM 2018, with exciting advances presented in many areas of systems biology, covering various different data types such as gene regulation, circular RNAs expression, single-cell RNA-Seq, inter-chromosomal interactions, metabolomics, proteomics and phosphoproteomics. Here, we describe ten high quality papers to be published in BMC Systems Biology.

Two-phase differential expression analysis for single cell RNA-seq.

Motivation: Single-cell RNA-sequencing (scRNA-seq) has brought the study of the transcriptome to higher resolution and makes it possible for scientists to provide answers with more clarity to the question of 'differential expression'. However, most computational methods still stick with the old mentality of viewing differential expression as a simple 'up or down' phenomenon. We advocate that we should fully embrace the features of single cell data, which allows us to observe binary (from Off to On) as well as continuous (the amount of expression) regulations. Results: We develop a method, termed SC2P, that first identifies the phase of expression a gene is in, by taking into account of both cell- and gene-specific contexts, in a model-based and data-driven fashion. We then identify two forms of transcription regulation: phase transition, and magnitude tuning. We demonstrate that compared with existing methods, SC2P provides substantial improvement in sensitivity without sacrificing the control of false discovery, as well as better robustness. Furthermore, the analysis provides better interpretation of the nature of regulation types in different genes. Availability and implementation: SC2P is implemented as an open source R package publicly available at https://github.com/haowulab/SC2P. Supplementary information: Supplementary data are available at Bioinformatics online.

Genotype-based gene signature of glioma risk.

BACKGROUND: Glioma accounts for 80% of malignant brain tumors, but its etiologic determinants remain elusive. Despite genetic susceptibility loci identified by genome-wide association study (GWAS), the agnostic approach leaves open the possibility that other susceptibility genes remain to be discovered. Here we conduct a gene-centric integrative GWAS (iGWAS) of glioma risk that combines transcriptomics and genetics. METHODS: We synthesized a brain transcriptomics dataset (n = 354), a GWAS dataset (n = 4203), and an advanced glioma tumor transcriptomic dataset (n = 483) to conduct an iGWAS. Using the expression quantitative trait loci (eQTL) dataset, we built models to predict gene expression for the GWAS data, based on eQTL genotypes. With the predicted gene expression, iGWAS analyses were performed using a novel statistical method. Gene signature risk score was constructed using a penalized logistic regression model. RESULTS: A total of 30527 transcripts were analyzed using the iGWAS approach. Four novel glioma susceptibility genes were identified with internal and external validation, including DRD5 (P = 3.0 × 10-79), WDR1 (P = 8.4 × 10-77), NOMO1 (P = 1.3 × 10-25), and PDXDC1 (P = 8.3 × 10-24). The genotype-predicted transcription pattern between cases and controls is consistent with that between tumor and its matched normal tissue. The genotype-based 4-gene signature improved the classification between glioma cases and controls based on age, gender, and population stratification, with area under the receiver operating characteristic curve increasing from 0.77 to 0.85 (P = 8.1 × 10-23). CONCLUSION: A new genotype-based gene signature of glioma was identified using a novel iGWAS approach, which integrates multiplatform genomic data as well as different genetic association studies.

Mitonuclear Interactions Mediate Transcriptional Responses to Hypoxia in Drosophila.

Among the major challenges in quantitative genetics and personalized medicine is to understand how gene × gene interactions (G × G: epistasis) and gene × environment interactions (G × E) underlie phenotypic variation. Here, we use the intimate relationship between mitochondria and oxygen availability to dissect the roles of nuclear DNA (nDNA) variation, mitochondrial DNA (mtDNA) variation, hypoxia, and their interactions on gene expression in Drosophila melanogaster. Mitochondria provide an important evolutionary and medical context for understanding G × G and G × E given their central role in integrating cellular signals. We hypothesized that hypoxia would alter mitonuclear communication and gene expression patterns. We show that first order nDNA, mtDNA, and hypoxia effects vary between the sexes, along with mitonuclear epistasis and G × G × E effects. Females were generally more sensitive to genetic and environmental perturbation. While dozens to hundreds of genes are altered by hypoxia in individual genotypes, we found very little overlap among mitonuclear genotypes for genes that were significantly differentially expressed as a consequence of hypoxia; excluding the gene hairy. Oxidative phosphorylation genes were among the most influenced by hypoxia and mtDNA, and exposure to hypoxia increased the signature of mtDNA effects, suggesting retrograde signaling between mtDNA and nDNA. We identified nDNA-encoded genes in the electron transport chain (succinate dehydrogenase) that exhibit female-specific mtDNA effects. Our findings have important implications for personalized medicine, the sex-specific nature of mitonuclear communication, and gene × gene coevolution under variable or changing environments.

Mitochondrial-Nuclear Interactions Mediate Sex-Specific Transcriptional Profiles in Drosophila.

The assembly and function of mitochondria require coordinated expression from two distinct genomes, the mitochondrial DNA (mtDNA) and nuclear DNA (nDNA). Mutations in either genome can be a source of phenotypic variation, yet their coexpression has been largely overlooked as a source of variation, particularly in the emerging paradigm of mitochondrial replacement therapy. Here we tested how the transcriptome responds to mtDNA and nDNA variation, along with mitonuclear interactions (mtDNA × nDNA) in Drosophila melanogaster We used two mtDNA haplotypes that differ in a substantial number of single nucleotide polymorphisms, with >100 amino acid differences. We placed each haplotype on each of two D. melanogaster nuclear backgrounds and tested for transcription differences in both sexes. We found that large numbers of transcripts were differentially expressed between nuclear backgrounds, and that mtDNA type altered the expression of nDNA genes, suggesting a retrograde, trans effect of mitochondrial genotype. Females were generally more sensitive to genetic perturbation than males, and males demonstrated an asymmetrical effect of mtDNA in each nuclear background; mtDNA effects were nuclear-background specific. mtDNA-sensitive genes were not enriched in male- or female-limited expression space in either sex. Using a variety of differential expression analyses, we show the responses to mitonuclear covariation to be substantially different between the sexes, yet the mtDNA genes were consistently differentially expressed across nuclear backgrounds and sexes. Our results provide evidence that the main mtDNA effects can be consistent across nuclear backgrounds, but the interactions between mtDNA and nDNA can lead to sex-specific global transcript responses.

Stem-loop binding protein is a multifaceted cellular regulator of HIV-1 replication.

A rare subset of HIV-1-infected individuals is able to maintain plasma viral load (VL) at low levels without antiretroviral treatment. Identifying the mechanisms underlying this atypical response to infection may lead to therapeutic advances for treating HIV-1. Here, we developed a proteomic analysis to compare peripheral blood cell proteomes in 20 HIV-1-infected individuals who maintained either high or low VL with the aim of identifying host factors that impact HIV-1 replication. We determined that the levels of multiple histone proteins were markedly decreased in cohorts of individuals with high VL. This reduction was correlated with lower levels of stem-loop binding protein (SLBP), which is known to control histone metabolism. Depletion of cellular SLBP increased promoter engagement with the chromatin structures of the host gene high mobility group protein A1 (HMGA1) and viral long terminal repeat (LTR), which led to higher levels of HIV-1 genomic integration and proviral transcription. Further, we determined that TNF-α regulates expression of SLBP and observed that plasma TNF-α levels in HIV-1-infected individuals correlated directly with VL levels and inversely with cellular SLBP levels. Our findings identify SLBP as a potentially important cellular regulator of HIV-1, thereby establishing a link between histone metabolism, inflammation, and HIV-1 infection.