Computational identification of variables in neonatal vocalizations predictive for postpubertal social behaviors in a mouse model of 16p11.2 deletion.

Autism spectrum disorder (ASD) is often signaled by atypical cries during infancy. Copy number variants (CNVs) provide genetically identifiable cases of ASD, but how early atypical cries predict a later onset of ASD among CNV carriers is not understood in humans. Genetic mouse models of CNVs have provided a reliable tool to experimentally isolate the impact of CNVs and identify early predictors for later abnormalities in behaviors relevant to ASD. However, many technical issues have confounded the phenotypic characterization of such mouse models, including systematically biased genetic backgrounds and weak or absent behavioral phenotypes. To address these issues, we developed a coisogenic mouse model of human proximal 16p11.2 hemizygous deletion and applied computational approaches to identify hidden variables within neonatal vocalizations that have predictive power for postpubertal dimensions relevant to ASD. After variables of neonatal vocalizations were selected by least absolute shrinkage and selection operator (Lasso), random forest, and Markov model, regression models were constructed to predict postpubertal dimensions relevant to ASD. While the average scores of many standard behavioral assays designed to model dimensions did not differentiate a model of 16p11.2 hemizygous deletion and wild-type littermates, specific call types and call sequences of neonatal vocalizations predicted individual variability of postpubertal reciprocal social interaction and olfactory responses to a social cue in a genotype-specific manner. Deep-phenotyping and computational analyses identified hidden variables within neonatal social communication that are predictive of postpubertal behaviors.

Antioxidants inhibit the inflammatory and apoptotic processes in an intermittent hypoxia model of sleep apnea.

BACKGROUND: Sleep apnea causes intermittent hypoxia (IH). We aimed to investigate the proteins related to oxidative stress, inflammation and apoptosis in liver tissue subjected to IH as a simulation of sleep apnea in conjunction with the administration of either melatonin (MEL, 200 µL/kg) or N-acetylcysteine (NAC, 10 mg/kg). METHODS: Seventy-two adult male Balb-C mice were divided: simulation of IH (SIH), SIH + MEL, SIH + NAC, IH, IH + MEL and IH + NAC. The animals were subjected to simulations of sleep apnea for 8 h a day for 35 days. The data were analyzed with ANOVA and Tukey tests with the significance set at p < 0.05. RESULTS: In IH, there was a significant increase in oxidative stress and expression of HIF-1a. In addition, we observed increase in the activation levels of NF-kB. This increase may be responsible for the increased expression of TNF-alpha and iNOS as well as the significant increase of VEGF signaling and expression of caspase-3 and caspase-6, which suggests an increase in apoptosis. In the groups treated with antioxidants, the analysis showed that the enzyme activity and protein levels were similar to those of the non-simulated group. CONCLUSIONS: Thus, we show that IH causes liver inflammation and apoptosis, which may be protected with either MEL or NAC.

Three-dimensional protein structure prediction: Methods and computational strategies.

A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction.