The innate immunity protein IFITM3 modulates γ-secretase in Alzheimer's disease.

Innate immunity is associated with Alzheimer's disease1, but the influence of immune activation on the production of amyloid-ß is unknown2,3. Here we identify interferon-induced transmembrane protein 3 (IFITM3) as a γ-secretase modulatory protein, and establish a mechanism by which inflammation affects the generation of amyloid-ß. Inflammatory cytokines induce the expression of IFITM3 in neurons and astrocytes, which binds to γ-secretase and upregulates its activity, thereby increasing the production of amyloid-ß. The expression of IFITM3 is increased with ageing and in mouse models that express familial Alzheimer's disease genes. Furthermore, knockout of IFITM3 reduces γ-secretase activity and the formation of amyloid plaques in a transgenic mouse model (5xFAD) of early amyloid deposition. IFITM3 protein is upregulated in tissue samples from a subset of patients with late-onset Alzheimer's disease that exhibit higher γ-secretase activity. The amount of IFITM3 in the γ-secretase complex has a strong and positive correlation with γ-secretase activity in samples from patients with late-onset Alzheimer's disease. These findings reveal a mechanism in which γ-secretase is modulated by neuroinflammation via IFITM3 and the risk of Alzheimer's disease is thereby increased.

Circuit-Wide Gene Network Analysis Reveals Sex-Specific Roles for Phosphodiesterase 1b in Cocaine Addiction.

Cocaine use disorder is a significant public health issue without an effective pharmacological treatment. Successful treatments are hindered in part by an incomplete understanding of the molecular mechanisms that underlie long-lasting maladaptive plasticity and addiction-like behaviors. Here, we leverage a large RNA sequencing dataset to generate gene coexpression networks across six interconnected regions of the brain's reward circuitry from mice that underwent saline or cocaine self-administration. We identify phosphodiesterase 1b (Pde1b), a Ca2+/calmodulin-dependent enzyme that increases cAMP and cGMP hydrolysis, as a central hub gene within a nucleus accumbens (NAc) gene module that was bioinformatically associated with addiction-like behavior. Chronic cocaine exposure increases Pde1b expression in NAc D2 medium spiny neurons (MSNs) in male but not female mice. Viral-mediated Pde1b overexpression in NAc reduces cocaine self-administration in female rats but increases seeking in both sexes. In female mice, overexpressing Pde1b in D1 MSNs attenuates the locomotor response to cocaine, with the opposite effect in D2 MSNs. Overexpressing Pde1b in D1/D2 MSNs had no effect on the locomotor response to cocaine in male mice. At the electrophysiological level, Pde1b overexpression reduces sEPSC frequency in D1 MSNs and regulates the excitability of NAc MSNs. Lastly, Pde1b overexpression significantly reduced the number of differentially expressed genes (DEGs) in NAc following chronic cocaine, with discordant effects on gene transcription between sexes. Together, we identify novel gene modules across the brain's reward circuitry associated with addiction-like behavior and explore the role of Pde1b in regulating the molecular, cellular, and behavioral responses to cocaine.

Lifelong chronic psychosocial stress induces a proteomic signature of Alzheimer's disease in wildtype mice.

Late onset, sporadic Alzheimer's disease (AD) accounts for the vast majority of cases. Unlike familial AD, the factors that drive the onset of sporadic AD are poorly understood, although aging and stress play a role. The early onset/severity of neuropathology observed in most genetic mouse models of AD hampers the study of the role of aging and environmental factors; thus alternate strategies are necessary to understand the contributions of these factors to sporadic AD. We demonstrate that mice acquiring a low social status (subordinate) in a lifelong chronic psychosocial stress (CPS) model, accrue widespread proteomic changes in the frontal/temporal cortex during aging. To better understand the significance of these stress-induced changes, we compared the differentially expressed proteins (DEPs) of subordinate mice to those of patients at varying stages of dementia. Sixteen and fifteen DEPs upregulated in subordinate mice were also upregulated in patients with mild cognitive impairment (MCI) and AD, respectively. Six of those upregulated proteins (CPE, ERC2, GRIN2B, SLC6A1, SYN1, WFS1) were shared by subordinate mice and patients with MCI or AD. Finally, comparison with a spatially detailed transcriptomic database revealed that the superior frontal gyrus and hippocampus had the greatest overlap between mice subjected to lifelong CPS and AD patients. Overall, most of the overlapping proteins were functionally associated with enhanced NMDA receptor mediated glutamatergic signaling, an excitotoxicity mechanism known to affect neurodegeneration. These findings support the association between stress and AD progression and provide valuable insight into potential early biomarkers and protein mediators of this relationship.

EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to improve prediction accuracy.

Motivation: Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify the therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov-Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods. Results: We first designed an expression weighted cosine (EWCos) method to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed ensemble of multiple drug repositioning approaches (EMUDRA) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs. Availability and implementation: The EMUDRA R package is available at doi: 10.7303/syn11510888. Supplementary information: Supplementary data are available at Bioinformatics online.

GO-function: deriving biologically relevant functions from statistically significant functions.

In high-throughput studies of diseases, terms enriched with disease-related genes based on Gene Ontology (GO) are routinely found. However, most current algorithms used to find significant GO terms cannot handle the redundancy that results from the dependencies of GO terms. Simply based on some numerical considerations, current algorithms developed for reducing this redundancy may produce results that do not account for biologically interesting cases. In this article, we present several rules used to design a tool called GO-function for extracting biologically relevant terms from statistically significant GO terms for a disease. Using one gene expression profile for colorectal cancer, we compared GO-function with four algorithms designed to treat redundancy. Then, we validated results obtained in this data set by GO-function using another data set for colorectal cancer. Our analysis showed that GO-function can identify disease-related terms that are more statistically and biologically meaningful than those found by the other four algorithms.

Transcriptomic landscape of human induced pluripotent stem cell-derived osteogenic differentiation identifies a regulatory role of KLF16.

Osteogenic differentiation is essential for bone development and metabolism, but the underlying gene regulatory networks have not been well investigated. We differentiated mesenchymal stem cells, derived from 20 human induced pluripotent stem cell lines, into preosteoblasts and osteoblasts, and performed systematic RNA-seq analyses of 60 samples for differential gene expression. We noted a highly significant correlation in expression patterns and genomic proximity among transcription factor (TF) and long noncoding RNA (lncRNA) genes. We identified TF-TF regulatory networks, regulatory roles of lncRNAs on their neighboring coding genes for TFs and splicing factors, and differential splicing of TF, lncRNA, and splicing factor genes. TF-TF regulatory and gene co-expression network analyses suggested an inhibitory role of TF KLF16 in osteogenic differentiation. We demonstrate that in vitro overexpression of human KLF16 inhibits osteogenic differentiation and mineralization, and in vivo Klf16+/- mice exhibit increased bone mineral density, trabecular number, and cortical bone area. Thus, our model system highlights the regulatory complexity of osteogenic differentiation and identifies novel osteogenic genes.

Rank-based predictors for response and prognosis of neoadjuvant taxane-anthracycline-based chemotherapy in breast cancer.

For neoadjuvant taxane and anthracycline-based chemotherapy for breast cancer, patients with pathological complete response (pCR) have a favorable prognosis compared with patients with residual disease (RD). Although a number of pCR predictors based on microarray profiles have been proposed to guide neoadjuvant chemotherapy, most of these have not been independently validated in inter-laboratory datasets, possibly owing to the fact that microarray measurements are sensitive to experimental batch effects and inter-array data normalizations. In this study, we developed a rank-based method to tackle this difficulty. First, we extracted from two datasets a combination of gene pairs, each of which had opposing relative expression orders in patients with pCR and those with RD, and used these to build a pCR predictor. This pCR predictor was found to have sensitivities of 74 and 86 % and specificities of 71 and 68 % in another two independent datasets from multiple laboratories, and these results were better than the performances of three previously reported predictors. Considering that patients with minimal RD also tend to have a good prognosis, we then developed a prognosis predictor for RD as a complement to the pCR predictor, in order to identify a group of patients likely to have a good prognosis, taking into account both the RD level and the intrinsic risk factors. In the independent validation, there was a significant difference (P = 0.001) in distant relapse-free survival between the patients likely to and those not likely to have a good prognosis according to our prognosis predictor. In conclusion, the rank-based predictors for response and prognosis can accurately and robustly predict patients with improved prognosis who might benefit from neoadjuvant taxane and anthracycline-based chemotherapy.

A relative ordering-based predictor for tamoxifen-treated estrogen receptor-positive breast cancer patients: multi-laboratory cohort validation.

Current predictors for estrogen receptor-positive (ER-positive) breast cancer patients receiving tamoxifen are often invalid in inter-laboratory validation. We aim to develop a robust predictor based on the relative ordering of expression measurement (ROE) in gene pairs. Using a large integrated dataset of 420 normal controls and 1,129 ER-positive breast tumor samples, we identified the gene pairs with stable ROEs in normal control and significantly reversed ROEs in ER-positive tumor. Using these gene pairs, we characterized each sample of a cohort of 292 ER-positive patients who received tamoxifen monotherapy for 5 years and then identified relapse risk-associated gene pairs. We extracted a gene pair subset that resulted in the largest positive and negative predictive values for predicting 10-year relapse-free survival (RFS) using a genetic algorithm. A predictor was developed based on the gene pair subset and was validated in 2 large multi-laboratory cohorts (N = 250 and 248, respectively) of ER-positive patients who received 5-year tamoxifen alone. In the first validation cohort, the patients predicted to be tamoxifen sensitive had a 10-year RFS of 91 % (95 % confidence interval [CI] 85-97 %) with an absolute risk reduction of 34 % (95 % CI 17-51 %). The patients predicted to be tamoxifen insensitive had a significantly higher relapse risk than the patients predicted to be tamoxifen sensitive (hazard ratio = 4.99, 95 % CI 2.45-10.17, P = 9.13 × 10(-7)). Similar performance was achieved for the second validation cohort. The predictor performed well in both node-negative and node-positive subsets and added significant predictive power to the clinical parameters. In contrast, 2 previously proposed predictors did not achieve significantly better performances than the baselines of the validation cohorts. In summary, the proposed predictor can accurately and robustly predict tamoxifen sensitivity of ER-positive breast cancer patients and identified patients with a high probability of 10-year RFS following tamoxifen monotherapy.

Smarca4 deficiency induces Pttg1 oncogene upregulation and hyperproliferation of tubular and interstitial cells during kidney development.

Kidney formation and nephrogenesis are controlled by precise spatiotemporal gene expression programs, which are coordinately regulated by cell-cycle, cell type-specific transcription factors and epigenetic/chromatin regulators. However, the roles of epigenetic/chromatin regulators in kidney development and disease remain poorly understood. In this study, we investigated the impact of deleting the chromatin remodeling factor Smarca4 (Brg1), a human Wilms tumor-associated gene, in Wnt4-expressing cells. Smarca4 deficiency led to severe tubular defects and a shortened medulla. Through unbiased single-cell RNA sequencing analyses, we identified multiple types of Wnt4 Cre-labeled interstitial cells, along with nephron-related cells. Smarca4 deficiency increased interstitial cells but markedly reduced tubular cells, resulting in cells with mixed identity and elevated expression of cell-cycle regulators and genes associated with extracellular matrix and epithelial-to-mesenchymal transition/fibrosis. We found that Smarca4 loss induced a significant upregulation of the oncogene Pttg1 and hyperproliferation of Wnt4 Cre-labeled cells. These changes in the cellular state could hinder the cellular transition into characteristic tubular structures, eventually leading to fibrosis. In conclusion, our findings shed light on novel cell types and genes associated with Wnt4 Cre-labeled cells and highlight the critical role of Smarca4 in regulating tubular cell differentiation and the expression of the cancer-causing gene Pttg1 in the kidney. These findings may provide valuable insights into potential therapeutic strategies for renal cell carcinoma resulting from SMARCA4 deficiency.

Pseudo-temporal dynamics of chemoresistant triple negative breast cancer cells reveal EGFR/HER2 inhibition as synthetic lethal during mid-neoadjuvant chemotherapy.

In the absence of targetable hormonal axes, chemoresistance for triple-negative breast cancer (TNBC) often compromises patient outcomes. To investigate the underlying tumor dynamics, we performed trajectory analysis on the single-nuclei RNA-seq (snRNA-seq) of chemoresistant tumor clones during neoadjuvant chemotherapy (NAC). It revealed a common tumor trajectory across multiple patients with HER2-like expansions during NAC. Genome-wide CRISPR-Cas9 knock-out on mammary epithelial cells revealed chemosensitivity-promoting knock-outs were up-regulated along the tumor trajectory. Furthermore, we derived a consensus gene signature of TNBC chemoresistance by comparing the trajectory transcriptome with chemoresistant transcriptomes from TNBC cell lines and poor prognosis patient samples to predict FDA-approved drugs, including afatinib (pan-HER inhibitor), targeting the consensus signature. We validated the synergistic efficacy of afatinib and paclitaxel in chemoresistant TNBC cells and confirmed pharmacological suppression of the consensus signature. The study provides a dynamic model of chemoresistant tumor transcriptome, and computational framework for pharmacological intervention.

Multi-omics-based analysis of high grade serous ovarian cancer subtypes reveals distinct molecular processes linked to patient prognosis.

Despite advancements in treatment, high-grade serous ovarian cancer (HGSOC) is still characterized by poor patient outcomes. To understand the molecular heterogeneity of this disease, which underlies the challenge in selecting optimal treatments for HGSOC patients, we have integrated genomic, transcriptomic, and epigenetic information to identify seven new HGSOC subtypes using a multiscale clustering method. These subtypes not only have significantly distinct overall survival, but also exhibit unique patterns of gene expression, microRNA expression, DNA methylation, and copy number alterations. As determined by our analysis, patients with similar clinical outcomes have distinct profiles of activated or repressed cellular processes, including cell cycle, epithelial-to-mesenchymal transition, immune activation, interferon response, and cilium organization. Furthermore, we performed a multiscale gene co-expression network analysis to identify subtype-specific key regulators and predicted optimal targeted therapies based on subtype-specific gene expression. In summary, this study provides new insights into the cellular heterogeneity of the HGSOC genomic, epigenetic, and transcriptomic landscapes and provides a basis for future studies into precision medicine for HGSOC patients.

Multiscale network analysis identifies potential receptors for SARS-CoV-2 and reveals their tissue-specific and age-dependent expression.

The coronavirus disease 2019 (COVID-19) pandemic has affected tens of millions of individuals and caused hundreds of thousands of deaths worldwide. Here, we present a comprehensive, multiscale network analysis of the transcriptional response to the virus. In particular, we focused on key regulators, cell receptors, and host processes that were hijacked by the virus for its advantage. ACE2-controlled processes involved CD300e (a TYROBP receptor) as a key regulator and the activation of IL-2 pro-inflammatory cytokine signaling. We further investigated the age dependency of such receptors in different tissues. In summary, this study provides novel insights into the gene regulatory organization during the SARS-CoV-2 infection and the tissue-specific, age-dependent expression of the cell receptors involved in COVID-19.

Multiscale protein networks systematically identify aberrant protein interactions and oncogenic regulators in seven cancer types.

Global proteomic data generated by advanced mass spectrometry (MS) technologies can help bridge the gap between genome/transcriptome and functions and hold great potential in elucidating unbiased functional models of pro-tumorigenic pathways. To this end, we collected the high-throughput, whole-genome MS data and conducted integrative proteomic network analyses of 687 cases across 7 cancer types including breast carcinoma (115 tumor samples; 10,438 genes), clear cell renal carcinoma (100 tumor samples; 9,910 genes), colorectal cancer (91 tumor samples; 7,362 genes), hepatocellular carcinoma (101 tumor samples; 6,478 genes), lung adenocarcinoma (104 tumor samples; 10,967 genes), stomach adenocarcinoma (80 tumor samples; 9,268 genes), and uterine corpus endometrial carcinoma UCEC (96 tumor samples; 10,768 genes). Through the protein co-expression network analysis, we identified co-expressed protein modules enriched for differentially expressed proteins in tumor as disease-associated pathways. Comparison with the respective transcriptome network models revealed proteome-specific cancer subnetworks associated with heme metabolism, DNA repair, spliceosome, oxidative phosphorylation and several oncogenic signaling pathways. Cross-cancer comparison identified highly preserved protein modules showing robust pan-cancer interactions and identified endoplasmic reticulum-associated degradation (ERAD) and N-acetyltransferase activity as the central functional axes. We further utilized these network models to predict pan-cancer protein regulators of disease-associated pathways. The top predicted pan-cancer regulators including RSL1D1, DDX21 and SMC2, were experimentally validated in lung, colon, breast cancer and fetal kidney cells. In summary, this study has developed interpretable network models of cancer proteomes, showcasing their potential in unveiling novel oncogenic regulators, elucidating underlying mechanisms, and identifying new therapeutic targets.

Molecular differences in brain regional vulnerability to aging between males and females.

Background: Aging-related cognitive decline is associated with brain structural changes and synaptic loss. However, the molecular mechanisms of cognitive decline during normal aging remain elusive. Results: Using the GTEx transcriptomic data from 13 brain regions, we identified aging-associated molecular alterations and cell-type compositions in males and females. We further constructed gene co-expression networks and identified aging-associated modules and key regulators shared by both sexes or specific to males or females. A few brain regions such as the hippocampus and the hypothalamus show specific vulnerability in males, while the cerebellar hemisphere and the anterior cingulate cortex regions manifest greater vulnerability in females than in males. Immune response genes are positively correlated with age, whereas those involved in neurogenesis are negatively correlated with age. Aging-associated genes identified in the hippocampus and the frontal cortex are significantly enriched for gene signatures implicated in Alzheimer's disease (AD) pathogenesis. In the hippocampus, a male-specific co-expression module is driven by key synaptic signaling regulators including VSNL1, INA, CHN1 and KCNH1; while in the cortex, a female-specific module is associated with neuron projection morphogenesis, which is driven by key regulators including SRPK2, REPS2 and FXYD1. In the cerebellar hemisphere, a myelination-associated module shared by males and females is driven by key regulators such as MOG, ENPP2, MYRF, ANLN, MAG and PLP1, which have been implicated in the development of AD and other neurodegenerative diseases. Conclusions: This integrative network biology study systematically identifies molecular signatures and networks underlying brain regional vulnerability to aging in males and females. The findings pave the way for understanding the molecular mechanisms of gender differences in developing neurodegenerative diseases such as AD.

Shared and divergent transcriptomic regulation in nucleus accumbens D1 and D2 medium spiny neurons by cocaine and morphine.

Substance use disorders (SUDs) induce widespread molecular dysregulation in the nucleus accumbens (NAc), a brain region pivotal for coordinating motivation and reward. These molecular changes are thought to support lasting neural and behavioral disturbances that promote drug-seeking in addiction. However, different drug classes exert unique influences on neural circuits, cell types, physiology, and gene expression despite the overlapping symptomatology of SUDs. To better understand common and divergent molecular mechanisms governing SUD pathology, our goal was to survey cell-type-specific restructuring of the NAc transcriptional landscape in after psychostimulant or opioid exposure. We combined fluorescence-activated nuclei sorting and RNA sequencing to profile NAc D1 and D2 medium spiny neurons (MSNs) across cocaine and morphine exposure paradigms, including initial exposure, prolonged withdrawal after repeated exposure, and re-exposure post-withdrawal. Our analyses reveal that D1 MSNs display many convergent transcriptional responses across drug classes during exposure, whereas D2 MSNs manifest mostly divergent responses between cocaine and morphine, with morphine causing more adaptations in this cell type. Utilizing multiscale embedded gene co-expression network analysis (MEGENA), we discerned transcriptional regulatory networks subserving biological functions shared between cocaine and morphine. We observed largely integrative engagement of overlapping gene networks across drug classes in D1 MSNs, but opposite regulation of key D2 networks, highlighting potential therapeutic gene network targets within MSNs. These studies establish a landmark, cell-type-specific atlas of transcriptional regulation induced by cocaine and by morphine that can serve as a foundation for future studies towards mechanistic understanding of SUDs. Our findings, and future work leveraging this dataset, will pave the way for the development of targeted therapeutic interventions, addressing the urgent need for more effective treatments for cocaine use disorder and enhancing the existing strategies for opioid use disorder.

Sex specific molecular networks and key drivers of Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a progressive and age-associated neurodegenerative disorder that affects women disproportionally. However, the underlying mechanisms are poorly characterized. Moreover, while the interplay between sex and ApoE genotype in AD has been investigated, multi-omics studies to understand this interaction are limited. Therefore, we applied systems biology approaches to investigate sex-specific molecular networks of AD. METHODS: We integrated large-scale human postmortem brain transcriptomic data of AD from two cohorts (MSBB and ROSMAP) via multiscale network analysis and identified key drivers with sexually dimorphic expression patterns and/or different responses to APOE genotypes between sexes. The expression patterns and functional relevance of the top sex-specific network driver of AD were further investigated using postmortem human brain samples and gene perturbation experiments in AD mouse models. RESULTS: Gene expression changes in AD versus control were identified for each sex. Gene co-expression networks were constructed for each sex to identify AD-associated co-expressed gene modules shared by males and females or specific to each sex. Key network regulators were further identified as potential drivers of sex differences in AD development. LRP10 was identified as a top driver of the sex differences in AD pathogenesis and manifestation. Changes of LRP10 expression at the mRNA and protein levels were further validated in human AD brain samples. Gene perturbation experiments in EFAD mouse models demonstrated that LRP10 differentially affected cognitive function and AD pathology in sex- and APOE genotype-specific manners. A comprehensive mapping of brain cells in LRP10 over-expressed (OE) female E4FAD mice suggested neurons and microglia as the most affected cell populations. The female-specific targets of LRP10 identified from the single cell RNA-sequencing (scRNA-seq) data of the LRP10 OE E4FAD mouse brains were significantly enriched in the LRP10-centered subnetworks in female AD subjects, validating LRP10 as a key network regulator of AD in females. Eight LRP10 binding partners were identified by the yeast two-hybrid system screening, and LRP10 over-expression reduced the association of LRP10 with one binding partner CD34. CONCLUSIONS: These findings provide insights into key mechanisms mediating sex differences in AD pathogenesis and will facilitate the development of sex- and APOE genotype-specific therapies for AD.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Sex-Specific Transcriptional Changes in Response to Adolescent Social Stress in the Brain's Reward Circuitry.

BACKGROUND: Sex differences in addiction have been described in humans and animal models. A key factor that influences addiction in both males and females is adolescent experience. Adolescence is associated with higher vulnerability to substance use disorders, and male rodents subjected to adolescent social isolation (SI) stress form stronger preferences for drugs of abuse in adulthood. However, little is known about how females respond to SI, and few studies have investigated the transcriptional changes induced by SI in the brain's reward circuitry. METHODS: We tested the hypothesis that SI alters the transcriptome in a persistent and sex-specific manner in prefrontal cortex, nucleus accumbens, and ventral tegmental area. Mice were isolated or group housed from postnatal day P22 to P42, then group housed until ∼P90. Transcriptome-wide changes were investigated by RNA sequencing after acute or chronic cocaine or saline administration. RESULTS: We found that SI disrupts sex-specific transcriptional responses to cocaine and reduces sex differences in gene expression across all three brain regions. Furthermore, SI induces gene expression profiles in males that more closely resemble group-housed females, suggesting that SI "feminizes" the male transcriptome. Coexpression analysis reveals that such disruption of sex differences in gene expression alters sex-specific gene networks and identifies potential sex-specific key drivers of these transcriptional changes. CONCLUSIONS: Together, these data show that SI has region-specific effects on sex-specific transcriptional responses to cocaine and provide a better understanding of reward-associated transcription that differs in males and females.

Dynamic plasticity of prostate cancer intermediate cells during androgen receptor-targeted therapy.

Treatment-emergent small cell neuroendocrine prostate cancer (t-SCNC) is associated with an epithelial lineage switch from an androgen receptor (AR)-positive to neuroendocrine (NE)-marker-positive status. Understanding the potential for reversibility of this aggressive disease state has been hampered by the paucity of models suitable for studying rate-limiting, transitional, or intermediate tumor cell subpopulations. We define a dual reporter model that measures acute transcriptional changes in response to castration or AR targeting agents. We identify steady-state transcriptional heterogeneity in AR and NE biomarkers, including intermediate subpopulations that are coordinately high for prostate-specific antigen (PSA) and neuron-specific enoclase (NSE) promoter activity. In the presence of castration or AR inhibitors, intermediate cells were necessary and sufficient for therapy-induced conversion of human PC cells to an NSE-high transcriptional status. Using hormone add-back studies, treatment-induced PSA-NSE transcriptional plasticity was reversible in PTEN-deficient PC cells but not in the presence of secondary genetic driver genes, including MYCN.

Common and species-specific molecular signatures, networks, and regulators of influenza virus infection in mice, ferrets, and humans.

Molecular responses to influenza A virus (IAV) infections vary between mammalian species. To identify conserved and species-specific molecular responses, we perform a comparative study of transcriptomic data derived from blood cells, primary epithelial cells, and lung tissues collected from IAV-infected humans, ferrets, and mice. The molecular responses in the human host have unique functions such as antigen processing that are not observed in mice or ferrets. Highly conserved gene coexpression modules across the three species are enriched for IAV infection-induced pathways including cell cycle and interferon (IFN) signaling. TDRD7 is predicted as an IFN-inducible host factor that is up-regulated upon IAV infection in the three species. TDRD7 is required for antiviral IFN response, potentially modulating IFN signaling via the JAK/STAT/IRF9 pathway. Identification of the common and species-specific molecular signatures, networks, and regulators of IAV infection provides insights into host-defense mechanisms and will facilitate the development of novel therapeutic interventions against IAV infection.