In Silico Functional Annotation of Genomic Variation

USP7/Maged1-mediated H2A monoubiquitination in the paraventricular thalamus: an epigenetic mechanism involved in cocaine use disorder

The risk of developing drug addiction is strongly influenced by the epigenetic landscape and chromatin remodeling. While histone modifications such as methylation and acetylation have been studied in the ventral tegmental area and nucleus accumbens (NAc), the role of H2A monoubiquitination remains unknown. Our investigations, initially focused on the scaffold protein melanoma-associated antigen D1 (Maged1), reveal that H2A monoubiquitination in the paraventricular thalamus (PVT) significantly contributes to cocaine-adaptive behaviors and transcriptional repression induced by cocaine. Chronic cocaine use increases H2A monoubiquitination, regulated by Maged1 and its partner USP7. Accordingly, Maged1 specific inactivation in thalamic Vglut2 neurons, or USP7 inhibition, blocks cocaine-evoked H2A monoubiquitination and cocaine locomotor sensitization. Additionally, genetic variations in MAGED1 and USP7 are linked to altered susceptibility to cocaine addiction and cocaine-associated symptoms in humans. These findings unveil an epigenetic modification in a non-canonical reward pathway of the brain and a potent marker of epigenetic risk factors for drug addiction in humans.

Purging of deleterious burden in the endangered Iberian lynx

SignificanceThe dynamics of deleterious variation under contrasting demographic scenarios remain poorly understood in spite of their relevance in evolutionary and conservation terms. Here we apply a genomic approach to study differences in the burden of deleterious alleles between the endangered Iberian lynx (Lynx pardinus) and the widespread Eurasian lynx (Lynx lynx). Our analysis unveils a significantly lower deleterious burden in the former species that should be ascribed to genetic purging, that is, to the increased opportunities of selection against recessive homozygotes due to the inbreeding caused by its smaller population size, as illustrated by our analytical predictions. This research provides theoretical and empirical evidence on the evolutionary relevance of genetic purging under certain demographic conditions.

Clustering suicidal phenotypes and genetic associations with brain-derived neurotrophic factor in patients with substance use disorders

Suicide attempts (SA), especially recurrent SA or serious SA, are common in substance use disorders (SUD). However, the genetic component of SA in SUD samples remains unclear. Brain-derived neurotrophic factor (BDNF) alleles and levels have been repeatedly involved in stress-related psychopathology. This investigation uses a within-cases study of BDNF and associated factors in three suicidal phenotypes ('any', 'recurrent', and 'serious') of outpatients seeking treatment for opiate and/or cocaine use disorder. Phenotypic characterization was ascertained using a semi-structured interview. After thorough quality control, 98 SNPs of BDNF and associated factors (the BDNF pathway) were extracted from whole-genome data, leaving 411 patients of Caucasian ancestry, who had reliable data regarding their SA history. Binary and multinomial regression with the three suicidal phenotypes were further performed to adjust for possible confounders, along with hierarchical clustering and compared to controls (N = 2504). Bayesian analyses were conducted to detect pleiotropy across the suicidal phenotypes. Among 154 (37%) ever suicide attempters, 104 (68%) reported at least one serious SA and 96 (57%) two SA or more. The median number of non-tobacco SUDs was three. The BDNF gene remained associated with lifetime SA in SNP-based (rs7934165, rs10835210) and gene-based tests within the clinical sample. rs10835210 clustered with serious SA. Bayesian analysis identified genetic correlation between 'any' and 'serious' SA regarding rs7934165. Despite limitations, 'serious' SA was shown to share both clinical and genetic risk factors of SA-not otherwise specified, suggesting a shared BDNF-related pathophysiology of SA in this population with multiple SUDs.

The cleft palate candidate gene BAG6 supports FoxO1 acetylation to promote FasL-mediated apoptosis during palate fusion

BACKGROUND

Cleft palate is a common craniofacial defect, which occurs when the palate fails to fuse during development. During fusion, the palatal shelves migrate towards the embryonic midline to form a seam. Apoptotic elimination of medial edge epithelium (MEE) cells along this seam is required for the completion of palate fusion.

METHODS

Whole exome sequencing (WES) of six Chinese cleft palate families was applied to identify novel cleft palate-associated gene variants. Palatal fusion and immunofluorescence studies were performed in a murine palatal shelf organ culture model. Gene and protein expression were analyzed by qPCR and immunoblotting in murine MEE cells during seam formation in vivo. Mechanistic immunoprecipitation studies were performed in murine MEE cells in vitro.

RESULTS

WES identified Bcl-2 associated anthanogene 6 (BAG6) as a novel cleft palate-associated gene. In murine MEE cells, we discovered upregulation of Bag6 and the transcription factor forkhead box protein O1 (FoxO1) during seam formation in vivo. Using a palatal shelf organ culture model, we demonstrate that nuclear-localized Bag6 enhances MEE cell apoptosis by promoting p300's acetylation of FoxO1, thereby promoting transcription of the pro-apoptotic Fas ligand (FasL). Subsequent gain- and loss-of-function studies in the organ culture model demonstrated that FasL is required for Bag6/acFoxO1-mediated activation of pro-apoptotic Bax/caspase-3 signaling, MEE apoptosis, and palate fusion. Palatal shelf contact was shown to enhance Bag6 nuclear localization and upregulate nuclear acFoxO1 in MEE cells.

CONCLUSIONS

These findings demonstrate that nuclear-localized Bag6 and p300 co-operatively enhance FoxO1 acetylation to promote FasL-mediated MEE apoptosis during palate fusion.

Review on the Computational Genome Annotation of Sequences Obtained by Next-Generation Sequencing

Next-Generation Sequencing (NGS) has made it easier to obtain genome-wide sequence data and it has shifted the research focus into genome annotation. The challenging tasks involved in annotation rely on the currently available tools and techniques to decode the information contained in nucleotide sequences. This information will improve our understanding of general aspects of life and evolution and improve our ability to diagnose genetic disorders. Here, we present a summary of both structural and functional annotations, as well as the associated comparative annotation tools and pipelines. We highlight visualization tools that immensely aid the annotation process and the contributions of the scientific community to the annotation. Further, we discuss quality-control practices and the need for re-annotation, and highlight the future of annotation.

Significant Association between Microrna Gene Polymorphisms and Type 2 Diabetes Mellitus Susceptibility in Asian Population: A Meta-Analysis

BACKGROUND

The gene polymorphisms in microRNA might relate to susceptibility of type 2 diabetes mellitus (T2DM). However, the results of existing studies were inconsistent and obscure. To investigate the precise associations between microRNA gene polymorphisms and T2DM risk, the present meta-analysis was performed.

METHODS

The literatures were searched from four electronic databases, PubMed, Embase, CNKI and Wan-fang. Subsequently, odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) were both used to evaluate the associations between two single nucleotide polymorphisms (SNPs) (microRNA146a rs2910164 (G>C), microRNA124a rs531564 (C>G)) and risk of T2DM in Asian population.

RESULTS

Totally, there were 4 studies included in our present analysis in the language of English and Chinese. There were partly significant associations between susceptibility of T2DM and SNPs (microRNA146a rs2910164 (G>C), microRNA124a rs531564 (C>G)). The G allele in microRNA146a rs2910164 (G>C) and C allele in microRNA124a rs531564 (C>G) both presented remarkably reduced risk of T2DM when compared with the healthy population.

CONCLUSION

The microRNA146a rs2910164 (G allele) and microRNA124a rs531564 (C allele) might function as protective factors in the pathogenetic process of T2DM in Asian population.

Molecular profiling for precision cancer therapies

The number of druggable tumor-specific molecular aberrations has grown substantially in the past decade, with a significant survival benefit obtained from biomarker matching therapies in several cancer types. Molecular pathology has therefore become fundamental not only to inform on tumor diagnosis and prognosis but also to drive therapeutic decisions in daily practice. The introduction of next-generation sequencing technologies and the rising number of large-scale tumor molecular profiling programs across institutions worldwide have revolutionized the field of precision oncology. As comprehensive genomic analyses become increasingly available in both clinical and research settings, healthcare professionals are faced with the complex tasks of result interpretation and translation. This review summarizes the current and upcoming approaches to implement precision cancer medicine, highlighting the challenges and potential solutions to facilitate the interpretation and to maximize the clinical utility of molecular profiling results. We describe novel molecular characterization strategies beyond tumor DNA sequencing, such as transcriptomics, immunophenotyping, epigenetic profiling, and single-cell analyses. We also review current and potential applications of liquid biopsies to evaluate blood-based biomarkers, such as circulating tumor cells and circulating nucleic acids. Last, lessons learned from the existing limitations of genotype-derived therapies provide insights into ways to expand precision medicine beyond genomics.

Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning

Next-generation sequencing methods have not only allowed an understanding of genome sequence variation during the evolution of organisms but have also provided invaluable information about genetic variants in inherited disease and the emergence of resistance to drugs in cancers and infectious disease. A challenge is to distinguish mutations that are drivers of disease or drug resistance, from passengers that are neutral or even selectively advantageous to the organism. This requires an understanding of impacts of missense mutations in gene expression and regulation, and on the disruption of protein function by modulating protein stability or disturbing interactions with proteins, nucleic acids, small molecule ligands, and other biological molecules. Experimental approaches to understanding differences between wild-type and mutant proteins are most accurate but are also time-consuming and costly. Computational tools used to predict the impacts of mutations can provide useful information more quickly. Here, we focus on two widely used structure-based approaches, originally developed in the Blundell lab: site-directed mutator (SDM), a statistical approach to analyze amino acid substitutions, and mutation cutoff scanning matrix (mCSM), which uses graph-based signatures to represent the wild-type structural environment and machine learning to predict the effect of mutations on protein stability. Here, we describe DUET that uses machine learning to combine the two approaches. We discuss briefly the development of mCSM for understanding the impacts of mutations on interfaces with other proteins, nucleic acids, and ligands, and we exemplify the wide application of these approaches to understand human genetic disorders and drug resistance mutations relevant to cancer and mycobacterial infections. STATEMENT FOR A BROADER AUDIENCE: Genetic or somatic changes in genes can lead to mutations in human proteins, which give rise to genetic disorders or cancer, or to genes of pathogens leading to drug resistance. Computer software described here, using statistical approaches or machine learning, uses the information from genome sequencing of humans and pathogens, together with experimental or modeled 3D structures of gene products, the proteins, to predict impacts of mutations in genetic disease, cancer and drug resistance.

Genomic Mutation Identification in Mice Using Illumina Sequencing and Linux-Based Computational Methods

Genetically modified mice are an essential tool for modeling disease-causing mechanisms and discovering gene function. SNP genotyping was traditionally used to associate candidate regions with traits in the mouse, but failed to reveal novel variants without further targeted sequencing. Using a robust set of computational protocols, we present a platform to enable scientists to detect variants arising from whole-genome and exome sequencing experiments. This article guides researchers on aligning reads to the mouse genome, quality-assurance strategies, mutation discovery, comparing mutations to previously discovered mouse SNPs, and the annotation of novel variants, in order to predict mutation consequences on the protein level. Challenges unique to the mouse are discussed, and two protocols use self-contained containers to maintain version control and allow users to adapt our approach to new techniques by upgrading container versions. Our protocols are suited for servers or office workstations and are usable by non-bioinformatics specialists. © 2019 by John Wiley & Sons, Inc.

Bridging the Gaps in Personalized Medicine Value Assessment: A Review of the Need for Outcome Metrics across Stakeholders and Scientific Disciplines

Despite monumental advances in genomics, relatively few health care provider organizations in the United States offer personalized or precision medicine as part of the routine clinical workflow. The gaps between research and applied genomic medicine may be a result of a cultural gap across various stakeholders representing scientists, clinicians, patients, policy makers, and third party payers. Scientists are trained to assess the health care value of genomics by either quantifying population-scale effects, or through the narrow lens of clinical trials where the standard of care is compared with the predictive power of a single or handful of genetic variants. While these metrics are an essential first step in assessing and documenting the clinical utility of genomics, they are rarely followed up with other assessments of health care value that are critical to stakeholders who use different measures to define value. The limited value assessment in both the research and implementation science of precision medicine is likely due to necessary logistical constraints of these teams; engaging bioethicists, health care economists, and individual patient belief systems is incredibly daunting for geneticists and informaticians conducting research. In this narrative review, we concisely describe several definitions of value through various stakeholder viewpoints. We highlight the existing gaps that prevent clinical translation of scientific findings generally as well as more specifically using two present-day, extreme scenarios: (1) genetically guided warfarin dosing representing a handful of genetic markers and more than 10 years of basic and translational research, and (2) next-generation sequencing representing genome-dense data lacking substantial evidence for implementation. These contemporary scenarios highlight the need for various stakeholders to broadly adopt frameworks designed to define and collect multiple value measures across different disciplines to ultimately impact more universal acceptance of and reimbursement for genomic medicine.

GenePy - a score for estimating gene pathogenicity in individuals using next-generation sequencing data

BACKGROUND

Next-generation sequencing is revolutionising diagnosis and treatment of rare diseases, however its application to understanding common disease aetiology is limited. Rare disease applications binarily attribute genetic change(s) at a single locus to a specific phenotype. In common diseases, where multiple genetic variants within and across genes contribute to disease, binary modelling cannot capture the burden of pathogenicity harboured by an individual across a given gene/pathway. We present GenePy, a novel gene-level scoring system for integration and analysis of next-generation sequencing data on a per-individual basis that transforms NGS data interpretation from variant-level to gene-level. This simple and flexible scoring system is intuitive and amenable to integration for machine learning, network and topological approaches, facilitating the investigation of complex phenotypes.

RESULTS

Whole-exome sequencing data from 508 individuals were used to generate GenePy scores. For each variant a score is calculated incorporating: i) population allele frequency estimates; ii) individual zygosity, determined through standard variant calling pipelines and; iii) any user defined deleteriousness metric to inform on functional impact. GenePy then combines scores generated for all variants observed into a single gene score for each individual. We generated a matrix of ~ 14,000 GenePy scores for all individuals for each of sixteen popular deleteriousness metrics. All per-gene scores are corrected for gene length. The majority of genes generate GenePy scores < 0.01 although individuals harbouring multiple rare highly deleterious mutations can accumulate extremely high GenePy scores. In the absence of a comparator metric, we examine GenePy performance in discriminating genes known to be associated with three common, complex diseases. A Mann-Whitney U test conducted on GenePy scores for this positive control gene in cases versus controls demonstrates markedly more significant results (p = 1.37 × 10- 4) compared to the most commonly applied association tool that combines common and rare variation (p = 0.003).

CONCLUSIONS

Per-gene per-individual GenePy scores are intuitive when assessing genetic variation in individual patients or comparing scores between groups. GenePy outperforms the currently accepted best practice tools for combining common and rare variation. GenePy scores are suitable for downstream data integration with transcriptomic and proteomic data that also report at the gene level.

Innovative strategies for annotating the "relationSNP" between variants and molecular phenotypes

Characterizing how variation at the level of individual nucleotides contributes to traits and diseases has been an area of growing interest since the completion of sequencing the first human genome. Our understanding of how a single nucleotide polymorphism (SNP) leads to a pathogenic phenotype on a genome-wide scale is a fruitful endeavor for anyone interested in developing diagnostic tests, therapeutics, or simply wanting to understand the etiology of a disease or trait. To this end, many datasets and algorithms have been developed as resources/tools to annotate SNPs. One of the most common practices is to annotate coding SNPs that affect the protein sequence. Synonymous variants are often grouped as one type of variant, however there are in fact many tools available to dissect their effects on gene expression. More recently, large consortiums like ENCODE and GTEx have made it possible to annotate non-coding regions. Although annotating variants is a common technique among human geneticists, the constant advances in tools and biology surrounding SNPs requires an updated summary of what is known and the trajectory of the field. This review will discuss the history behind SNP annotation, commonly used tools, and newer strategies for SNP annotation. Additionally, we will comment on the caveats that distinguish approaches from one another, along with gaps in the current state of knowledge, and potential future directions. We do not intend for this to be a comprehensive review for any specific area of SNP annotation, but rather it will be an excellent resource for those unfamiliar with computational tools used to functionally characterize SNPs. In summary, this review will help illustrate how each SNP annotation method impacts the way in which the genetic and molecular etiology of a disease is explored in-silico.

Insights Into de novo Mutation Variation in Lithuanian Exome

In the last decade, one of the biggest challenges in genomics research has been to distinguish definitive pathogenic variants from all likely pathogenic variants identified by next-generation sequencing. This task is particularly complex because of our lack of knowledge regarding overall genome variation and pathogenicity of the variants. Therefore, obtaining sufficient information about genome variants in the general population is necessary as such data could be used for the interpretation of de novo mutations (DNMs) in the context of patient's phenotype in cases of sporadic genetic disease. In this study, data from whole-exome sequencing of the general population in Lithuania were directly examined. In total, 84 (VarScan) and 95 (VarSeqTM) DNMs were identified and validated using different algorithms. Thirty-nine of these mutations were considered likely to be pathogenic based on gene function, evolutionary conservation, and mutation impact. The mutation rate estimated per position pair per generation was 2.74 × 10-8 [95% CI: 2.24 × 10-8-3.35 × 10-8] (VarScan) and 2.4 × 10-8 [95% CI: 1.96 × 10-8-2.99 × 10-8] (VarSeqTM), with 1.77 × 10-8 [95% CI: 6.03 × 10-9-5.2 × 10-8] de novo indels per position per generation. The rate of germline DNMs in the Lithuanian population and the effects of the genomic and epigenetic context on DNM formation were calculated for the first time in this study, providing a basis for further analysis of DNMs in individuals with genetic diseases. Considering these findings, additional studies in patient groups with genetic diseases with unclear etiology may facilitate our ability to distinguish certain pathogenic or adaptive DNMs from tolerated background DNMs and to reliably identify disease-causing DNMs by their properties through direct observation.