CloudATAC: a cloud-based framework for ATAC-Seq data analysis.

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) generates genome-wide chromatin accessibility profiles, providing valuable insights into epigenetic gene regulation at both pooled-cell and single-cell population levels. Comprehensive analysis of ATAC-seq data involves the use of various interdependent programs. Learning the correct sequence of steps needed to process the data can represent a major hurdle. Selecting appropriate parameters at each stage, including pre-analysis, core analysis, and advanced downstream analysis, is important to ensure accurate analysis and interpretation of ATAC-seq data. Additionally, obtaining and working within a limited computational environment presents a significant challenge to non-bioinformatic researchers. Therefore, we present Cloud ATAC, an open-source, cloud-based interactive framework with a scalable, flexible, and streamlined analysis framework based on the best practices approach for pooled-cell and single-cell ATAC-seq data. These frameworks use on-demand computational power and memory, scalability, and a secure and compliant environment provided by the Google Cloud. Additionally, we leverage Jupyter Notebook's interactive computing platform that combines live code, tutorials, narrative text, flashcards, quizzes, and custom visualizations to enhance learning and analysis. Further, leveraging GPU instances has significantly improved the run-time of the single-cell framework. The source codes and data are publicly available through NIH Cloud lab https://github.com/NIGMS/ATAC-Seq-and-Single-Cell-ATAC-Seq-Analysis. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

A Machine Learning Model for the Prediction of COVID-19 Severity Using RNA-Seq, Clinical, and Co-Morbidity Data.

The premise for this study emanated from the need to understand SARS-CoV-2 infections at the molecular level and to develop predictive tools for managing COVID-19 severity. With the varied clinical outcomes observed among infected individuals, creating a reliable machine learning (ML) model for predicting the severity of COVID-19 became paramount. Despite the availability of large-scale genomic and clinical data, previous studies have not effectively utilized multi-modality data for disease severity prediction using data-driven approaches. Our primary goal is to predict COVID-19 severity using a machine-learning model trained on a combination of patients' gene expression, clinical features, and co-morbidity data. Employing various ML algorithms, including Logistic Regression (LR), XGBoost (XG), Naïve Bayes (NB), and Support Vector Machine (SVM), alongside feature selection methods, we sought to identify the best-performing model for disease severity prediction. The results highlighted XG as the superior classifier, with 95% accuracy and a 0.99 AUC (Area Under the Curve), for distinguishing severity groups. Additionally, the SHAP analysis revealed vital features contributing to prediction, including several genes such as COX14, LAMB2, DOLK, SDCBP2, RHBDL1, and IER3-AS1. Notably, two clinical features, the absolute neutrophil count and Viremia Categories, emerged as top contributors. Integrating multiple data modalities has significantly improved the accuracy of disease severity prediction compared to using any single modality. The identified features could serve as biomarkers for COVID-19 prognosis and patient care, allowing clinicians to optimize treatment strategies and refine clinical decision-making processes for enhanced patient outcomes.

The single-cell opioid responses in the context of HIV (SCORCH) consortium.

Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.

Neurobehavioral Characterization of Perinatal Oxycodone-Exposed Offspring in Early Adolescence.

The opioid epidemic has received considerable attention, but the impact on perinatal opioid-exposed (POE) offspring remains underexplored. This study addresses the emerging public health challenge of understanding and treating POE children. We examined two scenarios using preclinical models: offspring exposed to oxycodone (OXY) in utero (IUO) and acute postnatal OXY (PNO). We hypothesized exposure to OXY during pregnancy primes offspring for neurodevelopmental deficits and severity of deficits is dependent on timing of exposure. Notable findings include reduced head size and brain weight in offspring. Molecular analyses revealed significantly lower levels of inflammasome-specific genes in the prefrontal cortex (PFC). Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA) highlighted the enrichment of genes associated with mitochondrial and synapse dysfunction in POE offspring. Western blot analysis validated IPA predictions of mitochondrial dysfunction in PFC-derived synaptosomes. Behavioral studies identified significant social deficits in POE offspring. This study presents the first comparative analysis of acute PNO- and IUO-offspring during early adolescence finding acute PNO-offspring have considerably greater deficits. The striking difference in deficit severity in acute PNO-offspring suggests that exposure to opioids in late pregnancy pose the greatest risk for offspring well-being.

iCluF: an unsupervised iterative cluster-fusion method for patient stratification using multiomics data.

Motivation: Patient stratification is crucial for the effective treatment or management of heterogeneous diseases, including cancers. Multiomic technologies facilitate molecular characterization of human diseases; however, the complexity of data warrants the need for the development of robust data integration tools for patient stratification using machine-learning approaches. Results: iCluF iteratively integrates three types of multiomic data (mRNA, miRNA, and DNA methylation) using pairwise patient similarity matrices built from each omic data. The intermediate omic-specific neighborhood matrices implement iterative matrix fusion and message passing among the similarity matrices to derive a final integrated matrix representing all the omics profiles of a patient, which is used to further cluster patients into subtypes. iCluF outperforms other methods with significant differences in the survival profiles of 8581 patients belonging to 30 different cancers in TCGA. iCluF also predicted the four intrinsic subtypes of Breast Invasive Carcinomas with adjusted rand index and Fowlkes-Mallows scores of 0.72 and 0.83, respectively. The Gini importance score showed that methylation features were the primary decisive players, followed by mRNA and miRNA to identify disease subtypes. iCluF can be applied to stratify patients with any disease containing multiomic datasets. Availability and implementation: Source code and datasets are available at https://github.com/GudaLab/iCluF_core.

Coordination among frequent genetic variants imparts substance use susceptibility and pathogenesis.

Determining the key genetic variants is a crucial step to comprehensively understand substance use disorders (SUDs). In this study, utilizing whole exome sequences of five multi-generational pedigrees with SUDs, we used an integrative omics-based approach to uncover candidate genetic variants that impart susceptibility to SUDs and influence addition traits. We identified several SNPs and rare, protein-function altering variants in genes, GRIA3, NCOR1, and SHANK1; compound heterozygous variants in LNPEP, LRP1, and TBX2, that play a significant role in the neurotransmitter-neuropeptide axis, specifically in the dopaminergic circuits. We also noted a greater frequency of heterozygous and recessive variants in genes involved in the structural and functional integrity of synapse receptors, CHRNA4, CNR2, GABBR1, DRD4, NPAS4, ADH1B, ADH1C, OPRM1, and GABBR2. Variant analysis in upstream promoter regions revealed regulatory variants in NEK9, PRRX1, PRPF4B, CELA2A, RABGEF1, and CRBN, crucial for dopamine regulation. Using family-and pedigree-based data, we identified heterozygous recessive alleles in LNPEP, LRP1 (4 frameshift deletions), and TBX2 (2 frameshift deletions) linked to SUDs. GWAS overlap identified several SNPs associated with SUD susceptibility, including rs324420 and rs1229984. Furthermore, miRNA variant analysis revealed notable variants in mir-548 U and mir-532. Pathway studies identified the presence of extensive coordination among these genetic variants to impart substance use susceptibility and pathogenesis. This study identified variants that were found to be overrepresented among genes of dopaminergic circuits participating in the neurotransmitter-neuropeptide axis, suggesting pleiotropic influences in the development and sustenance of chronic substance use. The presence of a diverse set of haploinsufficient variants in varying frequencies demonstrates the existence of extraordinary coordination among them in attributing risk and modulating severity to SUDs.

Effects of Acute and Chronic Gabapentin Treatment on Cardiovascular Function of Rats.

Gabapentin (GBP), a GABA analogue, is primarily used as an anticonvulsant for the treatment of partial seizures and neuropathic pain. Whereas a majority of the side effects are associated with the nervous system, emerging evidence suggests there is a high risk of heart diseases in patients taking GBP. In the present study, we first used a preclinical model of rats to investigate, firstly, the acute cardiovascular responses to GBP (bolus i.v. injection, 50 mg/kg) and secondly the effects of chronic GBP treatment (i.p. 100 mg/kg/day × 7 days) on cardiovascular function and the myocardial proteome. Under isoflurane anesthesia, rat blood pressure (BP), heart rate (HR), and left ventricular (LV) hemodynamics were measured using Millar pressure transducers. The LV myocardium and brain cortex were analyzed by proteomics, bioinformatics, and western blot to explore the molecular mechanisms underlying GBP-induced cardiac dysfunction. In the first experiment, we found that i.v. GBP significantly decreased BP, HR, maximal LV pressure, and maximal and minimal dP/dt, whereas it increased IRP-AdP/dt, Tau, systolic, diastolic, and cycle durations (* p < 0.05 and ** p < 0.01 vs. baseline; n = 4). In the second experiment, we found that chronic GBP treatment resulted in hypotension, bradycardia, and LV systolic dysfunction, with no change in plasma norepinephrine. In the myocardium, we identified 109 differentially expressed proteins involved in calcium pathways, cholesterol metabolism, and galactose metabolism. Notably, we found that calmodulin, a key protein of intracellular calcium signaling, was significantly upregulated by GBP in the heart but not in the brain. In summary, we found that acute and chronic GBP treatments suppressed cardiovascular function in rats, which is attributed to abnormal calcium signaling in cardiomyocytes. These data reveal a novel side effect of GBP independent of the nervous system, providing important translational evidence to suggest that GBP can evoke adverse cardiovascular events by depression of myocardial function.

Repeated Social Defeat Stress Induces an Inflammatory Gut Milieu by Altering the Mucosal Barrier Integrity and Gut Microbiota Homeostasis.

Background: Posttraumatic stress disorder (PTSD) is a mental health condition triggered by exposure to traumatic events in an individual's life. Patients with PTSD are also at a higher risk for comorbidities. However, it is not well understood how PTSD affects human health and/or promotes the risk for comorbidities. Nevertheless, patients with PTSD harbor a proinflammatory milieu and dysbiotic gut microbiota. Gut barrier integrity helps to maintain normal gut homeostasis and its dysregulation promotes gut dysbiosis and inflammation. Methods: We used a mouse model of repeated social defeat stress (RSDS), a preclinical model of PTSD. Behavioral studies, metagenomics analysis of the microbiome, gut permeability assay (on mouse colon, using an Ussing chamber), immunoblotting, and immunohistochemical analyses were performed. Polarized intestinal epithelial cells and 3-dimensional crypt cultures were used for mechanistic analysis. Results: The RSDS mice harbor a heightened proinflammatory gut environment and microbiota dysbiosis. The RSDS mice further showed significant dysregulation of gut barrier functions, including transepithelial electrical resistance, mucin homeostasis, and antimicrobial responses. RSDS mice also showed a specific increase in intestinal expression of claudin-2, a tight junction protein, and epinephrine, a stress-induced neurotransmitter. Treating intestinal epithelial cells or 3-dimensional cultured crypts with norepinephrine or intestinal luminal contents (fecal contents) upregulated claudin-2 expression and inhibited transepithelial electrical resistance. Conclusions: Traumatic stress induces dysregulation of gut barrier functions, which may underlie the observed gut microbiota changes and proinflammatory gut milieu, all of which may have an interdependent effect on the health and increased risk of comorbidities in patients with PTSD.

Oocyte quality is enhanced by hypoglycosylated FSH through increased cell-to-cell interaction during mouse follicle development.

Macroheterogeneity in follicle-stimulating hormone (FSH) ß-subunit N-glycosylation results in distinct FSH glycoforms. Hypoglycosylated FSH21 is the abundant and more bioactive form in pituitaries of females under 35 years of age, whereas fully glycosylated FSH24 is less bioactive and increases with age. To investigate whether the shift in FSH glycoform abundance contributes to the age-dependent decline in oocyte quality, the direct effects of FSH glycoforms on folliculogenesis and oocyte quality were determined using an encapsulated in vitro mouse follicle growth system. Long-term culture (10-12 days) with FSH21 (10 ng/ml) enhanced follicle growth, estradiol secretion and oocyte quality compared with FSH24 (10 ng/ml) treatment. FSH21 enhanced establishment of transzonal projections, gap junctions and cell-to-cell communication within 24 h in culture. Transient inhibition of FSH21-mediated bidirectional communication abrogated the positive effects of FSH21 on follicle growth, estradiol secretion and oocyte quality. Our data indicate that FSH21 promotes folliculogenesis and oocyte quality in vitro by increasing cell-to-cell communication early in folliculogenesis, and that the shift in in vivo abundance from FSH21 to FSH24 with reproductive aging may contribute to the age-dependent decline in oocyte quality.

Novel RNA-Seq Signatures Post-Methamphetamine and Oxycodone Use in a Model of HIV-Associated Neurocognitive Disorders.

In the 21st century, the effects of HIV-associated neurocognitive disorders (HAND) have been significantly reduced in individuals due to the development of antiretroviral therapies (ARTs). However, the growing epidemic of polysubstance use (PSU) has led to concern for the effects of PSU on HIV-seropositive individuals. To effectively treat individuals affected by HAND, it is critical to understand the biological mechanisms affected by PSU, including the identification of novel markers. To fill this important knowledge gap, we used an in vivo HIV-1 Transgenic (HIV-1 Tg) animal model to investigate the effects of the combined use of chronic methamphetamine (METH) and oxycodone (oxy). A RNA-Seq analysis on the striatum-a brain region that is primarily targeted by both HIV and drugs of abuse-identified key differentially expressed markers post-METH and oxy exposure. Furthermore, ClueGO analysis and Ingenuity Pathway Analysis (IPA) revealed crucial molecular and biological functions associated with ATP-activated adenosine receptors, neuropeptide hormone activity, and the oxytocin signaling pathway to be altered between the different treatment groups. The current study further reveals the harmful effects of chronic PSU and HIV infection that can subsequently impact neurological outcomes in polysubstance users with HAND.

Critical contributions of protein cargos to the functions of macrophage-derived extracellular vesicles.

BACKGROUND: Macrophages are highly plastic innate immune cells that play key roles in host defense, tissue repair, and homeostasis maintenance. In response to divergent stimuli, macrophages rapidly alter their functions and manifest a wide polarization spectrum with two extremes: M1 or classical activation and M2 or alternative activation. Extracellular vesicles (EVs) secreted from differentially activated macrophages have been shown to have diverse functions, which are primarily attributed to their microRNA cargos. The role of protein cargos in these EVs remains largely unexplored. Therefore, in this study, we focused on the protein cargos in macrophage-derived EVs. RESULTS: Naïve murine bone marrow-derived macrophages were treated with lipopolysaccharide or interlukin-4 to induce M1 or M2 macrophages, respectively. The proteins of EVs and their parental macrophages were subjected to quantitative proteomics analyses, followed by bioinformatic analyses. The enriched proteins of M1-EVs were involved in proinflammatory pathways and those of M2-EVs were associated with immunomodulation and tissue remodeling. The signature proteins of EVs shared a limited subset of the proteins of their respective progenitor macrophages, but they covered many of the typical pathways and functions of their parental cells, suggesting their respective M1-like and M2-like phenotypes and functions. Experimental examination validated that protein cargos in M1- or M2-EVs induced M1 or M2 polarization, respectively. More importantly, proteins in M1-EVs promoted viability, proliferation, and activation of T lymphocytes, whereas proteins in M2-EVs potently protected the tight junction structure and barrier integrity of epithelial cells from disruption. Intravenous administration of M2-EVs in colitis mice led to their accumulation in the colon, alleviation of colonic inflammation, promotion of M2 macrophage polarization, and improvement of gut barrier functions. Protein cargos in M2-EVs played a key role in their protective function in colitis. CONCLUSION: This study has yielded a comprehensive unbiased dataset of protein cargos in macrophage-derived EVs, provided a systemic view of their potential functions, and highlighted the important engagement of protein cargos in the pathophysiological functions of these EVs.

StrainIQ: A Novel n-Gram-Based Method for Taxonomic Profiling of Human Microbiota at the Strain Level.

The emergence of next-generation sequencing (NGS) technology has greatly influenced microbiome research and led to the development of novel bioinformatics tools to deeply analyze metagenomics datasets. Identifying strain-level variations in microbial communities is important to understanding the onset and progression of diseases, host-pathogen interrelationships, and drug resistance, in addition to designing new therapeutic regimens. In this study, we developed a novel tool called StrainIQ (strain identification and quantification) based on a new n-gram-based (series of n number of adjacent nucleotides in the DNA sequence) algorithm for predicting and quantifying strain-level taxa from whole-genome metagenomic sequencing data. We thoroughly evaluated our method using simulated and mock metagenomic datasets and compared its performance with existing methods. On average, it showed 85.8% sensitivity and 78.2% specificity on simulated datasets. It also showed higher specificity and sensitivity using n-gram models built from reduced reference genomes and on models with lower coverage sequencing data. It outperforms alternative approaches in genus- and strain-level prediction and strain abundance estimation. Overall, the results show that StrainIQ achieves high accuracy by implementing customized model-building and is an efficient tool for site-specific microbial community profiling.

Evaluation of Five Mammalian Models for Human Disease Research Using Genomic and Bioinformatic Approaches.

The suitability of an animal model for use in studying human diseases relies heavily on the similarities between the two species at the genetic, epigenetic, and metabolic levels. However, there is a lack of consistent data from different animal models at each level to evaluate this suitability. With the availability of genome sequences for many mammalian species, it is now possible to compare animal models based on genomic similarities. Herein, we compare the coding sequences (CDSs) of five mammalian models, including rhesus macaque, marmoset, pig, mouse, and rat models, with human coding sequences. We identified 10,316 conserved CDSs across the five organisms and the human genome based on sequence similarity. Mapping the human-disease-associated single-nucleotide polymorphisms (SNPs) from these conserved CDSs in each species has identified species-specific associations with various human diseases. While associations with a disease such as colon cancer were prevalent in multiple model species, the rhesus macaque showed the most model-specific human disease associations. Based on the percentage of disease-associated SNP-containing genes, marmoset models are well suited to study many human ailments, including behavioral and cardiovascular diseases. This study demonstrates a genomic similarity evaluation of five animal models against human CDSs that could help investigators select a suitable animal model for studying their target disease.

Microbiome diversity in African American, European American, and Egyptian colorectal cancer patients.

Purpose: Although there is an established role for microbiome dysbiosis in the pathobiology of colorectal cancer (CRC), CRC patients of various race/ethnicities demonstrate distinct clinical behaviors. Thus, we investigated microbiome dysbiosis in Egyptian, African American (AA), and European American (EA) CRC patients. Patients and methods: CRCs and their corresponding normal tissues from Egyptian (n = 17) patients of the Alexandria University Hospital, Egypt, and tissues from AA (n = 18) and EA (n = 19) patients at the University of Alabama at Birmingham were collected. DNA was isolated from frozen tissues, and the microbiome composition was analyzed by 16S rRNA sequencing. Differential microbial abundance, diversity, and metabolic pathways were identified using linear discriminant analysis (LDA) effect size analyses. Additionally, we compared these profiles with our previously published microbiome data derived from Kenyan CRC patients. Results: Differential microbiome analysis of CRCs across all racial/ethnic groups showed dysbiosis. There were high abundances of Herbaspirillum and Staphylococcus in CRCs of Egyptians, Leptotrichia in CRCs of AAs, Flexspiria and Streptococcus in CRCs of EAs, and Akkermansia muciniphila and Prevotella nigrescens in CRCs of Kenyans (LDA score >4, adj. p-value <0.05). Functional analyses showed distinct microbial metabolic pathways in CRCs compared to normal tissues within the racial/ethnic groups. Egyptian CRCs, compared to normal tissues, showed lower l-methionine biosynthesis and higher galactose degradation pathways. Conclusions: Our findings showed altered mucosa-associated microbiome profiles of CRCs and their metabolic pathways across racial/ethnic groups. These findings provide a basis for future studies to link racial/ethnic microbiome differences with distinct clinical behaviors in CRC.

The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers.

BACKGROUND: Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers. METHODS: We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers. RESULTS: We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort. CONCLUSIONS: This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies.

RNA-seq analysis identifies age-dependent changes in expression of mRNAs - encoding N-glycosylation pathway enzymes in mouse gonadotropes.

Follicle-stimulating hormone (FSH) is a glycoprotein that is assembled as a heterodimer of α/ß subunits in gonadotropes. Each subunit contains two N-glycan chains. Our previous in vivo genetic studies identified that at least one N-glycan chain must be present on the FSHß subunit for efficient FSH dimer assembly and secretion. Moreover, macroheterogeneity observed uniquely on human FSHß results in ratiometric changes in age-specific FSH glycoforms, particularly during menopausal transition. Despite the recognition of many prominent roles of sugars on FSH including dimer assembly and secretion, serum half-life, receptor binding and signal transduction, the N-glycosylation machinery in gonadotropes has never been defined. Here, we used a mouse model in which gonadotropes are GFP-labeled in vivo and achieved rapid purification of GFP+ gonadotropes from pituitaries of female mice at reproductively young, middle, and old ages. We identified by RNA-seq analysis 52 mRNAs encoding N-glycosylation pathway enzymes expressed in 3- and 8-10-month-old mouse gonadotropes. We hierarchically mapped and localized the enzymes to distinct subcellular organelles within the N-glycosylation biosynthetic pathway. Of the 52 mRNAs, we found 27 mRNAs are differentially expressed between the 3- and 8-10-month old mice. We subsequently selected 8 mRNAs which showed varying changes in expression for confirmation of abundance in vivo via qPCR analysis, using more expanded aging time points with distinct 8-month and 14-month age groups. Real time qPCR analysis indicated dynamic changes in expression of N-glycosylation pathway enzyme-encoding mRNAs across the life span. Notably, computational analysis predicted the promoters of genes encoding these 8 mRNAs contain multiple high probability binding sites for estrogen receptor-1 and progesterone receptor. Collectively, our studies define the N-glycome and identify age-specific dynamic changes in mRNAs encoding N-glycosylation pathway enzymes in mouse gonadotropes. Our studies suggest the age-related decline in ovarian steroids may regulate expression of N-glycosylation enzymes in mouse gonadotropes and explain the age-related N-glycosylation shift previously observed on human FSHß subunit in pituitaries of women.

Transcriptome profile changes in the jejunum of nonhuman primates exposed to supralethal dose of total- or partial-body radiation.

The risk of exposure of the general public or military personnel to high levels of ionizing radiation from nuclear weapons or radiological accidents is a dire national security matter. The development of advanced molecular biodosimetry methods, those that measure biological response, such as transcriptomics, to screen large populations of radiation-exposed victims is key to improving survival outcomes during radiological mass casualty scenarios. In this study, nonhuman primates were exposed to either 12.0 Gy cobalt-60 gamma (total-body irradiation, TBI) or X-ray (partial-body irradiation, PBI) 24 h after administration of a potential radiation medical countermeasure, gamma-tocotrienol (GT3). Changes in the jejunal transcriptomic profiles in GT3-treated and irradiated animals were compared to healthy controls to assess the extent of radiation damage. No major effect of GT3 on radiation-induced transcriptome at this radiation dose was identified. About 80% of the pathways with a known activation or repression state were commonly observed between both exposures. Several common pathways activated due to irradiation include FAK signaling, CREB signaling in the neurons, phagosome formation, and G-protein coupled signaling pathway. Sex-specific differences associated with excessive mortality among irradiated females were identified in this study, including Estrogen receptor signaling. Differential pathway activation was also identified across PBI and TBI, pointing towards altered molecular response for different degrees of bone marrow sparing and radiation doses. This study provides insight into radiation-induced changes in jejunal transcriptional profiles, supporting the investigation for the identification of biomarkers for radiation injury and countermeasure efficacy.

Molecular Subtyping and Survival Analysis of Osteosarcoma Reveals Prognostic Biomarkers and Key Canonical Pathways.

Osteosarcoma (OS) is a common bone malignancy in children and adolescents. Although histological subtyping followed by improved OS treatment regimens have helped achieve favorable outcomes, a lack of understanding of the molecular subtypes remains a challenge to characterize its genetic heterogeneity and subsequently to identify diagnostic and prognostic biomarkers for developing effective treatments. In the present study, global analysis of DNA methylation, and mRNA and miRNA gene expression in OS patient samples were correlated with their clinical characteristics. The mucin family of genes, MUC6, MUC12, and MUC4, were found to be highly mutated in the OS patients. Results revealed the enrichment of molecular pathways including Wnt signaling, Calcium signaling, and PI3K-Akt signaling in the OS tumors. Survival analyses showed that the expression levels of several genes such as RAMP1, CRIP1, CORT, CHST13, and DDX60L, miRNAs and lncRNAs were associated with survival of OS patients. Molecular subtyping using Cluster-Of-Clusters Analysis (COCA) for mRNA, lncRNA, and miRNA expression; DNA methylation; and mutation data from the TARGET dataset revealed two distinct molecular subtypes, each with a distinctive gene expression profile. Between the two subtypes, three upregulated genes, POP4, HEY1, CERKL, and seven downregulated genes, CEACAM1, ABLIM1, LTBP2, ISLR, LRRC32, PTPRF, and GPX3, associated with OS metastasis were found to be differentially regulated. Thus, the molecular subtyping results provide a strong basis for classification of OS patients that could be used to develop better prognostic treatment strategies.

Identification of YWHAH as a Novel Brain-Derived Extracellular Vesicle Marker Post Long-Term Midazolam Exposure during Early Development.

Recently, the long-term use of sedative agents in the neonatal intensive care unit (NICU) has raised concerns about neurodevelopmental outcomes in exposed neonates. Midazolam (MDZ), a common neonatal sedative in the NICU, has been suggested to increase learning disturbances and cognitive impairment in children. However, molecular mechanisms contributing to such outcomes with long-term MDZ use during the early stages of life remain unclear. In this study, we for the first time elucidate the role of brain-derived extracellular vesicles (BDEVs), including mining the BDEV proteome post long-term MDZ exposure during early development. Employing our previously established rodent model system that mimics the exposure of MDZ in the NICU using an increasing dosage regimen, we isolated BDEVs from postnatal 21-days-old control and MDZ groups using a differential sucrose density gradient. BDEVs from the control and MDZ groups were then characterized using a ZetaView nanoparticle tracking analyzer and transmission electron microscopy analysis. Next, using RT-qPCR, we examined the expression of key ESCRT-related genes involved in EV biogenesis. Lastly, using quantitative mass spectrometry-based proteomics, we mined the BDEV protein cargo that revealed key differentially expressed proteins and associated molecular pathways to be altered post long-term MDZ exposure. Our study characterized the proteome in BDEV cargo from long-term MDZ exposure at early development. Importantly, we identified and validated the expression of YWHAH as a potential target for further characterization of its downstream mechanism and a potential biomarker for the early onset of neurodevelopment and neurodegenerative diseases. Overall, the present study demonstrated long-term exposure to MDZ at early development stages could influence BDEV protein cargo, which potentially impact neural functions and behavior at later stages of development.