The ancestry of eastern paraguay: A typical south american profile with a unique pattern of admixture

Immigrants from diverse origins have arrived in Paraguay and produced important demographic changes in a territory initially inhabited by indigenous Guarani. Few studies have been performed to estimate the proportion of Native ancestry that is still preserved in Paraguay and the role of females and males in admixture processes. Therefore, 548 individuals from eastern Paraguay were genotyped for three marker sets: mtDNA, Y-SNPs and autosomal AIM-InDels. A genetic homogeneity was found between departments for each set of markers, supported by the demographic data collected, which showed that only 43% of the individuals have the same birthplace as their parents. The results show a sex-biased intermarriage, with higher maternal than paternal Native American ancestry. Within the native mtDNA lineages in Paraguay (87.2% of the total), most haplogroups have a broad distribution across the subcontinent, and only few are concentrated around the Paraná River basin. The frequency distribution of the European paternal lineages in Paraguay (92.2% of the total) showed a major contribution from the Iberian region. In addition to the remaining legacy of the colonial period, the joint analysis of the different types of markers included in this study revealed the impact of post-war migrations on the current genetic background of Paraguay.Funding: F.S. and L.G. were supported by Fundação de Amparo à Pesquisa do Rio de Janeiro–FAPERJ, Brazil (process E-26/202.275/2019 and CNE-2018). L.G. was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico–CNPq, Brazil (ref. 306342/2019-7). V.G. is supported by FCT under the program contract provided in Decree-Law no.57/2016 of August 29

OptFlux: an open-source software platform for in silico metabolic engineering

<p>Abstract</p> <p>Background</p> <p>Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications.</p> <p>Results</p> <p><it>OptFlux </it>is an open-source and modular software aimed at being the reference computational application in the field. It is the first tool to incorporate strain optimization tasks, i.e., the identification of Metabolic Engineering targets, using Evolutionary Algorithms/Simulated Annealing metaheuristics or the previously proposed OptKnock algorithm. It also allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes.</p> <p><it>OptFlux </it>also contemplates several methods for model simplification and other pre-processing operations aimed at reducing the search space for optimization algorithms.</p> <p>The software supports importing/exporting to several flat file formats and it is compatible with the SBML standard. <it>OptFlux </it>has a visualization module that allows the analysis of the model structure that is compatible with the layout information of <it>Cell Designer</it>, allowing the superimposition of simulation results with the model graph.</p> <p>Conclusions</p> <p>The <it>OptFlux </it>software is freely available, together with documentation and other resources, thus bridging the gap from research in strain optimization algorithms and the final users. It is a valuable platform for researchers in the field that have available a number of useful tools. Its open-source nature invites contributions by all those interested in making their methods available for the community.</p> <p>Given its plug-in based architecture it can be extended with new functionalities. Currently, several plug-ins are being developed, including network topology analysis tools and the integration with Boolean network based regulatory models.</p

Metabolic Network Topology Reveals Transcriptional Regulatory Signatures of Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites—metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM. An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM