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

Rheumatoid arthritis - clinical aspects: 134. Predictors of Joint Damage in South Africans with Rheumatoid Arthritis

Background: Rheumatoid arthritis (RA) causes progressive joint damage and functional disability. Studies on factors affecting joint damage as clinical outcome are lacking in Africa. The aim of the present study was to identify predictors of joint damage in adult South Africans with established RA. Methods: A cross-sectional study of 100 black patients with RA of >5 years were assessed for joint damage using a validated clinical method, the RA articular damage (RAAD) score. Potential predictors of joint damage that were documented included socio-demographics, smoking, body mass index (BMI), disease duration, delay in disease modifying antirheumatic drug (DMARD) initiation, global disease activity as measured by the disease activity score (DAS28), erythrocyte sedimentation rate (ESR), C reactive protein (CRP), and autoantibody status. The predictive value of variables was assessed by univariate and stepwise multivariate regression analyses. A p value <0.05 was considered significant. Results: The mean (SD) age was 56 (9.8) years, disease duration 17.5 (8.5) years, educational level 7.5 (3.5) years and DMARD lag was 9 (8.8) years. Female to male ratio was 10:1. The mean (SD) DAS28 was 4.9 (1.5) and total RAAD score was 28.3 (12.8). The mean (SD) BMI was 27.2 kg/m2 (6.2) and 93% of patients were rheumatoid factor (RF) positive. More than 90% of patients received between 2 to 3 DMARDs. Significant univariate predictors of a poor RAAD score were increasing age (p = 0.001), lower education level (p = 0.019), longer disease duration (p < 0.001), longer DMARD lag (p = 0.014), lower BMI (p = 0.025), high RF titre (p < 0.001) and high ESR (p = 0.008). The multivariate regression analysis showed that the only independent significant predictors of a higher mean RAAD score were older age at disease onset (p = 0.04), disease duration (p < 0.001) and RF titre (p < 0.001). There was also a negative association between BMI and the mean total RAAD score (p = 0.049). Conclusions: Patients with longstanding established RA have more severe irreversible joint damage as measured by the clinical RAAD score, contrary to other studies in Africa. This is largely reflected by a delay in the initiation of early effective treatment. Independent of disease duration, older age at disease onset and a higher RF titre are strongly associated with more joint damage. The inverse association between BMI and articular damage in RA has been observed in several studies using radiographic damage scores. The mechanisms underlying this paradoxical association are still widely unknown but adipokines have recently been suggested to play a role. Disclosure statement: C.I. has received a research grant from the Connective Tissue Diseases Research Fund, University of the Witwatersrand. All other authors have declared no conflicts of interes

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Genetic and Proteomic Interrogation of Lower Confidence Candidate Genes Reveals Signaling Networks in β-Catenin-Active Cancers

Genome-scale expression studies and comprehensive loss-of-function genetic screens have focused almost exclusively on the highest confidence candidate genes. Here, we describe a strategy for characterizing the lower confidence candidates identified by such approaches. We interrogated 177 genes that we classified as essential for the proliferation of cancer cells exhibiting constitutive β-catenin activity and integrated data for each of the candidates, derived from orthogonal short hairpin RNA (shRNA) knockdown and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9-mediated gene editing knockout screens, to yield 69 validated genes. We then characterized the relationships between sets of these genes using complementary assays: medium-throughput stable isotope labeling by amino acids in cell culture (SILAC)-based mass spectrometry, yielding 3,639 protein-protein interactions, and a CRISPR-mediated pairwise double knockout screen, yielding 375 combinations exhibiting greater- or lesser-than-additive phenotypic effects indicating genetic interactions. These studies identify previously unreported regulators of β-catenin, define functional networks required for the survival of β-catenin-active cancers, and provide an experimental strategy that may be applied to define other signaling networks

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

Atg16L1 T300A variant decreases selective autophagy resulting in altered cytokine signaling and decreased antibacterial defense

A coding polymorphism (Thr300Ala) in the essential autophagy gene, autophagy related 16-like 1 (ATG16L1), confers increased risk for the development of Crohn disease, although the mechanisms by which single disease-associated polymorphisms contribute to pathogenesis have been difficult to dissect given that environmental factors likely influence disease initiation in these patients. Here we introduce a knock-in mouse model expressing the Atg16L1 T300A variant. Consistent with the human polymorphism, T300A knock-in mice do not develop spontaneous intestinal inflammation, but exhibit morphological defects in Paneth and goblet cells. Selective autophagy is reduced in multiple cell types from T300A knock-in mice compared with WT mice. The T300A polymorphism significantly increases caspase 3- and caspase 7-mediated cleavage of Atg16L1, resulting in lower levels of full-length Atg16Ll T300A protein. Moreover, Atg16L1 T300A is associated with decreased antibacterial autophagy and increased IL-1β production in primary cells and in vivo. Quantitative proteomics for protein interactors of ATG16L1 identified previously unknown nonoverlapping sets of proteins involved in ATG16L1-dependent antibacterial autophagy or IL-1β production. These findings demonstrate how the T300A polymorphism leads to cell type- and pathway-specific disruptions of selective autophagy and suggest a mechanism by which this polymorphism contributes to disease.Crohn's and Colitis Foundation of America (Genetics Initiative)Leona M. and Harry B. Helmsley Charitable TrustNational Institutes of Health (U.S.) (Grant DK097485)National Institutes of Health (U.S.) (Grant DK043351)Deutsche Forschungsgemeinschaft (Fellowship Award KU2511/1-1)National Institutes of Health (U.S.) (Grant AI084887