A Boolean approach for novel hypoxia-related gene discovery

Abstract

Hypoxia plays a major role in the etiology and pathogenesis of most of the leading causes of morbidity and mortality, whether cardiovascular diseases, cancer, respiratory diseases or stroke. Despite active research on hypoxia-signaling pathways, the understanding of regulatory mechanisms, especially in specific tissues, still remain elusive. With the accessibility of thousands of potentially diverse genomic datasets, computational methods are utilized to generate new hypotheses. Here we utilized Boolean implication relationship, a powerful method to probe symmetrically and asymmetrically related genes, to identify novel hypoxia related genes. We used a well-known hypoxia-responsive gene, VEGFA, with very large human expression datasets (n = 25,955) to identify novel hypoxia-responsive candidate gene/s. Further, we utilized in-vitro analysis using human endothelial cells exposed to 1% O2 environment for 2, 8, 24 and 48 hours to validate top candidate genes. Out of the top candidate genes (n = 19), 84% genes were previously reported as hypoxia related, validating our results. However, we identified FAM114A1 as a novel candidate gene significantly upregulated in the endothelial cells at 8, 24 and 48 hours of 1% O2 environment. Additional evidence, particularly the localization of intronic miRNA and numerous HREs further support and strengthen our finding. Current results on FAM114A1 provide an example demonstrating the utility of powerful computational methods, like Boolean implications, in playing a major role in hypothesis building and discovery.

Detecting Boolean Asymmetric Relationships with a Loop Counting Technique and its Implications for Analyzing Heterogeneity within Gene Expression Datasets

“…A more general kind of interaction can involve 'asymmetric' relationships; knowledge of gene X may strongly inform the value of Y , without the reverse holding true. Interest in asymmetric-relationships has grown in recent years, and several informative asymmetric-relationships have been discovered in a variety of cellular-and geneticdata-sets, e.g., in the context of cancer data-sets [7], [8], [12], [13], [23], [38]- [40], hypoxia-related genes [34], and cell proliferation [9]. For instance, a boolean analysis in [9] identified more than two dozen new genes involved in cell cycle processes, with their effects subsequently validated using a recent RNA-sequencing data-set.…”

“…Boolean-asymmetric-relationships (BARs, described above) are relevant in many biological contexts, and have been found present in many different gene-expression data-sets [5]- [9], [12], [13], [23], [34]- [40]. As alluded to above, the genegene BARs each suggest an associated genetic interaction; one gene can be necessary for the other, or the two genes can be linked in an 'or' or 'nand' pair.…”

“…Several statistically-significant BARs have been observed relating certain genes to the developmental stages of tumors sampled in colon, bladder and prostate cancers [38]–[40]. Along similar lines, significant BARs have been found associated with celldifferentiation, cellular-development and tissue-function [9], [34]–[37]. More recent work has shown that, while many BARs differentiate between different cell-types in the micro-biome, there are also many BARs that are strongly conserved across microorganisms, potentially highlighting ‘universal’ gene-functions important for survival [6].…”