Systems medicine approach to model cell signaling activity uncovers disease mechanisms and predicts cancer outcome

Motivation: Gene expression measurements (microarray of RNA-seq) are affordable ways to survey cell activity. However, they constitute low-informative, decontextualized values that often lack a mechanistic link with real cell functional outcomes. Mathematical modeling of biological pathways is emerging as a useful tool to understand the molecular mechanisms that govern the cell behavior or fate, revealing disease mechanisms and drug mechanisms of action, and providing guidance on therapeutic decisions (Gustafsson et al., 2014). Methods: Signaling KEGG pathways are used as basic maps of cell functionality over which gene expression values are modeled to obtain probabilities of signal transduction and, consequently, cell function activations (Hidalgo et al, 2017).Results: Here we propose a new method that models cell signaling using biological knowledge on signal transduction (Hidalgo et al., 2017). The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Signaling circuit activities can predict cancer outcome and have also demonstrated to be excellent predictors of drug response (Amadoz et al., 2015)Conclusions: A comprehensive, systems-based understanding of the way in which genes interact to shape the phenotype is required to realistically manage complex diseases

Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes

Background: Identification of driver genes related to certain types of cancer is an important research topic. Several systems biology approaches have been suggested, in particular for the identification of breast cancer (BRCA) related genes. Such approaches usually rely on differential gene expression and/or mutational landscape data. In some cases interaction network data is also integrated to identify cancer-related modules computationally. Results: We provide a framework for the comparative graph-theoretical analysis of networks integrating the relevant gene expression, mutations, and potein-protein interaction network data. The comparisons involve a graph-theoretical analysis of normal and tumor network pairs across all instances of a given set of breast cancer samples. The network measures under consideration are based on appropriate formulations of various centrality measures: betweenness, clustering coefficients, degree centrality, random walk distances, graph-theoretical distances, and Jaccard index centrality. Conclusions: Among all the studied centrality-based graph-theoretical properties, we show that a betweenness-based measure differentiates BRCA genes across all normal versus tumor network pairs, than the rest of the popular centrality-based measures. The AUROC and AUPR values of the gene lists ordered with respect to the measures under study as compared to NCBI BioSystems pathway and the COSMIC database of cancer genes are the largest with the betweenness-based differentiation, followed by the measure based on degree centrality. In order to test the robustness of the suggested measures in prioritizing cancer genes, we further tested the two most promising measures, those based on betweenness and degree centralities, on randomly rewired networks. We show that both measures are quite resilient to noise in the input interaction network. We also compared the same measures against a state-of-the-art alternative disease gene prioritization method, UFFFINN. We show that both our graph-theoretical measures outperform MUFFINN prioritizations in terms of ROC and precions/recall analysis. Finally, we filter the ordered list of the best measure, the betweenness-based differentiation, via a maximum-weight independent set formulation and investigate the top 50 genes in regards to literature verification. We show that almost all genes in the list are verified by the breast cancer literature and three genes are presented as novel genes that may potentialy be BRCA-related but missing in literature.No sponso

Assessing the Biological Significance of Gene Expression Signatures and Co-Expression Modules by Studying Their Network Properties

Microarray experiments have been extensively used to define signatures, which are sets of genes that can be considered markers of experimental conditions (typically diseases). Paradoxically, in spite of the apparent functional role that might be attributed to such gene sets, signatures do not seem to be reproducible across experiments. Given the close relationship between function and protein interaction, network properties can be used to study to what extent signatures are composed of genes whose resulting proteins show a considerable level of interaction (and consequently a putative common functional role)

HPG pore: an efficient and scalable framework for nanopore sequencing data.

BACKGROUND: The use of nanopore technologies is expected to spread in the future because they are portable and can sequence long fragments of DNA molecules without prior amplification. The first nanopore sequencer available, the MinION™ from Oxford Nanopore Technologies, is a USB-connected, portable device that allows real-time DNA analysis. In addition, other new instruments are expected to be released soon, which promise to outperform the current short-read technologies in terms of throughput. Despite the flood of data expected from this technology, the data analysis solutions currently available are only designed to manage small projects and are not scalable. RESULTS: Here we present HPG Pore, a toolkit for exploring and analysing nanopore sequencing data. HPG Pore can run on both individual computers and in the Hadoop distributed computing framework, which allows easy scale-up to manage the large amounts of data expected to result from extensive use of nanopore technologies in the future. CONCLUSIONS: HPG Pore allows for virtually unlimited sequencing data scalability, thus guaranteeing its continued management in near future scenarios. HPG Pore is available in GitHub at http://github.com/opencb/hpg-pore

HCAD, closing the gap between breakpoints and genes

Recurrent chromosome aberrations are an important resource when associating human pathologies to specific genes. However, for technical reasons a large number of chromosome breakpoints are defined only at the level of cytobands and many of the genes involved remain unidentified. We developed a web-based information system that mines the scientific literature and generates textual and comprehensive information on all human breakpoints. We show that the statistical analysis of this textual information and its combination with genomic data can identify genes directly involved in DNA rearrangements. The Human Chromosome Aberration Database (HCAD) is publicly accessible at http://www.pdg.cnb.uam.es/UniPub/HCAD/

Diversification of the expanded teleost-specific toll-like receptor family in Atlantic cod, Gadus morhua.

BACKGROUND: Toll-like receptors (Tlrs) are major molecular pattern recognition receptors of the innate immune system. Atlantic cod (Gadus morhua) is the first vertebrate known to have lost most of the mammalian Tlr orthologues, particularly all bacterial recognising and other cell surface Tlrs. On the other hand, its genome encodes a unique repertoire of teleost-specific Tlrs. The aim of this study was to investigate if these duplicate Tlrs have been retained through adaptive evolution to compensate for the lack of other cell surface Tlrs in the cod genome. RESULTS: In this study, one tlr21, 12 tlr22 and two tlr23 genes representing the teleost-specific Tlr family have been cloned and characterised in cod. Phylogenetic analysis grouped all tlr22 genes under a single clade, indicating that the multiple cod paralogues have arisen through lineage-specific duplications. All tlrs examined were transcribed in immune-related tissues as well as in stomach, gut and gonads of adult cod and were differentially expressed during early development. These tlrs were also differentially regulated following immune challenge by immersion with Vibrio anguillarum, indicating their role in the immune response. An increase in water temperature from 4 to 12°C was associated with a 5.5-fold down-regulation of tlr22d transcript levels in spleen. Maximum likelihood analysis with different evolution models revealed that tlr22 genes are under positive selection. A total of 24 codons were found to be positively selected, of which 19 are in the ligand binding region of ectodomain. CONCLUSION: Positive selection pressure coupled with experimental evidence of differential expression strongly support the hypothesis that teleost-specific tlr paralogues in cod are undergoing neofunctionalisation and can recognise bacterial pathogen-associated molecular patterns to compensate for the lack of other cell surface Tlrs

Exploring the reasons for the large density of triplex-forming oligonucleotide target sequences in the human regulatory regions

BACKGROUND: DNA duplex sequences that can be targets for triplex formation are highly over-represented in the human genome, especially in regulatory regions. RESULTS: Here we studied using bioinformatics tools several properties of triplex target sequences in an attempt to determine those that make these sequences so special in the genome. CONCLUSION: Our results strongly suggest that the unique physical properties of these sequences make them particularly suitable as "separators" between protein-recognition sites in the promoter region

Chronic subordination stress selectively downregulates the insulin signaling pathway in liver and skeletal muscle but not in adipose tissue of male mice.

Chronic stress has been associated with obesity, glucose intolerance, and insulin resistance. We developed a model of chronic psychosocial stress (CPS) in which subordinate mice are vulnerable to obesity and the metabolic-like syndrome while dominant mice exhibit a healthy metabolic phenotype. Here we tested the hypothesis that the metabolic difference between subordinate and dominant mice is associated with changes in functional pathways relevant for insulin sensitivity, glucose and lipid homeostasis. Male mice were exposed to CPS for four weeks and fed either a standard diet or a high-fat diet (HFD). We first measured, by real-time PCR candidate genes, in the liver, skeletal muscle, and the perigonadal white adipose tissue (pWAT). Subsequently, we used a probabilistic analysis approach to analyze different ways in which signals can be transmitted across the pathways in each tissue. Results showed that subordinate mice displayed a drastic downregulation of the insulin pathway in liver and muscle, indicative of insulin resistance, already on standard diet. Conversely, pWAT showed molecular changes suggestive of facilitated fat deposition in an otherwise insulin-sensitive tissue. The molecular changes in subordinate mice fed a standard diet were greater compared to HFD-fed controls. Finally, dominant mice maintained a substantially normal metabolic and molecular phenotype even when fed a HFD. Overall, our data demonstrate that subordination stress is a potent stimulus for the downregulation of the insulin signaling pathway in liver and muscle and a major risk factor for the development of obesity, insulin resistance, and type 2 diabetes mellitus.Supported by UofMN Medical School start-up funds to AB, Medical Research Council MRC Disease Model Core and British Heart Foundation program grants to AVP, and BIO2011-27069 from the Spanish Ministry of Economy and Competitiveness and PROMETEOII/2014/025 from the GVA-FEDER to JD. VS was supported by a graduate student fellowship of the University of Parma. CC was supported by EU FP7-People Project(ref 316861) "MLPM2012: Machine Learning For Personalized Medicine".This is the final version of the article. It first appeared from Taylor & Francis via http://dx.doi.org/10.3109/10253890.2016.115149

Chronic subordination stress selectively downregulates the insulin signaling pathway in liver and skeletal muscle but not in adipose tissue of male mice

Chronic stress has been associated with obesity, glucose intolerance, and insulin resistance. We developed a model of chronic psychosocial stress (CPS) in which subordinate mice are vulnerable to obesity and the metabolic-like syndrome while dominant mice exhibit a healthy metabolic phenotype. Here we tested the hypothesis that the metabolic difference between subordinate and dominant mice is associated with changes in functional pathways relevant for insulin sensitivity, glucose and lipid homeostasis. Male mice were exposed to CPS for four weeks and fed either a standard diet or a high-fat diet (HFD). We first measured, by real-time PCR candidate genes, in the liver, skeletal muscle, and the perigonadal white adipose tissue (pWAT). Subsequently, we used a probabilistic analysis approach to analyze different ways in which signals can be transmitted across the pathways in each tissue. Results showed that subordinate mice displayed a drastic downregulation of the insulin pathway in liver and muscle, indicative of insulin resistance, already on standard diet. Conversely, pWAT showed molecular changes suggestive of facilitated fat deposition in an otherwise insulin-sensitive tissue. The molecular changes in subordinate mice fed a standard diet were greater compared to HFD-fed controls. Finally, dominant mice maintained a substantially normal metabolic and molecular phenotype even when fed a HFD. Overall, our data demonstrate that subordination stress is a potent stimulus for the downregulation of the insulin signaling pathway in liver and muscle and a major risk factor for the development of obesity, insulin resistance, and type 2 diabetes mellitus.Supported by UofMN Medical School start-up funds to AB, Medical Research Council MRC Disease Model Core and British Heart Foundation program grants to AVP, and BIO2011-27069 from the Spanish Ministry of Economy and Competitiveness and PROMETEOII/2014/025 from the GVA-FEDER to JD. VS was supported by a graduate student fellowship of the University of Parma. CC was supported by EU FP7-People Project(ref 316861) "MLPM2012: Machine Learning For Personalized Medicine".This is the final version of the article. It first appeared from Taylor & Francis via http://dx.doi.org/10.3109/10253890.2016.115149

Cell-level pathway scoring comparison with a biologically constrained variational autoencoder

This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in: Pang, J., Niehren, J. (eds) Computational Methods in Systems Biology. CMSB 2023. Lecture Notes in Computer Science, vol 14137. Springer, Cham. Available online at https://doi.org/10.1007/978-3-031-42697-1_