Modulation of neural gene networks by estradiol in old rhesus macaque females.

The postmenopausal decrease in circulating estradiol (E2) levels has been shown to contribute to several adverse physiological and psychiatric effects. To elucidate the molecular effects of E2 on the brain, we examined differential gene expression and DNA methylation (DNAm) patterns in the nonhuman primate brain following ovariectomy (Ov) and subsequent subcutaneous bioidentical E2 chronic treatment. We identified several dysregulated molecular networks, including MAPK signaling and dopaminergic synapse response, that are associated with ovariectomy and shared across two different brain areas, the occipital cortex (OC) and prefrontal cortex (PFC). The finding that hypomethylation (p = 1.6 × 10-51) and upregulation (p = 3.8 × 10-3) of UBE2M across both brain regions provide strong evidence for molecular differences in the brain induced by E2 depletion. Additionally, differential expression (p = 1.9 × 10-4; interaction p = 3.5 × 10-2) of LTBR in the PFC provides further support for the role E2 plays in the brain, by demonstrating that the regulation of some genes that are altered by ovariectomy may also be modulated by Ov followed by hormone replacement therapy (HRT). These results present real opportunities to understand the specific biological mechanisms that are altered with depleted E2. Given E2's potential role in cognitive decline and neuroinflammation, our findings could lead to the discovery of novel therapeutics to slow cognitive decline. Together, this work represents a major step toward understanding molecular changes in the brain that are caused by ovariectomy and how E2 treatment may revert or protect against the negative neuro-related consequences caused by a depletion in estrogen as women approach menopause.

The effectiveness of psychiatric genetic counseling training: An analysis of 13 international workshops.

Studies have consistently shown that psychiatric genetic counseling (pGC) helps people with psychiatric conditions by increasing empowerment and self-efficacy, and addressing emotions like guilt. Yet, it is not routinely provided. Genetic counselors and trainees express low confidence in their ability to provide meaningful pGC, especially in the absence of adequate training. Therefore, to address this gap a "Psychiatric Genetic Counseling for Genetic Counselors" (PG4GC) workshop was developed and delivered to 13 groups of participants (primarily qualified genetic counselors and trainees) between 2015 and 2023 (10 workshops were delivered in-person, and three virtually). Participants completed quantitative questionnaires both before and after completing the workshop to assess their comfort, knowledge, behavior, and feeling of being equipped to provide pGC. In total, 232 individuals completed the pre-workshop questionnaire and 154 completed the post-workshop questionnaire. Participants felt more comfortable, knowledgeable, and equipped to provide pGC, and reported being more likely to address psychiatric concerns after the workshop, regardless of whether they were trainees or practicing professionals and whether they completed the workshop in-person or virtually. This study suggests that the PG4GC workshop is an effective educational tool in pGC training that may aid in broader implementation of the service.

Cardiac Molecular Analysis Reveals Aging-Associated Metabolic Alterations Promoting Glycosaminoglycans Accumulation Via Hexosamine Biosynthetic Pathway.

Age is a prominent risk factor for cardiometabolic disease, and often leads to heart structural and functional changes. However, precise molecular mechanisms underlying cardiac remodeling and dysfunction resulting from physiological aging per se remain elusive. Understanding these mechanisms requires biological models with optimal translation to humans. Previous research demonstrated that baboons undergo age-related reduction in ejection fraction and increased heart sphericity, mirroring changes observed in humans. The goal of this study was to identify early cardiac molecular alterations that precede functional adaptations, shedding light on the regulation of age-associated changes. We performed unbiased transcriptomics of left ventricle (LV) samples from female baboons aged 7.5-22.1 years (human equivalent ~30-88 years). Weighted-gene correlation network and pathway enrichment analyses were performed to identify potential age-associated mechanisms in LV, with histological validation. Myocardial modules of transcripts negatively associated with age were primarily enriched for cardiac metabolism, including oxidative phosphorylation, tricarboxylic acid cycle, glycolysis, and fatty-acid ß-oxidation. Transcripts positively correlated with age suggest upregulation of glucose uptake, pentose phosphate pathway, and hexosamine biosynthetic pathway (HBP), indicating a metabolic shift towards glucose-dependent anabolic pathways. Upregulation of HBP commonly results in increased glycosaminoglycan precursor synthesis. Transcripts involved in glycosaminoglycan synthesis, modification, and intermediate metabolism were also upregulated in older animals, while glycosaminoglycan degradation transcripts were downregulated with age. These alterations would promote glycosaminoglycan accumulation, which was verified histologically. Upregulation of extracellular matrix (ECM)-induced signaling pathways temporally coincided with glycosaminoglycan accumulation. We found a subsequent upregulation of cardiac hypertrophy-related pathways and an increase in cardiomyocyte width. Overall, our findings revealed a transcriptional shift in metabolism from catabolic to anabolic pathways that leads to ECM glycosaminoglycan accumulation through HBP prior to upregulation of transcripts of cardiac hypertrophy-related pathways. This study illuminates cellular mechanisms that precede development of cardiac hypertrophy, providing novel potential targets to remediate age-related cardiac diseases.

Integrated multi-omics analysis of brain aging in female nonhuman primates reveals altered signaling pathways relevant to age-related disorders.

The prefrontal cortex (PFC) has been implicated as a key brain region responsible for age-related cognitive decline. Little is known about aging-related molecular changes in PFC that may mediate these effects. To date, no studies have used untargeted discovery methods with integrated analyses to determine PFC molecular changes in healthy female primates. We quantified PFC changes associated with healthy aging in female baboons by integrating multiple omics data types (transcriptomics, proteomics, metabolomics) from samples across the adult age span. Our integrated omics approach using unbiased weighted gene co-expression network analysis to integrate data and treat age as a continuous variable, revealed highly interconnected known and novel pathways associated with PFC aging. We found Gamma-aminobutyric acid (GABA) tissue content associated with these signaling pathways, providing 1 potential biomarker to assess PFC changes with age. These highly coordinated pathway changes during aging may represent early steps for aging-related decline in PFC functions, such as learning and memory, and provide potential biomarkers to assess cognitive status in humans.

Multi-omics Analysis of Aging Liver Reveals Changes in Endoplasmic Stress and Degradation Pathways in Female Nonhuman Primates.

The liver is critical for functions that support metabolism, immunity, digestion, detoxification, and vitamin storage. Aging is associated with severity and poor prognosis of various liver diseases such as nonalcoholic fatty liver disease (NAFLD). Previous studies have used multi-omic approaches to study liver diseases or to examine the effects of aging on the liver. However, to date, no studies have used an integrated omics approach to investigate aging-associated molecular changes in the livers of healthy female nonhuman primates. The goal of this study was to identify molecular changes associated with healthy aging in the livers of female baboons ( Papio sp., n=35) by integrating multiple omics data types (transcriptomics, proteomics, metabolomics) from samples across the adult age span. To integrate omics data, we performed unbiased weighted gene co-expression network analysis (WGCNA), and the results revealed 3 modules containing 3,149 genes and 33 proteins were positively correlated with age, and 2 modules containing 37 genes and 216 proteins were negatively correlated with age. Pathway enrichment analysis showed that unfolded protein response (UPR) and endoplasmic reticulum (ER) stress were positively associated with age, whereas xenobiotic metabolism and melatonin and serotonin degradation pathways were negatively associated with age. The findings of our study suggest that UPR and a reduction in reactive oxygen species generated from serotonin degradation could protect the liver from oxidative stress during the aging process in healthy female baboons.

Moderate maternal nutrient reduction in pregnancy alters fatty acid oxidation and RNA splicing in the nonhuman primate fetal liver.

Fetal liver tissue collected from a nonhuman primate (NHP) baboon model of maternal nutrient reduction (MNR) at four gestational time points (90, 120, 140, and 165 days gestation [dG], term in the baboon is ∼185 dG) was used to quantify MNR effects on the fetal liver transcriptome. 28 transcripts demonstrated different expression patterns between MNR and control livers during the second half of gestation, a developmental period when the fetus undergoes rapid weight gain and fat accumulation. Differentially expressed transcripts were enriched for fatty acid oxidation and RNA splicing-related pathways. Increased RNA splicing activity in MNR was reflected in greater abundances of transcript splice variant isoforms in the MNR group. It can be hypothesized that the increase in splice variants is deployed in an effort to adapt to the poor in utero environment and ensure near-normal development and energy metabolism. This study is the first to study developmental programming across four critical gestational stages during primate fetal liver development and reveals a potentially novel cellular response mechanism mediating fetal programming in response to MNR.

An Isobaric Labeling Approach to Enhance Detection and Quantification of Tissue-Derived Plasma Proteins as Potential Early Disease Biomarkers.

The proteomic analysis of plasma holds great promise to advance precision medicine and identify biomarkers of disease. However, it is likely that many potential biomarkers circulating in plasma originate from other tissues and are only present in low abundances in the plasma. Accurate detection and quantification of low abundance proteins by standard mass spectrometry approaches remain challenging. In addition, it is difficult to link low abundance plasma proteins back to their specific tissues or organs of origin with confidence. To address these challenges, we developed a mass spectrometry approach based on the use of tandem mass tags (TMT) and a tissue reference sample. By applying this approach to nonhuman primate plasma samples, we were able to identify and quantify 820 proteins by using a kidney tissue homogenate as reference. On average, 643 ± 16 proteins were identified per plasma sample. About 58% of proteins identified in replicate experiments were identified both times. A ratio of 50 µg kidney protein to 10 µg plasma protein, and the use of the TMT label with the highest molecular weight (131) for the kidney reference yielded the largest number of proteins in the analysis, and identified low abundance proteins in plasma that are prominently found in the kidney. Overall, this methodology promises efficient quantification of plasma proteins potentially released from specific tissues, thereby increasing the number of putative disease biomarkers for future study.

Modulation of neural gene networks by estradiol in old rhesus macaque females.

The postmenopausal decrease in circulating estradiol (E2) levels has been shown to contribute to several adverse physiological and psychiatric effects. To elucidate the molecular effects of E2 on the brain, we examined differential gene expression and DNA methylation (DNAm) patterns in the nonhuman primate brain following ovariectomy (Ov) and subsequent E2 treatment. We identified several dysregulated molecular networks, including MAPK signaling and dopaminergic synapse response, that are associated with ovariectomy and shared across two different brain areas, the occipital cortex (OC) and prefrontal cortex (PFC). The finding that hypomethylation (p=1.6×10-51) and upregulation (p=3.8×10-3) of UBE2M across both brain regions, provide strong evidence for molecular differences in the brain induced by E2 depletion. Additionally, differential expression (p=1.9×10-4; interaction p=3.5×10-2) of LTBR in the PFC, provides further support for the role E2 plays in the brain, by demonstrating that the regulation of some genes that are altered by ovariectomy may also be modulated by Ov followed by hormone replacement therapy (HRT). These results present real opportunities to understand the specific biological mechanisms that are altered with depleted E2. Given E2's potential role in cognitive decline and neuroinflammation, our findings could lead to the discovery of novel therapeutics to slow cognitive decline. Together, this work represents a major step towards understanding molecular changes in the brain that are caused by ovariectomy and how E2 treatment may revert or protect against the negative neuro-related consequences caused by a depletion in estrogen as women approach menopause.

Maternal obesity alters offspring liver and skeletal muscle metabolism in early post-puberty despite maintaining a normal post-weaning dietary lifestyle.

Maternal obesity (MO) during pregnancy is linked to increased and premature risk of age-related metabolic diseases in the offspring. However, the underlying molecular mechanisms still remain not fully understood. Using a well-established nonhuman primate model of MO, we analyzed tissue biopsies and plasma samples obtained from post-pubertal offspring (3-6.5 y) of MO mothers (n = 19) and from control animals born to mothers fed a standard diet (CON, n = 13). All offspring ate a healthy chow diet after weaning. Using untargeted gas chromatography-mass spectrometry metabolomics analysis, we quantified a total of 351 liver, 316 skeletal muscle, and 423 plasma metabolites. We identified 58 metabolites significantly altered in the liver and 46 in the skeletal muscle of MO offspring, with 8 metabolites shared between both tissues. Several metabolites were changed in opposite directions in males and females in both liver and skeletal muscle. Several tissue-specific and 4 shared metabolic pathways were identified from these dysregulated metabolites. Interestingly, none of the tissue-specific metabolic changes were reflected in plasma. Overall, our study describes characteristic metabolic perturbations in the liver and skeletal muscle in MO offspring, indicating that metabolic programming in utero persists postnatally, and revealing potential novel mechanisms that may contribute to age-related metabolic diseases later in life.

Assessment of label-free quantification and missing value imputation for proteomics in non-human primates.

BACKGROUND: Reliable and effective label-free quantification (LFQ) analyses are dependent not only on the method of data acquisition in the mass spectrometer, but also on the downstream data processing, including software tools, query database, data normalization and imputation. In non-human primates (NHP), LFQ is challenging because the query databases for NHP are limited since the genomes of these species are not comprehensively annotated. This invariably results in limited discovery of proteins and associated Post Translational Modifications (PTMs) and a higher fraction of missing data points. While identification of fewer proteins and PTMs due to database limitations can negatively impact uncovering important and meaningful biological information, missing data also limits downstream analyses (e.g., multivariate analyses), decreases statistical power, biases statistical inference, and makes biological interpretation of the data more challenging. In this study we attempted to address both issues: first, we used the MetaMorphues proteomics search engine to counter the limits of NHP query databases and maximize the discovery of proteins and associated PTMs, and second, we evaluated different imputation methods for accurate data inference. We used a generic approach for missing data imputation analysis without distinguising the potential source of missing data (either non-assigned m/z or missing values across runs). RESULTS: Using the MetaMorpheus proteomics search engine we obtained quantitative data for 1622 proteins and 10,634 peptides including 58 different PTMs (biological, metal and artifacts) across a diverse age range of NHP brain frontal cortex. However, among the 1622 proteins identified, only 293 proteins were quantified across all samples with no missing values, emphasizing the importance of implementing an accurate and statiscaly valid imputation method to fill in missing data. In our imputation analysis we demonstrate that Single Imputation methods that borrow information from correlated proteins such as Generalized Ridge Regression (GRR), Random Forest (RF), local least squares (LLS), and a Bayesian Principal Component Analysis methods (BPCA), are able to estimate missing protein abundance values with great accuracy. CONCLUSIONS: Overall, this study offers a detailed comparative analysis of LFQ data generated in NHP and proposes strategies for improved LFQ in NHP proteomics data.

Optimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabolomics Data.

Gas chromatography-coupled mass spectrometry (GC-MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC-MS instrument. By introducing missing values into the complete (i.e., data without any missing values) National Institute of Standards and Technology (NIST) plasma dataset, we demonstrate that random forest (RF), glmnet ridge regression (GRR), and Bayesian principal component analysis (BPCA) shared the lowest root mean squared error (RMSE) in technical replicate data. Further examination of these three methods in data from baboon plasma and liver samples demonstrated they all maintained high accuracy. Overall, our analysis suggests that any of the three imputation methods can be applied effectively to untargeted metabolomics datasets with high accuracy. However, it is important to note that imputation will alter the correlation structure of the dataset and bias downstream regression coefficients and p-values.

Antifibrotic factor KLF4 is repressed by the miR-10/TFAP2A/TBX5 axis in dermal fibroblasts: insights from twins discordant for systemic sclerosis.

OBJECTIVES: Systemic sclerosis (SSc) is a complex disease of unknown aetiology in which inflammation and fibrosis lead to multiple organ damage. There is currently no effective therapy that can halt the progression of fibrosis or reverse it, thus studies that provide novel insights into disease pathogenesis and identify novel potential therapeutic targets are critically needed. METHODS: We used global gene expression and genome-wide DNA methylation analyses of dermal fibroblasts (dFBs) from a unique cohort of twins discordant for SSc to identify molecular features of this pathology. We validated the findings using in vitro, ex vivo and in vivo models. RESULTS: Our results revealed distinct differentially expressed and methylated genes, including several transcription factors involved in stem cell differentiation and developmental programmes (KLF4, TBX5, TFAP2A and homeobox genes) and the microRNAs miR-10a and miR-10b which target several of these deregulated genes. We show that KLF4 expression is reduced in SSc dFBs and its expression is repressed by TBX5 and TFAP2A. We also show that KLF4 is antifibrotic, and its conditional knockout in fibroblasts promotes a fibrotic phenotype. CONCLUSIONS: Our data support a role for epigenetic dysregulation in mediating SSc susceptibility in dFBs, illustrating the intricate interplay between CpG methylation, miRNAs and transcription factors in SSc pathogenesis, and highlighting the potential for future use of epigenetic modifiers as therapies.

Single-cell expression quantitative trait loci (eQTL) analysis of SLE-risk loci in lupus patient monocytes.

BACKGROUND: We performed expression quantitative trait locus (eQTL) analysis in single classical (CL) and non-classical (NCL) monocytes from patients with systemic lupus erythematosus (SLE) to quantify the impact of well-established genetic risk alleles on transcription at single-cell resolution. METHODS: Single-cell gene expression was quantified using qPCR in purified monocyte subpopulations (CD14++CD16- CL and CD14dimCD16+ NCL) from SLE patients. Novel analysis methods were used to control for the within-person correlations observed, and eQTLs were compared between cell types and risk alleles. RESULTS: The SLE-risk alleles demonstrated significantly more eQTLs in NCLs as compared to CLs (p = 0.0004). There were 18 eQTLs exclusive to NCL cells, 5 eQTLs exclusive to CL cells, and only one shared eQTL, supporting large differences in the impact of the risk alleles between these monocyte subsets. The SPP1 and TNFAIP3 loci were associated with the greatest number of transcripts. Patterns of shared influence in which different SNPs impacted the same transcript also differed between monocyte subsets, with greater evidence for synergy in NCL cells. IRF1 expression demonstrated an on/off pattern, in which expression was zero in all of the monocytes studied from some individuals, and this pattern was associated with a number of SLE risk alleles. We observed corroborating evidence of this IRF1 expression pattern in public data sets. CONCLUSIONS: We document multiple SLE-risk allele eQTLs in single monocytes which differ greatly between CL and NCL subsets. These data support the importance of the SPP1 and TNFAIP3 risk variants and the IRF1 transcript in SLE patient monocyte function.

Nucleic Acid-Sensing and Interferon-Inducible Pathways Show Differential Methylation in MZ Twins Discordant for Lupus and Overexpression in Independent Lupus Samples: Implications for Pathogenic Mechanism and Drug Targeting.

Systemic lupus erythematosus (SLE) is a chronic, multisystem, autoimmune inflammatory disease with genomic and non-genomic contributions to risk. We hypothesize that epigenetic factors are a significant contributor to SLE risk and may be informative for identifying pathogenic mechanisms and therapeutic targets. To test this hypothesis while controlling for genetic background, we performed an epigenome-wide analysis of DNA methylation in genomic DNA from whole blood in three pairs of female monozygotic (MZ) twins of European ancestry, discordant for SLE. Results were replicated on the same array in four cell types from a set of four Danish female MZ twin pairs discordant for SLE. Genes implicated by the epigenetic analyses were then evaluated in 10 independent SLE gene expression datasets from the Gene Expression Omnibus (GEO). There were 59 differentially methylated loci between unaffected and affected MZ twins in whole blood, including 11 novel loci. All but two of these loci were hypomethylated in the SLE twins relative to the unaffected twins. The genes harboring these hypomethylated loci exhibited increased expression in multiple independent datasets of SLE patients. This pattern was largely consistent regardless of disease activity, cell type, or renal tissue type. The genes proximal to CpGs exhibiting differential methylation (DM) in the SLE-discordant MZ twins and exhibiting differential expression (DE) in independent SLE GEO cohorts (DM-DE genes) clustered into two pathways: the nucleic acid-sensing pathway and the type I interferon pathway. The DM-DE genes were also informatically queried for potential gene-drug interactions, yielding a list of 41 drugs including a known SLE therapy. The DM-DE genes delineate two important biologic pathways that are not only reflective of the heterogeneity of SLE but may also correlate with distinct IFN responses that depend on the source, type, and location of nucleic acid molecules and the activated receptors in individual patients. Cell- and tissue-specific analyses will be critical to the understanding of genetic factors dysregulating the nucleic acid-sensing and IFN pathways and whether these factors could be appropriate targets for therapeutic intervention.

Bulk and Single-Cell Profiling of Breast Tumors Identifies TREM-1 as a Dominant Immune Suppressive Marker Associated With Poor Outcomes.

BACKGROUND: Triggering receptor expressed on myeloid cells (TREM)-1 is a key mediator of innate immunity previously associated with the severity of inflammatory disorders, and more recently, the inferior survival of lung and liver cancer patients. Here, we investigated the prognostic impact and immunological correlates of TREM1 expression in breast tumors. METHODS: Breast tumor microarray and RNAseq expression profiles (n=4,364 tumors) were analyzed for associations between gene expression, tumor immune subtypes, distant metastasis-free survival (DMFS) and clinical response to neoadjuvant chemotherapy (NAC). Single-cell (sc)RNAseq was performed using the 10X Genomics platform. Statistical associations were assessed by logistic regression, Cox regression, Kaplan-Meier analysis, Spearman correlation, Student's t-test and Chi-square test. RESULTS: In pre-treatment biopsies, TREM1 and known TREM-1 inducible cytokines (IL1B, IL8) were discovered by a statistical ranking procedure as top genes for which high expression was associated with reduced response to NAC, but only in the context of immunologically "hot" tumors otherwise associated with a high NAC response rate. In surgical specimens, TREM1 expression varied among tumor molecular subtypes, with highest expression in the more aggressive subtypes (Basal-like, HER2-E). High TREM1 significantly and reproducibly associated with inferior distant metastasis-free survival (DMFS), independent of conventional prognostic markers. Notably, the association between high TREM1 and inferior DMFS was most prominent in the subset of immunogenic tumors that exhibited the immunologically hot phenotype and otherwise associated with superior DMFS. Further observations from bulk and single-cell RNAseq analyses indicated that TREM1 expression was significantly enriched in polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) and M2-like macrophages, and correlated with downstream transcriptional targets of TREM-1 (IL8, IL-1B, IL6, MCP-1, SPP1, IL1RN, INHBA) which have been previously associated with pro-tumorigenic and immunosuppressive functions. CONCLUSIONS: Together, these findings indicate that increased TREM1 expression is prognostic of inferior breast cancer outcomes and may contribute to myeloid-mediated breast cancer progression and immune suppression.

Hierarchicell: an R-package for estimating power for tests of differential expression with single-cell data.

BACKGROUND: Study design is a critical aspect of any experiment, and sample size calculations for statistical power that are consistent with that study design are central to robust and reproducible results. However, the existing power calculators for tests of differential expression in single-cell RNA-seq data focus on the total number of cells and not the number of independent experimental units, the true unit of interest for power. Thus, current methods grossly overestimate the power. RESULTS: Hierarchicell is the first single-cell power calculator to explicitly simulate and account for the hierarchical correlation structure (i.e., within sample correlation) that exists in single-cell RNA-seq data. Hierarchicell, an R-package available on GitHub, estimates the within sample correlation structure from real data to simulate hierarchical single-cell RNA-seq data and estimate power for tests of differential expression. This multi-stage approach models gene dropout rates, intra-individual dispersion, inter-individual variation, variable or fixed number of cells per individual, and the correlation among cells within an individual. Without modeling the within sample correlation structure and without properly accounting for the correlation in downstream analysis, we demonstrate that estimates of power are falsely inflated. Hierarchicell can be used to estimate power for binary and continuous phenotypes based on user-specified number of independent experimental units (e.g., individuals) and cells within the experimental unit. CONCLUSIONS: Hierarchicell is a user-friendly R-package that provides accurate estimates of power for testing hypotheses of differential expression in single-cell RNA-seq data. This R-package represents an important addition to single-cell RNA analytic tools and will help researchers design experiments with appropriate and accurate power, increasing discovery and improving robustness and reproducibility.

Genetic landscape of Gullah African Americans.

OBJECTIVES: Gullah African Americans are descendants of formerly enslaved Africans living in the Sea Islands along the coast of the southeastern U.S., from North Carolina to Florida. Their relatively high numbers and geographic isolation were conducive to the development and preservation of a unique culture that retains deep African features. Although historical evidence supports a West-Central African ancestry for the Gullah, linguistic and cultural evidence of a connection to Sierra Leone has led to the suggestion of this country/region as their ancestral home. This study sought to elucidate the genetic structure and ancestry of the Gullah. MATERIALS AND METHODS: We leveraged whole-genome genotype data from Gullah, African Americans from Jackson, Mississippi, African populations from Sierra Leone, and population reference panels from Africa and Europe to infer population structure, ancestry proportions, and global estimates of admixture. RESULTS: Relative to non-Gullah African Americans from the Southeast US, the Gullah exhibited higher mean African ancestry, lower European admixture, a similarly small Native American contribution, and increased male-biased European admixture. A slightly tighter bottleneck in the Gullah 13 generations ago suggests a largely shared demographic history with non-Gullah African Americans. Despite a slightly higher relatedness to populations from Sierra Leone, our data demonstrate that the Gullah are genetically related to many West African populations. DISCUSSION: This study confirms that subtle differences in African American population structure exist at finer regional levels. Such observations can help to inform medical genetics research in African Americans, and guide the interpretation of genetic data used by African Americans seeking to explore ancestral identities.

miR-181a Mediates Inflammatory Gene Expression After Intracerebral Hemorrhage: An Integrated Analysis of miRNA-seq and mRNA-seq in a Swine ICH Model.

Intracerebral hemorrhage (ICH) is a severe neurological disorder with no proven treatment. Inflammation after ICH contributes to clinical outcomes, but the relevant molecular mechanisms remain poorly understood. In studies of peripheral leukocyte counts and mRNA-sequencing (mRNA-seq), our group previously reported that monocytes and Interleukin-8 (IL-8) were important contributors to post-ICH inflammation. microRNA (miRNA) are powerful regulators of gene expression and promising therapeutic targets. We now report findings from an integrated analysis of miRNA-seq and mRNA-seq in peripheral blood mononuclear cells (PBMCs) from a swine ICH model. In 10 pigs, one PBMC sample was collected immediately prior to ICH induction and a second 6 h later; miRNA-seq and mRNA-seq were completed for each sample. An aggregate score calculation determined which miRNA regulated the differentially expressed mRNA. Networks of molecular interactions were generated for the combined miRNA/target mRNA. A total of 227 miRNA were identified, and 46 were differentially expressed after ICH (FDR < 0.05). The anti-inflammatory miR-181a was decreased post-ICH, and it was the most highly connected miRNA in the miRNA/mRNA bioinformatic network analysis. miR-181a has interconnected pathophysiology with IL-8 and monocytes; in prior studies, we found that IL-8 and monocytes contributed to post-ICH inflammation and ICH clinical outcome, respectively. miR-181a was a significant mediator of post-ICH inflammation and is promising for further study, including as a potential therapeutic target. This investigation also demonstrated feasible methodology for miRNA-seq/mRNA-seq analysis in swine that is innovative, and with unique challenges, compared with transcriptomics research in more established species.

A practical solution to pseudoreplication bias in single-cell studies.

Cells from the same individual share common genetic and environmental backgrounds and are not statistically independent; therefore, they are subsamples or pseudoreplicates. Thus, single-cell data have a hierarchical structure that many current single-cell methods do not address, leading to biased inference, highly inflated type 1 error rates, and reduced robustness and reproducibility. This includes methods that use a batch effect correction for individual as a means of accounting for within-sample correlation. Here, we document this dependence across a range of cell types and show that pseudo-bulk aggregation methods are conservative and underpowered relative to mixed models. To compute differential expression within a specific cell type across treatment groups, we propose applying generalized linear mixed models with a random effect for individual, to properly account for both zero inflation and the correlation structure among measures from cells within an individual. Finally, we provide power estimates across a range of experimental conditions to assist researchers in designing appropriately powered studies.