Interactome Networks and Human Disease

Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis.

Microdeletion syndromes (MSs) are a heterogeneous group of genetic diseases that can virtually affect all functions and organs in humans. Although systems biology approaches integrating multiomics and database information into biological networks have expanded our knowledge of genetic disorders, cytogenomic network-based analysis has rarely been applied to study MSs. In this study, we analyzed data of 28 MSs, using network-based approaches, to investigate the associations between the critical chromosome regions and the respective underlying biological network systems. We identified MSs-associated proteins that were organized in a network of linked modules within the human interactome. Certain MSs formed highly interlinked self-contained disease modules. Furthermore, we observed disease modules involving proteins from other disease groups in the MSs interactome. Moreover, analysis of integrated data from 564 genes located in known chromosomal critical regions, including those contributing to topological parameters, shared pathways, and gene-disease associations, indicated that complex biological systems and cellular networks may underlie many genotype to phenotype associations in MSs. In conclusion, we used a network-based analysis to provide resources that may contribute to better understanding of the molecular pathways involved in MSs.

Mol Syst Biol. 6: 443 Cellular processes are mediated by complex webs of interactions between macromolecules and metabolites, the complete set of which is often referred to as ‘interactome network’. Global and local properties of interactome networks appear to integrate genotypes into biological functions and phenotypes (Gavin et al , 2006). So far, empirical mapping efforts of cellular interactome networks have largely focused on interactions between macromolecules, such as protein–protein and DNA–protein interactions. Corresponding efforts to chart interactome networks between macromolecules and metabolites (sugars, nucleotides, amino acids or lipids) are still in their infancies. Lipids represent a large and diverse class of bioactive metabolites with mostly unknown molecular modes of action. Current knowledge about their ‘connectivity’ represents solitary islands on a vast open ocean rather than a comprehensive interconnected atlas. In an article just published in Molecular Systems Biology (Gallego et al , 2010), Gavin and colleagues describe a systematic screening strategy for protein–lipid interactions in Saccharomyces cerevisiae . Over 500 protein–lipid associations were catalogued, shedding light on the elusive modes of action of several bioactive lipids, and uncovering a novel dual‐binding specificity of a PH domain based on a novel structure. Additionally, a complete linkage analysis of protein–lipid‐binding fingerprints was modeled as predictors of protein localization (Figure 1). Figure 1. Yeast protein–lipid‐binding fingerprints as predictors of protein localization, domains and functions. ( A ) Yeast protein–lipid‐binding map summarizing protein–lipid‐binding frequencies, …

The Indonesian government is currently implementing two types of curriculums, namely the 2013 curriculum and the ‘Merdeka belajar’ curriculum which has been implemented for 2 years. Both of these differently named curriculums run together at different academic grades throughout Indonesia. Although they have different names, both curriculums have similarities in the application of their learning process, which is inquiry-based learning. Teachers can apply inquiry-based learning based on the guidelines specified in the curriculum or implement other inquiry-based learning methods, one of which is Wenning's levels of inquiry. This study aims to develop and strengthens the understanding of basic concepts in biology based on the student experience. This study investigates the impact of discovery learning on the development and strengthening of understanding basic concepts in biology, based on the experiences of students. The research aimed to explore the effectiveness of a discovery learning approach in enhancing students' comprehension and retention of fundamental biological principles. This study emphasizes the value of discovery learning as a powerful pedagogical approach for facilitating students' comprehension and mastery of basic concepts in biology. After carrying out the learning process with discovery learning steps, students get reinforcement of the basic concepts of the food digestive system and make it easier for students to carry out the next stage of inquiry.

High-throughput techniques involve large-scale detection of protein-protein interactions. This interaction data set from the genome-scale perspective is structured into an interactome network. Since the interaction evidence represents functional linkage, various graph-theoretic computational approaches have been applied to the interactome networks for functional characterization. However, this data is generally unreliable, and the typical genome-wide interactome networks have a complex connectivity. In this paper, the authors explore systematic analysis of protein interactome networks, and propose a $k$-round signal flow simulation algorithm to measure interaction reliability from connection patterns of the interactome networks. This algorithm quantitatively characterizes functional links between proteins by simulating the propagation of information signals through complex connections. In this regard, the algorithm efficiently estimates the strength of alternative paths for each interaction. The authors also present an algorithm for mining the complex interactome network structure. The algorithm restructures the network by hierarchical ordering of nodes, and this structure re-formatting process reveals hub proteins in the interactome networks. This paper demonstrates that two rounds of simulation accurately scores interaction reliability in terms of ontological correlation and functional consistency. Finally, the authors validate that the selected structural hubs represent functional core proteins.

The human interactome is instrumental in the systems-level study of the cell and the contextualization of disease-associated gene perturbations. However, reference organismal interactomes do not capture the cell-type-specific context in which proteins and modules preferentially act. Here, we introduce SCINET, a computational framework that reconstructs an ensemble of cell-type-specific interactomes by integrating a global, context-independent reference interactome with a single-cell gene-expression profile. SCINET addresses technical challenges of single-cell data by robustly imputing, transforming, and normalizing the initially noisy and sparse expression of data. Inferred cell-level gene interaction probabilities and group-level interaction strengths define cell-type-specific interactomes. We use SCINET to reconstruct and analyze interactomes of the major human brain and immune cell types, revealing specificity and modularity of perturbations associated with neurodegenerative, neuropsychiatric, and autoimmune disorders. We report cell-type interactomes for brain and immune cell types, together with the SCINET package.

A report on the joint Cold Spring Harbor/Wellcome Trust Meeting 'Interactome Networks', Hinxton, UK, 31 August-4 September 2005.

Metabolic networks: biology meets engineering sciences

Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein–protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well.

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

BackgroundThe emerging tools of protein‐protein interactome network offer a platform to explore not only the molecular complexity of human diseases, but also to identify risk genes and drug targets. Integration of the genome, transcriptome, proteome, and the interactome networks are essential for such identification, including Alzheimer’s disease (AD), Parkinson disease (PD), and Amyotrophic lateral sclerosis (ALS)MethodIn this study, we performed multi‐modal analyses of cross‐species protein interactome networks and human brain functional genomics data to identify risk genes and drug targets for neurodegenerative diseases. We presented a multi‐view topology‐based deep learning framework to identify disease‐associated genes for cross‐species interactome (TAG‐X). We re‐constructed comprehensive protein‐protein interactome networks for human, Drosophila melanogaster (fruit fly), Caenorhabditis elegans (worm), and Saccharomyces cerevisiae(yeast), by assembling high quality binary protein‐protein interactions (PPI). The fundamental premise of TAG‐X is that AD risk genes exhibit distinct functional characteristics compared to non‐risk genes and, therefore, can be distinguished by their aggregated human brain‐specific functional genomic features from various quantitative trait loci (x‐QTL), including expression QTL (eQTL), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL).ResultAfter integration genome‐wide association studies (GWAS) and x‐QTL data into the interactome networks via TAG‐X, we found that unique integration of fly, worm and yeast interactome networks boosted performance in risk gene prediction compared with the human protein‐protein interactome across AD (e.g., fly: NDUFAF6, CHRNA2; worm and yeast: TOMM40), PD (e.g., fly: SCARB2; worm: AGAP1; yeast: SLC2A13, BCKDK) and ALS (e.g., fly: SOD1; worm: SARM1; yeast: VCP, PRDX6). We found that human brain‐specific PPI network presented the strongest potential for AD risk gene discovery (e.g., AD: ACE, BIN1, INPP5D,MS4A4A, SYK; PD: SNCA, LRRK2, DGKQ; ALS: SCFD1, G2E3). Furthermore, interactome network‐predicted genes are significantly enriched in known drug targets and are significantly enriched in disease‐related pathobiological processes.ConclusionIn summary, we presented a cross‐species protein interactome network methodology that utilizes functional genomic and GWAS findings to identify disease risk genes and drug targets for AD and other neurodegenerative diseases if broadly applied. Functional observations of candidate targets and genes are warranted in the future.

Advances in genome-scale molecular biology and molecular genetics have greatly elevated our knowledge on the basic components of human biology and diseases. At the same time, the importance of cellular networks between those biological components is increasingly appreciated. Built upon these recent technological and conceptual advances, a new discipline called the network medicine, an approach to understand human diseases from a network point-of-view, is about to emerge. In this review article, we will survey some recent endeavours along this direction, centred on the concept and applications of the human diseasome and the human disease network. Questions, and partial answers thereof, such as how the connectivity between molecular parts translates into the relationships between the related disorders on a global scale and how central the disease-causing genetic components are in the cellular network, will be discussed. The use of the diseasome in combination with various interactome networks and other disease-related factors is also reviewed.

While understanding macromolecular structural elements and their roles in dictating cellular function is critical to grasp basic concepts in biology, it can be challenging for students to master this content—these elements naturally exist at the nanoscale and are not observable with the naked eye. Oftentimes this understanding is catalyzed by impactful illustrations and animations found online and in textbooks. In recent years, 3D printing technology has become readily accessible as an additional way to generate models and visualize entities of interest. In this report, we describe and discuss the efficacy of an approach using 3D-printed models in combination with online open-source molecular modeling analyses of the macromolecular structure of p53 to engage students with molecular concepts in cancer cell biology and human health. This pedagogy strategy has been successfully integrated into an upper-level undergraduate course at a primarily undergraduate institution and a graduate biology course at a public research university. We describe the potential benefits while providing tools for others to integrate this strategy into their teaching.

For over half a century it has been conjectured that macromolecules form complex networks of functionally interacting components, and that the molecular mechanisms underlying most biological processes correspond to particular steady states adopted by such cellular networks. However, until recently, systems‐level theoretical conjectures remained largely unappreciated, mainly because of lack of supporting experimental data.To generate the information necessary to eventually address how complex cellular networks relate to biology, we initiated, at the scale of the whole proteome, an integrated approach for modeling protein‐protein interaction or “interactome” networks. Our main questions are: How are interactome networks organized at the scale of the whole cell? How can we uncover local and global features underlying this organization, and how are interactome networks modified in human disease, such as cancer?

BackgroundSystematic approach for drug discovery is an emerging discipline in systems biology research area. It aims at integrating interaction data and experimental data to elucidate diseases and also raises new issues in drug discovery for cancer treatment. However, drug target discovery is still at a trial-and-error experimental stage and it is a challenging task to develop a prediction model that can systematically detect possible drug targets to deal with complex diseases.MethodsWe integrate gene expression, disease genes and interaction networks to identify the effective drug targets which have a strong influence on disease genes using network flow approach. In the experiments, we adopt the microarray dataset containing 62 prostate cancer samples and 41 normal samples, 108 known prostate cancer genes and 322 approved drug targets treated in human extracted from DrugBank database to be candidate proteins as our test data. Using our method, we prioritize the candidate proteins and validate them to the known prostate cancer drug targets.ResultsWe successfully identify potential drug targets which are strongly related to the well known drugs for prostate cancer treatment and also discover more potential drug targets which raise the attention to biologists at present. We denote that it is hard to discover drug targets based only on differential expression changes due to the fact that those genes used to be drug targets may not always have significant expression changes. Comparing to previous methods that depend on the network topology attributes, they turn out that the genes having potential as drug targets are weakly correlated to critical points in a network. In comparison with previous methods, our results have highest mean average precision and also rank the position of the truly drug targets higher. It thereby verifies the effectiveness of our method.ConclusionsOur method does not know the real ideal routes in the disease network but it tries to find the feasible flow to give a strong influence to the disease genes through possible paths. We successfully formulate the identification of drug target prediction as a maximum flow problem on biological networks and discover potential drug targets in an accurate manner.