In utero human intestine contains maternally derived bacterial metabolites.

Understanding when host-microbiome interactions are first established is crucial for comprehending normal development and identifying disease prevention strategies. Furthermore, bacterially derived metabolites play critical roles in shaping the intestinal immune system. Recent studies have demonstrated that memory T cells infiltrate human intestinal tissue early in the second trimester, suggesting that intestinal immune education begins in utero. Our previous study reported a unique fetal intestinal metabolomic profile with an abundance of several bacterially derived metabolites and aryl hydrocarbon receptor (AHR) ligands implicated in mucosal immune regulation. To follow up on this work, in the current study, we demonstrate that a number of microbial byproducts present in fetal intestines in utero are maternally derived and vertically transmitted to the fetus. Notably, these bacterially derived metabolites, particularly short chain fatty acids and secondary bile acids, are likely biologically active and functional in regulating the fetal immune system and preparing the gastrointestinal tract for postnatal microbial encounters, as the transcripts for their various receptors and carrier proteins are present in second trimester intestinal tissue through single-cell transcriptomic data.

Immune Infiltration Correlates with Transcriptomic Subtypes in Primary ER+ Invasive Lobular Breast Cancer.

Understanding interplay of breast cancer and microenvironment is critical. Here, we identified two transcriptomic subtypes and five immune infiltration patterns from RNA-seq and multiplex immunohistochemistry from 21 ER+/HER2- invasive lobular breast cancers. The proliferative subtype associated with increased immune infiltration especially by immunosuppressive regulatory T-cells and macrophages. We also defined a TAM-Low signature, which associated with lower infiltration of proliferative, pro-inflammatory TAM, and improved outcome in patients with ER+ tumors.

Accurate and Ultra-Efficient p-Value Calculation for Higher Criticism Tests.

In modern data science, higher criticism (HC) method is effective for detecting rare and weak signals. The computation, however, has long been an issue when the number of p-values combined ( K ) and/or the number of repeated HC tests ( N ) are large. Some computing methods have been developed, but they all have significant shortcomings, especially when a stringent significance level is required. In this paper, we propose an accurate and highly efficient computing strategy for four variations of HC. Specifically, we propose an unbiased cross-entropy-based importance sampling method ( IS C E ) to benchmark all existing computing methods, and develop a modified SetTest method (MST) that resolves numerical issues of the existing SetTest approach. We further develop an ultra-fast approach (UFI) combining pre-calculated statistical tables and cubic spline interpolation. Finally, following extensive simulations, we provide a computing strategy integrating MST, UFI and other existing methods with R package "HCp" for virtually any K and small p-values ( ∼ 10 - 20 ). The method is applied to a COVID-19 disease surveillance example for spatio-temporal outbreak detection from case numbers of 804 days in 3,342 counties in the United States. Results confirm viability of the computing strategy for large-scale inferences. Supplementary materials for this article are available online.

Molecular and cellular rhythms in excitatory and inhibitory neurons in the mouse prefrontal cortex.

Previous studies have shown that there are rhythms in gene expression in the mouse prefrontal cortex (PFC); however, the contribution of different cell types and potential variation by sex has not yet been determined. Of particular interest are excitatory pyramidal cells and inhibitory parvalbumin (PV) interneurons, as interactions between these cell types are essential for regulating the excitation/inhibition balance and controlling many of the cognitive functions regulated by the PFC. In this study, we identify cell-type specific rhythms in the translatome of PV and pyramidal cells in the mouse PFC and assess diurnal rhythms in PV cell electrophysiological properties. We find that while core molecular clock genes are conserved and synchronized between cell types, pyramidal cells have nearly twice as many rhythmic transcripts as PV cells (35% vs. 18%). Rhythmic transcripts in pyramidal cells also show a high degree of overlap between sexes, both in terms of which transcripts are rhythmic and in the biological processes associated with them. Conversely, in PV cells, rhythmic transcripts from males and females are largely distinct. Moreover, we find sex-specific effects of phase on action potential properties in PV cells that are eliminated by environmental circadian disruption. Together, this study demonstrates that rhythms in gene expression and electrophysiological properties in the mouse PFC vary by both cell type and sex. Moreover, the biological processes associated with these rhythmic transcripts may provide insight into the unique functions of rhythms in these cells, as well as their selective vulnerabilities to circadian disruption.

Disentangling transcriptomic heterogeneity within the human subgenual anterior cingulate cortex.

The subgenual anterior cingulate cortex (sgACC) is a critical site for understanding the neural correlates of affect and emotion. While the activity of the sgACC is functionally homogenous, it is comprised of multiple Brodmann Areas (BAs) that possess different cytoarchitectures. In some sgACC BAs, Layer 5 is sublaminated into L5a and L5b which has implications for its projection targets. To understand how the transcriptional profile differs between the BAs, layers, and sublayers of human sgACC, we collected layer strips using laser capture microdissection followed by RNA sequencing. We found no significant differences in transcript expression in these specific cortical layers between BAs within the sgACC. In contrast, we identified striking differences between Layers 3 and 5a or 5b that were concordant across sgACC BAs. We found that sublayers 5a and 5b were transcriptionally similar. Pathway analyses of L3 and L5 revealed overlapping biological processes related to synaptic function. However, L3 was enriched for pathways related to cell-to-cell junction and dendritic spines whereas L5 was enriched for pathways related to brain development and presynaptic function, indicating potential functional differences across layers. Our study provides important insight into normative transcriptional features of the sgACC.

Electronic Health Record-Based Nudge Intervention and Axillary Surgery in Older Women With Breast Cancer: A Nonrandomized Controlled Trial.

Importance: Choosing Wisely recommendations advocate against routine use of axillary staging in older women with early-stage, clinically node-negative (cN0), hormone receptor-positive (HR+), and HER2-negative breast cancer. However, rates of sentinel lymph node biopsy (SLNB) in this population remain persistently high. Objective: To evaluate whether an electronic health record (EHR)-based nudge intervention targeting surgeons in their first outpatient visit with patients meeting Choosing Wisely criteria decreases rates of SLNB. Design, Setting, and Participants: This nonrandomized controlled trial was a hybrid type 1 effectiveness-implementation study with subsequent postintervention semistructured interviews and lasted from October 2021 to October 2023. Data came from EHRs at 8 outpatient clinics within an integrated health care system; participants included 7 breast surgical oncologists. Data were collected for female patients meeting Choosing Wisely criteria for omission of SLNB (aged ≥70 years with cT1 and cT2, cN0, HR+/HER2- breast cancer). The study included a 12-month preintervention control period; baseline surveys assessing perceived acceptability, appropriateness, and feasibility of the designed intervention; and a 12-month intervention period. Intervention: A column nudge was embedded into the surgeon's schedule in the EHR identifying patients meeting Choosing Wisely criteria for potential SLNB omission. Main Outcomes and Measures: The primary outcome was rate of SLNB following nudge deployment into the EHR. Results: Similar baseline demographic and tumor characteristics were observed before (control period, n = 194) and after (intervention period, n = 193) nudge deployment. Patients in both the control and intervention period had a median (IQR) age of 75 (72-79) years. Compared with the control period, unadjusted rates of SLNB decreased by 23.1 percentage points (46.9% SLNB rate prenudge to 23.8% after; 95% CI, -32.9 to -13.8) in the intervention period. An interrupted time series model showed a reduction in the rate of SLNB following nudge deployment (adjusted odds ratio, 0.26; 95% CI, 0.07 to 0.90; P = .03). The participating surgeons scored the intervention highly on acceptability, appropriateness, and feasibility. Dominant themes from semistructured interviews indicated that the intervention helped remind the surgeons of potential Choosing Wisely applicability without the need for additional clicks or actions on the day of the patient visit, which facilitated use. Conclusions and Relevance: This study showed that a nudge intervention in the EHR significantly decreased low-value axillary surgery in older women with early-stage, cN0, HR+/HER2- breast cancer. This user-friendly and easily implementable EHR-based intervention could be a beneficial approach for decreasing low-value care in other practice settings or patient populations. Trial Registration: ClinicalTrials.gov Identifier: NCT06006910.

High-dimensional causal mediation analysis by partial sum statistic and sample splitting strategy in imaging genetics application.

Causal mediation analysis provides a systematic approach to explore the causal role of one or more mediators in the association between exposure and outcome. In omics or imaging data analysis, mediators are often high-dimensional, which brings new statistical challenges. Existing methods either violate causal assumptions or fail in interpretable variable selection. Additionally, mediators are often highly correlated, presenting difficulties in selecting and prioritizing top mediators. To address these issues, we develop a framework using Partial Sum Statistic and Sample Splitting Strategy, namely PS5, for high-dimensional causal mediation analysis. The method provides a powerful global mediation test satisfying causal assumptions, followed by an algorithm to select and prioritize active mediators with quantification of individual mediation contributions. We demonstrate its accurate type I error control, superior statistical power, reduced bias in mediation effect estimation, and accurate mediator selection using extensive simulations of varying levels of effect size, signal sparsity, and mediator correlations. Finally, we apply PS5 to an imaging genetics dataset of chronic obstructive pulmonary disease (COPD) patients ( N =8,897) in the COPDGene study to examine the causal mediation role of lung images ( p =5,810) in the associations between polygenic risk score and lung function and between smoking exposure and lung function, respectively. Both causal mediation analyses successfully estimate the global indirect effect and detect mediating image regions. Collectively, we find a region in the lower lobe of the right lung with a strong and concordant mediation effect for both genetic and environmental exposures. This suggests that targeted treatment toward this region might mitigate the severity of COPD due to genetic and smoking effects.

Model-based multifacet clustering with high-dimensional omics applications.

High-dimensional omics data often contain intricate and multifaceted information, resulting in the coexistence of multiple plausible sample partitions based on different subsets of selected features. Conventional clustering methods typically yield only one clustering solution, limiting their capacity to fully capture all facets of cluster structures in high-dimensional data. To address this challenge, we propose a model-based multifacet clustering (MFClust) method based on a mixture of Gaussian mixture models, where the former mixture achieves facet assignment for gene features and the latter mixture determines cluster assignment of samples. We demonstrate superior facet and cluster assignment accuracy of MFClust through simulation studies. The proposed method is applied to three transcriptomic applications from postmortem brain and lung disease studies. The result captures multifacet clustering structures associated with critical clinical variables and provides intriguing biological insights for further hypothesis generation and discovery.

Mutual information for detecting multi-class biomarkers when integrating multiple bulk or single-cell transcriptomic studies.

Motivation: Biomarker detection plays a pivotal role in biomedical research. Integrating omics studies from multiple cohorts can enhance statistical power, accuracy and robustness of the detection results. However, existing methods for horizontally combining omics studies are mostly designed for two-class scenarios (e.g., cases versus controls) and are not directly applicable for studies with multi-class design (e.g., samples from multiple disease subtypes, treatments, tissues, or cell types). Results: We propose a statistical framework, namely Mutual Information Concordance Analysis (MICA), to detect biomarkers with concordant multi-class expression pattern across multiple omics studies from an information theoretic perspective. Our approach first detects biomarkers with concordant multi-class patterns across partial or all of the omics studies using a global test by mutual information. A post hoc analysis is then performed for each detected biomarkers and identify studies with concordant pattern. Extensive simulations demonstrate improved accuracy and successful false discovery rate control of MICA compared to an existing MCC method. The method is then applied to two practical scenarios: four tissues of mouse metabolism-related transcriptomic studies, and three sources of estrogen treatment expression profiles. Detected biomarkers by MICA show intriguing biological insights and functional annotations. Additionally, we implemented MICA for single-cell RNA-Seq data for tumor progression biomarkers, highlighting critical roles of ribosomal function in the tumor microenvironment of triple-negative breast cancer and underscoring the potential of MICA for detecting novel therapeutic targets. Availability: https://github.com/jianzou75/MICA.

IFDlong: an isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data.

Advancements in long-read transcriptome sequencing (long-RNA-seq) technology have revolutionized the study of isoform diversity. These full-length transcripts enhance the detection of various transcriptome structural variations, including novel isoforms, alternative splicing events, and fusion transcripts. By shifting the open reading frame or altering gene expressions, studies have proved that these transcript alterations can serve as crucial biomarkers for disease diagnosis and therapeutic targets. In this project, we proposed IFDlong, a bioinformatics and biostatistics tool to detect isoform and fusion transcripts using bulk or single-cell long-RNA-seq data. Specifically, the software performed gene and isoform annotation for each long-read, defined novel isoforms, quantified isoform expression by a novel expectation-maximization algorithm, and profiled the fusion transcripts. For evaluation, IFDlong pipeline achieved overall the best performance when compared with several existing tools in large-scale simulation studies. In both isoform and fusion transcript quantification, IFDlong is able to reach more than 0.8 Spearman's correlation with the truth, and more than 0.9 cosine similarity when distinguishing multiple alternative splicing events. In novel isoform simulation, IFDlong can successfully balance the sensitivity (higher than 90%) and specificity (higher than 90%). Furthermore, IFDlong has proved its accuracy and robustness in diverse in-house and public datasets on healthy tissues, cell lines and multiple types of diseases. Besides bulk long-RNA-seq, IFDlong pipeline has proved its compatibility to single-cell long-RNA-seq data. This new software may hold promise for significant impact on long-read transcriptome analysis. The IFDlong software is available at https://github.com/wenjiaking/IFDlong.

An Electroencephalogram Signature of Melanin-Concentrating Hormone Neuron Activities Predicts Cocaine Seeking.

BACKGROUND: Identifying biomarkers that predict substance use disorder propensity may better strategize antiaddiction treatment. Melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus critically mediate interactions between sleep and substance use; however, their activities are largely obscured in surface electroencephalogram (EEG) measures, hindering the development of biomarkers. METHODS: Surface EEG signals and real-time calcium (Ca2+) activities of lateral hypothalamus MCH neurons (Ca2+MCH) were simultaneously recorded in male and female adult rats. Mathematical modeling and machine learning were then applied to predict Ca2+MCH using EEG derivatives. The robustness of the predictions was tested across sex and treatment conditions. Finally, features extracted from the EEG-predicted Ca2+MCH either before or after cocaine experience were used to predict future drug-seeking behaviors. RESULTS: An EEG waveform derivative-a modified theta-delta-theta peak ratio (EEGTDT ratio)-accurately tracked real-time Ca2+MCH in rats. The prediction was robust during rapid eye movement sleep (REMS), persisted through vigilance states, sleep manipulations, and circadian phases, and was consistent across sex. Moreover, cocaine self-administration and long-term withdrawal altered EEGTDT ratio, suggesting shortening and circadian redistribution of synchronous MCH neuron activities. In addition, features of EEGTDT ratio indicative of prolonged synchronous MCH neuron activities predicted lower subsequent cocaine seeking. EEGTDT ratio also exhibited advantages over conventional REMS measures for the predictions. CONCLUSIONS: The identified EEGTDT ratio may serve as a noninvasive measure for assessing MCH neuron activities in vivo and evaluating REMS; it may also serve as a potential biomarker for predicting drug use propensity.

Cell-type and sex-specific rhythmic gene expression in the nucleus accumbens.

Circadian rhythms are critical for human health and are highly conserved across species. Disruptions in these rhythms contribute to many diseases, including psychiatric disorders. Previous results suggest that circadian genes modulate behavior through specific cell types in the nucleus accumbens (NAc), particularly dopamine D1-expressing medium spiny neurons (MSNs). However, diurnal rhythms in transcript expression have not been investigated in NAc MSNs. In this study we identified and characterized rhythmic transcripts in D1- and D2-expressing neurons and compared rhythmicity results to homogenate as well as astrocyte samples taken from the NAc of male and female mice. We find that all cell types have transcripts with diurnal rhythms and that top rhythmic transcripts are largely core clock genes, which peak at approximately the same time of day in each cell type and sex. While clock-controlled rhythmic transcripts are enriched for protein regulation pathways across cell type, cell signaling and signal transduction related processes are most commonly enriched in MSNs. In contrast to core clock genes, these clock-controlled rhythmic transcripts tend to reach their peak in expression about 2-h later in females than males, suggesting diurnal rhythms in reward may be delayed in females. We also find sex differences in pathway enrichment for rhythmic transcripts peaking at different times of day. Protein folding and immune responses are enriched in transcripts that peak in the dark phase, while metabolic processes are primarily enriched in transcripts that peak in the light phase. Importantly, we also find that several classic markers used to categorize MSNs are rhythmic in the NAc. This is critical since the use of rhythmic markers could lead to over- or under-enrichment of targeted cell types depending on the time at which they are sampled. This study greatly expands our knowledge of how individual cell types contribute to rhythms in the NAc.

An EEG Signature of MCH Neuron Activities Predicts Cocaine Seeking.

Background: Identifying biomarkers that predict substance use disorder (SUD) propensity may better strategize anti-addiction treatment. The melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus (LH) critically mediates interactions between sleep and substance use; however, their activities are largely obscured in surface electroencephalogram (EEG) measures, hindering the development of biomarkers. Methods: Surface EEG signals and real-time Ca2+ activities of LH MCH neurons (Ca2+MCH) were simultaneously recorded in male and female adult rats. Mathematical modeling and machine learning were then applied to predict Ca2+MCH using EEG derivatives. The robustness of the predictions was tested across sex and treatment conditions. Finally, features extracted from the EEG-predicted Ca2+MCH either before or after cocaine experience were used to predict future drug-seeking behaviors. Results: An EEG waveform derivative - a modified theta-to-delta ratio (EEG Ratio) - accurately tracks real-time Ca2+MCH in rats. The prediction was robust during rapid eye movement sleep (REMS), persisted through REMS manipulations, wakefulness, circadian phases, and was consistent across sex. Moreover, cocaine self-administration and long-term withdrawal altered EEG Ratio suggesting shortening and circadian redistribution of synchronous MCH neuron activities. In addition, features of EEG Ratio indicative of prolonged synchronous MCH neuron activities predicted lower subsequent cocaine seeking. EEG Ratio also exhibited advantages over conventional REMS measures for the predictions. Conclusions: The identified EEG Ratio may serve as a non-invasive measure for assessing MCH neuron activities in vivo and evaluating REMS; it may also serve as a potential biomarker predicting drug use propensity.

Sex differences in plasma proteomic markers in late-life depression.

Previous studies have shown significant sex-specific differences in major depressive disorder (MDD) in multiple biological parameters. Most studies focused on young and middle-aged adults, and there is a paucity of information about sex-specific biological differences in older adults with depression (aka, late-life depression (LLD)). To address this gap, this study aimed to evaluate sex-specific biological abnormalities in a large group of individuals with LLD using an untargeted proteomic analysis. We quantified 344 plasma proteins using a multiplex assay in 430 individuals with LLD and 140 healthy comparisons (HC) (age range between 60 and 85 years old for both groups). Sixty-six signaling proteins were differentially expressed in LLD (both sexes). Thirty-three proteins were uniquely associated with LLD in females, while six proteins were uniquely associated with LLD in males. The main biological processes affected by these proteins in females were related to immunoinflammatory control. In contrast, despite the smaller number of associated proteins, males showed dysregulations in a broader range of biological pathways, including immune regulation pathways, cell cycle control, and metabolic control. Sex has a significant impact on biomarker changes in LLD. Despite some overlap in differentially expressed biomarkers, males and females show different patterns of biomarkers changes, and males with LLD exhibit abnormalities in a larger set of biological processes compared to females. Our findings can provide novel targets for sex-specific interventions in LLD.

Single nuclei transcriptomics in human and non-human primate striatum in opioid use disorder.

In brain, the striatum is a heterogenous region involved in reward and goal-directed behaviors. Striatal dysfunction is linked to psychiatric disorders, including opioid use disorder (OUD). Striatal subregions are divided based on neuroanatomy, each with unique roles in OUD. In OUD, the dorsal striatum is involved in altered reward processing, formation of habits, and development of negative affect during withdrawal. Using single nuclei RNA-sequencing, we identified both canonical (e.g., dopamine receptor subtype) and less abundant cell populations (e.g., interneurons) in human dorsal striatum. Pathways related to neurodegeneration, interferon response, and DNA damage were significantly enriched in striatal neurons of individuals with OUD. DNA damage markers were also elevated in striatal neurons of opioid-exposed rhesus macaques. Sex-specific molecular differences in glial cell subtypes associated with chronic stress were found in OUD, particularly female individuals. Together, we describe different cell types in human dorsal striatum and identify cell type-specific alterations in OUD.

Comparative rhythmic transcriptome profiling of human and mouse striatal subregions.

The human striatum can be subdivided into the caudate, putamen, and nucleus accumbens (NAc). In mice, this roughly corresponds to the dorsal medial striatum (DMS), dorsal lateral striatum (DLS), and ventral striatum (NAc). Each of these structures have some overlapping and distinct functions related to motor control, cognitive processing, motivation, and reward. Previously, we used a "time-of-death" approach to identify diurnal rhythms in RNA transcripts in these three human striatal subregions. Here, we identify molecular rhythms across similar striatal subregions collected from C57BL/6J mice across 6 times of day and compare results to the human striatum. Pathway analysis indicates a large degree of overlap between species in rhythmic transcripts involved in processes like cellular stress, energy metabolism, and translation. Notably, a striking finding in humans is that small nucleolar RNAs (snoRNAs) and long non-coding RNAs (lncRNAs) are among the most highly rhythmic transcripts in the NAc and this is not conserved in mice, suggesting the rhythmicity of RNA processing in this region could be uniquely human. Furthermore, the peak timing of overlapping rhythmic genes is altered between species, but not consistently in one direction. Taken together, these studies reveal conserved as well as distinct transcriptome rhythms across the human and mouse striatum and are an important step in understanding the normal function of diurnal rhythms in humans and model organisms in these regions and how disruption could lead to pathology.

Systems approach for congruence and selection of cancer models towards precision medicine.

Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.

Glucose dysregulation in antipsychotic-naive first-episode psychosis: in silico exploration of gene expression signatures.

Antipsychotic (AP)-naive first-episode psychosis (FEP) patients display early dysglycemia, including insulin resistance and prediabetes. Metabolic dysregulation may therefore be intrinsic to psychosis spectrum disorders (PSDs), independent of the metabolic effects of APs. However, the potential biological pathways that overlap between PSDs and dysglycemic states remain to be identified. Using meta-analytic approaches of transcriptomic datasets, we investigated whether AP-naive FEP patients share overlapping gene expression signatures with non-psychiatrically ill early dysglycemia individuals. We meta-analyzed peripheral transcriptomic datasets of AP-naive FEP patients and non-psychiatrically ill early dysglycemia subjects to identify common gene expression signatures. Common signatures underwent pathway enrichment analysis and were then used to identify potential new pharmacological compounds via Integrative Library of Integrated Network-Based Cellular Signatures (iLINCS). Our search results yielded 5 AP-naive FEP studies and 4 early dysglycemia studies which met inclusion criteria. We discovered that AP-naive FEP and non-psychiatrically ill subjects exhibiting early dysglycemia shared 221 common signatures, which were enriched for pathways related to endoplasmic reticulum stress and abnormal brain energetics. Nine FDA-approved drugs were identified as potential drug treatments, of which the antidiabetic metformin, the first-line treatment for type 2 diabetes, has evidence to attenuate metabolic dysfunction in PSDs. Taken together, our findings support shared gene expression changes and biological pathways associating PSDs with dysglycemic disorders. These data suggest that the pathobiology of PSDs overlaps and potentially contributes to dysglycemia. Finally, we find that metformin may be a potential treatment for early metabolic dysfunction intrinsic to PSDs.

Central insulin dysregulation in antipsychotic-naïve first-episode psychosis: In silico exploration of gene expression signatures.

Antipsychotic drug (AP)-naïve first-episode psychosis (FEP) patients display premorbid cognitive dysfunctions and dysglycemia. Brain insulin resistance may link metabolic and cognitive disorders in humans. This suggests that central insulin dysregulation represents a component of the pathophysiology of psychosis spectrum disorders (PSDs). Nonetheless, the links between central insulin dysregulation, dysglycemia, and cognitive deficits in PSDs are poorly understood. We investigated whether AP-naïve FEP patients share overlapping brain gene expression signatures with central insulin perturbation (CIP) in rodent models. We systematically compiled and meta-analyzed peripheral transcriptomic datasets of AP-naïve FEP patients along with hypothalamic and hippocampal datasets of CIP rodent models to identify common transcriptomic signatures. The common signatures were used for pathway analysis and to identify potential drug treatments with discordant (reverse) signatures. AP-naïve FEP and CIP (hypothalamus and hippocampus) shared 111 and 346 common signatures respectively, which were associated with pathways related to inflammation, endoplasmic reticulum stress, and neuroplasticity. Twenty-two potential drug treatments were identified, including antidiabetic agents. The pathobiology of PSDs may include central insulin dysregulation, which contribute to dysglycemia and cognitive dysfunction independently of AP treatment. The identified treatments may be tested in early psychosis patients to determine if dysglycemia and cognitive deficits can be mitigated.

Circadian rhythm disruptions associated with opioid use disorder in synaptic proteomes of human dorsolateral prefrontal cortex and nucleus accumbens.

Opioid craving and relapse vulnerability is associated with severe and persistent sleep and circadian rhythm disruptions. Understanding the neurobiological underpinnings of circadian rhythms and opioid use disorder (OUD) may prove valuable for developing new treatments for opioid addiction. Previous work indicated molecular rhythm disruptions in the human brain associated with OUD, highlighting synaptic alterations in the dorsolateral prefrontal cortex (DLPFC) and nucleus accumbens (NAc)-key brain regions involved in cognition and reward, and heavily implicated in the pathophysiology of OUD. To provide further insights into the synaptic alterations in OUD, we used mass-spectrometry based proteomics to deeply profile protein expression alterations in bulk tissue and synaptosome preparations from DLPFC and NAc of unaffected and OUD subjects. We identified 55 differentially expressed (DE) proteins in DLPFC homogenates, and 44 DE proteins in NAc homogenates, between unaffected and OUD subjects. In synaptosomes, we identified 161 and 56 DE proteins in DLPFC and NAc, respectively, of OUD subjects. By comparing homogenate and synaptosome protein expression, we identified proteins enriched specifically in synapses that were significantly altered in both DLPFC and NAc of OUD subjects. Across brain regions, synaptic protein alterations in OUD subjects were primarily identified in glutamate, GABA, and circadian rhythm signaling. Using time-of-death (TOD) analyses, where the TOD of each subject is used as a time-point across a 24-h cycle, we were able to map circadian-related changes associated with OUD in synaptic proteomes associated with vesicle-mediated transport and membrane trafficking in the NAc and platelet-derived growth factor receptor beta signaling in DLPFC. Collectively, our findings lend further support for molecular rhythm disruptions in synaptic signaling in the human brain as a key factor in opioid addiction.