A functional analysis of omic network embedding spaces reveals key altered functions in cancer.

MOTIVATION: Advances in omics technologies have revolutionized cancer research by producing massive datasets. Common approaches to deciphering these complex data are by embedding algorithms of molecular interaction networks. These algorithms find a low-dimensional space in which similarities between the network nodes are best preserved. Currently available embedding approaches mine the gene embeddings directly to uncover new cancer-related knowledge. However, these gene-centric approaches produce incomplete knowledge, since they do not account for the functional implications of genomic alterations. We propose a new, function-centric perspective and approach, to complement the knowledge obtained from omic data. RESULTS: We introduce our Functional Mapping Matrix (FMM) to explore the functional organization of different tissue-specific and species-specific embedding spaces generated by a Non-negative Matrix Tri-Factorization algorithm. Also, we use our FMM to define the optimal dimensionality of these molecular interaction network embedding spaces. For this optimal dimensionality, we compare the FMMs of the most prevalent cancers in human to FMMs of their corresponding control tissues. We find that cancer alters the positions in the embedding space of cancer-related functions, while it keeps the positions of the noncancer-related ones. We exploit this spacial 'movement' to predict novel cancer-related functions. Finally, we predict novel cancer-related genes that the currently available methods for gene-centric analyses cannot identify; we validate these predictions by literature curation and retrospective analyses of patient survival data. AVAILABILITY AND IMPLEMENTATION: Data and source code can be accessed at https://github.com/gaiac/FMM.

Identification of transcription factor high accumulation DNA zones.

BACKGROUND: Transcription factors (TF) play a crucial role in the regulation of gene transcription; alterations of their activity and binding to DNA areas are strongly involved in cancer and other disease onset and development. For proper biomedical investigation, it is hence essential to correctly trace TF dense DNA areas, having multiple bindings of distinct factors, and select DNA high occupancy target (HOT) zones, showing the highest accumulation of such bindings. Indeed, systematic and replicable analysis of HOT zones in a large variety of cells and tissues would allow further understanding of their characteristics and could clarify their functional role. RESULTS: Here, we propose, thoroughly explain and discuss a full computational procedure to study in-depth DNA dense areas of transcription factor accumulation and identify HOT zones. This methodology, developed as a computationally efficient parametric algorithm implemented in an R/Bioconductor package, uses a systematic approach with two alternative methods to examine transcription factor bindings and provide comparative and fully-reproducible assessments. It offers different resolutions by introducing three distinct types of accumulation, which can analyze DNA from single-base to region-oriented levels, and a moving window, which can estimate the influence of the neighborhood for each DNA base under exam. CONCLUSIONS: We quantitatively assessed the full procedure by using our implemented software package, named TFHAZ, in two example applications of biological interest, proving its full reliability and relevance.

Association rule mining to identify transcription factor interactions in genomic regions.

MOTIVATION: Genome regulatory networks have different layers and ways to modulate cellular processes, such as cell differentiation, proliferation, and adaptation to external stimuli. Transcription factors and other chromatin-associated proteins act as combinatorial protein complexes that control gene transcription. Thus, identifying functional interaction networks among these proteins is a fundamental task to understand the genome regulation framework. RESULTS: We developed a novel approach to infer interactions among transcription factors in user-selected genomic regions, by combining the computation of association rules and of a novel Importance Index on ChIP-seq datasets. The hallmark of our method is the definition of the Importance Index, which provides a relevance measure of the interaction among transcription factors found associated in the computed rules. Examples on synthetic data explain the index use and potential. A straightforward pre-processing pipeline enables the easy extraction of input data for our approach from any set of ChIP-seq experiments. Applications on ENCODE ChIP-seq data prove that our approach can reliably detect interactions between transcription factors, including known interactions that validate our approach. AVAILABILITY AND IMPLEMENTATION: A R/Bioconductor package implementing our association rules and Importance Index-based method is available at http://bioconductor.org/packages/release/bioc/html/TFARM.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The axes of biology: a novel axes-based network embedding paradigm to decipher the functional mechanisms of the cell.

Summary: Common approaches for deciphering biological networks involve network embedding algorithms. These approaches strictly focus on clustering the genes' embedding vectors and interpreting such clusters to reveal the hidden information of the networks. However, the difficulty in interpreting the genes' clusters and the limitations of the functional annotations' resources hinder the identification of the currently unknown cell's functioning mechanisms. We propose a new approach that shifts this functional exploration from the embedding vectors of genes in space to the axes of the space itself. Our methodology better disentangles biological information from the embedding space than the classic gene-centric approach. Moreover, it uncovers new data-driven functional interactions that are unregistered in the functional ontologies, but biologically coherent. Furthermore, we exploit these interactions to define new higher-level annotations that we term Axes-Specific Functional Annotations and validate them through literature curation. Finally, we leverage our methodology to discover evolutionary connections between cellular functions and the evolution of species. Availability and implementation: Data and source code can be accessed at https://gitlab.bsc.es/sdoria/axes-of-biology.git.

Multi-omics integration of scRNA-seq time series data predicts new intervention points for Parkinson's disease.

Parkinson's disease (PD) is a complex neurodegenerative disorder without a cure. The onset of PD symptoms corresponds to 50% loss of midbrain dopaminergic (mDA) neurons, limiting early-stage understanding of PD. To shed light on early PD development, we study time series scRNA-seq datasets of mDA neurons obtained from patient-derived induced pluripotent stem cell differentiation. We develop a new data integration method based on Non-negative Matrix Tri-Factorization that integrates these datasets with molecular interaction networks, producing condition-specific "gene embeddings". By mining these embeddings, we predict 193 PD-related genes that are largely supported (49.7%) in the literature and are specific to the investigated PINK1 mutation. Enrichment analysis in Kyoto Encyclopedia of Genes and Genomes pathways highlights 10 PD-related molecular mechanisms perturbed during early PD development. Finally, investigating the top 20 prioritized genes reveals 12 previously unrecognized genes associated with PD that represent interesting drug targets.

A phenotype driven integrative framework uncovers molecular mechanisms of a rare hereditary thrombophilia.

Antithrombin resistance is a rare subtype of hereditary thrombophilia caused by prothrombin gene variants, leading to thrombotic disorders. Recently, the Prothrombin Belgrade variant has been reported as a specific variant that leads to antithrombin resistance in two Serbian families with thrombosis. However, due to clinical data scarcity and the inapplicability of traditional genome-wide association studies (GWAS), a broader perspective on molecular and phenotypic mechanisms associated with the Prothrombin Belgrade variant is yet to be uncovered. Here, we propose an integrative framework to address the lack of genomic samples and support the genomic signal from the full genome sequences of five heterozygous subjects by integrating it with subjects' phenotypes and the genes' molecular interactions. Our goal is to identify candidate thrombophilia-related genes for which our subjects possess germline variants by focusing on the resulting gene clusters of our integrative framework. We applied a Non-negative Matrix Tri-Factorization-based method to simultaneously integrate different data sources, taking into account the observed phenotypes. In other words, our data-integration framework reveals gene clusters involved with this rare disease by fusing different datasets. Our results are in concordance with the current literature about antithrombin resistance. We also found candidate disease-related genes that need to be further investigated. CD320, RTEL1, UCP2, APOA5 and PROZ participate in healthy-specific or disease-specific subnetworks involving thrombophilia-annotated genes and are related to general thrombophilia mechanisms according to the literature. Moreover, the ADRA2A and TBXA2R subnetworks analysis suggested that their variants may have a protective effect due to their connection with decreased platelet activation. The results show that our method can give insights into antithrombin resistance even if a small amount of genetic data is available. Our framework is also customizable, meaning that it applies to any other rare disease.

A multi-omics integrative approach unravels novel genes and pathways associated with senescence escape after targeted therapy in NRAS mutant melanoma.

Therapy Induced Senescence (TIS) leads to sustained growth arrest of cancer cells. The associated cytostasis has been shown to be reversible and cells escaping senescence further enhance the aggressiveness of cancers. Chemicals specifically targeting senescent cells, so-called senolytics, constitute a promising avenue for improved cancer treatment in combination with targeted therapies. Understanding how cancer cells evade senescence is needed to optimise the clinical benefits of this therapeutic approach. Here we characterised the response of three different NRAS mutant melanoma cell lines to a combination of CDK4/6 and MEK inhibitors over 33 days. Transcriptomic data show that all cell lines trigger a senescence programme coupled with strong induction of interferons. Kinome profiling revealed the activation of Receptor Tyrosine Kinases (RTKs) and enriched downstream signaling of neurotrophin, ErbB and insulin pathways. Characterisation of the miRNA interactome associates miR-211-5p with resistant phenotypes. Finally, iCell-based integration of bulk and single-cell RNA-seq data identifies biological processes perturbed during senescence and predicts 90 new genes involved in its escape. Overall, our data associate insulin signaling with persistence of a senescent phenotype and suggest a new role for interferon gamma in senescence escape through the induction of EMT and the activation of ERK5 signaling.

Predicting Drug Synergism by Means of Non-Negative Matrix Tri-Factorization.

Traditional drug experiments to find synergistic drug pairs are time-consuming and expensive due to the numerous possible combinations of drugs that have to be examined. Thus, computational methods that can give suggestions for synergistic drug investigations are of great interest. Here, we propose a Non-negative Matrix Tri-Factorization (NMTF) based approach that leverages the integration of different data types for predicting synergistic drug pairs in multiple specific cell lines. Our computational framework relies on a network-based representation of available data about drug synergism, which also allows integrating genomic information about cell lines. We computationally evaluate the performances of our method in finding missing relationships between synergistic drug pairs and cell lines, and in computing synergy scores between drug pairs in a specific cell line, as well as we estimate the benefit of adding cell line genomic data to the network. Our approach obtains very good performance (Average Precision Score equal to 0.937, Pearson's correlation coefficient equal to 0.760) when cell line genomic data and rich data about synergistic drugs in a cell line are considered. Finally, we systematically searched our top-scored predictions in the available literature and in the NCI ALMANAC, a well-known database of drug combination experiments, proving the goodness of our findings.

Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers.

The complexity of cancer has always been a huge issue in understanding the source of this disease. However, by appreciating its complexity, we can shed some light on crucial gene associations across and in specific cancer types. In this study, we develop a general framework to infer relevant gene biomarkers and their gene-to-gene associations using multiple gene co-expression networks for each cancer type. Specifically, we infer computationally and biologically interesting communities of genes from kidney renal clear cell carcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma data sets of The Cancer Genome Atlas (TCGA) database. The gene communities are extracted through a data-driven pipeline and then evaluated through both functional analyses and literature findings. Furthermore, we provide a computational validation of their relevance for each cancer type by comparing the performance of normal/cancer classification for our identified gene sets and other gene signatures, including the typically-used differentially expressed genes. The hallmark of this study is its approach based on gene co-expression networks from different similarity measures: using a combination of multiple gene networks and then fusing normal and cancer networks for each cancer type, we can have better insights on the overall structure of the cancer-type-specific network.