The variation game: Cracking complex genetic disorders with NGS and omics data

Genetic and Non-Genetic Contributions to Eosinophilic Granulomatosis with Polyangiitis: Current Knowledge and Future Perspectives

In this work, we present a comprehensive overview of the genetic and non-genetic complexity of eosinophilic granulomatosis with polyangiitis (EGPA). EGPA is a rare complex systemic disease that occurs in people presenting with severe asthma and high eosinophilia. After briefly introducing EGPA and its relationship with the anti-neutrophil cytoplasmic autoantibodies (ANCA)-associated vasculitis (AAVs), we delve into the complexity of this disease. At first, the two main biological actors, ANCA and eosinophils, are presented. Biological and clinical phenotypes related to ANCA positivity or negativity are explained, as well as the role of eosinophils and their pathological subtypes, pointing out their intricate relations with EGPA. Then, the genetics of EGPA are described, providing an overview of the research effort to unravel them. Candidate gene studies have investigated biologically relevant candidate genes; the more recent genome-wide association studies and meta-analyses, able to analyze the whole genome, have confirmed previous associations and discovered novel risk loci; in the end, family-based studies have dissected the contribution of rare variants and the heritability of EGPA. Then, we briefly present the environmental contribution to EGPA, reporting seasonal events and pollutants as triggering factors. In the end, the latest omic research is discussed and the most recent epigenomic, transcriptomic and microbiome studies are presented, highlighting the current challenges, open questions and suggesting approaches to unraveling this complex disease.

The Extent of Edgetic Perturbations in the Human Interactome Caused by Population-Specific Mutations

Until recently, efforts in population genetics have been focused primarily on people of European ancestry. To attenuate this bias, global population studies, such as the 1000 Genomes Project, have revealed differences in genetic variation across ethnic groups. How many of these differences can be attributed to population-specific traits? To answer this question, the mutation data must be linked with functional outcomes. A new "edgotype" concept has been proposed, which emphasizes the interaction-specific, "edgetic", perturbations caused by mutations in the interacting proteins. In this work, we performed systematic in silico edgetic profiling of ~50,000 non-synonymous SNVs (nsSNVs) from the 1000 Genomes Project by leveraging our semi-supervised learning approach SNP-IN tool on a comprehensive set of over 10,000 protein interaction complexes. We interrogated the functional roles of the variants and their impact on the human interactome and compared the results with the pathogenic variants disrupting PPIs in the same interactome. Our results demonstrated that a considerable number of nsSNVs from healthy populations could rewire the interactome. We also showed that the proteins enriched with interaction-disrupting mutations were associated with diverse functions and had implications in a broad spectrum of diseases. Further analysis indicated that distinct gene edgetic profiles among major populations could shed light on the molecular mechanisms behind the population phenotypic variances. Finally, the network analysis revealed that the disease-associated modules surprisingly harbored a higher density of interaction-disrupting mutations from healthy populations. The variation in the cumulative network damage within these modules could potentially account for the observed disparities in disease susceptibility, which are distinctly specific to certain populations. Our work demonstrates the feasibility of a large-scale in silico edgetic study, and reveals insights into the orchestrated play of population-specific mutations in the human interactome.

Contribution of TEX15 genetic variants to the risk of developing severe non-obstructive oligozoospermia

Background: Severe spermatogenic failure (SPGF) represents one of the most relevant causes of male infertility. This pathological condition can lead to extreme abnormalities in the seminal sperm count, such as severe oligozoospermia (SO) or non-obstructive azoospermia (NOA). Most cases of SPGF have an unknown aetiology, and it is known that this idiopathic form of male infertility represents a complex condition. In this study, we aimed to evaluate whether common genetic variation in TEX15, which encodes a key player in spermatogenesis, is involved in the susceptibility to idiopathic SPGF. Materials and Methods: We designed a genetic association study comprising a total of 727 SPGF cases (including 527 NOA and 200 SO) and 1,058 unaffected men from the Iberian Peninsula. Following a tagging strategy, three tag single-nucleotide polymorphisms (SNPs) of TEX15 (rs1362912, rs323342, and rs323346) were selected for genotyping using TaqMan probes. Case-control association tests were then performed by logistic regression models. In silico analyses were also carried out to shed light into the putative functional implications of the studied variants. Results: A significant increase in TEX15-rs1362912 minor allele frequency (MAF) was observed in the group of SO patients (MAF = 0.0842) compared to either the control cohort (MAF = 0.0468, OR = 1.90, p = 7.47E-03) or the NOA group (MAF = 0.0472, OR = 1.83, p = 1.23E-02). The genotype distribution of the SO population was also different from those of both control (p = 1.14E-02) and NOA groups (p = 4.33-02). The analysis of functional annotations of the human genome suggested that the effect of the SO-associated TEX15 variants is likely exerted by alteration of the binding affinity of crucial transcription factors for spermatogenesis. Conclusion: Our results suggest that common variation in TEX15 is involved in the genetic predisposition to SO, thus supporting the notion of idiopathic SPGF as a complex trait.

Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning

Alternative splicing introduces an additional layer of protein diversity and complexity in regulating cellular functions that can be specific to the tissue and cell type, physiological state of a cell, or disease phenotype. Recent high-throughput experimental studies have illuminated the functional role of splicing events through rewiring protein-protein interactions; however, the extent to which the macromolecular interactions are affected by alternative splicing has yet to be fully understood. In silico methods provide a fast and cheap alternative to interrogating functional characteristics of thousands of alternatively spliced isoforms. Here, we develop an accurate feature-based machine learning approach that predicts whether a protein-protein interaction carried out by a reference isoform is perturbed by an alternatively spliced isoform. Our method, called the alternatively spliced interactions prediction (ALT-IN) tool, is compared with the state-of-the-art PPI prediction tools and shows superior performance, achieving 0.92 in precision and recall values.

TP53 Mutations as a Driver of Metastasis Signaling in Advanced Cancer Patients

Molecular profiling with next generation sequencing (NGS) delivers key information on mutant gene sequences, copy number alterations, gene-fusions, and with immunohistochemistry (IHC), is a valuable tool in clinical decision making for patients entering investigational agent trials. Our objective was to elucidate mutational profiles from primary versus metastatic sites from advanced cancer patients to guide rational therapy. All phase I patients (n = 203) with advanced cancer were profiled by commercially available NGS platforms. The samples were annotated by histology, primary and metastatic site, biopsy site, gene mutations, mutation count/gene, and mutant TP53. A molecular profile of each patient was categorized into common and unique mutations, signaling pathways for each profile and TP53 mutations mapped to 3D-structure of p53 bound to DNA and pre/post therapy molecular response. Of the 171 patients analyzed, 145 had genetic alterations from primary and metastatic sites. The predominant histology was adenocarcinoma followed by squamous cell carcinoma, carcinoma of unknown primary site (CUPS), and melanoma. Of 790 unique mutations, TP53 is the most common followed by APC, KRAS, PIK3CA, ATM, PTEN, NOTCH1, BRCA2, BRAF, KMT2D, LRP1B, and CDKN2A. TP53 was found in most metastatic sites and appears to be a key driver of acquired drug resistance. We highlight examples of acquired mutational profiles pre-/post- targeted therapy in multiple tumor types with a menu of potential targeted agents. Conclusion: The mutational profiling of primary and metastatic lesions in cancer patients provides an opportunity to identify TP53 driver 'pathways' that may predict for drug sensitivity/resistance and guide rational drug combinations in clinical trials.

Genetic determinants of fracture non-union: A systematic review from the literature

Approximately 10-15% of fracture patients suffer impaired healing, which is either delayed or even results in non-union. We performed a Systematic Review, aiming to examine the types and frequency of specific genetic abnormalities in patients experiencing bone fracture and to ascertain whether a genetic association exists regarding the tendency for some patients to suffer fracture non-union or postoperative non-union events. GO and KEGG analyses were used to identify the likely function of the genes involved. Furthermore, we evaluated the functional significance of single nucleotide polymorphisms using RegulomeDB and GTEx. Seven eligible studies involving 29 genes and 89 SNPs were analyzed in this review. We found that the polymorphisms in gene NOS2, NOG, BMP4, CYR61, IL1β and FGFR1 apparently predisposed patients to fracture non-union, while the polymorphisms in gene MMP13, BMP6 and FAM5C appeared to provide protection from non-union. Bioinformatics analysis suggested that these genes were enriched in inflammatory pathways, suggesting that inflammation may be a potential factor involved in fracture non-union. Three SNPs (rs17563, rs3753793 and rs2853550) had smaller RegulomeDB scores, indicating significant biological function. In conclusion, we have identified a number of genes and their polymorphisms that might contribute to a genetic susceptibility to fracture non-union. Further studies with larger cohorts will enhance our understanding of fracture non-union and may inform and direct early interventions.

DISPOT: a simple knowledge-based protein domain interaction statistical potential

MOTIVATION

The complexity of protein-protein interactions (PPIs) is further compounded by the fact that an average protein consists of two or more domains, structurally and evolutionary independent subunits. Experimental studies have demonstrated that an interaction between a pair of proteins is not carried out by all domains constituting each protein, but rather by a select subset. However, determining which domains from each protein mediate the corresponding PPI is a challenging task.

RESULTS

Here, we present domain interaction statistical potential (DISPOT), a simple knowledge-based statistical potential that estimates the propensity of an interaction between a pair of protein domains, given their structural classification of protein (SCOP) family annotations. The statistical potential is derived based on the analysis of >352 000 structurally resolved PPIs obtained from DOMMINO, a comprehensive database of structurally resolved macromolecular interactions.

AVAILABILITY AND IMPLEMENTATION

DISPOT is implemented in Python 2.7 and packaged as an open-source tool. DISPOT is implemented in two modes, basic and auto-extraction. The source code for both modes is available on GitHub: https://github.com/korkinlab/dispot and standalone docker images on DockerHub: https://hub.docker.com/r/korkinlab/dispot. The web server is freely available at http://dispot.korkinlab.org/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases

Rapid progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein-protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers.

Genetic and Epigenetic Regulation of Immune Response and Resistance to Infectious Diseases in Domestic Ruminants

Infectious diseases are the outcome of complex interactions between the host, pathogen, and environment. After exposure to a pathogen, the host immune system uses various mechanisms to remove the pathogen. However, environmental factors and characteristics of pathogens can compromise the host immune responses and subsequently alter the outcome of infection. In this article, genetic and epigenetic factors that shape the individual variation in mounting protective responses are reviewed. Different approaches that have been used by researchers to investigate the genetic regulation of immunity in ruminants and various sources of genetic information are discussed.

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.

Rapid and Easy Protocol for Quantification of Next-Generation Sequencing Libraries

The emergence of next-generation sequencing (NGS) over the last 10 years has increased the efficiency of DNA sequencing in terms of speed, ease, and price. However, the exact quantification of a NGS library is crucial in order to obtain good data on sequencing platforms developed by the current market leader Illumina. Different approaches for DNA quantification are available currently and the most commonly used are based on analysis of the physical properties of the DNA through spectrophotometric or fluorometric methods. Although these methods are technically simple, they do not allow exact quantification as can be achieved using a real-time quantitative PCR (qPCR) approach. A qPCR protocol for DNA quantification with applications in NGS library preparation studies is presented here. This can be applied in various fields of study such as medical disorders resulting from nutritional programming disturbances.

Prioritizing single-nucleotide polymorphisms and variants associated with clinical mastitis

Next-generation sequencing technology has provided resources to easily explore and identify candidate single-nucleotide polymorphisms (SNPs) and variants. However, there remains a challenge in identifying and inferring the causal SNPs from sequence data. A problem with different methods that predict the effect of mutations is that they produce false positives. In this hypothesis, we provide an overview of methods known for identifying causal variants and discuss the challenges, fallacies, and prospects in discerning candidate SNPs. We then propose a three-point classification strategy, which could be an additional annotation method in identifying causalities.

Effect-specific analysis of pathogenic SNVs in human interactome: Leveraging edge-based network robustness

Study of genetic variants in the context of molecular networks has recently gained much attention. However, many of these studies suffer from the lack of functional information about the network rewiring effect of genetic variants. After large-scale homology modeling, plus extracting native structure from PDB database, we performed structure-based prediction about the rewiring effect using our SNP-IN tool, and it covers significantly more variants than experimental characterization. The analysis result confirms the widespread perturbations in human interactome and reveals the network rewiring behavior based on edge-based network robustness concept.

Long contiguous stretches of homozygosity spanning shortly the imprinted loci are associated with intellectual disability, autism and/or epilepsy

BACKGROUND

Long contiguous stretches of homozygosity (LCSH) (regions/runs of homozygosity) are repeatedly detected by single-nucleotide polymorphism (SNP) chromosomal microarrays. Providing important clues regarding parental relatedness (consanguinity), uniparental disomy, chromosomal recombination or rearrangements, LCSH are rarely considered as a possible epigenetic cause of neurodevelopmental disorders. Additionally, despite being relevant to imprinting, LCSH at imprinted loci have not been truly addressed in terms of pathogenicity. In this study, we examined LCSH in children with unexplained intellectual disability, autism, congenital malformations and/or epilepsy focusing on chromosomal regions which harbor imprinted disease genes.

RESULTS

Out of 267 cases, 14 (5.2 %) were found to have LCSH at imprinted loci associated with a clinical outcome. There were 5 cases of LCSH at 15p11.2, 4 cases of LCSH at 7q31.2, 3 cases of LCSH at 11p15.5, and 2 cases of LCSH at 7q21.3. Apart from a case of LCSH at 7q31.33q32.3 (~4 Mb in size), all causative LCSH were 1-1.5 Mb in size. Clinically, these cases were characterized by a weak resemblance to corresponding imprinting diseases (i.e., Silver-Russell, Beckwith-Wiedemann, and Prader-Willi/Angelman syndromes), exhibiting distinctive intellectual disability, autistic behavior, developmental delay, seizures and/or facial dysmorphisms. Parental consanguinity was detected in 8 cases (3 %), and these cases did not exhibit LCSH at imprinted loci.

CONCLUSIONS

This study demonstrates that shorter LCSH at chromosomes 7q21.3, 7q31.2, 11p15.5, and 15p11.2 occur with a frequency of about 5 % in the children with intellectual disability, autism, congenital malformations and/or epilepsy. Consequently, this type of epigenetic mutations appears to be the most common one among children with neurodevelopmental diseases. Finally, since LCSH less than 2.5-10 Mb in size are generally ignored in diagnostic SNP microarray studies, one can conclude that an important epigenetic cause of intellectual disability, autism or epilepsy is actually overlooked.