To establish chronic infections, viruses must develop strategies to evade the host’s immune responses. Many retroviruses, including mouse mammary tumor virus (MMTV), are transmitted most efficiently through mucosal surfaces rich in microbiota. We found that MMTV, when ingested by newborn mice, stimulates a state of unresponsiveness toward viral antigens. This process required the intestinal microbiota, as antibiotic-treated mice or germ-free mice did not transmit infectious virus to their offspring. MMTV-bound bacterial lipopolysaccharide triggered Toll-like receptor 4 and subsequent interleukin-6 (IL-6)–dependent induction of the inhibitory cytokine IL-10. Thus, MMTV has evolved to rely on the interaction with the microbiota to induce an immune evasion pathway. Together, these findings reveal the fundamental importance of commensal microbiota in viral infections.

Most neuropsychiatric disease risk variants are in noncoding sequences and lack functional interpretation. Because regulatory sequences often reside in open chromatin, we reasoned that neuropsychiatric disease risk variants may affect chromatin accessibility during neurodevelopment. Using human induced pluripotent stem cell (iPSC)–derived neurons that model developing brains, we identified thousands of genetic variants exhibiting allele-specific open chromatin (ASoC). These neuronal ASoCs were partially driven by altered transcription factor binding, overrepresented in brain gene enhancers and expression quantitative trait loci, and frequently associated with distal genes through chromatin contacts. ASoCs were enriched for genetic variants associated with brain disorders, enabling identification of functional schizophrenia risk variants and their cis-target genes. This study highlights ASoC as a functional mechanism of noncoding neuropsychiatric risk variants, providing a powerful framework for identifying disease causal variants and genes.

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Background The BIN1 locus contains the second-most significant genetic risk factor for late-onset Alzheimer’s disease. BIN1 undergoes alternate splicing to generate tissue- and cell-type-specific BIN1 isoforms, which regulate membrane dynamics in a range of crucial cellular processes. Whilst the expression of BIN1 in the brain has been characterized in neurons and oligodendrocytes in detail, information regarding microglial BIN1 expression is mainly limited to large-scale transcriptomic and proteomic data. Notably, BIN1 protein expression and its functional roles in microglia, a cell type most relevant to Alzheimer’s disease, have not been examined in depth. Methods Microglial BIN1 expression was analyzed by immunostaining mouse and human brain, as well as by immunoblot and RT-PCR assays of isolated microglia or human iPSC-derived microglial cells. Bin1 expression was ablated by siRNA knockdown in primary microglial cultures in vitro and Cre-lox mediated conditional deletion in adult mouse brain microglia in vivo. Regulation of neuroinflammatory microglial signatures by BIN1 in vitro and in vivo was characterized using NanoString gene panels and flow cytometry methods. The transcriptome data was explored by in silico pathway analysis and validated by complementary molecular approaches. Results Here, we characterized microglial BIN1 expression in vitro and in vivo and ascertained microglia expressed BIN1 isoforms. By silencing Bin1 expression in primary microglial cultures, we demonstrate that BIN1 regulates the activation of proinflammatory and disease-associated responses in microglia as measured by gene expression and cytokine production. Our transcriptomic profiling revealed key homeostatic and lipopolysaccharide (LPS)-induced inflammatory response pathways, as well as transcription factors PU.1 and IRF1 that are regulated by BIN1. Microglia-specific Bin1 conditional knockout in vivo revealed novel roles of BIN1 in regulating the expression of disease-associated genes while counteracting CX3CR1 signaling. The consensus from in vitro and in vivo findings showed that loss of Bin1 impaired the ability of microglia to mount type 1 interferon responses to proinflammatory challenge, particularly the upregulation of a critical type 1 immune response gene, Ifitm3. Conclusions Our convergent findings provide novel insights into microglial BIN1 function and demonstrate an essential role of microglial BIN1 in regulating brain inflammatory response and microglial phenotypic changes. Moreover, for the first time, our study shows a regulatory relationship between Bin1 and Ifitm3, two Alzheimer’s disease-related genes in microglia. The requirement for BIN1 to regulate Ifitm3 upregulation during inflammation has important implications for inflammatory responses during the pathogenesis and progression of many neurodegenerative diseases. Graphical Abstract

Genomewide association studies (GWAS) have identified a large number of loci associated with neuropsychiatric traits, however, understanding the molecular mechanisms underlying these loci remains difficult. To help prioritize causal variants and interpret their functions, computational methods have been developed to predict regulatory effects of non-coding variants. An emerging approach to variant annotation is deep learning models that predict regulatory functions from DNA sequences alone. While such models have been trained on large publicly available dataset such as ENCODE, neuropsychiatric trait-related cell types are under-represented in these datasets, thus there is an urgent need of better tools and resources to annotate variant functions in such cellular contexts. To fill this gap, we collected a large collection of neurodevelopment-related cell/tissue types, and trained deep Convolutional Neural Networks (ResNet) using such data. Furthermore, our model, called MetaChrom, borrows information from public epigenomic consortium to improve the accuracy via transfer learning. We show that MetaChrom is substantially better in predicting experimentally determined chromatin accessibility variants than popular variant annotation tools such as CADD and delta-SVM. By combining GWAS data with MetaChrom predictions, we prioritized 31 SNPs for Schizophrenia, suggesting potential risk genes and the biological contexts where they act. In summary, MetaChrom provides functional annotations of any DNA variants in the neuro-development context and the general method of MetaChrom can also be extended to other disease-related cell or tissue types.