Exploring the human genome with functional maps

AP3B1 facilitates PDIA3/ERP57 function to regulate rabies virus glycoprotein selective degradation and viral entry

Rabies virus causes an estimated 59,000 annual fatalities worldwide and promising therapeutic treatments are necessary to develop. In this study, affinity tag-purification mass spectrometry was employed to delineate RABV glycoprotein and host protein interactions, and PDIA3/ERP57 was identified as a potential inhibitor of RABV infection. PDIA3 restricted RABV infection with follow mechanisms: PDIA3 mediated the degradation of RABV G protein by targeting lysine 332 via the selective macroautophagy/autophagy pathway; The PDIA3 interactor, AP3B1 (adaptor related protein complex 3 subunit beta 1) was indispensable in PDIA3-triggered selective degradation of the G protein; Furthermore, PDIA3 competitively bound with NCAM1/NCAM (neural cell adhesion molecule 1) to block RABV G, hindering viral entry into host cells. PDIA3 190-199 aa residues bound to the RABV G protein were necessary and sufficient to defend against RABV. These results demonstrated the therapeutic potential of biologics that target PDIA3 or utilize PDIA3 190-199 aa peptide to treat clinical rabies.Abbreviation: aa: amino acids; ANXA2: annexin A2; AP-MS: affinity tag purification-mass spectrometry; AP3B1: adaptor related protein complex 3 subunit beta 1; ATP6V1A: ATPase H+ transporting V1 subunit A; ATP6V1H: ATPase H+ transporting V1 subunit H; BafA1: bafilomycin A1; CHX: cycloheximide; co-IP: co-immunoprecipitation; DDX17: DEAD-box helicase 17; DmERp60: drosophila melanogaster endoplasmic reticulum p60; EBOV: Zaire ebolavirus virus; EV: empty vector; GANAB: glucosidase II alpha subunit; G protein: glycoprotein; GRM2/mGluR2: glutamate metabotropic receptor 2; HsPDIA3: homo sapiens protein disulfide isomerase family A member 3; IAV: influenza virus; ILF2: interleukin enhancer binding factor 2; KO: knockout; MAGT1: magnesium transporter 1; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MmPDIA3: mus musculus protein disulfide isomerase associated 3; NCAM1/NCAM: neural cell adhesion molecule 1; NGFR/p75NTR: nerve growth factor receptor; NGLY1: N-glycanase 1; OTUD4: OTU deubiquitinase 4; PDI: protein disulfide isomerase; PPIs: protein-protein interactions; RABV: rabies virus; RUVBL2: RuvB like AAA ATPase 2; SCAMP3: secretory carrier membrane protein 3; ScPdi1: Saccharomyces cerevisiae s288c protein disulfide isomerase 1; SLC25A6: solute carrier family 25 member 6; SQSTM1/p62: sequestosome 1; VSV: vesicular stomatitis virus.

Noncanonical usage of stop codons in ciliates expands proteins with structurally flexible Q-rich motifs

Serine(S)/threonine(T)-glutamine(Q) cluster domains (SCDs), polyglutamine (polyQ) tracts and polyglutamine/asparagine (polyQ/N) tracts are Q-rich motifs found in many proteins. SCDs often are intrinsically disordered regions that mediate protein phosphorylation and protein-protein interactions. PolyQ and polyQ/N tracts are structurally flexible sequences that trigger protein aggregation. We report that due to their high percentages of STQ or STQN amino acid content, four SCDs and three prion-causing Q/N-rich motifs of yeast proteins possess autonomous protein expression-enhancing activities. Since these Q-rich motifs can endow proteins with structural and functional plasticity, we suggest that they represent useful toolkits for evolutionary novelty. Comparative Gene Ontology (GO) analyses of the near-complete proteomes of 26 representative model eukaryotes reveal that Q-rich motifs prevail in proteins involved in specialized biological processes, including Saccharomyces cerevisiae RNA-mediated transposition and pseudohyphal growth, Candida albicans filamentous growth, ciliate peptidyl-glutamic acid modification and microtubule-based movement, Tetrahymena thermophila xylan catabolism and meiosis, Dictyostelium discoideum development and sexual cycles, Plasmodium falciparum infection, and the nervous systems of Drosophila melanogaster, Mus musculus and Homo sapiens. We also show that Q-rich-motif proteins are expanded massively in 10 ciliates with reassigned TAAQ and TAGQ codons. Notably, the usage frequency of CAGQ is much lower in ciliates with reassigned TAAQ and TAGQ codons than in organisms with expanded and unstable Q runs (e.g. D. melanogaster and H. sapiens), indicating that the use of noncanonical stop codons in ciliates may have coevolved with codon usage biases to avoid triplet repeat disorders mediated by CAG/GTC replication slippage.

Highly Sensitive and Specific Responses of Oyster Hemocytes to Copper Exposure: Single-Cell Transcriptomic Analysis of Different Cell Populations

Oyster hemocytes are the primary vehicles transporting and detoxifying metals and are regarded as important cells for the occurrence of colored oysters due to copper (Cu) contamination. However, its heterogeneous responses under Cu exposure have not been studied. Single-cell transcriptome profiling (scRNA-seq) provides high-resolution visual insights into tissue dynamics and environmental responses. Here, we used scRNA-seq to study the responses of different cell populations of hemocytes under Cu exposure in an estuarine oyster Crassostrea hongkongensis. The 1900 population-specific Cu-responsive genes were identified in 12 clusters of hemocytes, which provided a more sensitive technique for examining Cu exposure. The granulocyte, semigranulocyte, and hyalinocyte had specific responses, while the granulocyte was the most important responsive cell type and displayed heterogeneity responses of its two subtypes. In one subtype, Cu was transported with metal transporters and chelated with Cu chaperons in the cytoplasm. Excess Cu disturbed oxidative phosphorylation and induced reactive oxygen species production. However, in the other subtype, endocytosis was mainly responsible for Cu internalization, which was sequestered in membrane-bound granules. Collectively, our results provided the first mRNA expression profile of hemocytes in oysters and revealed the heterogeneity responses under Cu exposure.

Time to infer miRNA sponge modules

Inferring competing endogenous RNA (ceRNA) or microRNA (miRNA) sponge modules is a challenging and meaningful task for revealing ceRNA regulation mechanism at the module level. Modules in this context refer to groups of miRNA sponges which have mutual competitions and act as functional units for achieving biological processes. The recent development of computational methods based on heterogeneous data provides a novel way to discern the competitive effects of miRNA sponges on human complex diseases. This article aims to provide a comprehensive perspective of miRNA sponge module discovery methods. We first review the publicly available databases of cancer-related miRNA sponges, as the miRNA sponges involved in human cancers contribute to the discovery of cancer-associated modules. Then we review the existing computational methods for inferring miRNA sponge modules. Furthermore, we conduct an assessment on the performance of the module discovery methods with the pan-cancer dataset, and the comparison study indicates that it is useful to infer biologically meaningful miRNA sponge modules by directly mapping heterogeneous data to the competitive modules. Finally, we discuss the future directions and associated challenges in developing in silico methods to infer miRNA sponge modules. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

Decoding disease: from genomes to networks to phenotypes

Interpreting the effects of genetic variants is key to understanding individual susceptibility to disease and designing personalized therapeutic approaches. Modern experimental technologies are enabling the generation of massive compendia of human genome sequence data and associated molecular and phenotypic traits, together with genome-scale expression, epigenomics and other functional genomic data. Integrative computational models can leverage these data to understand variant impact, elucidate the effect of dysregulated genes on biological pathways in specific disease and tissue contexts, and interpret disease risk beyond what is feasible with experiments alone. In this Review, we discuss recent developments in machine learning algorithms for genome interpretation and for integrative molecular-level modelling of cells, tissues and organs relevant to disease. More specifically, we highlight existing methods and key challenges and opportunities in identifying specific disease-causing genetic variants and linking them to molecular pathways and, ultimately, to disease phenotypes.

Comparing Host Module Activation Patterns and Temporal Dynamics in Infection by Influenza H1N1 Viruses

Influenza is a serious global health threat that shows varying pathogenicity among different virus strains. Understanding similarities and differences among activated functional pathways in the host responses can help elucidate therapeutic targets responsible for pathogenesis. To compare the types and timing of functional modules activated in host cells by four influenza viruses of varying pathogenicity, we developed a new DYNAmic MOdule (DYNAMO) method that addresses the need to compare functional module utilization over time. This integrative approach overlays whole genome time series expression data onto an immune-specific functional network, and extracts conserved modules exhibiting either different temporal patterns or overall transcriptional activity. We identified a common core response to influenza virus infection that is temporally shifted for different viruses. We also identified differentially regulated functional modules that reveal unique elements of responses to different virus strains. Our work highlights the usefulness of combining time series gene expression data with a functional interaction map to capture temporal dynamics of the same cellular pathways under different conditions. Our results help elucidate conservation of the immune response both globally and at a granular level, and provide mechanistic insight into the differences in the host response to infection by influenza strains of varying pathogenicity.

GEOlimma: differential expression analysis and feature selection using pre-existing microarray data

Background

Differential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.

Results

In this study, we propose a novel differential expression and feature selection method—GEOlimma—which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset.

Conclusions

Our results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets.

Responsible, practical genomic data sharing that accelerates research

Data sharing anchors reproducible science, but expectations and best practices are often nebulous. Communities of funders, researchers and publishers continue to grapple with what should be required or encouraged. To illuminate the rationales for sharing data, the technical challenges and the social and cultural challenges, we consider the stakeholders in the scientific enterprise. In biomedical research, participants are key among those stakeholders. Ethical sharing requires considering both the value of research efforts and the privacy costs for participants. We discuss current best practices for various types of genomic data, as well as opportunities to promote ethical data sharing that accelerates science by aligning incentives.

Transcriptomic Analysis of circRNAs and mRNAs Reveals a Complex Regulatory Network That Participate in Follicular Development in Chickens

Follicular development plays a key role in poultry reproduction, affecting clutch traits and thus egg production. Follicular growth is determined by granulosa cells (GCs), theca cells (TCs), and oocyte at the transcription, translation, and secretion levels. With the development of bioinformatic and experimental techniques, non-coding RNAs have been shown to participate in many life events. In this study, we investigated the transcriptomes of GCs and TCs in three different physiological stages: small yellow follicle (SYF), smallest hierarchical follicle (F6), and largest hierarchical follicle (F1) stages. A differential expression (DE) analysis, weighted gene co-expression network analysis (WGCNA), and bioinformatic analyses were performed. A total of 18,016 novel circular RNAs (circRNAs) were detected in GCs and TCs, 8127 of which were abundantly expressed in both cell types. and more circRNAs were differentially expressed between GCs and TCs than mRNAs. Enrichment analysis showed that the DE transcripts were mainly involved in cell growth, proliferation, differentiation, and apoptosis. In the WGCNA analysis, we identified six specific modules that were related to the different cell types in different stages of development. A series of central hub genes, including MAPK1, CITED4, SOD2, STC1, MOS, GDF9, MDH1, CAPN2, and novel_circ0004730, were incorporated into a Cytoscape network. Notably, using both DE analysis and WGCNA, ESR1 was identified as a key gene during follicular development. Our results provide valuable information on the circRNAs involved in follicle development and identify potential genes for further research to determine their roles in the regulation of different biological processes during follicle growth.

Assessment of network module identification across complex diseases

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

EviNet: a web platform for network enrichment analysis with flexible definition of gene sets

The new web resource EviNet provides an easily run interface to network enrichment analysis for exploration of novel, experimentally defined gene sets. The major advantages of this analysis are (i) applicability to any genes found in the global network rather than only to those with pathway/ontology term annotations, (ii) ability to connect genes via different molecular mechanisms rather than within one high-throughput platform, and (iii) statistical power sufficient to detect enrichment of very small sets, down to individual genes. The users’ gene sets are either defined prior to upload or derived interactively from an uploaded file by differential expression criteria. The pathways and networks used in the analysis can be chosen from the collection menu. The calculation is typically done within seconds or minutes and the stable URL is provided immediately. The results are presented in both visual (network graphs) and tabular formats using jQuery libraries. Uploaded data and analysis results are kept in separated project directories not accessible by other users. EviNet is available at https://www.evinet.org/.

GIANT 2.0: genome-scale integrated analysis of gene networks in tissues

GIANT2 (Genome-wide Integrated Analysis of gene Networks in Tissues) is an interactive web server that enables biomedical researchers to analyze their proteins and pathways of interest and generate hypotheses in the context of genome-scale functional maps of human tissues. The precise actions of genes are frequently dependent on their tissue context, yet direct assay of tissue-specific protein function and interactions remains infeasible in many normal human tissues and cell-types. With GIANT2, researchers can explore predicted tissue-specific functional roles of genes and reveal changes in those roles across tissues, all through interactive multi-network visualizations and analyses. Additionally, the NetWAS approach available through the server uses tissue-specific/cell-type networks predicted by GIANT2 to re-prioritize statistical associations from GWAS studies and identify disease-associated genes. GIANT2 predicts tissue-specific interactions by integrating diverse functional genomics data from now over 61 400 experiments for 283 diverse tissues and cell-types. GIANT2 does not require any registration or installation and is freely available for use at http://giant-v2.princeton.edu.

Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data

Despite the widening range of high-throughput platforms and exponential growth of generated data volume, the validation of biomarkers discovered from large-scale data remains a challenging field. In order to tackle cancer heterogeneity and comply with the data dimensionality, a number of network and pathway approaches were invented but rarely systematically applied to this task. We propose a new method, called NEAmarker, for finding sensitive and robust biomarkers at the pathway level. scores from network enrichment analysis transform the original space of altered genes into a lower-dimensional space of pathways. These dimensions are then correlated with phenotype variables. The method was first tested using in vitro data from three anti-cancer drug screens and then on clinical data of The Cancer Genome Atlas. It proved superior to the single-gene and alternative enrichment analyses in terms of (1) universal applicability to different data types with a possibility of cross-platform integration, (2) consistency of the discovered correlates between independent drug screens, and (3) ability to explain differential survival of treated patients. Our new screen of anti-cancer compounds validated the performance of multivariate models of drug sensitivity. The previously proposed methods of enrichment analysis could achieve comparable levels of performance in certain tests. However, only our method could discover predictors of both in vitro response and patient survival given administration of the same drug.

Interpretation of an individual functional genomics experiment guided by massive public data

A key unmet challenge in interpreting omics experiments is inferring biological meaning in the context of public functional genomics data. We developed a computational framework, Your Evidence Tailored Integration (YETI; http://yeti.princeton.edu/ ), which creates specialized functional interaction maps from large public datasets relevant to an individual omics experiment. Using this tailored integration, we predicted and experimentally confirmed an unexpected divergence in viral replication after seasonal or pandemic human influenza virus infection.

An integrative tissue-network approach to identify and test human disease genes

Effective discovery of causal disease genes must overcome the statistical challenges of quantitative genetics studies and the practical limitations of human biology experiments. Here we developed diseaseQUEST, an integrative approach that combines data from human genome-wide disease studies with in silico network models of tissue- and cell-type-specific function in model organisms to prioritize candidates within functionally conserved processes and pathways. We used diseaseQUEST to predict candidate genes for 25 different diseases and traits, including cancer, longevity, and neurodegenerative diseases. Focusing on Parkinson's disease (PD), a diseaseQUEST-directed Caenhorhabditis elegans behavioral screen identified several candidate genes, which we experimentally verified and found to be associated with age-dependent motility defects mirroring PD clinical symptoms. Furthermore, knockdown of the top candidate gene, bcat-1, encoding a branched chain amino acid transferase, caused spasm-like 'curling' and neurodegeneration in C. elegans, paralleling decreased BCAT1 expression in PD patient brains. diseaseQUEST is modular and generalizable to other model organisms and human diseases of interest.

The Multiple Levels of Mitonuclear Coregulation

Together, the nuclear and mitochondrial genomes encode the oxidative phosphorylation (OXPHOS) complexes that reside in the mitochondrial inner membrane and enable aerobic life. Mitochondria maintain their own genome that is expressed and regulated by factors distinct from their nuclear counterparts. For optimal function, the cell must ensure proper stoichiometric production of OXPHOS subunits by coordinating two physically separated and evolutionarily distinct gene expression systems. Here, we review our current understanding of mitonuclear coregulation primarily at the levels of transcription and translation. Additionally, we discuss other levels of coregulation that may exist but remain largely unexplored, including mRNA modification and stability and posttranslational protein degradation.

Transcriptomic, Functional, and Network Analyses Reveal Novel Genes Involved in the Interaction Between Caenorhabditis elegans and Stenotrophomonas maltophilia

The bacterivorous nematode Caenorhabditis elegans is an excellent model for the study of innate immune responses to a variety of bacterial pathogens, including the emerging nosocomial bacterial pathogen Stenotrophomonas maltophilia. The study of this interaction has ecological and medical relevance as S. maltophilia is found in association with C. elegans and other nematodes in the wild and is an emerging opportunistic bacterial pathogen. We identified 393 genes that were differentially expressed when exposed to virulent and avirulent strains of S. maltophilia and an avirulent strain of E. coli. We then used a probabilistic functional gene network model (WormNet) to determine that 118 of the 393 differentially expressed genes formed an interacting network and identified a set of highly connected genes with eight or more predicted interactions. We hypothesized that these highly connected genes might play an important role in the defense against S. maltophila and found that mutations of six of seven highly connected genes have a significant effect on nematode survival in response to these bacteria. Of these genes, C48B4.1, mpk-2, cpr-4, clec-67, and lys-6 are needed for combating the virulent S. maltophilia JCMS strain, while dod-22 was solely involved in response to the avirulent S. maltophilia K279a strain. We further found that dod-22 and clec-67 were up regulated in response to JCMS vs. K279a, while C48B4.1, mpk-2, cpr-4, and lys-6 were down regulated. Only dod-22 had a documented role in innate immunity, which demonstrates the merit of our approach in the identification of novel genes that are involved in combating S. maltophilia infection.

The lysosomal membrane protein LAMP2A promotes autophagic flux and prevents SNCA-induced Parkinson disease-like symptoms in the Drosophila brain

The autophagy-lysosome pathway plays a fundamental role in the clearance of aggregated proteins and protection against cellular stress and neurodegenerative conditions. Alterations in autophagy processes, including macroautophagy and chaperone-mediated autophagy (CMA), have been described in Parkinson disease (PD). CMA is a selective autophagic process that depends on LAMP2A (lysosomal-associated membrane protein 2A), a mammal and bird-specific membrane glycoprotein that translocates cytosolic proteins containing a KFERQ-like peptide motif across the lysosomal membrane. Drosophila reportedly lack CMA and use endosomal microautophagy (eMI) as an alternative selective autophagic process. Here we report that neuronal expression of human LAMP2A protected Drosophila against starvation and oxidative stress, and delayed locomotor decline in aging flies without extending their lifespan. LAMP2A also prevented the progressive locomotor and oxidative defects induced by neuronal expression of PD-associated human SNCA (synuclein alpha) with alanine-to-proline mutation at position 30 (SNCAA30P). Using KFERQ-tagged fluorescent biosensors, we observed that LAMP2A expression stimulated selective autophagy in the adult brain and not in the larval fat body, but did not increase this process under starvation conditions. Noteworthy, we found that neurally expressed LAMP2A markedly upregulated levels of Drosophila Atg5, a key macroautophagy initiation protein, and that it increased the density of Atg8a/LC3-positive puncta, which reflects the formation of autophagosomes. Furthermore, LAMP2A efficiently prevented accumulation of the autophagy defect marker Ref(2)P/p62 in the adult brain under acute oxidative stress. These results indicate that LAMP2A can potentiate autophagic flux in the Drosophila brain, leading to enhanced stress resistance and neuroprotection.

ABBREVIATIONS

Act5C: actin 5C; a.E.: after eclosion; Atg5: autophagy-related 5; Atg8a/LC3: autophagy-related 8a; CMA: chaperone-mediated autophagy; DHE: dihydroethidium; elav: embryonic lethal abnormal vision; eMI: endosomal microautophagy; ESCRT: endosomal sorting complexes required for transport; GABARAP: GABA typeA receptor-associated protein; Hsc70-4: heat shock protein cognate 4; HSPA8/Hsc70: heat shock protein family A (Hsp70) member 8; LAMP2: lysosomal associated membrane protein 2; MDA: malondialdehyde; PA-mCherry: photoactivable mCherry; PBS: phosphate-buffered saline; PCR: polymerase chain reaction; PD: Parkinson disease; Ref(2)P/p62: refractory to sigma P; ROS: reactive oxygen species; RpL32/rp49: ribosomal protein L32; RT-PCR: reverse transcription polymerase chain reaction; SING: startle-induced negative geotaxis; SNCA/α-synuclein: synuclein alpha; SQSTM1/p62: sequestosome 1; TBS: Tris-buffered saline; UAS: upstream activating sequence.

Tissue-specific Network Analysis of Genetic Variants Associated with Coronary Artery Disease

Coronary artery disease (CAD) is a leading cause of death worldwide. Recent genome-wide association studies have identified more than one hundred susceptibility loci associated with CAD. However, the underlying mechanism of these genetic loci to CAD susceptibility is still largely unknown. We performed a tissue-specific network analysis of CAD using the summary statistics from one of the largest genome-wide association studies. Variant-level associations were summarized into gene-level associations, and a CAD-related interaction network was built using experimentally validated gene interactions and gene coexpression in coronary artery. The network contained 102 genes, of which 53 were significantly associated with CAD. Pathway enrichment analysis revealed that many genes in the network were involved in the regulation of peripheral arteries. In summary, we performed a tissue-specific network analysis and found abnormalities in the peripheral arteries might be an important pathway underlying the pathogenesis of CAD. Future functional characterization might further validate our findings and identify potential therapeutic targets for CAD.

Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods

Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two) before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number aberrations, and we show that such strategies can further improve our prediction capabilities. In conclusion, our approaches allow to discriminate patients in high-and low-risk groups using few potential biomarkers and therefore, can help clinicians to provide more precise prognoses and to facilitate the subsequent clinical management of patients at risk of disease.

Gene co-opening network deciphers gene functional relationships

Genome sequencing technology has generated a vast amount of genomic and epigenomic data, and has provided us a great opportunity to study gene functions on a global scale from an epigenomic view. In the last decade, network-based studies, such as those based on PPI networks and co-expression networks, have shown good performance in capturing functional relationships between genes. However, the functions of a gene and the mechanism of interaction of genes with each other to elucidate their functions are still not entirely clear. Here, we construct a gene co-opening network based on chromatin accessibility of genes. We show that genes related to a specific biological process or the same disease tend to be clustered in the co-opening network. This understanding allows us to detect functional clusters from the network and to predict new functions for genes. We further apply the network to prioritize disease genes for Psoriasis, and demonstrate the power of the joint analysis of the co-opening network and GWAS data in identifying disease genes. Taken together, the co-opening network provides a new viewpoint for the elucidation of gene associations and the interpretation of disease mechanisms.

Gene locations may contribute to predicting gene regulatory relationships

We propose that locations of genes on chromosomes can contribute to the prediction of gene regulatory relationships. We constructed a time-based gene regulatory network of zebrafish cardiogenesis on the basis of a spatio-temporal neighborhood method. Through the network, specific regulatory pathways and order of gene expression during zebrafish cardiogenesis were obtained. By comparing the order with locations of these genes on chromosomes, we discovered that there exists a reversal phenomenon between the order and order of gene locations. The discovery provides an inherent rule to instruct exploration of gene regulatory relationships. Specifically, the discovery can help to predict if regulatory relationships between genes exist and contribute to evaluating the correctness of discovered gene regulatory relationships.

The angiogenic switch leads to a metabolic shift in human glioblastoma

Background

Invasion and angiogenesis are major hallmarks of glioblastoma (GBM) growth. While invasive tumor cells grow adjacent to blood vessels in normal brain tissue, tumor cells within neovascularized regions exhibit hypoxic stress and promote angiogenesis. The distinct microenvironments likely differentially affect metabolic processes within the tumor cells.

Methods

In the present study, we analyzed gene expression and metabolic changes in a human GBM xenograft model that displayed invasive and angiogenic phenotypes. In addition, we used glioma patient biopsies to confirm the results from the xenograft model.

Results

We demonstrate that the angiogenic switch in our xenograft model is linked to a proneural-to-mesenchymal transition that is associated with upregulation of the transcription factors BHLHE40, CEBPB, and STAT3. Metabolic analyses revealed that angiogenic xenografts employed higher rates of glycolysis compared with invasive xenografts. Likewise, patient biopsies exhibited higher expression of the glycolytic enzyme lactate dehydrogenase A and glucose transporter 1 in hypoxic areas compared with the invasive edge and lower-grade tumors. Analysis of the mitochondrial respiratory chain showed reduction of complex I in angiogenic xenografts and hypoxic regions of GBM samples compared with invasive xenografts, nonhypoxic GBM regions, and lower-grade tumors. In vitro hypoxia experiments additionally revealed metabolic adaptation of invasive tumor cells, which increased lactate production under long-term hypoxia.

Conclusions

The use of glycolysis versus mitochondrial respiration for energy production within human GBM tumors is highly dependent on the specific microenvironment. The metabolic adaptability of GBM cells highlights the difficulty of targeting one specific metabolic pathway for effective therapeutic intervention.

Network-based machine learning and graph theory algorithms for precision oncology

Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

Common developmental genome deprogramming in schizophrenia - Role of Integrative Nuclear FGFR1 Signaling (INFS)

The watershed-hypothesis of schizophrenia asserts that over 200 different mutations dysregulate distinct pathways that converge on an unspecified common mechanism(s) that controls disease ontogeny. Consistent with this hypothesis, our RNA-sequencing of neuron committed cells (NCCs) differentiated from established iPSCs of 4 schizophrenia patients and 4 control subjects uncovered a dysregulated transcriptome of 1349 mRNAs common to all patients. Data reveals a global dysregulation of developmental genome, deconstruction of coordinated mRNA networks, and the formation of aberrant, new coordinated mRNA networks indicating a concerted action of the responsible factor(s). Sequencing of miRNA transcriptomes demonstrated an overexpression of 16 miRNAs and deconstruction of interactive miRNA-mRNA networks in schizophrenia NCCs. ChiPseq revealed that the nuclear (n) form of FGFR1, a pan-ontogenic regulator, is overexpressed in schizophrenia NCCs and overtargets dysregulated mRNA and miRNA genes. The nFGFR1 targeted 54% of all human gene promoters and 84.4% of schizophrenia dysregulated genes. The upregulated genes reside within major developmental pathways that control neurogenesis and neuron formation, whereas downregulated genes are involved in oligodendrogenesis. Our results indicate (i) an early (preneuronal) genomic etiology of schizophrenia, (ii) dysregulated genes and new coordinated gene networks are common to unrelated cases of schizophrenia, (iii) gene dysregulations are accompanied by increased nFGFR1-genome interactions, and (iv) modeling of increased nFGFR1 by an overexpression of a nFGFR1 lead to up or downregulation of selected genes as observed in schizophrenia NCCs. Together our results designate nFGFR1 signaling as a potential common dysregulated mechanism in investigated patients and potential therapeutic target in schizophrenia.

GenePANDA-a novel network-based gene prioritizing tool for complex diseases

Here we describe GenePANDA, a novel network-based tool for prioritizing candidate disease genes. GenePANDA assesses whether a gene is likely a candidate disease gene based on its relative distance to known disease genes in a functional association network. A unique feature of GenePANDA is the introduction of adjusted network distance derived by normalizing the raw network distance between two genes with their respective mean raw network distance to all other genes in the network. The use of adjusted network distance significantly improves GenePANDA’s performance on prioritizing complex disease genes. GenePANDA achieves superior performance over five previously published algorithms for prioritizing disease genes. Finally, GenePANDA can assist in prioritizing functionally important SNPs identified by GWAS.

OncoBinder facilitates interpretation of proteomic interaction data by capturing coactivation pairs in cancer

High-throughput methods such as co-immunoprecipitationmass spectrometry (coIP-MS) and yeast 2 hybridization (Y2H) have suggested a broad range of unannotated protein-protein interactions (PPIs), and interpretation of these PPIs remains a challenging task. The advancements in cancer genomic researches allow for the inference of "coactivation pairs" in cancer, which may facilitate the identification of PPIs involved in cancer. Here we present OncoBinder as a tool for the assessment of proteomic interaction data based on the functional synergy of oncoproteins in cancer. This decision tree-based method combines gene mutation, copy number and mRNA expression information to infer the functional status of protein-coding genes. We applied OncoBinder to evaluate the potential binders of EGFR and ERK2 proteins based on the gastric cancer dataset of The Cancer Genome Atlas (TCGA). As a result, OncoBinder identified high confidence interactions (annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) or validated by low-throughput assays) more efficiently than co-expression based method. Taken together, our results suggest that evaluation of gene functional synergy in cancer may facilitate the interpretation of proteomic interaction data. The OncoBinder toolbox for Matlab is freely accessible online.

A Functional Genomic Meta-Analysis of Clinical Trials in Systemic Sclerosis: Toward Precision Medicine and Combination Therapy

Systemic sclerosis is an orphan, systemic autoimmune disease with no FDA-approved treatments. Its heterogeneity and rarity often result in underpowered clinical trials making the analysis and interpretation of associated molecular data challenging. We performed a meta-analysis of gene expression data from skin biopsies of patients with systemic sclerosis treated with five therapies: mycophenolate mofetil, rituximab, abatacept, nilotinib, and fresolimumab. A common clinical improvement criterion of -20% or -5 modified Rodnan skin score was applied to each study. We applied a machine learning approach that captured features beyond differential expression and was better at identifying targets of therapies than the differential expression alone. Regardless of treatment mechanism, abrogation of inflammatory pathways accompanied clinical improvement in multiple studies suggesting that high expression of immune-related genes indicates active and targetable disease. Our framework allowed us to compare different trials and ask if patients who failed one therapy would likely improve on a different therapy, based on changes in gene expression. Genes with high expression at baseline in fresolimumab nonimprovers were downregulated in mycophenolate mofetil improvers, suggesting that immunomodulatory or combination therapy may have benefitted these patients. This approach can be broadly applied to increase tissue specificity and sensitivity of differential expression results.

Modular transcriptional repertoire and MicroRNA target analyses characterize genomic dysregulation in the thymus of Down syndrome infants

Trisomy 21-driven transcriptional alterations in human thymus were characterized through gene coexpression network (GCN) and miRNA-target analyses. We used whole thymic tissue--obtained at heart surgery from Down syndrome (DS) and karyotipically normal subjects (CT)--and a network-based approach for GCN analysis that allows the identification of modular transcriptional repertoires (communities) and the interactions between all the system's constituents through community detection. Changes in the degree of connections observed for hierarchically important hubs/genes in CT and DS networks corresponded to community changes. Distinct communities of highly interconnected genes were topologically identified in these networks. The role of miRNAs in modulating the expression of highly connected genes in CT and DS was revealed through miRNA-target analysis. Trisomy 21 gene dysregulation in thymus may be depicted as the breakdown and altered reorganization of transcriptional modules. Leading networks acting in normal or disease states were identified. CT networks would depict the "canonical" way of thymus functioning. Conversely, DS networks represent a "non-canonical" way, i.e., thymic tissue adaptation under trisomy 21 genomic dysregulation. This adaptation is probably driven by epigenetic mechanisms acting at chromatin level and through the miRNA control of transcriptional programs involving the networks' high-hierarchy genes.

DegreeCox - a network-based regularization method for survival analysis

Background

Modeling survival oncological data has become a major challenge as the increase in the amount of molecular information nowadays available means that the number of features greatly exceeds the number of observations. One possible solution to cope with this dimensionality problem is the use of additional constraints in the cost function optimization. Lasso and other sparsity methods have thus already been successfully applied with such idea. Although this leads to more interpretable models, these methods still do not fully profit from the relations between the features, specially when these can be represented through graphs. We propose DegreeCox, a method that applies network-based regularizers to infer Cox proportional hazard models, when the features are genes and the outcome is patient survival. In particular, we propose to use network centrality measures to constrain the model in terms of significant genes.

Results

We applied DegreeCox to three datasets of ovarian cancer carcinoma and tested several centrality measures such as weighted degree, betweenness and closeness centrality. The a priori network information was retrieved from Gene Co-Expression Networks and Gene Functional Maps. When compared with Ridge and Lasso, DegreeCox shows an improvement in the classification of high and low risk patients in a par with Net-Cox. The use of network information is especially relevant with datasets that are not easily separated. In terms of RMSE and C-index, DegreeCox gives results that are similar to those of the best performing methods, in a few cases slightly better.

Conclusions

Network-based regularization seems a promising framework to deal with the dimensionality problem. The centrality metrics proposed can be easily expanded to accommodate other topological properties of different biological networks.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1310-4) contains supplementary material, which is available to authorized users.

Systems Medicine as an Emerging Tool for Cardiovascular Genetics

Cardiovascular disease (CVD) is a major contributor to morbidity and mortality worldwide. However, the pathogenesis of CVD is complex and remains elusive. Within the last years, systems medicine has emerged as a novel tool to study the complex genetic, molecular, and physiological interactions leading to diseases. In this review, we provide an overview about the current approaches for systems medicine in CVD. They include bioinformatical and experimental tools such as cell and animal models, omics technologies, network, and pathway analyses. Additionally, we discuss challenges and current literature examples where systems medicine has been successfully applied for the study of CVD.

Cancer Markers Selection Using Network-Based Cox Regression: A Methodological and Computational Practice

International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation-based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for investigating cancers regulatory networks.

Excess sphingomyelin disturbs ATG9A trafficking and autophagosome closure

Sphingomyelin is an essential cellular lipid that traffics between plasma membrane and intracellular organelles until directed to lysosomes for SMPD1 (sphingomyelin phosphodiesterase 1)-mediated degradation. Inactivating mutations in the SMPD1 gene result in Niemann-Pick diseases type A and B characterized by sphingomyelin accumulation and severely disturbed tissue homeostasis. Here, we report that sphingomyelin overload disturbs the maturation and closure of autophagic membranes. Niemann-Pick type A patient fibroblasts and SMPD1-depleted cancer cells accumulate elongated and unclosed autophagic membranes as well as abnormally swollen autophagosomes in the absence of normal autophagosomes and autolysosomes. The immature autophagic membranes are rich in WIPI2, ATG16L1 and MAP1LC3B but display reduced association with ATG9A. Contrary to its normal trafficking between plasma membrane, intracellular organelles and autophagic membranes, ATG9A concentrates in transferrin receptor-positive juxtanuclear recycling endosomes in SMPD1-deficient cells. Supporting a causative role for ATG9A mistrafficking in the autophagy defect observed in SMPD1-deficient cells, ectopic ATG9A effectively reverts this phenotype. Exogenous C12-sphingomyelin induces a similar juxtanuclear accumulation of ATG9A and subsequent defect in the maturation of autophagic membranes in healthy cells while the main sphingomyelin metabolite, ceramide, fails to revert the autophagy defective phenotype in SMPD1-deficient cells. Juxtanuclear accumulation of ATG9A and defective autophagy are also evident in tissues of smpd1-deficient mice with a subsequent inability to cope with kidney ischemia-reperfusion stress. These data reveal sphingomyelin as an important regulator of ATG9A trafficking and maturation of early autophagic membranes.

A Network of Splice Isoforms for the Mouse

The laboratory mouse is the primary mammalian species used for studying alternative splicing events. Recent studies have generated computational models to predict functions for splice isoforms in the mouse. However, the functional relationship network, describing the probability of splice isoforms participating in the same biological process or pathway, has not yet been studied in the mouse. Here we describe a rich genome-wide resource of mouse networks at the isoform level, which was generated using a unique framework that was originally developed to infer isoform functions. This network was built through integrating heterogeneous genomic and protein data, including RNA-seq, exon array, protein docking and pseudo-amino acid composition. Through simulation and cross-validation studies, we demonstrated the accuracy of the algorithm in predicting isoform-level functional relationships. We showed that this network enables the users to reveal functional differences of the isoforms of the same gene, as illustrated by literature evidence with Anxa6 (annexin a6) as an example. We expect this work will become a useful resource for the mouse genetics community to understand gene functions. The network is publicly available at: http://guanlab.ccmb.med.umich.edu/isoformnetwork.

Computational Reconstruction of NFκB Pathway Interaction Mechanisms during Prostate Cancer

Molecular research in cancer is one of the largest areas of bioinformatic investigation, but it remains a challenge to understand biomolecular mechanisms in cancer-related pathways from high-throughput genomic data. This includes the Nuclear-factor-kappa-B (NFκB) pathway, which is central to the inflammatory response and cell proliferation in prostate cancer development and progression. Despite close scrutiny and a deep understanding of many of its members’ biomolecular activities, the current list of pathway members and a systems-level understanding of their interactions remains incomplete. Here, we provide the first steps toward computational reconstruction of interaction mechanisms of the NFκB pathway in prostate cancer. We identified novel roles for ATF3, CXCL2, DUSP5, JUNB, NEDD9, SELE, TRIB1, and ZFP36 in this pathway, in addition to new mechanistic interactions between these genes and 10 known NFκB pathway members. A newly predicted interaction between NEDD9 and ZFP36 in particular was validated by co-immunoprecipitation, as was NEDD9's potential biological role in prostate cancer cell growth regulation. We combined 651 gene expression datasets with 1.4M gene product interactions to predict the inclusion of 40 additional genes in the pathway. Molecular mechanisms of interaction among pathway members were inferred using recent advances in Bayesian data integration to simultaneously provide information specific to biological contexts and individual biomolecular activities, resulting in a total of 112 interactions in the fully reconstructed NFκB pathway: 13 (11%) previously known, 29 (26%) supported by existing literature, and 70 (63%) novel. This method is generalizable to other tissue types, cancers, and organisms, and this new information about the NFκB pathway will allow us to further understand prostate cancer and to develop more effective prevention and treatment strategies.

In molecular research in cancer it remains challenging to uncover biomolecular mechanisms in cancer-related pathways from high-throughput genomic data, including the Nuclear-factor-kappa-B (NFκB) pathway. Despite close scrutiny and a deep understanding of many of the NFκB pathway members’ biomolecular activities, the current list of pathway members and a systems-level understanding of their interactions remains incomplete. In this study, we provide the first steps toward computational reconstruction of interaction mechanisms of the NFκB pathway in prostate cancer. We identified novel roles for 8 genes in this pathway and new mechanistic interactions between these genes and 10 known pathway members. We combined 651 gene expression datasets with 1.4M interactions to predict the inclusion of 40 additional genes in the pathway. Molecular mechanisms of interaction were inferred using recent advances in Bayesian data integration to simultaneously provide information specific to biological contexts and individual biomolecular activities, resulting in 112 interactions in the fully reconstructed NFκB pathway. This method is generalizable, and this new information about the NFκB pathway will allow us to further understand prostate cancer.

A graph theoretical approach to data fusion

The rapid development of high throughput experimental techniques has resulted in a growing diversity of genomic datasets being produced and requiring analysis. Therefore, it is increasingly being recognized that we can gain deeper understanding about underlying biology by combining the insights obtained from multiple, diverse datasets. Thus we propose a novel scalable computational approach to unsupervised data fusion. Our technique exploits network representations of the data to identify similarities among the datasets. We may work within the Bayesian formalism, using Bayesian nonparametric approaches to model each dataset; or (for fast, approximate, and massive scale data fusion) can naturally switch to more heuristic modeling techniques. An advantage of the proposed approach is that each dataset can initially be modeled independently (in parallel), before applying a fast post-processing step to perform data integration. This allows us to incorporate new experimental data in an online fashion, without having to rerun all of the analysis. We first demonstrate the applicability of our tool on artificial data, and then on examples from the literature, which include yeast cell cycle, breast cancer and sporadic inclusion body myositis datasets.

Epithelial, metabolic and innate immunity transcriptomic signatures differentiating the rumen from other sheep and mammalian gastrointestinal tract tissues

Background. Ruminants are successful herbivorous mammals, in part due to their specialized forestomachs, the rumen complex, which facilitates the conversion of feed to soluble nutrients by micro-organisms. Is the rumen complex a modified stomach expressing new epithelial (cornification) and metabolic programs, or a specialised stratified epithelium that has acquired new metabolic activities, potentially similar to those of the colon? How has the presence of the rumen affected other sections of the gastrointestinal tract (GIT) of ruminants compared to non-ruminants? Methods. Transcriptome data from 11 tissues covering the sheep GIT, two stratified epithelial and two control tissues, was analysed using principal components to cluster tissues based on gene expression profile similarity. Expression profiles of genes along the sheep GIT were used to generate a network to identify genes enriched for expression in different compartments of the GIT. The data from sheep was compared to similar data sets from two non-ruminants, pigs (closely related) and humans (more distantly related). Results. The rumen transcriptome clustered with the skin and tonsil, but not the GIT transcriptomes, driven by genes from the epidermal differentiation complex, and genes encoding stratified epithelium keratins and innate immunity proteins. By analysing all of the gene expression profiles across tissues together 16 major clusters were identified. The strongest of these, and consistent with the high turnover rate of the GIT, showed a marked enrichment of cell cycle process genes (P = 1.4 E-46), across the whole GIT, relative to liver and muscle, with highest expression in the caecum followed by colon and rumen. The expression patterns of several membrane transporters (chloride, zinc, nucleosides, amino acids, fatty acids, cholesterol and bile acids) along the GIT was very similar in sheep, pig and humans. In contrast, short chain fatty acid uptake and metabolism appeared to be different between the species and different between the rumen and colon in sheep. The importance of nitrogen and iodine recycling in sheep was highlighted by the highly preferential expression of SLC14A1-urea (rumen), RHBG-ammonia (intestines) and SLC5A5-iodine (abomasum). The gene encoding a poorly characterized member of the maltase-glucoamylase family (MGAM2), predicted to play a role in the degradation of starch or glycogen, was highly expressed in the small and large intestines. Discussion. The rumen appears to be a specialised stratified cornified epithelium, probably derived from the oesophagus, which has gained some liver-like and other specialized metabolic functions, but probably not by expression of pre-existing colon metabolic programs. Changes in gene transcription downstream of the rumen also appear have occurred as a consequence of the evolution of the rumen and its effect on nutrient composition flowing down the GIT.

Exome sequencing reveals recurrent germ line variants in patients with familial Waldenström macroglobulinemia

Familial aggregation of Waldenström macroglobulinemia (WM) cases, and the clustering of B-cell lymphoproliferative disorders among first-degree relatives of WM patients, has been reported. Nevertheless, the possible contribution of inherited susceptibility to familial WM remains unrevealed. We performed whole exome sequencing on germ line DNA obtained from 4 family members in which coinheritance for WM was documented in 3 of them, and screened additional independent 246 cases by using gene-specific mutation sequencing. Among the shared germ line variants, LAPTM5(c403t) and HCLS1(g496a) were the most recurrent, being present in 3/3 affected members of the index family, detected in 8% of the unrelated familial cases, and present in 0.5% of the nonfamilial cases and in <0.05 of a control population. LAPTM5 and HCLS1 appeared as relevant WM candidate genes that characterized familial WM individuals and were also functionally relevant to the tumor clone. These findings highlight potentially novel contributors for the genetic predisposition to familial WM and indicate that LAPTM5(c403t) and HCLS1(g496a) may represent predisposition alleles in patients with familial WM.

High-throughput transcriptome sequencing of the cold seep mussel Bathymodiolus platifrons

Bathymodiolid mussels dominate hydrothermal vents, cold methane/sulfide-hydrocarbon seeps, and other sites of organic enrichment. Here, we aimed to explore the innate immune system and detoxification mechanism of the deep sea mussel Bathymodiolus platifrons collected from a methane seep in the South China Sea. We sequenced the transcriptome of the mussels’ gill, foot and mantle tissues and generated a transcriptomic database containing 96,683 transcript sequences. Based on GO and KEGG annotations, we reported transcripts that were related to the innate immune system, heavy metal detoxification and sulfide metabolic genes. Our in-depth analysis on the isoforms of peptidoglycan recognition protein (PGRP) that have different cellular location and potentially differential selectivity towards peptidoglycan (PGN) from gram-positive and gram-negative bacteria were differentially expressed in different tissues. We also reported a potentially novel form of metallothionein and the production of phytochelatin in B. platifrons, which has not been reported in any of its coastal relative Mytilus mussel species. Overall, the present study provided new insights into heavy metal and sulfide metabolism in B. platifrons and can be served as the basis for future molecular studies on host-symbiont interactions in cold seep mussels.

Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery

Precision medicine will revolutionize the way we treat and prevent disease. A major barrier to the implementation of precision medicine that clinicians and translational scientists face is understanding the underlying mechanisms of disease. We are starting to address this challenge through automatic approaches for information extraction, representation and analysis. Recent advances in text and data mining have been applied to a broad spectrum of key biomedical questions in genomics, pharmacogenomics and other fields. We present an overview of the fundamental methods for text and data mining, as well as recent advances and emerging applications toward precision medicine.

Turning publicly available gene expression data into discoveries using gene set context analysis

Gene Set Context Analysis (GSCA) is an open source software package to help researchers use massive amounts of publicly available gene expression data (PED) to make discoveries. Users can interactively visualize and explore gene and gene set activities in 25,000+ consistently normalized human and mouse gene expression samples representing diverse biological contexts (e.g. different cells, tissues and disease types, etc.). By providing one or multiple genes or gene sets as input and specifying a gene set activity pattern of interest, users can query the expression compendium to systematically identify biological contexts associated with the specified gene set activity pattern. In this way, researchers with new gene sets from their own experiments may discover previously unknown contexts of gene set functions and hence increase the value of their experiments. GSCA has a graphical user interface (GUI). The GUI makes the analysis convenient and customizable. Analysis results can be conveniently exported as publication quality figures and tables. GSCA is available at https://github.com/zji90/GSCA. This software significantly lowers the bar for biomedical investigators to use PED in their daily research for generating and screening hypotheses, which was previously difficult because of the complexity, heterogeneity and size of the data.

Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases

Many functionally important interactions between genes and proteins involved in immunological diseases and processes are unknown. The exponential growth in public high-throughput data offers an opportunity to expand this knowledge. To unlock human-immunology-relevant insight contained in the global biomedical research effort, including all public high-throughput datasets, we performed immunological-pathway-focused Bayesian integration of a comprehensive, heterogeneous compendium comprising 38,088 genome-scale experiments. The distillation of this knowledge into immunological networks of functional relationships between molecular entities (ImmuNet), and tools to mine this resource, are accessible to the public at http://immunet.princeton.edu. The predictive capacity of ImmuNet, established by rigorous statistical validation, is easily accessed by experimentalists to generate data-driven hypotheses. We demonstrate the power of this approach through the identification of unique host-virus interaction responses, and we show how ImmuNet complements genetic studies by predicting disease-associated genes. ImmuNet should be widely beneficial for investigating the mechanisms of the human immune system and immunological diseases.

Mycobacterium avium MAV_2941 mimics phosphoinositol-3-kinase to interfere with macrophage phagosome maturation

Mycobacterium avium subsp hominissuis (M. avium) is a pathogen that infects and survives in macrophages. Previously, we have identified the M. avium MAV_2941 gene encoding a 73 amino acid protein exported by the oligopeptide transporter OppA to the macrophage cytoplasm. Mutations in MAV_2941 were associated with significant impairment of M. avium growth in THP-1 macrophages. In this study, we investigated the molecular mechanism of MAV_2941 action and demonstrated that MAV_2941 interacts with the vesicle trafficking proteins syntaxin-8 (STX8), adaptor-related protein complex 3 (AP-3) complex subunit beta-1 (AP3B1) and Archain 1 (ARCN1) in mononuclear phagocytic cells. Sequencing analysis revealed that the binding site of MAV_2941 is structurally homologous to the human phosphatidylinositol 3-kinase (PI3K) chiefly in the region recognized by vesicle trafficking proteins. The β3A subunit of AP-3, encoded by AP3B1, is essential for trafficking cargo proteins, including lysosomal-associated membrane protein 1 (LAMP-1), to the phagosome and lysosome-related organelles. Here, we show that while the heat-killed M. avium when ingested by macrophages co-localizes with LAMP-1 protein, transfection of MAV_2941 in macrophages results in significant decrease of LAMP-1 co-localization with the heat-killed M. avium phagosomes. Mutated MAV_2941, where the amino acids homologous to the binding region of PI3K were changed, failed to interact with trafficking proteins. Inactivation of the AP3B1 gene led to alteration in the trafficking of LAMP-1. These results suggest that M. avium MAV_2941 interferes with the protein trafficking within macrophages altering the maturation of phagosome.

Algorithms for modeling global and context-specific functional relationship networks

Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling functional relationship networks, which summarize the probability of gene co-functionality relationships. Such modeling can be based on either expression data only or heterogeneous data integration. Numerous methods have been deployed to infer the functional relationship networks, while most of them target the global (non-context-specific) functional relationship networks. However, it is expected that functional relationships consistently reprogram under different tissues or biological processes. Thus, advanced methods have been developed targeting tissue-specific or developmental stage-specific networks. This article brings together the state-of-the-art functional relationship network modeling methods, emphasizes the need for heterogeneous genomic data integration and context-specific network modeling and outlines future directions for functional relationship networks.