Understanding the genetic structure of Native American populations is important to clarify their diversity, demographic history, and to identify genetic factors relevant for biomedical traits. Here, we show a demographic history reconstruction from 12 Native American whole genomes belonging to six distinct ethnic groups representing the three main described genetic clusters of Mexico (Northern, Southern, and Maya). Effective population size estimates of all Native American groups remained below 2,000 individuals for up to 10,000 years ago. The proportion of missense variants predicted as damaging is higher for undescribed (~ 30%) than for previously reported variants (~ 15%). Several variants previously associated with biological traits are highly frequent in the Native American genomes. These findings suggest that the demographic and adaptive processes that occurred in these groups shaped their genetic architecture and could have implications in biological processes of the Native Americans and Mestizos of today.

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Cardiovascular diseases are the leading cause of human mortality worldwide. Among the many factors associated with the etiology, incidence, and evolution of such diseases; social and environmental issues constitute an important and often overlooked component. Understanding to a greater extent the scope to which such social determinants of cardiovascular diseases (SDCVD) occur as well as the connections among them would be useful for public health policy making. Here, we will explore the historical trends and associations among the main SDCVD in the published literature. Our aim will be finding meaningful relations among those that will help us to have an integrated view on this complex phenomenon by providing historical context and a relational framework. To uncover such relations, we used a data mining approach to the current literature, followed by network analysis of the interrelationships discovered. To this end, we systematically mined the PubMed/MEDLINE database for references of published studies on the subject, as outlined by the World Health Organization’s framework on social determinants of health. The analyzed structured corpus consisted in circa 1190 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor. The use of data analytics techniques allowed us to find a number of non-trivial connections among SDCVDs. Such relations may be relevant to get a deeper understanding of the social and environmental issues associated with cardiovascular disease and are often overlooked by traditional literature survey approaches, such as systematic reviews and meta-analyses.

It has been proposed that the history and evolution of scientific ideas may reflect certain aspects of the underlying socio-cognitive frameworks in which science itself is developing. Systematic analyses of the development of scientific knowledge may help us to construct models of the collective dynamics of science. Aiming at scientific rigor, these models should be built upon solid empirical evidence, analyzed with formal tools leading to ever-improving results that support the related conclusions. Along these lines we studied the dynamics and structure of the development of research in genomics as represented by the entire collection of genomics-related scientific papers contained in the PubMed database. The analyzed corpus consisted in more than 49,000 articles published in the years 1987 (first appeareance of the term Genomics) to 2011, categorized by means of the Medical Subheadings (MeSH) content-descriptors. Complex networks were built where two MeSH terms were connected if they are descriptors of the same article(s). The analysis of such networks revealed a complex structure and dynamics that to certain extent resembled small-world networks. The evolution of such networks in time reflected interesting phenomena in the historical development of genomic research, including what seems to be a phase-transition in a period marked by the completion of the first draft of the Human Genome Project. We also found that different disciplinary areas have different dynamic evolution patterns in their MeSH connectivity networks. In the case of areas related to science, changes in topology were somewhat fast while retaining a certain core-stucture, whereas in the humanities, the evolution was pretty slow and the structure resulted highly redundant and in the case of technology related issues, the evolution was very fast and the structure remained tree-like with almost no overlapping terms.

Gene regulatory networks present a wide variety of dynamical responses to intrinsic and extrinsic perturbations. Arguably, one of the most important of such coordinated responses is the one of amplification cascades, in which activation of a few key-responsive transcription factors (termed master regulators, MRs) lead to a large series of transcriptional activation events. This is so since master regulators are transcription factors controlling the expression of other transcription factor molecules and so on. MRs hold a central position related to transcriptional dynamics and control of gene regulatory networks and are often involved in complex feedback and feedforward loops inducing non-trivial dynamics. Recent studies have pointed out to the myocyte enhancing factor 2C (MEF2C, also known as MADS box transcription enhancer factor 2, polypeptide C) as being one of such master regulators involved in the pathogenesis of primary breast cancer. In this work, we perform an integrative genomic analysis of the transcriptional regulation activity of MEF2C and its target genes to evaluate to what extent are these molecules inducing collective responses leading to gene expression deregulation and carcinogenesis. We also analyzed a number of induced dynamic responses, in particular those associated with transcriptional bursts, and nonlinear cascading to evaluate the influence they may have in malignant phenotypes and cancer.