Systematic analysis, comparison, and integration of disease based human genetic association data and mouse genetic phenotypic information

The human neuropsychiatric risk gene Drd2 is necessary for social functioning across evolutionary distant species

The Drd2 gene, encoding the dopamine D2 receptor (D2R), was recently indicated as a potential target in the etiology of lowered sociability (i.e., social withdrawal), a symptom of several neuropsychiatric disorders such as Schizophrenia and Major Depression. Many animal species show social withdrawal in response to stimuli, including the vinegar fly Drosophila melanogaster and mice, which also share most human disease-related genes. Here we will test for causality between Drd2 and sociability and for its evolutionary conserved function in these two distant species, as well as assess its mechanism as a potential therapeutic target. During behavioral observations in groups of freely interacting D. melanogaster, Drd2 homologue mutant showed decreased social interactions and locomotor activity. After confirming Drd2's social effects in flies, conditional transgenic mice lacking Drd2 in dopaminergic cells (autoreceptor KO) or in serotonergic cells (heteroreceptor KO) were studied in semi-natural environments, where they could freely interact. Autoreceptor KOs showed increased sociability, but reduced activity, while no overall effect of Drd2 deletion was observed in heteroreceptor KOs. To determine acute effects of D2R signaling on sociability, we also showed that a direct intervention with the D2R agonist Sumanirole decreased sociability in wild type mice, while the antagonist showed no effects. Using a computational ethological approach, this study demonstrates that Drd2 regulates sociability across evolutionary distant species, and that activation of the mammalian D2R autoreceptor, in particular, is necessary for social functioning.

Transcriptional changes in the rat brain induced by repetitive transcranial magnetic stimulation

Introduction

Transcranial Magnetic Stimulation (TMS) is a noninvasive technique that uses pulsed magnetic fields to affect the physiology of the brain and central nervous system. Repetitive TMS (rTMS) has been used to study and treat several neurological conditions, but its complex molecular basis is largely unexplored.

Methods

Utilizing three experimental rat models (in vitro, ex vivo, and in vivo) and employing genome-wide microarray analysis, our study reveals the extensive impact of rTMS treatment on gene expression patterns.

Results

These effects are observed across various stimulation protocols, in diverse tissues, and are influenced by time and age. Notably, rTMS-induced alterations in gene expression span a wide range of biological pathways, such as glutamatergic, GABAergic, and anti-inflammatory pathways, ion channels, myelination, mitochondrial energetics, multiple neuron-and synapse-specific genes.

Discussion

This comprehensive transcriptional analysis induced by rTMS stimulation serves as a foundational characterization for subsequent experimental investigations and the exploration of potential clinical applications.

Atlas of regulated target genes of transcription factors (ART-TF) in human ES cells

Background

Transcription factors (TFs) play central roles in maintaining “stemness” of embryonic stem (ES) cells and their differentiation into several hundreds of adult cell types. The regulatory competence of TFs is routinely assessed by detecting target genes to which they bind. However, these data do not indicate which target genes are activated, repressed, or not affected by the change of TF abundance. There is a lack of large-scale studies that compare the genome binding of TFs with the expression change of target genes after manipulation of each TF.

Results

In this paper we associated human TFs with their target genes by two criteria: binding to genes, evaluated from published ChIP-seq data (n = 1868); and change of target gene expression shortly after induction of each TF in human ES cells. Lists of direction- and strength-specific regulated target genes are generated for 311 TFs (out of 351 TFs tested) with expected proportion of false positives less than or equal to 0.30, including 63 new TFs not present in four existing databases of target genes. Our lists of direction-specific targets for 152 TFs (80.0%) are larger that in the TRRUST database. In average, 30.9% of genes that respond greater than or equal to twofold to the induction of TFs are regulated targets. Regulated target genes indicate that the majority of TFs are either strong activators or strong repressors, whereas sets of genes that responded greater than or equal to twofold to the induction of TFs did not show strong asymmetry in the direction of expression change. The majority of human TFs (82.1%) regulated their target genes primarily via binding to enhancers. Repression of target genes is more often mediated by promoter-binding than activation of target genes. Enhancer-promoter loops are more abundant among strong activator and repressor TFs.

Conclusions

We developed an atlas of regulated targets of TFs (ART-TF) in human ES cells by combining data on TF binding with data on gene expression change after manipulation of individual TFs. Sets of regulated gene targets were identified with a controlled rate of false positives. This approach contributes to the understanding of biological functions of TFs and organization of gene regulatory networks. This atlas should be a valuable resource for ES cell-based regenerative medicine studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04924-3.

Deep learning-based medical diagnostic services: A secure, lightweight, and accurate realization1

In this paper, we propose CryptMed, a system framework that enables medical service providers to offer secure, lightweight, and accurate medical diagnostic service to their customers via an execution of neural network inference in the ciphertext domain. CryptMed ensures the privacy of both parties with cryptographic guarantees. Our technical contributions include: 1) presenting a secret sharing based inference protocol that can well cope with the commonly-used linear and non-linear NN layers; 2) devising optimized secure comparison function that can efficiently support comparison-based activation functions in NN architectures; 3) constructing a suite of secure smooth functions built on precise approximation approaches for accurate medical diagnoses. We evaluate CryptMed on 6 neural network architectures across a wide range of non-linear activation functions over two benchmark and four real-world medical datasets. We comprehensively compare our system with prior art in terms of end-to-end service workload and prediction accuracy. Our empirical results demonstrate that CryptMed achieves up to respectively 413 ×, 19 ×, and 43 × bandwidth savings for MNIST, CIFAR-10, and medical applications compared with prior art. For the smooth activation based inference, the best choice of our proposed approximations preserve the precision of original functions, with less than 1.2% accuracy loss and could enhance the precision due to the newly introduced activation function family.

Topoisomerase 3β knockout mice show transcriptional and behavioural impairments associated with neurogenesis and synaptic plasticity

Topoisomerase 3β (Top3β) is the only dual-activity topoisomerase in animals that can change topology for both DNA and RNA, and facilitate transcription on DNA and translation on mRNAs. Top3β mutations have been linked to schizophrenia, autism, epilepsy, and cognitive impairment. Here we show that Top3β knockout mice exhibit behavioural phenotypes related to psychiatric disorders and cognitive impairment. The mice also display impairments in hippocampal neurogenesis and synaptic plasticity. Notably, the brains of the mutant mice exhibit impaired global neuronal activity-dependent transcription in response to fear conditioning stress, and the affected genes include many with known neuronal functions. Our data suggest that Top3β is essential for normal brain function, and that defective neuronal activity-dependent transcription may be a mechanism by which Top3β deletion causes cognitive impairment and psychiatric disorders.

Involvement of inflammatory gene expression pathways in depressed patients with hyperphagia

The pathophysiology of major depressive disorder (MDD) is highly heterogeneous. Previous evidence at the DNA level as well as on the serum protein level suggests that the role of inflammation in MDD pathology is stronger in patients with hyperphagia during an active episode. Which inflammatory pathways differ in MDD patients with hyperphagia inflammatory pathways in terms of gene expression is unknown. We analyzed whole-blood gene expression profiles of 881 current MDD cases and 331 controls from the Netherlands Study of Depression and Anxiety (NESDA). The MDD patients were stratified according to patients with hyperphagia (characterized by increased appetite and/or weight, N = 246) or hypophagia (characterized by decreased appetite and/or weight, N = 342). Using results of differential gene expression analysis between controls and the MDD subgroups, enrichment of curated inflammatory pathways was estimated. The majority of the pathways were significantly (FDR < 0.1) enriched in the expression profiles of MDD cases with hyperphagia, including top pathways related to factors responsible for the onset of inflammatory response ('caspase', 'GATA3', 'NFAT', and 'inflammasomes' pathways). Only two pathways ('adaptive immune system' and 'IL-8- and CXCR2-mediated signaling') were enriched in the MDD with hypophagia subgroup, these were also enriched in the total current MDD group and the group with hyperphagia. This confirms the importance of inflammation in MDD pathology of patients with hyperphagia, and suggests that distinguishing more uniform MDD phenotypes can help in finding their pathophysiological basis.

Human Ageing Genomic Resources: new and updated databases

In spite of a growing body of research and data, human ageing remains a poorly understood process. Over 10 years ago we developed the Human Ageing Genomic Resources (HAGR), a collection of databases and tools for studying the biology and genetics of ageing. Here, we present HAGR’s main functionalities, highlighting new additions and improvements. HAGR consists of six core databases: (i) the GenAge database of ageing-related genes, in turn composed of a dataset of >300 human ageing-related genes and a dataset with >2000 genes associated with ageing or longevity in model organisms; (ii) the AnAge database of animal ageing and longevity, featuring >4000 species; (iii) the GenDR database with >200 genes associated with the life-extending effects of dietary restriction; (iv) the LongevityMap database of human genetic association studies of longevity with >500 entries; (v) the DrugAge database with >400 ageing or longevity-associated drugs or compounds; (vi) the CellAge database with >200 genes associated with cell senescence. All our databases are manually curated by experts and regularly updated to ensure a high quality data. Cross-links across our databases and to external resources help researchers locate and integrate relevant information. HAGR is freely available online (http://genomics.senescence.info/).

ENTPRISE-X: Predicting disease-associated frameshift and nonsense mutations

To exploit the plethora of information provided by Next Generation Sequencing, the identification of the genetic mutations responsible for disease in general or cancer in particular, among the thousands of neutral germline or somatic variations is a crucial task. Genome-wide association studies for the detection of disease-associated genes or cancer drivers can only identify common variations or driver genes in a cohort of patients. Thus, they cannot discover unique disease-associated mutations or cancer driver genes on a personal basis. Moreover, even when there are such common variations, their significance is unknown. Here, we extend the machine learning based approach ENTPRISE developed for predicting the disease association of missense mutations to frameshift and nonsense mutations. The new approach, ENTPRISE-X, is shown to outperform the state-of-the-art methods VEST-indel and DDIG-in for predicting the disease association of germline frameshift mutations in terms of balanced measure Matthew’s correlation coefficient, MCC, with a MCC of 0.586 for ENTPRISE-X, versus 0.412 by VEST-indel and 0.321 by DDIG-in, respectively. Large scale testing on the ExAC dataset shows ENTPRISE-X has a much lower fraction of 16% of variations classified as disease causing, as compared to VEST-indel’s 26% and DDIG-in’s 65% of predictions as being disease-associated. A web server for ENTPRISE-X is freely available for academic users at http://cssb2.biology.gatech.edu/entprise-x.

Systematic analysis of the gerontome reveals links between aging and age-related diseases

In model organisms, over 2,000 genes have been shown to modulate aging, the collection of which we call the ‘gerontome’. Although some individual aging-related genes have been the subject of intense scrutiny, their analysis as a whole has been limited. In particular, the genetic interaction of aging and age-related pathologies remain a subject of debate. In this work, we perform a systematic analysis of the gerontome across species, including human aging-related genes. First, by classifying aging-related genes as pro- or anti-longevity, we define distinct pathways and genes that modulate aging in different ways. Our subsequent comparison of aging-related genes with age-related disease genes reveals species-specific effects with strong overlaps between aging and age-related diseases in mice, yet surprisingly few overlaps in lower model organisms. We discover that genetic links between aging and age-related diseases are due to a small fraction of aging-related genes which also tend to have a high network connectivity. Other insights from our systematic analysis include assessing how using datasets with genes more or less studied than average may result in biases, showing that age-related disease genes have faster molecular evolution rates and predicting new aging-related drugs based on drug-gene interaction data. Overall, this is the largest systems-level analysis of the genetics of aging to date and the first to discriminate anti- and pro-longevity genes, revealing new insights on aging-related genes as a whole and their interactions with age-related diseases.

Genetic and functional characterization of disease associations explains comorbidity

Understanding relationships between diseases, such as comorbidities, has important socio-economic implications, ranging from clinical study design to health care planning. Most studies characterize disease comorbidity using shared genetic origins, ignoring pathway-based commonalities between diseases. In this study, we define the disease pathways using an interactome-based extension of known disease-genes and introduce several measures of functional overlap. The analysis reveals 206 significant links among 94 diseases, giving rise to a highly clustered disease association network. We observe that around 95% of the links in the disease network, though not identified by genetic overlap, are discovered by functional overlap. This disease network portraits rheumatoid arthritis, asthma, atherosclerosis, pulmonary diseases and Crohn's disease as hubs and thus pointing to common inflammatory processes underlying disease pathophysiology. We identify several described associations such as the inverse comorbidity relationship between Alzheimer's disease and neoplasms. Furthermore, we investigate the disruptions in protein interactions by mapping mutations onto the domains involved in the interaction, suggesting hypotheses on the causal link between diseases. Finally, we provide several proof-of-principle examples in which we model the effect of the mutation and the change of the association strength, which could explain the observed comorbidity between diseases caused by the same genetic alterations.

A knowledge-based approach for predicting gene-disease associations

MOTIVATION

Recent advances of next-generation sequence technologies have made it possible to rapidly and inexpensively identify gene variations. Knowing the disease association of these gene variations is important for early intervention to treat deadly diseases and provide possible targets to cure these diseases. Genome-wide association studies (GWAS) have identified many individual genes associated with common diseases. To exploit the large amount of data obtained from GWAS studies and leverage our understanding of common as well as rare diseases, we have developed a knowledge-based approach to predict gene-disease associations. We first derive gene-gene mutual information by utilizing the cooccurrence of genes in known gene-disease association data. Subsequently, the mutual information is combined with known protein-protein interaction networks by a boosted tree regression method.

RESULTS

The method called Know-GENE is compared with the method of random walking on the heterogeneous network using the same input data. For a set of 960 diseases, using the same training data in testing in 3-fold cross-validation, the average recall rate within the top ranked 100 genes by Know-GENE is 65.0% compared with 37.9% by the state of the art random walking on heterogeneous network. This significant improvement is mostly due to the inclusion of knowledge-based mutual information.

AVAILABILITY AND IMPLEMENTATION

Predictions for genes associated with the 960 diseases are available at http://cssb2.biology.gatech.edu/knowgene

CONTACT

: skolnick@gatech.edu.

Induction of specific neuron types by overexpression of single transcription factors

Specific neuronal types derived from embryonic stem cells (ESCs) can facilitate mechanistic studies and potentially aid in regenerative medicine. Existing induction methods, however, mostly rely on the effects of the combined action of multiple added growth factors, which generally tend to result in mixed populations of neurons. Here, we report that overexpression of specific transcription factors (TFs) in ESCs can rather guide the differentiation of ESCs towards specific neuron lineages. Analysis of data on gene expression changes 2 d after induction of each of 185 TFs implicated candidate TFs for further ESC differentiation studies. Induction of 23 TFs (out of 49 TFs tested) for 6 d facilitated neural differentiation of ESCs as inferred from increased proportion of cells with neural progenitor marker PSA-NCAM. We identified early activation of the Notch signaling pathway as a common feature of most potent inducers of neural differentiation. The majority of neuron-like cells generated by induction of Ascl1, Smad7, Nr2f1, Dlx2, Dlx4, Nr2f2, Barhl2, and Lhx1 were GABA-positive and expressed other markers of GABAergic neurons. In the same way, we identified Lmx1a and Nr4a2 as inducers for neurons bearing dopaminergic markers and Isl1, Fezf2, and St18 for cholinergic motor neurons. A time-course experiment with induction of Ascl1 showed early upregulation of most neural-specific messenger RNA (mRNA) and microRNAs (miRNAs). Sets of Ascl1-induced mRNAs and miRNAs were enriched in Ascl1 targets. In further studies, enrichment of cells obtained with the induction of Ascl1, Smad7, and Nr2f1 using microbeads resulted in essentially pure population of neuron-like cells with expression profiles similar to neural tissues and expressed markers of GABAergic neurons. In summary, this study indicates that induction of transcription factors is a promising approach to generate cultures that show the transcription profiles characteristic of specific neural cell types.

Exercise-mimetic AICAR transiently benefits brain function

Exercise enhances learning and memory in animals and humans. The role of peripheral factors that may trigger the beneficial effects of running on brain function has been sparsely examined. In particular, it is unknown whether AMP-kinase (AMPK) activation in muscle can predict enhancement of brain plasticity. Here we compare the effects of running and administration of AMPK agonist 5-Aminoimidazole-4-carboxamide 1-β-D-ribofuranoside (AICAR, 500 mg/kg), for 3, 7 or 14 days in one-month-old male C57BL/6J mice, on muscle AMPK signaling. At the time-points where we observed equivalent running- and AICAR-induced muscle pAMPK levels (7 and 14 days), cell proliferation, synaptic plasticity and gene expression, as well as markers of oxidative stress and inflammation in the dentate gyrus (DG) of the hippocampus and lateral entorhinal cortex (LEC) were evaluated. At the 7-day time-point, both regimens increased new DG cell number and brain-derived neurotrophic factor (BDNF) protein levels. Furthermore, microarray analysis of DG and LEC tissue showed a remarkable overlap between running and AICAR in the regulation of neuronal, mitochondrial and metabolism related gene classes. Interestingly, while similar outcomes for both treatments were stable over time in muscle, in the brain an inversion occurred at fourteen days. The compound no longer increased DG cell proliferation or neurotrophin levels, and upregulated expression of apoptotic genes and inflammatory cytokine interleukin-1β. Thus, an exercise mimetic that produces changes in muscle consistent with those of exercise does not have the same sustainable positive effects on the brain, indicating that only running consistently benefits brain function.

Generation and gene expression profiling of 48 transcription-factor-inducible mouse embryonic stem cell lines

Mouse embryonic stem cells (ESCs) can differentiate into a wide range – and possibly all cell types in vitro, and thus provide an ideal platform to study systematically the action of transcription factors (TFs) in cell differentiation. Previously, we have generated and analyzed 137 TF-inducible mouse ESC lines. As an extension of this “NIA Mouse ESC Bank,” we generated and characterized 48 additional mouse ESC lines, in which single TFs in each line could be induced in a doxycycline-controllable manner. Together, with the previous ESC lines, the bank now comprises 185 TF-manipulable ESC lines (>10% of all mouse TFs). Global gene expression (transcriptome) profiling revealed that the induction of individual TFs in mouse ESCs for 48 hours shifts their transcriptomes toward specific differentiation fates (e.g., neural lineages by Myt1 Isl1, and St18; mesodermal lineages by Pitx1, Pitx2, Barhl2, and Lmx1a; white blood cells by Myb, Etv2, and Tbx6, and ovary by Pitx1, Pitx2, and Dmrtc2). These data also provide and lists of inferred target genes of each TF and possible functions of these TFs. The results demonstrate the utility of mouse ESC lines and their transcriptome data for understanding the mechanism of cell differentiation and the function of TFs.

Nur77-deficiency in bone marrow-derived macrophages modulates inflammatory responses, extracellular matrix homeostasis, phagocytosis and tolerance

Background

The nuclear orphan receptor Nur77 (NR4A1, TR3, or NGFI-B) has been shown to modulate the inflammatory response of macrophages. To further elucidate the role of Nur77 in macrophage physiology, we compared the transcriptome of bone marrow-derived macrophages (BMM) from wild-type (WT) and Nur77-knockout (KO) mice.

Results

In line with previous observations, SDF-1α (CXCL12) was among the most upregulated genes in Nur77-deficient BMM and we demonstrated that Nur77 binds directly to the SDF-1α promoter, resulting in inhibition of SDF-1α expression. The cytokine receptor CX3CR1 was strongly downregulated in Nur77-KO BMM, implying involvement of Nur77 in macrophage tolerance. Ingenuity pathway analyses (IPA) to identify canonical pathways regulation and gene set enrichment analyses (GSEA) revealed a potential role for Nur77 in extracellular matrix homeostasis. Nur77-deficiency increased the collagen content of macrophage extracellular matrix through enhanced expression of several collagen subtypes and diminished matrix metalloproteinase (MMP)-9 activity. IPA upstream regulator analyses discerned the small GTPase Rac1 as a novel regulator of Nur77-mediated gene expression. We identified an inhibitory feedback loop with increased Rac1 activity in Nur77-KO BMM, which may explain the augmented phagocytic activity of these cells. Finally, we predict multiple chronic inflammatory diseases to be influenced by macrophage Nur77 expression. GSEA and IPA associated Nur77 to osteoarthritis, chronic obstructive pulmonary disease, rheumatoid arthritis, psoriasis, and allergic airway inflammatory diseases.

Conclusions

Altogether these data identify Nur77 as a modulator of macrophage function and an interesting target to treat chronic inflammatory disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2469-9) contains supplementary material, which is available to authorized users.

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels

Background

A deeper understanding of differences and similarities in transcriptional regulation between species can uncover important information about gene functions and the role of genes in disease. Deciphering such patterns between mice and humans is especially important since mice play an essential role in biomedical research.

Results

Here, in order to characterize evolutionary changes between humans and mice, we compared gene co-expression maps to evaluate the conservation of co-expression. We show that the conservation of co-expression connectivity of homologous genes is negatively correlated with molecular evolution rates, as expected. Then we investigated evolutionary aspects of gene sets related to functions, tissues, pathways and diseases. Genes expressed in the testis, eye and skin, and those associated with regulation of transcription, olfaction, PI3K signalling, response to virus and bacteria were more divergent between mice and humans in terms of co-expression connectivity. Surprisingly, a deeper investigation of the PI3K signalling cascade revealed that its divergence is caused by the most crucial genes of this pathway, such as mTOR and AKT2. On the other hand, our analysis revealed that genes expressed in the brain and in the bone, and those associated with cell adhesion, cell cycle, DNA replication and DNA repair are most strongly conserved in terms of co-expression network connectivity as well as having a lower rate of duplication events. Genes involved in lipid metabolism and genes specific to blood showed a signature of increased co-expression connectivity in the mouse. In terms of diseases, co-expression connectivity of genes related to metabolic disorders is the most strongly conserved between mice and humans and tumor-related genes the most divergent.

Conclusions

This work contributes to discerning evolutionary patterns between mice and humans in terms of gene interactions. Conservation of co-expression is a powerful approach to identify gene targets and processes with potential similarity and divergence between mice and humans, which has implications for drug testing and other studies employing the mouse as a model organism.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-015-0534-7) contains supplementary material, which is available to authorized users.

Antiapoptotic activity of 30 kDa lipoproteins family from fat body tissue of silkworm, Bombyx mori

The family of 30 kDa lipoproteins (LP1-5) is abundant in silkworm pupa fat body (FB) and hemolymph. One of its members, the 29 kDa protein decreased in concentration from peripheral (PP) FB tissue but was sustained in perivisceral (PV) FB tissue at the time of apoptosis. This study investigated the correlation of the 30 kDa proteins with FB apoptosis. Two protein fractions were purified, a 29 and a 30/31 kDa protein fraction, and they were used to test for activity against actinomycin D-induced apoptosis in the FB tissues. Concentrations as little as 50 μg/mL of the 29 kDa protein fraction efficiently inhibited apoptosis. Less antiapoptotic activity was detected for the higher MW fraction; DNA fragmentation was observed in FB tissue treated with 50 μg/mL of the 30/31 kDa fraction. The viability of the cells in the 29 kDa protein-supplemented culture was 40% higher than in the 31 kDa protein-supplemented culture. However, the 30 kDa lipoproteins were not able to prevent scheduled FB degeneration during silkworm metamorphosis. Thus, it is hypothesized that the antiapoptotic 29 kDa protein needs to be proteolytically degraded by a regulatory mechanism to allow programmed cell death of FB tissue.

Estimating a Ranked List of Human Genetic Diseases by Associating Phenotype-Gene with Gene-Disease Bipartite Graphs

With vast amounts of medical knowledge available on the Internet, it is becoming increasingly practical to help doctors in clinical diagnostics by suggesting plausible diseases predicted by applying data and text mining technologies. Recently, Genome-Wide Association Studies ( GWAS ) have proved useful as a method for exploring phenotypic associations with diseases. However, since genetic diseases are difficult to diagnose because of their low prevalence, large number, and broad diversity of symptoms, genetic disease patients are often misdiagnosed or experience long diagnostic delays. In this article, we propose a method for ranking genetic diseases for a set of clinical phenotypes. In this regard, we associate a phenotype-gene bipartite graph ( PGBG ) with a gene-disease bipartite graph ( GDBG ) by producing a phenotype-disease bipartite graph ( PDBG ), and we estimate the candidate weights of diseases. In our approach, all paths from a phenotype to a disease are explored by considering causative genes to assign a weight based on path frequency, and the phenotype is linked to the disease in a new PDBG. We introduce the Bidirectionally induced Importance Weight ( BIW ) prediction method to PDBG for approximating the weights of the edges of diseases with phenotypes by considering link information from both sides of the bipartite graph. The performance of our system is compared to that of other known related systems by estimating Normalized Discounted Cumulative Gain ( NDCG ), Mean Average Precision ( MAP ), and Kendall’s tau metrics. Further experiments are conducted with well-known TF · IDF , BM25 , and Jenson-Shannon divergence as baselines. The result shows that our proposed method outperforms the known related tool Phenomizer in terms of NDCG@10, NDCG@20, MAP@10, and MAP@20; however, it performs worse than Phenomizer in terms of Kendall’s tau-b metric at the top-10 ranks. It also turns out that our proposed method has overall better performance than the baseline methods.

ExAtlas: An interactive online tool for meta-analysis of gene expression data

We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users' own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher's methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein-protein interaction) are pre-loaded and can be used for functional annotations.

Co-morditities of environmental diseases: A common cause

The global pandemic of non-vector borne environmental diseases may, in large part, be attributed to chronic exposures to ever increasing levels of exogenous lipophilic chemicals. These chemicals include persistent organic pollutants, semi-volatile compounds and low molecular weight hydrocarbons. Such chemicals facilitate the sequential absorption of otherwise not absorbed more toxic hydrophilic species that attack numerous body organs and systems, leading to environmental disease. Co-morbidities of non-communicable environmental diseases are alarmingly high, with as many as half of all individuals chronically ill with two or more diseases. Co-morbidity is to be anticipated, since all of the causative chemicals identified have independently been shown to trigger the individual diseases.

Insights into the miRNA regulations in human disease genes

BACKGROUND

MicroRNAs are a class of short non-coding RNAs derived from either cellular or viral transcripts that act post-transcriptionally to regulate mRNA stability and translation. In recent days, increasing numbers of miRNAs have been shown to be involved in the development and progression of a variety of diseases. We, therefore, intend to enumerate miRNA targets in several known disease classes to explore the degree of miRNA regulations on them which is unexplored till date.

RESULTS

Here, we noticed that miRNA hits in cancer genes are remarkably higher than other diseases in human. Our observation suggests that UTRs and the transcript length of cancer related genes have a significant contribution in higher susceptibility to miRNA regulation. Moreover, gene duplication, mRNA stability, AREScores and evolutionary rate were likely to have implications for more miRNA targeting on cancer genes. Consequently, the regression analysis have confirmed that the AREScores plays most important role in detecting miRNA targets on disease genes. Interestingly, we observed that epigenetic modifications like CpG methylation and histone modification are less effective than miRNA regulations in controlling the gene expression of cancer genes.

CONCLUSIONS

The intrinsic properties of cancer genes studied here, for higher miRNA targeting will enhance the knowledge on cancer gene regulation.

In silico prediction of physical protein interactions and characterization of interactome orphans

Protein-protein interactions (PPIs) are useful for understanding signaling cascades, predicting protein function, associating proteins with disease and fathoming drug mechanism of action. Currently, only ∼ 10% of human PPIs may be known, and about one-third of human proteins have no known interactions. We introduce FpClass, a data mining-based method for proteome-wide PPI prediction. At an estimated false discovery rate of 60%, we predicted 250,498 PPIs among 10,531 human proteins; 10,647 PPIs involved 1,089 proteins without known interactions. We experimentally tested 233 high- and medium-confidence predictions and validated 137 interactions, including seven novel putative interactors of the tumor suppressor p53. Compared to previous PPI prediction methods, FpClass achieved better agreement with experimentally detected PPIs. We provide an online database of annotated PPI predictions (http://ophid.utoronto.ca/fpclass/) and the prediction software (http://www.cs.utoronto.ca/~juris/data/fpclass/).

Complete dosage compensation and sex-biased gene expression in the moth Manduca sexta

Sex chromosome dosage compensation balances homogametic sex chromosome expression with autosomal expression in the heterogametic sex, leading to sex chromosome expression parity between the sexes. If compensation is incomplete, this can lead to expression imbalance and sex-biased gene expression. Recent work has uncovered an intriguing and variable pattern of dosage compensation across species that includes a lack of complete dosage compensation in ZW species compared with XY species. This has led to the hypothesis that ZW species do not require complete compensation or that complete compensation would negatively affect their fitness. To date, only one study, a study of the moth Bombyx mori, has discovered evidence for complete dosage compensation in a ZW species. We examined another moth species, Manduca sexta, using high-throughput sequencing to survey gene expression in the head tissue of males and females. We found dosage compensation to be complete in M. sexta with average expression between the Z chromosome in males and females being equal. When genes expressed at very low levels are removed by filtering, we found that average autosome expression was highly similar to average Z expression, suggesting that the majority of genes in M. sexta are completely dosage compensated. Further, this compensation was accompanied by sex-specific gene expression associated with important sexually dimorphic traits. We suggest that complete dosage compensation in ZW species might be more common than previously appreciated and linked to additional selective processes, such as sexual selection. More ZW and lepidopteran species should now be examined in a phylogenetic framework, to understand the evolution of dosage compensation.

Applications of Business Analytics in Healthcare

The American healthcare system is at a crossroads, and analytics, as an organizational skill, figures to play a pivotal role in its future. As more healthcare systems capture information electronically and as they begin to collect more novel forms of data, such as human DNA, how will we leverage these resources and use them to improve human health at a manageable cost? In this article, we argue that analytics will play a fundamental role in the transformation of the American healthcare system. However, there are numerous challenges to the application and use of analytics, namely the lack of data standards, barriers to the collection of high-quality data, and a shortage of qualified personnel to conduct such analyses. There are also multiple managerial issues, such as how to get end users of electronic data to employ it consistently for improving healthcare delivery, and how to manage the public reporting and sharing of data. In this article, we explore applications of analytics in healthcare, barriers and facilitators to its widespread adoption, and how analytics can help us achieve the goals of the modern healthcare system: high-quality, responsive, affordable, and efficient care.

Suberoylanilide hydroxamic acid (SAHA)-induced dynamics of a human histone deacetylase protein interaction network

Histone deacetylases (HDACs) are targets for cancer therapy. Suberoylanilide hydroxamic acid (SAHA) is an HDAC inhibitor approved by the U.S. Food and Drug Administration for the treatment of cutaneous T-cell lymphoma. To obtain a better mechanistic understanding of the Sin3/HDAC complex in cancer, we extended its protein-protein interaction network and identified a mutually exclusive pair within the complex. We then assessed the effects of SAHA on the disruption of the complex network through six homologous baits. SAHA perturbs multiple protein interactions and therefore compromises the composition of large parts of the Sin3/HDAC network. A comparison of the effect of SAHA treatment on gene expression in breast cancer cells to a knockdown of the ING2 subunit indicated that a portion of the anticancer effects of SAHA may be attributed to the disruption of ING2's association with the complex. Our dynamic protein interaction network resource provides novel insights into the molecular mechanism of SAHA action and demonstrates the potential for drugs to rewire networks.

Chromosome-centric Human Proteome Project (C-HPP): Chromosome 12

Following an official announcement of the Chromosome-centric Human Proteome Project (C-HPP), the Chromosome 12 (Ch12) Consortium has been established by five representative teams from five Asian countries including Thailand (Siriraj Hospital, Mahidol University), Singapore (National University of Singapore), Taiwan (Academia Sinica), Hong Kong (The Chinese University of Hong Kong), and India (Institute of Bioinformatics). We have worked closely together to extensively and systematically analyze all missing and known proteins encoded by Ch12 for their tissue/cellular/subcellular localizations. The target organs/tissues/cells include kidney, brain, gastrointestinal tissues, blood/immune cells, and stem cells. In the later phase, post-translational modifications and functional significance of Ch12-encoded proteins as well as their associations with human diseases (i.e., immune diseases, metabolic disorders, and cancers) will be defined. We have collaborated with other chromosome teams, Human Kidney and Urine Proteome Project (HKUPP), AOHUPO Membrane Proteomics Initiative, and other existing HUPO initiatives in the Biology/Disease-Based Human Proteome Project (B/D-HPP) to delineate functional roles and medical implications of Ch12-encoded proteins. The data set to be obtained from this multicountry consortium will be an important piece of the jigsaw puzzle to fulfill the missions and goals of the C-HPP and the global Human Proteome Project (HPP).

Systems genetics: a paradigm to improve discovery of candidate genes and mechanisms underlying complex traits

Understanding the control of any trait optimally requires the detection of causal genes, gene interaction, and mechanism of action to discover and model the biochemical pathways underlying the expressed phenotype. Functional genomics techniques, including RNA expression profiling via microarray and high-throughput DNA sequencing, allow for the precise genome localization of biological information. Powerful genetic approaches, including quantitative trait locus (QTL) and genome-wide association study mapping, link phenotype with genome positions, yet genetics is less precise in localizing the relevant mechanistic information encoded in DNA. The coupling of salient functional genomic signals with genetically mapped positions is an appealing approach to discover meaningful gene-phenotype relationships. Techniques used to define this genetic-genomic convergence comprise the field of systems genetics. This short review will address an application of systems genetics where RNA profiles are associated with genetically mapped genome positions of individual genes (eQTL mapping) or as gene sets (co-expression network modules). Both approaches can be applied for knowledge independent selection of candidate genes (and possible control mechanisms) underlying complex traits where multiple, likely unlinked, genomic regions might control specific complex traits.

The integrin adhesome: from genes and proteins to human disease

The adhesive interactions of cells with their environment through the integrin family of transmembrane receptors have key roles in regulating multiple aspects of cellular physiology, including cell proliferation, viability, differentiation and migration. Consequently, failure to establish functional cell adhesions, and thus the assembly of associated cytoplasmic scaffolding and signalling networks, can have severe pathological effects. The roles of specific constituents of integrin-mediated adhesions, which are collectively known as the 'integrin adhesome', in diverse pathological states are becoming clear. Indeed, the prominence of mutations in specific adhesome molecules in various human diseases is now appreciated, and experimental as well as in silico approaches provide insights into the molecular mechanisms underlying these pathological conditions.

Evolutionary conservation in genes underlying human psychiatric disorders

Many psychiatric diseases observed in humans have tenuous or absent analogs in other species. Most notable among these are schizophrenia and autism. One hypothesis has posited that these diseases have arisen as a consequence of human brain evolution, for example, that the same processes that led to advances in cognition, language, and executive function also resulted in novel diseases in humans when dysfunctional. Here, the molecular evolution of the protein-coding regions of genes associated with these and other psychiatric disorders are compared among species. Genes associated with psychiatric disorders are drawn from the literature and orthologous sequences are collected from eleven primate species (human, chimpanzee, bonobo, gorilla, orangutan, gibbon, macaque, baboon, marmoset, squirrel monkey, and galago) and 34 non-primate mammalian species. Evolutionary parameters, including d_N/d_S, are calculated for each gene and compared between disease classes and among species, focusing on humans and primates compared to other mammals, and on large-brained taxa (cetaceans, rhinoceros, walrus, bear, and elephant) compared to their small-brained sister species. Evidence of differential selection in humans to the exclusion of non-human primates was absent, however elevated d_N/d_S was detected in catarrhines as a whole, as well as in cetaceans, possibly as part of a more general trend. Although this may suggest that protein changes associated with schizophrenia and autism are not a cost of the higher brain function found in humans, it may also point to insufficiencies in the study of these diseases including incomplete or inaccurate gene association lists and/or a greater role of regulatory changes or copy number variation. Through this work a better understanding of the molecular evolution of the human brain, the pathophysiology of disease, and the genetic basis of human psychiatric disease is gained.

Network-based study reveals potential infection pathways of hepatitis-C leading to various diseases

Protein-protein interaction network-based study of viral pathogenesis has been gaining popularity among computational biologists in recent days. In the present study we attempt to investigate the possible pathways of hepatitis-C virus (HCV) infection by integrating the HCV-human interaction network, human protein interactome and human genetic disease association network. We have proposed quasi-biclique and quasi-clique mining algorithms to integrate these three networks to identify infection gateway host proteins and possible pathways of HCV pathogenesis leading to various diseases. Integrated study of three networks, namely HCV-human interaction network, human protein interaction network, and human proteins-disease association network reveals potential pathways of infection by the HCV that lead to various diseases including cancers. The gateway proteins have been found to be biologically coherent and have high degrees in human interactome compared to the other virus-targeted proteins. The analyses done in this study provide possible targets for more effective anti-hepatitis-C therapeutic involvement.

ANIA: ANnotation and Integrated Analysis of the 14-3-3 interactome

The dimeric 14-3-3 proteins dock onto pairs of phosphorylated Ser and Thr residues on hundreds of proteins, and thereby regulate many events in mammalian cells. To facilitate global analyses of these interactions, we developed a web resource named ANIA: ANnotation and Integrated Analysis of the 14-3-3 interactome, which integrates multiple data sets on 14-3-3-binding phosphoproteins. ANIA also pinpoints candidate 14-3-3-binding phosphosites using predictor algorithms, assisted by our recent discovery that the human 14-3-3-interactome is highly enriched in 2R-ohnologues. 2R-ohnologues are proteins in families of two to four, generated by two rounds of whole genome duplication at the origin of the vertebrate animals. ANIA identifies candidate ‘lynchpins’, which are 14-3-3-binding phosphosites that are conserved across members of a given 2R-ohnologue protein family. Other features of ANIA include a link to the catalogue of somatic mutations in cancer database to find cancer polymorphisms that map to 14-3-3-binding phosphosites, which would be expected to interfere with 14-3-3 interactions. We used ANIA to map known and candidate 14-3-3-binding enzymes within the 2R-ohnologue complement of the human kinome. Our projections indicate that 14-3-3s dock onto many more human kinases than has been realized. Guided by ANIA, PAK4, 6 and 7 (p21-activated kinases 4, 6 and 7) were experimentally validated as a 2R-ohnologue family of 14-3-3-binding phosphoproteins. PAK4 binding to 14-3-3 is stimulated by phorbol ester, and involves the ‘lynchpin’ site phosphoSer99 and a major contribution from Ser181. In contrast, PAK6 and PAK7 display strong phorbol ester-independent binding to 14-3-3, with Ser113 critical for the interaction with PAK6. These data point to differential 14-3-3 regulation of PAKs in control of cell morphology.

Database URL: https://ania-1433.lifesci.dundee.ac.uk/prediction/webserver/index.py

AMPK agonist AICAR improves cognition and motor coordination in young and aged mice

Normal aging can result in a decline of memory and muscle function. Exercise may prevent or delay these changes. However, aging-associated frailty can preclude physical activity. In young sedentary animals, pharmacological activation of AMP-activated protein kinase (AMPK), a transcriptional regulator important for muscle physiology, enhanced spatial memory function, and endurance. In the present study we investigated effects of AMPK agonist 5-aminoimidazole-4-carboxamide riboside (AICAR) on memory and motor function in young (5- to 7-wk-old) and aged (23-mo-old) female C57Bl/6 mice, and in young (4- to 6-wk-old) transgenic mice with muscle-specific mutated AMPK α2-subunit (AMPK-DN). Mice were injected with AICAR (500 mg/kg) for 3–14 d. Two weeks thereafter animals were tested in the Morris water maze, rotarod, and open field. Improved water maze performance and motor function were observed, albeit at longer duration of administration, in aged (14-d AICAR) than in young (3-d AICAR) mice. In the AMPK-DN mice, the compound did not enhance behavior, providing support for a muscle-mediated mechanism. In addition, microarray analysis of muscle and hippocampal tissue derived from aged mice treated with AICAR revealed changes in gene expression in both tissues, which correlated with behavioral effects in a dose-dependent manner. Pronounced up-regulation of mitochondrial genes in muscle was observed. In the hippocampus, genes relevant to neuronal development and plasticity were enriched. Altogether, endurance-related factors may mediate both muscle and brain health in aging, and could play a role in new therapeutic interventions.

Mining emerging biomedical literature for understanding disease associations in drug discovery

Systematically evaluating the exponentially growing body of scientific literature has become a critical task that every drug discovery organization must engage in in order to understand emerging trends for scientific investment and strategy development. Developing trends analysis uses the number of publications within a 3-year window to determine concepts derived from well-established disease and gene ontologies to aid in recognizing and predicting emerging areas of scientific discoveries relevant to that space. In this chapter, we describe such a method and use obesity and psoriasis as use-case examples by analyzing the frequency of disease-related MeSH terms in PubMed abstracts over time. We share how our system can be used to predict emerging trends at a relatively early stage and we analyze the literature-identified genes for genetic associations, druggability, and biological pathways to explore any potential biological connections between the two diseases that could be utilized for drug discovery.

Circ2Traits: a comprehensive database for circular RNA potentially associated with disease and traits

Circular RNAs are new players in regulation of post transcriptional gene expression. Animal genomes express many circular RNAs from diverse genomic locations. A recent study has validated a fairly large number of circular RNAs in human, mouse, and nematode. Circular RNAs play a crucial role in fine tuning the level of miRNA mediated regulation of gene expression by sequestering the miRNAs. Their interaction with disease associated miRNAs indicates that circular RNAs are important for disease regulation. In this paper we studied the potential association of circular RNAs (circRNA) with human diseases in two different ways. Firstly, the interactions of circRNAs with disease associated miRNAs were identified, following which the likelihood of a circRNA being associated with a disease was calculated. For the miRNAs associated with individual diseases, we constructed a network of predicted interactions between the miRNAs and protein coding, long non-coding and circular RNA genes. We carried out gene ontology (GO) enrichment analysis on the set of protein coding genes in the miRNA- circRNA interactome of individual diseases to check the enrichment of genes associated with particular biological processes. Secondly, disease associated SNPs were mapped on circRNA loci, and Argonaute (Ago) interaction sites on circular RNAs were identified. We compiled a database of disease-circRNA association in Circ2Traits (http://gyanxet-beta.com/circdb/), the first comprehensive knowledgebase of potential association of circular RNAs with diseases in human.

Development of a versatile enrichment analysis tool reveals associations between the maternal brain and mental health disorders, including autism

Background

A recent study of lateral septum (LS) suggested a large number of autism-related genes with altered expression in the postpartum state. However, formally testing the findings for enrichment of autism-associated genes proved to be problematic with existing software. Many gene-disease association databases have been curated which are not currently incorporated in popular, full-featured enrichment tools, and the use of custom gene lists in these programs can be difficult to perform and interpret. As a simple alternative, we have developed the Modular Single-set Enrichment Test (MSET), a minimal tool that enables one to easily evaluate expression data for enrichment of any conceivable gene list of interest.

Results

The MSET approach was validated by testing several publicly available expression data sets for expected enrichment in areas of autism, attention deficit hyperactivity disorder (ADHD), and arthritis. Using nine independent, unique autism gene lists extracted from association databases and two recent publications, a striking consensus of enrichment was detected within gene expression changes in LS of postpartum mice. A network of 160 autism-related genes was identified, representing developmental processes such as synaptic plasticity, neuronal morphogenesis, and differentiation. Additionally, maternal LS displayed enrichment for genes associated with bipolar disorder, schizophrenia, ADHD, and depression.

Conclusions

The transition to motherhood includes the most fundamental social bonding event in mammals and features naturally occurring changes in sociability. Some individuals with autism, schizophrenia, or other mental health disorders exhibit impaired social traits. Genes involved in these deficits may also contribute to elevated sociability in the maternal brain. To date, this is the first study to show a significant, quantitative link between the maternal brain and mental health disorders using large scale gene expression data. Thus, the postpartum brain may provide a novel and promising platform for understanding the complex genetics of improved sociability that may have direct relevance for multiple psychiatric illnesses. This study also provides an important new tool that fills a critical analysis gap and makes evaluation of enrichment using any database of interest possible with an emphasis on ease of use and methodological transparency.

HMPAS: Human Membrane Protein Analysis System

Background

Membrane proteins perform essential roles in diverse cellular functions and are regarded as major pharmaceutical targets. The significance of membrane proteins has led to the developing dozens of resources related with membrane proteins. However, most of these resources are built for specific well-known membrane protein groups, making it difficult to find common and specific features of various membrane protein groups.

Methods

We collected human membrane proteins from the dispersed resources and predicted novel membrane protein candidates by using ortholog information and our membrane protein classifiers. The membrane proteins were classified according to the type of interaction with the membrane, subcellular localization, and molecular function. We also made new feature dataset to characterize the membrane proteins in various aspects including membrane protein topology, domain, biological process, disease, and drug. Moreover, protein structure and ICD-10-CM based integrated disease and drug information was newly included. To analyze the comprehensive information of membrane proteins, we implemented analysis tools to identify novel sequence and functional features of the classified membrane protein groups and to extract features from protein sequences.

Results

We constructed HMPAS with 28,509 collected known membrane proteins and 8,076 newly predicted candidates. This system provides integrated information of human membrane proteins individually and in groups organized by 45 subcellular locations and 1,401 molecular functions. As a case study, we identified associations between the membrane proteins and diseases and present that membrane proteins are promising targets for diseases related with nervous system and circulatory system. A web-based interface of this system was constructed to facilitate researchers not only to retrieve organized information of individual proteins but also to use the tools to analyze the membrane proteins.

Conclusions

HMPAS provides comprehensive information about human membrane proteins including specific features of certain membrane protein groups. In this system, user can acquire the information of individual proteins and specified groups focused on their conserved sequence features, involved cellular processes, and diseases. HMPAS may contribute as a valuable resource for the inference of novel cellular mechanisms and pharmaceutical targets associated with the human membrane proteins. HMPAS is freely available at http://fcode.kaist.ac.kr/hmpas.

Social environmental effects on gene regulation

Social environmental conditions, particularly the experience of social adversity, have long been connected with health and mortality in humans and other social mammals. Efforts to identify the physiological basis for these effects have historically focused on their neurological, endocrinological, and immunological consequences. Recently, this search has been extended to understanding the role of gene regulation in sensing, mediating, and determining susceptibility to social environmental variation. Studies in laboratory rodents, captive primates, and human populations have revealed correlations between social conditions and the regulation of a large number of genes, some of which are likely causal. Gene expression responses to the social environment are, in turn, mediated by a set of underlying regulatory mechanisms, of which epigenetic marks are the best studied to date. Importantly, a number of genes involved in the response to the social environment are also associated with susceptibility to other external stressors, as well as certain diseases. Hence, gene regulatory studies are a promising avenue for understanding, and potentially developing strategies to address, the effects of social adversity on health.

Genome-wide modeling of complex phenotypes in Caenorhabditis elegans and Drosophila melanogaster

Background

The genetic and molecular basis for many intermediate and end stage phenotypes in model systems such as C. elegans and D. melanogaster has long been known to involve pleiotropic effects and complex multigenic interactions. Gene sets are groups of genes that contribute to multiple biological or molecular phenomena. They have been used in the analysis of large molecular datasets such as microarray data, Next Generation sequencing, and other genomic datasets to reveal pleiotropic and multigenic contributions to phenotypic outcomes. Many model systems lack species specific organized phenotype based gene sets to enable high throughput analysis of large molecular datasets.

Results and discussion

Here, we describe two novel collections of gene sets in C. elegans and D. melanogaster that are based exclusively on genetically determined phenotypes and use a controlled phenotypic ontology. We use these collections to build genome-wide models of thousands of defined phenotypes in both model species. In addition, we demonstrate the utility of these gene sets in systems analysis and in analysis of gene expression-based molecular datasets and show how they are useful in analysis of genomic datasets connecting multigenic gene inputs to complex phenotypes.

Conclusions

Phenotypic based gene sets in both C. elegans and D. melanogaster are developed, characterized, and shown to be useful in the analysis of large scale species-specific genomic datasets. These phenotypic gene set collections will contribute to the understanding of complex phenotypic outcomes in these model systems.

A model-based analysis of GC-biased gene conversion in the human and chimpanzee genomes

GC-biased gene conversion (gBGC) is a recombination-associated process that favors the fixation of G/C alleles over A/T alleles. In mammals, gBGC is hypothesized to contribute to variation in GC content, rapidly evolving sequences, and the fixation of deleterious mutations, but its prevalence and general functional consequences remain poorly understood. gBGC is difficult to incorporate into models of molecular evolution and so far has primarily been studied using summary statistics from genomic comparisons. Here, we introduce a new probabilistic model that captures the joint effects of natural selection and gBGC on nucleotide substitution patterns, while allowing for correlations along the genome in these effects. We implemented our model in a computer program, called phastBias, that can accurately detect gBGC tracts about 1 kilobase or longer in simulated sequence alignments. When applied to real primate genome sequences, phastBias predicts gBGC tracts that cover roughly 0.3% of the human and chimpanzee genomes and account for 1.2% of human-chimpanzee nucleotide differences. These tracts fall in clusters, particularly in subtelomeric regions; they are enriched for recombination hotspots and fast-evolving sequences; and they display an ongoing fixation preference for G and C alleles. They are also significantly enriched for disease-associated polymorphisms, suggesting that they contribute to the fixation of deleterious alleles. The gBGC tracts provide a unique window into historical recombination processes along the human and chimpanzee lineages. They supply additional evidence of long-term conservation of megabase-scale recombination rates accompanied by rapid turnover of hotspots. Together, these findings shed new light on the evolutionary, functional, and disease implications of gBGC. The phastBias program and our predicted tracts are freely available.

Interpreting patterns of DNA sequence variation in the genomes of closely related species is critically important for understanding the causes and functional effects of nucleotide substitutions. Classical models describe patterns of substitution in terms of the fundamental forces of mutation, recombination, neutral drift, and natural selection. However, an entirely separate force, called GC-biased gene conversion (gBGC), also appears to have an important influence on substitution patterns in many species. gBGC is a recombination-associated evolutionary process that favors the fixation of strong (G/C) over weak (A/T) alleles. In mammals, gBGC is thought to promote variation in GC content, rapidly evolving sequences, and the fixation of deleterious mutations. However, its genome-wide influence remains poorly understood, in part because, it is difficult to incorporate gBGC into statistical models of evolution. In this paper, we describe a new evolutionary model that jointly describes the effects of selection and gBGC and apply it to the human and chimpanzee genomes. Our genome-wide predictions of gBGC tracts indicate that gBGC has been an important force in recent human evolution. Our publicly available computer program, called phastBias, and our genome-wide predictions will enable other researchers to consider gBGC in their analyses.

Experimental characterization of the human non-sequence-specific nucleic acid interactome

Background

The interactions between proteins and nucleic acids have a fundamental function in many biological processes, including gene transcription, RNA homeostasis, protein translation and pathogen sensing for innate immunity. While our knowledge of the ensemble of proteins that bind individual mRNAs in mammalian cells has been greatly augmented by recent surveys, no systematic study on the non-sequence-specific engagement of native human proteins with various types of nucleic acids has been reported.

Results

We designed an experimental approach to achieve broad coverage of the non-sequence-specific RNA and DNA binding space, including methylated cytosine, and tested for interaction potential with the human proteome. We used 25 rationally designed nucleic acid probes in an affinity purification mass spectrometry and bioinformatics workflow to identify proteins from whole cell extracts of three different human cell lines. The proteins were profiled for their binding preferences to the different general types of nucleic acids. The study identified 746 high-confidence direct binders, 139 of which were novel and 237 devoid of previous experimental evidence. We could assign specific affinities for sub-types of nucleic acid probes to 219 distinct proteins and individual domains. The evolutionarily conserved protein YB-1, previously associated with cancer and drug resistance, was shown to bind methylated cytosine preferentially, potentially conferring upon YB-1 an epigenetics-related function.

Conclusions

The dataset described here represents a rich resource of experimentally determined nucleic acid-binding proteins, and our methodology has great potential for further exploration of the interface between the protein and nucleic acid realms.

Evolution of signal multiplexing by 14-3-3-binding 2R-ohnologue protein families in the vertebrates

14-3-3 proteins regulate cellular responses to stimuli by docking onto pairs of phosphorylated residues on target proteins. The present study shows that the human 14-3-3-binding phosphoproteome is highly enriched in 2R-ohnologues, which are proteins in families of two to four members that were generated by two rounds of whole genome duplication at the origin of the vertebrates. We identify 2R-ohnologue families whose members share a ‘lynchpin’, defined as a 14-3-3-binding phosphosite that is conserved across members of a given family, and aligns with a Ser/Thr residue in pro-orthologues from the invertebrate chordates. For example, the human receptor expression enhancing protein (REEP) 1–4 family has the commonest type of lynchpin motif in current datasets, with a phosphorylatable serine in the –2 position relative to the 14-3-3-binding phosphosite. In contrast, the second 14-3-3-binding sites of REEPs 1–4 differ and are phosphorylated by different kinases, and hence the REEPs display different affinities for 14-3-3 dimers. We suggest a conceptual model for intracellular regulation involving protein families whose evolution into signal multiplexing systems was facilitated by 14-3-3 dimer binding to lynchpins, which gave freedom for other regulatory sites to evolve. While increased signalling complexity was needed for vertebrate life, these systems also generate vulnerability to genetic disorders.

Multiple samples aCGH analysis for rare CNVs detection

Background

DNA copy number variations (CNV) constitute an important source of genetic variability. The standard method used for CNV detection is array comparative genomic hybridization (aCGH).

Results

We propose a novel multiple sample aCGH analysis methodology aiming in rare CNVs detection. In contrast to the majority of previous approaches, which deal with cancer datasets, we focus on constitutional genomic abnormalities identified in a diverse spectrum of diseases in human. Our method is tested on exon targeted aCGH array of 366 patients affected with developmental delay/intellectual disability, epilepsy, or autism. The proposed algorithms can be applied as a post–processing filtering to any given segmentation method.

Conclusions

Thanks to the additional information obtained from multiple samples, we could efficiently detect significant segments corresponding to rare CNVs responsible for pathogenic changes. The robust statistical framework applied in our method enables to eliminate the influence of widespread technical artifact termed ‘waves’.

Integration of genome-wide approaches identifies lncRNAs of adult neural stem cells and their progeny in vivo

Long noncoding RNAs (lncRNAs) have been described in cell lines and various whole tissues, but lncRNA analysis of development in vivo is limited. Here, we comprehensively analyze lncRNA expression for the adult mouse subventricular zone neural stem cell lineage. We utilize complementary genome-wide techniques including RNA-seq, RNA CaptureSeq, and ChIP-seq to associate specific lncRNAs with neural cell types, developmental processes, and human disease states. By integrating data from chromatin state maps, custom microarrays, and FACS purification of the subventricular zone lineage, we stringently identify lncRNAs with potential roles in adult neurogenesis. shRNA-mediated knockdown of two such lncRNAs, Six3os and Dlx1as, indicate roles for lncRNAs in the glial-neuronal lineage specification of multipotent adult stem cells. Our data and workflow thus provide a uniquely coherent in vivo lncRNA analysis and form the foundation of a user-friendly online resource for the study of lncRNAs in development and disease.

Integration of genome-wide approaches identifies lncRNAs of adult neural stem cells and their progeny in vivo

Long noncoding RNAs (lncRNAs) have been described in cell lines and various whole tissues, but lncRNA analysis of development in vivo is limited. Here, we comprehensively analyze lncRNA expression for the adult mouse subventricular zone neural stem cell lineage. We utilize complementary genome-wide techniques including RNA-seq, RNA CaptureSeq, and ChIP-seq to associate specific lncRNAs with neural cell types, developmental processes, and human disease states. By integrating data from chromatin state maps, custom microarrays, and FACS purification of the subventricular zone lineage, we stringently identify lncRNAs with potential roles in adult neurogenesis. shRNA-mediated knockdown of two such lncRNAs, Six3os and Dlx1as, indicate roles for lncRNAs in the glial-neuronal lineage specification of multipotent adult stem cells. Our data and workflow thus provide a uniquely coherent in vivo lncRNA analysis and form the foundation of a user-friendly online resource for the study of lncRNAs in development and disease.