Microglia play essential roles in central nervous system (CNS) homeostasis and influence diverse aspects of neuronal function. However, the transcriptional mechanisms that specify human microglia phenotypes are largely unknown. We examined the transcriptomes and epigenetic landscapes of human microglia isolated from surgically resected brain tissue ex vivo and following transition to an in vitro environment. Transfer to a tissue culture environment results in rapid and extensive downregulation of microglia-specific genes that are induced in primitive mouse macrophages following migration into the fetal brain. Substantial subsets of these genes exhibit altered expression in neurodegenerative and behavioral diseases and are associated with non-coding risk variants. These findings reveal an environment-dependent transcriptional network specifying microglia-specific programs of gene expression and facilitate efforts to understand the roles of microglia in human disease.

Non-coding genetic variation is a major driver of phenotypic diversity and allows the investigation of mechanisms that control gene expression. Here, we systematically investigated the effects of >50 million variations from five strains of mice on mRNA, nascent transcription, transcription start sites, and transcription factor binding in resting and activated macrophages. We observed substantial differences associated with distinct molecular pathways. Evaluating genetic variation provided evidence for roles of ∼100 TFs in shaping lineage-determining factor binding. Unexpectedly, a substantial fraction of strain-specific factor binding could not be explained by local mutations. Integration of genomic features with chromatin interaction data provided evidence for hundreds of connected cis-regulatory domains associated with differences in transcription factor binding and gene expression. This system and the >250 datasets establish a substantial new resource for investigation of how genetic variation affects cellular phenotypes.

No abstract

Macrophages (MÂs) are key immune infiltrates in solid tumors and serve as major drivers behind tumor growth, immune suppression, and inhibition of adaptive immune responses in the tumor microenvironment (TME). Bromodomain and extraterminal (BET) protein, BRD4, which binds to acetylated lysine on histone tails, has recently been reported to promote gene transcription of proinflammatory cytokines but has rarely been explored for its role in IL4driven MÂ transcriptional programming and MÂ-mediated immunosuppression in the TME. Herein, we report that BET bromodomain inhibitor, JQ1, blocks association of BRD4 with promoters of arginase and other IL4-driven MÂ genes, which promote immunosuppression in TME. Pharmacolog-ic inhibition of BRD4 using JQ1 and/or PI3K using dual PI3K/BRD4 inhibitor SF2523 (previously reported by our group as a potent inhibitor to block tumor growth and metastasis in various cancer models) suppresses tumor growth in syngeneic and spontaneous murine cancer models; reduces infiltration of myeloid-derived suppressor cells; blocks polarization of immunosuppressive MÂs; restores CD8 þ T-cell activity; and stimulates antitumor immune responses. Finally, our results suggest that BRD4 regulates the immunosuppressive myeloid TME, and BET inhibitors and dual PI3K/BRD4 inhibitors are therapeutic strategies for cancers driven by the MÂ-dependent immunosuppressive TME. Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis):

◥ Macrophages (MÈ) play a critical role in tumor growth, immunosuppression, and inhibition of adaptive immune responses in cancer. Hence, targeting signaling pathways in MÈs that promote tumor immunosuppression will provide therapeutic benefit. PI3Kg has been recently established by our group and others as a novel immuno-oncology target. Herein, we report that an MÈ Syk-PI3K axis drives polarization of immunosuppressive MÈs that establish an immunosuppressive tumor microenvironment in in vivo syngeneic tumor models. Genetic or pharmacologic inhibition of Syk and/or PI3Kg in MÈs promotes a proinflammatory MÈ phenotype, restores CD8 þ T-cell activity, destabilizes HIF under hypoxia, and stimulates an antitumor immune response. Assay for transposaseaccessible Chromatin using Sequencing (ATAC-seq) analyses on the bone marrow-derived macrophages (BMDM) show that inhibition of Syk kinase promotes activation and binding of NF-kB motif in Syk MC-KO BMDMs, thus stimulating immunostimulatory transcriptional programming in MÈs to suppress tumor growth. Finally, we have developed in silico the "first-in-class" dual Syk/ PI3K inhibitor, SRX3207, for the combinatorial inhibition of Syk and PI3K in one small molecule. This chemotype demonstrates efficacy in multiple tumor models and represents a novel combinatorial approach to activate antitumor immunity.

Genome sequences diverge more rapidly in mammals than in other animal lineages, such as birds or insects. However, the effect of this rapid divergence on transcriptional evolution remains unclear. Recent reports have indicated a faster divergence of transcription factor binding in mammals than in insects, but others found the reverse for mRNA expression. Here, we show that these conflicting interpretations resulted from differing methodologies. We performed an integrated analysis of transcriptional network evolution by examining mRNA expression, transcription factor binding and cis-regulatory motifs across >25 animal species, including mammals, birds and insects. Strikingly, we found that transcriptional networks evolve at a common rate across the three animal lineages. Furthermore, differences in rates of genome divergence were greatly reduced when restricting comparisons to chromatin-accessible sequences. The evolution of transcription is thus decoupled from the global rate of genome sequence evolution, suggesting that a small fraction of the genome regulates transcription.DOI: http://dx.doi.org/10.7554/eLife.11615.001

Hematopoietic stem cells (HSCs) maintain a quiescent state during homeostasis, but with acute infection, they exit the quiescent state to increase the output of immune cells, the so-called "emergency hematopoiesis." However, HSCs' response to severe infection during septic shock and the pathological impact remain poorly elucidated. Here, we report that the histone demethylase KDM1A/LSD1, serving as a critical regulator of mammalian hematopoiesis, is a negative regulator of the response to inflammation in HSCs during endotoxic shock typically observed during acute bacterial or viral infection. Inflammation-induced LSD1 deficiency results in an acute expansion of a pathological population of hyperproliferative and hyperinflammatory myeloid progenitors, resulting in a septic shock phenotype and acute death. Unexpectedly, in vivo administration of bacterial lipopolysaccharide (LPS) to wild-type mice results in acute suppression of LSD1 in HSCs with a septic shock phenotype that resembles that observed following induced deletion of The suppression of LSD1 in HSCs is caused, at least in large part, by a cohort of inflammation-induced microRNAs. Significantly, reconstitution of mice with bone marrow progenitor cells expressing inhibitors of these inflammation-induced microRNAs blocked the suppression of LSD1 in vivo following acute LPS administration and prevented mortality from endotoxic shock. Our results indicate that LSD1 activators or miRNA antagonists could serve as a therapeutic approach for life-threatening septic shock characterized by dysfunction of HSCs.

Motivation:Extraction of biomedical knowledge from unstructured text poses a great challenge in the biomedical field. Named entity recognition (NER) promises to improve information extraction and retrieval. However, existing approaches require manual annotation of large training text corpora, which is laborious and time-consuming. To address this problem we adopted deep learning technique that repurposes the 43,900,000 Entity-free-text pairs available in metadata associated with the NCBI BioSample archive to train a scalable NER model. This NER model can assist in biospecimen metadata annotation by extracting named-entities from user-supplied free-text descriptions.Results: We evaluated our model against two validation sets, namely data sets consisting of short-phrases and long sentences. We achieved an accuracy of 93.29% and 93.40% in the short-phrase validation set and long sentence validation set respectively.Availability: All the analyses, pre-trained model, environments, and Jupyter notebooks pertaining to this manuscript are available on Github: https://github.com/brianyiktaktsui/DEEP_NLP . Contact: hkcarter@ucsd.eduFig1. Repurposing public biospecimen data for NER training ( A ) Depiction of training NER model using pre-annotated Entity-free-text pairs available from public biospecimen annotation data (BioSamples) from NCBI ( A.1 ) Example of Entity-free-text pairs from BioSamples. In this example, the free-text phrase Glioblastoma stage 4 system is a Disease entity. ( A.2 ) Expected results of an NER model recognizing biomedical concepts from sentences. ( B ) Histogram of the 30 most frequently used entities (x-axis) available in the current set of BioSamples. These atomic named entities (blue labels) can be used to extract concepts from composite entities TITLE and DESCRIPTION (red labels).