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Expression microarrays are powerful technology that allows large-scale analysis of RNA profiles in a tissue; these platforms include underexploited detection scores outputs. We developed an algorithm using the detection score, to generate a detection profile of shared elements in retinoblastoma as well as to determine its transcriptomic size and structure. We analyzed eight briefly cultured primary retinoblastomas with the Human transcriptome array 2.0 (HTA2.0). Transcripts and genes detection scores were determined using the Detection Above Background algorithm (DABG). We used unsupervised and supervised computational tools to analyze detected and undetected elements; WebGestalt was used to explore functions encoded by genes in relevant clusters and performed experimental validation. We found a core cluster with 7,513 genes detected and shared by all samples, 4,321 genes in a cluster that was commonly absent, and 7,681 genes variably detected across the samples accounting for tumor heterogeneity. Relevant pathways identified in the core cluster relate to cell cycle, RNA transport, and DNA replication. We performed a kinome analysis of the core cluster and found 4 potential therapeutic kinase targets. Through analysis of the variably detected genes, we discovered 123 differentially expressed transcripts between bilateral and unilateral cases. This novel analytical approach allowed determining the retinoblastoma transcriptomic size, a shared active transcriptomic core among the samples, potential therapeutic target kinases shared by all samples, transcripts related to inter tumor heterogeneity, and to determine transcriptomic profiles without the need of control tissues. This approach is useful to analyze other cancer or tissue types.

There has been limited study of Native American whole genome diversity to date, which impairs effective implementation of personalized medicine and a detailed description of its demographic history. Here we report high coverage whole genome sequencing of 76 unrelated individuals, from 27 indigenous groups across Mexico, with more than 97% average Native American ancestry. On average, each individual has 3.26 million Single Nucleotide Variants and short indels, that together comprise a catalog of 9,737,152 variants, 44,118 of which are novel. We report 497 common Single Nucleotide Variants (with allele frequency > 5%) mapped to drug responses and 316,577 in enhancer or promoter elements; interestingly we found some of these enhancer variants in PPARG, a nuclear receptor involved in highly prevalent health problems in Mexican population, such as obesity, diabetes, and insulin resistance. By detecting signals of positive selection we report 24 enriched key pathways under selection, most of them related to immune mechanisms. No missense variants in ACE2, the receptor responsible for the entry of the SARS CoV-2 virus, were found in any individual. Population genomics and phylogenetic analyses demonstrated stratification in a Northern-Central-Southern axis, with major substructure in the Central region. The Seri, a northern group with the most genetic divergence in our study, showed a distinctive genomic context with the most novel variants, and the most population specific genotypes. Genome-wide analysis showed that the average haplotype blocks are longer in Native Mexicans than in other world populations. With this dataset we describe previously undetected population level variation in Native Mexicans, helping to reduce the gap in genomic data representation of such groups.

Transcription factors are responsible for both tissue-specific gene expression and the regulation of gene activity in response to specific stimuli. Analysis of whole genome gene expression data and studies on the specificities of transcription factor binding to genomic DNA sequences are common ways of inferring transcriptional regulatory networks. The interplay between different levels of regulation occurs when a series of transcription factors triggers a gene cascade that controls the whole regulatory program for cells under associated phenotypes. In this chapter, we discuss the role that transcription factors and other related molecules have on the intricate series of processes leading to genome-wide transcriptional regulatory programs. Experimental, computational, and modeling approaches to this problem are presented, and some general methods and algorithms are described in greater detail. Finally, we present an overview section on how to incorporate epigenomic and other regulatory processes in the context of networks of transcription factors.

Analysis of gene regulatory networks allows the identification of master transcriptional factors that control specific groups of genes. In this work, we inferred a gene regulatory network from a large dataset of breast cancer samples to identify the master transcriptional factors that control the genes within signal transduction pathways. The focus in a particular subset of relevant genes constitutes an extension of the original Master Regulator Inference Algorithm (MARINa) analysis. This modified version of MARINa utilizes a restricted molecular signature containing genes from the 25 human pathways in KEGG's signal transduction category. Our breast cancer RNAseq expression dataset consists of 881 samples comprising tumors and normal mammary gland tissue. The top 10 master transcriptional factors found to regulate signal transduction pathways in breast cancer we identified are: TSHZ2, HOXA2, MEIS2, HOXA3, HAND2, HOXA5, TBX18, PEG3, GLI2, and CLOCK. The functional enrichment of the regulons of these master transcriptional factors showed an important proportion of processes related to morphogenesis. Our results suggest that, as part of the aberrant regulation of signaling pathways in breast cancer, pathways similar to the regulation of cell differentiation, cardiovascular system development, and vasculature development may be dysregulated and co-opted in favor of tumor development through the action of these transcription factors.

Graphical abstractDisplay Omitted Gene regulatory networks account for the delicate mechanisms that control gene expression. Under certain circumstances, gene regulatory programs may give rise to amplification cascades. Such transcriptional cascades are events in which activation of key-responsive transcription factors called master regulators trigger a series of gene expression events. The action of transcriptional master regulators is then important for the establishment of certain programs like cell development and differentiation. However, such cascades have also been related with the onset and maintenance of cancer phenotypes. Here we present a systematic implementation of a series of algorithms aimed at the inference of a gene regulatory network and analysis of transcriptional master regulators in the context of primary breast cancer cells. Such studies were performed in a highly curated database of 880 microarray gene expression experiments on biopsy-captured tissue corresponding to primary breast cancer and healthy controls. Biological function and biochemical pathway enrichment analyses were also performed to study the role that the processes controlled - at the transcriptional level - by such master regulators may have in relation to primary breast cancer. We found that transcription factors such as AGTR2, ZNF132, TFDP3 and others are master regulators in this gene regulatory network. Sets of genes controlled by these regulators are involved in processes that are well-known hallmarks of cancer. This kind of analyses may help to understand the most upstream events in the development of phenotypes, in particular, those regarding cancer biology.

Gene expression in eukaryotic cells is an extremely complex and interesting phenomenon whose dynamics are controlled by a large number of subtle physicochemical processes commonly described by means of gene regulatory networks. Such networks consist in a series of coupled chemical reactions, conformational changes, and other biomolecular processes involving the interaction of the DNA molecule itself with a number of proteins usually called transcription factors as well as enzymes and other components. The kinetics behind the functioning of such gene regulatory networks are largely unknown, though its description in terms of non-equilibrium thermodynamics has been discussed recently. In this work we will derive general kinetic equations for a gene regulatory network from a non-equilibrium thermodynamical description and discuss its use in understanding the free energy constrains imposed in the network structure. We also will discuss explicit expressions for the kinetics of a simple model of gene regulation and show that the kinetic role of mRNA decay during the RNA synthesis stage (or transcription) is somehow limited due to the comparatively low values of decay rates. At the level discussed here, this implies a decoupling of the kinetics of mRNA synthesis and degradation a fact that may become quite useful when modeling gene regulatory networks from experimental data on whole genome gene expression.

Gene regulatory networks present a wide variety of dynamical responses to intrinsic and extrinsic perturbations. An outstanding case of such coordinated responses is that of transcriptional amplification cascades, in which activation of a few key-responsive transcription factors (termed master regulators) leads to a large series of transcriptional activation events. Recent studies have pointed to the protein called myocyte enhancing factor 2C (MEF2C) as being one of such master regulators involved in the pathogenesis of primary breast cancer. A systems biology analysis of the transcriptional regulation activity of MEF2C and its target genes, has revealed that this molecule induces collective responses leading to system-level gene expression deregulation and carcinogenesis. We found extensive evidence for this. Being this the case, one may wonder what set of physicochemical, structural and thermodynamic constrains need to be satisfied if a protein is to become a transcription factor, and moreover a master regulator? Some hints will be discussed.