Sensory-thresholded switch of neural firing states in a computational model of the ventromedial hypothalamus.

The mouse ventromedial hypothalamus (VMH) is both necessary and sufficient for defensive responses to predator and social threats. Defensive behaviors typically involve cautious approach toward potentially threatening stimuli aimed at obtaining information about the risk involved, followed by sudden avoidance and flight behavior to escape harm. In vivo neural recording studies in mice have identified two major populations of VMH neurons that either increase their firing activity as the animal approaches the threat (called Assessment+ cells) or increase their activity as the animal flees the threat (called Flight+ cells). Interestingly, Assessment+ and Flight+ cells abruptly decrease and increase their firing activity, respectively, at the decision point for flight, creating an escape-related "switch" in functional state. This suggests that the activity of the two cell types in VMH is coordinated and could result from local circuit interactions. Here, we used computational modeling to test if a local inhibitory feedback circuit could give rise to key features of the neural activity seen in VMH during the approach-to-flight transition. Starting from a simple dual-population inhibitory feedback circuit receiving repeated trains of monotonically increasing sensory input to mimic approach to threat, we tested the requirement for balanced sensory input, balanced feedback, short-term synaptic plasticity, rebound excitation, and inhibitory feedback exclusivity to reproduce an abrupt, sensory-thresholded reciprocal firing change that resembles Assessment+ and Flight+ cell activity seen in vivo. Our work demonstrates that a relatively simple local circuit architecture is sufficient for the emergence of firing patterns similar to those seen in vivo and suggests that a reiterative process of experimental and computational work may be a fruitful avenue for better understanding the functional organization of mammalian instinctive behaviors at the circuit level.

Plasma and urine metabolomic analyses in aortic valve stenosis reveal shared and biofluid-specific changes in metabolite levels.

Aortic valve stenosis (AVS) is a prevalent condition among the elderly population that eventually requires aortic valve replacement. The lack of reliable biomarkers for AVS poses a challenge for its early diagnosis and the application of preventive measures. Untargeted gas chromatography mass spectrometry (GC-MS) metabolomics was applied in 46 AVS cases and 46 controls to identify plasma and urine metabolites underlying AVS risk. Multivariate data analyses were performed on pre-processed data (e.g. spectral peak alignment), in order to detect changes in metabolite levels in AVS patients and to evaluate their performance in group separation and sensitivity of AVS prediction, followed by regression analyses to test for their association with AVS. Through untargeted analysis of 190 urine and 130 plasma features that could be detected and quantified in the GC-MS spectra, we identified contrasting levels of 22 urine and 21 plasma features between AVS patients and control subjects. Following metabolite assignment, we observed significant changes in the concentration of known metabolites in urine (n = 14) and plasma (n = 15) that distinguish the metabolomic profiles of AVS patients from healthy controls. Associations with AVS were replicated in both plasma and urine for about half of these metabolites. Among these, 2-Oxovaleric acid, elaidic acid, myristic acid, palmitic acid, estrone, myo-inositol showed contrasting trends of regulation in the two biofluids. Only trans-Aconitic acid and 2,4-Di-tert-butylphenol showed consistent patterns of regulation in both plasma and urine. These results illustrate the power of metabolomics in identifying potential disease-associated biomarkers and provide a foundation for further studies towards early diagnostic applications in severe heart conditions that may prevent surgery in the elderly.

Expanded genome-wide comparisons give novel insights into population structure and genetic heterogeneity of Leishmania tropica complex.

Leishmania tropica is one of the main causative agents of cutaneous leishmaniasis (CL). Population structures of L. tropica appear to be genetically highly diverse. However, the relationship between L. tropica strains genomic diversity, protein coding gene evolution and biogeography are still poorly understood. In this study, we sequenced the genomes of three new clinical L. tropica isolates, two derived from a recent outbreak of CL in camps hosting Syrian refugees in Lebanon and one historical isolate from Azerbaijan to further refine comparative genome analyses. In silico multilocus microsatellite typing (MLMT) was performed to integrate the current diversity of genome sequence data in the wider available MLMT genetic population framework. Single nucleotide polymorphism (SNPs), gene copy number variations (CNVs) and chromosome ploidy were investigated across the available 18 L. tropica genomes with a main focus on protein coding genes. MLMT divided the strains in three populations that broadly correlated with their geographical distribution but not populations defined by SNPs. Unique SNPs profiles divided the 18 strains into five populations based on principal component analysis. Gene ontology enrichment analysis of the protein coding genes with population specific SNPs profiles revealed various biological processes, including iron acquisition, sterols synthesis and drug resistance. This study further highlights the complex links between L. tropica important genomic heterogeneity and the parasite broad geographic distribution. Unique sequence features in protein coding genes identified in distinct populations reveal potential novel markers that could be exploited for the development of more accurate typing schemes to further improve our knowledge of the evolution and epidemiology of the parasite as well as highlighting protein variants of potential functional importance underlying L. tropica specific biology.