A network of networks approach for modeling interconnected brain tissue-specific networks

Embryos assist morphogenesis of others through calcium and ATP signaling mechanisms in collective teratogen resistance

Information for organismal patterning can come from a variety of sources. We investigate the possibility that instructive influences for normal embryonic development are provided not only at the level of cells within the embryo, but also via interactions between embryos. To explore this, we challenge groups of embryos with disruptors of normal development while varying group size. Here, we show that Xenopus laevis embryos are much more sensitive to a diverse set of chemical and molecular-biological perturbations when allowed to develop alone or in small groups, than in large groups. Keeping per-embryo exposure constant, we find that increasing the number of exposed embryos in a cohort increases the rate of survival while incidence of defects decreases. This inter-embryo assistance effect is mediated by short-range diffusible signals and involves the P2 ATP receptor. Our data and computational model emphasize that morphogenesis is a collective phenomenon not only at the level of cells, but also of whole bodies, and that cohort size is a crucial variable in studies of ecotoxicology, teratogenesis, and developmental plasticity.

Current progress and challenges in the development of brain tissue models: How to grow up the changeable brain in vitro?

In vitro modeling of brain tissue is a promising but not yet resolved problem in modern neurobiology and neuropharmacology. Complexity of the brain structure and diversity of cell-to-cell communication in (patho)physiological conditions make this task almost unachievable. However, establishment of novel in vitro brain models would ultimately lead to better understanding of development-associated or experience-driven brain plasticity, designing efficient approaches to restore aberrant brain functioning. The main goal of this review is to summarize the available data on methodological approaches that are currently in use, and to identify the most prospective trends in development of neurovascular unit, blood-brain barrier, blood-cerebrospinal fluid barrier, and neurogenic niche in vitro models. The manuscript focuses on the regulation of adult neurogenesis, cerebral microcirculation and fluids dynamics that should be reproduced in the in vitro 4D models to mimic brain development and its alterations in brain pathology. We discuss approaches that are critical for studying brain plasticity, deciphering the individual person-specific trajectory of brain development and aging, and testing new drug candidates in the in vitro models.

Oxytocin-Receptor Gene Modulates Reward-Network Connection and Relationship with Empathy Performance

Introduction

Empathy traits are highly heritable and linked with reward processing. It is implicated that common variations of the oxytocin-receptor gene (OXTR) play a modulatory effect on empathic performance. However, it is unclear about the neural substrates underlying the modulatory effect of the OXTR genotype on empathic performance. This study aimed to characterize the modulatory effect of common OXTR variations on reward-circuitry function and its relationship with empathy.

Methods

Based on the seed of the nucleus accumbens (NAcc; a key hub of reward circuitry), we examined differences in spontaneous local activity and functional connectivity between OXTR rs2268493 genotype groups and their relationship with empathic performance among 402 high-homogeneity participants.

Results

Comparing with C carriers (CC/CT) group, the individuals with the rs2268493 TT genotype exhibited lower functional connectivity of the right NAcc with the medial prefrontal cortex (mPFC) and inferior frontal gyrus. Similarly lower functional connectivity was found between the left NAcc and mPFC. Consequently, no significant difference was found in the spontaneous local activity of NAcc.

Discussion

Our findings suggested that common OXTR variations have a modulatory effect on the connection of the NAcc with the hub of empathic networks (mPFC and IFG), which may provide insight on the neural substrate underlying the modulatory effect of OXTR on empathic behavior.

Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic

Breakthrough advances in informatics over the last decade have thoroughly influenced the field of immunology. The intermingling of machine learning with wet lab applications and clinical results has hatched the newly defined immunoinformatics society. Immunoinformatics of the central neural system, referred to as neuroimmunoinformatics (NII), investigates symmetrical and asymmetrical interactions of the brain-immune interface. This interdisciplinary overview on NII is addressed to bioscientists and computer scientists. We delineate the dominating trajectories and field-shaping achievements and elaborate on future directions using bridging language and terminology. Computation, varying from linear modeling to complex deep learning approaches, fuels neuroimmunology through three core directions. Firstly, by providing big-data analysis software for high-throughput methods such as next-generation sequencing and genome-wide association studies. Secondly, by designing models for the prediction of protein morphology, functions, and symmetrical and asymmetrical protein–protein interactions. Finally, NII boosts the output of quantitative pathology by enabling the automatization of tedious processes such as cell counting, tracing, and arbor analysis. The new classification of microglia, the brain’s innate immune cells, was an NII achievement. Deep sequencing classifies microglia in “sensotypes” to accurately describe the versatility of immune responses to physiological and pathological challenges, as well as to experimental conditions such as xenografting and organoids. NII approaches complex tasks in the brain-immune interface, recognizes patterns and allows for hypothesis-free predictions with ultimate targeted individualized treatment strategies, and personalizes disease prognosis and treatment response.

An Overview of Bioinformatics Methods for Analyzing Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders and are well recognized to be biologically heterogeneous in which various factors are associated, including genetic, metabolic, and environmental ones. Despite its high prevalence, only a few drugs have been approved for the treatment of ASD. Therefore, extensive studies have been conducted to identify ASD risk genes and novel drug targets. Since many genes and many other factors are associated with ASD, various bioinformatics methods have also been developed for the analysis of ASD. In this paper, we review bioinformatics methods for analyzing ASD data with the focus on computational aspects. We classify existing methods into two categories: (i) methods based on genomic variants and gene expression data, and (ii) methods using biological networks, which include gene co-expression networks and protein-protein interaction networks. Next, for each method, we provide an overall flow and elaborate on the computational techniques used. We also briefly review other approaches and discuss possible future directions and strategies for developing bioinformatics approaches to analyze ASD.