Comprehensive comparison of large-scale tissue expression datasets

The impact of host microRNAs on the development of conserved mutations of SARS-CoV-2

SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has undergone various genetic alterations due to evolutionary pressures exerted by host cells, including intracellular antiviral mechanisms such as targeting by human microRNAs (miRNAs). This study investigates the impact of miRNAs hsa-miR-3132 and hsa-miR-4650 on the viral genome. Sequence alignment revealed conserved mutations in the binding sites of these miRNAs in adapted strains compared to the original Wuhan-Hu-1 strain, leading to their deletion. Despite modest expression of these miRNAs in SARS-CoV-2 target tissues, their efficacy against mutant strains is reduced due to the loss of binding sites. Structural analysis indicates that the mutant genome is more stable than the Wuhan-Hu-1 genome. Luciferase and virus titration assays demonstrate that hsa-miR-3132 and hsa-miR-4650 effectively target the Nsp3 gene in the Wuhan-Hu-1 strain but not in mutant strains lacking their binding sites. These findings suggest that the observed mutations help the virus evade selective pressure from human miRNAs, contributing to its adaptation.

Long noncoding RNA GATA2AS influences human erythropoiesis by transcription factor and chromatin landscape modulation

ABSTRACT

Long noncoding RNAs (lncRNAs) are extensively expressed in eukaryotic cells and have been revealed to be important for regulating cell differentiation. Many lncRNAs have been found to regulate erythroid differentiation in the mouse. However, given the low sequence conservation of lncRNAs between mouse and human, our understanding of lncRNAs in human erythroid differentiation remains incomplete. lncRNAs are often transcribed opposite to protein coding genes and regulate their expression. Here, we characterized a human erythrocyte-expressed lncRNA, GATA2AS, which is transcribed opposite to erythroid transcription regulator GATA2. GATA2AS is a 2080-bp long, primarily nucleus-localized noncoding RNA that is expressed in erythroid progenitor cells and decreases during differentiation. Knockout of GATA2AS in human HUDEP2 erythroid progenitor cells using CRISPR-Cas9 genome editing to remove the transcription start site accelerated erythroid differentiation and dysregulated erythroblast gene expression. We identified GATA2AS as a novel GATA2 and HBG activator. Chromatin isolation by RNA purification showed that GATA2AS binds to thousands of genomic sites and colocalizes at a subset of sites with erythroid transcription factors including LRF and KLF1. RNA pulldown and RNA immunoprecipitation confirmed interaction between GATA2AS and LRF and KLF1. Chromatin immunoprecipitation sequencing (ChIP-seq) showed that knockout of GATA2AS reduces binding of these transcription factors genome wide. Assay for transposase-accessible chromatin sequencing (ATAC-seq) and H3K27ac ChIP-seq showed that GATA2AS is essential to maintain the chromatin regulatory landscape during erythroid differentiation. Knockdown of GATA2AS in human primary CD34+ cells mimicked results in HUDEP2 cells. Overall, our results implicate human-specific lncRNA GATA2AS as a regulator of erythroid differentiation by influencing erythroid transcription factor binding and the chromatin regulatory landscape.

The phosphatase DUSP22 inhibits UBR2-mediated K63-ubiquitination and activation of Lck downstream of TCR signalling

DUSP22 is a dual-specificity phosphatase that inhibits T cell activation by inactivating the kinase Lck. Here we show that the E3 ubiquitin ligase UBR2 is a positive upstream regulator of Lck during T-cell activation. DUSP22 dephosphorylates UBR2 at specific Serine residues, leading to ubiquitin-mediated UBR2 degradation. UBR2 is also modified by the SCF E3 ubiquitin ligase complex via Lys48-linked ubiquitination at multiple Lysine residues. Single-cell RNA sequencing analysis and UBR2 loss of function experiments showed that UBR2 is a positive regulator of proinflammatory cytokine expression. Mechanistically, UBR2 induces Lys63-linked ubiquitination of Lck at Lys99 and Lys276 residues, followed by Lck Tyr394 phosphorylation and activation as part of TCR signalling. Inflammatory phenotypes induced by TCR-triggered Lck activation or knocking out DUSP22, are attenuated by genomic deletion of UBR2. UBR2-Lck interaction and Lck Lys63-linked ubiquitination are induced in the peripheral blood T cells of human SLE patients, which demonstrate the relevance of the UBR2-mediated regulation of inflammation to human pathology. In summary, we show here an important regulatory mechanism of T cell activation, which finetunes the balance between T cell response and aggravated inflammation.

Bioinformatics and cheminformatics approaches to identify pathways, molecular mechanisms and drug substances related to genetic basis of cervical cancer

Cervical cancer (CC) is a global threat to women and our knowledge is frighteningly little about its underlying genomic contributors. Our research aimed to understand the underlying molecular and genetic mechanisms of CC by integrating bioinformatics and network-based study. Transcriptomic analyses of three microarray datasets identified 218 common differentially expressed genes (DEGs) within control samples and CC specimens. KEGG pathway analysis revealed pathways in cell cycle, drug metabolism, DNA replication and the significant GO terms were cornification, proteolysis, cell division and DNA replication. Protein-protein interaction (PPI) network analysis identified 20 hub genes and survival analyses validated CDC45, MCM2, PCNA and TOP2A as CC biomarkers. Subsequently, 10 transcriptional factors (TFs) and 10 post-transcriptional regulators were detected through TFs-DEGs and miRNAs-DEGs regulatory network assessment. Finally, the CC biomarkers were subjected to a drug-gene relationship analysis to find the best target inhibitors. Standard cheminformatics method including in silico ADMET and molecular docking study substantiated PD0325901 and Selumetinib as the most potent candidate-drug for CC treatment. Overall, this meticulous study holds promises for further in vitro and in vivo research on CC diagnosis, prognosis and therapies. Communicated by Ramaswamy H. Sarma.

Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts

BACKGROUND

Knowledge graphs support biomedical research efforts by providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. These databases are populated via manual curation, which is challenging to scale with an exponentially rising publication rate. Data programming is a paradigm that circumvents this arduous manual process by combining databases with simple rules and heuristics written as label functions, which are programs designed to annotate textual data automatically. Unfortunately, writing a useful label function requires substantial error analysis and is a nontrivial task that takes multiple days per function. This bottleneck makes populating a knowledge graph with multiple nodes and edge types practically infeasible. Thus, we sought to accelerate the label function creation process by evaluating how label functions can be re-used across multiple edge types.

RESULTS

We obtained entity-tagged abstracts and subsetted these entities to only contain compounds, genes, and disease mentions. We extracted sentences containing co-mentions of certain biomedical entities contained in a previously described knowledge graph, Hetionet v1. We trained a baseline model that used database-only label functions and then used a sampling approach to measure how well adding edge-specific or edge-mismatch label function combinations improved over our baseline. Next, we trained a discriminator model to detect sentences that indicated a biomedical relationship and then estimated the number of edge types that could be recalled and added to Hetionet v1. We found that adding edge-mismatch label functions rarely improved relationship extraction, while control edge-specific label functions did. There were two exceptions to this trend, Compound-binds-Gene and Gene-interacts-Gene, which both indicated physical relationships and showed signs of transferability. Across the scenarios tested, discriminative model performance strongly depends on generated annotations. Using the best discriminative model for each edge type, we recalled close to 30% of established edges within Hetionet v1.

CONCLUSIONS

Our results show that this framework can incorporate novel edges into our source knowledge graph. However, results with label function transfer were mixed. Only label functions describing very similar edge types supported improved performance when transferred. We expect that the continued development of this strategy may provide essential building blocks to populating biomedical knowledge graphs with discoveries, ensuring that these resources include cutting-edge results.

Larynx proteomics after jellyfish collagen IL: Increased ECM/collagen and suppressed inflammation

Objectives/Hypothesis

Compare proteomic profiles of rabbit vocal folds (VFs) injected with micronized cross-linked jellyfish collagen "collagen Type 0" (MX-JC) against two clinical products for injection medialization laryngoplasty (IL).

Study Design

Animal model.

Methods

Left recurrent laryngeal nerve sectioning and IL were performed in New Zealand White rabbits (N = 6/group). Group 1 received (MX-JC) and adipose-derived stem cells (ADSCs), Group 2, MX-JC alone; Group 3, cross-linked hyaluronic acid; and Group 4, micronized acellular dermis. Animals were sacrificed at 4 and 12 weeks. Proteomic profiling of injected versus noninjected VFs by nano-liquid chromatography, tandem mass spectrometry, and reactome gene ontology analysis was performed.

Results

Overall, 37-61 proteins were found to be upregulated and 60-284 downregulated in injected versus non-injected VFs (>1.5 fold, false discovery rate-adjusted p < .05). Over-representation analysis (% of total) revealed top up-regulated pathways at 4 and 12 weeks, respectively: Group 1, keratan sulfate metabolism (46%) and cellular processes (29%); Group 2, extracellular matrix (ECM)/collagen processes (33%) and beta oxidation (39%); Group 3, cellular processes (50%) and energy metabolism (100%); and Group 4, keratan sulfate metabolism (31%) and inflammation (50%). Top downregulated pathways were: Group 1, Inflammation (36%) and glucose/citric acid metabolism (42%); Group 2, cell signaling (38%) and glucose/citric acid metabolism (35%); Group 3, keratan sulfate metabolism (31%) and ECM/collagen processes (48%); and Group 4, glucose/citric acid metabolism (33%) and ECM/collagen processes (43%).

Conclusions

MX-JC "collagen Type 0" upregulates pathways related to ECM/collagen formation and downregulates pathways related to inflammation suggesting that it is promising biomaterial for IL.

Level of Evidence

NA.

Transcription Factors YAP/TAZ and SRF Cooperate To Specify Renal Myofibroblasts in the Developing Mouse Kidney

BACKGROUND

The embryonic renal stroma consists of multiple molecularly distinct cell subpopulations, the functional significance of which is largely unknown. Previous work has demonstrated that the transcription factors YAP and TAZ play roles in the development and morphogenesis of the nephrons, collecting ducts, and nephron progenitor cells.

METHODS

In embryonic mouse kidneys, we identified a subpopulation of stromal cells with enriched activity in YAP and TAZ. To evaluate the function of these cell types, we genetically ablated both Yap and Taz from the stromal progenitor population and examined how gene activity and development of YAP/TAZ mutant kidneys are affected over a developmental time course.

RESULTS

We found that YAP and TAZ are active in a subset of renal interstitium and that stromal-specific coablation of YAP/TAZ disrupts cortical fibroblast, pericyte, and myofibroblast development, with secondary effects on peritubular capillary differentiation. We also demonstrated that the transcription factor SRF cooperates with YAP/TAZ to drive expression of at least a subset of renal myofibroblast target genes and to specify myofibroblasts but not cortical fibroblasts or pericytes.

CONCLUSIONS

These findings reveal a critical role for YAP/TAZ in specific embryonic stromal cells and suggest that interaction with cofactors, such as SRF, influence the expression of cell type-specific target genes, thus driving stromal heterogeneity. Further, this work reveals functional roles for renal stroma heterogeneity in creating unique microenvironments that influence the differentiation and maintenance of the renal parenchyma.

Large-Scale Multi-Omics Studies Provide New Insights into Blood Pressure Regulation

Recent genome-wide association studies uncovered part of blood pressure’s heritability. However, there is still a vast gap between genetics and biology that needs to be bridged. Here, we followed up blood pressure genome-wide summary statistics of over 750,000 individuals, leveraging comprehensive epigenomic and transcriptomic data from blood with a follow-up in cardiovascular tissues to prioritise likely causal genes and underlying blood pressure mechanisms. We first prioritised genes based on coding consequences, multilayer molecular associations, blood pressure-associated expression levels, and coregulation evidence. Next, we followed up the prioritised genes in multilayer studies of genomics, epigenomics, and transcriptomics, functional enrichment, and their potential suitability as drug targets. Our analyses yielded 1880 likely causal genes for blood pressure, tens of which are targets of the available licensed drugs. We identified 34 novel genes for blood pressure, supported by more than one source of biological evidence. Twenty-eight (82%) of these new genes were successfully replicated by transcriptome-wide association analyses in a large independent cohort (n = ~220,000). We also found a substantial mediating role for epigenetic regulation of the prioritised genes. Our results provide new insights into genetic regulation of blood pressure in terms of likely causal genes and involved biological pathways offering opportunities for future translation into clinical practice.

Overcoming Sparseness of Biomedical Networks to Identify Drug Repositioning Candidates

Modeling complex biological systems is necessary to understand biochemical interactions behind pharmacological effects of drugs. Successful in silico drug repurposing relies on exploration of diverse biochemical concepts and their relationships, including drug's adverse reactions, drug targets, disease symptoms, as well as disease associated genes and their pathways, to name a few. We present a computational method for inferring drug-disease associations from complex but incomplete and biased biological networks. Our method employs matrix completion to overcome the sparseness of biomedical data and to enrich the set of relationships between different biomedical entities. We present a strategy for identifying network paths supportive of drug efficacy as well as a computational procedure capable of combining different network patterns to better distinguish treatments from non-treatments. The algorithms is available at http://bioinfo.cs.uni.edu/AEONET.html.

Crohn's Disease and Early Exposure to Thiopurines are Independent Risk Factors for Mosaic Chromosomal Alterations in Patients with Inflammatory Bowel Diseases

BACKGROUND AND AIMS

Mosaic chromosomal alterations [mCAs] increase the risk for haematopoietic malignancies and may be risk factors for several other diseases. Inflammatory bowel diseases [IBDs], including Crohn's disease [CD] and ulcerative colitis [UC], are associated with mCAs, and patients may be at risk for haematopoietic malignancy development and/or modification of IBD phenotypes. In the present study, we screened patients with IBD for the presence of mCAs and explored the possible pathophysiological and genetic risk factors for mCAs.

METHODS

We analysed mCAs in peripheral blood from 3339 patients with IBD and investigated the clinical and genetic risk factors for mCAs.

RESULTS

CD and exposure to thiopurines before the age of 20 years were identified as novel independent risk factors for mCAs [odds ratio = 2.15 and 5.68, p = 1.17e-2 and 1.60e-3, respectively]. In contrast, there were no significant associations of disease duration, anti-tumour necrosis factor alpha antibodies, or other clinical factors with mCAs. Gene ontology enrichment analysis revealed that genes specifically located in the mCAs in patients with CD were significantly associated with factors related to mucosal immune responses. A genome-wide association study revealed that ERBIN, CD96, and AC068672.2 were significantly associated with mCAs in patients with CD [p = 1.56e-8, 1.65e-8, and 4.92e-8, respectively].

CONCLUSIONS

The difference in mCAs between patients with CD and UC supports the higher incidence of haematopoietic malignancies in CD. Caution should be exercised when using thiopurines in young patients with IBD, particularly CD, in light of possible chromosomal alterations.

Functional validation of variants of unknown significance using CRISPR gene editing and transcriptomics: A Kleefstra syndrome case study

There are an estimated > 400 million people living with a rare disease globally, with genetic variants the cause of approximately 80% of cases. Next Generation Sequencing (NGS) rapidly identifies genetic variants however they are often of unknown significance. Low throughput functional validation in specialist laboratories is the current ad hoc approach for functional validation of genetic variants, which creating major bottlenecks in patient diagnosis. This study investigates the application of CRISPR gene editing followed by genome wide transcriptomic profiling to facilitate patient diagnosis. As proof-of-concept, we introduced a variant in the Euchromatin histone methyl transferase (EHMT1) gene into HEK293T cells. We identified changes in the regulation of the cell cycle, neural gene expression and suppression of gene expression changes on chromosome 19 and chromosome X, that are in keeping with Kleefstra syndrome clinical phenotype and/or provide insight into disease mechanism. This study demonstrates the utility of genome editing followed by functional readouts to rapidly and systematically validating the function of variants of unknown significance in patients suffering from rare diseases.

Complementary evolution of coding and noncoding sequence underlies mammalian hairlessness

Body hair is a defining mammalian characteristic, but several mammals, such as whales, naked mole-rats, and humans, have notably less hair. To find the genetic basis of reduced hair quantity, we used our evolutionary-rates-based method, RERconverge, to identify coding and noncoding sequences that evolve at significantly different rates in so-called hairless mammals compared to hairy mammals. Using RERconverge, we performed a genome-wide scan over 62 mammal species using 19,149 genes and 343,598 conserved noncoding regions. In addition to detecting known and potential novel hair-related genes, we also discovered hundreds of putative hair-related regulatory elements. Computational investigation revealed that genes and their associated noncoding regions show different evolutionary patterns and influence different aspects of hair growth and development. Many genes under accelerated evolution are associated with the structure of the hair shaft itself, while evolutionary rate shifts in noncoding regions also included the dermal papilla and matrix regions of the hair follicle that contribute to hair growth and cycling. Genes that were top ranked for coding sequence acceleration included known hair and skin genes KRT2, KRT35, PKP1, and PTPRM that surprisingly showed no signals of evolutionary rate shifts in nearby noncoding regions. Conversely, accelerated noncoding regions are most strongly enriched near regulatory hair-related genes and microRNAs, such as mir205, ELF3, and FOXC1, that themselves do not show rate shifts in their protein-coding sequences. Such dichotomy highlights the interplay between the evolution of protein sequence and regulatory sequence to contribute to the emergence of a convergent phenotype.

High-quality reference genome of Fasciola gigantica: Insights into the genomic signatures of transposon-mediated evolution and specific parasitic adaption in tropical regions

Fasciola gigantica and Fasciola hepatica are causative pathogens of fascioliasis, with the widest latitudinal, longitudinal, and altitudinal distribution; however, among parasites, they have the largest sequenced genomes, hindering genomic research. In the present study, we used various sequencing and assembly technologies to generate a new high-quality Fasciola gigantica reference genome. We improved the integration of gene structure prediction, and identified two independent transposable element expansion events contributing to (1) the speciation between Fasciola and Fasciolopsis during the Cretaceous-Paleogene boundary mass extinction, and (2) the habitat switch to the liver during the Paleocene-Eocene Thermal Maximum, accompanied by gene length increment. Long interspersed element (LINE) duplication contributed to the second transposon-mediated alteration, showing an obvious trend of insertion into gene regions, regardless of strong purifying effect. Gene ontology analysis of genes with long LINE insertions identified membrane-associated and vesicle secretion process proteins, further implicating the functional alteration of the gene network. We identified 852 predicted excretory/secretory proteins and 3300 protein-protein interactions between Fasciola gigantica and its host. Among them, copper/zinc superoxide dismutase genes, with specific gene copy number variations, might play a central role in the phase I detoxification process. Analysis of 559 single-copy orthologs suggested that Fasciola gigantica and Fasciola hepatica diverged at 11.8 Ma near the Middle and Late Miocene Epoch boundary. We identified 98 rapidly evolving gene families, including actin and aquaporin, which might explain the large body size and the parasitic adaptive character resulting in these liver flukes becoming epidemic in tropical and subtropical regions.

Genomic Signatures of Coevolution between Nonmodel Mammals and Parasitic Roundworms

Antagonistic coevolution between host and parasite drives species evolution. However, most of the studies only focus on parasitism adaptation and do not explore the coevolution mechanisms from the perspective of both host and parasite. Here, through the de novo sequencing and assembly of the genomes of giant panda roundworm, red panda roundworm, and lion roundworm parasitic on tiger, we investigated the genomic mechanisms of coevolution between nonmodel mammals and their parasitic roundworms and those of roundworm parasitism in general. The genome-wide phylogeny revealed that these parasitic roundworms have not phylogenetically coevolved with their hosts. The CTSZ and prolyl 4-hydroxylase subunit beta (P4HB) immunoregulatory proteins played a central role in protein interaction between mammals and parasitic roundworms. The gene tree comparison identified that seven pairs of interactive proteins had consistent phylogenetic topology, suggesting their coevolution during host–parasite interaction. These coevolutionary proteins were particularly relevant to immune response. In addition, we found that the roundworms of both pandas exhibited higher proportions of metallopeptidase genes, and some positively selected genes were highly related to their larvae’s fast development. Our findings provide novel insights into the genetic mechanisms of coevolution between nonmodel mammals and parasites and offer the valuable genomic resources for scientific ascariasis prevention in both pandas.

Three Signatures of Adaptive Polymorphism Exemplified by Malaria-Associated Genes

Malaria has been one of the strongest selective pressures on our species. Many of the best-characterized cases of adaptive evolution in humans are in genes tied to malaria resistance. However, the complex evolutionary patterns at these genes are poorly captured by standard scans for nonneutral evolution. Here, we present three new statistical tests for selection based on population genetic patterns that are observed more than once among key malaria resistance loci. We assess these tests using forward-time evolutionary simulations and apply them to global whole-genome sequencing data from humans, and thus we show that they are effective at distinguishing selection from neutrality. Each test captures a distinct evolutionary pattern, here called Divergent Haplotypes, Repeated Shifts, and Arrested Sweeps, associated with a particular period of human prehistory. We clarify the selective signatures at known malaria-relevant genes and identify additional genes showing similar adaptive evolutionary patterns. Among our top outliers, we see a particular enrichment for genes involved in erythropoiesis and for genes previously associated with malaria resistance, consistent with a major role for malaria in shaping these patterns of genetic diversity. Polymorphisms at these genes are likely to impact resistance to malaria infection and contribute to ongoing host-parasite coevolutionary dynamics.

MicroRNAs for Virus Pathogenicity and Host Responses, Identified in SARS-CoV-2 Genomes, May Play Roles in Viral-Host Co-Evolution in Putative Zoonotic Host Species

Our recent study identified seven key microRNAs (miR-8066, 5197, 3611, 3934-3p, 1307-3p, 3691-3p, 1468-5p) similar between SARS-CoV-2 and the human genome, pointing at miR-related mechanisms in viral entry and the regulatory effects on host immunity. To identify the putative roles of these miRs in zoonosis, we assessed their conservation, compared with humans, in some key wild and domestic animal carriers of zoonotic viruses, including bat, pangolin, pig, cow, rat, and chicken. Out of the seven miRs under study, miR-3611 was the most strongly conserved across all species; miR-5197 was the most conserved in pangolin, pig, cow, bat, and rat; miR-1307 was most strongly conserved in pangolin, pig, cow, bat, and human; miR-3691-3p in pangolin, cow, and human; miR-3934-3p in pig and cow, followed by pangolin and bat; miR-1468 was most conserved in pangolin, pig, and bat; while miR-8066 was most conserved in pangolin and pig. In humans, miR-3611 and miR-1307 were most conserved, while miR-8066, miR-5197, miR-3334-3p and miR-1468 were least conserved, compared with pangolin, pig, cow, and bat. Furthermore, we identified that changes in the miR-5197 nucleotides between pangolin and human can generate three new miRs, with differing tissue distribution in the brain, lung, intestines, lymph nodes, and muscle, and with different downstream regulatory effects on KEGG pathways. This may be of considerable importance as miR-5197 is localized in the spike protein transcript area of the SARS-CoV-2 genome. Our findings may indicate roles for these miRs in viral-host co-evolution in zoonotic hosts, particularly highlighting pangolin, bat, cow, and pig as putative zoonotic carriers, while highlighting the miRs' roles in KEGG pathways linked to viral pathogenicity and host responses in humans. This in silico study paves the way for investigations into the roles of miRs in zoonotic disease.

Loss of the fragile X syndrome protein FMRP results in misregulation of nonsense-mediated mRNA decay

Loss of the fragile X protein FMRP is a leading cause of intellectual disability and autism^1,2, but the underlying mechanism remains poorly understood. We report that FMRP deficiency results in hyperactivated nonsense-mediated mRNA decay (NMD)^3,4 in human SH-SY5Y neuroblastoma cells and fragile X syndrome (FXS) fibroblast-derived induced pluripotent stem cells (iPSCs). We examined the underlying mechanism and found that the key NMD factor UPF1 binds directly to FMRP, promoting FMRP binding to NMD targets. Our data indicate that FMRP acts as an NMD repressor. In the absence of FMRP, NMD targets are relieved from FMRP-mediated translational repression so that their half-lives are decreased and, for those NMD targets encoding NMD factors, increased translation produces abnormally high factor levels despite their hyperactivated NMD. Transcriptome-wide alterations caused by NMD hyperactivation have a role in the FXS phenotype. Consistent with this, small-molecule-mediated inhibition of hyperactivated NMD, which typifies iPSCs derived from patients with FXS, restores a number of neurodifferentiation markers, including those not deriving from NMD targets. Our mechanistic studies reveal that many molecular abnormalities in FMRP-deficient cells are attributable-either directly or indirectly-to misregulated NMD.

Informatics Inference of Exercise-Induced Modulation of Brain Pathways Based on Cerebrospinal Fluid Micro-RNAs in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Introduction: The post-exertional malaise of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) was modeled by comparing micro-RNA (miRNA) in cerebrospinal fluid from subjects who had no exercise versus submaximal exercise. Materials and Methods: Differentially expressed miRNAs were examined by informatics methods to predict potential targets and regulatory pathways affected by exercise. Results: miR-608, miR-328, miR-200a-5p, miR-93-3p, and miR-92a-3p had higher levels in subjects who rested overnight (nonexercise n=45) compared to subjects who had exercised before their lumbar punctures (n=15). The combination was examined in DIANA MiRpath v3.0, TarBase, Cytoscape, and Ingenuity software® to select the intersection of target mRNAs. DIANA found 33 targets that may be elevated after exercise, including TGFBR1, IGFR1, and CDC42. Adhesion and adherens junctions were the most frequent pathways. Ingenuity selected seven targets that had complementary mechanistic pathways involving GNAQ, ADCY3, RAP1B, and PIK3R3. Potential target cells expressing high levels of these genes included choroid plexus, neurons, and microglia. Conclusion: The reduction of this combination of miRNAs in cerebrospinal fluid after exercise suggested upregulation of phosphoinositol signaling pathways and altered adhesion during the post-exertional malaise of ME/CFS. Clinical Trial Registration Nos.: NCT01291758 and NCT00810225.

Interaction screening by Kendall's partial correlation for ultrahigh-dimensional data with survival trait

MOTIVATION

In gene expression and genome-wide association studies, the identification of interaction effects is an important and challenging issue owing to its ultrahigh-dimensional nature. In particular, contaminated data and right-censored survival outcome make the associated feature screening even challenging.

RESULTS

In this article, we propose an inverse probability-of-censoring weighted Kendall's tau statistic to measure association of a survival trait with biomarkers, as well as a Kendall's partial correlation statistic to measure the relationship of a survival trait with an interaction variable conditional on the main effects. The Kendall's partial correlation is then used to conduct interaction screening. Simulation studies under various scenarios are performed to compare the performance of our proposal with some commonly available methods. In the real data application, we utilize our proposed method to identify epistasis associated with the clinical survival outcomes of non-small-cell lung cancer, diffuse large B-cell lymphoma and lung adenocarcinoma patients. Both simulation and real data studies demonstrate that our method performs well and outperforms existing methods in identifying main and interaction biomarkers.

AVAILABILITY AND IMPLEMENTATION

R-package 'IPCWK' is available to implement this method, together with a reference manual describing how to perform the 'IPCWK' package.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Dynamics of a Protein Interaction Network Associated to the Aggregation of polyQ-Expanded Ataxin-1

Background: Several experimental models of polyglutamine (polyQ) diseases have been previously developed that are useful for studying disease progression in the primarily affected central nervous system. However, there is a missing link between cellular and animal models that would indicate the molecular defects occurring in neurons and are responsible for the disease phenotype in vivo. Methods: Here, we used a computational approach to identify dysregulated pathways shared by an in vitro and an in vivo model of ATXN1(Q82) protein aggregation, the mutant protein that causes the neurodegenerative polyQ disease spinocerebellar ataxia type-1 (SCA1). Results: A set of common dysregulated pathways were identified, which were utilized to construct cerebellum-specific protein-protein interaction (PPI) networks at various time-points of protein aggregation. Analysis of a SCA1 network indicated important nodes which regulate its function and might represent potential pharmacological targets. Furthermore, a set of drugs interacting with these nodes and predicted to enter the blood–brain barrier (BBB) was identified. Conclusions: Our study points to molecular mechanisms of SCA1 linked from both cellular and animal models and suggests drugs that could be tested to determine whether they affect the aggregation of pathogenic ATXN1 and SCA1 disease progression.

Combination Treatment with GSK126 and Pomalidomide Induces B-Cell Differentiation in EZH2 Gain-of-Function Mutant Diffuse Large B-Cell Lymphoma

Simple Summary

To overcome the potential threat of drug resistance or limit of potency, the combination treatment of drugs is a promising strategy. Around 22% of patients with GCB-DLBCL carry EZH2 gain-of-function mutations and several PRC2 inhibitors are under clinical trials. Herein, we demonstrate that combination of GSK126 with pomalidomide synergistically inhibit tumor growth through inducing B-cell maturation and apoptosis in EZH2 gain-of-function mutant DLBCL. Our study provides the molecular basis of the combination strategy of PRC2 inhibitors and IMiDs in DLBCLs harboring EZH2 hyperactive mutation.

Abstract

Enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2), the catalytic subunit of polycomb repressive complex 2 (PRC2), regulates genes involved in cell lineage and differentiation through methylating lysine 27 on histone H3 (H3K27me3). Recurrent gain-of-function mutations of EZH2 have been identified in various cancer types, in particular, diffuse large B-cell lymphoma (DLBCL), through large-scale genome-wide association studies and EZH2 depletion or pharmacological inhibition has been shown to exert an antiproliferative effect on cancer cells, both in vitro and in vivo. In the current study, a combination of pomalidomide and GSK126 synergistically inhibited the growth of EZH2 gain-of-function mutant Diffuse large B-cell lymphoma (DLBCL) cells. Furthermore, this synergistic effect appeared to be dependent on cereblon (CRBN), a cellular receptor of pomalidomide, but not degradation of IKAROS family zinc finger 1 (IKZF1) or IKAROS family zinc finger 3 (IKZF3). RNA sequencing analyses revealed that co-treatment with GSK126 and pomalidomide induced specific gene sets involved in B-cell differentiation and apoptosis. Synergistic growth inhibition and B-cell differentiation were further validated in xenograft mouse models. Our collective results provide a molecular basis for the mechanisms underlying the combined therapeutic effects of PRC2 inhibitors and pomalidomide on EZH2-mutated DLBCL.

Identification and characterization of cellular heterogeneity within the developing renal interstitium

Kidney formation requires the coordinated growth of multiple cell types including the collecting ducts, nephrons, vasculature and interstitium. There is a long-held belief that interactions between progenitors of the collecting ducts and nephrons are primarily responsible for kidney development. However, over the last several years, it has become increasingly clear that multiple aspects of kidney development require signaling from the interstitium. How the interstitium orchestrates these various roles is poorly understood. Here, we show that during development the interstitium is a highly heterogeneous patterned population of cells that occupies distinct positions correlated to the adjacent parenchyma. Our analysis indicates that the heterogeneity is not a mere reflection of different stages in a linear developmental trajectory but instead represents several novel differentiated cell states. Further, we find that β-catenin has a cell autonomous role in the development of a medullary subset of the interstitium and that this non-autonomously affects the development of the adjacent epithelia. These findings suggest the intriguing possibility that the different interstitial subtypes may create microenvironments that play unique roles in development of the adjacent epithelia and endothelia.

Exploring the functional impact of alternative splicing on human protein isoforms using available annotation sources

In recent years, the emphasis of scientific inquiry has shifted from whole-genome analyses to an understanding of cellular responses specific to tissue, developmental stage or environmental conditions. One of the central mechanisms underlying the diversity and adaptability of the contextual responses is alternative splicing (AS). It enables a single gene to encode multiple isoforms with distinct biological functions. However, to date, the functions of the vast majority of differentially spliced protein isoforms are not known. Integration of genomic, proteomic, functional, phenotypic and contextual information is essential for supporting isoform-based modeling and analysis. Such integrative proteogenomics approaches promise to provide insights into the functions of the alternatively spliced protein isoforms and provide high-confidence hypotheses to be validated experimentally. This manuscript provides a survey of the public databases supporting isoform-based biology. It also presents an overview of the potential global impact of AS on the human canonical gene functions, molecular interactions and cellular pathways.

Implications of SARS-CoV-2 Mutations for Genomic RNA Structure and Host microRNA Targeting

The SARS-CoV-2 virus is a recently-emerged zoonotic pathogen already well adapted to transmission and replication in humans. Although the mutation rate is limited, recently introduced mutations in SARS-CoV-2 have the potential to alter viral fitness. In addition to amino acid changes, mutations could affect RNA secondary structure critical to viral life cycle, or interfere with sequences targeted by host miRNAs. We have analysed subsets of genomes from SARS-CoV-2 isolates from around the globe and show that several mutations introduce changes in Watson-Crick pairing, with resultant changes in predicted secondary structure. Filtering to targets matching miRNAs expressed in SARS-CoV-2-permissive host cells, we identified ten separate target sequences in the SARS-CoV-2 genome; three of these targets have been lost through conserved mutations. A genomic site targeted by the highly abundant miR-197-5p, overexpressed in patients with cardiovascular disease, is lost by a conserved mutation. Our results are compatible with a model that SARS-CoV-2 replication within the human host is constrained by host miRNA defences. The impact of these and further mutations on secondary structures, miRNA targets or potential splice sites offers a new context in which to view future SARS-CoV-2 evolution, and a potential platform for engineering conditional attenuation to vaccine development, as well as providing a better understanding of viral tropism and pathogenesis.

Epimutations in Developmental Genes Underlie the Onset of Domestication in Farmed European Sea Bass

Domestication of wild animals induces a set of phenotypic characteristics collectively known as the domestication syndrome. However, how this syndrome emerges is still not clear. Recently, the neural crest cell deficit hypothesis proposed that it is generated by a mildly disrupted neural crest cell developmental program, but clear support is lacking due to the difficulties of distinguishing pure domestication effects from preexisting genetic differences between farmed and wild mammals and birds. Here, we use a farmed fish as model to investigate the role of persistent changes in DNA methylation (epimutations) in the process of domestication. We show that early domesticates of sea bass, with no genetic differences with wild counterparts, contain epimutations in tissues with different embryonic origins. About one fifth of epimutations that persist into adulthood are established by the time of gastrulation and affect genes involved in developmental processes that are expressed in embryonic structures, including the neural crest. Some of these genes are differentially expressed in sea bass with lower jaw malformations, a key feature of domestication syndrome. Interestingly, these epimutations significantly overlap with cytosine-to-thymine polymorphisms after 25 years of selective breeding. Furthermore, epimutated genes coincide with genes under positive selection in other domesticates. We argue that the initial stages of domestication include dynamic alterations in DNA methylation of developmental genes that affect the neural crest. Our results indicate a role for epimutations during the beginning of domestication that could be fixed as genetic variants and suggest a conserved molecular process to explain Darwin’s domestication syndrome across vertebrates.

CoCoScore: context-aware co-occurrence scoring for text mining applications using distant supervision

MOTIVATION

Information extraction by mining the scientific literature is key to uncovering relations between biomedical entities. Most existing approaches based on natural language processing extract relations from single sentence-level co-mentions, ignoring co-occurrence statistics over the whole corpus. Existing approaches counting entity co-occurrences ignore the textual context of each co-occurrence.

RESULTS

We propose a novel corpus-wide co-occurrence scoring approach to relation extraction that takes the textual context of each co-mention into account. Our method, called CoCoScore, scores the certainty of stating an association for each sentence that co-mentions two entities. CoCoScore is trained using distant supervision based on a gold-standard set of associations between entities of interest. Instead of requiring a manually annotated training corpus, co-mentions are labeled as positives/negatives according to their presence/absence in the gold standard. We show that CoCoScore outperforms previous approaches in identifying human disease-gene and tissue-gene associations as well as in identifying physical and functional protein-protein associations in different species. CoCoScore is a versatile text mining tool to uncover pairwise associations via co-occurrence mining, within and beyond biomedical applications.

AVAILABILITY AND IMPLEMENTATION

CoCoScore is available at: https://github.com/JungeAlexander/cocoscore.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

LSD1 Cooperates with Noncanonical NF-κB Signaling to Regulate Marginal Zone B Cell Development

Marginal zone B cells (MZB) are a mature B cell subset that rapidly respond to blood-borne pathogens. Although the transcriptional changes that occur throughout MZB development are known, the corresponding epigenetic changes and epigenetic modifying proteins that facilitate these changes are poorly understood. The histone demethylase LSD1 is an epigenetic modifier that promotes plasmablast formation, but its role in B cell development has not been explored. In this study, a role for LSD1 in the development of B cell subsets was examined. B cell-conditional deletion of LSD1 in mice resulted in a decrease in MZB whereas follicular B cells and bone marrow B cell populations were minimally affected. LSD1 repressed genes in MZB that were normally upregulated in the myeloid and follicular B cell lineages. Correspondingly, LSD1 regulated chromatin accessibility at the motifs of transcription factors known to regulate splenic B cell development, including NF-κB motifs. The importance of NF-κB signaling was examined through an ex vivo MZB development assay, which showed that both LSD1-deficient and NF-κB-inhibited transitional B cells failed to undergo full MZB development. Gene expression and chromatin accessibility analyses of in vivo- and ex vivo-generated LSD1-deficient MZB indicated that LSD1 regulated the downstream target genes of noncanonical NF-κB signaling. Additionally LSD1 was found to interact with the noncanonical NF-κB transcription factor p52. Together, these data reveal that the epigenetic modulation of the noncanonical NF-κB signaling pathway by LSD1 is an essential process during the development of MZB.

Gene ORGANizer: linking genes to the organs they affect

One of the biggest challenges in studying how genes work is understanding their effect on the physiology and anatomy of the body. Existing tools try to address this using indirect features, such as expression levels and biochemical pathways. Here, we present Gene ORGANizer (geneorganizer.huji.ac.il), a phenotype-based tool that directly links human genes to the body parts they affect. It is built upon an exhaustive curated database that links >7000 genes to ∼150 anatomical parts using >150 000 gene-organ associations. The tool offers user-friendly platforms to analyze the anatomical effects of individual genes, and identify trends within groups of genes. We demonstrate how Gene ORGANizer can be used to make new discoveries, showing that chromosome X is enriched with genes affecting facial features, that positive selection targets genes with more constrained phenotypic effects, and more. We expect Gene ORGANizer to be useful in a variety of evolutionary, medical and molecular studies aimed at understanding the phenotypic effects of genes.

The Promoter Regions of Intellectual Disability-Associated Genes Are Uniquely Enriched in LTR Sequences of the MER41 Primate-Specific Endogenous Retrovirus: An Evolutionary Connection Between Immunity and Cognition

Social behavior and neuronal connectivity in rodents have been shown to be shaped by the prototypical T lymphocyte-derived pro-inflammatory cytokine Interferon-gamma (IFNγ). It has also been demonstrated that STAT1 (Signal Transducer And Activator Of Transcription 1), a transcription factor (TF) crucially involved in the IFNγ pathway, binds consensus sequences that, in humans, are located with a high frequency in the LTRs (Long Terminal Repeats) of the MER41 family of primate-specific HERVs (Human Endogenous Retroviruses). However, the putative role of an IFNγ/STAT1/MER41 pathway in human cognition and/or behavior is still poorly documented. Here, we present evidence that the promoter regions of intellectual disability-associated genes are uniquely enriched in LTR sequences of the MER41 HERVs. This observation is specific to MER41 among more than 130 HERVs examined. Moreover, we have not found such a significant enrichment in the promoter regions of genes that associate with autism spectrum disorder (ASD) or schizophrenia. Interestingly, ID-associated genes exhibit promoter-localized MER41 LTRs that harbor TF binding sites (TFBSs) for not only STAT1 but also other immune TFs such as, in particular, NFKB1 (Nuclear Factor Kappa B Subunit 1) and STAT3 (Signal Transducer And Activator Of Transcription 3). Moreover, IL-6 (Interleukin 6) rather than IFNγ, is identified as the main candidate cytokine regulating such an immune/MER41/cognition pathway. Of note, differences between humans and chimpanzees are observed regarding the insertion sites of MER41 LTRs in the promoter regions of ID-associated genes. Finally, a survey of the human proteome has allowed us to map a protein-protein network which links the identified immune/MER41/cognition pathway to FOXP2 (Forkhead Box P2), a key TF involved in the emergence of human speech. Our work suggests that together with the evolution of immune genes, the stepped self-domestication of MER41 in the genomes of primates could have contributed to cognitive evolution. We further propose that non-inherited forms of ID might result from the untimely or quantitatively inappropriate expression of immune signals, notably IL-6, that putatively regulate cognition-associated genes via promoter-localized MER41 LTRs.

Design, implementation, and operation of a rapid, robust named entity recognition web service

Most BioCreative tasks to date have focused on assessing the quality of text-mining annotations in terms of precision and recall. Interoperability, speed, and stability are, however, other important factors to consider for practical applications of text mining. For about a decade, we have run named entity recognition (NER) web services, which are designed to be efficient, implemented using a multi-threaded queueing system to robustly handle many simultaneous requests, and hosted at a supercomputer facility. To participate in this new task, we extended the existing NER tagging service with support for the BeCalm API. The tagger suffered no downtime during the challenge and, as in earlier tests, proved to be highly efficient, consistently processing requests of 5000 abstracts in less than half a minute. In fact, the majority of this time was spent not on the NER task but rather on retrieving the document texts from the challenge servers. The latter was found to be the main bottleneck even when hosting a copy of the tagging service on a Raspberry Pi 3, showing that local document storage or caching would be desirable features to include in future revisions of the API standard.

Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing

Risk for late-onset Alzheimer's disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of clinically diagnosed LOAD (94,437 individuals). We confirm 20 previous LOAD risk loci and identify five new genome-wide loci (IQCK, ACE, ADAM10, ADAMTS1, and WWOX), two of which (ADAM10, ACE) were identified in a recent genome-wide association (GWAS)-by-familial-proxy of Alzheimer's or dementia. Fine-mapping of the human leukocyte antigen (HLA) region confirms the neurological and immune-mediated disease haplotype HLA-DR15 as a risk factor for LOAD. Pathway analysis implicates immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein (APP) metabolism, showing that genetic variants affecting APP and Aβ processing are associated not only with early-onset autosomal dominant Alzheimer's disease but also with LOAD. Analyses of risk genes and pathways show enrichment for rare variants (P = 1.32 × 10-7), indicating that additional rare variants remain to be identified. We also identify important genetic correlations between LOAD and traits such as family history of dementia and education.

Analysis of Predicted Host-Parasite Interactomes Reveals Commonalities and Specificities Related to Parasitic Lifestyle and Tissues Tropism

The study of molecular host–parasite interactions is essential to understand parasitic infection and adaptation within the host system. As well, prevention and treatment of infectious diseases require a clear understanding of the molecular crosstalk between parasites and their hosts. Yet, large-scale experimental identification of host–parasite molecular interactions remains challenging, and the use of computational predictions becomes then necessary. Here, we propose a computational integrative approach to predict host—parasite protein—protein interaction (PPI) networks resulting from the human infection by 15 different eukaryotic parasites. We used an orthology-based approach to transfer high-confidence intraspecies interactions obtained from the STRING database to the corresponding interspecies homolog protein pairs in the host–parasite system. Our approach uses either the parasites predicted secretome and membrane proteins, or only the secretome, depending on whether they are uni- or multi-cellular, respectively, to reduce the number of false predictions. Moreover, the host proteome is filtered for proteins expressed in selected cellular localizations and tissues supporting the parasite growth. We evaluated the inferred interactions by analyzing the enriched biological processes and pathways in the predicted networks and their association with known parasitic invasion and evasion mechanisms. The resulting PPI networks were compared across parasites to identify common mechanisms that may define a global pathogenic hallmark. We also provided a study case focusing on a closer examination of the human–S. mansoni predicted interactome, detecting central proteins that have relevant roles in the human–S. mansoni network, and identifying tissue-specific interactions with key roles in the life cycle of the parasite. The predicted PPI networks can be visualized and downloaded at http://orthohpi.jensenlab.org.

How to Develop a Drug Target Ontology: KNowledge Acquisition and Representation Methodology (KNARM)

Technological advancements in many fields have led to huge increases in data production, including data volume, diversity, and the speed at which new data is becoming available. In accordance with this, there is a lack of conformity in the ways data is interpreted. This era of "big data" provides unprecedented opportunities for data-driven research and "big picture" models. However, in-depth analyses-making use of various data types and data sources and extracting knowledge-have become a more daunting task. This is especially the case in life sciences where simplification and flattening of diverse data types often lead to incorrect predictions. Effective applications of big data approaches in life sciences require better, knowledge-based, semantic models that are suitable as a framework for big data integration, while avoiding oversimplifications, such as reducing various biological data types to the gene level. A huge hurdle in developing such semantic knowledge models, or ontologies, is the knowledge acquisition bottleneck. Automated methods are still very limited, and significant human expertise is required. In this chapter, we describe a methodology to systematize this knowledge acquisition and representation challenge, termed KNowledge Acquisition and Representation Methodology (KNARM). We then describe application of the methodology while implementing the Drug Target Ontology (DTO). We aimed to create an approach, involving domain experts and knowledge engineers, to build useful, comprehensive, consistent ontologies that will enable big data approaches in the domain of drug discovery, without the currently common simplifications.

A Guide to Dictionary-Based Text Mining

PubMed contains more than 27 million documents, and this number is growing at an estimated 4% per year. Even within specialized topics, it is no longer possible for a researcher to read any field in its entirety, and thus nobody has a complete picture of the scientific knowledge in any given field at any time. Text mining provides a means to automatically read this corpus and to extract the relations found therein as structured information. Having data in a structured format is a huge boon for computational efforts to access, cross reference, and mine the data stored therein. This is increasingly useful as biological research is becoming more focused on systems and multi-omics integration. This chapter provides an overview of the steps that are required for text mining: tokenization, named entity recognition, normalization, event extraction, and benchmarking. It discusses a variety of approaches to these tasks and then goes into detail on how to prepare data for use specifically with the JensenLab tagger. This software uses a dictionary-based approach and provides the text mining evidence for STRING and several other databases.

TISSUES 2.0: an integrative web resource on mammalian tissue expression

Physiological and molecular similarities between organisms make it possible to translate findings from simpler experimental systems—model organisms—into more complex ones, such as human. This translation facilitates the understanding of biological processes under normal or disease conditions. Researchers aiming to identify the similarities and differences between organisms at the molecular level need resources collecting multi-organism tissue expression data. We have developed a database of gene–tissue associations in human, mouse, rat and pig by integrating multiple sources of evidence: transcriptomics covering all four species and proteomics (human only), manually curated and mined from the scientific literature. Through a scoring scheme, these associations are made comparable across all sources of evidence and across organisms. Furthermore, the scoring produces a confidence score assigned to each of the associations. The TISSUES database (version 2.0) is publicly accessible through a user-friendly web interface and as part of the STRING app for Cytoscape. In addition, we analyzed the agreement between datasets, across and within organisms, and identified that the agreement is mainly affected by the quality of the datasets rather than by the technologies used or organisms compared.

Database URL: http://tissues.jensenlab.org/

Rare Variants in Known Susceptibility Loci and Their Contribution to Risk of Lung Cancer

BACKGROUND

Genome-wide association studies are widely used to map genomic regions contributing to lung cancer (LC) susceptibility, but they typically do not identify the precise disease-causing genes/variants. To unveil the inherited genetic variants that cause LC, we performed focused exome-sequencing analyses on genes located in 121 genome-wide association study-identified loci previously implicated in the risk of LC, chronic obstructive pulmonary disease, pulmonary function level, and smoking behavior.

METHODS

Germline DNA from 260 case patients with LC and 318 controls were sequenced by utilizing VCRome 2.1 exome capture. Filtering was based on enrichment of rare and potential deleterious variants in cases (risk alleles) or controls (protective alleles). Allelic association analyses of single-variant and gene-based burden tests of multiple variants were performed. Promising candidates were tested in two independent validation studies with a total of 1773 case patients and 1123 controls.

RESULTS

We identified 48 rare variants with deleterious effects in the discovery analysis and validated 12 of the 43 candidates that were covered in the validation platforms. The top validated candidates included one well-established truncating variant, namely, BRCA2, DNA repair associated gene (BRCA2) K3326X (OR = 2.36, 95% confidence interval [CI]: 1.38-3.99), and three newly identified variations, namely, lymphotoxin beta gene (LTB) p.Leu87Phe (OR = 7.52, 95% CI: 1.01-16.56), prolyl 3-hydroxylase 2 gene (P3H2) p.Gln185His (OR = 5.39, 95% CI: 0.75-15.43), and dishevelled associated activator of morphogenesis 2 gene (DAAM2) p.Asp762Gly (OR = 0.25, 95% CI: 0.10-0.79). Burden tests revealed strong associations between zinc finger protein 93 gene (ZNF93), DAAM2, bromodomain containing 9 gene (BRD9), and the gene LTB and LC susceptibility.

CONCLUSION

Our results extend the catalogue of regions associated with LC and highlight the importance of germline rare coding variants in LC susceptibility.

Context-dependent prediction of protein complexes by SiComPre

Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein-protein interactions and resulting in variations in the complexome. Quantitative and qualitative characterization of context-specific protein complexes will help to better understand context-dependent variations in the physiological behavior of cells. Here, we present SiComPre 1.0, a computational tool that predicts context-specific protein complexes by integrating multi-omics sources. SiComPre outperforms other protein complex prediction tools in qualitative predictions and is unique in giving quantitative predictions on the complexome depending on the specific interactions and protein abundances defined by the user. We provide tutorials and examples on the complexome prediction of common model organisms, various human tissues and how the complexome is affected by drug treatment.

Site-specific characterization of endogenous SUMOylation across species and organs

Small ubiquitin-like modifiers (SUMOs) are post-translational modifications that play crucial roles in most cellular processes. While methods exist to study exogenous SUMOylation, large-scale characterization of endogenous SUMO2/3 has remained technically daunting. Here, we describe a proteomics approach facilitating system-wide and in vivo identification of lysines modified by endogenous and native SUMO2. Using a peptide-level immunoprecipitation enrichment strategy, we identify 14,869 endogenous SUMO2/3 sites in human cells during heat stress and proteasomal inhibition, and quantitatively map 1963 SUMO sites across eight mouse tissues. Characterization of the SUMO equilibrium highlights striking differences in SUMO metabolism between cultured cancer cells and normal tissues. Targeting preferences of SUMO2/3 vary across different organ types, coinciding with markedly differential SUMOylation states of all enzymes involved in the SUMO conjugation cascade. Collectively, our systemic investigation details the SUMOylation architecture across species and organs and provides a resource of endogenous SUMOylation sites on factors important in organ-specific functions.

Proteomics is a powerful method to study protein SUMOylation, but system-wide insights into endogenous SUMO2/3 modification events are still sparse. Here, the authors develop a more sensitive SUMO proteomics approach, providing detailed maps of endogenous SUMO2/3 sites in human cells and mouse tissues.

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success. We obtained features from the recently published comprehensive resource: Harmonizome. Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes, but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias. We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features (AUROC = 0.57 and AUPR = 0.81). The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues, where successful targets tended to have lower mean expression and higher expression variance than failed targets. This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Overall, our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection. We anticipate deeper insights and better models in the future, as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features. Code, documentation, and data for this study have been deposited on GitHub at https://github.com/arouillard/omic-features-successful-targets.

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823-2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein-protein, disease-gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates

BACKGROUND

In current statistical methods for calling differentially expressed genes in RNA-Seq experiments, the assumption is that an adjusted observed gene count represents an unknown true gene count. This adjustment usually consists of a normalization step to account for heterogeneous sample library sizes, and then the resulting normalized gene counts are used as input for parametric or non-parametric differential gene expression tests. A distribution of true gene counts, each with a different probability, can result in the same observed gene count. Importantly, sequencing coverage information is currently not explicitly incorporated into any of the statistical models used for RNA-Seq analysis.

RESULTS

We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data.

CONCLUSIONS

Our results suggest that our method can be used to overcome analytical bottlenecks in experiments with limited number of replicates and low sequencing coverage. The method is implemented in CORNAS (Coverage-dependent RNA-Seq), and is available at https://github.com/joel-lzb/CORNAS .

Drug target ontology to classify and integrate drug discovery data

Background

One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome.

Results

As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships.

Conclusions

DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/, Github (http://github.com/DrugTargetOntology/DTO), and the NCBO Bioportal (http://bioportal.bioontology.org/ontologies/DTO). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Electronic supplementary material

The online version of this article (10.1186/s13326-017-0161-x) contains supplementary material, which is available to authorized users.

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

Neuroinflammation and ALS: Transcriptomic Insights into Molecular Disease Mechanisms and Therapeutic Targets

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting the motor nervous system. Despite the mechanism underlying motor neuron death is not yet clarified, multiple pathogenic processes have been proposed to account for ALS. Among these, inflammatory/immune responses have recently gained particular interest, although there are conflicting reports on the role of these processes in ALS pathogenesis and treatment. This apparent discrepancy may be due to the absence of an effective stratification of ALS patients into subgroups with markedly different clinical, biological, and molecular features. Our research group recently described genome-wide characterization of motor cortex samples from sporadic ALS (SALS) patients, revealing the existence of molecular and functional heterogeneity in SALS. Here, we reexamine data coming from our previous work, focusing on transcriptomic changes of inflammatory-related genes, in order to investigate their potential contribution in ALS. A total of 1573 inflammatory genes were identified as differentially expressed between SALS patients and controls, characterizing distinct topological pathways and networks, suggestive of specific inflammatory molecular signatures for different patient subgroups. Besides providing promising insights into the intricate relationship between inflammation and ALS, this paper represents a starting point for the rationale design and development of novel and more effective diagnostic and therapeutic applications.

Computational polypharmacology: a new paradigm for drug discovery

INTRODUCTION

Over the past couple of years, the cost of drug development has sharply increased along with the high rate of clinical trial failures. Such increase in expenses is partially due to the inability of the "one drug - one target" approach to predict drug side effects and toxicities. To tackle this issue, an alternative approach, known as polypharmacology, is being adopted to study small molecule interactions with multiple targets. Apart from developing more potent and effective drugs, this approach allows for studies of off-target activities and the facilitation of drug repositioning. Although exhaustive polypharmacology studies in-vitro or in-vivo are not practical, computational methods of predicting unknown targets or side effects are being developed. Areas covered: This article describes various computational approaches that have been developed to study polypharmacology profiles of small molecules. It also provides a brief description of the algorithms used in these state-of-the-art methods. Expert opinion: Recent success in computational prediction of multi-targeting drugs has established polypharmacology as a promising alternative approach to tackle some of the daunting complications in drug discovery. This will not only help discover more effective agents, but also present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.