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.

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.

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.

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.

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.

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.

The microRNA target site profile is a novel biomarker in the immunotherapy response

MicroRNAs (miRNAs) bind on the 3' untranslated region (3'UTR) of messenger RNAs (mRNAs) and regulate mRNA expression in physiological and pathological conditions, including cancer. Thus, studies have identified miRNAs as potential biomarkers by correlating the miRNA expression with the expression of important mRNAs and/or clinical outcomes in cancers. However, tumors undergo pervasive 3'UTR shortening/lengthening events through alternative polyadenylation (APA), which varies the number of miRNA target sites in mRNA, raising the number of miRNA target sites (numTS) as another important regulatory axis of the miRNA binding effects. In this study, we developed the first statistical method, BIOMATA-APA, to identify predictive miRNAs based on numTS features. Running BIOMATA-APA on The Cancer Genome Atlas (TCGA) and independent cohort data both with immunotherapy and no immunotherapy, we demonstrated for the first time that the numTS feature 1) distinguishes different cancer types, 2) predicts tumor proliferation and immune infiltration status, 3) explains more variation in the proportion of tumor-infiltrating immune cells, 4) predicts response to immune checkpoint blockade (ICB) therapy, and 5) adds prognostic power beyond clinical and miRNA expression. To the best of our knowledge, this is the first pan-cancer study to systematically demonstrate numTS as a novel type of biomarker representing the miRNA binding effects underlying tumorigenesis and pave the way to incorporate miRNA target sites for miRNA biomarker identification. Another advantage of examining the miRNA binding effect using numTS is that it requires only RNA-Seq data, not miRNAs, thus resulting in high power in the miRNA biomarker identification.

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.

Circadian rhythm disruptions associated with opioid use disorder in the synaptic proteomes of the 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- hour cycle, we were able to map circadian-related changes associated with OUD in synaptic proteomes related to 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.

Experimental design and power calculation in omics circadian rhythmicity detection using the cosinor model

Circadian clocks are 24-h endogenous oscillators in physiological and behavioral processes. Though recent transcriptomic studies have been successful in revealing the circadian rhythmicity in gene expression, the power calculation for omics circadian analysis have not been fully explored. In this paper, we develop a statistical method, namely CircaPower, to perform power calculation for circadian pattern detection. Our theoretical framework is determined by three key factors in circadian gene detection: sample size, intrinsic effect size and sampling design. Via simulations, we systematically investigate the impact of these key factors on circadian power calculation. We not only demonstrate that CircaPower is fast and accurate, but also show its underlying cosinor model is robust against variety of violations of model assumptions. In real applications, we demonstrate the performance of CircaPower using mouse pan-tissue data and human post-mortem brain data, and illustrate how to perform circadian power calculation using mouse skeleton muscle RNA-Seq pilot as case study. Our method CircaPower has been implemented in an R package, which is made publicly available on GitHub ( https://github.com/circaPower/circaPower).

The EstroGene Database Reveals Diverse Temporal, Context-Dependent, and Bidirectional Estrogen Receptor Regulomes in Breast Cancer

As one of the most successful cancer therapeutic targets, estrogen receptor-α (ER/ESR1) has been extensively studied over the past few decades. Sequencing technological advances have enabled genome-wide analysis of ER action. However, comparison of individual studies is limited by different experimental designs, and few meta-analyses are available. Here, we established the EstroGene database through unified processing of data from 246 experiments including 136 transcriptomic, cistromic, and epigenetic datasets focusing on estradiol (E2)-triggered ER activation across 19 breast cancer cell lines. A user-friendly browser (https://estrogene.org/) was generated for multiomic data visualization involving gene inquiry under user-defined experimental conditions and statistical thresholds. Notably, annotation of metadata associated with public datasets revealed a considerable lack of experimental details. Comparison of independent RNA-seq or ER ChIP-seq data with the same design showed large variability and only strong effects could be consistently detected. Temporal estrogen response metasignatures were defined, and the association of E2 response rate with temporal transcriptional factors, chromatin accessibility, and heterogeneity of ER expression was evaluated. Unexpectedly, harmonizing 146 E2-induced transcriptomic datasets uncovered a subset of genes harboring bidirectional E2 regulation, which was linked to unique transcriptional factors and highly associated with immune surveillance in the clinical setting. Furthermore, the context dependent E2 response programs were characterized in MCF7 and T47D cell lines, the two most frequently used models in the EstroGene database. Collectively, the EstroGene database provides an informative and practical resource to the cancer research community to uniformly evaluate key reproducible features of ER regulomes and unravels modes of ER signaling.

SIGNIFICANCE

A resource database integrating 246 publicly available ER profiling datasets facilitates meta-analyses and identifies estrogen response temporal signatures, a bidirectional program, and model-specific biases.

Differential Effects of Cocaine and Morphine on the Diurnal Regulation of the Mouse Nucleus Accumbens Proteome

Substance use disorders are associated with disruptions in sleep and circadian rhythms that persist during abstinence and may contribute to relapse risk. Repeated use of substances such as psychostimulants and opioids may lead to significant alterations in molecular rhythms in the nucleus accumbens (NAc), a brain region central to reward and motivation. Previous studies have identified rhythm alterations in the transcriptome of the NAc and other brain regions following the administration of psychostimulants or opioids. However, little is known about the impact of substance use on the diurnal rhythms of the proteome in the NAc. We used liquid chromatography coupled to tandem mass spectrometry-based quantitative proteomics, along with a data-independent acquisition analysis pipeline, to investigate the effects of cocaine or morphine administration on diurnal rhythms of proteome in the mouse NAc. Overall, our data reveal cocaine and morphine differentially alter diurnal rhythms of the proteome in the NAc, with largely independent differentially expressed proteins dependent on time-of-day. Pathways enriched from cocaine altered protein rhythms were primarily associated with glucocorticoid signaling and metabolism, whereas morphine was associated with neuroinflammation. Collectively, these findings are the first to characterize the diurnal regulation of the NAc proteome and demonstrate a novel relationship between the phase-dependent regulation of protein expression and the differential effects of cocaine and morphine on the NAc proteome. The proteomics data in this study are available via ProteomeXchange with identifier PXD042043.

Immune landscape in invasive ductal and lobular breast cancer reveals a divergent macrophage-driven microenvironment

T cell-centric immunotherapies have shown modest clinical benefit thus far for estrogen receptor-positive (ER+) breast cancer. Despite accounting for 70% of all breast cancers, relatively little is known about the immunobiology of ER+ breast cancer in women with invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC). To investigate this, we performed phenotypic, transcriptional and functional analyses for a cohort of treatment-naive IDC (n = 94) and ILC (n = 87) tumors. We show that macrophages, and not T cells, are the predominant immune cells infiltrating the tumor bed and the most transcriptionally diverse cell subset between IDC and ILC. Analysis of cellular neighborhoods revealed an interplay between macrophages and T cells associated with longer disease-free survival in IDC but not ILC. Our datasets provide a rich resource for further interrogation into immune cell dynamics in ER+ IDC and ILC and highlight macrophages as a potential target for ER+ breast cancer.

Adaptively Integrative Association between Multivariate Phenotypes and Transcriptomic Data for Complex Diseases

Phenotype–gene association studies can uncover disease mechanisms for translational research. Association with multiple phenotypes or clinical variables in complex diseases has the advantage of increasing statistical power and offering a holistic view. Existing multi-variate association methods mostly focus on SNP-based genetic associations. In this paper, we extend and evaluate two adaptive Fisher’s methods, namely AFp and AFz, from the p-value combination perspective for phenotype–mRNA association analysis. The proposed method effectively aggregates heterogeneous phenotype–gene effects, allows association with different data types of phenotypes, and performs the selection of the associated phenotypes. Variability indices of the phenotype–gene effect selection are calculated by bootstrap analysis, and the resulting co-membership matrix identifies gene modules clustered by phenotype–gene effect. Extensive simulations demonstrate the superior performance of AFp compared to existing methods in terms of type I error control, statistical power and biological interpretation. Finally, the method is separately applied to three sets of transcriptomic and clinical datasets from lung disease, breast cancer, and brain aging and generates intriguing biological findings.

Differential Effects of Cocaine and Morphine on the Diurnal Regulation of the Mouse Nucleus Accumbens Proteome

Substance use disorders (SUDs) are associated with disruptions in sleep and circadian rhythms that persist during abstinence and may contribute to relapse risk. Repeated use of substances such as psychostimulants and opioids may lead to significant alterations in molecular rhythms in the nucleus accumbens (NAc), a brain region central to reward and motivation. Previous studies have identified rhythm alterations in the transcriptome of the NAc and other brain regions following the administration of psychostimulants or opioids. However, little is known about the impact of substance use on the diurnal rhythms of the proteome in the NAc. We used liquid chromatography coupled to tandem mass spectrometry-based (LC-MS/MS) quantitative proteomics, along with a data-independent acquisition (DIA) analysis pipeline, to investigate the effects of cocaine or morphine administration on diurnal rhythms of proteome in the mouse NAc. Overall, our data reveals cocaine and morphine differentially alters diurnal rhythms of the proteome in the NAc, with largely independent differentially expressed proteins dependent on time-of-day. Pathways enriched from cocaine altered protein rhythms were primarily associated with glucocorticoid signaling and metabolism, whereas morphine was associated with neuroinflammation. Collectively, these findings are the first to characterize the diurnal regulation of the NAc proteome and demonstrate a novel relationship between phase-dependent regulation of protein expression and the differential effects of cocaine and morphine on the NAc proteome.

Transcriptomic congruence analysis for evaluating model organisms

Model organisms are instrumental substitutes for human studies to expedite basic, translational, and clinical research. Despite their indispensable role in mechanistic investigation and drug development, molecular congruence of animal models to humans has long been questioned and debated. Little effort has been made for an objective quantification and mechanistic exploration of a model organism's resemblance to humans in terms of molecular response under disease or drug treatment. We hereby propose a framework, namely Congruence Analysis for Model Organisms (CAMO), for transcriptomic response analysis by developing threshold-free differential expression analysis, quantitative concordance/discordance scores incorporating data variabilities, pathway-centric downstream investigation, knowledge retrieval by text mining, and topological gene module detection for hypothesis generation. Instead of a genome-wide vague and dichotomous answer of "poorly" or "greatly" mimicking humans, CAMO assists researchers to numerically quantify congruence, to dissect true cross-species differences from unwanted biological or cohort variabilities, and to visually identify molecular mechanisms and pathway subnetworks that are best or least mimicked by model organisms, which altogether provides foundations for hypothesis generation and subsequent translational decisions.

EstroGene database reveals diverse temporal, context-dependent and directional estrogen receptor regulomes in breast cancer

As one of the most successful cancer therapeutic targets, estrogen receptor-α (ER/ESR1) has been extensively studied in decade-long. Sequencing technological advances have enabled genome-wide analysis of ER action. However, reproducibility is limited by different experimental design. Here, we established the EstroGene database through centralizing 246 experiments from 136 transcriptomic, cistromic and epigenetic datasets focusing on estradiol-treated ER activation across 19 breast cancer cell lines. We generated a user-friendly browser ( https://estrogene.org/ ) for data visualization and gene inquiry under user-defined experimental conditions and statistical thresholds. Notably, documentation-based meta-analysis revealed a considerable lack of experimental details. Comparison of independent RNA-seq or ER ChIP-seq data with the same design showed large variability and only strong effects could be consistently detected. We defined temporal estrogen response metasignatures and showed the association with specific transcriptional factors, chromatin accessibility and ER heterogeneity. Unexpectedly, harmonizing 146 transcriptomic analyses uncovered a subset of E2-bidirectionally regulated genes, which linked to immune surveillance in the clinical setting. Furthermore, we defined context dependent E2 response programs in MCF7 and T47D cell lines, the two most frequently used models in the field. Collectively, the EstroGene database provides an informative resource to the cancer research community and reveals a diverse mode of ER signaling.

Diurnal Alterations in Gene Expression Across Striatal Subregions in Psychosis

BACKGROUND

Psychosis is a defining feature of schizophrenia and highly prevalent in bipolar disorder. Notably, individuals with these illnesses also have major disruptions in sleep and circadian rhythms, and disturbances of sleep and circadian rhythms can precipitate or exacerbate psychotic symptoms. Psychosis is associated with the striatum, though to our knowledge, no study to date has directly measured molecular rhythms and determined how they are altered in the striatum of subjects with psychosis.

METHODS

We performed RNA sequencing and both differential expression and rhythmicity analyses to investigate diurnal alterations in gene expression in human postmortem striatal subregions (nucleus accumbens, caudate, and putamen) in subjects with psychosis (n = 36) relative to unaffected comparison subjects (n = 36).

RESULTS

Across regions, we found differential expression of immune-related transcripts and a substantial loss of rhythmicity in core circadian clock genes in subjects with psychosis. In the nucleus accumbens, mitochondrial-related transcripts had decreased expression in subjects with psychosis, but only in those who died at night. Additionally, we found a loss of rhythmicity in small nucleolar RNAs and a gain of rhythmicity in glutamatergic signaling in the nucleus accumbens of subjects with psychosis. Between-region comparisons indicated that rhythmicity in the caudate and putamen was far more similar in subjects with psychosis than in matched comparison subjects.

CONCLUSIONS

Together, these findings reveal differential and rhythmic gene expression differences across the striatum that may contribute to striatal dysfunction and psychosis in psychotic disorders.

Twelve-hour rhythms in transcript expression within the human dorsolateral prefrontal cortex are altered in schizophrenia

Twelve-hour (12 h) ultradian rhythms are a well-known phenomenon in coastal marine organisms. While 12 h cycles are observed in human behavior and physiology, no study has measured 12 h rhythms in the human brain. Here, we identify 12 h rhythms in transcripts that either peak at sleep/wake transitions (approximately 9 AM/PM) or static times (approximately 3 PM/AM) in the dorsolateral prefrontal cortex, a region involved in cognition. Subjects with schizophrenia (SZ) lose 12 h rhythms in genes associated with the unfolded protein response and neuronal structural maintenance. Moreover, genes involved in mitochondrial function and protein translation, which normally peak at sleep/wake transitions, peak instead at static times in SZ, suggesting suboptimal timing of these essential processes.

Rapid Eye Movement Sleep Engages Melanin-Concentrating Hormone Neurons to Reduce Cocaine Seeking

BACKGROUND

Persistent sleep disruptions following withdrawal from abused drugs may hold keys to battle drug relapse. It is posited that there may be sleep signatures that predict relapse propensity, identifying which may open new avenues for treating substance use disorders.

METHODS

We trained male rats (approximately postnatal day 56) to self-administer cocaine. After long-term drug withdrawal (approximately postnatal day 100), we examined the correlations between the intensity of cocaine seeking and key sleep features. To test for causal relationships, we then used behavioral, chemogenetic, or optogenetic methods to selectively increase rapid eye movement sleep (REMS) and measured behavioral and electrophysiological outcomes to probe for cellular and circuit mechanisms underlying REMS-mediated regulation of cocaine seeking.

RESULTS

A selective set of REMS features was preferentially associated with the intensity of cue-induced cocaine seeking after drug withdrawal. Moreover, selectively increasing REMS time and continuity by environmental warming attenuated a withdrawal time-dependent intensification of cocaine seeking, or incubation of cocaine craving, suggesting that REMS may benefit withdrawal. Warming increased the activity of lateral hypothalamic melanin-concentrating hormone (MCH) neurons selectively during prolonged REMS episodes and counteracted cocaine-induced synaptic accumulation of calcium-permeable AMPA receptors in the nucleus accumbens-a critical substrate for incubation. Finally, the warming effects were partly mimicked by chemogenetic or optogenetic stimulations of MCH neurons during sleep, or intra-accumbens infusions of MCH peptide during the rat's inactive phase.

CONCLUSIONS

REMS may encode individual vulnerability to relapse, and MCH neuron activities can be selectively targeted during REMS to reduce drug relapse.

Longitudinal Modulation of Loco-Regional Immunity in Ovarian Cancer Patients Receiving Intraperitoneal Chemotherapy

The immune tumor microenvironment (TME) of epithelial ovarian cancer (EOC) carries both effector and suppressive functions. To define immune correlates of chemotherapy-induced tumor involution, we performed longitudinal evaluation of biomarker expression on serial biological specimens collected during intraperitoneal (IP) platinum-based chemotherapy. Serial biological samples were collected at several time points during IP chemotherapy. RNA from IP fluid cells and tumor tissue was analyzed via NanoString. Meso Scale Discovery (MSD) multiplex assay and ELISA for MUC1 antibodies were performed on plasma and IP fluid. Differentially expressed genes in IP fluid demonstrate an upregulation of B cell function and activation of Th2 immune response along with dampening of Th1 immunity during chemotherapy. MSD analysis of IP fluid and gene expression analysis of tumor tissue revealed activation of Th2 immunity and the complement system. Anti-MUC1 antibodies were detected in IP fluid samples. IP fluid analysis in a secondary cohort also identified chemotherapy-induced B cell function genes. This study shows that serial IP fluid sampling is an effective method to capture changes in the immune TME during chemotherapy and reveals treatment induced changes in B cell function and Th2 immunity.

Brain region- and sex-specific transcriptional profiles of microglia

Microglia are resident macrophages of the brain, performing roles related to brain homeostasis, including modulation of synapses, trophic support, phagocytosis of apoptotic cells and debris, as well as brain protection and repair. Studies assessing morphological and transcriptional features of microglia found regional differences as well as sex differences in some investigated brain regions. However, markers used to isolate microglia in many previous studies are not expressed exclusively by microglia or cannot be used to identify and isolate microglia in all contexts. Here, fluorescent activated cell sorting was used to isolate cells expressing the microglia-specific marker TMEM119 from prefrontal cortex (PFC), striatum, and midbrain in mice. RNA-sequencing was used to assess the transcriptional profile of microglia, focusing on brain region and sex differences. We found striking brain region differences in microglia-specific transcript expression. Most notable was the distinct transcriptional profile of midbrain microglia, with enrichment for pathways related to immune function; these midbrain microglia exhibited a profile similar to disease-associated or immune-surveillant microglia. Transcripts more highly expressed in PFC isolated microglia were enriched for synapse-related pathways while microglia isolated from the striatum were enriched for pathways related to microtubule polymerization. We also found evidence for a gradient of expression of microglia-specific transcripts across the rostral-to-caudal axes of the brain, with microglia extracted from the striatum exhibiting a transcriptional profile intermediate between that of the PFC and midbrain. We also found sex differences in expression of microglia-specific transcripts in all 3 brain regions, with many selenium-related transcripts more highly expressed in females across brain regions. These results suggest that the transcriptional profile of microglia varies between brain regions under homeostatic conditions, suggesting that microglia perform diverse roles in different brain regions and even based on sex.

High-dimension to high-dimension screening for detecting genome-wide epigenetic and noncoding RNA regulators of gene expression

MOTIVATION

The advancement of high-throughput technology characterizes a wide variety of epigenetic modifications and noncoding RNAs across the genome involved in disease pathogenesis via regulating gene expression. The high dimensionality of both epigenetic/noncoding RNA and gene expression data make it challenging to identify the important regulators of genes. Conducting univariate test for each possible regulator-gene pair is subject to serious multiple comparison burden, and direct application of regularization methods to select regulator-gene pairs is computationally infeasible. Applying fast screening to reduce dimension first before regularization is more efficient and stable than applying regularization methods alone.

RESULTS

We propose a novel screening method based on robust partial correlation to detect epigenetic and noncoding RNA regulators of gene expression over the whole genome, a problem that includes both high-dimensional predictors and high-dimensional responses. Compared to existing screening methods, our method is conceptually innovative that it reduces the dimension of both predictor and response, and screens at both node (regulators or genes) and edge (regulator-gene pairs) levels. We develop data-driven procedures to determine the conditional sets and the optimal screening threshold, and implement a fast iterative algorithm. Simulations and applications to long noncoding RNA and microRNA regulation in Kidney cancer and DNA methylation regulation in Glioblastoma Multiforme illustrate the validity and advantage of our method.

AVAILABILITY AND IMPLEMENTATION

The R package, related source codes and real datasets used in this article are provided at https://github.com/kehongjie/rPCor.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Astrocyte Molecular Clock Function in the Nucleus Accumbens Is Important for Reward-Related Behavior

BACKGROUND

Substance use disorders are associated with disruptions in circadian rhythms. Both human and animal work have shown the integral role for circadian clocks in the modulation of reward behaviors. Astrocytes have emerged as key regulators of circadian rhythmicity. However, no studies to date have identified the role of circadian astrocyte function in the nucleus accumbens (NAc), a hub for reward regulation, or determined the importance of these rhythms for reward-related behavior.

METHODS

Using astrocyte-specific RNA sequencing across time of day, we first characterized diurnal variation of the NAc astrocyte transcriptome. We then investigated the functional significance of this circadian regulation through viral-mediated disruption of molecular clock function in NAc astrocytes, followed by assessment of reward-related behaviors, metabolic-related molecular assays, and whole-cell electrophysiology in the NAc.

RESULTS

Strikingly, approximately 43% of the astrocyte transcriptome has a diurnal rhythm, and key metabolic pathways were enriched among the top rhythmic genes. Moreover, mice with a viral-mediated loss of molecular clock function in NAc astrocytes show a significant increase in locomotor response to novelty, exploratory drive, operant food self-administration, and motivation. At the molecular level, these animals also show disrupted metabolic gene expression, along with significant downregulation of both lactate and glutathione levels in the NAc. Loss of NAc astrocyte clock function also significantly altered glutamatergic signaling onto neighboring medium spiny neurons, alongside upregulated glutamate-related gene expression.

CONCLUSIONS

Taken together, these findings demonstrate a novel role for astrocyte circadian molecular clock function in the regulation of the NAc and reward-related behaviors.

ESR1 mutant breast cancers show elevated basal cytokeratins and immune activation

Estrogen receptor alpha (ER/ESR1) is frequently mutated in endocrine resistant ER-positive (ER+) breast cancer and linked to ligand-independent growth and metastasis. Despite the distinct clinical features of ESR1 mutations, their role in intrinsic subtype switching remains largely unknown. Here we find that ESR1 mutant cells and clinical samples show a significant enrichment of basal subtype markers, and six basal cytokeratins (BCKs) are the most enriched genes. Induction of BCKs is independent of ER binding and instead associated with chromatin reprogramming centered around a progesterone receptor-orchestrated insulated neighborhood. BCK-high ER+ primary breast tumors exhibit a number of enriched immune pathways, shared with ESR1 mutant tumors. S100A8 and S100A9 are among the most induced immune mediators and involve in tumor-stroma paracrine crosstalk inferred by single-cell RNA-seq from metastatic tumors. Collectively, these observations demonstrate that ESR1 mutant tumors gain basal features associated with increased immune activation, encouraging additional studies of immune therapeutic vulnerabilities.

Hotspot ESR1 Mutations Are Multimodal and Contextual Modulators of Breast Cancer Metastasis

Constitutively active estrogen receptor α (ER/ESR1) mutations have been identified in approximately one-third of ER+ metastatic breast cancers. Although these mutations are known as mediators of endocrine resistance, their potential role in promoting metastatic disease has not yet been mechanistically addressed. In this study, we show the presence of ESR1 mutations exclusively in distant but not local recurrences in five independent breast cancer cohorts. In concordance with transcriptomic profiling of ESR1-mutant tumors, genome-edited ESR1 Y537S and D538G-mutant cell models exhibited a reprogrammed cell adhesive gene network via alterations in desmosome/gap junction genes and the TIMP3/MMP axis, which functionally conferred enhanced cell-cell contacts while decreasing cell-extracellular matrix adhesion. In vivo studies showed ESR1-mutant cells were associated with larger multicellular circulating tumor cell (CTC) clusters with increased compactness compared with ESR1 wild-type CTCs. These preclinical findings translated to clinical observations, where CTC clusters were enriched in patients with ESR1-mutated metastatic breast cancer. Conversely, context-dependent migratory phenotypes revealed cotargeting of Wnt and ER as a vulnerability in a D538G cell model. Mechanistically, mutant ESR1 exhibited noncanonical regulation of several metastatic pathways, including secondary transcriptional regulation and de novo FOXA1-driven chromatin remodeling. Collectively, these data provide evidence for ESR1 mutation-modulated metastasis and suggest future therapeutic strategies for targeting ESR1-mutant breast cancer.

SIGNIFICANCE

Context- and allele-dependent transcriptome and cistrome reprogramming in mutant ESR1 cell models elicit diverse metastatic phenotypes related to cell adhesion and migration, which can be pharmacologically targeted in metastatic breast cancer.

Molecular rhythm alterations in prefrontal cortex and nucleus accumbens associated with opioid use disorder

Severe and persistent disruptions to sleep and circadian rhythms are common in people with opioid use disorder (OUD). Preclinical evidence suggests altered molecular rhythms in the brain modulate opioid reward and relapse. However, whether molecular rhythms are disrupted in the brains of people with OUD remained an open question, critical to understanding the role of circadian rhythms in opioid addiction. Using subjects' times of death as a marker of time of day, we investigated transcriptional rhythms in the brains of subjects with OUD compared to unaffected comparison subjects. We discovered rhythmic transcripts in both the dorsolateral prefrontal cortex (DLPFC) and nucleus accumbens (NAc), key brain areas involved in OUD, that were largely distinct between OUD and unaffected subjects. Fewer rhythmic transcripts were identified in DLPFC of subjects with OUD compared to unaffected subjects, whereas in the NAc, nearly double the number of rhythmic transcripts was identified in subjects with OUD. In NAc of subjects with OUD, rhythmic transcripts peaked either in the evening or near sunrise, and were associated with an opioid, dopamine, and GABAergic neurotransmission. Associations with altered neurotransmission in NAc were further supported by co-expression network analysis which identified OUD-specific modules enriched for transcripts involved in dopamine, GABA, and glutamatergic synaptic functions. Additionally, rhythmic transcripts in DLPFC and NAc of subjects with OUD were enriched for genomic loci associated with sleep-related GWAS traits, including sleep duration and insomnia. Collectively, our findings connect transcriptional rhythm changes in opioidergic, dopaminergic, GABAergic signaling in the human brain to sleep-related traits in opioid addiction.

Abstract PD1-08: Esr1 mutant breast cancers show elevated basal cytokeratins and immune activation

Estrogen receptor alpha (ER/ESR1) is mutated in 30-40% of endocrine resistant ER-positive (ER+) breast cancer. ESR1 mutations cause ligand-independent growth and increased metastasis in vivo and in vitro. Despite the distinct clinical features and changes in therapeutic response associated with ESR1 mutations, there are no data about their potential role in intrinsic subtype switching.Applying five different luminal and basal gene set pairs derived from cell lines and tumors, ESR1 mutant cell models and clinical samples showed a significant enrichment of basal subtype markers. Among them, the six basal cytokeratins (BCKs) were the most enriched genes (KRT5/6A/6B/14/16/17) uniquely in ESR1 mutant cells but not other endocrine resistant cell models. BCKs were observed to heterogeneously express in a minor cell subpopulation in ESR1 mutant cell models and clinical specimens. ER ChIP-seq showed the mutant-specific induction of BCKs was independent of ER binding and instead selectively expressed in clones with low ER expression. In contrast, BCKs are associated with chromatin reprogramming centered around a progesterone receptor-orchestrated 154 kb insulated neighborhood at the KRT14/16/17 genomic region. Stronger CTCF binding was detected at the bases of chromatin loop in ESR1 mutant cells. Knockdown of progesterone receptor but not glucocorticoid receptor drastically blocked the induction of KRT14/16/17 in ESR1 mutant cells. Unexpectedly, high BCK expression in ER+ primary breast cancer is associated with good prognosis, and these tumors show enriched activation of a number of immune pathways, a distinctive feature shared with ESR1 mutant tumors. While the BCK-associated immune activation is not related to tumor mutation burdens, S100A8 and S100A9 were identified as the most highly induced immune mediators shared between high-BCKs ER+ and ESR1 mutant tumors, which was further validated in the plasma samples of a cohort of 18 patients with ER+ metastases (11 WT vs 7 mutant). Finally, single-cell RNA-seq analysis in an ER+ bone metastasis case inferred the involvement of S100A8 and S100A9 in paracrine crosstalk between epithelial and stromal cells, particularly macrophages and fibroblasts through TLR4 signaling. Collectively, these observations demonstrate that ESR1 mutant tumors gain basal features with induction of basal cytokeratins via epigenetic mechanisms in rare subpopulation of cells. This is associated with increased immune activation, encouraging additional studies of immune therapeutic vulnerabilities in ESR1 mutant tumors.

Citation Format: Zheqi Li, Yang Wu, Olivia Mcginn, Amir Bahreini, Nolan M. Priedigkeit, Kai Ding, Sayali Onkar, Carol A. Sartorius, Lori Miller, Margaret Rosenzweig, Nikhil Wagle, Jennifer K. Richer, William J. Muller, Laki Buluwela, Simak Ali, Dario A.A. Vignali, Yusi Fang, Li Zhu, George C. Tseng, Jason Gertz, Jennifer M. Atkinson, Adrian V. Lee, Steffi Oesterreich. Esr1 mutant breast cancers show elevated basal cytokeratins and immune activation [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD1-08.

Likelihood-based tests for detecting circadian rhythmicity and differential circadian patterns in transcriptomic applications

Circadian rhythmicity in transcriptomic profiles has been shown in many physiological processes, and the disruption of circadian patterns has been found to associate with several diseases. In this paper, we developed a series of likelihood-based methods to detect (i) circadian rhythmicity (denoted as LR_rhythmicity) and (ii) differential circadian patterns comparing two experimental conditions (denoted as LR_diff). In terms of circadian rhythmicity detection, we demonstrated that our proposed LR_rhythmicity could better control the type I error rate compared to existing methods under a wide variety of simulation settings. In terms of differential circadian patterns, we developed methods in detecting differential amplitude, differential phase, differential basal level and differential fit, which also successfully controlled the type I error rate. In addition, we demonstrated that the proposed LR_diff could achieve higher statistical power in detecting differential fit, compared to existing methods. The superior performance of LR_rhythmicity and LR_diff was demonstrated in four real data applications, including a brain aging data (gene expression microarray data of human postmortem brain), a time-restricted feeding data (RNA sequencing data of human skeletal muscles) and a scRNAseq data (single cell RNA sequencing data of mouse suprachiasmatic nucleus). An R package for our methods is publicly available on GitHub https://github.com/diffCircadian/diffCircadian.

Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients

Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However, significant numbers of patients with HCC experience cancer recurrence, and the selection of suitable candidates for liver transplant remains a challenge. We developed a model to predict the likelihood of HCC recurrence after liver transplantation based on transcriptome and whole‐exome sequencing analyses. We used a training cohort and a subsequent testing cohort based on liver transplantation performed before or after the first half of 2012. We found that the combination of transcriptome and mutation pathway analyses using a random forest machine learning correctly predicted HCC recurrence in 86.8% of the training set. The same algorithm yielded a correct prediction of HCC recurrence of 76.9% in the testing set. When the cohorts were combined, the prediction rate reached 84.4% in the leave‐one‐out cross‐validation analysis. When the transcriptome analysis was combined with Milan criteria using the k‐top scoring pairs (k‐TSP) method, the testing cohort prediction rate improved to 80.8%, whereas the training cohort and the combined cohort prediction rates were 79% and 84.4%, respectively. Application of the transcriptome/mutation pathways RF model on eight tumor nodules from 3 patients with HCC yielded 8/8 consistency, suggesting a robust prediction despite the heterogeneity of HCC. Conclusion: The genome prediction model may hold promise as an alternative in selecting patients with HCC for liver transplant.

Transcriptional Alterations in Dorsolateral Prefrontal Cortex and Nucleus Accumbens Implicate Neuroinflammation and Synaptic Remodeling in Opioid Use Disorder

BACKGROUND

Prevalence rates of opioid use disorder (OUD) have increased dramatically, accompanied by a surge of overdose deaths. While opioid dependence has been extensively studied in preclinical models, an understanding of the biological alterations that occur in the brains of people who chronically use opioids and who are diagnosed with OUD remains limited. To address this limitation, RNA sequencing was conducted on the dorsolateral prefrontal cortex and nucleus accumbens, regions heavily implicated in OUD, from postmortem brains in subjects with OUD.

METHODS

We performed RNA sequencing on the dorsolateral prefrontal cortex and nucleus accumbens from unaffected comparison subjects (n = 20) and subjects diagnosed with OUD (n = 20). Our transcriptomic analyses identified differentially expressed transcripts and investigated the transcriptional coherence between brain regions using rank-rank hypergeometric orderlap. Weighted gene coexpression analyses identified OUD-specific modules and gene networks. Integrative analyses between differentially expressed transcripts and genome-wide association study datasets using linkage disequilibrium scores assessed the genetic liability of psychiatric-related phenotypes in OUD.

RESULTS

Rank-rank hypergeometric overlap analyses revealed extensive overlap in transcripts between the dorsolateral prefrontal cortex and nucleus accumbens in OUD, related to synaptic remodeling and neuroinflammation. Identified transcripts were enriched for factors that control proinflammatory cytokine, chondroitin sulfate, and extracellular matrix signaling. Cell-type deconvolution implicated a role for microglia as a potential driver for opioid-induced neuroplasticity. Linkage disequilibrium score analysis suggested genetic liabilities for risky behavior, attention-deficit/hyperactivity disorder, and depression in subjects with OUD.

CONCLUSIONS

Overall, our findings suggest connections between the brain's immune system and opioid dependence in the human brain.

A sparse negative binomial mixture model for clustering RNA-seq count data

Clustering with variable selection is a challenging yet critical task for modern small-n-large-p data. Existing methods based on sparse Gaussian mixture models or sparse $K$-means provide solutions to continuous data. With the prevalence of RNA-seq technology and lack of count data modeling for clustering, the current practice is to normalize count expression data into continuous measures and apply existing models with a Gaussian assumption. In this article, we develop a negative binomial mixture model with lasso or fused lasso gene regularization to cluster samples (small $n$) with high-dimensional gene features (large $p$). A modified EM algorithm and Bayesian information criterion are used for inference and determining tuning parameters. The method is compared with existing methods using extensive simulations and two real transcriptomic applications in rat brain and breast cancer studies. The result shows the superior performance of the proposed count data model in clustering accuracy, feature selection, and biological interpretation in pathways.

The Utility of Next-Generation Sequencing in Advanced Breast and Gynecologic Cancers

OBJECTIVES

Next-generation sequencing (NGS) has the potential to identify genetic alterations that are actionable with targeted therapy. Our objective was to identify the impact of NGS testing on advanced breast and gynecologic malignancies.

METHODS

A retrospective review of 108 patients who underwent NGS testing between 2015 and 2019 was performed. The NGS clinical action rate was calculated based on documentation of positive clinical action taken in cases with an actionable NGS result.

RESULTS

The 108 specimens tested included 35 breast cancers and 73 gynecologic malignancies, with most of the testing performed at Foundation Medicine (90%). Actionable mutation(s) were identified in 79 (73%) of 108 cases. The overall clinical action rate of NGS testing was 38% (30 of 79 cases). Overall, 47 (44%) of 108 patients died, all succumbing to disease. The average survival was 10.9 months. The survival difference between patients with actionable NGS result and targeted treatment, actionable NGS result but no targeted treatment, and patients with nonactionable NGS result was not significant (log-rank test, P = .5160).

CONCLUSIONS

NGS testing for advanced breast and gynecologic cancers at our institution has a 38% clinical action rate. However, the increased clinical action rate over the years did not translate into improved survival.

Valproate reverses mania-like behaviors in mice via preferential targeting of HDAC2

Valproate (VPA) has been used in the treatment of bipolar disorder since the 1990s. However, the therapeutic targets of VPA have remained elusive. Here we employ a preclinical model to identify the therapeutic targets of VPA. We find compounds that inhibit histone deacetylase proteins (HDACs) are effective in normalizing manic-like behavior, and that class I HDACs (e.g., HDAC1 and HDAC2) are most important in this response. Using an RNAi approach, we find that HDAC2, but not HDAC1, inhibition in the ventral tegmental area (VTA) is sufficient to normalize behavior. Furthermore, HDAC2 overexpression in the VTA prevents the actions of VPA. We used RNA sequencing in both mice and human induced pluripotent stem cells (iPSCs) derived from bipolar patients to further identify important molecular targets. Together, these studies identify HDAC2 and downstream targets for the development of novel therapeutics for bipolar mania.

A Novel Mouse Model for SNP in Steroid Receptor Co-Activator-1 Reveals Role in Bone Density and Breast Cancer Metastasis

The steroid receptor coactivator-1 (SRC-1) is a nuclear receptor co-activator, known to play key roles in both estrogen response in bone and in breast cancer metastases. We previously demonstrated that the P1272S single nucleotide polymorphism (SNP; P1272S; rs1804645) in SRC-1 decreases the activity of estrogen receptor in the presence of selective estrogen receptor modulators (SERMs) and that it is associated with a decrease in bone mineral density (BMD) after tamoxifen therapy, suggesting it may disrupt the agonist action of tamoxifen. Given such dual roles of SRC-1 in the bone microenvironment and in tumor cell-intrinsic phenotypes, we hypothesized that SRC-1 and a naturally occurring genetic variant, P1272S, may promote breast cancer bone metastases. We developed a syngeneic, knock-in mouse model to study if the SRC-1 SNP is critical for normal bone homeostasis and bone metastasis. Our data surprisingly reveal that the homozygous SRC-1 SNP knock-in increases tamoxifen-induced bone protection after ovariectomy. The presence of the SRC-1 SNP in mammary glands resulted in decreased expression levels of SRC-1 and reduced tumor burden after orthotopic injection of breast cancer cells not bearing the SRC-1 SNP, but increased metastases to the lungs in our syngeneic mouse model. Interestingly, the P1272S SNP identified in a small, exploratory cohort of bone metastases from breast cancer patients was significantly associated with earlier development of bone metastasis. This study demonstrates the importance of the P1272S SNP in both the effect of SERMs on BMD and the development of tumor in the bone.

A Metabolite Composite Score Attenuated a Substantial Portion of the Higher Mortality Risk Associated With Frailty Among Community-Dwelling Older Adults

BACKGROUND

Frailty is more prevalent among black versus white older Americans. We previously identified 37 metabolites associated with the vigor to frailty spectrum using the Scale of Aging Vigor in Epidemiology (SAVE) among older black men from the Health, Aging, and Body Composition (Health ABC) study. Here, we sought to develop a metabolite composite score based on the 37 SAVE-associated metabolites and determine whether the composite score predicts mortality and whether it attenuates the association between frailty and mortality among older black men.

METHODS

Plasma metabolites were measured using liquid chromatography-mass spectrometry. Most of the 37 metabolites were organic acids/derivatives or lipids. Metabolites were ranked into tertiles: tertiles associated with more vigorous SAVE scores were scored 0, mid-tertiles were scored 1, and tertiles associated with frailer SAVE scores were scored 2. Composite scores were the sum of metabolite tertile scores. We examined mortality associations using Cox regression. Percent attenuation estimated the extent to which metabolites attenuated the association between frailty and mortality.

RESULTS

One standard deviation frailer SAVE was associated with 30% higher mortality, adjusting for age and site (p = .0002); this association was attenuated by 56% after additionally adjusting for the metabolite composite score. In this model, one standard deviation higher metabolite composite score was associated with 46% higher mortality (p < .0001). Metabolite composite scores also predicted mortality (p = .045) in a validation sample of 120 older adults (40% men, 90% white).

CONCLUSION

These metabolites may provide a deeper characterization of the higher mortality that is associated with frailty among older adults.

Older molecular brain age in severe mental illness

Psychiatric disorders are associated with accelerated aging and enhanced risk for neurodegenerative disorders. Brain aging is associated with molecular, cellular, and structural changes that are robust on the group level, yet show substantial inter-individual variability. Here we assessed deviations in gene expression from normal age-dependent trajectories, and tested their validity as predictors of risk for major mental illnesses and neurodegenerative disorders. We performed large-scale gene expression and genotype analyses in postmortem samples of two frontal cortical brain regions from 214 control subjects aged 20-90 years. Individual estimates of "molecular age" were derived from age-dependent genes, identified by robust regression analysis. Deviation from chronological age was defined as "delta age". Genetic variants associated with deviations from normal gene expression patterns were identified by expression quantitative trait loci (cis-eQTL) of age-dependent genes or genome-wide association study (GWAS) on delta age, combined into distinct polygenic risk scores (PRScis-eQTL and PRSGWAS), and tested for predicting brain disorders or pathology in independent postmortem expression datasets and clinical cohorts. In these validation datasets, molecular ages, defined by 68 and 76 age-related genes for two brain regions respectively, were positively correlated with chronological ages (r = 0.88/0.91), elevated in bipolar disorder (BP) and schizophrenia (SCZ), and unchanged in major depressive disorder (MDD). Exploratory analyses in independent clinical datasets show that PRSs were associated with SCZ and MDD diagnostics, and with cognition in SCZ and pathology in Alzheimer's disease (AD). These results suggest that older molecular brain aging is a common feature of severe mental illnesses and neurodegeneration.

Abstract 2848: Neomorphic cell-cell adhesion reprogramming facilitates metastasis of ESR1 mutant breast cancer

Background: Hotspot estrogen receptor-α (ER/ERα/ESR1) mutations occur in 30-40% endocrine resistant ER+ breast cancer and are associated with worse outcome. How these mutations facilitate metastasis remains ambiguous. It is necessary to identify a clear mechanism for therapeutic intervention.

Methods: ESR1 mutations were detected by ddPCR. Transcriptome data were derived from cell lines and clinical samples. Y537S and D538G genome-edited MCF7 and T47D cell lines were used for in vitro phenotypic characterization. Cell-cell adhesive properties were assessed using calcein-labelled adhesion, spontaneous cell aggregation and ibidi microfluidic assays. Altered cell-cell adhesion genes were validated using qRT-PCR, immunoblot and immunostaining. Desmosome blocking peptides and carbenoxolone were used for blockade of adhesion. ER cistromes were profiled by ChIP-seq. Tail vein injection was performed on athymic nude mice to evaluate metastasis in vivo. Circulating tumor cell (CTC) clustering propensity in vivo was assessed via intracardiac injection followed by CTC microfilter capture. CTC-cluster gene signatures were generated from ER+ breast cancer CTC RNA-seq data.

Results: ESR1 mutations were significantly enriched in distant metastases (12/48) vs local (0/27) recurrences, confirming their critical role in promoting metastasis. Transcriptomic analysis revealed altered cell-cell interaction pathways in ESR1 mutant tumors. ESR1 mutant cells formed more compact spheroids in suspension culture, and exhibited stronger cell-cell adhesion in static condition. Under microfluidic condition with physiological shear stress, mutant ESR1 cells derived more and larger clusters, which were prominently preserved from pre-existing clusters. This effect was correlated with increased expression of desmosome and gap junction genes in mutant cells and pharmacological blockade significantly reduced the enhanced cell-cell adhesion. ER ChIP-seq revealed no de novo mutant ER binding sites at the loci of the target genes, suggesting indirect regulation by mutant ER. This was exemplified by a secondary regulation from cFos/AP1 signaling of GJA1 expression, and an epigenetic regulation at DSC1/DSG1 loci through enhanced H3K4me2 and H3K27ac modification. In vivo studies showed ESR1 mutant cells derived more distant metastases, and MCF7 Y537S cells formed larger CTC clusters with increased compactness compared to WT cells. Finally, ER+ CTC-cluster signatures were enriched in ESR1 mutant tumors, suggesting their unique dependence on this pathway during metastasis.

Conclusion: Hotspot ESR1 mutations induce expression of multiple desmosome and gap junction genes and confer enhanced cell-cell adhesion, which facilitates breast cancer metastasis via increased CTCs clustering propensity. These findings provide insights to the development of drugs targeting gap junction in ER mutant tumors.

Citation Format: Yang Wu, Zheqi Li, Amir Bahreini, Jian Chen, Ye Qin, Kevin M. Levine, Nilgun Tasdemir, Nolan M. Priedigkeit, Li Zhu, George C. Tseng, Yu Jiang, Benjamin Troness, Laki Buluwela, Simak Ali, Spencer Arnesen, Jason Gertz, Ben Ho Park, Jennifer M. Atkinson, Dorraya El-Ashry, Adrian V. Lee, Steffi Oesterreich. Neomorphic cell-cell adhesion reprogramming facilitates metastasis of ESR1 mutant breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2848.

Unpaired data empowers association tests

Motivation

There is growing interest in the biomedical research community to incorporate retrospective data, available in healthcare systems, to shed light on associations between different biomarkers. Understanding the association between various types of biomedical data, such as genetic, blood biomarkers, imaging, etc. can provide a holistic understanding of human diseases. To formally test a hypothesized association between two types of data in Electronic Health Records (EHRs), one requires a substantial sample size with both data modalities to achieve a reasonable power. Current association test methods only allow using data from individuals who have both data modalities. Hence, researchers cannot take advantage of much larger EHR samples that includes individuals with at least one of the data types, which limits the power of the association test.

Results

We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and to improve the power of the association test. We study the properties of the new test theoretically and empirically, through a series of simulations and by applying our method on real studies in the context of Chronic Obstructive Pulmonary Disease. We are able to identify an association between the high-dimensional characterization of Computed Tomography chest images and several blood biomarkers as well as the expression of dozens of genes involved in the immune system.

Availability and implementation

Code is available on https://github.com/batmanlab/Semi-paired-Association-Test.

Supplementary information

Supplementary data are available at Bioinformatics online.

The microRNA target site landscape is a novel molecular feature associating alternative polyadenylation with immune evasion activity in breast cancer

Alternative polyadenylation (APA) in breast tumor samples results in the removal/addition of cis-regulatory elements such as microRNA (miRNA) target sites in the 3'-untranslated region (3'-UTRs) of genes. Although previous computational APA studies focused on a subset of genes strongly affected by APA (APA genes), we identify miRNAs of which widespread APA events collectively increase or decrease the number of target sites [probabilistic inference of microRNA target site modification through APA (PRIMATA-APA)]. Using PRIMATA-APA on the cancer genome atlas (TCGA) breast cancer data, we found that the global APA events change the number of the target sites of particular microRNAs [target sites modified miRNA (tamoMiRNA)] enriched for cancer development and treatments. We also found that when knockdown (KD) of NUDT21 in HeLa cells induces a different set of widespread 3'-UTR shortening than TCGA breast cancer data, it changes the target sites of the common tamoMiRNAs. Since the NUDT21 KD experiment previously demonstrated the tumorigenic role of APA events in a miRNA dependent fashion, this result suggests that the APA-initiated tumorigenesis is attributable to the miRNA target site changes, not the APA events themselves. Further, we found that the miRNA target site changes identify tumor cell proliferation and immune cell infiltration to the tumor microenvironment better than the miRNA expression levels or the APA events themselves. Altogether, our computational analyses provide a proof-of-concept demonstration that the miRNA target site information indicates the effect of global APA events with a potential as predictive biomarker.

Simultaneous estimation of cluster number and feature sparsity in high-dimensional cluster analysis

Estimating the number of clusters (K) is a critical and often difficult task in cluster analysis. Many methods have been proposed to estimate K, including some top performers using resampling approach. When performing cluster analysis in high-dimensional data, simultaneous clustering and feature selection is needed for improved interpretation and performance. To our knowledge, little has been studied for simultaneous estimation of K and feature sparsity parameter in a high-dimensional exploratory cluster analysis. In this paper, we propose a resampling method to bridge this gap and evaluate its performance under the sparse K-means clustering framework. The proposed target function balances between sensitivity and specificity of clustering evaluation of pairwise subjects from clustering of full and subsampled data. Through extensive simulations, the method performs among the best over classical methods in estimating K in low-dimensional data. For high-dimensional simulation data, it also shows superior performance to simultaneously estimate K and feature sparsity parameter. Finally, we evaluated the methods in four microarray, two RNA-seq, one SNP, and two nonomics datasets. The proposed method achieves better clustering accuracy with fewer selected predictive genes in almost all real applications.

Metabolites Associated with Walking Ability Among the Oldest Old from the CHS All Stars Study

BACKGROUND

Low walking ability is highly prevalent with advancing age and predicts major health outcomes. Metabolomics may help to better characterize differences in walking ability among older adults, providing insight into potentially altered molecular processes underlying age-related decline in functioning. We sought to identify metabolites and metabolic pathways associated with high versus low walking ability among 120 participants ages 79-95 from the CHS All Stars study.

METHODS

Using a nested case-control design, 60 randomly selected participants with low walking ability were matched one-to-one on age, gender, race, and fasting time with 60 participants with high walking ability. High versus low walking ability was defined as being in the best versus worst tertiles for both gait speed (≥0.9 vs <0.7 m/s) and the Walking Ability Index (7-9 vs 0-1). Using liquid chromatography-mass spectrometry, 569 metabolites were identified in overnight-fasting plasma.

RESULTS

Ninety-six metabolites were associated with walking ability, where 24% were triacylglycerols. Triacylglycerols that were higher among those with high walking ability consisted mostly of polyunsaturated fatty acids, whereas triacylglycerols that were lower among those with high walking ability consisted mostly of saturated or monounsaturated fatty acids. Body composition partly explained associations between some metabolites and walking ability. Proline and arginine metabolism was a top pathway associated with walking ability.

CONCLUSION

These results may partly reflect pathways of modifiable risk factors, including excess dietary lipids and lack of physical activity, contributing to obesity and further alterations in metabolic pathways that lead to age-related decline in walking ability in this older adult cohort.

Abstract 4917: Estrogen receptor D538G mutation promotes cell migration via hyperactivation of Wnt signaling pathway

Background: Hotspot estrogen receptor-α (ERα/ESR1) mutations occur in 30-40% endocrine resistant ER+ breast cancer, with Y537S and D538G as the two most frequent hotspot mutations. A mostly unanswered question is and how these mutations facilitate metastatic processes.

Methods: Frequencies of different ESR1 hotspot mutations were compared within three publicly available cohorts. Y537S and D538G genome-edited MCF7 and T47D cell models were used for in vitro characterization. Wound scratching, spheroid collective migration, chemotaxis assays were applied to examine different metastatic properties. Tail vein injection in nude mice followed by human CK19 lung section immune staining was used to characterize in vivo metastasis. Canonical Wnt pathway activity was studied using top-flash reporter assays, immunoblotting, and overexpression of dominant negative TCF4. ChIP-seq and ATAC-seq were utilized to profile ER global binding patterns and chromatin accessibility in cell models, respectively. Porcupine inhibitor-LGK974 was used to evaluate the efficiency of Wnt-targeted therapy.

Results: D538G mutations were more frequent in ER+ metastatic lesions compared to Y537S among three metastatic breast cancer cohorts with unbiased SNV detection. In line with this, T47D-D538G ESR1 mutant cells showed strongly enhanced cell migration in vitro which was not observed in Y537S cells. We also observed increased lung micro-metastasis in vivo after tail vein injection of the D538G cells. Cell line transcriptomic analysis revealed uniquely hyperactivated Wnt pathway in D538G mutant cells, which was further confirmed in vitro by top-flash reporter and immunoblot. Suppression of canonical Wnt pathways blocked T47D-D538G specific cell migration. Combination treatment of fulvestrant and LGK974 synergistically inhibited D538G specific migration. Mechanistically, multiple Wnt regulator genes were found uniquely upregulated in D538G cells. Interestingly, none of these targets gained ER binding peaks at proximal regulatory regions, suggesting potential epigenetic regulation, which was confirmed in ATAC-seq results. Knockdown of FOXA1, a well-characterized pioneer factor, decreased canonical Wnt activity and abrogated the D538G-unique cell migration in short-term.

Conclusion: T47D-D538G cells showed uniquely enhanced cell migration via Wnt hyperactivation, potentially mediated via epigenetic remodeling. These findings suggest the further study of potential combinatorial targeting of Wnt and ER signaling in D538G ESR1 mutant tumors.

Citation Format: Zheqi Li, Kevin M. Levine, Spencer Arneson, Kristofer C. Berrett, Nolan M. Priedigkeit, Amir Bahreini, Jian Chen, Li Zhu, Jason S. Carroll, George C. Tseng, Peter C. Lucas, Jennifer M. Atkinson, Jason Gertz, Adrian V. Lee, Steffi Oesterreich. Estrogen receptor D538G mutation promotes cell migration via hyperactivation of Wnt signaling pathway [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4917.

Comparative Pathway Integrator: A Framework of Meta-Analytic Integration of Multiple Transcriptomic Studies for Consensual and Differential Pathway Analysis

Pathway enrichment analysis provides a knowledge-driven approach to interpret differentially expressed genes associated with disease status. Many tools have been developed to analyze a single study. However, when multiple studies of different conditions are jointly analyzed, novel integrative tools are needed. In addition, pathway redundancy introduced by combining multiple public pathway databases hinders interpretation and knowledge discovery. We present a meta-analytic integration tool, Comparative Pathway Integrator (CPI), to address these issues using adaptively weighted Fisher’s method to discover consensual and differential enrichment patterns, a tight clustering algorithm to reduce pathway redundancy, and a text mining algorithm to assist interpretation of the pathway clusters. We applied CPI to jointly analyze six psychiatric disorder transcriptomic studies to demonstrate its effectiveness, and found functions confirmed by previous biological studies as well as novel enrichment patterns. CPI’s R package is accessible online on Github metaOmics/MetaPath.

MetaOmics: analysis pipeline and browser-based software suite for transcriptomic meta-analysis

SUMMARY

The rapid advances of omics technologies have generated abundant genomic data in public repositories and effective analytical approaches are critical to fully decipher biological knowledge inside these data. Meta-analysis combines multiple studies of a related hypothesis to improve statistical power, accuracy and reproducibility beyond individual study analysis. To date, many transcriptomic meta-analysis methods have been developed, yet few thoughtful guidelines exist. Here, we introduce a comprehensive analytical pipeline and browser-based software suite, called MetaOmics, to meta-analyze multiple transcriptomic studies for various biological purposes, including quality control, differential expression analysis, pathway enrichment analysis, differential co-expression network analysis, prediction, clustering and dimension reduction. The pipeline includes many public as well as >10 in-house transcriptomic meta-analytic methods with data-driven and biological-aim-driven strategies, hands-on protocols, an intuitive user interface and step-by-step instructions.

AVAILABILITY AND IMPLEMENTATION

MetaOmics is freely available at https://github.com/metaOmics/metaOmics.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transcriptome Characterization of Matched Primary Breast and Brain Metastatic Tumors to Detect Novel Actionable Targets

BACKGROUND

Breast cancer brain metastases (BrMs) are defined by complex adaptations to both adjuvant treatment regimens and the brain microenvironment. Consequences of these alterations remain poorly understood, as does their potential for clinical targeting. We utilized genome-wide molecular profiling to identify therapeutic targets acquired in metastatic disease.

METHODS

Gene expression profiling of 21 patient-matched primary breast tumors and their associated brain metastases was performed by TrueSeq RNA-sequencing to determine clinically actionable BrM target genes. Identified targets were functionally validated using small molecule inhibitors in a cohort of resected BrM ex vivo explants (n = 4) and in a patient-derived xenograft (PDX) model of BrM. All statistical tests were two-sided.

RESULTS

Considerable shifts in breast cancer cell-specific gene expression profiles were observed (1314 genes upregulated in BrM; 1702 genes downregulated in BrM; DESeq; fold change > 1.5, Padj < .05). Subsequent bioinformatic analysis for readily druggable targets revealed recurrent gains in RET expression and human epidermal growth factor receptor 2 (HER2) signaling. Small molecule inhibition of RET and HER2 in ex vivo patient BrM models (n = 4) resulted in statistically significantly reduced proliferation (P < .001 in four of four models). Furthermore, RET and HER2 inhibition in a PDX model of BrM led to a statistically significant antitumor response vs control (n = 4, % tumor growth inhibition [mean difference; SD], anti-RET = 86.3% [1176; 258.3], P < .001; anti-HER2 = 91.2% [1114; 257.9], P < .01).

CONCLUSIONS

RNA-seq profiling of longitudinally collected specimens uncovered recurrent gene expression acquisitions in metastatic tumors, distinct from matched primary tumors. Critically, we identify aberrations in key oncogenic pathways and provide functional evidence for their suitability as therapeutic targets. Altogether, this study establishes recurrent, acquired vulnerabilities in BrM that warrant immediate clinical investigation and suggests paired specimen expression profiling as a compelling and underutilized strategy to identify targetable dependencies in advanced cancers.