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.

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.

A phase II study of Mirvetuximab Soravtansine in triple-negative breast cancer.

Folate receptor alpha (FRα) has been reported to be expressed in up to 80% of triple-negative breast cancers (TNBC) with limited expression in normal tissues, making it a promising therapeutic target. Mirvetuximab soravtansine (mirvetuximab-s) is an antibody drug conjugate which has shown promise in the treatment of FRα-positive solid tumors in early phase clinical trials. Herein, are the results of the first prospective phase II trial evaluating mirvetuximab-s in metastatic TNBC. Patients with advanced, FRα-positive TNBC were enrolled on this study. Mirvetuximab-s was administered at a dose of 6.0 mg/kg every 3 weeks. 96 patients with advanced TNBC consented for screening. FRα staining was performed on tumor tissue obtained from 80 patients. The rate of FRα positivity by immunohistochemistry was 10.0% (8/80). Two patients were treated on study, with best overall responses of stable disease in one and progressive disease in the other. Adverse events were consistent with earlier studies. The study was terminated early due to the low rate of FRα positivity in the screened patient population and lack of disease response in the two patients treated. The observed rate of FRα positivity was considerably lower than previously reported and none of the patients had a partial or complete response. Treatment with mirvetuximab-s should only be further explored in TNBC if an alternate biomarker strategy is developed for patient selection on the basis of additional preclinical data.

Cocaine-induced neural adaptations in the lateral hypothalamic melanin-concentrating hormone neurons and the role in regulating rapid eye movement sleep after withdrawal.

Sleep abnormalities are often a prominent contributor to withdrawal symptoms following chronic drug use. Notably, rapid eye movement (REM) sleep regulates emotional memory, and persistent REM sleep impairment after cocaine withdrawal negatively impacts relapse-like behaviors in rats. However, it is not understood how cocaine experience may alter REM sleep regulatory machinery, and what may serve to improve REM sleep after withdrawal. Here, we focus on the melanin-concentrating hormone (MCH) neurons in the lateral hypothalamus (LH), which regulate REM sleep initiation and maintenance. Using adult male Sprague-Dawley rats trained to self-administer intravenous cocaine, we did transcriptome profiling of LH MCH neurons after long-term withdrawal using RNA-sequencing, and performed functional assessment using slice electrophysiology. We found that 3 weeks after withdrawal from cocaine, LH MCH neurons exhibit a wide range of gene expression changes tapping into cell membrane signaling, intracellular signaling, and transcriptional regulations. Functionally, they show reduced membrane excitability and decreased glutamatergic receptor activity, consistent with increased expression of voltage-gated potassium channel gene Kcna1 and decreased expression of metabotropic glutamate receptor gene Grm5. Finally, chemogenetic or optogenetic stimulations of LH MCH neural activity increase REM sleep after long-term withdrawal with important differences. Whereas chemogenetic stimulation promotes both wakefulness and REM sleep, optogenetic stimulation of these neurons in sleep selectively promotes REM sleep. In summary, cocaine exposure persistently alters gene expression profiles and electrophysiological properties of LH MCH neurons. Counteracting cocaine-induced hypoactivity of these neurons selectively in sleep enhances REM sleep quality and quantity after long-term withdrawal.

DNA methylation in the human frontal cortex reveals a putative mechanism for age-by-disease interactions.

A consistent gene set undergoes age-associated expression changes in the human cerebral cortex, and our Age-by-Disease Model posits that these changes contribute to psychiatric diseases by "pushing" the expression of disease-associated genes in disease-promoting directions. DNA methylation (DNAm) is an attractive candidate mechanism for age-associated gene expression changes. We used the Illumina HumanMethylation450 array to characterize genome-wide DNAm in the postmortem orbital frontal cortex from 20 younger (<42 years) and 19 older (>60 years) subjects. DNAm data were integrated with existing normal brain aging expression data and sets of psychiatric disease risk genes to test the hypothesis that age-associated DNAm changes contribute to age-associated gene expression changes and, by extension, susceptibility to psychiatric diseases. We found that age-associated differentially methylated regions (aDMRs) are common, robust, bidirectional, concentrated in CpG island shelves and sea, depleted in CpG islands, and enriched among genes undergoing age-associated expression changes (OR = 2.30, p = 1.69 × 10-27). We found the aDMRs are enriched among genetic association-based risk genes for schizophrenia, Alzheimer's disease (AD), and major depressive disorder (MDD) (OR = 2.51, p = 0.00015; OR = 2.38, p = 0.036; and OR = 3.08, p = 0.018, respectively) as well as expression-based MDD-associated genes (OR = 1.48, p = 0.00012). Similar patterns of enrichment were found for aDMRs that correlate with local gene expression. These results were replicated in a large publically-available dataset, and confirmed by meta-analysis of the two datasets. Our findings suggest DNAm is a molecular mechanism for age-associated gene expression changes and support a role for DNAm in age-by-disease interactions through preferential targeting of disease-associated genes.

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.