Transcriptomic analyses of black women in neighborhoods with high levels of violence.

Chronic stress threatens an individual's capacity to maintain psychological and physiological homeostasis, but the molecular processes underlying the biological embedding of these experiences are not well understood. This is particularly true for marginalized groups, presenting a fundamental challenge to decreasing racial, economic, and gender-based health disparities. Physical and social environments influence genome function, including the transcriptional activity of core stress responsive genes. We studied the relationship between social experiences that are associated with systemic inequality (e.g., racial segregation, poverty, and neighborhood violence) and blood cell (leukocytes) gene expression, focusing on the activation of transcription factors (TF) critical to stress response pathways. The study used data from 68 women collected from a convenience sample in 2013 from the Southside of Chicago. Comparing single, low-income Black mothers living in neighborhoods with high levels of violence (self-reported and assessed using administrative police records) to those with low levels of violence we found no significant differences in expression of 51 genes associated with the Conserved Transcriptional Response to Adversity (CTRA). Using TELiS analysis of promoter TF-binding motif prevalence we found that mothers who self-reported higher levels of neighborhood stress showed greater expression of genes regulated by the glucocorticoid receptor (GR). These findings may reflect increased cortisol output from the hypothalamic-pituitary-adrenal (HPA) axis, or increased GR transcriptional sensitivity. Transcript origin analyses identified monocytes and dendritic cells as the primary cellular sources of gene transcripts up-regulated in association with neighborhood stress. The prominence of GR-related transcripts and the absence of sympathetic nervous system-related CTRA transcripts suggest that a subjective perception of elevated chronic neighborhood stress may be associated with an HPA-related defeat-withdrawal phenotype rather than a fight-or-flight phenotype. The defeat-withdrawal phenotype has been previously observed in animal models of severe, overwhelming threat. These results demonstrate the importance of studying biological embedding in diverse environments and communities, specifically marginalized populations such as low-income Black women.

Involving Urban Single Low-Income African American Mothers in Genomic Research: Giving Voice to How Place Matters in Health Disparities and Prevention Strategies.

This article describes the process of using principles from community-based participatory action research to involve low-income, single, African American mothers on the south side of Chicago in genomic research, including as citizen scientists. The South Chicago Black Mothers' Resiliency Project used a mixed methods design to investigate how the stress of living in neighborhoods with high levels of violence affects mothers' mental and physical health. This article seeks to serve as a model for physicians and scholars interested in successfully involving low-income African American mothers in genomic research, and other health-related activities in ways that are culturally sensitive and transformative. The lives of Black mothers who struggle under interlocking systems of oppression that are often hidden from view of most Americans are at the center of this article. Therefore, we provide extensive information about the procedures used to collect the various types of data, the rationale for our procedures, the setting, the responses of mothers in our sample and methodological challenges. This study also has implications for the current COVID-19 pandemic and the need to train a corps of citizen scientists in health and wellness to avoid future extreme loss of life such as the 106,195 lives lost in the United States as of June 1, 2020.

Markov chain Monte Carlo simulation of a Bayesian mixture model for gene network inference.

BACKGROUND: Simultaneous measurement of gene expression level for thousands of genes contains the rich information about many different aspects of biological mechanisms. A major computational challenge is to find methods to extract new biological insights from this wealth of data. Complex biological processes are often regulated under the various conditions or circumstances and associated gene interactions are dynamically changed depending on different biological contexts. Thus, inference of such dynamic relationships between genes with consideration of biological conditions is very challenging. METHOD: In this study, we propose a comprehensive and integrated approach to infer the dynamic relationships between genes and evaluate this approach on three distinct gene networks. RESULTS: This study demonstrates the advantage of integrating Markov chain Monte Carlo (MCMC) simulation into a Bayesian mixture model to overcome the high-dimension, low sample size (HDLSS) problem as well as to identify context-specific biological modules. Such biological modules were identified through the summarization of sampled network structures obtained from MCMC simulation. CONCLUSION: This novel approach gives a comprehensive understanding of the dynamically regulated biological modules.

Contribution of semen trait selection, artificial insemination technique, and semen dose to the profitability of pig production systems: A simulation study.

The economic impact of selection for semen traits on pig production systems and potential interaction with artificial insemination (AI) technique and semen dose remains partially understood. The objectives of this study were to compare the financial indicators (gross return, net profit, cost) in a three-tier pig production system under one of two selection strategies: a traditional strategy including nine paternal and maternal traits (S9) and an advanced strategy that adds four semen traits (S13). Maternal traits included the number of pigs born alive, litter birth weight, adjusted 21-day litter weight, and the number of pigs at 21 days, and paternal traits included days to 113.5 kg, back fat, average daily gain, feed efficiency, and carcass lean percentage. The four semen traits included volume, concentration, progressive motility of spermatozoa, and abnormal spermatozoa. Simultaneously, the impact of two AI techniques and a range of fresh refrigerated semen doses including cervical AI with 3 × 10(9) (CAI3) and 2 × 10(9) (CAI2) sperm cells/dose, and intrauterine AI with 1.5 × 10(9) (IUI1.5), 0.75 × 10(9) (IUI0.75), and 0.5 × 10(9) (IUI0.5) sperm cells/dose were evaluated. These factors were also evaluated using a range of farrowing rates (60%-90%), litter sizes (8-14 live-born pigs), and a selected semen collection frequency. The financial impact of the factors was assessed through simulation of a three-way crossbreeding system (maternal nucleus lines A and B and paternal nucleus line C) using ZPLAN. The highest return on investment (profit/cost) of boars was observed at 2.33 collections/wk (three periods of 24 hours between collections). Under this schedule, a significant (P < 0.0001) interaction between the selection strategy and the AI technique-dose combination was identified for the gross return; meanwhile, significant (P < 0.0001) additive effects of the selection strategy and AI technique-dose combination were observed for the net profit. The highest gross return was obtained under S13 with IUI0.75 and IUI0.5. The net profit of S13 was 34.37% higher than the traditional S9 (P < 0.0001). The net profit favored IUI0.5 with relative differences of 4.13%, 2.41%, 1.72%, and 0.43% compared to CAI3, CAI2, IUI1.5, and IUI0.75, respectively. The advanced selection strategy proposed including four semen traits is recommended on the basis of the higher profitability relative to the traditional strategy.

Accurate assignment of significance to neuropeptide identifications using Monte Carlo k-permuted decoy databases.

In support of accurate neuropeptide identification in mass spectrometry experiments, novel Monte Carlo permutation testing was used to compute significance values. Testing was based on k-permuted decoy databases, where k denotes the number of permutations. These databases were integrated with a range of peptide identification indicators from three popular open-source database search software (OMSSA, Crux, and X! Tandem) to assess the statistical significance of neuropeptide spectra matches. Significance p-values were computed as the fraction of the sequences in the database with match indicator value better than or equal to the true target spectra. When applied to a test-bed of all known manually annotated mouse neuropeptides, permutation tests with k-permuted decoy databases identified up to 100% of the neuropeptides at p-value < 10(-5). The permutation test p-values using hyperscore (X! Tandem), E-value (OMSSA) and Sp score (Crux) match indicators outperformed all other match indicators. The robust performance to detect peptides of the intuitive indicator "number of matched ions between the experimental and theoretical spectra" highlights the importance of considering this indicator when the p-value was borderline significant. Our findings suggest permutation decoy databases of size 1×105 are adequate to accurately detect neuropeptides and this can be exploited to increase the speed of the search. The straightforward Monte Carlo permutation testing (comparable to a zero order Markov model) can be easily combined with existing peptide identification software to enable accurate and effective neuropeptide detection. The source code is available at http://stagbeetle.animal.uiuc.edu/pepshop/MSMSpermutationtesting.

A genomewide assessment of inbreeding depression: gene number, function, and mode of action.

Although the genetic basis of inbreeding depression is still being debated, most fitness effects are thought to be the result of increased homozygosity for recessive or partially recessive deleterious alleles rather than the loss of overdominant genes. It is unknown how many loci are associated with inbreeding depression, the genes or gene pathways involved, or their mode of action. To uncover genes associated with variation in fitness following inbreeding, we generated a set of inbred lines of Drosophila melanogaster for which only the third chromosome varied among lines and measured male competitive reproductive success among these lines to estimate inbreeding depression. Male competitive reproductive success for different lines validated our prediction that equally inbred lines show variation in inbreeding depression. To begin to assess the molecular basis of inbreeding depression for male competitive reproductive success, we detected variation in whole-genome gene expression across these inbred lines with commercially available high-density oligonucleotide microarrays. A total of 567 genes were differentially expressed among these inbred lines, indicating that inbreeding directly or indirectly affects a large number of genes: genes that are disproportionately involved in metabolism, stress and defense responses. Subsequently, we generated a set of outbred lines by crossing the highest inbreeding depression lines to each other and contrasted gene expression between parental inbred lines and F(1) hybrids with transcript abundance as a quantitative phenotype to determine the mode of action of the genes associated with inbreeding depression. Although our results indicated that approximately 75% of all genes involved in inbreeding depression were additive, partially additive, or dominant, about 25% of all genes expressed patterns of overdominance. These results should be viewed with caution given that they may be confounded by issues of statistical inference or associative overdominance.