P-value evaluation, variability index and biomarker categorization for adaptively weighted Fisher's meta-analysis method in omics applications

Sex differences in plasma proteomic markers in late-life depression

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

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.

Transcriptomic meta-analysis reveals ERRα-mediated oxidative phosphorylation is downregulated in Fuchs' endothelial corneal dystrophy

BACKGROUND

Late-onset Fuchs' endothelial corneal dystrophy (FECD) is a degenerative disease of cornea and the leading indication for corneal transplantation. Genetically, FECD patients can be categorized as with (RE+) or without (RE-) the CTG trinucleotide repeat expansion in the transcription factor 4 gene. The molecular mechanisms underlying FECD remain unclear, though there are plausible pathogenic models proposed for RE+ FECD.

METHOD

In this study, we performed a meta-analysis on RNA sequencing datasets of FECD corneal endothelium including 3 RE+ datasets and 2 RE- datasets, aiming to compare the transcriptomic profiles of RE+ and RE- FECD. Gene differential expression analysis, co-expression networks analysis, and pathway analysis were conducted.

RESULTS

There was a striking similarity between RE+ and RE- transcriptomes. There were 1,184 genes significantly upregulated and 1,018 genes significantly downregulated in both RE+ and RE- cases. Pathway analysis identified multiple biological processes significantly enriched in both-mitochondrial functions, energy-related processes, ER-nucleus signaling pathway, demethylation, and RNA splicing were negatively enriched, whereas small GTPase mediated signaling, actin-filament processes, extracellular matrix organization, stem cell differentiation, and neutrophil mediated immunity were positively enriched. The translational initiation process was downregulated in the RE+ transcriptomes. Gene co-expression analysis identified modules with relatively distinct biological processes enriched including downregulation of mitochondrial respiratory chain complex assembly. The majority of oxidative phosphorylation (OXPHOS) subunit genes, as well as their upstream regulator gene estrogen-related receptor alpha (ESRRA), encoding ERRα, were downregulated in both RE+ and RE- cases, and the expression level of ESRRA was correlated with that of OXPHOS subunit genes.

CONCLUSION

Meta-analysis increased the power of detecting differentially expressed genes. Integrating differential expression analysis with co-expression analysis helped understand the underlying molecular mechanisms. FECD RE+ and RE- transcriptomic profiles are much alike with the hallmark of downregulation of genes in pathways related to ERRα-mediated OXPHOS.

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

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

Metabolome panels as potential noninvasive biomarkers for primary glomerulonephritis sub-types: meta-analysis of profiling metabolomics studies

Primary glomerulonephritis diseases (PGDs) are known as the top causes of chronic kidney disease worldwide. Renal biopsy, an invasive method, is the main approach to diagnose PGDs. Studying the metabolome profiles of kidney diseases is an inclusive approach to identify the disease's underlying pathways and discover novel non-invasive biomarkers. So far, different experiments have explored the metabolome profiles in different PGDs, but the inconsistencies might hinder their clinical translations. The main goal of this meta-analysis study was to achieve consensus panels of dysregulated metabolites in PGD sub-types. The PGDs-related metabolome profiles from urine samples in humans were selected in a comprehensive search. Amanida package in R software was utilized for performing the meta-analysis. Through sub-type analyses, the consensus list of metabolites in each category was obtained. To identify the most affected pathways, functional enrichment analysis was performed. Also, a gene-metabolite network was constructed to identify the key metabolites and their connected proteins. After a vigorous search, among the 11 selected studies (15 metabolite profiles), 270 dysregulated metabolites were recognized in urine of 1154 PGDs and control samples. Through sub-type analyses by Amanida package, the consensus list of metabolites in each category was obtained. Top dysregulated metabolites (vote score of ≥ 4 or ≤ - 4) in PGDs urines were selected as main panel of meta-metabolites including glucose, leucine, choline, betaine, dimethylamine, fumaric acid, citric acid, 3-hydroxyisovaleric acid, pyruvic acid, isobutyric acid, and hippuric acid. The enrichment analyses results revealed the involvement of different biological pathways such as the TCA cycle and amino acid metabolisms in the pathogenesis of PGDs. The constructed metabolite-gene interaction network revealed the high centralities of several metabolites, including pyruvic acid, leucine, and choline. The identified metabolite panels could shed a light on the underlying pathological pathways and be considered as non-invasive biomarkers for the diagnosis of PGD sub-types.

Deciphering the causal relationship between blood pressure and regional white matter integrity: A two-sample Mendelian randomization study

Elevated arterial blood pressure (BP) is a common risk factor for cerebrovascular and cardiovascular diseases, but no causal relationship has been established between BP and cerebral white matter (WM) integrity. In this study, we performed a two-sample Mendelian randomization (MR) analysis with individual-level data by defining two nonoverlapping sets of European ancestry individuals (genetics-exposure set: N = 203,111; mean age = 56.71 years, genetics-outcome set: N = 16,156; mean age = 54.61 years) from UK Biobank to evaluate the causal effects of BP on regional WM integrity, measured by fractional anisotropy of diffusion tensor imaging. Two BP traits: systolic and diastolic blood pressure were used as exposures. Genetic variant was carefully selected as instrumental variable (IV) under the MR analysis assumptions. We existing large-scale genome-wide association study summary data for validation. The main method used was a generalized version of inverse-variance weight method while other MR methods were also applied for consistent findings. Two additional MR analyses were performed to exclude the possibility of reverse causality. We found significantly negative causal effects (FDR-adjusted p < .05; every 10 mmHg increase in BP leads to a decrease in FA value by .4% ~ 2%) of BP traits on a union set of 17 WM tracts, including brain regions related to cognitive function and memory. Our study extended the previous findings of association to causation for regional WM integrity, providing insights into the pathological processes of elevated BP that might chronically alter the brain microstructure in different regions.

LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications

OMIC is a novel approach that analyses entire genetic or molecular profiles in humans and other organisms. It involves identifying and quantifying biological molecules that contribute to a species' structure, function, and dynamics. Finding the secrets of OMIC is like deciphering the biochemical code, but building data-driven models to mine the hidden phenotypic trait information has been a research hotspot. Transcriptome analysis is a popular biological technology for characterizing living systems' overall health, including cells and tissues. Individual transcript expression levels are known to be correlated with those of other transcripts. Nevertheless, most computational studies do not fully exploit these inter-feature correlations. Differential expression analyses, for example, assume that the expression levels of the transcripts are independent. Thus, we propose extracting these inter-feature correlations using the convolutional neural network (CNN) and transforming the transcriptomic features into a new space of convolutional transcriptomic (LaCOme) features. On most transcriptomic datasets in use, a series of comprehensive experiments have demonstrated that engineered LaCOme features outperform the original transcriptomic features in classification performances. Based on experimental results, OMIC data from biological samples could be further enriched using CNN to enhance computational analysis results. Also, feature rough screening can be used to extract valuable information from OMIC, regardless of the algorithm used to select features. It may always be better to create a novel feature than to keep the original. Furthermore, we investigated the feasibility of the feature construction method through cross-validation and independent verification, hoping to develop a more efficient and effective method.

Ranking Antibody Binding Epitopes and Proteins Across Samples from Whole Proteome Tiled Linear Peptides

Ultradense peptide binding arrays that can probe millions of linear peptides comprising the entire proteomes or immunomes of human or mouse, or numerous microbes, are powerful tools for studying the abundance of different antibody repertoire in serum samples to understand adaptive immune responses. There are few statistical analysis tools for exploring high-dimensional, significant and reproducible antibody targets for ultradense peptide binding arrays at the linear peptide, epitope (grouping of adjacent peptides), and protein level across multiple samples/subjects (I.e. epitope spread or immunogenic regions within each protein) for understanding the heterogeneity of immune responses. We developed HERON (Hierarchical antibody binding Epitopes and pROteins from liNear peptides), an R package, which allows users to identify immunogenic epitopes using meta-analyses and spatial clustering techniques to explore antibody targets at various resolution and confidence levels, that can be found consistently across a specified number of samples through the entire proteome to study antibody responses for diagnostics or treatment. Our approach estimates significance values at the linear peptide (probe), epitope, and protein level to identify top candidates for validation. We test the performance of predictions on all three levels using correlation between technical replicates and comparison of epitope calls on 2 datasets, which shows HERON's competitiveness in estimating false discovery rates and finding general and sample-level regions of interest for antibody binding. The code is available as an R package downloadable from http://github.com/Ong-Research/HERON.

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.

Integrative systems immunology uncovers molecular networks of the cell cycle that stratify COVID-19 severity

Several perturbations in the number of peripheral blood leukocytes, such as neutrophilia and lymphopenia associated with Coronavirus disease 2019 (COVID-19) severity, point to systemic molecular cell cycle alterations during severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. However, the landscape of cell cycle alterations in COVID-19 remains primarily unexplored. Here, we performed an integrative systems immunology analysis of publicly available proteome and transcriptome data to characterize global changes in the cell cycle signature of COVID-19 patients. We found significantly enriched cell cycle-associated gene co-expression modules and an interconnected network of cell cycle-associated differentially expressed proteins (DEPs) and genes (DEGs) by integrating the molecular data of 1469 individuals (981 SARS-CoV-2 infected patients and 488 controls [either healthy controls or individuals with other respiratory illnesses]). Among these DEPs and DEGs are several cyclins, cell division cycles, cyclin-dependent kinases, and mini-chromosome maintenance proteins. COVID-19 patients partially shared the expression pattern of some cell cycle-associated genes with other respiratory illnesses but exhibited some specific differential features. Notably, the cell cycle signature predominated in the patients' blood leukocytes (B, T, and natural killer cells) and was associated with COVID-19 severity and disease trajectories. These results provide a unique global understanding of distinct alterations in cell cycle-associated molecules in COVID-19 patients, suggesting new putative pathways for therapeutic intervention.

Pathogenomic Signature and Aberrant Neurogenic Events in Experimental Cerebral Ischemic Stroke: A Neurotranscriptomic-Based Implication for Dementia

BACKGROUND

Cerebral ischemic stroke is caused due to neurovascular damage or thrombosis, leading to neuronal dysfunction, neuroinflammation, neurodegeneration, and regenerative failure responsible for neurological deficits and dementia. The valid therapeutic targets against cerebral stroke remain obscure. Thus, insight into neuropathomechanisms resulting from the aberrant expression of genes appears to be crucial.

OBJECTIVE

In this study, we have elucidated how neurogenesis-related genes are altered in experimental stroke brains from the available transcriptome profiles in correlation with transcriptome profiles of human postmortem stroke brain tissues.

METHODS

The transcriptome datasets available on the middle cerebral artery occlusion (MCAo) rat brains were obtained from the Gene Expression Omnibus, National Center for Biotechnology Information. Of the available datasets, 97 samples were subjected to the meta-analysis using the network analyst tool followed by Cytoscape-based enrichment mapping analysis. The key differentially expressed genes (DEGs) were validated and compared with transcriptome profiling of human stroke brains.

RESULTS

Results revealed 939 genes are differently expressed in the brains of the MCAo rat model of stroke, in which 30 genes are key markers of neural stem cells, and regulators of neurogenic processes. Its convergence with DEGs from human stroke brains has revealed common targets.

CONCLUSION

This study has established a panel of highly important DEGs to signify the potential therapeutic targets for neuroregenerative strategy against pathogenic events associated with cerebral stroke. The outcome of the findings can be translated to mitigate neuroregeneration failure seen in various neurological and metabolic disease manifestations with neurocognitive impairments.

Molecular characterization of cell types in the squid Loligo vulgaris

Cephalopods are set apart from other mollusks by their advanced behavioral abilities and the complexity of their nervous systems. Because of the great evolutionary distance that separates vertebrates from cephalopods, it is evident that higher cognitive features have evolved separately in these clades despite the similarities that they share. Alongside their complex behavioral abilities, cephalopods have evolved specialized cells and tissues, such as the chromatophores for camouflage or suckers to grasp prey. Despite significant progress in genome and transcriptome sequencing, the molecular identities of cell types in cephalopods remain largely unknown. We here combine single-cell transcriptomics with in situ gene expression analysis to uncover cell type diversity in the European squid Loligo vulgaris. We describe cell types that are conserved with other phyla such as neurons, muscles, or connective tissues but also cephalopod-specific cells, such as chromatophores or sucker cells. Moreover, we investigate major components of the squid nervous system including progenitor and developing cells, differentiated cells of the brain and optic lobes, as well as sensory systems of the head. Our study provides a molecular assessment for conserved and novel cell types in cephalopods and a framework for mapping the nervous system of L. vulgaris.

Fused inverse-normal method for integrated differential expression analysis of RNA-seq data

Background

Use of next-generation sequencing technologies to transcriptomics (RNA-seq) for gene expression profiling has found widespread application in studying different biological conditions including cancers. However, RNA-seq experiments are still small sample size experiments due to the cost. Recently, an increased focus has been on meta-analysis methods for integrated differential expression analysis for exploration of potential biomarkers. In this study, we propose a p-value combination method for meta-analysis of multiple independent but related RNA-seq studies that accounts for sample size of a study and direction of expression of genes in individual studies.

Results

The proposed method generalizes the inverse-normal method without an increase in statistical or computational complexity and does not pre- or post-hoc filter genes that have conflicting direction of expression in different studies. Thus, the proposed method, as compared to the inverse-normal, has better potential for the discovery of differentially expressed genes (DEGs) with potentially conflicting differential signals from multiple studies related to disease. We demonstrated the use of the proposed method in detection of biologically relevant DEGs in glioblastoma (GBM), the most aggressive brain cancer. Our approach notably enabled the identification of over-expressed tumour suppressor gene RAD51 in GBM compared to healthy controls, which has recently been shown to be a target for inhibition to enhance radiosensitivity of GBM cells during treatment. Pathway analysis identified multiple aberrant GBM related pathways as well as novel regulators such as TCF7L2 and MAPT as important upstream regulators in GBM.

Conclusions

The proposed meta-analysis method generalizes the existing inverse-normal method by providing a way to establish differential expression status for genes with conflicting direction of expression in individual RNA-seq studies. Hence, leading to further exploration of them as potential biomarkers for the disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04859-9.

Amanida: an R package for meta-analysis of metabolomics non-integral data

SUMMARY

The combination, analysis and evaluation of different studies which try to answer or solve the same scientific question, also known as a meta-analysis, plays a crucial role in answering relevant clinical relevant questions. Unfortunately, metabolomics studies rarely disclose all the statistical information needed to perform a meta-analysis. Here, we present a meta-analysis approach using only the most reported statistical parameters in this field: P-value and fold-change. The P-values are combined via Fisher's method and fold-changes by averaging, both weighted by the study size (n). The amanida package includes several visualization options: a volcano plot for quantitative results, a vote plot for total regulation behaviours (up/down regulations) for each compound, and a explore plot of the vote-counting results with the number of times a compound is found upregulated or downregulated. In this way, it is very easy to detect discrepancies between studies at a first glance.

AVAILABILITY AND IMPLEMENTATION

Amanida code and documentation are at CRAN and https://github.com/mariallr/amanida.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Reviewing and assessing existing meta-analysis models and tools

Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.

Biomarker Categorization in Transcriptomic Meta-Analysis by Concordant Patterns With Application to Pan-Cancer Studies

With the increasing availability and dropping cost of high-throughput technology in recent years, many-omics datasets have accumulated in the public domain. Combining multiple transcriptomic studies on related hypothesis via meta-analysis can improve statistical power and reproducibility over single studies. For differential expression (DE) analysis, biomarker categorization by DE pattern across studies is a natural but critical task following biomarker detection to help explain between study heterogeneity and classify biomarkers into categories with potentially related functionality. In this paper, we propose a novel meta-analysis method to categorize biomarkers by simultaneously considering the concordant pattern and the biological and statistical significance across studies. Biomarkers with the same DE pattern can be analyzed together in downstream pathway enrichment analysis. In the presence of different types of transcripts (e.g., mRNA, miRNA, and lncRNA, etc.), integrative analysis including miRNA/lncRNA target enrichment analysis and miRNA-mRNA and lncRNA-mRNA causal regulatory network analysis can be conducted jointly on all the transcripts of the same category. We applied our method to two Pan-cancer transcriptomic study examples with single or multiple types of transcripts available. Targeted downstream analysis identified categories of biomarkers with unique functionality and regulatory relationships that motivate new hypothesis in Pan-cancer analysis.

Circadian-Dependent and Sex-Dependent Increases in Intravenous Cocaine Self-Administration in Npas2 Mutant Mice

Substance use disorder (SUD) is associated with disruptions in circadian rhythms. The circadian transcription factor neuronal PAS domain protein 2 (NPAS2) is enriched in reward-related brain regions and regulates reward, but its role in SU is unclear. To examine the role of NPAS2 in drug taking, we measured intravenous cocaine self-administration (acquisition, dose-response, progressive ratio, extinction, cue-induced reinstatement) in wild-type (WT) and Npas2 mutant mice at different times of day. In the light (inactive) phase, cocaine self-administration, reinforcement, motivation and extinction responding were increased in all Npas2 mutants. Sex differences emerged during the dark (active) phase with Npas2 mutation increasing self-administration, extinction responding, and reinstatement only in females as well as reinforcement and motivation in males and females. To determine whether circulating hormones are driving these sex differences, we ovariectomized WT and Npas2 mutant females and confirmed that unlike sham controls, ovariectomized mutant mice showed no increase in self-administration. To identify whether striatal brain regions are activated in Npas2 mutant females, we measured cocaine-induced ΔFosB expression. Relative to WT, ΔFosB expression was increased in D1+ neurons in the nucleus accumbens (NAc) core and dorsolateral (DLS) striatum in Npas2 mutant females after dark phase self-administration. We also identified potential target genes that may underlie the behavioral responses to cocaine in Npas2 mutant females. These results suggest NPAS2 regulates reward and activity in specific striatal regions in a sex and time of day (TOD)-specific manner. Striatal activation could be augmented by circulating sex hormones, leading to an increased effect of Npas2 mutation in females.SIGNIFICANCE STATEMENT Circadian disruptions are a common symptom of substance use disorders (SUDs) and chronic exposure to drugs of abuse alters circadian rhythms, which may contribute to subsequent SU. Diurnal rhythms are commonly found in behavioral responses to drugs of abuse with drug sensitivity and motivation peaking during the dark (active) phase in nocturnal rodents. Emerging evidence links disrupted circadian genes to SU vulnerability and drug-induced alterations to these genes may augment drug-seeking. The circadian transcription factor neuronal PAS domain protein 2 (NPAS2) is enriched in reward-related brain regions and regulates reward, but its role in SU is unclear. To examine the role of NPAS2 in drug taking, we measured intravenous cocaine self-administration in wild-type (WT) and Npas2 mutant mice at different times of day.

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.

Integrated Analysis of Small RNA, Transcriptome and Degradome Sequencing Provides New Insights into Floral Development and Abscission in Yellow Lupine (Lupinus luteus L.)

The floral development in an important legume crop yellow lupine (Lupinus luteus L., Taper cv.) is often affected by the abscission of flowers leading to significant economic losses. Small non-coding RNAs (sncRNAs), which have a proven effect on almost all developmental processes in other plants, might be of key players in a complex net of molecular interactions regulating flower development and abscission. This study represents the first comprehensive sncRNA identification and analysis of small RNA, transcriptome and degradome sequencing data in lupine flowers to elucidate their role in the regulation of lupine generative development. As shedding in lupine primarily concerns flowers formed at the upper part of the inflorescence, we analyzed samples from extreme parts of raceme separately and conducted an additional analysis of pedicels from abscising and non-abscising flowers where abscission zone forms. A total of 394 known and 28 novel miRNAs and 316 phased siRNAs were identified. In flowers at different stages of development 59 miRNAs displayed differential expression (DE) and 46 DE miRNAs were found while comparing the upper and lower flowers. Identified tasiR-ARFs were DE in developing flowers and were strongly expressed in flower pedicels. The DEmiR-targeted genes were preferentially enriched in the functional categories related to carbohydrate metabolism and plant hormone transduction pathways. This study not only contributes to the current understanding of how lupine flowers develop or undergo abscission but also holds potential for research aimed at crop improvement.