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Background: PM2.5 is a complex mixture, with water-soluble inorganic ions (WSII), mainly NH4+, SO42−, and NO3−, constituting major components. Early-life PM2.5 exposure has been shown to induce adverse health consequence but it is difficult to determine whether such an effect occurs prenatally (preconception, gestational) or postnatally in human studies. Methods: Four groups of C57BL/6 J mice were assigned to four exposure conditions: PM2.5 NO3−, PM2.5 SO42−, PM2.5 NH4+ and clean air, and exposure started at 4 weeks old. At 8 weeks old, mice bred within group. The exposure continued during gestation. After delivery, both the maternal and F1 mice (offspring) were kept in clean air without exposure to PM2.5. Respiratory function and pulmonary pathology were assessed in offspring mice at 8 weeks of age. In parallel, placenta tissue was collected for transcriptome profiling and mechanistic investigation. Results: F1 mice in PM2.5 NH4+, SO42- and NO3− groups had 32.2 % (p=6.0e-10), 30.3 % (p=3.8e-10) and 16.9 % (p=5.7e-8) lower peak expiratory flow (PEF) than the clean air group. Importantly, the exposure-induced lung function decline was greater in male than female offspring. Moreover, exposure to PM2.5 WSII before conception and during gestation was linked to increased airway wall thickness and elevated pulmonary neutrophil and macrophage counts in the offspring mice. At the molecular level, the exposure significantly disrupted gene expression in the placenta, affecting crucial functional pathways related to sex hormone response and inflammation. Conclusions: PM2.5 WSII exposure during preconception and gestational period alone without post-natal exposure substantially impacted offspring’s respiratory function as measured at adolescent age. Our results support the paradigm of fetal origin of environmentally associated chronic lung disease and highlight sex differences in susceptibility to air pollution exposure.

Abstract Alcohol consumption during pregnancy can result in a range of adverse postnatal outcomes among exposed children. However, identifying at-risk children is challenging given the difficulty to confirm prenatal alcohol exposure and the lack of early diagnostic tools. Placental surveys present an important opportunity to uncover early biomarkers to identify those at risk. Here, we report the first transcriptome-wide evaluation to comprehensively evaluate human placental pathways altered by fetal alcohol exposure. In a prospective longitudinal birth cohort in Cape Town, South Africa, we performed bulk tissue RNAseq in placenta samples from 32 women reporting heavy drinking during pregnancy and 30 abstainers/light drinkers. Weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis were performed to assess associations between fetal alcohol exposure and placental gene expression patterns at a network-wide and single gene level, respectively. The results revealed altered expression in genes related to erythropoiesis and angiogenesis, which are implicated in established postnatal phenotypes related to alcohol exposure, including disruptions in iron homeostasis, growth, and neurodevelopment. The reported findings provide insights into the molecular pathways affected by prenatal alcohol exposure and highlight the potential of placental biomarkers for detecting and understanding the effects of alcohol on fetal development.

Tackling the intricacies of network dismantling in complex systems poses significant challenges. This task has relevance across various practical domains, yet traditional approaches focus primarily on singular metrics, such as the number of nodes in the Giant Connected Component (GCC) or the average pairwise connectivity. In contrast, we propose a unique metric that concurrently targets nodes with the highest degree and reduces the GCC size. Given the NP-hard nature of optimizing this metric, we introduce MaxShot, an innovative end-to-end solution that leverages graph representation learning and reinforcement learning. Through comprehensive evaluations on both synthetic and real-world datasets, our method consistently outperforms leading benchmarks in accuracy and efficiency. These results highlight MaxShot’s potential as a superior approach to effectively addressing the network dismantling problem.

Abstract Despite national and international efforts for the prevention of metabolic syndrome and its underlying diseases/disorders, its prevalence is still rising, especially in the middle-aged population. In this study, we explore the effect of high fat diet on the development of metabolic syndrome in middle-aged mice and to evaluate the potential benefits of voluntary physical exercise on the periphery as well as brain cognitive function, and to explore the potential mechanisms. We found that metabolic syndrome developed at middle age significantly impairs cognitive function and the impairment is associated with gene dysregulation in metabolic pathways that are largely affecting astrocytes in the brain. Eight-week voluntary wheel running at a frequency of three times a week, not only improves peripheral glucose control but also significantly improves learning and memory. The improvement of cognitive function is associated with restoration of gene expression involved in energy metabolism in the brain. Our study suggests that voluntary physical exercise is beneficial for metabolic syndrome-induced peripheral as well as cognitive dysfunction and can be recommended as therapeutic intervention for metabolic syndrome and associated diseases.

Background: Electronic cigarette (e-cig) use is increasing worldwide, especially among young individuals. Spirometry measures airflow obstruction and is the primary tool for diagnosing/monitoring respiratory diseases in clinical settings. This study aims to assess the effects of chronic e-cig exposure on spirometric traits, and directly compare to conventional combustible-cigarette (c-cig). Methods: We employed an e- and c-cig aerosol generation system that resembled human smoking/vaping scenario. Fifty 6-week old C57BL/6 mice were equally divided into five groups and exposed to clean air (control), e-cig aerosol (low- and high-dose), and c-cig aerosol (low- and high-dose), respectively, for 10 weeks. Afterwards, growth trajectory, spirometry and pulmonary pathology were analyzed. Results: Both e- and c-cig exposure slowed down growth and weight gain. Low dose e-cig exposure (1 h exposure per day) resulted in minimal respiratory function damage. At high dose (2 h exposure per day), e-cig exposure deteriorated 7 spirometry traits but by a smaller magnitude than c-cig exposure. For example, comparing to clean air controls, high dose e- and c-cig exposure increased inspiratory resistance by 24.3% (p = 0.026) and 66.7% (p = 2.6e-5), respectively. Low-dose e-cig exposure increased alveolar macrophage count but did not lead to airway remodeling. In contrast, even low-dose c-cig caused alveoli break down and thickening of the small airway, hallmarks of airway obstructive disease. Conclusions: We conducted well-controlled animal exposure experiments assessing chronic e-cig exposure’s effects on spirometry traits. Further, mechanistic study characterized airway remodeling, alveolar tissue lesion and inflammation induced by e- and c-cig exposure. Our findings provided scientific and public health insights on e-cig’s health consequences, especially in adolescent users.

This study integrates Alzheimer’s disease (AD) GWAS data with myeloid cell genomics, and reports that myeloid active enhancers are most burdened by AD risk alleles. The authors also nominate candidate causal regulatory elements, variants and genes that likely modulate the risk for AD.

GWAS identified variants associated with birth weight (BW), childhood obesity (CO) and childhood BMI (CBMI), and placenta is a critical organ for fetal development and postnatal health. We examined the role of placental transcriptome and eQTLs in mediating the genetic causes for BW, CO and CBMI, and applied integrative analysis (Colocalization and MetaXcan). GWAS loci associated with BW, CO, and CBMI were substantially enriched for placenta eQTLs (6.76, 4.83 and 2.26 folds, respectively). Importantly, compared to eQTLs of adult tissues, only placental eQTLs contribute significantly to both anthropometry outcomes at birth (BW) and childhood phenotypes (CO/CBMI). Eight, six and one transcripts colocalized with BW, CO and CBMI risk loci, respectively. Our study reveals that placental transcription in utero likely plays a key role in determining postnatal body size, and as such may hold new possibilities for therapeutic interventions to prevent childhood obesity.

An amendment to this paper has been published and can be accessed via the original article.

Background: Increasing number of eQTL (Expression Quantitative Trait Loci) datasets facilitate genetics and systems biology research. Meta-analysis tools are in need to jointly analyze datasets of same or similar issue types to improve statistical power especially in trans-eQTL mapping. Meta-analysis framework is also necessary for ChrX eQTL discovery. Results: We developed a novel tool, meta-eqtl, for fast eQTL meta-analysis of arbitrary sample size and arbitrary number of datasets. Further, this tool accommodates versatile modeling, eg. non-parametric model and mixed effect models. In addition, meta-eqtl readily handles calculation of chrX eQTLs. Conclusions: We demonstrated and validated meta-eqtl as fast and comprehensive tool to meta-analyze multiple datasets and ChrX eQTL discovery. Meta-eqtl is a set of command line utilities written in R, with some computationally intensive parts written in C. The software runs on Linux platforms and is designed to intelligently adapt to high performance computing (HPC) cluster. We applied the novel tool to liver and adipose tissue data, and revealed eSNPs underlying diabetes GWAS loci. [ABSTRACT FROM AUTHOR]

Apolipoprotein L1 (APOL1) risk alleles in donor kidneys associate with graft loss, but whether recipient risk allele expression affects transplant outcomes is unclear. To test whether recipient APOL1 risk alleles independently correlate with transplant outcomes, we analyzed genome-wide SNP genotyping data on donors and recipients from 2 kidney transplant cohorts: Genomics of Chronic Allograft Rejection (GOCAR) and Clinical Trials in Organ Transplantation 01/17 (CTOT-01/17). We estimated genetic ancestry (quantified as the proportion of African ancestry, or pAFR) by ADMIXTURE and correlated APOL1 genotypes and pAFR with outcomes. In the GOCAR discovery set, we noted that the number of recipient APOL1 G1/G2 alleles (R-nAPOL1) associated with an increased risk of death-censored allograft loss (DCAL), independent of ancestry (HR = 2.14; P = 0.006), as well as within the subgroup of African American and Hispanic (AA/H) recipients (HR = 2.36; P = 0.003). R-nAPOL1 also associated with an increased risk of any T cell–mediated rejection (TCMR) event. These associations were validated in CTOT-01/17. Ex vivo studies of PMBCs revealed, unexpectedly, high expression levels of APOL1 in activated CD4+ /CD8+ T cells and NK cells. We detected enriched immune response gene pathways in risk allele carriers compared with noncarriers on the kidney transplant waitlist and among healthy controls. Our findings demonstrate an immunomodulatory role for recipient APOL1 risk alleles associated with TCMR and DCAL. We believe this finding has broader implications for immune-mediated injury to native kidneys. [ABSTRACT FROM AUTHOR]