USP15 regulates type I interferon response and is required for pathogenesis of neuroinflammation.

Genes and pathways in which inactivation dampens tissue inflammation present new opportunities for understanding the pathogenesis of common human inflammatory diseases, including inflammatory bowel disease, rheumatoid arthritis and multiple sclerosis. We identified a mutation in the gene encoding the deubiquitination enzyme USP15 (Usp15L749R) that protected mice against both experimental cerebral malaria (ECM) induced by Plasmodium berghei and experimental autoimmune encephalomyelitis (EAE). Combining immunophenotyping and RNA sequencing in brain (ECM) and spinal cord (EAE) revealed that Usp15L749R-associated resistance to neuroinflammation was linked to dampened type I interferon responses in situ. In hematopoietic cells and in resident brain cells, USP15 was coexpressed with, and functionally acted together with the E3 ubiquitin ligase TRIM25 to positively regulate type I interferon responses and to promote pathogenesis during neuroinflammation. The USP15-TRIM25 dyad might be a potential target for intervention in acute or chronic states of neuroinflammation.

'Single-subject studies'-derived analyses unveil altered biomechanisms between very small cohorts: implications for rare diseases.

MOTIVATION: Identifying altered transcripts between very small human cohorts is particularly challenging and is compounded by the low accrual rate of human subjects in rare diseases or sub-stratified common disorders. Yet, single-subject studies (S3) can compare paired transcriptome samples drawn from the same patient under two conditions (e.g. treated versus pre-treatment) and suggest patient-specific responsive biomechanisms based on the overrepresentation of functionally defined gene sets. These improve statistical power by: (i) reducing the total features tested and (ii) relaxing the requirement of within-cohort uniformity at the transcript level. We propose Inter-N-of-1, a novel method, to identify meaningful differences between very small cohorts by using the effect size of 'single-subject-study'-derived responsive biological mechanisms. RESULTS: In each subject, Inter-N-of-1 requires applying previously published S3-type N-of-1-pathways MixEnrich to two paired samples (e.g. diseased versus unaffected tissues) for determining patient-specific enriched genes sets: Odds Ratios (S3-OR) and S3-variance using Gene Ontology Biological Processes. To evaluate small cohorts, we calculated the precision and recall of Inter-N-of-1 and that of a control method (GLM+EGS) when comparing two cohorts of decreasing sizes (from 20 versus 20 to 2 versus 2) in a comprehensive six-parameter simulation and in a proof-of-concept clinical dataset. In simulations, the Inter-N-of-1 median precision and recall are > 90% and >75% in cohorts of 3 versus 3 distinct subjects (regardless of the parameter values), whereas conventional methods outperform Inter-N-of-1 at sample sizes 9 versus 9 and larger. Similar results were obtained in the clinical proof-of-concept dataset. AVAILABILITY AND IMPLEMENTATION: R software is available at Lussierlab.net/BSSD.

Analysis of protein-coding genetic variation in 60,706 humans.

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.

Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes.

The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile ('personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about 'average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's 'personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive 'personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.

binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions.

BACKGROUND: In this era of data science-driven bioinformatics, machine learning research has focused on feature selection as users want more interpretation and post-hoc analyses for biomarker detection. However, when there are more features (i.e., transcripts) than samples (i.e., mice or human samples) in a study, it poses major statistical challenges in biomarker detection tasks as traditional statistical techniques are underpowered in high dimension. Second and third order interactions of these features pose a substantial combinatoric dimensional challenge. In computational biology, random forest (RF) classifiers are widely used due to their flexibility, powerful performance, their ability to rank features, and their robustness to the "P > > N" high-dimensional limitation that many matrix regression algorithms face. We propose binomialRF, a feature selection technique in RFs that provides an alternative interpretation for features using a correlated binomial distribution and scales efficiently to analyze multiway interactions. RESULTS: In both simulations and validation studies using datasets from the TCGA and UCI repositories, binomialRF showed computational gains (up to 5 to 300 times faster) while maintaining competitive variable precision and recall in identifying biomarkers' main effects and interactions. In two clinical studies, the binomialRF algorithm prioritizes previously-published relevant pathological molecular mechanisms (features) with high classification precision and recall using features alone, as well as with their statistical interactions alone. CONCLUSION: binomialRF extends upon previous methods for identifying interpretable features in RFs and brings them together under a correlated binomial distribution to create an efficient hypothesis testing algorithm that identifies biomarkers' main effects and interactions. Preliminary results in simulations demonstrate computational gains while retaining competitive model selection and classification accuracies. Future work will extend this framework to incorporate ontologies that provide pathway-level feature selection from gene expression input data.

Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions.

An amendment to this paper has been published and can be accessed via the original article.

kMEn: Analyzing noisy and bidirectional transcriptional pathway responses in single subjects.

MOTIVATION: Understanding dynamic, patient-level transcriptomic response to therapy is an important step forward for precision medicine. However, conventional transcriptome analysis aims to discover cohort-level change, lacking the capacity to unveil patient-specific response to therapy. To address this gap, we previously developed two N-of-1-pathways methods, Wilcoxon and Mahalanobis distance, to detect unidirectionally responsive transcripts within a pathway using a pair of samples from a single subject. Yet, these methods cannot recognize bidirectionally (up and down) responsive pathways. Further, our previous approaches have not been assessed in presence of background noise and are not designed to identify differentially expressed mRNAs between two samples of a patient taken in different contexts (e.g. cancer vs non cancer), which we termed responsive transcripts (RTs). METHODS: We propose a new N-of-1-pathways method, k-Means Enrichment (kMEn), that detects bidirectionally responsive pathways, despite background noise, using a pair of transcriptomes from a single patient. kMEn identifies transcripts responsive to the stimulus through k-means clustering and then tests for an over-representation of the responsive genes within each pathway. The pathways identified by kMEn are mechanistically interpretable pathways significantly responding to a stimulus. RESULTS: In ∼9000 simulations varying six parameters, superior performance of kMEn over previous single-subject methods is evident by: (i) improved precision-recall at various levels of bidirectional response and (ii) lower rates of false positives (1-specificity) when more than 10% of genes in the genome are differentially expressed (background noise). In a clinical proof-of-concept, personal treatment-specific pathways identified by kMEn correlate with therapeutic response (p-value<0.01). CONCLUSION: Through improved single-subject transcriptome dynamics of bidirectionally-regulated signals, kMEn provides a novel approach to identify mechanism-level biomarkers.

The mouse Gene Expression Database (GXD): 2014 update.

The Gene Expression Database (GXD; http://www.informatics.jax.org/expression.shtml) is an extensive and well-curated community resource of mouse developmental expression information. GXD collects different types of expression data from studies of wild-type and mutant mice, covering all developmental stages and including data from RNA in situ hybridization, immunohistochemistry, RT-PCR, northern blot and western blot experiments. The data are acquired from the scientific literature and from researchers, including groups doing large-scale expression studies. Integration with the other data in Mouse Genome Informatics (MGI) and interconnections with other databases places GXD's gene expression information in the larger biological and biomedical context. Since the last report, the utility of GXD has been greatly enhanced by the addition of new data and by the implementation of more powerful and versatile search and display features. Web interface enhancements include the capability to search for expression data for genes associated with specific phenotypes and/or human diseases; new, more interactive data summaries; easy downloading of data; direct searches of expression images via associated metadata; and new displays that combine image data and their associated annotations. At present, GXD includes >1.4 million expression results and 250,000 images that are accessible to our search tools.

THEMIS is required for pathogenesis of cerebral malaria and protection against pulmonary tuberculosis.

We identify an N-ethyl-N-nitrosourea (ENU)-induced I23N mutation in the THEMIS protein that causes protection against experimental cerebral malaria (ECM) caused by infection with Plasmodium berghei ANKA. Themis(I23N) homozygous mice show reduced CD4(+) and CD8(+) T lymphocyte numbers. ECM resistance in P. berghei ANKA-infected Themis(I23N) mice is associated with decreased cerebral cellular infiltration, retention of blood-brain barrier integrity, and reduced proinflammatory cytokine production. THEMIS(I23N) protein expression is absent from mutant mice, concurrent with the decreased THEMIS(I23N) stability observed in vitro. Biochemical studies in vitro and functional complementation in vivo in Themis(I23N/+):Lck(-/+) doubly heterozygous mice demonstrate that functional coupling of THEMIS to LCK tyrosine kinase is required for ECM pathogenesis. Damping of proinflammatory responses in Themis(I23N) mice causes susceptibility to pulmonary tuberculosis. Thus, THEMIS is required for the development and ultimately the function of proinflammatory T cells. Themis(I23N) mice can be used to study the newly discovered association of THEMIS (6p22.33) with inflammatory bowel disease and multiple sclerosis.

Irf8-regulated genomic responses drive pathological inflammation during cerebral malaria.

Interferon Regulatory Factor 8 (IRF8) is required for development, maturation and expression of anti-microbial defenses of myeloid cells. BXH2 mice harbor a severely hypomorphic allele at Irf8 (Irf8(R294C)) that causes susceptibility to infection with intracellular pathogens including Mycobacterium tuberculosis. We report that BXH2 are completely resistant to the development of cerebral malaria (ECM) following Plasmodium berghei ANKA infection. Comparative transcriptional profiling of brain RNA as well as chromatin immunoprecipitation and high-throughput sequencing (ChIP-seq) was used to identify IRF8-regulated genes whose expression is associated with pathological acute neuroinflammation. Genes increased by infection were strongly enriched for IRF8 binding sites, suggesting that IRF8 acts as a transcriptional activator in inflammatory programs. These lists were enriched for myeloid-specific pathways, including interferon responses, antigen presentation and Th1 polarizing cytokines. We show that inactivation of several of these downstream target genes (including the Irf8 transcription partner Irf1) confers protection against ECM. ECM-resistance in Irf8 and Irf1 mutants is associated with impaired myeloid and lymphoid cells function, including production of IL12p40 and IFNγ. We note strong overlap between genes bound and regulated by IRF8 during ECM and genes regulated in the lungs of M. tuberculosis infected mice. This IRF8-dependent network contains several genes recently identified as risk factors in acute and chronic human inflammatory conditions. We report a common core of IRF8-bound genes forming a critical inflammatory host-response network.

Evidence for additive and interaction effects of host genotype and infection in malaria.

The host mechanisms responsible for protection against malaria remain poorly understood, with only a few protective genetic effects mapped in humans. Here, we characterize a host-specific genome-wide signature in whole-blood transcriptomes of Plasmodium falciparum-infected West African children and report a demonstration of genotype-by-infection interactions in vivo. Several associations involve transcripts sensitive to infection and implicate complement system, antigen processing and presentation, and T-cell activation (i.e., SLC39A8, C3AR1, FCGR3B, RAD21, RETN, LRRC25, SLC3A2, and TAPBP), including one association that validated a genome-wide association candidate gene (SCO1), implicating binding variation within a noncoding regulatory element. Gene expression profiles in mice infected with Plasmodium chabaudi revealed and validated similar responses and highlighted specific pathways and genes that are likely important responders in both hosts. These results suggest that host variation and its interplay with infection affect children's ability to cope with infection and suggest a polygenic model mounted at the transcriptional level for susceptibility.

Genetic newborn screening and digital technologies: A project protocol based on a dual approach to shorten the rare diseases diagnostic path in Europe.

Since 72% of rare diseases are genetic in origin and mostly paediatrics, genetic newborn screening represents a diagnostic "window of opportunity". Therefore, many gNBS initiatives started in different European countries. Screen4Care is a research project, which resulted of a joint effort between the European Union Commission and the European Federation of Pharmaceutical Industries and Associations. It focuses on genetic newborn screening and artificial intelligence-based tools which will be applied to a large European population of about 25.000 infants. The neonatal screening strategy will be based on targeted sequencing, while whole genome sequencing will be offered to all enrolled infants who may show early symptoms but have resulted negative at the targeted sequencing-based newborn screening. We will leverage artificial intelligence-based algorithms to identify patients using Electronic Health Records (EHR) and to build a repository "symptom checkers" for patients and healthcare providers. S4C will design an equitable, ethical, and sustainable framework for genetic newborn screening and new digital tools, corroborated by a large workout where legal, ethical, and social complexities will be addressed with the intent of making the framework highly and flexibly translatable into the diverse European health systems.

Host resistance to malaria: using mouse models to explore the host response.

Malaria is a disease that infects over 500 million people, causing at least 1 million deaths every year, with the majority occurring in developing countries. The current antimalarial arsenal is becoming dulled due to the rapid rate of resistance of the parasite. However, in populations living in malaria-endemic regions there are many examples of genetic-based resistance to the severe effects of the parasite Plasmodium. Defining the genetic factors behind host resistance has been an area of great scientific interest over the last few decades; this review summarizes the current knowledge of the genetic loci involved. Perhaps the lessons learned from the natural variation in both the human populations and experimental mouse models of infection may pave the way for novel resistance-proof antimalarials.

Mutations in VANGL1 associated with neural-tube defects.

Neural-tube defects such as anencephaly and spina bifida constitute a group of common congenital malformations caused by complex genetic and environmental factors. We have identified three mutations in the VANGL1 gene in patients with familial types (V239I and R274Q) and a sporadic type (M328T) of the disease, including a spontaneous mutation (V239I) appearing in a familial setting. In a protein-protein interaction assay V239I abolished interaction of VANGL1 protein with its binding partners, disheveled-1, -2, and -3. These findings implicate VANGL1 as a risk factor in human neural-tube defects.

Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine.

BACKGROUND: Gene expression profiling has benefited medicine by providing clinically relevant insights at the molecular candidate and systems levels. However, to adopt a more 'precision' approach that integrates individual variability including 'omics data into risk assessments, diagnoses, and therapeutic decision making, whole transcriptome expression needs to be interpreted meaningfully for single subjects. We propose an "all-against-one" framework that uses biological replicates in isogenic conditions for testing differentially expressed genes (DEGs) in a single subject (ss) in the absence of an appropriate external reference standard or replicates. To evaluate our proposed "all-against-one" framework, we construct reference standards (RSs) with five conventional replicate-anchored analyses (NOISeq, DEGseq, edgeR, DESeq, DESeq2) and the remainder were treated separately as single-subject sample pairs for ss analyses (without replicates). RESULTS: Eight ss methods (NOISeq, DEGseq, edgeR, mixture model, DESeq, DESeq2, iDEG, and ensemble) for identifying genes with differential expression were compared in Yeast (parental line versus snf2 deletion mutant; n = 42/condition) and a MCF7 breast-cancer cell line (baseline versus stimulated with estradiol; n = 7/condition). Receiver-operator characteristic (ROC) and precision-recall plots were determined for eight ss methods against each of the five RSs in both datasets. Consistent with prior analyses of these data, ~ 50% and ~ 15% DEGs were obtained in Yeast and MCF7 datasets respectively, regardless of the RSs method. NOISeq, edgeR, and DESeq were the most concordant for creating a RS. Single-subject versions of NOISeq, DEGseq, and an ensemble learner achieved the best median ROC-area-under-the-curve to compare two transcriptomes without replicates regardless of the RS method and dataset (> 90% in Yeast, > 0.75 in MCF7). Further, distinct specific single-subject methods perform better according to different proportions of DEGs. CONCLUSIONS: The "all-against-one" framework provides a honest evaluation framework for single-subject DEG studies since these methods are evaluated, by design, against reference standards produced by unrelated DEG methods. The ss-ensemble method was the only one to reliably produce higher accuracies in all conditions tested in this conservative evaluation framework. However, single-subject methods for identifying DEGs from paired samples need improvement, as no method performed with precision> 90% and obtained moderate levels of recall. http://www.lussiergroup.org/publications/EnsembleBiomarker.

Precision drug repurposing via convergent eQTL-based molecules and pathway targeting independent disease-associated polymorphisms.

Repurposing existing drugs for new therapeutic indications can improve success rates and streamline development. Use of large-scale biomedical data repositories, including eQTL regulatory relationships and genome-wide disease risk associations, offers opportunities to propose novel indications for drugs targeting common or convergent molecular candidates associated to two or more diseases. This proposed novel computational approach scales across 262 complex diseases, building a multi-partite hierarchical network integrating (i) GWAS-derived SNP-to-disease associations, (ii) eQTL-derived SNP-to-eGene associations incorporating both cis- and trans-relationships from 19 tissues, (iii) protein target-to-drug, and (iv) drug-to-disease indications with (iv) Gene Ontology-based information theoretic semantic (ITS) similarity calculated between protein target functions. Our hypothesis is that if two diseases are associated to a common or functionally similar eGene - and a drug targeting that eGene/protein in one disease exists - the second disease becomes a potential repurposing indication. To explore this, all possible pairs of independently segregating GWAS-derived SNPs were generated, and a statistical network of similarity within each SNP-SNP pair was calculated according to scale-free overrepresentation of convergent biological processes activity in regulated eGenes (ITSeGENE-eGENE) and scale-free overrepresentation of common eGene targets between the two SNPs (ITSSNP-SNP). Significance of ITSSNP-SNP was conservatively estimated using empirical scale-free permutation resampling keeping the node-degree constant for each molecule in each permutation. We identified 26 new drug repurposing indication candidates spanning 89 GWAS diseases, including a potential repurposing of the calcium-channel blocker Verapamil from coronary disease to gout. Predictions from our approach are compared to known drug indications using DrugBank as a gold standard (odds ratio=13.1, p-value=2.49x10-8). Because of specific disease-SNPs associations to candidate drug targets, the proposed method provides evidence for future precision drug repositioning to a patient's specific polymorphisms.

Workshop during the Pacific Symposium of Biocomputing, Jan 3-7, 2019: Reading between the genes: interpreting non-coding DNA in high-throughput.

Identifying functional elements and predicting mechanistic insight from non-coding DNA and noncoding variation remains a challenge. Advances in genome-scale, high-throughput technology, however, have brought these answers closer within reach than ever, though there is still a need for new computational approaches to analysis and integration. This workshop aims to explore these resources and new computational methods applied to regulatory elements, chromatin interactions, non-protein-coding genes, and other non-coding DNA.

Interpretation of 'Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes.

Calculating Differentially Expressed Genes (DEGs) from RNA-sequencing requires replicates to estimate gene-wise variability, a requirement that is at times financially or physiologically infeasible in clinics. By imposing restrictive transcriptome-wide assumptions limiting inferential opportunities of conventional methods (edgeR, NOISeq-sim, DESeq, DEGseq), comparing two conditions without replicates (TCWR) has been proposed, but not evaluated. Under TCWR conditions (e.g., unaffected tissue vs. tumor), differences of transformed expression of the proposed individualized DEG (iDEG) method follow a distribution calculated across a local partition of related transcripts at baseline expression; thereafter the probability of each DEG is estimated by empirical Bayes with local false discovery rate control using a two-group mixture model. In extensive simulation studies of TCWR methods, iDEG and NOISeq are more accurate at 5%90%, recall>75%, false_positive_rate<1%) and 30%

Single subject transcriptome analysis to identify functionally signed gene set or pathway activity.

Analysis of single-subject transcriptome response data is an unmet need of precision medicine, made challenging by the high dimension, dynamic nature and difficulty in extracting meaningful signals from biological or stochastic noise. We have proposed a method for single subject analysis that uses a mixture model for transcript fold-change clustering from isogenically paired samples, followed by integration of these distributions with Gene Ontology Biological Processes (GO-BP) to reduce dimension and identify functional attributes. We then extended these methods to develop functional signing metrics for gene set process regulation by incorporating biological repressor relationships encoded in GO-BP as negatively_regulates edges. Results revealed reproducible and biologically meaningful signals from analysis of a single subject's response, opening the door to future transcriptomic studies where subject and resource availability are currently limiting. We used inbred mouse strains fed different diets to provide isogenic biological replicates, permitting rigorous validation of our method. We compared significant genotype-specific GO-BP term results for overlap and rank order across three replicate pairs per genotype, and cross-methods to reference standards (limma+FET, SAM+FET, and GSEA). All single-subject analytics findings were robust and highly reproducible (median area under the ROC curve=0.96, n=24 genotypes × 3 replicates), providing confidence and validation of this approach for analyses in single subjects. R code is available online at http://www.lussiergroup.org/publications/PathwayActivity.