Transcriptomic analysis identifies dysregulated pathways and therapeutic targets in PMM2-CDG

PMM2-CDG (MIM # 212065), the most common congenital disorder of glycosylation, is caused by the deficiency of phosphomannomutase 2 (PMM2). It is a multisystemic disease of variable severity that particularly affects the nervous system; however, its molecular pathophysiology remains poorly understood. Currently, there is no effective treatment. We performed an RNA-seq based transcriptomic study using patient-derived fibroblasts to gain insight into the mechanisms underlying the clinical symptomatology and to identify druggable targets. Systems biology methods were used to identify cellular pathways potentially affected by PMM2 deficiency, including Senescence, Bone regulation, Cell adhesion and Extracellular Matrix (ECM) and Response to cytokines. Functional validation assays using patients' fibroblasts revealed defects related to cell proliferation, cell cycle, the composition of the ECM and cell migration, and showed a potential role of the inflammatory response in the pathophysiology of the disease. Furthermore, treatment with a previously described pharmacological chaperone reverted the differential expression of some of the dysregulated genes. The results presented from transcriptomic data might serve as a platform for identifying therapeutic targets for PMM2-CDG, as well as for monitoring the effectiveness of therapeutic strategies, including pharmacological candidates and mannose-1-P, drug repurposing.

CoMentG: comprehensive retrieval of generic relationships between biomedical concepts from the scientific literature

The CoMentG resource contains millions of relationships between terms of biomedical interest obtained from the scientific literature. At the core of the system is a methodology for detecting significant co-mentions of concepts in the entire PubMed corpus. That method was applied to nine sets of terms covering the most important classes of biomedical concepts: diseases, symptoms/clinical signs, molecular functions, biological processes, cellular compartments, anatomic parts, cell types, bacteria and chemical compounds. We obtained more than 7 million relationships between more than 74 000 terms, and many types of relationships were not available in any other resource. As the terms were obtained from widely used resources and ontologies, the relationships are given using the standard identifiers provided by them and hence can be linked to other data. A web interface allows users to browse these associations, searching for relationships for a set of terms of interests provided as input, such as between a disease and their associated symptoms, underlying molecular processes or affected tissues. The results are presented in an interactive interface where the user can explore the reported relationships in different ways and follow links to other resources. Database URL: https://csbg.cnb.csic.es/CoMentG/.

Cytokine profiling and transcriptomics in mononuclear cells define immune variants in Meniere Disease

Meniere Disease (MD) is a chronic inner ear disorder characterized by vertigo attacks, sensorineural hearing loss, tinnitus, and aural fullness. Extensive evidence supporting the inflammatory etiology of MD has been found, therefore, by using transcriptome analysis, we aim to describe the inflammatory variants of MD. We performed Bulk RNAseq on 45 patients with definite MD and 15 healthy controls. MD patients were classified according to their basal levels of IL-1β into 2 groups: high and low. Differentially expression analysis was performed using the ExpHunter Suite, and cell type proportion was evaluated using the estimation algorithms xCell, ABIS, and CIBERSORTx. MD patients showed 15 differentially expressed genes (DEG) compared to controls. The top DEGs include IGHG1 (p = 1.64  ×  10-6) and IGLV3-21 (p = 6.28  ×  10-3), supporting a role in the adaptative immune response. Cytokine profiling defines a subgroup of patients with high levels of IL-1β with up-regulation of IL6 (p = 7.65  ×  10-8) and INHBA (p = 3.39  ×  10-7) genes. Transcriptomic data from peripheral blood mononuclear cells support a proinflammatory subgroup of MD patients with high levels of IL6 and an increase in naïve B-cells, and memory CD8+ T cells.

Bone metabolism and inflammatory biomarkers in radiographic and non-radiographic axial spondyloarthritis patients: a comprehensive evaluation

Introduction

Axial spondyloarthritis (axSpA) is a heterogeneous disease that can be represented by radiographic axSpA (r-axSpA) and non-radiographic axSpA (nr-axSpA). This study aimed to evaluate the relationship between the markers of inflammation and bone turnover in r-axSpA patients and nr-axSpA patients.

Methods

A cross-sectional study included 29 r-axSpA patients, 10 nr-axSpA patients, and 20 controls matched for age and sex. Plasma markers related to bone remodeling such as human procollagen type 1 N-terminal propeptide (P1NP), sclerostin, tartrate-resistant acid phosphatase 5b (TRACP5b), receptor activator of nuclear factor kappa B ligand (RANKL), and osteoprotegerin (OPG) were measured by an ELISA kit. A panel of 92 inflammatory molecules was analyzed by proximity extension assay.

Results

R-axSpA patients had decreased plasma levels of P1NP, a marker of bone formation, compared to controls. In addition, r-axSpA patients exhibited decreased plasma levels of sclerostin, an anti-anabolic bone hormone, which would not explain the co-existence of decreased plasma P1NP concentration; however, sclerostin levels could also be influenced by inflammatory processes. Plasma markers of osteoclast activity were similar in all groups. Regarding inflammation-related molecules, nr-axSpA patients showed increased levels of serum interleukin 13 (IL13) as compared with both r-axSpA patients and controls, which may participate in the prevention of inflammation. On the other hand, r-axSpA patients had higher levels of pro-inflammatory molecules compared to controls (i.e., IL6, Oncostatin M, and TNF receptor superfamily member 9). Correlation analysis showed that sclerostin was inversely associated with IL6 and Oncostatin M among others.

Conclusion

Altogether, different inflammatory profiles may play a role in the development of the skeletal features in axSpA patients particularly related to decreased bone formation. The relationship between sclerostin and inflammation and the protective actions of IL13 could be of relevance in the axSpA pathology, which is a topic for further investigation.

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease

Hirschsprung's disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.

Exploring miRNA-target gene pair detection in disease with coRmiT

A wide range of approaches can be used to detect micro RNA (miRNA)-target gene pairs (mTPs) from expression data, differing in the ways the gene and miRNA expression profiles are calculated, combined and correlated. However, there is no clear consensus on which is the best approach across all datasets. Here, we have implemented multiple strategies and applied them to three distinct rare disease datasets that comprise smallRNA-Seq and RNA-Seq data obtained from the same samples, obtaining mTPs related to the disease pathology. All datasets were preprocessed using a standardized, freely available computational workflow, DEG_workflow. This workflow includes coRmiT, a method to compare multiple strategies for mTP detection. We used it to investigate the overlap of the detected mTPs with predicted and validated mTPs from 11 different databases. Results show that there is no clear best strategy for mTP detection applicable to all situations. We therefore propose the integration of the results of the different strategies by selecting the one with the highest odds ratio for each miRNA, as the optimal way to integrate the results. We applied this selection-integration method to the datasets and showed it to be robust to changes in the predicted and validated mTP databases. Our findings have important implications for miRNA analysis. coRmiT is implemented as part of the ExpHunterSuite Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite.

Multi-Omics Approach Reveals Prebiotic and Potential Antioxidant Effects of Essential Oils from the Mediterranean Diet on Cardiometabolic Disorder Using Humanized Gnotobiotic Mice

Essential oils sourced from herbs commonly used in the Mediterranean diet have demonstrated advantageous attributes as nutraceuticals and prebiotics within a model of severe cardiometabolic disorder. The primary objective of this study was to assess the influences exerted by essential oils derived from thyme (Thymus vulgaris) and oregano (Origanum vulgare) via a comprehensive multi-omics approach within a gnotobiotic murine model featuring colonic microbiota acquired from patients diagnosed with coronary artery disease (CAD) and type-2 diabetes mellitus (T2DM). Our findings demonstrated prebiotic and potential antioxidant effects elicited by these essential oils. We observed a substantial increase in the relative abundance of the Lactobacillus genus in the gut microbiota, accompanied by higher levels of short-chain fatty acids and a reduction in trimethylamine N-oxide levels and protein oxidation in the plasma. Moreover, functional enrichment analysis of the cardiac tissue proteome unveiled an over-representation of pathways related to mitochondrial function, oxidative stress, and cardiac contraction. These findings provide compelling evidence of the prebiotic and antioxidant actions of thyme- and oregano-derived essential oils, which extend to cardiac function. These results encourage further investigation into the promising utility of essential oils derived from herbs commonly used in the Mediterranean diet as potential nutraceutical interventions for mitigating chronic diseases linked to CAD and T2DM.

Integrating differential expression, co-expression and gene network analysis for the identification of common genes associated with tumor angiogenesis deregulation

Angiogenesis is essential for tumor growth and cancer metastasis. Identifying the molecular pathways involved in this process is the first step in the rational design of new therapeutic strategies to improve cancer treatment. In recent years, RNA-seq data analysis has helped to determine the genetic and molecular factors associated with different types of cancer. In this work we performed integrative analysis using RNA-seq data from human umbilical vein endothelial cells (HUVEC) and patients with angiogenesis-dependent diseases to find genes that serve as potential candidates to improve the prognosis of tumor angiogenesis deregulation and understand how this process is orchestrated at the genetic and molecular level. We downloaded four RNA-seq datasets (including cellular models of tumor angiogenesis and ischaemic heart disease) from the Sequence Read Archive. Our integrative analysis includes a first step to determine differentially and co-expressed genes. For this, we used the ExpHunter Suite, an R package that performs differential expression, co-expression and functional analysis of RNA-seq data. We used both differentially and co-expressed genes to explore the human gene interaction network and determine which genes were found in the different datasets that may be key for the angiogenesis deregulation. Finally, we performed drug repositioning analysis to find potential targets related to angiogenesis inhibition. We found that that among the transcriptional alterations identified, SEMA3D and IL33 genes are deregulated in all datasets. Microenvironment remodeling, cell cycle, lipid metabolism and vesicular transport are the main molecular pathways affected. In addition to this, interacting genes are involved in intracellular signaling pathways, especially in immune system and semaphorins, respiratory electron transport and fatty acid metabolism. The methodology presented here can be used for finding common transcriptional alterations in other genetically-based diseases.

Advancing in Schaaf-Yang syndrome pathophysiology: from bedside to subcellular analyses of truncated MAGEL2

BACKGROUND

Schaaf-Yang syndrome (SYS) is caused by truncating mutations in MAGEL2, mapping to the Prader-Willi region (15q11-q13), with an observed phenotype partially overlapping that of Prader-Willi syndrome. MAGEL2 plays a role in retrograde transport and protein recycling regulation. Our aim is to contribute to the characterisation of SYS pathophysiology at clinical, genetic and molecular levels.

METHODS

We performed an extensive phenotypic and mutational revision of previously reported patients with SYS. We analysed the secretion levels of amyloid-β 1-40 peptide (Aβ_1-40) and performed targeted metabolomic and transcriptomic profiles in fibroblasts of patients with SYS (n=7) compared with controls (n=11). We also transfected cell lines with vectors encoding wild-type (WT) or mutated MAGEL2 to assess stability and subcellular localisation of the truncated protein.

RESULTS

Functional studies show significantly decreased levels of secreted Aβ_1-40 and intracellular glutamine in SYS fibroblasts compared with WT. We also identified 132 differentially expressed genes, including non-coding RNAs (ncRNAs) such as HOTAIR, and many of them related to developmental processes and mitotic mechanisms. The truncated form of MAGEL2 displayed a stability similar to the WT but it was significantly switched to the nucleus, compared with a mainly cytoplasmic distribution of the WT MAGEL2. Based on the updated knowledge, we offer guidelines for the clinical management of patients with SYS.

CONCLUSION

A truncated MAGEL2 protein is stable and localises mainly in the nucleus, where it might exert a pathogenic neomorphic effect. Aβ_1-40 secretion levels and HOTAIR mRNA levels might be promising biomarkers for SYS. Our findings may improve SYS understanding and clinical management.

Regulation and directed inhibition of ECP production by human neutrophils

Background

Neutrophils are involved in the pathophysiology of allergic asthma, where the Eosinophil Cationic Protein (ECP) is a critical inflammatory mediator. Although ECP production is attributed to eosinophils, we reported that ECP is also present in neutrophils from allergic patients where, in contrast to eosinophils, it is produced in an IgE-dependent manner. Given the key role of ECP in asthma, we investigated the molecular mechanisms involved in ECP production as well as the effects induced by agonists and widely used clinical approaches. We also analyzed the correlation between ECP production and lung function.

Methods

Neutrophils from allergic asthmatic patients were challenged with allergens, alone or in combination with cytokines, in the presence of cell-signaling inhibitors and clinical drugs. We analyzed ECP levels by ELISA and confocal microscopy. Lung function was assessed by spirometry.

Results

IgE-mediated ECP release is dependent on phosphoinositide 3-kinase, the extracellular signal-regulated kinase (ERK1/2) and the production of reactive oxygen species by NADPH-oxidase. Calcineurin phosphatase and the transcription factor NFAT are also involved. ECP release is enhanced by the cytokines interleukin (IL)-5 and granulocyte macrophage-colony stimulating factor, and inhibited by interferon-γ, IL-10, clinical drugs (formoterol, tiotropium and budesonide) and allergen-specific IT. We also found an inverse correlation between asthma severity and ECP levels.

Conclusions

Our results suggest the molecular pathways involved in ECP production and potential therapeutic targets. We also provide a new method to evaluate disease severity in asthmatic patients based on the quantification of in vitro ECP production by peripheral neutrophils.

Deepening the knowledge of rare diseases dependent on angiogenesis through semantic similarity clustering and network analysis

BACKGROUND

Angiogenesis is regulated by multiple genes whose variants can lead to different disorders. Among them, rare diseases are a heterogeneous group of pathologies, most of them genetic, whose information may be of interest to determine the still unknown genetic and molecular causes of other diseases. In this work, we use the information on rare diseases dependent on angiogenesis to investigate the genes that are associated with this biological process and to determine if there are interactions between the genes involved in its deregulation.

RESULTS

We propose a systemic approach supported by the use of pathological phenotypes to group diseases by semantic similarity. We grouped 158 angiogenesis-related rare diseases in 18 clusters based on their phenotypes. Of them, 16 clusters had traceable gene connections in a high-quality interaction network. These disease clusters are associated with 130 different genes. We searched for genes associated with angiogenesis througth ClinVar pathogenic variants. Of the seven retrieved genes, our system confirms six of them. Furthermore, it allowed us to identify common affected functions among these disease clusters.

AVAILABILITY

https://github.com/ElenaRojano/angio_cluster.

CONTACT

seoanezonjic@uma.es and elenarojano@uma.es.

CIBERER: Spanish National Network for Research on Rare Diseases: a highly productive collaborative initiative

CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low‐prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15‐year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research.

Assigning protein function from domain-function associations using DomFun

Background

Protein function prediction remains a key challenge. Domain composition affects protein function. Here we present DomFun, a Ruby gem that uses associations between protein domains and functions, calculated using multiple indices based on tripartite network analysis. These domain-function associations are combined at the protein level, to generate protein-function predictions.

Results

We analysed 16 tripartite networks connecting homologous superfamily and FunFam domains from CATH-Gene3D with functional annotations from the three Gene Ontology (GO) sub-ontologies, KEGG, and Reactome. We validated the results using the CAFA 3 benchmark platform for GO annotation, finding that out of the multiple association metrics and domain datasets tested, Simpson index for FunFam domain-function associations combined with Stouffer’s method leads to the best performance in almost all scenarios. We also found that using FunFams led to better performance than superfamilies, and better results were found for GO molecular function compared to GO biological process terms. DomFun performed as well as the highest-performing method in certain CAFA 3 evaluation procedures in terms of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_{max}$$\end{document}Fmax and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S_{min}$$\end{document}Smin We also implemented our own benchmark procedure, Pathway Prediction Performance (PPP), which can be used to validate function prediction for additional annotations sources, such as KEGG and Reactome. Using PPP, we found similar results to those found with CAFA 3 for GO, moreover we found good performance for the other annotation sources. As with CAFA 3, Simpson index with Stouffer’s method led to the top performance in almost all scenarios.

Conclusions

DomFun shows competitive performance with other methods evaluated in CAFA 3 when predicting proteins function with GO, although results vary depending on the evaluation procedure. Through our own benchmark procedure, PPP, we have shown it can also make accurate predictions for KEGG and Reactome. It performs best when using FunFams, combining Simpson index derived domain-function associations using Stouffer’s method. The tool has been implemented so that it can be easily adapted to incorporate other protein features, such as domain data from other sources, amino acid k-mers and motifs. The DomFun Ruby gem is available from https://rubygems.org/gems/DomFun. Code maintained at https://github.com/ElenaRojano/DomFun. Validation procedure scripts can be found at https://github.com/ElenaRojano/DomFun_project.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04565-6.

Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer

Exhaustive and comprehensive analysis of pathological traits is essential to understanding genetic diseases, performing precise diagnosis and prescribing personalized treatments. It is particularly important for disease cohorts, as thoroughly detailed phenotypic profiles allow patients to be compared and contrasted. However, many disease cohorts contain patients that have been ascribed low numbers of very general and relatively uninformative phenotypes. We present Cohort Analyzer, a tool that measures the phenotyping quality of patient cohorts. It calculates multiple statistics to give a general overview of the cohort status in terms of the depth and breadth of phenotyping, allowing us to detect less well-phenotyped patients for re-examining or excluding from further analyses. In addition, it performs clustering analysis to find subgroups of patients that share similar phenotypic profiles. We used it to analyse three cohorts of genetic diseases patients with very different properties. We found that cohorts with the most specific and complete phenotypic characterization give more potential insights into the disease than those that were less deeply characterised by forming more informative clusters. For two of the cohorts, we also analysed genomic data related to the patients, and linked the genomic data to the patient-subgroups by mapping shared variants to genes and functions. The work highlights the need for improved phenotyping in this era of personalized medicine. The tool itself is freely available alongside a workflow to allow the analyses shown in this work to be applied to other datasets.

Protein residues determining interaction specificity in paralogous families

MOTIVATION

Predicting the residues controlling a protein's interaction specificity is important not only to better understand its interactions but also to design mutations aimed at fine-tuning or swapping them as well.

RESULTS

In this work, we present a methodology that combines sequence information (in the form of multiple sequence alignments) with interactome information to detect that kind of residues in paralogous families of proteins. The interactome is used to define pairwise similarities of interaction contexts for the proteins in the alignment. The method looks for alignment positions with patterns of amino-acid changes reflecting the similarities/differences in the interaction neighborhoods of the corresponding proteins. We tested this new methodology in a large set of human paralogous families with structurally characterized interactions, and discuss in detail the results for the RasH family. We show that this approach is a better predictor of interfacial residues than both, sequence conservation and an equivalent 'unsupervised' method that does not use interactome information.

AVAILABILITY AND IMPLEMENTATION

http://csbg.cnb.csic.es/pazos/Xdet/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Decoding Neuromuscular Disorders Using Phenotypic Clusters Obtained From Co-Occurrence Networks

Neuromuscular disorders (NMDs) represent an important subset of rare diseases associated with elevated morbidity and mortality whose diagnosis can take years. Here we present a novel approach using systems biology to produce functionally-coherent phenotype clusters that provide insight into the cellular functions and phenotypic patterns underlying NMDs, using the Human Phenotype Ontology as a common framework. Gene and phenotype information was obtained for 424 NMDs in OMIM and 126 NMDs in Orphanet, and 335 and 216 phenotypes were identified as typical for NMDs, respectively. ‘Elevated serum creatine kinase’ was the most specific to NMDs, in agreement with the clinical test of elevated serum creatinine kinase that is conducted on NMD patients. The approach to obtain co-occurring NMD phenotypes was validated based on co-mention in PubMed abstracts. A total of 231 (OMIM) and 150 (Orphanet) clusters of highly connected co-occurrent NMD phenotypes were obtained. In parallel, a tripartite network based on phenotypes, diseases and genes was used to associate NMD phenotypes with functions, an approach also validated by literature co-mention, with KEGG pathways showing proportionally higher overlap than Gene Ontology and Reactome. Phenotype-function pairs were crossed with the co-occurrent NMD phenotype clusters to obtain 40 (OMIM) and 72 (Orphanet) functionally coherent phenotype clusters. As expected, many of these overlapped with known diseases and confirmed existing knowledge. Other clusters revealed interesting new findings, indicating informative phenotypes for differential diagnosis, providing deeper knowledge of NMDs, and pointing towards specific cell dysfunction caused by pleiotropic genes. This work is an example of reproducible research that i) can help better understand NMDs and support their diagnosis by providing a new tool that exploits existing information to obtain novel clusters of functionally-related phenotypes, and ii) takes us another step towards personalised medicine for NMDs.

Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases

Genetic and molecular analysis of rare disease is made difficult by the small numbers of affected patients. Phenotypic comorbidity analysis can help rectify this by combining information from individuals with similar phenotypes and looking for overlap in terms of shared genes and underlying functional systems. However, few studies have combined comorbidity analysis with genomic data. We present a computational approach that connects patient phenotypes based on phenotypic co-occurence and uses genomic information related to the patient mutations to assign genes to the phenotypes, which are used to detect enriched functional systems. These phenotypes are clustered using network analysis to obtain functionally coherent phenotype clusters. We applied the approach to the DECIPHER database, containing phenotypic and genomic information for thousands of patients with heterogeneous rare disorders and copy number variants. Validity was demonstrated through overlap with known diseases, co-mention within the biomedical literature, semantic similarity measures, and patient cluster membership. These connected pairs formed multiple phenotype clusters, showing functional coherence, and mapped to genes and systems involved in similar pathological processes. Examples include claudin genes from the 22q11 genomic region associated with a cluster of phenotypes related to DiGeorge syndrome and genes related to the GO term anterior/posterior pattern specification associated with abnormal development. The clusters generated can help with the diagnosis of rare diseases, by suggesting additional phenotypes for a given patient and potential underlying functional systems. Other tools to find causal genes based on phenotype were also investigated. The approach has been implemented as a workflow, named PhenCo, which can be adapted to any set of patients for which phenomic and genomic data is available. Full details of the analysis, including the clusters formed, their constituent functional systems and underlying genes are given. Code to implement the workflow is available from GitHub.

Although rare diseases each affect a small number of people, taken together they affect millions. Better diagnosis and understanding of the underlying mechanisms are needed. By combining phenotypic data for many rare disease patients, we can build clusters of comorbid phenotypes that tend to co-occur together. By using genomic information, we can supplement these clusters and look for related genes and functional systems, such as pathways and molecular mechanisms. We applied such an approach to thousands of rare disease patients from the DECIPHER resources. We were able to detect hundreds of pairs of comorbid phenotypes, and use them to build tens of phenotype clusters. By mapping genes to these phenotypes, based on data from the same patients, we were able to detect related genes and functional systems, such as genes mapping to the 22q11 genomic region underlying a cluster of phenotypes related to DiGeorge syndrome. To ensure that these clusters made sensible predictions, results were validated using literature co-mention, overlap with known disease and semantic similarity measures. These comorbidity patterns, along with their underlying molecular systems, can give important insights into disease mechanisms, moreover they can be used to direct differential-diagnosis of rare disease patients.

Regulatory variants: from detection to predicting impact

Variants within non-coding genomic regions can greatly affect disease. In recent years, increasing focus has been given to these variants, and how they can alter regulatory elements, such as enhancers, transcription factor binding sites and DNA methylation regions. Such variants can be considered regulatory variants. Concurrently, much effort has been put into establishing international consortia to undertake large projects aimed at discovering regulatory elements in different tissues, cell lines and organisms, and probing the effects of genetic variants on regulation by measuring gene expression. Here, we describe methods and techniques for discovering disease-associated non-coding variants using sequencing technologies. We then explain the computational procedures that can be used for annotating these variants using the information from the aforementioned projects, and prediction of their putative effects, including potential pathogenicity, based on rule-based and machine learning approaches. We provide the details of techniques to validate these predictions, by mapping chromatin-chromatin and chromatin-protein interactions, and introduce Clustered Regularly Interspaced Short Palindromic Repeats-Associated Protein 9 (CRISPR-Cas9) technology, which has already been used in this field and is likely to have a big impact on its future evolution. We also give examples of regulatory variants associated with multiple complex diseases. This review is aimed at bioinformaticians interested in the characterization of regulatory variants, molecular biologists and geneticists interested in understanding more about the nature and potential role of such variants from a functional point of views, and clinicians who may wish to learn about variants in non-coding genomic regions associated with a given disease and find out what to do next to uncover how they impact on the underlying mechanisms.

Applications of molecular networks in biomedicine

Due to the large interdependence between the molecular components of living systems, many phenomena, including those related to pathologies, cannot be explained in terms of a single gene or a small number of genes. Molecular networks, representing different types of relationships between molecular entities, embody these large sets of interdependences in a framework that allow their mining from a systemic point of view to obtain information. These networks, often generated from high-throughput omics datasets, are used to study the complex phenomena of human pathologies from a systemic point of view. Complementing the reductionist approach of molecular biology, based on the detailed study of a small number of genes, systemic approaches to human diseases consider that these are better reflected in large and intricate networks of relationships between genes. These networks, and not the single genes, provide both better markers for diagnosing diseases and targets for treating them. Network approaches are being used to gain insight into the molecular basis of complex diseases and interpret the large datasets associated with them, such as genomic variants. Network formalism is also suitable for integrating large, heterogeneous and multilevel datasets associated with diseases from the molecular level to organismal and epidemiological scales. Many of these approaches are available to nonexpert users through standard software packages.

Structural and Functional View of Polypharmacology

Protein domains mediate drug-protein interactions and this principle can guide the design of multi-target drugs i.e. polypharmacology. In this study, we associate multi-target drugs with CATH functional families through the overrepresentation of targets of those drugs in CATH functional families. Thus, we identify CATH functional families that are currently enriched in drugs (druggable CATH functional families) and we use the network properties of these druggable protein families to analyse their association with drug side effects. Analysis of selected druggable CATH functional families, enriched in drug targets, show that relatives exhibit highly conserved drug binding sites. Furthermore, relatives within druggable CATH functional families occupy central positions in a human protein functional network, cluster together forming network neighbourhoods and are less likely to be within proteins associated with drug side effects. Our results demonstrate that CATH functional families can be used to identify drug-target interactions, opening a new research direction in target identification.

How can functional annotations be derived from profiles of phenotypic annotations?

Background

Loss-of-function phenotypes are widely used to infer gene function using the principle that similar phenotypes are indicative of similar functions. However, converting phenotypic to functional annotations requires careful interpretation of phenotypic descriptions and assessment of phenotypic similarity. Understanding how functions and phenotypes are linked will be crucial for the development of methods for the automatic conversion of gene loss-of-function phenotypes to gene functional annotations.

Results

We explored the relation between cellular phenotypes from RNAi-based screens in human cells and gene annotations of cellular functions as provided by the Gene Ontology (GO). Comparing different similarity measures, we found that information content-based measures of phenotypic similarity were the best at capturing gene functional similarity. However, phenotypic similarities did not map to the Gene Ontology organization of gene function but to functions defined as groups of GO terms with shared gene annotations.

Conclusions

Our observations have implications for the use and interpretation of phenotypic similarities as a proxy for gene functions both in RNAi screen data analysis and curation and in the prediction of disease genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1503-5) contains supplementary material, which is available to authorized users.

Systematic identification of phenotypically enriched loci using a patient network of genomic disorders

Background

Network medicine is a promising new discipline that combines systems biology approaches and network science to understand the complexity of pathological phenotypes. Given the growing availability of personalized genomic and phenotypic profiles, network models offer a robust integrative framework for the analysis of "omics" data, allowing the characterization of the molecular aetiology of pathological processes underpinning genetic diseases.

Methods

Here we make use of patient genomic data to exploit different network-based analyses to study genetic and phenotypic relationships between individuals. For this method, we analyzed a dataset of structural variants and phenotypes for 6,564 patients from the DECIPHER database, which encompasses one of the most comprehensive collections of pathogenic Copy Number Variations (CNVs) and their associated ontology-controlled phenotypes. We developed a computational strategy that identifies clusters of patients in a synthetic patient network according to their genetic overlap and phenotype enrichments.

Results

Many of these clusters of patients represent new genotype-phenotype associations, suggesting the identification of newly discovered phenotypically enriched loci (indicative of potential novel syndromes) that are currently absent from reference genomic disorder databases such as ClinVar, OMIM or DECIPHER itself.

Conclusions

We provide a high-resolution map of pathogenic phenotypes associated with their respective significant genomic regions and a new powerful tool for diagnosis of currently uncharacterized mutations leading to deleterious phenotypes and syndromes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2569-6) contains supplementary material, which is available to authorized users.

Biocomputational resources useful for drug discovery against compartmentalized targets

It has been estimated that the cost of bringing a new drug onto the market is 10 years and 0.5-2 billions of dollars, making it a non-profitable project, particularly in the case of low prevalence diseases. The advances in Systems Biology have been absolutely decisive for drug discovery, as iterative rounds of predictions made from in silico models followed by selected experimental validations have resulted in a substantial saving of time and investments. Many diseases have their origins in proteins that are not located in the cytosol but in intracellular compartments (i.e. mitochondria, lysosome, peroxisome and others) or cell membranes. In these cases, biocomputational approaches present limitations to their study. In the present work, we review them and propose new initiatives to advance towards a safer, more efficient and personalized pharmacology. This focus could be especially useful for drug discovery and the reposition of known drugs in rare and emergent diseases associated with compartmentalized proteins.

Global analysis of the human pathophenotypic similarity gene network merges disease module components

The molecular complexity of genetic diseases requires novel approaches to break it down into coherent biological modules. For this purpose, many disease network models have been created and analyzed. We highlight two of them, "the human diseases networks" (HDN) and "the orphan disease networks" (ODN). However, in these models, each single node represents one disease or an ambiguous group of diseases. In these cases, the notion of diseases as unique entities reduces the usefulness of network-based methods. We hypothesize that using the clinical features (pathophenotypes) to define pathophenotypic connections between disease-causing genes improve our understanding of the molecular events originated by genetic disturbances. For this, we have built a pathophenotypic similarity gene network (PSGN) and compared it with the unipartite projections (based on gene-to-gene edges) similar to those used in previous network models (HDN and ODN). Unlike these disease network models, the PSGN uses semantic similarities. This pathophenotypic similarity has been calculated by comparing pathophenotypic annotations of genes (human abnormalities of HPO terms) in the "Human Phenotype Ontology". The resulting network contains 1075 genes (nodes) and 26197 significant pathophenotypic similarities (edges). A global analysis of this network reveals: unnoticed pairs of genes showing significant pathophenotypic similarity, a biological meaningful re-arrangement of the pathological relationships between genes, correlations of biochemical interactions with higher similarity scores and functional biases in metabolic and essential genes toward the pathophenotypic specificity and the pleiotropy, respectively. Additionally, pathophenotypic similarities and metabolic interactions of genes associated with maple syrup urine disease (MSUD) have been used to merge into a coherent pathological module.Our results indicate that pathophenotypes contribute to identify underlying co-dependencies among disease-causing genes that are useful to describe disease modularity.

The function of histamine receptor H4R in the brain revealed by interaction partners

The histamine H4 receptor is mainly expressed in haematopoietic cells, hence is linked to inflammatory and immune system conditions. It has been recently discovered that the receptor is expressed also in the mammalian central nervous system (CNS), but its role in the brain remains unclear. We address the potential functions of the histamine H4 receptor in the human brain using a 'guilty by association' logic, by close examination of protein-protein functional associations networks in the human proteome.

Evolutionary expansion of the Ras switch regulatory module in eukaryotes

Ras proteins control many aspects of eukaryotic cell homeostasis by switching between active (GTP-bound) and inactive (GDP-bound) conformations, a reaction catalyzed by GTPase exchange factors (GEF) and GTPase activating proteins (GAP) regulators, respectively. Here, we show that the complexity, measured as number of genes, of the canonical Ras switch genetic system (including Ras, RasGEF, RasGAP and RapGAP families) from 24 eukaryotic organisms is correlated with their genome size and is inversely correlated to their evolutionary distances from humans. Moreover, different gene subfamilies within the Ras switch have contributed unevenly to the module’s expansion and speciation processes during eukaryote evolution. The Ras system remarkably reduced its genetic expansion after the split of the Euteleostomi clade and presently looks practically crystallized in mammals. Supporting evidence points to gene duplication as the predominant mechanism generating functional diversity in the Ras system, stressing the leading role of gene duplication in the Ras family expansion. Domain fusion and alternative splicing are significant sources of functional diversity in the GAP and GEF families but their contribution is limited in the Ras family. An evolutionary model of the Ras system expansion is proposed suggesting an inherent ‘decision making’ topology with the GEF input signal integrated by a homologous molecular mechanism and bifurcation in GAP signaling propagation.

Finding the "dark matter" in human and yeast protein network prediction and modelling

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or "dark matter" of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions.

Assessment of protein domain fusions in human protein interaction networks prediction: application to the human kinetochore model

In order to understand how biological systems function it is necessary to determine the interactions and associations between proteins. Some proteins, involved in a common biological process and encoded by separate genes in one organism, can be found fused within a single protein chain in other organisms. By detecting these triplets, a functional relationship can be established between the unfused proteins. Here we use a domain fusion prediction method to predict these protein interactions for the human interactome. We observed that gene fusion events are more related to physical interaction between proteins than to other weaker functional relationships such as participation in a common biological pathway. These results suggest that domain fusion is an appropriate method for predicting protein complexes. The most reliable fused domain predictions were used to build protein-protein interaction (PPI) networks. These predicted PPI network models showed the same topological features as real biological networks and different features from random behaviour. We built the PPI domain fusion sub-network model of the human kinetochore and observed that the majority of the predicted interactions have not yet been experimentally characterised in the publicly available PPI repositories. The study of the human kinetochore domain fusion sub-network reveals undiscovered kinetochore proteins with presumably relevant functions, such as hubs with many connections in the kinetochore sub-network. These results suggest that experimentally hidden regions in the predicted PPI networks contain key functional elements, associated with important functional areas, still undiscovered in the human interactome. Until novel experiments shed light on these hidden regions; domain fusion predictions provide a valuable approach for exploring them.

CODA: accurate detection of functional associations between proteins in eukaryotic genomes using domain fusion

Background

In order to understand how biological systems function it is necessary to determine the interactions and associations between proteins. Gene fusion prediction is one approach to detection of such functional relationships. Its use is however known to be problematic in higher eukaryotic genomes due to the presence of large homologous domain families. Here we introduce CODA (Co-Occurrence of Domains Analysis), a method to predict functional associations based on the gene fusion idiom.

Methodology/Principal Findings

We apply a novel scoring scheme which takes account of the genome-specific size of homologous domain families involved in fusion to improve accuracy in predicting functional associations. We show that CODA is able to accurately predict functional similarities in human with comparison to state-of-the-art methods and show that different methods can be complementary. CODA is used to produce evidence that a currently uncharacterised human protein may be involved in pathways related to depression and that another is involved in DNA replication.

Conclusions/Significance

The relative performance of different gene fusion methodologies has not previously been explored. We find that they are largely complementary, with different methods being more or less appropriate in different genomes. Our method is the only one currently available for download and can be run on an arbitrary dataset by the user. The CODA software and datasets are freely available from ftp://ftp.biochem.ucl.ac.uk/pub/gene3d_data/v6.1.0/CODA/. Predictions are also available via web services from http://funcnet.eu/.

Predicting protein function with hierarchical phylogenetic profiles: the Gene3D Phylo-Tuner method applied to eukaryotic genomes

“Phylogenetic profiling” is based on the hypothesis that during evolution functionally or physically interacting genes are likely to be inherited or eliminated in a codependent manner. Creating presence–absence profiles of orthologous genes is now a common and powerful way of identifying functionally associated genes. In this approach, correctly determining orthology, as a means of identifying functional equivalence between two genes, is a critical and nontrivial step and largely explains why previous work in this area has mainly focused on using presence–absence profiles in prokaryotic species. Here, we demonstrate that eukaryotic genomes have a high proportion of multigene families whose phylogenetic profile distributions are poor in presence–absence information content. This feature makes them prone to orthology mis-assignment and unsuited to standard profile-based prediction methods. Using CATH structural domain assignments from the Gene3D database for 13 complete eukaryotic genomes, we have developed a novel modification of the phylogenetic profiling method that uses genome copy number of each domain superfamily to predict functional relationships. In our approach, superfamilies are subclustered at ten levels of sequence identity—from 30% to 100%—and phylogenetic profiles built at each level. All the profiles are compared using normalised Euclidean distances to identify those with correlated changes in their domain copy number. We demonstrate that two protein families will “auto-tune” with strong co-evolutionary signals when their profiles are compared at the similarity levels that capture their functional relationship. Our method finds functional relationships that are not detectable by the conventional presence–absence profile comparisons, and it does not require a priori any fixed criteria to define orthologous genes.

The vast number of protein sequences being determined by the international genomics projects means that it is not possible to functionally characterise all the proteins through direct experimentation. One of the more successful electronic methods for detecting functionally associated genes has been through the comparison of genes' phylogenetic profiles. This method is based on the hypothesis that two functionally related genes will show very similar presence–absence profile patterns throughout different organisms. Whilst these methods have grown increasingly sophisticated, they have largely been based on detecting functionally homologous genes in different species (technically known as orthologous genes) and thus better suited to prokaryotic genomes, where this can be done more easily. We have developed a new type of hierarchical phylogenetic profile by subdividing protein families into subclusters in different sequence identity levels. This new approach encapsulates a more realistic model of the functional variation that uneven natural selection pressure produces on different protein families and organisms, and it can detect functional relationships between protein families without the initial application of rigid sequence similarity thresholds or complex protocols for orthology assignment. These advantages are especially useful in eukaryotes since the larger average size of eukaryotic multigene families makes them more prone to orthology mis-assignment than in prokaryotes.

Protein Superfamily Evolution and the Last Universal Common Ancestor (LUCA)

By exploiting three-dimensional structure comparison, which is more sensitive than conventional sequence-based methods for detecting remote homology, we have identified a set of 140 ancestral protein domains using very restrictive criteria to minimize the potential error introduced by horizontal gene transfer. These domains are highly likely to have been present in the Last Universal Common Ancestor (LUCA) based on their universality in almost all of 114 completed prokaryotic (Bacteria and Archaea) and eukaryotic genomes. Functional analysis of these ancestral domains reveals a genetically complex LUCA with practically all the essential functional systems present in extant organisms, supporting the theory that life achieved its modern cellular status much before the main kingdom separation (Doolittle 2000). In addition, we have calculated different estimations of the genetic and functional versatility of all the superfamilies and functional groups in the prokaryote subsample. These estimations reveal that some ancestral superfamilies have been more versatile than others during evolution allowing more genetic and functional variation. Furthermore, the differences in genetic versatility between protein families are more attributable to their functional nature rather than the time that they have been evolving. These differences in tolerance to mutation suggest that some protein families have eroded their phylogenetic signal faster than others, hiding in many cases, their ancestral origin and suggesting that the calculation of 140 ancestral domains is probably an underestimate.

TSEMA: interactive prediction of protein pairings between interacting families

An entire family of methodologies for predicting protein interactions is based on the observed fact that families of interacting proteins tend to have similar phylogenetic trees due to co-evolution. One application of this concept is the prediction of the mapping between the members of two interacting protein families (which protein within one family interacts with which protein within the other). The idea is that the real mapping would be the one maximizing the similarity between the trees. Since the exhaustive exploration of all possible mappings is not feasible for large families, current approaches use heuristic techniques which do not ensure the best solution to be found. This is why it is important to check the results proposed by heuristic techniques and to manually explore other solutions. Here we present TSEMA, the server for efficient mapping assessment. This system calculates an initial mapping between two families of proteins based on a Monte Carlo approach and allows the user to interactively modify it based on performance figures and/or specific biological knowledge. All the explored mappings are graphically shown over a representation of the phylogenetic trees. The system is freely available at . Standalone versions of the software behind the interface are available upon request from the authors.

Exploiting protein structure data to explore the evolution of protein function and biological complexity

New directions in biology are being driven by the complete sequencing of genomes, which has given us the protein repertoires of diverse organisms from all kingdoms of life. In tandem with this accumulation of sequence data, worldwide structural genomics initiatives, advanced by the development of improved technologies in X-ray crystallography and NMR, are expanding our knowledge of structural families and increasing our fold libraries. Methods for detecting remote sequence similarities have also been made more sensitive and this means that we can map domains from these structural families onto genome sequences to understand how these families are distributed throughout the genomes and reveal how they might influence the functional repertoires and biological complexities of the organisms. We have used robust protocols to assign sequences from completed genomes to domain structures in the CATH database, allowing up to 60% of domain sequences in these genomes, depending on the organism, to be assigned to a domain family of known structure. Analysis of the distribution of these families throughout bacterial genomes identified more than 300 universal families, some of which had expanded significantly in proportion to genome size. These highly expanded families are primarily involved in metabolism and regulation and appear to make major contributions to the functional repertoire and complexity of bacterial organisms. When comparisons are made across all kingdoms of life, we find a smaller set of universal domain families (approx. 140), of which families involved in protein biosynthesis are the largest conserved component. Analysis of the behaviour of other families reveals that some (e.g. those involved in metabolism, regulation) have remained highly innovative during evolution, making it harder to trace their evolutionary ancestry. Structural analyses of metabolic families provide some insights into the mechanisms of functional innovation, which include changes in domain partnerships and significant structural embellishments leading to modulation of active sites and protein interactions.

Assessing Protein Co-evolution in the Context of the Tree of Life Assists in the Prediction of the Interactome

The identification of the whole set of protein interactions taking place in an organism is one of the main tasks in genomics, proteomics and systems biology. One of the computational techniques used by many investigators for studying and predicting protein interactions is the comparison of evolutionary histories (phylogenetic trees), under the hypothesis that interacting proteins would be subject to a similar evolutionary pressure resulting in a similar topology of the corresponding trees. Here, we present a new approach to predict protein interactions from phylogenetic trees, which incorporates information on the overall evolutionary histories of the species (i.e. the canonical "tree of life") in order to correct by the expected background similarity due to the underlying speciation events. We test the new approach in the largest set of annotated interacting proteins for Escherichia coli. This assessment of co-evolution in the context of the tree of life leads to a highly significant improvement (P(N) by sign test approximately 10E-6) in predicting interaction partners with respect to the previous technique, which does not incorporate information on the overall speciation tree. For half of the proteins we found a real interactor among the 6.4% top scores, compared with the 16.5% by the previous method. We applied the new method to the whole E.coli proteome and propose functions for some hypothetical proteins based on their predicted interactors. The new approach allows us also to detect non-canonical evolutionary events, in particular horizontal gene transfers. We also show that taking into account these non-canonical evolutionary events when assessing the similarity between evolutionary trees improves the performance of the method predicting interactions.

Microeconomic principles explain an optimal genome size in bacteria

Bacteria can clearly enhance their survival by expanding their genetic repertoire. However, the tight packing of the bacterial genome and the fact that the most evolved species do not necessarily have the biggest genomes suggest there are other evolutionary factors limiting their genome expansion. To clarify these restrictions on size, we studied those protein families contributing most significantly to bacterial-genome complexity. We found that all bacteria apply the same basic and ancestral 'molecular technology' to optimize their reproductive efficiency. The same microeconomics principles that define the optimum size in a factory can also explain the existence of a statistical optimum in bacterial genome size. This optimum is reached when the bacterial genome obtains the maximum metabolic complexity (revenue) for minimal regulatory genes (logistic cost).

Evolution of protein superfamilies and bacterial genome size

We present the structural annotation of 56 different bacterial species based on the assignment of genes to 816 evolutionary superfamilies in the CATH domain structure database. These assignments have enabled us to analyse the recurrence of specific superfamilies within and across the genomes. We have selected the superfamilies that have a very broad representation and therefore appear to be universally distributed in a significant number of bacterial lineages. Occurrence profiles of these universally distributed superfamilies are compared with genome size in order to estimate the correlation between superfamily duplication and the increase in proteome size. This distinguishes between those size-dependent superfamilies where frequency of occurrence is highly correlated with increase in genome size, and size-independent superfamilies where no correlation is observed. Consideration of the size correlation and the ratio between the mean and the standard deviations for all the superfamily profiles allows more detailed subdivisions and classification of superfamilies. For example, within the size-independent superfamilies, we distinguished a group that are distributed evenly amongst all the genomes. Within the size-dependent superfamilies we differentiated two groups: linearly distributed and non-linearly distributed. Functional annotation using the COG database was performed for all superfamilies in each of these groups, and this revealed significant differences amongst the three sets of superfamilies. Evenly distributed, size-independent domains are shown to be involved primarily in protein translation and biosynthesis. For the size-dependent superfamilies, linearly distributed superfamilies are involved mainly in metabolism, and non-linearly distributed superfamily domains are involved principally in gene regulation.