McKusick's Online Mendelian Inheritance in Man (OMIM)

The time is ripe to investigate human centromeres by long-read sequencing†

The complete sequencing of human centromeres, which are filled with highly repetitive elements, has long been challenging. In human centromeres, α-satellite monomers of about 171 bp in length are the basic repeating units, but α-satellite monomers constitute the higher-order repeat (HOR) units, and thousands of copies of highly homologous HOR units form large arrays, which have hampered sequence assembly of human centromeres. Because most HOR unit occurrences are covered by long reads of about 10 kb, the recent availability of much longer reads is expected to enable observation of individual HOR occurrences in terms of their single-nucleotide or structural variants. The time has come to examine the complete sequence of human centromeres.

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network

Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data

Genomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate the data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNVs) and copy number variations (CNVs) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNVs and CNVs compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigation.

Finding commonalities in rare diseases through the undiagnosed diseases network

Objective

When studying any specific rare disease, heterogeneity and scarcity of affected individuals has historically hindered investigators from discerning on what to focus to understand and diagnose a disease. New nongenomic methodologies must be developed that identify similarities in seemingly dissimilar conditions.

Materials and Methods

This observational study analyzes 1042 patients from the Undiagnosed Diseases Network (2015-2019), a multicenter, nationwide research study using phenotypic data annotated by specialized staff using Human Phenotype Ontology terms. We used Louvain community detection to cluster patients linked by Jaccard pairwise similarity and 2 support vector classifier to assign new cases. We further validated the clusters’ most representative comorbidities using a national claims database (67 million patients).

Results

Patients were divided into 2 groups: those with symptom onset before 18 years of age (n = 810) and at 18 years of age or older (n = 232) (average symptom onset age: 10 [interquartile range, 0-14] years). For 810 pediatric patients, we identified 4 statistically significant clusters. Two clusters were characterized by growth disorders, and developmental delay enriched for hypotonia presented a higher likelihood of diagnosis. Support vector classifier showed 0.89 balanced accuracy (0.83 for Human Phenotype Ontology terms only) on test data.

Discussions

To set the framework for future discovery, we chose as our endpoint the successful grouping of patients by phenotypic similarity and provide a classification tool to assign new patients to those clusters.

Conclusion

This study shows that despite the scarcity and heterogeneity of patients, we can still find commonalities that can potentially be harnessed to uncover new insights and targets for therapy.

The genetic architecture of primary biliary cholangitis

Primary biliary cholangitis (PBC) is a rare autoimmune disease of the liver affecting the small bile ducts. From a genetic point of view, PBC is a complex trait and several genetic and environmental factors have been called in action to explain its etiopathogenesis. Similarly to other complex traits, PBC has benefited from the introduction of genome-wide association studies (GWAS), which identified many variants predisposing or protecting toward the development of the disease. While a progressive endeavour toward the characterization of candidate loci and downstream pathways is currently ongoing, there is still a relatively large portion of heritability of PBC to be revealed. In addition, genetic variation behind progression of the disease and therapeutic response are mostly to be investigated yet. This review outlines the state-of-the-art regarding the genetic architecture of PBC and provides some hints for future investigations, focusing on the study of gene-gene interactions, the application of whole-genome sequencing techniques, and the investigation of X chromosome that can be helpful to cover the missing heritability gap in PBC.

Insights into the molecular mechanisms of Huangqi decoction on liver fibrosis via computational systems pharmacology approaches

BACKGROUND

The traditional Chinese medicine Huangqi decoction (HQD) consists of Radix Astragali and Radix Glycyrrhizae in a ratio of 6: 1, which has been used for the treatment of liver fibrosis. In this study, we tried to elucidate its action of mechanism (MoA) via a combination of metabolomics data, network pharmacology and molecular docking methods.

METHODS

Firstly, we collected prototype components and metabolic products after administration of HQD from a publication. With known and predicted targets, compound-target interactions were obtained. Then, the global compound-liver fibrosis target bipartite network and the HQD-liver fibrosis protein-protein interaction network were constructed, separately. KEGG pathway analysis was applied to further understand the mechanisms related to the target proteins of HQD. Additionally, molecular docking simulation was performed to determine the binding efficiency of compounds with targets. Finally, considering the concentrations of prototype compounds and metabolites of HQD, the critical compound-liver fibrosis target bipartite network was constructed.

RESULTS

68 compounds including 17 prototype components and 51 metabolic products were collected. 540 compound-target interactions were obtained between the 68 compounds and 95 targets. Combining network analysis, molecular docking and concentration of compounds, our final results demonstrated that eight compounds (three prototype compounds and five metabolites) and eight targets (CDK1, MMP9, PPARD, PPARG, PTGS2, SERPINE1, TP53, and HIF1A) might contribute to the effects of HQD on liver fibrosis. These interactions would maintain the balance of ECM, reduce liver damage, inhibit hepatocyte apoptosis, and alleviate liver inflammation through five signaling pathways including p53, PPAR, HIF-1, IL-17, and TNF signaling pathway.

CONCLUSIONS

This study provides a new way to understand the MoA of HQD on liver fibrosis by considering the concentrations of components and metabolites, which might be a model for investigation of MoA of other Chinese herbs.

Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS

Electronic health records (EHRs) are a rich source of data for researchers, but extracting meaningful information out of this highly complex data source is challenging. Phecodes represent one strategy for defining phenotypes for research using EHR data. They are a high-throughput phenotyping tool based on ICD (International Classification of Diseases) codes that can be used to rapidly define the case/control status of thousands of clinically meaningful diseases and conditions. Phecodes were originally developed to conduct phenome-wide association studies to scan for phenotypic associations with common genetic variants. Since then, phecodes have been used to support a wide range of EHR-based phenotyping methods, including the phenotype risk score. This review aims to comprehensively describe the development, validation, and applications of phecodes and suggest some future directions for phecodes and high-throughput phenotyping.

Design, Synthesis, Biological Evaluation and Silico Prediction of Novel Sinomenine Derivatives

Sinomenine is a morphinan alkaloid with a variety of biological activities. Its derivatives have shown significant cytotoxic activity against different cancer cell lines in many studies. In this study, two series of sinomenine derivatives were designed and synthesized by modifying the active positions C₁ and C₄ on the A ring of sinomenine. Twenty-three compounds were synthesized and characterized by spectroscopy (IR, ¹H-NMR, ¹³C-NMR, and HRMS). They were further evaluated for their cytotoxic activity against five cancer cell lines, MCF-7, Hela, HepG2, SW480 and A549, and a normal cell line, Hek293, using MTT and CCK8 methods. The chlorine-containing compounds exhibited significant cytotoxic activity compared to the nucleus structure of sinomenine. Furthermore, we searched for cancer-related core targets and verified their interaction with derivatives through molecular docking. The chlorine-containing compounds 5g, 5i, 5j, 6a, 6d, 6e, and 6g exhibited the best against four core targets AKT1, EGFR, HARS and KARS. The molecular docking results were consistent with the cytotoxic results. Overall, results indicate that chlorine-containing derivatives might be a promising lead for the development of new anticancer agents.

Neuronal Cytoskeleton in Intellectual Disability: From Systems Biology and Modeling to Therapeutic Opportunities

Intellectual disability (ID) is a pathological condition characterized by limited intellectual functioning and adaptive behaviors. It affects 1–3% of the worldwide population, and no pharmacological therapies are currently available. More than 1000 genes have been found mutated in ID patients pointing out that, despite the common phenotype, the genetic bases are highly heterogeneous and apparently unrelated. Bibliomic analysis reveals that ID genes converge onto a few biological modules, including cytoskeleton dynamics, whose regulation depends on Rho GTPases transduction. Genetic variants exert their effects at different levels in a hierarchical arrangement, starting from the molecular level and moving toward higher levels of organization, i.e., cell compartment and functions, circuits, cognition, and behavior. Thus, cytoskeleton alterations that have an impact on cell processes such as neuronal migration, neuritogenesis, and synaptic plasticity rebound on the overall establishment of an effective network and consequently on the cognitive phenotype. Systems biology (SB) approaches are more focused on the overall interconnected network rather than on individual genes, thus encouraging the design of therapies that aim to correct common dysregulated biological processes. This review summarizes current knowledge about cytoskeleton control in neurons and its relevance for the ID pathogenesis, exploiting in silico modeling and translating the implications of those findings into biomedical research.

Computational analysis of deleterious single nucleotide polymorphisms in catechol O-Methyltransferase conferring risk to post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) is one of the prevalent neurological disorder which is drawing increased attention over the past few decades. Major risk factors for PTSD can be categorized into environmental and genetic factors. Among the genetic risk factors, polymorphisms in the catechol-O-methyltransferase (COMT) gene is known to be associated with the risk for PTSD. In the present study, we analysed the impact of deleterious single nucleotide polymorphisms (SNPs) in the COMT gene conferring risk to PTSD using computational based approaches followed by molecular dynamic simulations. The data on COMT gene associated with PTSD were collected from several databases including Online Mendelian Inheritance in Man (OMIM) search. Datasets related to SNP were downloaded from the dbSNP database. To study the structural and dynamic effects of COMT wild type and mutant forms, we performed molecular dynamics simulations (MD simulations) at a time scale of 300 ns. Results from screening the SNPs using the computational tools SIFT and Polyphen-2 demonstrated that the SNP rs4680 (V158M) in COMT has a deleterious effect with phenotype in PTSD. Results from the MD simulations showed that there is some major fluctuations in the structural features including root mean square deviation (RMSD), radius of gyration (Rg), root mean square fluctuation (RMSF) and secondary structural elements including α-helices, sheets and turns between wild-type (WT) and mutant forms of COMT protein. In conclusion, our study provides novel insights into the deleterious effects and impact of V158M mutation on COMT protein structure which plays a key role in PTSD.

X Chromosome Contribution to the Genetic Architecture of Primary Biliary Cholangitis

BACKGROUND & AIMS

Genome-wide association studies in primary biliary cholangitis (PBC) have failed to find X chromosome (chrX) variants associated with the disease. Here, we specifically explore the chrX contribution to PBC, a sexually dimorphic complex autoimmune disease.

METHODS

We performed a chrX-wide association study, including genotype data from 5 genome-wide association studies (from Italy, United Kingdom, Canada, China, and Japan; 5244 case patients and 11,875 control individuals).

RESULTS

Single-marker association analyses found approximately 100 loci displaying P < 5 × 10-4, with the most significant being a signal within the OTUD5 gene (rs3027490; P = 4.80 × 10-6; odds ratio [OR], 1.39; 95% confidence interval [CI], 1.028-1.88; Japanese cohort). Although the transethnic meta-analysis evidenced only a suggestive signal (rs2239452, mapping within the PIM2 gene; OR, 1.17; 95% CI, 1.09-1.26; P = 9.93 × 10-8), the population-specific meta-analysis showed a genome-wide significant locus in East Asian individuals pointing to the same region (rs7059064, mapping within the GRIPAP1 gene; P = 6.2 × 10-9; OR, 1.33; 95% CI, 1.21-1.46). Indeed, rs7059064 tags a unique linkage disequilibrium block including 7 genes: TIMM17B, PQBP1, PIM2, SLC35A2, OTUD5, KCND1, and GRIPAP1, as well as a superenhancer (GH0XJ048933 within OTUD5) targeting all these genes. GH0XJ048933 is also predicted to target FOXP3, the main T-regulatory cell lineage specification factor. Consistently, OTUD5 and FOXP3 RNA levels were up-regulated in PBC case patients (1.75- and 1.64-fold, respectively).

CONCLUSIONS

This work represents the first comprehensive study, to our knowledge, of the chrX contribution to the genetics of an autoimmune liver disease and shows a novel PBC-related genome-wide significant locus.

Nucleocytoplasmic transport of the RNA-binding protein CELF2 regulates neural stem cell fates

The development of the cerebral cortex requires balanced expansion and differentiation of neural stem/progenitor cells (NPCs), which rely on precise regulation of gene expression. Because NPCs often exhibit transcriptional priming of cell-fate-determination genes, the ultimate output of these genes for fate decisions must be carefully controlled in a timely fashion at the post-transcriptional level, but how that is achieved is poorly understood. Here, we report that de novo missense variants in an RNA-binding protein CELF2 cause human cortical malformations and perturb NPC fate decisions in mice by disrupting CELF2 nucleocytoplasmic transport. In self-renewing NPCs, CELF2 resides in the cytoplasm, where it represses mRNAs encoding cell fate regulators and neurodevelopmental disorder-related factors. The translocation of CELF2 into the nucleus releases mRNA for translation and thereby triggers NPC differentiation. Our results reveal that CELF2 translocation between subcellular compartments orchestrates mRNA at the translational level to instruct cell fates in cortical development.

Identification of a variant in NDP associated with X-linked retinal dysplasia in the English cocker spaniel dog

Purpose

Three related male English Cocker Spaniels (ECS) were reported to be congenitally blind. Examination of one of these revealed complete retinal detachment. A presumptive diagnosis of retinal dysplasia (RD) was provided and pedigree analysis was suggestive of an X-linked mode of inheritance. We sought to investigate the genetic basis of RD in this family of ECS.

Methods

Following whole genome sequencing (WGS) of the one remaining male RD-affected ECS, two distinct investigative approaches were employed: a candidate gene approach and a whole genome approach. In the candidate gene approach, COL9A2, COL9A3, NHEJ1, RS1 and NDP genes were investigated based on their known associations with RD and retinal detachment in dogs and humans. In the whole genome approach, affected WGS was compared with 814 unaffected canids to identify candidate variants, which were filtered based on appropriate segregation and predicted pathogenic effects followed by subsequent investigation of gene function. Candidate variants were tested for appropriate segregation in the ECS family and association with disease was assessed using samples from a total of 180 ECS.

Results

The same variant in NDP (c.653_654insC, p.Met114Hisfs*16) that was predicted to result in 15 aberrant amino acids before a premature stop in norrin protein, was identified independently by both approaches and was shown to segregate appropriately within the ECS family. Association of this variant with X-linked RD was significant (P = 0.0056).

Conclusions

For the first time, we report a variant associated with canine X-linked RD. NDP variants are already known to cause X-linked RD, along with other abnormalities, in human Norrie disease. Thus, the dog may serve as a useful large animal model for research.

Why does SARS-CoV-2 hit in different ways? Host genetic factors can influence the acquisition or the course of COVID-19

The identification of high-risk factors for the infection by SARS-CoV-2 and the negative outcome of COVID-19 is crucial. The genetic background of the host might account for individual responses to SARS-CoV-2 infection besides age and comorbidities. A list of candidate polymorphisms is needed to drive targeted screens, given the existence of frequent polymorphisms in the general population. We carried out text mining in the scientific literature to draw up a list of genes referable to the term "SARS-CoV*". We looked for frequent mutations that are likely to affect protein function in these genes. Ten genes, mostly involved in innate immunity, and thirteen common variants were identified, for some of these the involvement in COVID-19 is supported by publicly available epidemiological data. We looked for available data on the population distribution of these variants and we demonstrated that the prevalence of five of them, Arg52Cys (rs5030737), Gly54Asp (rs1800450) and Gly57Glu (rs1800451) in MBL2, Ala59Thr (rs25680) in CD27, and Val197Met (rs12329760) in TMPRSS2, correlates with the number of cases and/or deaths of COVID-19 observed in different countries. The association of the TMPRSS2 variant provides epidemiological evidence of the usefulness of transmembrane protease serine 2 inhibitors for the cure of COVID-19. The identified genetic variants represent a basis for the design of a cost-effective assay for population screening of genetic risk factors in the COVID-19 pandemic.

The landscape of molecular chaperones across human tissues reveals a layered architecture of core and variable chaperones

The sensitivity of the protein-folding environment to chaperone disruption can be highly tissue-specific. Yet, the organization of the chaperone system across physiological human tissues has received little attention. Through computational analyses of large-scale tissue transcriptomes, we unveil that the chaperone system is composed of core elements that are uniformly expressed across tissues, and variable elements that are differentially expressed to fit with tissue-specific requirements. We demonstrate via a proteomic analysis that the muscle-specific signature is functional and conserved. Core chaperones are significantly more abundant across tissues and more important for cell survival than variable chaperones. Together with variable chaperones, they form tissue-specific functional networks. Analysis of human organ development and aging brain transcriptomes reveals that these functional networks are established in development and decline with age. In this work, we expand the known functional organization of de novo versus stress-inducible eukaryotic chaperones into a layered core-variable architecture in multi-cellular organisms.

Low Diversity of Human Variation Despite Mostly Mild Functional Impact of De Novo Variants

Non-synonymous Single Nucleotide Variants (nsSNVs), resulting in single amino acid variants (SAVs), are important drivers of evolutionary adaptation across the tree of life. Humans carry on average over 10,000 SAVs per individual genome, many of which likely have little to no impact on the function of the protein they affect. Experimental evidence for protein function changes as a result of SAVs remain sparse – a situation that can be somewhat alleviated by predicting their impact using computational methods. Here, we used SNAP to examine both observed and in silico generated human variation in a set of 1,265 proteins that are consistently found across a number of diverse species. The number of SAVs that are predicted to have any functional effect on these proteins is smaller than expected, suggesting sequence/function optimization over evolutionary timescales. Additionally, we find that only a few of the yet-unobserved SAVs could drastically change the function of these proteins, while nearly a quarter would have only a mild functional effect. We observed that variants common in the human population localized to less conserved protein positions and carried mild to moderate functional effects more frequently than rare variants. As expected, rare variants carried severe effects more frequently than common variants. In line with current assumptions, we demonstrated that the change of the human reference sequence amino acid to the reference of another species (a cross-species variant) is unlikely to significantly impact protein function. However, we also observed that many cross-species variants may be weakly non-neutral for the purposes of quick adaptation to environmental changes, but may not be identified as such by current state-of-the-art methodology.

ClinSV: clinical grade structural and copy number variant detection from whole genome sequencing data

Whole genome sequencing (WGS) has the potential to outperform clinical microarrays for the detection of structural variants (SV) including copy number variants (CNVs), but has been challenged by high false positive rates. Here we present ClinSV, a WGS based SV integration, annotation, prioritization, and visualization framework, which identified 99.8% of simulated pathogenic ClinVar CNVs > 10 kb and 11/11 pathogenic variants from matched microarrays. The false positive rate was low (1.5-4.5%) and reproducibility high (95-99%). In clinical practice, ClinSV identified reportable variants in 22 of 485 patients (4.7%) of which 35-63% were not detectable by current clinical microarray designs. ClinSV is available at https://github.com/KCCG/ClinSV .

Improving the phenotype risk score as a scalable approach to identifying patients with Mendelian disease

OBJECTIVE

The Phenotype Risk Score (PheRS) is a method to detect Mendelian disease patterns using phenotypes from the electronic health record (EHR). We compared the performance of different approaches mapping EHR phenotypes to Mendelian disease features.

MATERIALS AND METHODS

PheRS utilizes Mendelian diseases descriptions annotated with Human Phenotype Ontology (HPO) terms. In previous work, we presented a map linking phecodes (based on International Classification of Diseases [ICD]-Ninth Revision) to HPO terms. For this study, we integrated ICD-Tenth Revision codes and lab data. We also created a new map between HPO terms using customized groupings of ICD codes. We compared the performance with cases and controls for 16 Mendelian diseases using 2.5 million de-identified medical records.

RESULTS

PheRS effectively distinguished cases from controls for all 15 positive controls and all approaches tested (P < 4 × 1016). Adding lab data led to a statistically significant improvement for 4 of 14 diseases. The custom ICD groupings improved specificity, leading to an average 8% increase for precision at 100 (-2% to 22%). Eight of 10 adults with cystic fibrosis tested had PheRS in the 95th percentile prio to diagnosis.

DISCUSSION

Both phecodes and custom ICD groupings were able to detect differences between affected cases and controls at the population level. The ICD map showed better precision for the highest scoring individuals. Adding lab data improved performance at detecting population-level differences.

CONCLUSIONS

PheRS is a scalable method to study Mendelian disease at the population level using electronic health record data and can potentially be used to find patients with undiagnosed Mendelian disease.

An Overview of Benefits and Challenges of Rare Disease Biobanking in Africa, Focusing on South Africa

The North-West University's Centre for Human Metabolomics (CHM) is in the process of establishing the first rare disease (RD) biobank in South Africa and Africa. The CHM Biobank's main focus is on the collection of samples and information for rare congenital disorders. Approximately 72% of all RDs have a genetic origin, of which 70% have an exclusive pediatric onset. The need for such a biobank was identified by the CHM diagnostic laboratory. Feedback toward this initiative was overwhelmingly positive at the first stakeholder meeting in August 2019. However, gaining support from the public sector and recruiting of participants have proven to be challenging. Problems experienced to date include lack of support from government and clinicians; lack of knowledge on RDs (patients and clinicians); public health care focus not directed toward RDs; patients not returning for follow-up visits; and unwillingness to participate due to fear of exploitation. The CHM Biobank's vision and goals are aligned to address a national and international research need: it will provide a valuable resource for scientists to improve what is known about these diseases; to better understand the natural history and pathophysiology; to optimize diagnostic methods; and to potentially develop treatments. The genetic variability of the South African population provides added value to the RD biobank. This review provides a brief overview of the literature on the challenges and benefits of an RD biobank and how this relates to low- and middle-income countries (LMIC) like South Africa. The aim of the review is to draw attention to the potential benefits of such an undertaking and to create awareness, at both local and global level, toward some of the unique collective considerations that an RD biobank in LMIC (also unique South African challenges) faces on an operational, collaborate, and sustainability level.

A Network Pharmacology Approach to Estimate Potential Targets of the Active Ingredients of Epimedium for Alleviating Mild Cognitive Impairment and Treating Alzheimer's Disease

BACKGROUND

The present study made use of a network pharmacological approach to evaluate the mechanisms and potential targets of the active ingredients of Epimedium for alleviating mild cognitive impairment (MCI) and treating Alzheimer's disease (AD).

METHODS

The active ingredients of Epimedium were acquired from the Traditional Chinese Medicine System Pharmacology database, and potential targets were predicted using the TCMSP target module, SwissTargetPrediction, and PharmMapper database. Target proteins correlating with MCI and AD were downloaded from the GeneCards, DisGeNet, and OMIM databases. The common targets of Epimedium, MCI, and AD were identified using the Jvenn online tool, and a protein-protein interaction (PPI) network was constructed using the String database and Cytoscape. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the common targets was performed using DAVID, and molecular docking between active ingredients and target genes was modeled using AutoDock Vina.

RESULTS

A total of 20 active ingredients were analyzed, and 337 compound-related targets were identified for Epimedium. Out of 236 proteins associated with MCI and AD, 54 overlapped with the targets of Epimedium. The top 30 interacting proteins in this set were ranked by topological analysis. GO and KEGG enrichment analysis suggested that the common targets participated in diverse biological processes and pathways, including cell proliferation and apoptosis, inflammatory response, signal transduction, and protein phosphorylation through cancer pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, sphingolipid signaling pathway, FoxO signaling pathway, and TNF signaling pathway. Molecular docking analysis suggested that the 20 active ingredients could bind to the top 5 protein targets.

CONCLUSIONS

The present study provides theoretical evidence for in-depth analysis of the mechanisms and molecular targets by which Epimedium protects against MCI, AD, and other neurodegenerative diseases and lays the foundation for pragmatic clinical applications and potential new drug development.

A Review of Genetic and Physiological Disease Mechanisms Associated With Cav1 Channels: Implications for Incomplete Congenital Stationary Night Blindness Treatment

Calcium channels are crucial to a number of cellular functions. The high voltage-gated calcium channel family comprise four heteromeric channels (Cav1.1-1.4) that function in a similar manner, but that have distinct expression profiles. Three of the pore-forming α₁ subunits are located on autosomes and the forth on the X chromosome, which has consequences for the type of pathogenic mutation and the disease mechanism associated with each gene. Mutations in this family of channels are associated with malignant hyperthermia (Cav1.1), various QT syndromes (Cav1.2), deafness (Cav1.3), and incomplete congenital stationary night blindness (iCSNB; Cav1.4). In this study we performed a bioinformatic analysis on reported mutations in all four Cav α₁ subunits and correlated these with variant frequency in the general population, phenotype and the effect on channel conductance to produce a comprehensive composite Cav1 mutation analysis. We describe regions of mutation clustering, identify conserved residues that are mutated in multiple family members and regions likely to cause a loss- or gain-of-function in Cav1.4. Our research highlights that therapeutic treatments for each of the Cav1 channels will have to consider channel-specific mechanisms, especially for the treatment of X-linked iCSNB.

Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery

In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein-protein interaction network (PPI, or interactome) to predict novel disease-disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.

Analysis of pharmacological mechanisms of Yinyanghuo as treatment of erectile dysfunction with network pharmacology-based strategy

Erectile dysfunction is considered an important health problem that impacts the quality of life of men. Yinyanghuo, also called Epimedium or Horny Goat Weed, is a frequently used Chinese traditional herbal medicine, commonly used in treating erectile dysfunction in China. A network pharmacology method was performed systematically, at a molecular level, to analyse the pharmacological mechanism of Yinyanghuo as erectile dysfunction therapy. The network pharmacology method used in this study primarily includes prescreening of the active compounds, prediction of targets, network analysis and gene enrichment analysis. This network analysis proved that 4 targets (AR, NR3C2, PDE5A and BMP2) could be the targets of Yinyanghuo therapy on erectile dysfunction. Besides, gene enrichment analysis predicted that Yinyanghuo might have a role in erectile dysfunction by regulating 10 molecular functions, 8 cellular components, 10 biological processes and 36 possible targets related to 10 signalling pathways. Our study demonstrated the molecular and pharmacological mechanisms of Yinyanghuo against erectile dysfunction with a holistic approach and demonstrated a powerful method for analysing pharmacological mechanisms and rational utilisation of Traditional Chinese Medicine clinically.

Rapid and ongoing evolution of repetitive sequence structures in human centromeres

Our understanding of centromere sequence variation across human populations is limited by its extremely long nested repeat structures called higher-order repeats that are challenging to sequence. Here, we analyzed chromosomes 11, 17, and X using long-read sequencing data for 36 individuals from diverse populations including a Han Chinese trio and 21 Japanese. We revealed substantial structural diversity with many previously unidentified variant higher-order repeats specific to individuals characterizing rapid, haplotype-specific evolution of human centromeric arrays, while frequent single-nucleotide variants are largely conserved. We found a characteristic pattern shared among prevalent variants in human and chimpanzee. Our findings pave the way for studying sequence evolution in human and primate centromeres.

Insights into the mechanism of Arnebia euchroma on leukemia via network pharmacology approach

BACKGROUND

Arnebia euchroma (A. euchroma) is a traditional Chinese medicine (TCM) used for the treatment of blood diseases including leukemia. In recent years, many studies have been conducted on the anti-tumor effect of shikonin and its derivatives, the major active components of A. euchroma. However, the underlying mechanism of action (MoA) for all the components of A. euchroma on leukemia has not been explored systematically.

METHODS

In this study, we analyzed the MoA of A. euchroma on leukemia via network pharmacology approach. Firstly, the chemical components and their concentrations in A. euchroma as well as leukemia-related targets were collected. Next, we predicted compound-target interactions (CTIs) with our balanced substructure-drug-target network-based inference (bSDTNBI) method. The known and predicted targets of A. euchroma and leukemia-related targets were merged together to construct A. euchroma-leukemia protein-protein interactions (PPIs) network. Then, weighted compound-target bipartite network was constructed according to combination of eight central attributes with concentration information through Cytoscape. Additionally, molecular docking simulation was performed to calculate whether the components and predicted targets have interactions or not.

RESULTS

A total of 65 components of A. euchroma were obtained and 27 of them with concentration information, which were involved in 157 targets and 779 compound-target interactions (CTIs). Following the calculation of eight central attributes of targets in A. euchroma-leukemia PPI network, 37 targets with all central attributes greater than the median values were selected to construct the weighted compound-target bipartite network and do the KEGG pathway analysis. We found that A. euchroma candidate targets were significantly associated with several apoptosis and inflammation-related biological pathways, such as MAPK signaling, PI3K-Akt signaling, IL-17 signaling, and T cell receptor signaling pathways. Moreover, molecular docking simulation demonstrated that there were eight pairs of predicted CTIs had the strong binding free energy.

CONCLUSIONS

This study deciphered that the efficacy of A. euchroma in the treatment of leukemia might be attributed to 10 targets and 14 components, which were associated with inhibiting leukemia cell survival and inducing apoptosis, relieving inflammatory environment and inhibiting angiogenesis.

Disruptive natural selection by male reproductive potential prevents underexpression of protein-coding genes on the human Y chromosome as a self-domestication syndrome

Background

In population ecology, the concept of reproductive potential denotes the most vital indicator of chances to produce and sustain a healthy descendant until his/her reproductive maturity under the best conditions. This concept links quality of life and longevity of an individual with disease susceptibilities encoded by his/her genome. Female reproductive potential has been investigated deeply, widely, and comprehensively in the past, but the male one has not received an equal amount of attention. Therefore, here we focused on the human Y chromosome and found candidate single-nucleotide polymorphism (SNP) markers of male reproductive potential.

Results

Examining in silico (i.e., using our earlier created Web-service SNP_TATA_Z-tester) all 1206 unannotated SNPs within 70 bp proximal promoters of all 63 Y-linked genes, we found 261 possible male-reproductive-potential SNP markers that can significantly alter the binding affinity of TATA-binding protein (TBP) for these promoters. Among them, there are candidate SNP markers of spermatogenesis disorders (e.g., rs1402972626), pediatric cancer (e.g., rs1483581212) as well as male anxiety damaging family relationships and mother’s and children’s health (e.g., rs187456378). First of all, we selectively verified in vitro both absolute and relative values of the analyzed TBP–promoter affinity, whose Pearson’s coefficients of correlation between predicted and measured values were r = 0.84 (significance p <  0.025) and r = 0.98 (p <  0.025), respectively. Next, we found that there are twofold fewer candidate SNP markers decreasing TBP–promoter affinity relative to those increasing it, whereas in the genome-wide norm, SNP-induced damage to TBP–promoter complexes is fourfold more frequent than SNP-induced improvement (p <  0.05, binomial distribution). This means natural selection against underexpression of these genes. Meanwhile, the numbers of candidate SNP markers of an increase and decrease in male reproductive potential were indistinguishably equal to each other (p <  0.05) as if male self-domestication could have happened, with its experimentally known disruptive natural selection. Because there is still not enough scientific evidence that this could have happened, we discuss the human diseases associated with candidate SNP markers of male reproductive potential that may correspond to domestication-related disorders in pets.

Conclusions

Overall, our findings seem to support a self-domestication syndrome with disruptive natural selection by male reproductive potential preventing Y-linked underexpression of a protein.

Gene Set Analysis: Challenges, Opportunities, and Future Research

Gene set analysis methods are widely used to provide insight into high-throughput gene expression data. There are many gene set analysis methods available. These methods rely on various assumptions and have different requirements, strengths and weaknesses. In this paper, we classify gene set analysis methods based on their components, describe the underlying requirements and assumptions for each class, and provide directions for future research in developing and evaluating gene set analysis methods.

Machine learning-based approaches for disease gene prediction

Disease gene prediction is an essential issue in biomedical research. In the early days, annotation-based approaches were proposed for this problem. With the development of high-throughput technologies, interaction data between genes/proteins have grown quickly and covered almost genome and proteome; thus, network-based methods for the problem become prominent. In parallel, machine learning techniques, which formulate the problem as a classification, have also been proposed. Here, we firstly show a roadmap of the machine learning-based methods for the disease gene prediction. In the beginning, the problem was usually approached using a binary classification, where positive and negative training sample sets are comprised of disease genes and non-disease genes, respectively. The disease genes are ones known to be associated with diseases; meanwhile, non-disease genes were randomly selected from those not yet known to be associated with diseases. However, the later may contain unknown disease genes. To overcome this uncertainty of defining the non-disease genes, more realistic approaches have been proposed for the problem, such as unary and semi-supervised classification. Recently, more advanced methods, including ensemble learning, matrix factorization and deep learning, have been proposed for the problem. Secondly, 12 representative machine learning-based methods for the disease gene prediction were examined and compared in terms of prediction performance and running time. Finally, their advantages, disadvantages, interpretability and trust were also analyzed and discussed.

Construction of an integrated human osteosarcoma database, HOsDb, based on literature mining, microarray analysis, and database retrieval

Background

Osteosarcoma (OS) is the most frequent primary malignancy of bone with a high incidence in adolescence. This study aimed to construct a publicly available, integrated database of human OS, named HOsDb.

Methods

Microarray data, current databases, and a literature search of PubMed were used to extract information relevant to human OS-related genes and their transcription factors (TFs) and single nucleotide polymorphisms (SNPs), as well as methylation sites and microRNAs (miRNAs). This information was collated for constructing the HOsDb.

Results

In total, we identified 7191 OS tumor-related genes, 763 OS metastasis-related genes, and 1589 OS drug-related genes, corresponding to 190,362, 21,131, and 41,135 gene-TF pairs, respectively, 3,749,490, 358,361, and 767,674 gene-miRNA pairs, respectively; and 28,386, 2532, and 3943 SNPs, respectively. Additionally, 240 OS-related miRNAs, 1695 genes with copy number variations in OS, and 18 genes with methylation sites in OS were identified. These data were collated to construct the HOsDb, which is available at www.hosdatabase.com. Users can search OS-related molecules using this database.

Conclusion

The HOsDb provides a platform that is comprehensive, quick, and easily accessible, and it will enrich our current knowledge of OS.

Genetic Counseling and Genome Sequencing in Pediatric Rare Disease

Both genome sequencing (GS) and exome sequencing (ES) have proven to be revolutionary in the diagnosis of pediatric rare disease. The diagnostic potential and increasing affordability make GS and ES more accessible as a routine clinical test in some centers. Herein, I review aspects of rare disease in pediatrics associated with the use of genomic technologies with an emphasis on the benefits and limitations of both ES and GS, complexities of variant classification, and the importance of genetic counseling. Indications for testing, the role of genetic counselors in genomic test selection, and the diagnostic potential of ES and GS in various pediatric multisystem disorders are discussed. The neonatal population represents an important cohort in pediatric rare disease. Rapid ES and GS in critically ill neonates can have an immediate impact on medical management and present unique genetic counseling challenges. This work includes reviews of recommendations for genetic counseling for families considering genome-wide sequencing, and issues of access to genetic counseling that affect clinical use and will necessitate implementation of innovative methods such as online decision aids. Finally, this work will also review the challenges of having a child with a rare disease, the impact of results from ES and GS on these families, and the role of various support agencies.

George-Hyslop P, Londos E, Zetterberg H, Morgan K, Troakes C, Al-Sarraj S, Lashley T, Holton J, Compta Y, Van Deerlin V, Trojanowski JQ, Serrano GE, Beach TG, Lesage S, Galasko D, Masliah E, Santana I, Pastor P, Tienari PJ, Myllykangas L, Oinas M, Revesz T, Lees A, Boeve BF, Petersen RC, Ferman TJ, Escott-Price V, Graff-Radford N, Cairns NJ, Morris JC, Pickering-Brown S, Mann D, Halliday G, Stone DJ, Dickson DW, Hardy J, Singleton A, Guerreiro R, Bras J. Analysis of neurodegenerative disease-causing genes in dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is a clinically heterogeneous disorder with a substantial burden on healthcare. Despite this, the genetic basis of the disorder is not well defined and its boundaries with other neurodegenerative diseases are unclear. Here, we performed whole exome sequencing of a cohort of 1118 Caucasian DLB patients, and focused on genes causative of monogenic neurodegenerative diseases. We analyzed variants in 60 genes implicated in DLB, Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia, and atypical parkinsonian or dementia disorders, in order to determine their frequency in DLB. We focused on variants that have previously been reported as pathogenic, and also describe variants reported as pathogenic which remain of unknown clinical significance, as well as variants associated with strong risk. Rare missense variants of unknown significance were found in APP, CHCHD2, DCTN1, GRN, MAPT, NOTCH3, SQSTM1, TBK1 and TIA1. Additionally, we identified a pathogenic GRN p.Arg493* mutation, potentially adding to the diversity of phenotypes associated with this mutation. The rarity of previously reported pathogenic mutations in this cohort suggests that the genetic overlap of other neurodegenerative diseases with DLB is not substantial. Since it is now clear that genetics plays a role in DLB, these data suggest that other genetic loci play a role in this disease.

VarSite: Disease variants and protein structure

VarSite is a web server mapping known disease-associated variants from UniProt and ClinVar, together with natural variants from gnomAD, onto protein 3D structures in the Protein Data Bank. The analyses are primarily image-based and provide both an overview for each human protein, as well as a report for any specific variant of interest. The information can be useful in assessing whether a given variant might be pathogenic or benign. The structural annotations for each position in the protein include protein secondary structure, interactions with ligand, metal, DNA/RNA, or other protein, and various measures of a given variant's possible impact on the protein's function. The 3D locations of the disease-associated variants can be viewed interactively via the 3dmol.js JavaScript viewer, as well as in RasMol and PyMOL. Users can search for specific variants, or sets of variants, by providing the DNA coordinates of the base change(s) of interest. Additionally, various agglomerative analyses are given, such as the mapping of disease and natural variants onto specific Pfam or CATH domains. The server is freely accessible to all at: https://www.ebi.ac.uk/thornton-srv/databases/VarSite.

Bioinformatics classification of mutations in patients with Mucopolysaccharidosis IIIA

Mucopolysaccharidosis (MPS) IIIA, also known as Sanfilippo syndrome type A, is a severe, progressive disease that affects the central nervous system (CNS). MPS IIIA is inherited in an autosomal recessive manner and is caused by a deficiency in the lysosomal enzyme sulfamidase, which is required for the degradation of heparan sulfate. The sulfamidase is produced by the N-sulphoglucosamine sulphohydrolase (SGSH) gene. In MPS IIIA patients, the excess of lysosomal storage of heparan sulfate often leads to mental retardation, hyperactive behavior, and connective tissue impairments, which occur due to various known missense mutations in the SGSH, leading to protein dysfunction. In this study, we focused on three mutations (R74C, S66W, and R245H) based on in silico pathogenic, conservation, and stability prediction tool studies. The three mutations were further subjected to molecular dynamic simulation (MDS) analysis using GROMACS simulation software to observe the structural changes they induced, and all the mutants exhibited maximum deviation patterns compared with the native protein. Conformational changes were observed in the mutants based on various geometrical parameters, such as conformational stability, fluctuation, and compactness, followed by hydrogen bonding, physicochemical properties, principal component analysis (PCA), and salt bridge analyses, which further validated the underlying cause of the protein instability. Additionally, secondary structure and surrounding amino acid analyses further confirmed the above results indicating the loss of protein function in the mutants compared with the native protein. The present results reveal the effects of three mutations on the enzymatic activity of sulfamidase, providing a molecular explanation for the cause of the disease. Thus, this study allows for a better understanding of the effect of SGSH mutations through the use of various computational approaches in terms of both structure and functions and provides a platform for the development of therapeutic drugs and potential disease treatments.

A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases

Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.

A network pharmacology approach to explore active compounds and pharmacological mechanisms of epimedium for treatment of premature ovarian insufficiency

Background and purpose

Premature ovarian insufficiency (POI) refers to a hypergonadotropic hypoestrogenism and the condition of pre-onset ovarian function failure. Epimedium is a common traditional Chinese herbal medicine that is widely used to relieve POI in China. To systematically explore the pharmacological mechanism of epimedium on POI therapy, a network pharmacology approach was conducted at the molecular level.

Methods

In this study, we adopt the network pharmacology method, which mainly includes active ingredients prescreening, target prediction, gene enrichment analysis and network analysis.

Results

The network analysis revealed that 6 targets (ESR1, AR, ESR2, KDR, CYP19A1 and ESRRG) might be the therapeutic targets of epimedium on POI. In addition, gene-enrichment analysis suggested that epimedium appeared to play a role in POI by modulating 6 molecular functions, 5 cellular components, 15 biological processes and striking 52 potential targets involved in 13 signaling pathways.

Conclusion

This study predicted the pharmacological and molecular mechanism of epimedium against POI from a holistic perspective, as well as provided a powerful tool for exploring pharmacological mechanisms and rational clinical application of traditional Chinese medicine.

Global Text Mining and Development of Pharmacogenomic Knowledge Resource for Precision Medicine

Understanding patients’ genomic variations and their effect in protecting or predisposing them to drug response phenotypes is important for providing personalized healthcare. Several studies have manually curated such genotype–phenotype relationships into organized databases from clinical trial data or published literature. However, there are no text mining tools available to extract high-accuracy information from such existing knowledge. In this work, we used a semiautomated text mining approach to retrieve a complete pharmacogenomic (PGx) resource integrating disease–drug–gene-polymorphism relationships to derive a global perspective for ease in therapeutic approaches. We used an R package, pubmed.mineR, to automatically retrieve PGx-related literature. We identified 1,753 disease types, and 666 drugs, associated with 4,132 genes and 33,942 polymorphisms collated from 180,088 publications. With further manual curation, we obtained a total of 2,304 PGx relationships. We evaluated our approach by performance (precision = 0.806) with benchmark datasets like Pharmacogenomic Knowledgebase (PharmGKB) (0.904), Online Mendelian Inheritance in Man (OMIM) (0.600), and The Comparative Toxicogenomics Database (CTD) (0.729). We validated our study by comparing our results with 362 commercially used the US- Food and drug administration (FDA)-approved drug labeling biomarkers. Of the 2,304 PGx relationships identified, 127 belonged to the FDA list of 362 approved pharmacogenomic markers, indicating that our semiautomated text mining approach may reveal significant PGx information with markers for drug response prediction. In addition, it is a scalable and state-of-art approach in curation for PGx clinical utility.

A paradigm shift in medicine: A comprehensive review of network-based approaches

Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators. The complexity of these interactions embraces different types of information: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression and regulation, to metabolic and disease pathways up to drug-disease relationships. The analysis of these complex networks can reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. In this review, we offer a comprehensive overview of network types and algorithms used in the framework of network medicine. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Exploring the dark genome: implications for precision medicine

The increase in the number of both patients and healthcare practitioners who grew up using the Internet and computers (so-called "digital natives") is likely to impact the practice of precision medicine, and requires novel platforms for data integration and mining, as well as contextualized information retrieval. The "Illuminating the Druggable Genome Knowledge Management Center" (IDG KMC) quantifies data availability from a wide range of chemical, biological, and clinical resources, and has developed platforms that can be used to navigate understudied proteins (the "dark genome"), and their potential contribution to specific pathologies. Using the "Target Importance and Novelty Explorer" (TIN-X) highlights the role of LRRC10 (a dark gene) in dilated cardiomyopathy. Combining mouse and human phenotype data leads to increased strength of evidence, which is discussed for four additional dark genes: SLX4IP and its role in glucose metabolism, the role of HSF2BP in coronary artery disease, the involvement of ELFN1 in attention-deficit hyperactivity disorder and the role of VPS13D in mouse neural tube development and its confirmed role in childhood onset movement disorders. The workflow and tools described here are aimed at guiding further experimental research, particularly within the context of precision medicine.

Disease classification via gene network integrating modules and pathways

Disease classification based on gene information has been of significance as the foundation for achieving precision medicine. Previous works focus on classifying diseases according to the gene expression data of patient samples, and constructing disease network based on the overlap of disease genes, as many genes have been confirmed to be associated with diseases. In this work, the effects of diseases on human biological functions are assessed from the perspective of gene network modules and pathways, and the distances between diseases are defined to carry out the classification models. In total, 1728 diseases are divided into 12 and 14 categories by the intensity and scope of effects on pathways, respectively. Each category is a mix of several types of diseases identified based on congenital and acquired factors as well as diseased tissues and organs. The disease classification models on the basis of gene network are parallel with traditional pathology classification based on anatomic and clinical manifestations, and enable us to look at diseases in the viewpoint of commonalities in etiology and pathology. Our models provide a foundation for exploring combination therapy of diseases, which in turn may inform strategies for future gene-targeted therapy.

Targeted gene panel sequencing for the rapid diagnosis of acutely ill infants

Background

Exome/genome sequencing (ES/GS) have been recently used in neonatal and pediatric/cardiac intensive care units (NICU and PICU/CICU) to diagnose and care for acutely ill infants, but the effectiveness of targeted gene panels for these purposes remains unknown.

Methods

RapSeq, a newly developed panel targeting 4,503 disease‐causing genes, was employed on selected patients in our NICU/PICU/CICU. Twenty trios were sequenced from October 2015 to March 2017. We assessed diagnostic yield, turnaround times, and clinical consequences.

Results

A diagnosis was made in 10/20 neonates (50%); eight had de novo variants (ASXL1, CHD, FBN1, KMT2D, FANCB, FLNA, PAX3), one was a compound heterozygote for CHAT, and one had a maternally inherited GNAS variant. Preliminary reports were generated by 9.6 days (mean); final reports after Sanger sequencing at 16.3 days (mean). In all positive infants, the diagnosis changed management. In a case with congenital myasthenia, diagnosis and treatment occurred at 17 days versus 7 months in a historical control.

Conclusions

This study shows that a gene panel that includes the majority of known disease‐causing genes can rapidly identify a diagnosis in a large number of tested infants. Due to simpler deployment and interpretation and lower costs, this approach might represent an alternative to ES/GS in the NICU/PICU/CICU.

Structural Analysis and Conformational Dynamics of STN1 Gene Mutations Involved in Coat Plus Syndrome

The human CST complex (CTC1-STN1-TEN1) is associated with telomere functions including genome stability. We have systemically analyzed the sequence of STN and performed structure analysis to establish its association with the Coat Plus (CP) syndrome. Many deleterious non-synonymous SNPs have been identified and subjected for structure analysis to find their pathogenic association and aggregation propensity. A 100-ns all-atom molecular dynamics simulation of WT, R135T, and D157Y structures revealed significant conformational changes in the case of mutants. Changes in hydrogen bonds, secondary structure, and principal component analysis further support the structural basis of STN1 dysfunction in such mutations. Free energy landscape analysis revealed the presence of multiple energy minima, suggesting that R135T and D157Y mutations destabilize and alter the conformational dynamics of STN1 and thus may be associated with the CP syndrome. Our study provides a valuable direction to understand the molecular basis of CP syndrome and offer a newer therapeutics approach to address CP syndrome.

Centromeric Satellite DNAs: Hidden Sequence Variation in the Human Population

The central goal of medical genomics is to understand the inherited basis of sequence variation that underlies human physiology, evolution, and disease. Functional association studies currently ignore millions of bases that span each centromeric region and acrocentric short arm. These regions are enriched in long arrays of tandem repeats, or satellite DNAs, that are known to vary extensively in copy number and repeat structure in the human population. Satellite sequence variation in the human genome is often so large that it is detected cytogenetically, yet due to the lack of a reference assembly and informatics tools to measure this variability, contemporary high-resolution disease association studies are unable to detect causal variants in these regions. Nevertheless, recently uncovered associations between satellite DNA variation and human disease support that these regions present a substantial and biologically important fraction of human sequence variation. Therefore, there is a pressing and unmet need to detect and incorporate this uncharacterized sequence variation into broad studies of human evolution and medical genomics. Here I discuss the current knowledge of satellite DNA variation in the human genome, focusing on centromeric satellites and their potential implications for disease.

The multiplex network of human diseases

Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer. We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease communities, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers.

Pathway centrality in protein interaction networks identifies putative functional mediating pathways in pulmonary disease

Identification of functional pathways mediating molecular responses may lead to better understanding of disease processes and suggest new therapeutic approaches. We introduce a method to detect such mediating functions using topological properties of protein-protein interaction networks. We define the concept of pathway centrality, a measure of communication between disease genes and differentially expressed genes. Using pathway centrality, we identify mediating pathways in three pulmonary diseases (asthma; bronchopulmonary dysplasia (BPD); and chronic obstructive pulmonary disease (COPD)). We systematically evaluate the significance of all identified central pathways using genetic interactions. Mediating pathways shared by all three pulmonary disorders favor innate immune and inflammation-related processes, including toll-like receptor (TLR) signaling, PDGF- and angiotensin-regulated airway remodeling, the JAK-STAT signaling pathway, and interferon gamma. Disease-specific mediators, such as neurodevelopmental processes in BPD or adhesion molecules in COPD, are also highlighted. Some of our findings implicate pathways already in development as drug targets, while others may suggest new therapeutic approaches.

Systematically Characterize the Anti-Alzheimer's Disease Mechanism of Lignans from S. chinensis based on In-Vivo Ingredient Analysis and Target-Network Pharmacology Strategy by UHPLC⁻Q-TOF-MS

Lignans from Schisandra chinensis (Turcz.) Baill can ameliorate cognitive impairment in animals with Alzheimer’s disease (AD). However, the metabolism of absorbed ingredients and the potential targets of the lignans from S. chinensis in animals with AD have not been systematically investigated. Therefore, for the first time, we performed an in-vivo ingredient analysis and implemented a target-network pharmacology strategy to assess the effects of lignans from S. chinensis in rats with AD. Ten absorbed prototype constituents and 39 metabolites were identified or tentatively characterized in the plasma of dosed rats with AD using ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Based on the results of analysis of the effective constituents in vivo, the potential therapeutic mechanism of the effective constituents in the rats with AD was investigated using a target-network pharmacology approach and independent experimental validation. The results showed that the treatment effects of lignans from S. chinensis on cognitive impairment might involve the regulation of amyloid precursor protein metabolism, neurofibrillary tangles, neurotransmitter metabolism, inflammatory response, and antioxidant system. Overall, we identified the effective components of lignans in S. chinensis that can improve the cognitive impairment induced by AD and proposed potential therapeutic metabolic pathways. The results might serve as the basis for a fundamental strategy to explore effective therapeutic drugs to treat AD.

The computational prediction of drug-disease interactions using the dual-network L2,1-CMF method

BACKGROUND

Predicting drug-disease interactions (DDIs) is time-consuming and expensive. Improving the accuracy of prediction results is necessary, and it is crucial to develop a novel computing technology to predict new DDIs. The existing methods mostly use the construction of heterogeneous networks to predict new DDIs. However, the number of known interacting drug-disease pairs is small, so there will be many errors in this heterogeneous network that will interfere with the final results.

RESULTS

A novel method, known as the dual-network L2,1-collaborative matrix factorization, is proposed to predict novel DDIs. The Gaussian interaction profile kernels and L2,1-norm are introduced in our method to achieve better results than other advanced methods. The network similarities of drugs and diseases with their chemical and semantic similarities are combined in this method.

CONCLUSIONS

Cross validation is used to evaluate our method, and simulation experiments are used to predict new interactions using two different datasets. Finally, our prediction accuracy is better than other existing methods. This proves that our method is feasible and effective.