Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem

phuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets

Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics datasets.

Prioritizing drug targets by perturbing biological network response functions

Therapeutic interventions are designed to perturb the function of a biological system. However, there are many types of proteins that cannot be targeted with conventional small molecule drugs. Accordingly, many identified gene-regulatory drivers and downstream effectors are currently undruggable. Drivers and effectors are often connected by druggable signaling and regulatory intermediates. Methods to identify druggable intermediates therefore have general value in expanding the set of targets available for hypothesis-driven validation. Here we identify and prioritize potential druggable intermediates by developing a network perturbation theory, termed NetPert, for response functions of biological networks. Dynamics are defined by a network structure in which vertices represent genes and proteins, and edges represent gene-regulatory interactions and protein-protein interactions. Perturbation theory for network dynamics prioritizes targets that interfere with signaling from driver to response genes. Applications to organoid models for metastatic breast cancer demonstrate the ability of this mathematical framework to identify and prioritize druggable intermediates. While the short-time limit of the perturbation theory resembles betweenness centrality, NetPert is superior in generating target rankings that correlate with previous wet-lab assays and are more robust to incomplete or noisy network data. NetPert also performs better than a related graph diffusion approach. Wet-lab assays demonstrate that drugs for targets identified by NetPert, including targets that are not themselves differentially expressed, are active in suppressing additional metastatic phenotypes.

Transcriptional programs mediating neuronal toxicity and altered glial-neuronal signaling in a Drosophila knock-in tauopathy model

Missense mutations in the gene encoding the microtubule-associated protein TAU (current and approved symbol is MAPT) cause autosomal dominant forms of frontotemporal dementia. Multiple models of frontotemporal dementia based on transgenic expression of human TAU in experimental model organisms, including Drosophila, have been described. These models replicate key features of the human disease but do not faithfully recreate the genetic context of the human disorder. Here we use CRISPR-Cas-mediated gene editing to model frontotemporal dementia caused by the TAU P301L mutation by creating the orthologous mutation, P251L, in the endogenous Drosophila tau gene. Flies heterozygous or homozygous for Tau P251L display age-dependent neurodegeneration, display metabolic defects, and accumulate DNA damage in affected neurons. To understand the molecular events promoting neuronal dysfunction and death in knock-in flies, we performed single-cell RNA sequencing on approximately 130,000 cells from brains of Tau P251L mutant and control flies. We found that expression of disease-associated mutant tau altered gene expression cell autonomously in all neuronal cell types identified. Gene expression was also altered in glial cells, suggestive of non-cell-autonomous regulation. Cell signaling pathways, including glial-neuronal signaling, were broadly dysregulated as were brain region and cell type-specific protein interaction networks and gene regulatory programs. In summary, we present here a genetic model of tauopathy that faithfully recapitulates the genetic context and phenotypic features of the human disease, and use the results of comprehensive single-cell sequencing analysis to outline pathways of neurotoxicity and highlight the potential role of non-cell-autonomous changes in glia.

A systems-biology approach connects aging mechanisms with Alzheimer's disease pathogenesis

Age is the strongest risk factor for developing Alzheimer's disease, the most common neurodegenerative disorder. However, the mechanisms connecting advancing age to neurodegeneration in Alzheimer's disease are incompletely understood. We conducted an unbiased, genome-scale, forward genetic screen for age-associated neurodegeneration in Drosophila to identify the underlying biological processes required for maintenance of aging neurons. To connect genetic screen hits to Alzheimer's disease pathways, we measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimer's disease. We further identified Alzheimer's disease human genetic variants that modify expression in disease-vulnerable neurons. Through multi-omic, multi-species network integration of these data, we identified relationships between screen hits and tau-mediated neurotoxicity. Furthermore, we computationally and experimentally identified relationships between screen hits and DNA damage in Drosophila and human iPSC-derived neural progenitor cells. Our work identifies candidate pathways that could be targeted to attenuate the effects of age on neurodegeneration and Alzheimer's disease.

Transcriptional programs mediating neuronal toxicity and altered glial-neuronal signaling in a Drosophila knock-in tauopathy model

Missense mutations in the gene encoding the microtubule-associated protein tau cause autosomal dominant forms of frontotemporal dementia. Multiple models of frontotemporal dementia based on transgenic expression of human tau in experimental model organisms, including Drosophila, have been described. These models replicate key features of the human disease, but do not faithfully recreate the genetic context of the human disorder. Here we use CRISPR-Cas mediated gene editing to model frontotemporal dementia caused by the tau P301L mutation by creating the orthologous mutation, P251L, in the endogenous Drosophila tau gene. Flies heterozygous or homozygous for tau P251L display age-dependent neurodegeneration, metabolic defects and accumulate DNA damage in affected neurons. To understand the molecular events promoting neuronal dysfunction and death in knock-in flies we performed single-cell RNA sequencing on approximately 130,000 cells from brains of tau P251L mutant and control flies. We found that expression of disease-associated mutant tau altered gene expression cell autonomously in all neuronal cell types identified and non-cell autonomously in glial cells. Cell signaling pathways, including glial-neuronal signaling, were broadly dysregulated as were brain region and cell-type specific protein interaction networks and gene regulatory programs. In summary, we present here a genetic model of tauopathy, which faithfully recapitulates the genetic context and phenotypic features of the human disease and use the results of comprehensive single cell sequencing analysis to outline pathways of neurotoxicity and highlight the role of non-cell autonomous changes in glia.

Integrative analysis reveals a conserved role for the amyloid precursor protein in proteostasis during aging

Aβ peptides derived from the amyloid precursor protein (APP) have been strongly implicated in the pathogenesis of Alzheimer's disease. However, the normal function of APP and the importance of that role in neurodegenerative disease is less clear. We recover the Drosophila ortholog of APP, Appl, in an unbiased forward genetic screen for neurodegeneration mutants. We perform comprehensive single cell transcriptional and proteomic studies of Appl mutant flies to investigate Appl function in the aging brain. We find an unexpected role for Appl in control of multiple cellular pathways, including translation, mitochondrial function, nucleic acid and lipid metabolism, cellular signaling and proteostasis. We mechanistically define a role for Appl in regulating autophagy through TGFβ signaling and document the broader relevance of our findings using mouse genetic, human iPSC and in vivo tauopathy models. Our results demonstrate a conserved role for APP in controlling age-dependent proteostasis with plausible relevance to Alzheimer's disease.

Identification of the hub genes in polycystic ovary syndrome based on disease-associated molecule network

Revealing the key genes involved in polycystic ovary syndrome (PCOS) and elucidating its pathogenic mechanism is of extreme importance for the development of targeted clinical therapy for PCOS. Investigating disease by integrating several associated and interacting molecules in biological systems will make it possible to discover new pathogenic genes. In this study, an integrative disease-associated molecule network, combining protein-protein interactions and protein-metabolites interactions (PPMI) network was constructed based on the PCOS-associated genes and metabolites systematically collected. This new PPMI strategy identified several potential PCOS-associated genes, which have unreported in previous publications. Moreover, the systematic analysis of five benchmarks data sets indicated the DERL1 was identified as downregulated in PCOS granulosa cell and has good classification performance between PCOS patients and healthy controls. CCR2 and DVL3 were upregulated in PCOS adipose tissues and have good classification performance. The expression of novel gene FXR2 identified in this study is significantly increased in ovarian granulosa cells of PCOS patients compared with controls via quantitative analysis. Our study uncovers substantial differences in the PCOS-specific tissue and provides a plethora of information on dysregulated genes and metabolites that are linked to PCOS. This knowledgebase could have the potential to benefit the scientific and clinical community. In sum, the identification of novel gene associated with PCOS provides valuable insights into the underlying molecular mechanisms of PCOS and could potentially lead to the development of new diagnostic and therapeutic strategies.

Growing Directed Acyclic Graphs: Optimization Functions for Pathway Reconstruction Algorithms

A major challenge in molecular systems biology is to understand how proteins work to transmit external signals to changes in gene expression. Computationally reconstructing these signaling pathways from protein interaction networks can help understand what is missing from existing pathway databases. We formulate a new pathway reconstruction problem, one that iteratively grows directed acyclic graphs (DAGs) from a set of starting proteins in a protein interaction network. We present an algorithm that provably returns the optimal DAGs for two different cost functions and evaluate the pathway reconstructions when applied to six diverse signaling pathways from the NetPath database. The optimal DAGs outperform an existing k-shortest paths method for pathway reconstruction, and the new reconstructions are enriched for different biological processes. Growing DAGs is a promising step toward reconstructing pathways that provably optimize a specific cost function.

Computational systems approach towards phosphoproteomics and their downstream regulation

Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.

Risk-aware temporal cascade reconstruction to detect asymptomatic cases

This paper studies the problem of detecting asymptomatic cases in a temporal contact network in which multiple outbreaks have occurred. We show that the key to detecting asymptomatic cases well is taking into account both individual risk and the likelihood of disease-flow along edges. We consider both aspects by formulating the asymptomatic case detection problem as a directed prize-collecting Steiner tree (Directed PCST) problem. We present an approximation-preserving reduction from this problem to the directed Steiner tree problem and obtain scalable algorithms for the Directed PCST problem on instances with more than 1.5M edges obtained from both synthetic and fine-grained hospital data. On synthetic data, we demonstrate that our detection methods significantly outperform various baselines (with a gain of 3.6 × ). We apply our method to the infectious disease prediction task by using an additional feature set that captures exposure to detected asymptomatic cases and show that our method outperforms all baselines. We further use our method to detect infection sources ("patient zero") of outbreaks that outperform baselines. We also demonstrate that the solutions returned by our approach are clinically meaningful by presenting case studies.

De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet

Background

With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem.

Results

We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software.

Conclusion

The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.

Context dependent isoform specific PI3K inhibition confers drug resistance in hepatocellular carcinoma cells

BACKGROUND

Targeted therapies for Primary liver cancer (HCC) is limited to the multi-kinase inhibitors, and not fully effective due to the resistance to these agents because of the heterogeneous molecular nature of HCC developed during chronic liver disease stages and cirrhosis. Although combinatorial therapy can increase the efficiency of targeted therapies through synergistic activities, isoform specific effects of the inhibitors are usually ignored. This study concentrated on PI3K/Akt/mTOR pathway and the differential combinatory bioactivities of isoform specific PI3K-α inhibitor (PIK-75) or PI3K-β inhibitor (TGX-221) with Sorafenib dependent on PTEN context.

METHODS

The bioactivities of inhibitors on PTEN adequate Huh7 and deficient Mahlavu cells were investigated with real time cell growth, cell cycle and cell migration assays. Differentially expressed genes from RNA-Seq were identified by edgeR tool. Systems level network analysis of treatment specific pathways were performed with Prize Collecting Steiner Tree (PCST) on human interactome and enriched networks were visualized with Cytoscape platform.

RESULTS

Our data from combinatory treatment of Sorafenib and PIK-75 and TGX-221 showed opposite effects; while PIK-75 displays synergistic effects on Huh7 cells leading to apoptotic cell death, Sorafenib with TGX-221 display antagonistic effects and significantly promotes cell growth in PTEN deficient Mahlavu cells. Signaling pathways were reconstructed and analyzed in-depth from RNA-Seq data to understand mechanism of differential synergistic or antagonistic effects of PI3K-α (PIK-75) and PI3K-β (TGX-221) inhibitors with Sorafenib. PCST allowed as to identify AOX1 and AGER as targets in PI3K/Akt/mTOR pathway for this combinatory effect. The siRNA knockdown of AOX1 and AGER significantly reduced cell proliferation in HCC cells.

CONCLUSIONS

Simultaneously constructed and analyzed differentially expressed cellular networks presented in this study, revealed distinct consequences of isoform specific PI3K inhibition in PTEN adequate and deficient liver cancer cells. We demonstrated the importance of context dependent and isoform specific PI3K/Akt/mTOR signaling inhibition in drug resistance during combination therapies. ( https://github.com/cansyl/Isoform-spesific-PI3K-inhibitor-analysis ).

Autism genes converge on asynchronous development of shared neuron classes

Genetic risk for autism spectrum disorder (ASD) is associated with hundreds of genes spanning a wide range of biological functions^1-6. The alterations in the human brain resulting from mutations in these genes remain unclear. Furthermore, their phenotypic manifestation varies across individuals^7,8. Here we used organoid models of the human cerebral cortex to identify cell-type-specific developmental abnormalities that result from haploinsufficiency in three ASD risk genes-SUV420H1 (also known as KMT5B), ARID1B and CHD8-in multiple cell lines from different donors, using single-cell RNA-sequencing (scRNA-seq) analysis of more than 745,000 cells and proteomic analysis of individual organoids, to identify phenotypic convergence. Each of the three mutations confers asynchronous development of two main cortical neuronal lineages-γ-aminobutyric-acid-releasing (GABAergic) neurons and deep-layer excitatory projection neurons-but acts through largely distinct molecular pathways. Although these phenotypes are consistent across cell lines, their expressivity is influenced by the individual genomic context, in a manner that is dependent on both the risk gene and the developmental defect. Calcium imaging in intact organoids shows that these early-stage developmental changes are followed by abnormal circuit activity. This research uncovers cell-type-specific neurodevelopmental abnormalities that are shared across ASD risk genes and are finely modulated by human genomic context, finding convergence in the neurobiological basis of how different risk genes contribute to ASD pathology.

Proximity Interactome Map of the Vac14-Fig4 Complex Using BioID

Conversion between phosphatidylinositol-3-phosphate and phosphatidylinositol-3,5-bisphosphate on endosomal membranes is critical for the maturation of early endosomes to late endosomes/lysosomes and is regulated by the PIKfyve-Vac14-Fig4 complex. Despite the importance of this complex for endosomal homeostasis and vesicular trafficking, there is little known about how its activity is regulated or how it interacts with other cellular proteins. Here, we screened for the cellular interactome of Vac14 and Fig4 using proximity-dependent biotin labeling (BioID). After independently screening the interactomes of Vac14 and Fig4, we identified 89 high-confidence protein hits shared by both proteins. Network analysis of these hits revealed pathways with known involvement of the PIKfyve-Vac14-Fig4 complex, including vesicular organization and PI3K/Akt signaling, as well as novel pathways including cell cycle and mitochondrial regulation. We also identified subunits of coatomer complex I (COPI), a Golgi-associated complex with an emerging role in endosomal dynamics. Using proximity ligation assays, we validated the interaction between Vac14 and COPI subunit COPB1 and between Vac14 and Arf1, a GTPase required for COPI assembly. In summary, this study used BioID to comprehensively map the Vac14-Fig4 interactome, revealing potential roles for these proteins in diverse cellular processes and pathways, including preliminary evidence of an interaction between Vac14 and COPI. Data are available via ProteomeXchange with the identifier PXD027917.

Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.

CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data

Single-cell technology enables study of signal transduction in a complex tissue at unprecedented resolution. We describe CytoTalk for de novo construction of cell type-specific signaling networks using single-cell transcriptomic data. Using an integrated intracellular and intercellular gene network as the input, CytoTalk identifies candidate pathways using the prize-collecting Steiner forest algorithm. Using high-throughput spatial transcriptomic data and single-cell RNA sequencing data with receptor gene perturbation, we demonstrate that CytoTalk has substantial improvement over existing algorithms. To better understand plasticity of signaling networks across tissues and developmental stages, we perform a comparative analysis of signaling networks between macrophages and endothelial cells across human adult and fetal tissues. Our analysis reveals an overall increased plasticity of signaling networks across adult tissues and specific network nodes that contribute to increased plasticity. CytoTalk enables de novo construction of signal transduction pathways and facilitates comparative analysis of these pathways across tissues and conditions.

Automating parameter selection to avoid implausible biological pathway models

A common way to integrate and analyze large amounts of biological "omic" data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms' parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising algorithm to tune pathway reconstruction algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway reconstruction algorithms. We also demonstrate how pathway parameter advising can guide reconstruction of an influenza host factor network. Pathway parameter advising is method agnostic; it is applicable to any pathway reconstruction algorithm with tunable parameters.

Actionable Cytopathogenic Host Responses of Human Alveolar Type 2 Cells to SARS-CoV-2

Human transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causative pathogen of the COVID-19 pandemic, exerts a massive health and socioeconomic crisis. The virus infects alveolar epithelial type 2 cells (AT2s), leading to lung injury and impaired gas exchange, but the mechanisms driving infection and pathology are unclear. We performed a quantitative phosphoproteomic survey of induced pluripotent stem cell-derived AT2s (iAT2s) infected with SARS-CoV-2 at air-liquid interface (ALI). Time course analysis revealed rapid remodeling of diverse host systems, including signaling, RNA processing, translation, metabolism, nuclear integrity, protein trafficking, and cytoskeletal-microtubule organization, leading to cell cycle arrest, genotoxic stress, and innate immunity. Comparison to analogous data from transformed cell lines revealed respiratory-specific processes hijacked by SARS-CoV-2, highlighting potential novel therapeutic avenues that were validated by a high hit rate in a targeted small molecule screen in our iAT2 ALI system.

Analysis of Pan-omics Data in Human Interactome Network (APODHIN)

Analysis of Pan-omics Data in Human Interactome Network (APODHIN) is a platform for integrative analysis of transcriptomics, proteomics, genomics, and metabolomics data for identification of key molecular players and their interconnections exemplified in cancer scenario. APODHIN works on a meta-interactome network consisting of human protein-protein interactions (PPIs), miRNA-target gene regulatory interactions, and transcription factor-target gene regulatory relationships. In its first module, APODHIN maps proteins/genes/miRNAs from different omics data in its meta-interactome network and extracts the network of biomolecules that are differentially altered in the given scenario. Using this context specific, filtered interaction network, APODHIN identifies topologically important nodes (TINs) implementing graph theory based network topology analysis and further justifies their role via pathway and disease marker mapping. These TINs could be used as prospective diagnostic and/or prognostic biomarkers and/or potential therapeutic targets. In its second module, APODHIN attempts to identify cross pathway regulatory and PPI links connecting signaling proteins, transcription factors (TFs), and miRNAs to metabolic enzymes via utilization of single-omics and/or pan-omics data and implementation of mathematical modeling. Interconnections between regulatory components such as signaling proteins/TFs/miRNAs and metabolic pathways need to be elucidated more elaborately in order to understand the role of oncogene and tumor suppressors in regulation of metabolic reprogramming during cancer. APODHIN platform contains a web server component where users can upload single/multi omics data to identify TINs and cross-pathway links. Tabular, graphical and 3D network representations of the identified TINs and cross-pathway links are provided for better appreciation. Additionally, this platform also provides few example data analysis of cancer specific, single and/or multi omics dataset for cervical, ovarian, and breast cancers where meta-interactome networks, TINs, and cross-pathway links are provided. APODHIN platform is freely available at http://www.hpppi.iicb.res.in/APODHIN/home.html.

Network medicine in Cardiovascular Research

The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.

An Overview of Bioinformatics Methods for Analyzing Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders and are well recognized to be biologically heterogeneous in which various factors are associated, including genetic, metabolic, and environmental ones. Despite its high prevalence, only a few drugs have been approved for the treatment of ASD. Therefore, extensive studies have been conducted to identify ASD risk genes and novel drug targets. Since many genes and many other factors are associated with ASD, various bioinformatics methods have also been developed for the analysis of ASD. In this paper, we review bioinformatics methods for analyzing ASD data with the focus on computational aspects. We classify existing methods into two categories: (i) methods based on genomic variants and gene expression data, and (ii) methods using biological networks, which include gene co-expression networks and protein-protein interaction networks. Next, for each method, we provide an overall flow and elaborate on the computational techniques used. We also briefly review other approaches and discuss possible future directions and strategies for developing bioinformatics approaches to analyze ASD.

A dynamic programing approach to integrate gene expression data and network information for pathway model generation

MOTIVATION

As large amounts of biological data continue to be rapidly generated, a major focus of bioinformatics research has been aimed toward integrating these data to identify active pathways or modules under certain experimental conditions or phenotypes. Although biologically significant modules can often be detected globally by many existing methods, it is often hard to interpret or make use of the results toward pathway model generation and testing.

RESULTS

To address this gap, we have developed the IMPRes algorithm, a new step-wise active pathway detection method using a dynamic programing approach. IMPRes takes advantage of the existing pathway interaction knowledge in Kyoto Encyclopedia of Genes and Genomes. Omics data are then used to assign penalties to genes, interactions and pathways. Finally, starting from one or multiple seed genes, a shortest path algorithm is applied to detect downstream pathways that best explain the gene expression data. Since dynamic programing enables the detection one step at a time, it is easy for researchers to trace the pathways, which may lead to more accurate drug design and more effective treatment strategies. The evaluation experiments conducted on three yeast datasets have shown that IMPRes can achieve competitive or better performance than other state-of-the-art methods. Furthermore, a case study on human lung cancer dataset was performed and we provided several insights on genes and mechanisms involved in lung cancer, which had not been discovered before.

AVAILABILITY AND IMPLEMENTATION

IMPRes visualization tool is available via web server at http://digbio.missouri.edu/impres.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Reconstructing signaling pathways using regular language constrained paths

MOTIVATION

High-quality curation of the proteins and interactions in signaling pathways is slow and painstaking. As a result, many experimentally detected interactions are not annotated to any pathways. A natural question that arises is whether or not it is possible to automatically leverage existing pathway annotations to identify new interactions for inclusion in a given pathway.

RESULTS

We present RegLinker, an algorithm that achieves this purpose by computing multiple short paths from pathway receptors to transcription factors within a background interaction network. The key idea underlying RegLinker is the use of regular language constraints to control the number of non-pathway interactions that are present in the computed paths. We systematically evaluate RegLinker and five alternative approaches against a comprehensive set of 15 signaling pathways and demonstrate that RegLinker recovers withheld pathway proteins and interactions with the best precision and recall. We used RegLinker to propose new extensions to the pathways. We discuss the literature that supports the inclusion of these proteins in the pathways. These results show the broad potential of automated analysis to attenuate difficulties of traditional manual inquiry.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Murali-group/RegLinker.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ST-Steiner: a spatio-temporal gene discovery algorithm

MOTIVATION

Whole exome sequencing (WES) studies for autism spectrum disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited.

RESULTS

Here, we present a spatio-temporal gene discovery algorithm, which leverages information from evolving gene co-expression networks of neurodevelopment. The algorithm solves a prize-collecting Steiner forest-based problem on co-expression networks, adapted to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on ASD WES data of 3871 samples and identify risk clusters using BrainSpan co-expression networks of early- and mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the predictive power: predicted clusters are hit more and show higher enrichment in ASD-related functions compared with the state-of-the-art.

AVAILABILITY AND IMPLEMENTATION

The code is available at http://ciceklab.cs.bilkent.edu.tr/st-steiner.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks

Biomedical research studies have generated large multi-omic datasets to study complex diseases like Alzheimer’s disease (AD). An important aim of these studies is the identification of candidate genes that demonstrate congruent disease-related alterations across the different data types measured by the study. We developed a new method to detect such candidate genes in large multi-omic case-control studies that measure multiple data types in the same set of samples. The method is based on a gene-centric integrative coefficient quantifying to what degree consistent differences are observed in the different data types. For statistical inference, a Bayesian hierarchical model is used to study the distribution of the integrative coefficient. The model employs a conditional autoregressive prior to integrate a functional gene network and to share information between genes known to be functionally related. We applied the method to an AD dataset consisting of histone acetylation, DNA methylation, and RNA transcription data from human cortical tissue samples of 233 subjects, and we detected 816 genes with consistent differences between persons with AD and controls. The findings were validated in protein data and in RNA transcription data from two independent AD studies. Finally, we found three subnetworks of jointly dysregulated genes within the functional gene network which capture three distinct biological processes: myeloid cell differentiation, protein phosphorylation and synaptic signaling. Further investigation of the myeloid network indicated an upregulation of this network in early stages of AD prior to accumulation of hyperphosphorylated tau and suggested that increased CSF1 transcription in astrocytes may contribute to microglial activation in AD. Thus, we developed a method that integrates multiple data types and external knowledge of gene function to detect candidate genes, applied the method to an AD dataset, and identified several disease-related genes and processes demonstrating the usefulness of the integrative approach.

Recent technological advances have led to a new generation of studies that interrogate multiple molecular levels in the same target tissue of a set of subjects, generating complex multi-omic datasets with which to study disease mechanism. These datasets of genetic, epigenomic, transcriptomic, and other data have the potential to reveal novel biological insights; however, integrative analyses remain challenging and require new computational methods. We developed an integrative Bayesian approach to detect genes with consistent differences between case and control samples across multiple data types. The method further integrates prior knowledge about gene function in the form of a gene functional similarity network to improve statistical inference by sharing information between related genes. We applied our method to an Alzheimer’s disease dataset of epigenomic and transcriptomic data and detected and then validated several novel and known candidate genes as well as three major disease-related biological processes. One of these processes reflected microglial activation and included the cytokine CSF1. Single-nucleus data revealed that CSF1 was primarily upregulated in astrocytes, implicating the involvement of this cell type in microglial activation. Hence, we demonstrated that integrative analysis approaches to multi-omic datasets can improve candidate gene detection and thereby generate new insights into complex diseases.

PRODIGY: personalized prioritization of driver genes

MOTIVATION

Evolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, whereas most somatic mutations have no impact on progression. Distinguishing those mutated genes that drive tumorigenesis in a patient is a primary goal in cancer therapy: Knowledge of these genes and the pathways on which they operate can illuminate disease mechanisms and indicate potential therapies and drug targets. Current research focuses mainly on cohort-level driver gene identification but patient-specific driver gene identification remains a challenge.

METHODS

We developed a new algorithm for patient-specific ranking of driver genes. The algorithm, called PRODIGY, analyzes the expression and mutation profiles of the patient along with data on known pathways and protein-protein interactions. Prodigy quantifies the impact of each mutated gene on every deregulated pathway using the prize-collecting Steiner tree model. Mutated genes are ranked by their aggregated impact on all deregulated pathways.

RESULTS

In testing on five TCGA cancer cohorts spanning >2500 patients and comparison to validated driver genes, Prodigy outperformed extant methods and ranking based on network centrality measures. Our results pinpoint the pleiotropic effect of driver genes and show that Prodigy is capable of identifying even very rare drivers. Hence, Prodigy takes a step further toward personalized medicine and treatment.

AVAILABILITY AND IMPLEMENTATION

The Prodigy R package is available at: https://github.com/Shamir-Lab/PRODIGY.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrating protein localization with automated signaling pathway reconstruction

Background

Understanding cellular responses via signal transduction is a core focus in systems biology. Tools to automatically reconstruct signaling pathways from protein-protein interactions (PPIs) can help biologists generate testable hypotheses about signaling. However, automatic reconstruction of signaling pathways suffers from many interactions with the same confidence score leading to many equally good candidates. Further, some reconstructions are biologically misleading due to ignoring protein localization information.

Results

We propose LocPL, a method to improve the automatic reconstruction of signaling pathways from PPIs by incorporating information about protein localization in the reconstructions. The method relies on a dynamic program to ensure that the proteins in a reconstruction are localized in cellular compartments that are consistent with signal transduction from the membrane to the nucleus. LocPL and existing reconstruction algorithms are applied to two PPI networks and assessed using both global and local definitions of accuracy. LocPL produces more accurate and biologically meaningful reconstructions on a versatile set of signaling pathways.

Conclusion

LocPL is a powerful tool to automatically reconstruct signaling pathways from PPIs that leverages cellular localization information about proteins. The underlying dynamic program and signaling model are flexible enough to study cellular signaling under different settings of signaling flow across the cellular compartments.

Hierarchical HotNet: identifying hierarchies of altered subnetworks

Motivation

The analysis of high-dimensional ‘omics data is often informed by the use of biological interaction networks. For example, protein–protein interaction networks have been used to analyze gene expression data, to prioritize germline variants, and to identify somatic driver mutations in cancer. In these and other applications, the underlying computational problem is to identify altered subnetworks containing genes that are both highly altered in an ‘omics dataset and are topologically close (e.g. connected) on an interaction network.

Results

We introduce Hierarchical HotNet, an algorithm that finds a hierarchy of altered subnetworks. Hierarchical HotNet assesses the statistical significance of the resulting subnetworks over a range of biological scales and explicitly controls for ascertainment bias in the network. We evaluate the performance of Hierarchical HotNet and several other algorithms that identify altered subnetworks on the problem of predicting cancer genes and significantly mutated subnetworks. On somatic mutation data from The Cancer Genome Atlas, Hierarchical HotNet outperforms other methods and identifies significantly mutated subnetworks containing both well-known cancer genes and candidate cancer genes that are rarely mutated in the cohort. Hierarchical HotNet is a robust algorithm for identifying altered subnetworks across different ‘omics datasets.

Availability and implementation

http://github.com/raphael-group/hierarchical-hotnet.

Supplementary information

Supplementary material are available at Bioinformatics online.

Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma

As malignant transformation requires synchronization of growth-driving signaling (S) and metabolic (M) pathways, defining cancer-specific S-M interconnected networks (SMINs) could lead to better understanding of oncogenic processes. In a systems-biology approach, we developed a mathematical model for SMINs in mutated EGF receptor (EGFRvIII) compared to wild-type EGF receptor (EGFRwt) expressing glioblastoma multiforme (GBM). Starting with experimentally validated human protein-protein interactome data for S-M pathways, and incorporating proteomic data for EGFRvIII and EGFRwt GBM cells and patient transcriptomic data, we designed a dynamic model for EGFR-driven GBM-specific information flow. Key nodes and paths identified by in silico perturbation were validated experimentally when inhibition of signaling pathway proteins altered expression of metabolic proteins as predicted by the model. This demonstrated capacity of the model to identify unknown connections between signaling and metabolic pathways, explain the robustness of oncogenic SMINs, predict drug escape, and assist identification of drug targets and the development of combination therapies.

Complex and highly dynamic interconnected networks allow cancer to take different routes and circumvent chemotherapy. Therefore, understanding these context-specific networks and their dynamics of molecular interactions driven by different oncogenic signaling and metabolic pathways is very much needed to predict drug targets and the effect of therapeutics. We incorporated high-throughput transcriptome and proteome data into mathematical models to deduce properties of cancer cells through systems biology approach. Here we report the development, testing and validation of an integrated systems biology model of information flow between signaling and metabolic pathways to understand the regulation of the interconnection between them in cancer. Our model efficiently identified unique connections and key nodes important in signaling-metabolic information flow. We predicted some potential novel targets before performing actual drug tests. We have successfully applied this model to identify the interconnections altered in the constitutive signaling of the mutated EGFR by comparing EGF-dependent and wild-type EGFR signaling in glioblastoma multiforme.

A systems biology pipeline identifies regulatory networks for stem cell engineering

A major challenge for stem cell engineering is achieving a holistic understanding of the molecular networks and biological processes governing cell differentiation. To address this challenge, we describe a computational approach that combines gene expression analysis, previous knowledge from proteomic pathway informatics and cell signaling models to delineate key transitional states of differentiating cells at high resolution. Our network models connect sparse gene signatures with corresponding, yet disparate, biological processes to uncover molecular mechanisms governing cell fate transitions. This approach builds on our earlier CellNet and recent trajectory-defining algorithms, as illustrated by our analysis of hematopoietic specification along the erythroid lineage, which reveals a role for the EGF receptor family member, ErbB4, as an important mediator of blood development. We experimentally validate this prediction and perturb the pathway to improve erythroid maturation from human pluripotent stem cells. These results exploit an integrative systems perspective to identify new regulatory processes and nodes useful in cell engineering.

Stems cells, big data and compendium-based analyses for identifying cell types, signalling pathways and gene regulatory networks

Identification of new drug and cell therapy targets for disease treatment will be facilitated by a detailed molecular understanding of normal and disease development. Human pluripotent stem cells can provide a large in vitro source of human cell types and, in a growing number of instances, also three-dimensional multicellular tissues called organoids. The application of stem cell technology to discovery and development of new therapies will be aided by detailed molecular characterisation of cell identity, cell signalling pathways and target gene networks. Big data or 'omics' techniques-particularly transcriptomics and proteomics-facilitate cell and tissue characterisation using thousands to tens-of-thousands of genes or proteins. These gene and protein profiles are analysed using existing and/or emergent bioinformatics methods, including a growing number of methods that compare sample profiles against compendia of reference samples. This review assesses how compendium-based analyses can aid the application of stem cell technology for new therapy development. This includes via robust definition of differentiated stem cell identity, as well as elucidation of complex signalling pathways and target gene networks involved in normal and diseased states.

From Yeast to Humans: Leveraging New Approaches in Yeast to Accelerate Discovery of Therapeutic Targets for Synucleinopathies

Neurodegenerative diseases (ND) represent a growing, global health crisis, one that lacks any disease-modifying therapeutic strategy. This critical need for new therapies must be met with an exhaustive approach to exploit all tools available. A yeast (Saccharomyces cerevisiae) model of α-synuclein toxicity-the protein causally linked to Parkinson's disease and other synucleinopathies-offers a powerful approach that takes advantage of the unique offerings of this system: tractable genetics, robust high-throughput screening strategies, unparalleled data repositories, powerful computational tools, and extensive evolutionary conservation of fundamental biological pathways. These attributes have enabled genetic and small molecule screens that have revealed toxic phenotypes and drug targets that translate directly to patient-derived iPSC neurons. Extending these insights, recent advances in genetic network analyses have generated the first "humanized" α-synuclein network, which has identified druggable proteins and led to validation of the toxic phenotypes in patient-derived cells. Unbiased phenotypic small molecule screens can identify compounds targeting critical proteins within α-synuclein networks. While identification of direct drug targets for phenotypic screen hits represents a bottleneck, high-throughput chemical genetic methods provide a means to uncover cellular targets and pathways for large numbers of compounds in parallel. Taken together, the yeast α-synuclein model and associated tools can reveal insights into underlying cellular pathologies, lead molecules and their cognate targets, and strategies to translate mechanisms of toxicity and cytoprotection into complex neuronal systems.

Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

We present a method for automatically discovering signaling pathways from time-resolved phosphoproteomic data. The Temporal Pathway Synthesizer (TPS) algorithm uses constraint-solving techniques first developed in the context of formal verification to explore paths in an interaction network. It systematically eliminates all candidate structures for a signaling pathway where a protein is activated or inactivated before its upstream regulators. The algorithm can model more than one hundred thousand dynamic phosphosites and can discover pathway members that are not differentially phosphorylated. By analyzing temporal data, TPS defines signaling cascades without needing to experimentally perturb individual proteins. It recovers known pathways and proposes pathway connections when applied to the human epidermal growth factor and yeast osmotic stress responses. Independent kinase mutant studies validate predicted substrates in the TPS osmotic stress pathway.

Hepatic Dysfunction Caused by Consumption of a High-Fat Diet

Obesity is a major human health crisis that promotes insulin resistance and, ultimately, type 2 diabetes. The molecular mechanisms that mediate this response occur across many highly complex biological regulatory levels that are incompletely understood. Here, we present a comprehensive molecular systems biology study of hepatic responses to high-fat feeding in mice. We interrogated diet-induced epigenomic, transcriptomic, proteomic, and metabolomic alterations using high-throughput omic methods and used a network modeling approach to integrate these diverse molecular signals. Our model indicated that disruption of hepatic architecture and enhanced hepatocyte apoptosis are among the numerous biological processes that contribute to early liver dysfunction and low-grade inflammation during the development of diet-induced metabolic syndrome. We validated these model findings with additional experiments on mouse liver sections. In total, we present an integrative systems biology study of diet-induced hepatic insulin resistance that uncovered molecular features promoting the development and maintenance of metabolic disease.

Knowledge-Based Neuroendocrine Immunomodulation (NIM) Molecular Network Construction and Its Application

Growing evidence shows that the neuroendocrine immunomodulation (NIM) network plays an important role in maintaining and modulating body function and the homeostasis of the internal environment. The disequilibrium of NIM in the body is closely associated with many diseases. In the present study, we first collected a core dataset of NIM signaling molecules based on our knowledge and obtained 611 NIM signaling molecules. Then, we built a NIM molecular network based on the MetaCore database and analyzed the signaling transduction characteristics of the core network. We found that the endocrine system played a pivotal role in the bridge between the nervous and immune systems and the signaling transduction between the three systems was not homogeneous. Finally, employing the forest algorithm, we identified the molecular hub playing an important role in the pathogenesis of rheumatoid arthritis (RA) and Alzheimer’s disease (AD), based on the NIM molecular network constructed by us. The results showed that GSK3B, SMARCA4, PSMD7, HNF4A, PGR, RXRA, and ESRRA might be the key molecules for RA, while RARA, STAT3, STAT1, and PSMD14 might be the key molecules for AD. The molecular hub may be a potentially druggable target for these two complex diseases based on the literature. This study suggests that the NIM molecular network in this paper combined with the forest algorithm might provide a useful tool for predicting drug targets and understanding the pathogenesis of diseases. Therefore, the NIM molecular network and the corresponding online tool will not only enhance research on complex diseases and system biology, but also promote the communication of valuable clinical experience between modern medicine and Traditional Chinese Medicine (TCM).

Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications

Understanding the genetic basis of complex diseases is challenging. Prior work shows that disease-related proteins do not typically function in isolation. Rather, they often interact with each other to form a network module that underlies dysfunctional mechanistic pathways. Identifying such disease modules will provide insights into a systems-level understanding of molecular mechanisms of diseases. Owing to the incompleteness of our knowledge of disease proteins and limited information on the biological mediators of pathobiological processes, the key proteins (seed proteins) for many diseases appear scattered over the human protein-protein interactome and form a few small branches, rather than coherent network modules. In this paper, we develop a network-based algorithm, called the Seed Connector algorithm (SCA), to pinpoint disease modules by adding as few additional linking proteins (seed connectors) to the seed protein pool as possible. Such seed connectors are hidden disease module elements that are critical for interpreting the functional context of disease proteins. The SCA aims to connect seed disease proteins so that disease mechanisms and pathways can be decoded based on predicted coherent network modules. We validate the algorithm using a large corpus of 70 complex diseases and binding targets of over 200 drugs, and demonstrate the biological relevance of the seed connectors. Lastly, as a specific proof of concept, we apply the SCA to a set of seed proteins for coronary artery disease derived from a meta-analysis of large-scale genome-wide association studies and obtain a coronary artery disease module enriched with important disease-related signaling pathways and drug targets not previously recognized.

Tissue and cellular rigidity and mechanosensitive signaling activation in Alexander disease

Glial cells have increasingly been implicated as active participants in the pathogenesis of neurological diseases, but critical pathways and mechanisms controlling glial function and secondary non-cell autonomous neuronal injury remain incompletely defined. Here we use models of Alexander disease, a severe brain disorder caused by gain-of-function mutations in GFAP, to demonstrate that misregulation of GFAP leads to activation of a mechanosensitive signaling cascade characterized by activation of the Hippo pathway and consequent increased expression of A-type lamin. Importantly, we use genetics to verify a functional role for dysregulated mechanotransduction signaling in promoting behavioral abnormalities and non-cell autonomous neurodegeneration. Further, we take cell biological and biophysical approaches to suggest that brain tissue stiffness is increased in Alexander disease. Our findings implicate altered mechanotransduction signaling as a key pathological cascade driving neuronal dysfunction and neurodegeneration in Alexander disease, and possibly also in other brain disorders characterized by gliosis.

Alexander disease is a rare neurodegeneration caused by mutations in a glial gene GFAP. Here, Wang and colleagues show in animal models of Alexander disease that GFAP mutant brain and cells have greater tissue and cellular stiffness and greater activation of mechanosensitive signaling cascade.

Integrative Computational Network Analysis Reveals Site-Specific Mediators of Inflammation in Alzheimer's Disease

Alzheimer's disease (AD) is a major neurodegenerative disease and is one of the most common cause of dementia in older adults. Among several factors, neuroinflammation is known to play a critical role in the pathogenesis of chronic neurodegenerative diseases. In particular, studies of brains affected by AD show a clear involvement of several inflammatory pathways. Furthermore, depending on the brain regions affected by the disease, the nature and the effect of inflammation can vary. Here, in order to shed more light on distinct and common features of inflammation in different brain regions affected by AD, we employed a computational approach to analyze gene expression data of six site-specific neuronal populations from AD patients. Our network based computational approach is driven by the concept that a sustained inflammatory environment could result in neurotoxicity leading to the disease. Thus, our method aims to infer intracellular signaling pathways/networks that are likely to be constantly activated or inhibited due to persistent inflammatory conditions. The computational analysis identified several inflammatory mediators, such as tumor necrosis factor alpha (TNF-a)-associated pathway, as key upstream receptors/ligands that are likely to transmit sustained inflammatory signals. Further, the analysis revealed that several inflammatory mediators were mainly region specific with few commonalities across different brain regions. Taken together, our results show that our integrative approach aids identification of inflammation-related signaling pathways that could be responsible for the onset or the progression of AD and can be applied to study other neurodegenerative diseases. Furthermore, such computational approaches can enable the translation of clinical omics data toward the development of novel therapeutic strategies for neurodegenerative diseases.

Discovering Altered Regulation and Signaling Through Network-based Integration of Transcriptomic, Epigenomic, and Proteomic Tumor Data

With the extraordinary rise in available biological data, biologists and clinicians need unbiased tools for data integration in order to reach accurate, succinct conclusions. Network biology provides one such method for high-throughput data integration, but comes with its own set of algorithmic problems and needed expertise. We provide a step-by-step guide for using Omics Integrator, a software package designed for the integration of transcriptomic, epigenomic, and proteomic data. Omics Integrator can be found at http://fraenkel.mit.edu/omicsintegrator .

Functional Genomics Approach Identifies Novel Signaling Regulators of TGFα Ectodomain Shedding

Ectodomain shedding of cell-surface precursor proteins by metalloproteases generates important cellular signaling molecules. Of importance for disease is the release of ligands that activate the EGFR, such as TGFα, which is mostly carried out by ADAM17 [a member of the A-disintegrin and metalloprotease (ADAM) domain family]. EGFR ligand shedding has been linked to many diseases, in particular cancer development, growth and metastasis, as well as resistance to cancer therapeutics. Excessive EGFR ligand release can outcompete therapeutic EGFR inhibition or the inhibition of other growth factor pathways by providing bypass signaling via EGFR activation. Drugging metalloproteases directly have failed clinically because it indiscriminately affected shedding of numerous substrates. It is therefore essential to identify regulators for EGFR ligand cleavage. Here, integration of a functional shRNA genomic screen, computational network analysis, and dedicated validation tests succeeded in identifying several key signaling pathways as novel regulators of TGFα shedding in cancer cells. Most notably, a cluster of genes with NFκB pathway regulatory functions was found to strongly influence TGFα release, albeit independent of their NFκB regulatory functions. Inflammatory regulators thus also govern cancer cell growth-promoting ectodomain cleavage, lending mechanistic understanding to the well-known connection between inflammation and cancer.Implications: Using genomic screens and network analysis, this study defines targets that regulate ectodomain shedding and suggests new treatment opportunities for EGFR-driven cancers. Mol Cancer Res; 16(1); 147-61. ©2017 AACR.

The node-weighted Steiner tree approach to identify elements of cancer-related signaling pathways

Background

Cancer constitutes a momentous health burden in our society. Critical information on cancer may be hidden in its signaling pathways. However, even though a large amount of money has been spent on cancer research, some critical information on cancer-related signaling pathways still remains elusive. Hence, new works towards a complete understanding of cancer-related signaling pathways will greatly benefit the prevention, diagnosis, and treatment of cancer.

Results

We propose the node-weighted Steiner tree approach to identify important elements of cancer-related signaling pathways at the level of proteins. This new approach has advantages over previous approaches since it is fast in processing large protein-protein interaction networks. We apply this new approach to identify important elements of two well-known cancer-related signaling pathways: PI3K/Akt and MAPK. First, we generate a node-weighted protein-protein interaction network using protein and signaling pathway data. Second, we modify and use two preprocessing techniques and a state-of-the-art Steiner tree algorithm to identify a subnetwork in the generated network. Third, we propose two new metrics to select important elements from this subnetwork. On a commonly used personal computer, this new approach takes less than 2 s to identify the important elements of PI3K/Akt and MAPK signaling pathways in a large node-weighted protein-protein interaction network with 16,843 vertices and 1,736,922 edges. We further analyze and demonstrate the significance of these identified elements to cancer signal transduction by exploring previously reported experimental evidences.

Conclusions

Our node-weighted Steiner tree approach is shown to be both fast and effective to identify important elements of cancer-related signaling pathways. Furthermore, it may provide new perspectives into the identification of signaling pathways for other human diseases.

A convex optimization approach for identification of human tissue-specific interactomes

Motivation: Analysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/tissue-selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes.

Results: We present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (Edge Set Enrichment Analysis, Gene Ontology Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state-of-the-art techniques. Finally, using case studies of Alzheimer’s and Parkinson’s diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology and demonstrate the use of these pathways in identifying novel targets.

Availability and implementation:http://www.cs.purdue.edu/homes/mohammas/projects/ActPro.html

Contact:mohammadi@purdue.edu

PCSF: An R-package for network-based interpretation of high-throughput data

With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis.

Advances in mass spectrometry based strategies to study receptor tyrosine kinases

Receptor tyrosine kinases (RTKs) are key transmembrane environmental sensors that are capable of transmitting extracellular information into phenotypic responses, including cell proliferation, survival and metabolism. Advances in mass spectrometry (MS)-based phosphoproteomics have been instrumental in providing the foundations of much of our current understanding of RTK signalling networks and activation dynamics. Furthermore, new insights relating to the deregulation of RTKs in disease, for instance receptor co-activation and kinome reprogramming, have largely been identified using phosphoproteomic-based strategies. This review outlines the current approaches employed in phosphoproteomic workflows, including phosphopeptide enrichment and MS data-acquisition methods. Here, recent advances in the application of MS-based phosphoproteomics to bridge critical gaps in our knowledge of RTK signalling are focused on. The current limitations of the technology are discussed and emerging areas such as computational modelling, high-throughput phospho-proteomic workflows and next-generation single-cell approaches to further our understanding in new areas of RTK biology are highlighted.

Identifying niche-mediated regulatory factors of stem cell phenotypic state: a systems biology approach

Understanding how the cellular niche controls the stem cell phenotype is often hampered due to the complexity of variegated niche composition, its dynamics, and nonlinear stem cell–niche interactions. Here, we propose a systems biology view that considers stem cell–niche interactions as a many‐body problem amenable to simplification by the concept of mean field approximation. This enables approximation of the niche effect on stem cells as a constant field that induces sustained activation/inhibition of specific stem cell signaling pathways in all stem cells within heterogeneous populations exhibiting the same phenotype (niche determinants). This view offers a new basis for the development of single cell‐based computational approaches for identifying niche determinants, which has potential applications in regenerative medicine and tissue engineering.

Genome-Scale Networks Link Neurodegenerative Disease Genes to α-Synuclein through Specific Molecular Pathways

Numerous genes and molecular pathways are implicated in neurodegenerative proteinopathies, but their inter-relationships are poorly understood. We systematically mapped molecular pathways underlying the toxicity of alpha-synuclein (α-syn), a protein central to Parkinson's disease. Genome-wide screens in yeast identified 332 genes that impact α-syn toxicity. To "humanize" this molecular network, we developed a computational method, TransposeNet. This integrates a Steiner prize-collecting approach with homology assignment through sequence, structure, and interaction topology. TransposeNet linked α-syn to multiple parkinsonism genes and druggable targets through perturbed protein trafficking and ER quality control as well as mRNA metabolism and translation. A calcium signaling hub linked these processes to perturbed mitochondrial quality control and function, metal ion transport, transcriptional regulation, and signal transduction. Parkinsonism gene interaction profiles spatially opposed in the network (ATP13A2/PARK9 and VPS35/PARK17) were highly distinct, and network relationships for specific genes (LRRK2/PARK8, ATXN2, and EIF4G1/PARK18) were confirmed in patient induced pluripotent stem cell (iPSC)-derived neurons. This cross-species platform connected diverse neurodegenerative genes to proteinopathy through specific mechanisms and may facilitate patient stratification for targeted therapy.

The PathLinker app: Connect the dots in protein interaction networks

PathLinker is a graph-theoretic algorithm for reconstructing the interactions in a signaling pathway of interest. It efficiently computes multiple short paths within a background protein interaction network from the receptors to transcription factors (TFs) in a pathway. We originally developed PathLinker to complement manual curation of signaling pathways, which is slow and painstaking. The method can be used in general to connect any set of sources to any set of targets in an interaction network. The app presented here makes the PathLinker functionality available to Cytoscape users. We present an example where we used PathLinker to compute and analyze the network of interactions connecting proteins that are perturbed by the drug lovastatin.

A physarum-inspired prize-collecting steiner tree approach to identify subnetworks for drug repositioning

Background

Drug repositioning can reduce the time, costs and risks of drug development by identifying new therapeutic effects for known drugs. It is challenging to reposition drugs as pharmacological data is large and complex. Subnetwork identification has already been used to simplify the visualization and interpretation of biological data, but it has not been applied to drug repositioning so far. In this paper, we fill this gap by proposing a new Physarum-inspired Prize-Collecting Steiner Tree algorithm to identify subnetworks for drug repositioning.

Results

Drug Similarity Networks (DSN) are generated using the chemical, therapeutic, protein, and phenotype features of drugs. In DSNs, vertex prizes and edge costs represent the similarities and dissimilarities between drugs respectively, and terminals represent drugs in the cardiovascular class, as defined in the Anatomical Therapeutic Chemical classification system. A new Physarum-inspired Prize-Collecting Steiner Tree algorithm is proposed in this paper to identify subnetworks. We apply both the proposed algorithm and the widely-used GW algorithm to identify subnetworks in our 18 generated DSNs. In these DSNs, our proposed algorithm identifies subnetworks with an average Rand Index of 81.1%, while the GW algorithm can only identify subnetworks with an average Rand Index of 64.1%. We select 9 subnetworks with high Rand Index to find drug repositioning opportunities. 10 frequently occurring drugs in these subnetworks are identified as candidates to be repositioned for cardiovascular diseases.

Conclusions

We find evidence to support previous discoveries that nitroglycerin, theophylline and acarbose may be able to be repositioned for cardiovascular diseases. Moreover, we identify seven previously unknown drug candidates that also may interact with the biological cardiovascular system. These discoveries show our proposed Prize-Collecting Steiner Tree approach as a promising strategy for drug repositioning.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0371-3) contains supplementary material, which is available to authorized users.

Multi-label ℓ2-regularized logistic regression for predicting activation/inhibition relationships in human protein-protein interaction networks

Protein-protein interaction (PPI) networks are naturally viewed as infrastructure to infer signalling pathways. The descriptors of signal events between two interacting proteins such as upstream/downstream signal flow, activation/inhibition relationship and protein modification are indispensable for inferring signalling pathways from PPI networks. However, such descriptors are not available in most cases as most PPI networks are seldom semantically annotated. In this work, we extend ℓ₂-regularized logistic regression to the scenario of multi-label learning for predicting the activation/inhibition relationships in human PPI networks. The phenomenon that both activation and inhibition relationships exist between two interacting proteins is computationally modelled by multi-label learning framework. The problem of GO (gene ontology) sparsity is tackled by introducing the homolog knowledge as independent homolog instances. ℓ₂-regularized logistic regression is accordingly adopted here to penalize the homolog noise and to reduce the computational complexity of the double-sized training data. Computational results show that the proposed method achieves satisfactory multi-label learning performance and outperforms the existing phenotype correlation method on the experimental data of Drosophila melanogaster. Several predictions have been validated against recent literature. The predicted activation/inhibition relationships in human PPI networks are provided in the supplementary file for further biomedical research.