Natali Gulbahce

Network medicine: a network-based approach to human disease.

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

Global landscape of HIV-human protein complexes

Human immunodeficiency virus (HIV) has a small genome and therefore relies heavily on the host cellular machinery to replicate. Identifying which host proteins and complexes come into physical contact with the viral proteins is crucial for a comprehensive understanding of how HIV rewires the host’s cellular machinery during the course of infection. Here we report the use of affinity tagging and purification mass spectrometry to determine systematically the physical interactions of all 18 HIV-1 proteins and polyproteins with host proteins in two different human cell lines (HEK293 and Jurkat). Using a quantitative scoring system that we call MiST, we identified with high confidence 497 HIV–human protein–protein interactions involving 435 individual human proteins, with ∼40% of the interactions being identified in both cell types. We found that the host proteins hijacked by HIV, especially those found interacting in both cell types, are highly conserved across primates. We uncovered a number of host complexes targeted by viral proteins, including the finding that HIV protease cleaves eIF3d, a subunit of eukaryotic translation initiation factor 3. This host protein is one of eleven identified in this analysis that act to inhibit HIV replication. This data set facilitates a more comprehensive and detailed understanding of how the host machinery is manipulated during the course of HIV infection.

Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins

Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype–phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or ‘passenger’, cancer mutations from causal, or ‘driver’, mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.

MicroRNA-21 Integrates Pathogenic Signaling to Control Pulmonary Hypertension Results of a Network Bioinformatics Approach

Background— Pulmonary hypertension (PH) is driven by diverse pathogenic etiologies. Owing to their pleiotropic actions, microRNA molecules are potential candidates for coordinated regulation of these disease stimuli. Methods and Results— Using a network biology approach, we identify microRNA associated with multiple pathogenic pathways central to PH. Specifically, microRNA-21 (miR-21) is predicted as a PH-modifying microRNA, regulating targets integral to bone morphogenetic protein (BMP) and Rho/Rho-kinase signaling as well as functional pathways associated with hypoxia, inflammation, and genetic haploinsufficiency of BMP receptor type 2. To validate these predictions, we have found that hypoxia and BMP receptor type 2 signaling independently upregulate miR-21 in cultured pulmonary arterial endothelial cells. In a reciprocal feedback loop, miR-21 downregulates BMP receptor type 2 expression. Furthermore, miR-21 directly represses RhoB expression and Rho-kinase activity, inducing molecular changes consistent with decreased angiogenesis and vasodilation. In vivo, miR-21 is upregulated in pulmonary tissue from several rodent models of PH and in humans with PH. On induction of disease in miR-21–null mice, RhoB expression and Rho-kinase activity are increased, accompanied by exaggerated manifestations of PH. Conclusions— A network-based bioinformatic approach coupled with confirmatory in vivo data delineates a central regulatory role for miR-21 in PH. Furthermore, this study highlights the unique utility of network biology for identifying disease-modifying microRNA in PH.Background— Pulmonary hypertension (PH) is driven by diverse pathogenic etiologies. Owing to their pleiotropic actions, microRNA molecules are potential candidates for coordinated regulation of these disease stimuli. Methods and Results— Using a network biology approach, we identify microRNA associated with multiple pathogenic pathways central to PH. Specifically, microRNA-21 (miR-21) is predicted as a PH-modifying microRNA, regulating targets integral to bone morphogenetic protein (BMP) and Rho/Rho-kinase signaling as well as functional pathways associated with hypoxia, inflammation, and genetic haploinsufficiency of BMP receptor type 2. To validate these predictions, we have found that hypoxia and BMP receptor type 2 signaling independently upregulate miR-21 in cultured pulmonary arterial endothelial cells. In a reciprocal feedback loop, miR-21 downregulates BMP receptor type 2 expression. Furthermore, miR-21 directly represses RhoB expression and Rho-kinase activity, inducing molecular changes consistent with decreased angiogenesis and vasodilation. In vivo, miR-21 is upregulated in pulmonary tissue from several rodent models of PH and in humans with PH. On induction of disease in miR-21 –null mice, RhoB expression and Rho-kinase activity are increased, accompanied by exaggerated manifestations of PH. Conclusions— A network-based bioinformatic approach coupled with confirmatory in vivo data delineates a central regulatory role for miR-21 in PH. Furthermore, this study highlights the unique utility of network biology for identifying disease-modifying microRNA in PH. # Clinical Perspective {#article-title-52}

Predicting synthetic rescues in metabolic networks

An important goal of medical research is to develop methods to recover the loss of cellular function due to mutations and other defects. Many approaches based on gene therapy aim to repair the defective gene or to insert genes with compensatory function. Here, we propose an alternative, network‐based strategy that aims to restore biological function by forcing the cell to either bypass the functions affected by the defective gene, or to compensate for the lost function. Focusing on the metabolism of single‐cell organisms, we computationally study mutants that lack an essential enzyme, and thus are unable to grow or have a significantly reduced growth rate. We show that several of these mutants can be turned into viable organisms through additional gene deletions that restore their growth rate. In a rather counterintuitive fashion, this is achieved via additional damage to the metabolic network. Using flux balance‐based approaches, we identify a number of synthetically viable gene pairs, in which the removal of one enzyme‐encoding gene results in a non‐viable phenotype, while the deletion of a second enzyme‐encoding gene rescues the organism. The systematic network‐based identification of compensatory rescue effects may open new avenues for genetic interventions.

Global Mapping of the Inc-Human Interactome Reveals that Retromer Restricts Chlamydia Infection

Chlamydia trachomatis is a leading cause of genital and ocular infections for which no vaccine exists. Upon entry into host cells, C. trachomatis resides within a membrane-bound compartment—the inclusion—and secretes inclusion membrane proteins (Incs) that are thought to modulate the host-bacterium interface. To expand our understanding of Inc function(s), we subjected putative C. trachomatis Incs to affinity purification-mass spectroscopy (AP-MS). We identified Inc-human interactions for 38/58 Incs with enrichment in host processes consistent with Chlamydias intracellular life cycle. There is significant overlap between Inc targets and viral proteins, suggesting common pathogenic mechanisms among obligate intracellular microbes. IncE binds to sorting nexins (SNXs) 5/6, components of the retromer, which relocalizes SNX5/6 to the inclusion membrane and augments inclusion membrane tubulation. Depletion of retromer components enhances progeny production, revealing that retromer restricts Chlamydia infection. This study demonstrates the value of proteomics in unveiling host-pathogen interactions in genetically challenging microbes.

Viral perturbations of host networks reflect disease etiology.

Many human diseases, arising from mutations of disease susceptibility genes (genetic diseases), are also associated with viral infections (virally implicated diseases), either in a directly causal manner or by indirect associations. Here we examine whether viral perturbations of host interactome may underlie such virally implicated disease relationships. Using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV), we find that host targets of viral proteins reside in network proximity to products of disease susceptibility genes. Expression changes in virally implicated disease tissues and comorbidity patterns cluster significantly in the network vicinity of viral targets. The topological proximity found between cellular targets of viral proteins and disease genes was exploited to uncover a novel pathway linking HPV to Fanconi anemia.

The art of community detection

Networks in nature possess a remarkable amount of structure. Via a series of data‐driven discoveries, the cutting edge of network science has recently progressed from positing that the random graphs of mathematical graph theory might accurately describe real networks to the current viewpoint that networks in nature are highly complex and structured entities. The identification of high order structures in networks unveils insights into their functional organization. Recently, Clauset, Moore, and Newman, 1 introduced a new algorithm that identifies such heterogeneities in complex networks by utilizing the hierarchy that necessarily organizes the many levels of structure. Here, we anchor their algorithm in a general community detection framework and discuss the future of community detection. BioEssays 30:934–938, 2008.

A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma

Recent advances in genetics have spurred rapid progress towards the systematic identification of genes involved in complex diseases. Still, the detailed understanding of the molecular and physiological mechanisms through which these genes affect disease phenotypes remains a major challenge. Here, we identify the asthma disease module, i.e. the local neighborhood of the interactome whose perturbation is associated with asthma, and validate it for functional and pathophysiological relevance, using both computational and experimental approaches. We find that the asthma disease module is enriched with modest GWAS P-values against the background of random variation, and with differentially expressed genes from normal and asthmatic fibroblast cells treated with an asthma-specific drug. The asthma module also contains immune response mechanisms that are shared with other immune-related disease modules. Further, using diverse omics (genomics, gene-expression, drug response) data, we identify the GAB1 signaling pathway as an important novel modulator in asthma. The wiring diagram of the uncovered asthma module suggests a relatively close link between GAB1 and glucocorticoids (GCs), which we experimentally validate, observing an increase in the level of GAB1 after GC treatment in BEAS-2B bronchial epithelial cells. The siRNA knockdown of GAB1 in the BEAS-2B cell line resulted in a decrease in the NFkB level, suggesting a novel regulatory path of the pro-inflammatory factor NFkB by GAB1 in asthma.

Local structure of directed networks.

Previous work on undirected small-world networks established the paradigm that locally structured networks tend to have a high density of short loops. On the other hand, many realistic networks are directed. Here we investigate the local organization of directed networks and find, surprisingly, that real networks often have very few short loops as compared to random models. We develop a theory and derive conditions for determining if a given network has more or less loops than its randomized counterparts. These findings carry broad implications for structural and dynamical processes sustained by directed networks.