The increasing cost of drug development together with a significant drop in the number of new drug approvals raises the need for innovative approaches for target identification and efficacy ...prediction. Here, we take advantage of our increasing understanding of the network-based origins of diseases to introduce a drug-disease proximity measure that quantifies the interplay between drugs targets and diseases. By correcting for the known biases of the interactome, proximity helps us uncover the therapeutic effect of drugs, as well as to distinguish palliative from effective treatments. Our analysis of 238 drugs used in 78 diseases indicates that the therapeutic effect of drugs is localized in a small network neighborhood of the disease genes and highlights efficacy issues for drugs used in Parkinson and several inflammatory disorders. Finally, network-based proximity allows us to predict novel drug-disease associations that offer unprecedented opportunities for drug repurposing and the detection of adverse effects.
A network approach to finding disease modules
Shared genes represent a powerful but limited representation of the mechanistic relationship between two diseases. However, the analysis of ...protein-protein interactions has been hampered by the incompleteness of interactome maps. Menche
et al.
formulated the mathematical conditions needed to allow a disease module (a localized region of connections between disease-related proteins) to be observed. Only diseases with data coverage that exceeds a specific threshold have identifiable disease modules. The network-based distance between two disease modules revealed that disease pairs that are predicted to have overlapping modules had statistically significant molecular similarity. These similarities encompassed their protein components, gene expression, symptoms, and morbidity. Molecular-level links between diseases lacking shared disease genes could also be identified.
Science
, this issue
10.1126/science.1257601
Incomplete networks of protein-protein interactions help explain disease relationships, even in the absence of shared genes.
INTRODUCTION
A disease is rarely a straightforward consequence of an abnormality in a single gene, but rather reflects the interplay of multiple molecular processes. The relationships among these processes are encoded in the interactome, a network that integrates all physical interactions within a cell, from protein-protein to regulatory protein–DNA and metabolic interactions. The documented propensity of disease-associated proteins to interact with each other suggests that they tend to cluster in the same neighborhood of the interactome, forming a disease module, a connected subgraph that contains all molecular determinants of a disease. The accurate identification of the corresponding disease module represents the first step toward a systematic understanding of the molecular mechanisms underlying a complex disease. Here, we present a network-based framework to identify the location of disease modules within the interactome and use the overlap between the modules to predict disease-disease relationships.
RATIONALE
Despite impressive advances in high-throughput interactome mapping and disease gene identification, both the interactome and our knowledge of disease-associated genes remain incomplete. This incompleteness prompts us to ask to what extent the current data are sufficient to map out the disease modules, the first step toward an integrated approach toward human disease. To make progress, we must formulate mathematically the impact of network incompleteness on the identifiability of disease modules, quantifying the predictive power and the limitations of the current interactome.
RESULTS
Using the tools of network science, we show that we can only uncover disease modules for diseases whose number of associated genes exceeds a critical threshold determined by the network incompleteness. We find that disease proteins associated with 226 diseases are clustered in the same network neighborhood, displaying a statistically significant tendency to form identifiable disease modules. The higher the degree of agglomeration of the disease proteins within the interactome, the higher the biological and functional similarity of the corresponding genes. These findings indicate that many local neighborhoods of the interactome represent the observable part of the true, larger and denser disease modules.
If two disease modules overlap, local perturbations causing one disease can disrupt pathways of the other disease module as well, resulting in shared clinical and pathobiological characteristics. To test this hypothesis, we measure the network-based separation of each disease pair, observing a direct relation between the pathobiological similarity of diseases and their relative distance in the interactome. We find that disease pairs with overlapping disease modules display significant molecular similarity, elevated coexpression of their associated genes, and similar symptoms and high comorbidity. At the same time, non-overlapping disease pairs lack any detectable pathobiological relationships. The proposed network-based distance allows us to predict the pathobiological relationship even for diseases that do not share genes.
CONCLUSION
Despite its incompleteness, the interactome has reached sufficient coverage to allow the systematic investigation of disease mechanisms and to help uncover the molecular origins of the pathobiological relationships between diseases. The introduced network-based framework can be extended to address numerous questions at the forefront of network medicine, from interpreting genome-wide association study data to drug target identification and repurposing.
Diseases within the interactome.
The interactome collects all physical interactions between a cell’s molecular components. Proteins associated with the same disease form connected subgraphs, called disease modules, shown for multiple sclerosis (MS), peroxisomal disorders (PD), and rheumatoid arthritis (RA). Disease pairs with overlapping modules (MS and RA) have some phenotypic similarities and high comorbidity. Non-overlapping diseases, like MS and PD, lack detectable clinical relationships.
According to the disease module hypothesis, the cellular components associated with a disease segregate in the same neighborhood of the human interactome, the map of biologically relevant molecular interactions. Yet, given the incompleteness of the interactome and the limited knowledge of disease-associated genes, it is not obvious if the available data have sufficient coverage to map out modules associated with each disease. Here we derive mathematical conditions for the identifiability of disease modules and show that the network-based location of each disease module determines its pathobiological relationship to other diseases. For example, diseases with overlapping network modules show significant coexpression patterns, symptom similarity, and comorbidity, whereas diseases residing in separated network neighborhoods are phenotypically distinct. These tools represent an interactome-based platform to predict molecular commonalities between phenotypically related diseases, even if they do not share primary disease genes.
Despite exceptional experimental efforts to map out the human interactome, the continued data incompleteness limits our ability to understand the molecular roots of human disease. Computational tools ...offer a promising alternative, helping identify biologically significant, yet unmapped protein-protein interactions (PPIs). While link prediction methods connect proteins on the basis of biological or network-based similarity, interacting proteins are not necessarily similar and similar proteins do not necessarily interact. Here, we offer structural and evolutionary evidence that proteins interact not if they are similar to each other, but if one of them is similar to the other's partners. This approach, that mathematically relies on network paths of length three (L3), significantly outperforms all existing link prediction methods. Given its high accuracy, we show that L3 can offer mechanistic insights into disease mechanisms and can complement future experimental efforts to complete the human interactome.
Recent advances in DNA/RNA sequencing have made it possible to identify new targets rapidly and to repurpose approved drugs for treating heterogeneous diseases by the 'precise' targeting of ...individualized disease modules. In this study, we develop a Genome-wide Positioning Systems network (GPSnet) algorithm for drug repurposing by specifically targeting disease modules derived from individual patient's DNA and RNA sequencing profiles mapped to the human protein-protein interactome network. We investigate whole-exome sequencing and transcriptome profiles from ~5,000 patients across 15 cancer types from The Cancer Genome Atlas. We show that GPSnet-predicted disease modules can predict drug responses and prioritize new indications for 140 approved drugs. Importantly, we experimentally validate that an approved cardiac arrhythmia and heart failure drug, ouabain, shows potential antitumor activities in lung adenocarcinoma by uniquely targeting a HIF1α/LEO1-mediated cell metabolism pathway. In summary, GPSnet offers a network-based, in silico drug repurposing framework for more efficacious therapeutic selections.
Chaperones are central to the proteostasis network (PN) and safeguard the proteome from misfolding, aggregation, and proteotoxicity. We categorized the human chaperome of 332 genes into network ...communities using function, localization, interactome, and expression data sets. During human brain aging, expression of 32% of the chaperome, corresponding to ATP-dependent chaperone machines, is repressed, whereas 19.5%, corresponding to ATP-independent chaperones and co-chaperones, are induced. These repression and induction clusters are enhanced in the brains of those with Alzheimer’s, Huntington’s, or Parkinson’s disease. Functional properties of the chaperome were assessed by perturbation in C. elegans and human cell models expressing Aβ, polyglutamine, and Huntingtin. Of 219 C. elegans orthologs, knockdown of 16 enhanced both Aβ and polyQ-associated toxicity. These correspond to 28 human orthologs, of which 52% and 41% are repressed, respectively, in brain aging and disease and 37.5% affected Huntingtin aggregation in human cells. These results identify a critical chaperome subnetwork that functions in aging and disease.
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•Chaperome expression is dramatically affected in human brain aging•Chaperome dynamics correlate with aging and neurodegenerative disease•Functional analysis identifies a chaperome subset that safeguards proteostasis
Brehme et al. have examined the chaperome from C. elegans to humans using functional assays and expression as well as protein-interactome analysis. The authors identify a conserved C. elegans chaperome subnetwork of 16 chaperone genes, corresponding to 28 human orthologs that are affected in brain aging and diseases associated with protein aggregation.
While alternative splicing is known to diversify the functional characteristics of some genes, the extent to which protein isoforms globally contribute to functional complexity on a proteomic scale ...remains unknown. To address this systematically, we cloned full-length open reading frames of alternatively spliced transcripts for a large number of human genes and used protein-protein interaction profiling to functionally compare hundreds of protein isoform pairs. The majority of isoform pairs share less than 50% of their interactions. In the global context of interactome network maps, alternative isoforms tend to behave like distinct proteins rather than minor variants of each other. Interaction partners specific to alternative isoforms tend to be expressed in a highly tissue-specific manner and belong to distinct functional modules. Our strategy, applicable to other functional characteristics, reveals a widespread expansion of protein interaction capabilities through alternative splicing and suggests that many alternative “isoforms” are functionally divergent (i.e., “functional alloforms”).
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•Alternative splicing can produce isoforms with vastly different interaction profiles•These differences can be as great as those between proteins encoded by different genes•Isoform-specific partners exhibit distinct expression and functional characteristics
Alternatively spliced isoforms of proteins exhibit strikingly different interaction profiles and thus, in the context of global interactome networks, appear to behave as if encoded by distinct genes rather than as minor variants of each other.
Interactome modeling Vidal, Marc
FEBS letters,
March 21, 2005, Letnik:
579, Številka:
8
Journal Article
Recenzirano
Odprti dostop
A long-term goal of the field of interactome modeling is to understand how global and local properties of complex macromolecular networks impact on observable biological properties, and how changes ...in such properties can lead to human diseases. The information available at this stage of development of the field provides strong evidence for the existence of such interesting global and local properties, but also demonstrates that many more datasets will be needed to provide accurate models with increasingly predictive capacity. This review focuses on an early attempt at mapping a multicellular interactome network and on the lessons learned from that attempt.
human disease network Goh, Kwang-Il; Cusick, Michael E; Valle, David ...
Proceedings of the National Academy of Sciences - PNAS,
05/2007, Letnik:
104, Številka:
21
Journal Article
Recenzirano
Odprti dostop
A network of disorders and disease genes linked by known disorder-gene associations offers a platform to explore in a single graph-theoretic framework all known phenotype and disease gene ...associations, indicating the common genetic origin of many diseases. Genes associated with similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, supporting the existence of distinct disease-specific functional modules. We find that essential human genes are likely to encode hub proteins and are expressed widely in most tissues. This suggests that disease genes also would play a central role in the human interactome. In contrast, we find that the vast majority of disease genes are nonessential and show no tendency to encode hub proteins, and their expression pattern indicates that they are localized in the functional periphery of the network. A selection-based model explains the observed difference between essential and disease genes and also suggests that diseases caused by somatic mutations should not be peripheral, a prediction we confirm for cancer genes.
Plants generate effective responses to infection by recognizing both conserved and variable pathogen-encoded molecules. Pathogens deploy virulence effector proteins into host cells, where they ...interact physically with host proteins to modulate defense. We generated an interaction network of plant-pathogen effectors from two pathogens spanning the eukaryote-eubacteria divergence, three classes of "Arabidopsis" immune system proteins, and ∼8000 other "Arabidopsis" proteins. We noted convergence of effectors onto highly interconnected host proteins and indirect, rather than direct, connections between effectors and plant immune receptors. We demonstrated plant immune system functions for 15 of 17 tested host proteins that interact with effectors from both pathogens. Thus, pathogens from different kingdoms deploy independently evolved virulence proteins that interact with a limited set of highly connected cellular hubs to facilitate their diverse life-cycle strategies.
Cyclin D-dependent kinases (CDK4 and CDK6) are positive regulators of cell cycle entry and they are overactive in the majority of human cancers. However, it is currently not completely understood by ...which cellular mechanisms CDK4/6 promote tumorigenesis, largely due to the limited number of identified substrates. Here we performed a systematic screen for substrates of cyclin D1-CDK4 and cyclin D3-CDK6. We identified the Forkhead Box M1 (FOXM1) transcription factor as a common critical phosphorylation target. CDK4/6 stabilize and activate FOXM1, thereby maintain expression of G1/S phase genes, suppress the levels of reactive oxygen species (ROS), and protect cancer cells from senescence. Melanoma cells, unlike melanocytes, are highly reliant on CDK4/6-mediated senescence suppression, which makes them particularly susceptible to CDK4/6 inhibition.
► Assembly of an in vitro CDK4/6 substrate resource across the human proteome ► FOXM1 phosphorylation by CDK4/6 results in protein stabilization and activation ► FOXM1 is critical for CDK4/6-mediated cell cycle entry and senescence suppression ► CDK4/6 blockade in melanoma cells leads to FOXM1 degradation and massive senescence
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