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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.