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The naïve neonatal gut is sensitive to early life experiences. Events during this critical developmental window may have life-long impacts on the gut microbiota. Two experiences that have been associated with variation in the gut microbiome in infancy are mode of delivery and feeding practices (eg, breastfeeding). It remains unclear whether these early experiences are responsible for microbial differences beyond toddlerhood. Our study examined whether mode of delivery and infant feeding practices are associated with differences in the child and adolescent microbiome. We used an adoption-sibling design to compare genetically related siblings who were reared together or apart. Gut microbiome samples were collected from 73 children (M = 11 years, SD = 3 years, range = 3-18 years). Parents reported on child breastfeeding history, age, sex, height, and weight. Mode of delivery was collected through medical records and phone interviews. Negative binomial mixed effects models were used to identify whether mode of delivery and feeding practices were related to differences in phylum and genus-level abundance of bacteria found in the gut of child participants. Covariates included age, sex, and body mass index. Genetic relatedness and rearing environment were accounted for as random effects. We observed a significant association between lack of breastfeeding during infancy and a greater number of the genus Bacteroides in stool in childhood and adolescence. The absence of breastfeeding may impart lasting effects on the gut microbiome well into childhood.

What do \"microbes\" have to do with social equity? These microorganisms are integral to our health, that of our natural environment, and even the \"health\" of the environments we build. The loss, gain, and retention of microorganisms-their flow between humans and the environment-can greatly impact our health. It is well-known that inequalities in access to perinatal care, healthy foods, quality housing, and the natural environment can create and arise from social inequality. Here, we focus on the argument that access to beneficial microorganisms is a facet of public health, and health inequality may be compounded by inequitable microbial exposure.

Our results demonstrate that the early life home environment can significantly alter the gut microbiome in childhood, potentially altering health outcomes or risk for adverse health outcomes. A better understanding of the drivers of gut microbiome variation during childhood could lead to more effective intervention strategies for overall health starting in early life. The composition of the human gut microbiome is highly variable, and this variation has been repeatedly tied to variation in human health. However, the sources of microbial variation remain unclear, especially early in life. It is particularly important to understand sources of early life variation in the microbiome because the state of the microbiome in childhood can influence lifelong health. Here, we compared the gut microbiomes of children adopted in infancy to those of genetically unrelated children in the same household and genetically related children raised in other households. We observed that a shared home environment was the strongest predictor of overall microbiome similarity. Among those microbial taxa whose variation was significantly explained by our models, the abundance of a given taxon was more frequently explained by host genetic similarity (relatedness), while the presence of a given taxon was more dependent upon a shared home environment. This suggests that although the home environment may act as a species source pool for the gut microbiome in childhood, host genetic factors likely drive variation in microbial abundance once a species colonizes the gut. IMPORTANCE Our results demonstrate that the early life home environment can significantly alter the gut microbiome in childhood, potentially altering health outcomes or risk for adverse health outcomes. A better understanding of the drivers of gut microbiome variation during childhood could lead to more effective intervention strategies for overall health starting in early life.

Variation among host-associated microbiomes is well documented across species, populations, and individuals. While numerous factors can contribute to this variation, understanding the influence of host genetic differences on microbial variation is particularly important for predicting co-evolutionary dynamics between hosts and their microbiota. Functional understanding of host genetic and microbial covariation is also of biomedical relevance, for example, providing insights into why some humans are more susceptible to chronic disorders like inflammatory bowel diseases. Unfortunately, disentangling the relative contribution to microbiome variation of host genetics from the environment has been difficult, particularly in humans where confounding environmental effects cannot be completely controlled experimentally. While isogenic laboratory models can be used in controlled environments, the effects on microbiomes of induced large-effect mutations may not recapitulate those of genetic variation observed in nature. Few studies have tested for the direct influence of natural host genetic variation on microbiome differences within a highly controlled environment in which hosts interact freely. To fill this gap, we performed a common garden experiment using families of genetically divergent populations of threespine stickleback fish—an outbred model organism commonly used for determining the genetic basis of complex traits in the context of natural genetic variation. Using germ-free derivation of divergent lines and hybrids between them in this experimental framework, we detected a clear, positive association between stickleback genetic dissimilarity and microbiome dissimilarity. With RAD-seq data, we identified regions of the genome that contributed most significantly to this relationship, providing insight into the genomic architecture of gut microbiome variation. A major focus of host-microbe research is to understand how genetic differences, of various magnitudes, among hosts translate to differences in their microbiomes. This has been challenging for animal hosts, including humans, because it is difficult to control environmental variables tightly enough to isolate direct genetic effects on the microbiome. Our work in stickleback fish is a significant contribution because our experimental approach allowed strict control over environmental factors, including standardization of the microbiome from the earliest stage of development and unrestricted co-housing of fish in a truly common environment. Furthermore, we measured host genetic variation over 2,000 regions of the stickleback genome, comparing this information and microbiome composition data among fish from very similar and very different genetic backgrounds. Our findings highlight how differences in the host genome influence microbiome diversity and make a case for future manipulative microbiome experiments that use host systems with naturally occurring genetic variation.

Dietary and physical activity behaviors formed early in life can increase risk for childhood obesity and have continued negative consequences for lifelong health. Previous research has highlighted the importance of both genetic and environmental (e.g., cultural environment or parental lifestyle) contributions to obesity risk, although these studies typically involve genetically-related individuals residing in the same household, where genetic similarity and rearing environment are inextricably linked. Here we utilize a sibling-adoption design to independently estimate genetic and environmental contributions to obesity risk in childhood and describe how these influences might vary as children age. As part of a prospective adoption study, the current investigation used data from biological siblings reared either apart or together, and nonbiological siblings reared together to estimate the contributions of genetics and environment to body mass indices (BMI) in a large cohort of children (N = 711). We used a variance partitioning model to allocate variation in BMI to that which is due to shared genetics, common environment, or unique environment in this cohort during middle childhood and adolescence. We found 63% of the total variance in BMI could be attributed to heritable factors in middle childhood sibling pairs (age 5-11.99; 95% CI [0.41,0.85]). Additionally, we observed that common environment explained 31% of variation in BMI in this group (95% CI [0.11,0.5]), with unique environment and error explaining the remaining variance. We failed to detect an influence of genetics or common environment in older sibling pairs (12-18) or pairs spanning childhood and adolescence (large sibling age difference), but home type (adoptive versus birth) was an important predictor of BMI in adolescence. The presence of strong common environment effects during childhood suggests that early interventions at the family level in middle childhood could be effective in mitigating obesity risk in later childhood and adolescence.

New strategies to combat the global scourge of schistosomiasis may be revealed by increased understanding of the mechanisms by which the obligate snail host can resist the schistosome parasite. However, few molecular markers linked to resistance have been identified and characterized in snails. Here we test six independent genetic loci for their influence on resistance to Schistosoma mansoni strain PR1 in the 13-16-R1 strain of the snail Biomphalaria glabrata. We first identify a genomic region, RADres, showing the highest differentiation between susceptible and resistant inbred lines among 1611 informative restriction-site associated DNA (RAD) markers, and show that it significantly influences resistance in an independent set of 439 outbred snails. The additive effect of each RADres resistance allele is 2-fold, similar to that of the previously identified resistance gene sod1. The data fit a model in which both loci contribute independently and additively to resistance, such that the odds of infection in homozygotes for the resistance alleles at both loci (13% infected) is 16-fold lower than the odds of infection in snails without any resistance alleles (70% infected). Genome-wide linkage disequilibrium is high, with both sod1 and RADres residing on haplotype blocks >2 Mb, and with other markers in each block also showing significant effects on resistance; thus the causal genes within these blocks remain to be demonstrated. Other candidate loci had no effect on resistance, including the Guadeloupe Resistance Complex and three genes (aif, infPhox, and prx1) with immunological roles and expression patterns tied to resistance, which must therefore be trans-regulated. The loci RADres and sod1 both have strong effects on resistance to S. mansoni. Future approaches to control schistosomiasis may benefit from further efforts to characterize and harness this natural genetic variation.

Aim As climate change accelerates, assessing how climate shapes gene flow and neutral and adaptive genetic differentiation on landscapes is increasingly important. Aardvarks (Orycteropus afer) are ecologically important in sub‐Saharan Africa but are sensitive to human pressures and increasing aridity. We used individual, population and landscape genetic approaches to infer the influence of landscape, climate and potential adaptive differences on gene flow. Location We surveyed 8 protected and 4 privately owned areas in South Africa, 2 protected areas in Eswatini and one location in Kenya during 2016–2018. Methods We developed microsatellite markers and methods for DNA extraction from faeces, collected and genotyped faecal samples from focal areas and estimated genetic structure. We inferred space use from individual redetections, tested for close relatives and estimated genetic neighbourhood distance. We applied individual‐based landscape genetic analyses at multiple scales to test hypotheses about genetic differentiation by landscape resistance and potential adaptive differences. Results We developed 19 variable microsatellite loci and collected 253 faecal samples from 13 focal areas. We genotyped 104 samples successfully at ≥8 loci as needed for individual identification. The genetic structure suggested 3 regional divisions in South Africa. We detected individuals at locations ≤7.3 km distant and closely related individuals at ≤44 km; genetic neighbourhood distance was <55 km. Lower precipitation increased landscape resistance and strongly predicted genetic differentiation at most spatial scales. Temperature differences at sampling sites also influenced structure, suggesting climate‐associated adaptive differences. Main Conclusions The genetic structure of aardvarks is strongly shaped by climate, with arid areas limiting gene flow and reflects apparent isolation by adaptation associated with temperature. Dispersal distances likely are <55 km. The markers we developed will facilitate studies of space use, dispersal, population density or survival. Aridification will increase fragmentation and we recommend monitoring aardvark presence as an indicator of ecosystem change associated with aridification.

Objectives: People who inject drugs (PWID) are especially vulnerable to morbidity and mortality as a result of SARS-CoV-2 infection because of social and physical health vulnerabilities. Routine testing for SARS-CoV-2 is critical to reduce transmission. Contingency management—the provision of tangible rewards to reinforce positive behavior—can promote the use of health services among PWID. Evidence is scarce on the utility of contingency management to promote SARS-CoV-2 testing. The objective of this study was to evaluate the effectiveness of contingency management to increase testing among PWID. Methods: SARS-CoV-2 testing was implemented at 9 syringe exchange program sites in partnership with an Oregon-based nonprofit organization for 5 weeks without contingency management and for 6 weeks with contingency management (a $10 financial incentive for testing) from February 1 through mid-April 2021. We measured rates of testing among syringe exchange program clients before and after implementation of contingency management. Results: Before contingency management, SARS-CoV-2 testing occurred during approximately 131 of 1410 (9.3%) client encounters, and 123 of 997 (12.3%) unique clients were tested. During contingency management, testing occurred during approximately 571 of 1756 (32.5%) client encounters, and 407 of 1151 (35.4%) unique clients were tested. Rates of testing increased from 0.04 (SD, 0.04) before contingency management implementation to 0.25 (SD, 0.15) after implementation (t8 = −3.88; P = .005; Cohen d = 1.46). Conclusions: Contingency management facilitated uptake of SARS-CoV-2 testing among PWID. Contingency management may be an effective strategy for improving communicable disease testing beyond testing for SARS-CoV-2 and for improving vaccine uptake among PWID and warrants additional research.

Testing a vantage sensitivity model from differential susceptibility theory (DST), we examined a G × E × I hypothesis; that is, whether a military parenting intervention program (I) might buffer a G × E susceptibility for military deployed fathers exposed to deployment combat stress and trauma. We hypothesized that combat stress (E, referring to the natural environmental factor) would lead to increases in problem drinking, and that the effect of problem drinking would be amplified by genetic predisposition (G) for drinking reward systems, substance use, and addictive behaviors (i.e., differential vulnerability). Providing a preventive intervention designed to improve post-deployment family environments (I, vantage sensitivity) is hypothesized to buffer the negative impacts of combat exposure and genetic susceptibility. The sample included 185 post-deployed military fathers who consented to genotyping, from a larger sample of 294 fathers enrolled in a randomized effectiveness trial of the After Deployment Adaptive Parenting Tools (ADAPT) intervention. Trauma-exposed military fathers at genetic susceptibility for problem drinking assigned to the ADAPT intervention reported significantly more reductions in risky drinking compared with fathers at genetic susceptibility assigned to the control group, with a small effect size for the G × E × I interaction (d = .2). Trial Registration. The ADAPT trial is registered at the US National Institutes of Health (ClinicalTrials.gov) # NCT03522610.

The COVID-19 pandemic has disproportionately affected communities of color, including Latinx communities. Oregon Saludable: Juntos Podemos (OSJP) is a randomized clinical trial aimed at reducing this disparity by both increasing access to testing for SARS-CoV-2, the virus that causes COVID-19, for Oregon Latinx community members and studying the effectiveness of health and behavioral health interventions on turnout and health outcomes. OSJP established SARS-CoV-2 testing events at sites across Oregon. A critical early question was how to locate these sites to best serve Latinx community members. To propose sites in each participating county, we implemented an algorithmic approach solving a facilities location problem. This algorithm was based on minimizing driving time from Latinx population centers to SARS-CoV-2 testing locations. OSJP staff presented these proposed testing locations to community partners as a starting place for identifying final testing sites. Due to differences in geography, population distributions, and potential site accessibility, the study sites exhibited variation in how well the algorithmic optimization objectives could be satisfied. From this variation, we inferred the effects of the drive time optimization metric on the likelihood of Latinx community members utilizing SARS-CoV-2 testing services. After controlling for potential confounders, we found that minimizing the drive time optimization metric was strongly correlated with increased turnout among Latinx community members. This paper presents the algorithm and data sources used for site proposals and discusses challenges and opportunities for community-based health promotion research when translating algorithm proposals into action across a range of health outcomes.