ZNF91 deletion in human embryonic stem cells leads to ectopic activation of SVA retrotransposons and up-regulation of KRAB zinc finger gene clusters.

Transposable element (TE) invasions have shaped vertebrate genomes over the course of evolution. They have contributed an extra layer of species-specific gene regulation by providing novel transcription factor binding sites. In humans, SINE-VNTR-Alu (SVA) elements are one of three still active TE families; approximately 2800 SVA insertions exist in the human genome, half of which are human-specific. TEs are often silenced by KRAB zinc finger (KZNF) proteins recruiting corepressor proteins that establish a repressive chromatin state. A number of KZNFs have been reported to bind SVAs, but their individual contribution to repressing SVAs and their roles in suppressing SVA-mediated gene-regulatory effects remains elusive. We analyzed the genome-wide binding profile for ZNF91 in human cells and found that ZNF91 interacts with the VNTR region of SVAs. Through CRISPR-Cas9-mediated deletion of ZNF91 in human embryonic stem cells, we established that loss of ZNF91 results in increased transcriptional activity of SVAs. In contrast, SVA activation was not observed upon genetic deletion of the ZNF611 gene encoding another strong SVA interactor. Epigenetic profiling confirmed the loss of SVA repression in the absence of ZNF91 and revealed that mainly evolutionary young SVAs gain gene activation-associated epigenetic modifications. Genes close to activated SVAs showed a mild up-regulation, indicating SVAs adopt properties of cis-regulatory elements in the absence of repression. Notably, genome-wide derepression of SVAs elicited the communal up-regulation of KZNFs that reside in KZNF clusters. This phenomenon may provide new insights into the potential mechanisms used by the host genome to sense and counteract TE invasions.

Occurrence Prediction of Riffle Beetles (Coleoptera: Elmidae) in a Tropical Andean Basin of Ecuador Using Species Distribution Models.

Genera and species of Elmidae (riffle beetles) are sensitive to water pollution; however, in tropical freshwater ecosystems, their requirements regarding environmental factors need to be investigated. Species distribution models (SDMs) were established for five elmid genera in the Paute river basin (southern Ecuador) using the Random Forest (RF) algorithm considering environmental variables, i.e., meteorology, land use, hydrology, and topography. Each RF-based model was trained and optimised using cross-validation. Environmental variables that explained most of the Elmidae spatial variability were land use (i.e., riparian vegetation alteration and presence/absence of canopy), precipitation, and topography, mainly elevation and slope. The highest probability of occurrence for elmids genera was predicted in streams located within well-preserved zones. Moreover, specific ecological niches were spatially predicted for each genus. Macrelmis was predicted in the lower and forested areas, with high precipitation levels, towards the Amazon basin. Austrelmis was predicted to be in the upper parts of the basin, i.e., páramo ecosystems, with an excellent level of conservation of their riparian ecosystems. Austrolimnius and Heterelmis were also predicted in the upper parts of the basin but in more widespread elevation ranges, in the Heterelmis case, and even in some areas with a medium level of anthropisation. Neoelmis was predicted to be in the mid-region of the study basin in high altitudinal streams with a high degree of meandering. The main findings of this research are likely to contribute significantly to local conservation and restoration efforts being implemented in the study basin and could be extrapolated to similar eco-hydrological systems.

Early-life stress lastingly impacts microglial transcriptome and function under basal and immune-challenged conditions.

Early-life stress (ELS) leads to increased vulnerability to psychiatric disorders including depression later in life. Neuroinflammatory processes have been implicated in ELS-induced negative health outcomes, but how ELS impacts microglia, the main tissue-resident macrophages of the central nervous system, is unknown. Here, we determined the effects of ELS-induced by limited bedding and nesting material during the first week of life (postnatal days [P]2-9) on microglial (i) morphology; (ii) hippocampal gene expression; and (iii) synaptosome phagocytic capacity in male pups (P9) and adult (P200) mice. The hippocampus of ELS-exposed adult mice displayed altered proportions of morphological subtypes of microglia, as well as microglial transcriptomic changes related to the tumor necrosis factor response and protein ubiquitination. ELS exposure leads to distinct gene expression profiles during microglial development from P9 to P200 and in response to an LPS challenge at P200. Functionally, synaptosomes from ELS-exposed mice were phagocytosed less by age-matched microglia. At P200, but not P9, ELS microglia showed reduced synaptosome phagocytic capacity when compared to control microglia. Lastly, we confirmed the ELS-induced increased expression of the phagocytosis-related gene GAS6 that we observed in mice, in the dentate gyrus of individuals with a history of child abuse using in situ hybridization. These findings reveal persistent effects of ELS on microglial function and suggest that altered microglial phagocytic capacity is a key contributor to ELS-induced phenotypes.

Water quality assessment with emphasis in parameter optimisation using pattern recognition methods and genetic algorithm.

A non-supervised (k-means) and a supervised (k-Nearest Neighbour in combination with genetic algorithm optimisation, k-NN/GA) pattern recognition algorithms were applied for evaluating and interpreting a large complex matrix of water quality (WQ) data collected during five years (2008, 2010-2013) in the Paute river basin (southern Ecuador). 21 physical, chemical and microbiological parameters collected at 80 different WQ sampling stations were examined. At first, the k-means algorithm was carried out to identify classes of sampling stations regarding their associated WQ status by considering three internal validation indexes, i.e., Silhouette coefficient, Davies-Bouldin and Calinski-Harabasz. As a result, two WQ classes were identified, representing low (C1) and high (C2) pollution. The k-NN/GA algorithm was applied on the available data to construct a classification model with the two WQ classes, previously defined by the k-means algorithm, as the dependent variables and the 21 physical, chemical and microbiological parameters being the independent ones. This algorithm led to a significant reduction of the multidimensional space of independent variables to only nine, which are likely to explain most of the structure of the two identified WQ classes. These parameters are, namely, electric conductivity, faecal coliforms, dissolved oxygen, chlorides, total hardness, nitrate, total alkalinity, biochemical oxygen demand and turbidity. Further, the land use cover of the study basin revealed a very good agreement with the WQ spatial distribution suggested by the k-means algorithm, confirming the credibility of the main results of the used WQ data mining approach.