The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease

LeukmiR: a database for miRNAs and their targets in acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is one of the most common hematological malignancies in children. Recent studies suggest the involvement of multiple microRNAs in the tumorigenesis of various leukemias. However, until now, no comprehensive database exists for miRNAs and their cognate target genes involved specifically in ALL. Therefore, we developed 'LeukmiR' a dynamic database comprising in silico predicted microRNAs, and experimentally validated miRNAs along with the target genes they regulate in mouse and human. LeukmiR is a user-friendly platform with search strings for ALL-associated microRNAs, their sequences, description of target genes, their location on the chromosomes and the corresponding deregulated signaling pathways. For the user query, different search modules exist where either quick search can be carried out using any fuzzy term or by providing exact terms in specific modules. All entries for both human and mouse genomes can be retrieved through multiple options such as miRNA ID, their accession number, sequence, target genes, Ensemble-ID or Entrez-ID. User can also access miRNA: mRNA interaction networks in different signaling pathways, the genomic location of the targeted regions such as 3'UTR, 5'UTR and exons with their gene ontology and disease ontology information in both human and mouse systems. Herein, we also report 51 novel microRNAs which are not described earlier for ALL. Thus, LeukmiR database will be a valuable source of information for researchers to understand and investigate miRNAs and their targets with diagnostic and therapeutic potential in ALL. Database URL: http://tdb.ccmb.res.in/LeukmiR/.

Identification of infectious disease-associated host genes using machine learning techniques

Background

With the global spread of multidrug resistance in pathogenic microbes, infectious diseases emerge as a key public health concern of the recent time. Identification of host genes associated with infectious diseases will improve our understanding about the mechanisms behind their development and help to identify novel therapeutic targets.

Results

We developed a machine learning techniques-based classification approach to identify infectious disease-associated host genes by integrating sequence and protein interaction network features. Among different methods, Deep Neural Networks (DNN) model with 16 selected features for pseudo-amino acid composition (PAAC) and network properties achieved the highest accuracy of 86.33% with sensitivity of 85.61% and specificity of 86.57%. The DNN classifier also attained an accuracy of 83.33% on a blind dataset and a sensitivity of 83.1% on an independent dataset. Furthermore, to predict unknown infectious disease-associated host genes, we applied the proposed DNN model to all reviewed proteins from the database. Seventy-six out of 100 highly-predicted infectious disease-associated genes from our study were also found in experimentally-verified human-pathogen protein-protein interactions (PPIs). Finally, we validated the highly-predicted infectious disease-associated genes by disease and gene ontology enrichment analysis and found that many of them are shared by one or more of the other diseases, such as cancer, metabolic and immune related diseases.

Conclusions

To the best of our knowledge, this is the first computational method to identify infectious disease-associated host genes. The proposed method will help large-scale prediction of host genes associated with infectious-diseases. However, our results indicated that for small datasets, advanced DNN-based method does not offer significant advantage over the simpler supervised machine learning techniques, such as Support Vector Machine (SVM) or Random Forest (RF) for the prediction of infectious disease-associated host genes. Significant overlap of infectious disease with cancer and metabolic disease on disease and gene ontology enrichment analysis suggests that these diseases perturb the functions of the same cellular signaling pathways and may be treated by drugs that tend to reverse these perturbations. Moreover, identification of novel candidate genes associated with infectious diseases would help us to explain disease pathogenesis further and develop novel therapeutics.

GeVIR is a continuous gene-level metric that uses variant distribution patterns to prioritize disease candidate genes

With large-scale population sequencing projects gathering pace, there is a need for strategies that advance disease gene prioritization^1,2. Metrics that provide information about a gene and its ability to tolerate protein-altering variation can aid in clinical interpretation of human genomes and can advance disease gene discovery^1-4. Previous reported methods analyzed the total variant load in a gene^1-4, but did not analyze the distribution pattern of variants within a gene. Using data from 138,632 exome and genome sequences², we developed gene variation intolerance rank (GeVIR), a continuous gene-level metric for 19,361 genes that is able to prioritize both dominant and recessive Mendelian disease genes⁵, that outperforms missense constraint metrics³ and that is comparable-but complementary-to loss-of-function (LOF) constraint metrics². GeVIR is also able to prioritize short genes, for which LOF constraint cannot be estimated with confidence². The majority of the most intolerant genes identified here have no defined phenotype and are candidates for severe dominant disorders.

Computational resources associating diseases with genotypes, phenotypes and exposures

The causes of a disease and its therapies are not only related to genotypes, but also associated with other factors, including phenotypes, environmental exposures, drugs and chemical molecules. Distinguishing disease-related factors from many neutral factors is critical as well as difficult. Over the past two decades, bioinformaticians have developed many computational resources to integrate the omics data and discover associations among these factors. However, researchers and clinicians are experiencing difficulties in choosing appropriate resources from hundreds of relevant databases and software tools. Here, in order to assist the researchers and clinicians, we systematically review the public computational resources of human diseases related to genotypes, phenotypes, environment factors, drugs and chemical exposures. We briefly describe the development history of these computational resources, followed by the details of the relevant databases and software tools. We finally conclude with a discussion of current challenges and future opportunities as well as prospects on this topic.

Ontology-based prediction of cancer driver genes

Identifying and distinguishing cancer driver genes among thousands of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from non-driver mutations. We have developed a novel method for identifying cancer driver genes. Our approach utilizes multiple complementary types of information, specifically cellular phenotypes, cellular locations, functions, and whole body physiological phenotypes as features. We demonstrate that our method can accurately identify known cancer driver genes and distinguish between their role in different types of cancer. In addition to confirming known driver genes, we identify several novel candidate driver genes. We demonstrate the utility of our method by validating its predictions in nasopharyngeal cancer and colorectal cancer using whole exome and whole genome sequencing.

Analysis of pooled genome sequences from Djallonke and Sahelian sheep of Ghana reveals co-localisation of regions of reduced heterozygosity with candidate genes for disease resistance and adaptation to a tropical environment

BACKGROUND

The Djallonke sheep is well adapted to harsh environmental conditions, and is relatively resistant to Haemonchosis and resilient to animal trypanosomiasis. The larger Sahelian sheep, which cohabit the same region, is less well adapted to these disease challenges. Haemonchosis and Trypanosomiasis collectively cost the worldwide animal industry billions of dollars in production losses annually.

RESULTS

Here, we separately sequenced and then pooled according to breed the genomes from five unrelated individuals from each of the Djallonke and Sahelian sheep breeds (sourced from Ghana), at greater than 22-fold combined coverage for each breed. A total of approximately 404 million (97%) and 343 million (97%) sequence reads from the Djallonke and Sahelian breeds respectively, were successfully mapped to the sheep reference genome Oar v3.1. We identified approximately 11.1 million and 10.9 million single nucleotide polymorphisms (SNPs) in the Djallonke and Sahelian breeds, with approximately 15 and 16% respectively of these not previously reported in sheep. Multiple regions of reduced heterozygosity were also found; 70 co-localised within genomic regions harbouring genes that mediate disease resistance, immune response and adaptation in sheep or cattle. Thirty- three of the regions of reduced heterozygosity co-localised with previously reported genes for resistance to haemonchosis and trypanosomiasis.

CONCLUSIONS

Our analyses suggest that these regions of reduced heterozygosity may be signatures of selection for these economically important diseases.

Identification of DNAH6 mutations in infertile men with multiple morphological abnormalities of the sperm flagella

Male infertility due to spermatogenesis defects affects millions of men worldwide. However, the genetic etiology of the vast majority remains unclear. Here we describe three men with primary infertility due to multiple morphological abnormalities of the sperm flagella (MMAF) from two unrelated Han Chinese families. We performed whole-exome sequencing (WES) and Sanger sequencing on the proband of family 1, and found that he carried novel compound heterozygous missense mutations in dynein axonemal heavy chain 6 (DNAH6) that resulted in the substitution of a conserved amino acid residue and co-segregated with the MMAF phenotype in this family. Papanicolaou staining and transmission electron microscopy analysis revealed morphological and ultrastructural abnormalities in the sperm flagella in carriers of these genetic variants. Immunostaining experiments showed that DNAH6 was localized in the sperm tail. This is the first report identifying novel recessive mutations in DNAH6 as a cause of MMAF. These findings expand the spectrum of known MMAF mutations and phenotypes and provide information that can be useful for genetic and reproductive counseling of MMAF patients.

A functional enrichment test for molecular convergent evolution finds a clear protein-coding signal in echolocating bats and whales

Distantly related species entering similar biological niches often adapt by evolving similar morphological and physiological characters. How much genomic molecular convergence (particularly of highly constrained coding sequence) contributes to convergent phenotypic evolution, such as echolocation in bats and whales, is a long-standing fundamental question. Like others, we find that convergent amino acid substitutions are not more abundant in echolocating mammals compared to their outgroups. However, we also ask a more informative question about the genomic distribution of convergent substitutions by devising a test to determine which, if any, of more than 4,000 tissue-affecting gene sets is most statistically enriched with convergent substitutions. We find that the gene set most overrepresented (q-value = 2.2e-3) with convergent substitutions in echolocators, affecting 18 genes, regulates development of the cochlear ganglion, a structure with empirically supported relevance to echolocation. Conversely, when comparing to nonecholocating outgroups, no significant gene set enrichment exists. For aquatic and high-altitude mammals, our analysis highlights 15 and 16 genes from the gene sets most affected by molecular convergence which regulate skin and lung physiology, respectively. Importantly, our test requires that the most convergence-enriched set cannot also be enriched for divergent substitutions, such as in the pattern produced by inactivated vision genes in subterranean mammals. Showing a clear role for adaptive protein-coding molecular convergence, we discover nearly 2,600 convergent positions, highlight 77 of them in 3 organs, and provide code to investigate other clades across the tree of life.

Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes

Autism spectrum disorder (ASD) is a genetically heterogeneous condition, caused by a combination of rare de novo and inherited variants as well as common variants in at least several hundred genes. However, significantly larger sample sizes are needed to identify the complete set of genetic risk factors. We conducted a pilot study for SPARK (SPARKForAutism.org) of 457 families with ASD, all consented online. Whole exome sequencing (WES) and genotyping data were generated for each family using DNA from saliva. We identified variants in genes and loci that are clinically recognized causes or significant contributors to ASD in 10.4% of families without previous genetic findings. In addition, we identified variants that are possibly associated with ASD in an additional 3.4% of families. A meta-analysis using the TADA framework at a false discovery rate (FDR) of 0.1 provides statistical support for 26 ASD risk genes. While most of these genes are already known ASD risk genes, BRSK2 has the strongest statistical support and reaches genome-wide significance as a risk gene for ASD (p-value = 2.3e-06). Future studies leveraging the thousands of individuals with ASD who have enrolled in SPARK are likely to further clarify the genetic risk factors associated with ASD as well as allow accelerate ASD research that incorporates genetic etiology.

Biallelic mutations in CFAP65 lead to severe asthenoteratospermia due to acrosome hypoplasia and flagellum malformations

Background

The genetic causes for most male infertility due to severe asthenozoospermia remain unclear.

Objective

Our objective was to identify unknown genetic factors in 47 patients with severe asthenozoospermia from 45 unrelated Chinese families.

Methods

We performed whole exome sequencing of 47 individuals with severe asthenozoospermia from 45 unrelated families. Mutation screening was performed in a control cohort of 637 individuals, including 219 with oligoasthenospermia, 195 with non-obstructive azoospermia and 223 fertile controls. Ultrastructural and immunostaining analyses of patients’ spermatozoa were performed to characterise the effect of variants.

Results

One homozygous non-sense mutation (NM_194302, c.G5341T:p.E1781X), two compound heterozygous mutations (c.C2284T:p.R762X and c.1751delC:p.P584fs) and two compound heterozygous mutations (c.5714_5721del:p.L1905fs and c.C3021A:p.N1007K) were identified in CFAP65 of three individuals with completely immotile spermatozoa, respectively. No biallelic deleterious variants of CFAP65 were detected in the control cohort of 637 individuals. Ultrastructural and immunostaining analyses of spermatozoa from two patients showed highly aberrant sperm morphology with severe defects such as acrosome hypoplasia, disruption of the mitochondrial sheath and absence of the central pair complex.

Conclusion

To the best of our knowledge, we are the first to report that CFAP65 mutations may cause spermatozoa to be completely immotile.

Mouse Genome Database (MGD)-2018: knowledgebase for the laboratory mouse

The Mouse Genome Database (MGD; http://www.informatics.jax.org) is the key community mouse database which supports basic, translational and computational research by providing integrated data on the genetics, genomics, and biology of the laboratory mouse. MGD serves as the source for biological reference data sets related to mouse genes, gene functions, phenotypes and disease models with an increasing emphasis on the association of these data to human biology and disease. We report here on recent enhancements to this resource, including improved access to mouse disease model and human phenotype data and enhanced relationships of mouse models to human disease.

CAG Repeat Not Polyglutamine Length Determines Timing of Huntington's Disease Onset

Variable, glutamine-encoding, CAA interruptions indicate that a property of the uninterrupted HTT CAG repeat sequence, distinct from the length of huntingtin's polyglutamine segment, dictates the rate at which Huntington's disease (HD) develops. The timing of onset shows no significant association with HTT cis-eQTLs but is influenced, sometimes in a sex-specific manner, by polymorphic variation at multiple DNA maintenance genes, suggesting that the special onset-determining property of the uninterrupted CAG repeat is a propensity for length instability that leads to its somatic expansion. Additional naturally occurring genetic modifier loci, defined by GWAS, may influence HD pathogenesis through other mechanisms. These findings have profound implications for the pathogenesis of HD and other repeat diseases and question the fundamental premise that polyglutamine length determines the rate of pathogenesis in the "polyglutamine disorders."

High Functioning Autism with Missense Mutations in Synaptotagmin-Like Protein 4 (SYTL4) and Transmembrane Protein 187 (TMEM187) Genes: SYTL4- Protein Modeling, Protein-Protein Interaction, Expression Profiling and MicroRNA Studies

We describe a 7-year-old male with high functioning autism spectrum disorder (ASD) and maternally-inherited rare missense variant of Synaptotagmin-like protein 4 (SYTL4) gene (Xq22.1; c.835C>T; p.Arg279Cys) and an unknown missense variant of Transmembrane protein 187 (TMEM187) gene (Xq28; c.708G>T; p. Gln236His). Multiple in-silico predictions described in our study indicate a potentially damaging status for both X-linked genes. Analysis of predicted atomic threading models of the mutant and the native SYTL4 proteins suggest a potential structural change induced by the R279C variant which eliminates the stabilizing Arg279-Asp60 salt bridge in the N-terminal half of the SYTL4, affecting the functionality of the protein's critical RAB-Binding Domain. In the European (Non-Finnish) population, the allele frequency for this variant is 0.00042. The SYTL4 gene is known to directly interact with several members of the RAB family of genes, such as, RAB27A, RAB27B, RAB8A, and RAB3A which are known autism spectrum disorder genes. The SYTL4 gene also directly interacts with three known autism genes: STX1A, SNAP25 and STXBP1. Through a literature-based analytical approach, we identified three of five (60%) autism-associated serum microRNAs (miRs) with high predictive power among the total of 298 mouse Sytl4 associated/predicted microRNA interactions. Five of 13 (38%) miRs were differentially expressed in serum from ASD individuals which were predicted to interact with the mouse equivalent Sytl4 gene. TMEM187 gene, like SYTL4, is a protein-coding gene that belongs to a group of genes which host microRNA genes in their introns or exons. The novel Q236H amino acid variant in the TMEM187 in our patient is near the terminal end region of the protein which is represented by multiple sequence alignments and hidden Markov models, preventing comparative structural analysis of the variant harboring region. Like SYTL4, the TMEM187 gene is expressed in the brain and interacts with four known ASD genes, namely, HCFC1; TMLHE; MECP2; and GPHN. TMM187 is in linkage with MECP2, which is a well-known determinant of brain structure and size and is a well-known autism gene. Other members of the TMEM gene family, TMEM132E and TMEM132D genes are associated with bipolar and panic disorders, respectively, while TMEM231 is a known syndromic autism gene. Together, TMEM187 and SYTL4 genes directly interact with recognized important ASD genes, and their mRNAs are found in extracellular vesicles in the nervous system and stimulate target cells to translate into active protein. Our evidence shows that both these genes should be considered as candidate genes for autism. Additional biological testing is warranted to further determine the pathogenicity of these gene variants in the causation of autism.

Systems-wide Studies Uncover Commander, a Multiprotein Complex Essential to Human Development

Recent mass spectrometry maps of the human interactome independently support the existence of a large multiprotein complex, dubbed "Commander." Broadly conserved across animals and ubiquitously expressed in nearly every human cell type examined thus far, Commander likely plays a fundamental cellular function, akin to other ubiquitous machines involved in expression, proteostasis, and trafficking. Experiments on individual subunits support roles in endosomal protein sorting, including the trafficking of Notch proteins, copper transporters, and lipoprotein receptors. Commander is critical for vertebrate embryogenesis, and defects in the complex and its interaction partners disrupt craniofacial, brain, and heart development. Here, we review the synergy between large-scale proteomic efforts and focused studies in the discovery of Commander, describe its composition, structure, and function, and discuss how it illustrates the power of systems biology. Based on 3D modeling and biochemical data, we draw strong parallels between Commander and the retromer cargo-recognition complex, laying a foundation for future research into Commander's role in human developmental disorders.

PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research

Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the understanding of infectious agent pathogenesis, and support for research on disease mechanisms. We have developed PathoPhenoDB, a database containing pathogen-to-phenotype associations. PathoPhenoDB relies on manual curation of pathogen-disease relations, on ontology-based text mining as well as manual curation to associate host disease phenotypes with infectious agents. Using Semantic Web technologies, PathoPhenoDB also links to knowledge about drug resistance mechanisms and drugs used in the treatment of infectious diseases. PathoPhenoDB is accessible at http://patho.phenomebrowser.net/ , and the data are freely available through a public SPARQL endpoint.

Genes with High Network Connectivity Are Enriched for Disease Heritability

Recent studies have highlighted the role of gene networks in disease biology. To formally assess this, we constructed a broad set of pathway, network, and pathway+network annotations and applied stratified LD score regression to 42 diseases and complex traits (average N = 323K) to identify enriched annotations. First, we analyzed 18,119 biological pathways. We identified 156 pathway-trait pairs whose disease enrichment was statistically significant (FDR < 5%) after conditioning on all genes and 75 known functional annotations (from the baseline-LD model), a stringent step that greatly reduced the number of pathways detected; most significant pathway-trait pairs were previously unreported. Next, for each of four published gene networks, we constructed probabilistic annotations based on network connectivity. For each gene network, the network connectivity annotation was strongly significantly enriched. Surprisingly, the enrichments were fully explained by excess overlap between network annotations and regulatory annotations from the baseline-LD model, validating the informativeness of the baseline-LD model and emphasizing the importance of accounting for regulatory annotations in gene network analyses. Finally, for each of the 156 enriched pathway-trait pairs, for each of the four gene networks, we constructed pathway+network annotations by annotating genes with high network connectivity to the input pathway. For each gene network, these pathway+network annotations were strongly significantly enriched for the corresponding traits. Once again, the enrichments were largely explained by the baseline-LD model. In conclusion, gene network connectivity is highly informative for disease architectures, but the information in gene networks may be subsumed by regulatory annotations, emphasizing the importance of accounting for known annotations.

Integrative network analysis reveals biological pathways associated with Williams syndrome

BACKGROUND

Williams syndrome (WS) is a neurodevelopmental disorder that has been attributed to heterozygous deletions in chromosome 7q11.23 and exhibits a variety of physical, cognitive, and behavioral features. However, the genetic basis of this phenotypic variability is unclear. In this study, we identified genetic clues underlying these complex phenotypes.

METHODS

Neurobehavioral function was assessed in WS patients and healthy controls. Total RNA was extracted from peripheral blood and subjected to microarray analysis, RNA-sequencing, and qRT-PCR. Weighted gene co-expression network analysis was performed to identify specific alterations related to intermediate disease phenotypes. To functionally interpret each WS-related module, gene ontology and disease-related gene enrichment were examined. We also investigated the micro (mi)RNA expression profiles and miRNA co-expression networks to better explain the regulation of the transcriptome in WS.

RESULTS

Our analysis identified four significant co-expression modules related to intermediate WS phenotypes. Notably, the three upregulated WS-related modules were composed exclusively of genes located outside the 7q11.23 region. They were significantly enriched in genes related to B-cell activation, RNA processing, and RNA transport. BCL11A, which is known for its association with speech disorders and intellectual disabilities, was identified as one of the hub genes in the top WS-related module. Finally, these key upregulated mRNA co-expression modules appear to be inversely correlated with a specific downregulated WS-related miRNA co-expression module.

CONCLUSIONS

Dysregulation of the mRNA/miRNA network involving genes outside of the 7q11.23 region is likely related to the complex phenotypes observed in WS patients.

Exosomal miRNA derived from keratinocytes regulates pigmentation in melanocytes

BACKGROUND

Pigmentation is controlled by complex mechanisms. Evidence suggests that miRNAs can regulate pigmentation. However, the mechanism has not been fully elucidated. Objective In this study, we revealed a novel mechanism that regulates pigmentation involving exosomes, miRNAs and the crosstalk between keratinocytes and melanocytes.

METHODS

The expression and localization of exosome specific marker TSG101 in keratinocytes and melanocytes; Changes of melanin content in melanocytes after co-culture of exosome and melanocytes; Expression changes of target gene TYR and its related genes and inhibitory effect of miR-330-5p on pigmentation were studied by using various molecular biological techniques.

RESULTS

In this experiment, we used miR-330-5p in keratinocytes to verify the effect of keratinocyte derived exosome on melanocyte pigmentation. First, we found that keratinocytes secrete exosomes carrying miR-330-5p; moreover, greater miR-330-5p expression was found in exosomes derived from keratinocytes that overexpressed miR-330-5p. Second, we found that exosomes derived from keratinocytes with overexpression of miR-330-5p caused a significant increase in miR-330-5p in melanocytes. Finally, exosomes derived from keratinocytes that overexpressed miR-330-5p induced a significant decrease in the production of melanin and expression of TYR in melanocytes. Meanwhile, we overexpressed miR-330-5p in melanocytes, which also proved the inhibitory effect of miR-330-5p on pigmentation.

CONCLUSION

These findings suggest that keratinocytes crosstalk with melanocytes in the epidermal melanin unit via exosomal miRNAs. These studies reveal an important role of exosomes in melanocyte pigmentation, which opens a new pathway of melanogenesis.

Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing

Risk for late-onset Alzheimer's disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of clinically diagnosed LOAD (94,437 individuals). We confirm 20 previous LOAD risk loci and identify five new genome-wide loci (IQCK, ACE, ADAM10, ADAMTS1, and WWOX), two of which (ADAM10, ACE) were identified in a recent genome-wide association (GWAS)-by-familial-proxy of Alzheimer's or dementia. Fine-mapping of the human leukocyte antigen (HLA) region confirms the neurological and immune-mediated disease haplotype HLA-DR15 as a risk factor for LOAD. Pathway analysis implicates immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein (APP) metabolism, showing that genetic variants affecting APP and Aβ processing are associated not only with early-onset autosomal dominant Alzheimer's disease but also with LOAD. Analyses of risk genes and pathways show enrichment for rare variants (P = 1.32 × 10-7), indicating that additional rare variants remain to be identified. We also identify important genetic correlations between LOAD and traits such as family history of dementia and education.

Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is the most common autoimmune disease and affects about 1% of the population. The cause of RA remains largely unknown and could result from a complex interaction between genes and environment factors. Recent studies suggested that gut microbiota and their collective metabolic outputs exert profound effects on the host immune system and are implicated in RA. However, which and how gut microbial metabolites interact with host genetics in contributing to RA pathogenesis remains unknown. In this study, we present a data-driven study to understand how gut microbial metabolites contribute to RA at the genetic, functional and phenotypic levels.

Results

We used publicly available disease genetics, chemical genetics, human metabolome, genetic signaling pathways, mouse genome-wide mutation phenotypes, and mouse phenotype ontology data. We identified RA-associated microbial metabolites and prioritized them based on their genetic, functional and phenotypic relevance to RA. We evaluated the prioritization methods using short-chain fatty acids (SCFAs), which were previously shown to be involved in RA etiology. We validate the algorithms by showing that SCFAs are highly associated with RA at genetic, functional and phenotypic levels: SCFAs ranked at top 3.52% based on shared genes with RA, top 5.69% based on shared genetic pathways, and top 16.94% based on shared phenotypes. Based on the genetic-level analysis, human gut microbial metabolites directly interact with many RA-associated genes (as many as 18.1% of all 166 RA genes). Based on the functional-level analysis, human gut microbial metabolites participate in many RA-associated genetic pathways (as many as 71.4% of 311 genetic pathways significantly enriched for RA), including immune system pathways. Based on the phenotypic-level analysis, gut microbial metabolites affect many RA-related phenotypes (as many as 51.3% of 978 phenotypes significantly enriched for RA), including many immune system phenotypes.

Conclusions

Our study demonstrates strong gut-microbiome-immune-joint interactions in RA, which converged on both genetic, functional and phenotypic levels.

A noncanonical role for the engulfment gene ELMO1 in neutrophils that promotes inflammatory arthritis

Rheumatoid arthritis is characterized by progressive joint inflammation and affects ~1% of the human population. We noted single nucleotide polymorphisms (SNPs) in the apoptotic cell engulfment genes ELMO1, DOCK2, and RAC1 linked to rheumatoid arthritis. As ELMO1 promotes cytoskeletal reorganization during engulfment, we hypothesized that ELMO1 loss would worsen inflammatory arthritis. Surprisingly, Elmo1-deficient mice showed reduced joint inflammation in acute and chronic arthritis models. Genetic and cell biological studies revealed that ELMO1 associates with receptors linked to neutrophil function in arthritis and regulates activation and early neutrophil recruitment to the joints, without general inhibition of inflammatory responses. Further, neutrophils from peripheral blood of human donors that carry the SNP in ELMO1 associated with arthritis display increased migratory capacity, whereas ELMO1 knockdown reduces human neutrophil migration to chemokines linked to arthritis. These data identify ‘non-canonical’ roles for ELMO1 as an important cytoplasmic regulator of specific neutrophil receptors and promoter of arthritis.

The antiviral activity of rodent and lagomorph SERINC3 and SERINC5 is counteracted by known viral antagonists

A first step towards the development of a human immunodeficiency virus (HIV) animal model has been the identification and surmounting of species-specific barriers encountered by HIV along its replication cycle in cells from small animals. Serine incorporator proteins 3 (SERINC3) and 5 (SERINC5) were recently identified as restriction factors that reduce HIV-1 infectivity. Here, we compared the antiviral activity of SERINC3 and SERINC5 among mice, rats and rabbits, and their susceptibility to viral counteraction to their human counterparts. In the absence of viral antagonists, rodent and lagomorph SERINC3 and SERINC5 displayed anti-HIV activity in a similar range to human controls. Vesicular stomatitis virus G protein (VSV-G) pseudotyped virions were considerably less sensitive to restriction by all SERINC3/5 orthologs. Interestingly, HIV-1 Nef, murine leukemia virus (MLV) GlycoGag and equine infectious anemia virus (EIAV) S2 counteracted the antiviral activity of all SERINC3/5 orthologs with similar efficiency. Our results demonstrate that the antiviral activity of SERINC3/5 proteins is conserved in rodents and rabbits, and can be overcome by all three previously reported viral antagonists.

PSMD12 haploinsufficiency in a neurodevelopmental disorder with autistic features

Protein homeostasis is tightly regulated by the ubiquitin proteasome pathway. Disruption of this pathway gives rise to a host of neurological disorders. Through whole exome sequencing (WES) in families with neurodevelopmental disorders, we identified mutations in PSMD12, a core component of the proteasome, underlying a neurodevelopmental disorder with intellectual disability (ID) and features of autism spectrum disorder (ASD). We performed WES on six affected siblings from a multiplex family with ID and autistic features, the affected father, and two unaffected mothers, and a trio from a simplex family with one affected child with ID and periventricular nodular heterotopia. We identified an inherited heterozygous nonsense mutation in PSMD12 (NM_002816: c.367C>T: p.R123X) in the multiplex family and a de novo nonsense mutation in the same gene (NM_002816: c.601C>T: p.R201X) in the simplex family. PSMD12 encodes a non-ATPase regulatory subunit of the 26S proteasome. We confirm the association of PSMD12 with ID, present the first cases of inherited PSMD12 mutation, and demonstrate the heterogeneity of phenotypes associated with PSMD12 mutations.

Fas Promotes T Helper 17 Cell Differentiation and Inhibits T Helper 1 Cell Development by Binding and Sequestering Transcription Factor STAT1

The death receptor Fas removes activated lymphocytes through apoptosis. Previous transcriptional profiling predicted that Fas positively regulates interleukin-17 (IL-17)-producing T helper 17 (Th17) cells. Here, we demonstrate that Fas promoted the generation and stability of Th17 cells and prevented their differentiation into Th1 cells. Mice with T-cell- and Th17-cell-specific deletion of Fas were protected from induced autoimmunity, and Th17 cell differentiation and stability were impaired. Fas-deficient Th17 cells instead developed a Th1-cell-like transcriptional profile, which a new algorithm predicted to depend on STAT1. Experimentally, Fas indeed bound and sequestered STAT1, and Fas deficiency enhanced IL-6-induced STAT1 activation and nuclear translocation, whereas deficiency of STAT1 reversed the transcriptional changes induced by Fas deficiency. Thus, our computational and experimental approach identified Fas as a regulator of the Th17-to-Th1 cell balance by controlling the availability of opposing STAT1 and STAT3 to have a direct impact on autoimmunity.

Comparative expression profiling reveals widespread coordinated evolution of gene expression across eukaryotes

Comparative studies of gene expression across species have revealed many important insights, but have also been limited by the number of species represented. Here we develop an approach to identify orthologs between highly diverged transcriptome assemblies, and apply this to 657 RNA-seq gene expression profiles from 309 diverse unicellular eukaryotes. We analyzed the resulting data for coevolutionary patterns, and identify several hundred protein complexes and pathways whose expression levels have evolved in a coordinated fashion across the trillions of generations separating these species, including many gene sets with little or no within-species co-expression across environmental or genetic perturbations. We also detect examples of adaptive evolution, for example of tRNA ligase levels to match genome-wide codon usage. In sum, we find that comparative studies from extremely diverse organisms can reveal new insights into the evolution of gene expression, including coordinated evolution of some of the most conserved protein complexes in eukaryotes.

Gene pairs that are coexpressed across various environmental conditions in multiple species suggest functional similarity. Here the authors analyze patterns of gene expression co-evolution across diverse eukaryotes, and identify hundreds of protein complexes and pathways whose gene expression levels have co-evolved since their ancient divergence.

Identification of TGFβ-related genes regulated in murine osteoarthritis and chondrocyte hypertrophy by comparison of multiple microarray datasets

OBJECTIVE

Osteoarthritis (OA) is a joint disease characterized by progressive degeneration of articular cartilage. Some features of OA, including chondrocyte hypertrophy and focal calcification of articular cartilage, resemble the endochondral ossification processes. Alterations in transforming growth factor β (TGFβ) signaling have been associated with OA as well as with chondrocyte hypertrophy. Our aim was to identify novel candidate genes implicated in chondrocyte hypertrophy during OA pathogenesis by determining which TGFβ-related genes are regulated during murine OA and endochondral ossification.

METHODS

A list of 580 TGFβ-related genes, including TGFβ signaling pathway components and TGFβ-target genes, was generated. Regulation of these TGFβ-related genes was assessed in a microarray of murine OA cartilage: 1, 2 and 6 weeks after destabilization of the medial meniscus (DMM). Subsequently, genes regulated in the DMM model were studied in two independent murine microarray datasets on endochondral ossification: the growth plate and transient embryonic cartilage (joint development).

RESULTS

A total of 106 TGFβ-related genes were differentially expressed in articular cartilage of DMM-operated mice compared to sham-control. From these genes, 43 were similarly regulated during chondrocyte hypertrophy in the growth plate or embryonic joint development. Among these 43 genes, 18 genes have already been associated with OA. The remaining 25 genes were considered as novel candidate genes involved in OA pathogenesis and endochondral ossification. In supplementary data of published human OA microarrays we found indications that 15 of the 25 novel genes are indeed regulated in articular cartilage of human OA patients.

CONCLUSION

By focusing on TGFβ-related genes during OA and chondrocyte hypertrophy in mice, we identified 18 known and 25 new candidate genes potentially implicated in phenotypical changes in chondrocytes leading to OA. We propose that 15 of these candidates warrant further investigation as therapeutic target for OA as they are also regulated in articular cartilage of OA patients.

Contribution of Host Genetics to the Variation of Microbial Composition of Cecum Lumen and Feces in Pigs

Pigs are a perfect model for studying the interaction between host genetics and gut microbiome due to the high similarity of gastrointestine and digestive system with humans, and the easily controlled feeding conditions. In this study, two pig populations which were raised in uniformed farm conditions and provided with the same commercial formula diet were used as the experimental animals. A systematical investigation of host genetic effect on the gut microbial composition was separately performed in porcine cecum lumen and feces samples through the comparison of microbial composition among full-sibs, half-sibs and unrelated members, heritability estimate (h²), and genome-wide association study (GWAS). The results showed that full-sib members had a higher similarity of microbial composition than unrelated individuals. A significant correlation was observed between the microbial composition-based kinship and the host SNP-based kinship in both populations (P < 9.9 × 10^-5). We identified 81 and 67 microbial taxa having h² > 0.15 in fecal and cecum luminal samples, respectively, including 31 taxa with h² > 0.15 in both types of samples. GWAS identified 40 and 34 significant associations between host genomic loci and the abundance or presence/absence of bacterial taxa in the fecal and cecum luminal samples. Functional classifications of host candidate genes related to microbial taxa are mainly associated with metabolism, immunity functions and response, and signal transduction. The high similarity of heritable taxa and functional categories of candidate genes among pig, human and mouse suggests the similar mechanism of the host genetic effect on gut microbiome across mammalian species. The results from this study provided another evidence that host genetics contributes significantly to the gut microbiome.

Apparent bias toward long gene misregulation in MeCP2 syndromes disappears after controlling for baseline variations

Recent studies have suggested that genes longer than 100 kb are more likely to be misregulated in neurological diseases associated with synaptic dysfunction, such as autism and Rett syndrome. These length-dependent transcriptional changes are modest in MeCP2-mutant samples, but, given the low sensitivity of high-throughput transcriptome profiling technology, here we re-evaluate the statistical significance of these results. We find that the apparent length-dependent trends previously observed in MeCP2 microarray and RNA-sequencing datasets disappear after estimating baseline variability from randomized control samples. This is particularly true for genes with low fold changes. We find no bias with NanoString technology, so this long gene bias seems to be particular to polymerase chain reaction amplification-based platforms. In contrast, authentic long gene effects, such as those caused by topoisomerase inhibition, can be detected even after adjustment for baseline variability. We conclude that accurate characterization of length-dependent (or other) trends requires establishing a baseline from randomized control samples.

Whole exome analysis of primary immunodeficiency

The human primary immunodeficiency diseases (PIDs) refer to a rare heterogeneous group of single-gene inherited disorders causing malfunctions in the immune system, and thus the affected patients have a predisposition to severe life-threatening infections. The heterogeneous nature of PIDs, which involves at list 300 different genes, makes diagnosis of the disease a complex issue. Although studies revealed that six million people have a kind of PID, but due to a complex diagnosis procedure many affected individuals have not gotten a correct diagnosis. However, thanks to advancing in the DNA sequencing method and availability of sophisticated sequencers molecular characterization of genetic disorders have been revolutionized. The whole exome sequencing (WES) method can help clinicians detect Mendelian disease and other complex genetic disorders. The presented study used WES to investigate two infants with symptoms of primary immunodeficiency including hemophagocytic lymphohistio­cytosis (HLH) and severe combined immunodeficiency (SCID). It has been shown that the HLH patient had a mutation in the UNC13D gene (NM_199242.2:c.627delT), and the SCID patient had a mutation in the RAG1 gene (NM_000448.2:c.322C>G). It has been demonstrated that WES is a fast and cost-effective method facilitating genetic diagnosis in PID sufferers.

Genome-wide association study using haplotype alleles for the evaluation of reproductive traits in Nelore cattle

Zebu cattle (Bos taurus indicus) are highly adapted to tropical regions. However, females reach puberty after taurine heifers, which affects the economic efficiency of beef cattle breeding in the tropical regions. The aims of this study were to establish associations between the haplotype alleles of the bovine genome and age at first calving (AFC) in the Nelore cattle, and to identify the genes and quantitative trait loci (QTL) related to this phenotype. A total of 2,273 Nelore cattle (995 males and 1,278 females) genotyped using the Illumina BovineHD BeadChip were used in the current study. The association analysis included females with valid first calving records as well as open heifers. Linkage disequilibrium (LD) analysis among the markers was performed using blocks of 5, 10, and 15 markers, which were determined by sliding windows shifting one marker at a time. Then, the haplotype block size to be used in the association study was chosen based on the highest r² average among the SNPs in the block. The five HapAlleles most strongly associated with the trait (top five) were considered as significant associations. The results of the analysis revealed four genomic regions related to AFC, which overlapped with 20 QTL of the reproductive traits reported previously. Furthermore, there were 19 genes related to reproduction in those regions. In conclusion, the use of haplotypes allowed the detection of chromosomal regions associated with AFC in Nelore cattle, and provided the basis for elucidating the mechanisms underlying this trait.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network

The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein–protein interaction data set and the human signal transduction network—a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets.

Genetic interaction between two insulin-dependent diabetes susceptibility loci, Idd2 and Idd13, in determining immunoregulatory DN T cell proportion

Several immune regulatory cell types participate in the protection against autoimmune diseases such as autoimmune diabetes. Of these immunoregulatory cells, we and others have shown that peripheral CD4^-CD8^- double negative (DN) T cells can induce antigen-specific immune tolerance. Particularly, we have described that diabetes-prone mice exhibit a lower number of peripheral DN T cells compared to diabetes-resistant mice. Identifying the molecular pathways that influence the size of the DN T cell pool in peripheral lymphoid organs may thus be of interest for maintaining antigen-specific immune tolerance. Hence, through immunogenetic approaches, we found that two genetic loci linked to autoimmune diabetes susceptibility, namely Idd2 and Idd13, independently contribute to the partial restoration of DN T cell proportion in secondary lymphoid organs. We now extend these findings to show an interaction between the Idd2 and Idd13 loci in determining the number of DN T cells in secondary lymphoid organs. Using bioinformatics tools, we link potential biological pathways arising from interactions of genes encoded within the two loci. By focusing on cell cycle, we validate that both the Idd2 and Idd13 loci influence RAD51 expression as well as DN T cell progression through the cell cycle. Altogether, we find that genetic interactions between Idd2 and Idd13 loci modulate cell cycle progression, which contributes, at least in part, to defining the proportion of DN T cells in secondary lymphoid organs.

GC[Formula: see text]NMF: A Novel Matrix Factorization Framework for Gene-Phenotype Association Prediction

Gene-phenotype association prediction can be applied to reveal the inherited basis of human diseases and facilitate drug development. Gene-phenotype associations are related to complex biological processes and influenced by various factors, such as relationship between phenotypes and that among genes. While due to sparseness of curated gene-phenotype associations and lack of integrated analysis of the joint effect of multiple factors, existing applications are limited to prediction accuracy and potential gene-phenotype association detection. In this paper, we propose a novel method by exploiting weighted graph constraint learned from hierarchical structures of phenotype data and group prior information among genes by inheriting advantages of Non-negative Matrix Factorization (NMF), called Weighted Graph Constraint and Group Centric Non-negative Matrix Factorization (GC[Formula: see text]NMF). Specifically, first we introduce the depth of parent-child relationships between two adjacent phenotypes in hierarchical phenotypic data as weighted graph constraint for a better phenotype understanding. Second, we utilize intra-group correlation among genes in a gene group as group constraint for gene understanding. Such information provides us with the intuition that genes in a group probably result in similar phenotypes. The model not only allows us to achieve a high-grade prediction performance, but also helps us to learn interpretable representation of genes and phenotypes simultaneously to facilitate future biological analysis. Experimental results on biological gene-phenotype association datasets of mouse and human demonstrate that GC[Formula: see text]NMF can obtain superior prediction accuracy and good understandability for biological explanation over other state-of-the-arts methods.

A systems biology approach to predict and characterize human gut microbial metabolites in colorectal cancer

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths. It is estimated that about half the cases of CRC occurring today are preventable. Recent studies showed that human gut microbiota and their collective metabolic outputs play important roles in CRC. However, the mechanisms by which human gut microbial metabolites interact with host genetics in contributing CRC remain largely unknown. We hypothesize that computational approaches that integrate and analyze vast amounts of publicly available biomedical data have great potential in better understanding how human gut microbial metabolites are mechanistically involved in CRC. Leveraging vast amount of publicly available data, we developed a computational algorithm to predict human gut microbial metabolites for CRC. We validated the prediction algorithm by showing that previously known CRC-associated gut microbial metabolites ranked highly (mean ranking: top 10.52%; median ranking: 6.29%; p-value: 3.85E-16). Moreover, we identified new gut microbial metabolites likely associated with CRC. Through computational analysis, we propose potential roles for tartaric acid, the top one ranked metabolite, in CRC etiology. In summary, our data-driven computation-based study generated a large amount of associations that could serve as a starting point for further experiments to refute or validate these microbial metabolite associations in CRC cancer.

Combining mechanism-based prediction with patient-based profiling for psoriasis metabolomics biomarker discovery

Psoriasis is a chronic, debilitating skin condition that affects approximately 125 million individuals worldwide. The cause of psoriasis appears multifactorial, and no unified mitigating signal or single antigenic target has been identified to date. Metabolomic studies hold great potential for explaining disease mechanism, facilitating early diagnosis, and identifying potential therapeutic areas. Here, we present an integrated disease metabolomic biomarker discovery strategy that combines mechanism-based biomarker discovery with clinical sample-based metabolomic profiling. We applied this strategy in identifying and understanding metabolite biomarkers for psoriasis. The key innovation of our strategy is a novel mechanism-based metabolite prediction system, mmPredict, which assimilates vast amounts of existing knowledge of diseases and metabolites. mmPredict first constructed a psoriasis-specific mouse mutational phenotype profile. It then constructed phenotype profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. Metabolites were then prioritized based on the phenotypic similarities between disease- and metabolites. We evaluated mmPredict using 150 metabolites identified using our in-house metabolome profiling study of psoriasis patient samples. mmPredict found 96 of the 150 metabolites and ranked them highly (recall: 0.64, mean ranking: 8.73%, median ranking: 2.33%, p-value: 4.75E-44). These results show that mmPredict is consistent with, as well as a complement to, traditional human metabolomic profiling studies. We then developed a strategy to combine outputs from both systems and found that the oxidative product of linoleic acid, 13(S)-hydroxy-9Z,11E-octadecadienoic acid (13- HODE), ranked highly by both mmPredict and our in-house experiments. Our integrated analysis indicates that 13- HODE may be a mechanistic link between psoriasis and cardiovascular comorbidities associated with psoriasis. In summary, we developed an integrated metabolomic prediction system that combines both human metabolomic studies and mechanism-based prediction and demonstrated its application in the skin disease psoriasis. Our system is highly general and can be applied to other diseases when patient-based metabolomic profiling data becomes more increasingly available. Data is publicly available at: http://nlp.

CASE

edu/public/data/mmPredict_PSO.

A novel variant in GLIS3 is associated with osteoarthritis

OBJECTIVES

Osteoarthritis (OA) is a complex disease, but its genetic aetiology remains poorly characterised. To identify novel susceptibility loci for OA, we carried out a genome-wide association study (GWAS) in individuals from the largest UK-based OA collections to date.

METHODS

We carried out a discovery GWAS in 5414 OA individuals with knee and/or hip total joint replacement (TJR) and 9939 population-based controls. We followed-up prioritised variants in OA subjects from the interim release of the UK Biobank resource (up to 12 658 cases and 50 898 controls) and our lead finding in operated OA subjects from the full release of UK Biobank (17 894 cases and 89 470 controls). We investigated its functional implications in methylation, gene expression and proteomics data in primary chondrocytes from 12 pairs of intact and degraded cartilage samples from patients undergoing TJR.

RESULTS

We detect a genome-wide significant association at rs10116772 with TJR (P=3.7×10-8; for allele A: OR (95% CI) 0.97 (0.96 to 0.98)), an intronic variant in GLIS3, which is expressed in cartilage. Variants in strong correlation with rs10116772 have been associated with elevated plasma glucose levels and diabetes.

CONCLUSIONS

We identify a novel susceptibility locus for OA that has been previously implicated in diabetes and glycaemic traits.

Correlates of evolutionary rates in the murine sperm proteome

Background

Protein-coding genes expressed in sperm evolve at different rates. To gain deeper insight into the factors underlying this heterogeneity we examined the relative importance of a diverse set of previously described rate correlates in determining the evolution of murine sperm proteins.

Results

Using partial rank correlations we detected several major rate indicators: Phyletic gene age, numbers of protein-protein interactions, and survival essentiality emerged as particularly important rate correlates in murine sperm proteins. Tissue specificity, numbers of paralogs, and untranslated region lengths also correlate significantly with sperm genes’ evolutionary rates, albeit to a lesser extent. Multifunctionality, coding sequence or average intron lengths, and mean expression level have insignificant or virtually no independent effects on evolutionary rates in murine sperm genes. Gene ontology enrichment analyses of three equally sized murine sperm protein groups classified based on their evolutionary rates indicate strongest sperm-specific functional specialization in the most quickly evolving gene class.

Conclusions

We propose a model according to which slowly evolving murine sperm proteins tend to be constrained by factors such as survival essentiality, network connectivity, and/or broad expression. In contrast, evolutionary change may arise especially in less constrained sperm proteins, which might, moreover, be prone to specialize to reproduction-related functions. Our results should be taken into account in future studies on rate variations of reproductive genes.

Electronic supplementary material

The online version of this article (10.1186/s12862-018-1157-6) contains supplementary material, which is available to authorized users.

Forniceal deep brain stimulation induces gene expression and splicing changes that promote neurogenesis and plasticity

Clinical trials are currently underway to assess the efficacy of forniceal deep brain stimulation (DBS) for improvement of memory in Alzheimer's patients, and forniceal DBS has been shown to improve learning and memory in a mouse model of Rett syndrome (RTT), an intellectual disability disorder caused by loss-of-function mutations in MECP2. The mechanism of DBS benefits has been elusive, however, so we assessed changes in gene expression, splice isoforms, DNA methylation, and proteome following acute forniceal DBS in wild-type mice and mice lacking Mecp2. We found that DBS upregulates genes involved in synaptic function, cell survival, and neurogenesis and normalized expression of ~25% of the genes altered in Mecp2-null mice. Moreover, DBS induced expression of 17-24% of the genes downregulated in other intellectual disability mouse models and in post-mortem human brain tissue from patients with Major Depressive Disorder, suggesting forniceal DBS could benefit individuals with a variety of neuropsychiatric disorders.

Emerging roles for RNA-binding proteins as effectors and regulators of cardiovascular disease

The cardiovascular system comprises multiple cell types that possess the capacity to modulate their phenotype in response to acute or chronic injury. Transcriptional and post-transcriptional mechanisms play a key role in the regulation of remodelling and regenerative responses to damaged cardiovascular tissues. Simultaneously, insufficient regulation of cellular phenotype is tightly coupled with the persistence and exacerbation of cardiovascular disease. Recently, RNA-binding proteins such as Quaking, HuR, Muscleblind, and SRSF1 have emerged as pivotal regulators of these functional adaptations in the cardiovascular system by guiding a wide-ranging number of post-transcriptional events that dramatically impact RNA fate, including alternative splicing, stability, localization and translation. Moreover, homozygous disruption of RNA-binding protein genes is commonly associated with cardiac- and/or vascular complications. Here, we summarize the current knowledge on the versatile role of RNA-binding proteins in regulating the transcriptome during phenotype switching in cardiovascular health and disease. We also detail existing and potential DNA- and RNA-based therapeutic approaches that could impact the treatment of cardiovascular disease in the future.

The Effects of LW-AFC on the Hippocampal Transcriptome in Senescence-Accelerated Mouse Prone 8 Strain, a Mouse Model of Alzheimer's Disease

The senescence-accelerated mouse prone 8 (SAMP8) strain is considered a robust experimental model for developing preventative and therapeutic treatments for Alzheimer's disease (AD), a neurodegenerative disease which cannot be effectively prevented, halted, or cured. Our previous studies showed that LW-AFC, a new formula derived from the classical traditional Chinese medicinal prescription Liuwei Dihuang decoction, ameliorates cognitive deterioration in PrP-hAβPPswe/PS1ΔE9 transgenic mice and SAMP8 mice. This study aims to investigate the mechanism that mediates how LW-AFC improves cognitive deficit on the basis of the transcriptome. We conducted a genome-wide survey of gene expression in the hippocampus in mice from the senescence accelerated mouse resistant 1 (SAMR1) strain, from SAMP8 and from LW-AFC treated SAMP8. The results showed that LW-AFC reversed the transcriptome in the hippocampus of SAMP8 mice. The specific investigation of altered gene expression in subtypes defined by cognitive profiles indicated that the systemic lupus erythematosus pathway, spliceosomes, amyotrophic lateral sclerosis, and the insulin signaling were involved in the improvement of cognitive ability by LW-AFC. The expression of genes Enpp2, Etnk1, Epdr1, and Gm5900 in the hippocampus were correlated with that of LW-AFC's ameliorating cognitive impairment in SAMP8 mice. Because LW-AFC is composed of polysaccharides, glycosides, and oligosaccharides, we infer that LW-AFC has direct or indirect effects on altering gene expressions and regulating pathways in the hippocampus of SAMP8 mice. These data are helpful for the enhanced identification of LW-AFC as new therapeutic modalities to AD.

TCF7L1 suppresses primitive streak gene expression to support human embryonic stem cell pluripotency

Human embryonic stem cells (hESCs) are exquisitely sensitive to WNT ligands, which rapidly cause differentiation. Therefore, hESC self-renewal requires robust mechanisms to keep the cells in a WNT inactive but responsive state. How they achieve this is largely unknown. We explored the role of transcriptional regulators of WNT signaling, the TCF/LEFs. As in mouse ESCs, TCF7L1 is the predominant family member expressed in hESCs. Genome-wide, it binds a gene cohort involved in primitive streak formation at gastrulation, including NODAL, BMP4 and WNT3 Comparing TCF7L1-bound sites with those bound by the WNT signaling effector β-catenin indicates that TCF7L1 acts largely on the WNT signaling pathway. TCF7L1 overlaps less with the pluripotency regulators OCT4 and NANOG than in mouse ESCs. Gain- and loss-of-function studies indicate that TCF7L1 suppresses gene cohorts expressed in the primitive streak. Interestingly, we find that BMP4, another driver of hESC differentiation, downregulates TCF7L1, providing a mechanism of BMP and WNT pathway intersection. Together, our studies indicate that TCF7L1 plays a major role in maintaining hESC pluripotency, which has implications for human development during gastrulation.

Reactome graph database: Efficient access to complex pathway data

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types.

DMC1 mutation that causes human non-obstructive azoospermia and premature ovarian insufficiency identified by whole-exome sequencing

BACKGROUND

The genetic causes of the majority of male and female infertility caused by human non-obstructive azoospermia (NOA) and premature ovarian insufficiency (POI) with meiotic arrest are unknown.

OBJECTIVE

To identify the genetic cause of NOA and POI in two affected members from a consanguineous Chinese family.

METHODS

We performed whole-exome sequencing of DNA from both affected patients. The identified candidate causative gene was further verified by Sanger sequencing for pedigree analysis in this family. In silico analysis was performed to functionally characterise the mutation, and histological analysis was performed using the biopsied testicle sample from the male patient with NOA.

RESULTS

We identified a novel homozygous missense mutation (NM_007068.3: c.106G>A, p.Asp36Asn) in DMC1, which cosegregated with NOA and POI phenotypes in this family. The identified missense mutation resulted in the substitution of a conserved aspartic residue with asparaginate in the modified H3TH motif of DMC1. This substitution results in protein misfolding. Histological analysis demonstrated a lack of spermatozoa in the male patient's seminiferous tubules. Immunohistochemistry using a testis biopsy sample from the male patient showed that spermatogenesis was blocked at the zygotene stage during meiotic prophase I.

CONCLUSIONS

To the best of our knowledge, this is the first report identifying DMC1 as the causative gene for human NOA and POI. Furthermore, our pedigree analysis shows an autosomal recessive mode of inheritance for NOA and POI caused by DMC1 in this family.

Inferring ontology graph structures using OWL reasoning

BACKGROUND

Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge.

RESULTS

We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph .

CONCLUSIONS

Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

Contribution of Host Genetics to the Variation of Microbial Composition of Cecum Lumen and Feces in Pigs

Pigs are a perfect model for studying the interaction between host genetics and gut microbiome due to the high similarity of gastrointestine and digestive system with humans, and the easily controlled feeding conditions. In this study, two pig populations which were raised in uniformed farm conditions and provided with the same commercial formula diet were used as the experimental animals. A systematical investigation of host genetic effect on the gut microbial composition was separately performed in porcine cecum lumen and feces samples through the comparison of microbial composition among full-sibs, half-sibs and unrelated members, heritability estimate (h ²), and genome-wide association study (GWAS). The results showed that full-sib members had a higher similarity of microbial composition than unrelated individuals. A significant correlation was observed between the microbial composition-based kinship and the host SNP-based kinship in both populations (P < 9.9 × 10^-5). We identified 81 and 67 microbial taxa having h ² > 0.15 in fecal and cecum luminal samples, respectively, including 31 taxa with h ² > 0.15 in both types of samples. GWAS identified 40 and 34 significant associations between host genomic loci and the abundance or presence/absence of bacterial taxa in the fecal and cecum luminal samples. Functional classifications of host candidate genes related to microbial taxa are mainly associated with metabolism, immunity functions and response, and signal transduction. The high similarity of heritable taxa and functional categories of candidate genes among pig, human and mouse suggests the similar mechanism of the host genetic effect on gut microbiome across mammalian species. The results from this study provided another evidence that host genetics contributes significantly to the gut microbiome.

A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis

Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs) for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.

Better living through ontologies at the Immune Epitope Database

The Immune Epitope Database (IEDB) project incorporates independently developed ontologies and controlled vocabularies into its curation and search interface. This simplifies curation practices, improves the user query experience and facilitates interoperability between the IEDB and other resources. While the use of independently developed ontologies has long been recommended as a best practice, there continues to be a significant number of projects that develop their own vocabularies instead, or that do not fully utilize the power of ontologies that they are using. We describe how we use ontologies in the IEDB, providing a concrete example of the benefits of ontologies in practice.

Database URL:www.iedb.org