Using an adoption–biological family design to examine associations between maternal trauma, maternal depressive symptoms, and child internalizing and externalizing behaviors

Maternal trauma is a complex risk factor that has been linked to adverse child outcomes, yet the mechanisms underlying this association are not well understood. This study, which included adoptive and biological families, examined the heritable and environmental mechanisms by which maternal trauma and associated depressive symptoms are linked to child internalizing and externalizing behaviors. Path analyses were used to analyze data from 541 adoptive mother–adopted child (AM–AC) dyads and 126 biological mother–biological child (BM–BC) dyads; the two family types were linked through the same biological mother. Rearing mother’s trauma was associated with child internalizing and externalizing behaviors in AM–AC and BM–BC dyads, and this association was mediated by rearing mothers’ depressive symptoms, with the exception of biological child externalizing behavior, for which biological mother trauma had a direct influence only. Significant associations between maternal trauma and child behavior in dyads that share only environment (i.e., AM–AC dyads) suggest an environmental mechanism of influence for maternal trauma. Significant associations were also observed between maternal depressive symptoms and child internalizing and externalizing behavior in dyads that were only genetically related, with no shared environment (i.e., BM–AC dyads), suggesting a heritable pathway of influence via maternal depressive symptoms

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

The RAST Server: Rapid Annotations using Subsystems Technology

<p>Abstract</p> <p>Background</p> <p>The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them.</p> <p>Description</p> <p>We describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes, predicts which subsystems are represented in the genome, uses this information to reconstruct the metabolic network and makes the output easily downloadable for the user. In addition, the annotated genome can be browsed in an environment that supports comparative analysis with the annotated genomes maintained in the SEED environment.</p> <p>The service normally makes the annotated genome available within 12–24 hours of submission, but ultimately the quality of such a service will be judged in terms of accuracy, consistency, and completeness of the produced annotations. We summarize our attempts to address these issues and discuss plans for incrementally enhancing the service.</p> <p>Conclusion</p> <p>By providing accurate, rapid annotation freely to the community we have created an important community resource. The service has now been utilized by over 120 external users annotating over 350 distinct genomes.</p

An immune clock of human pregnancy

The maintenance of pregnancy relies on finely tuned immune adaptations. We demonstrate that these adaptations are precisely timed, reflecting an immune clock of pregnancy in women delivering at term. Using mass cytometry, the abundance and functional responses of all major immune cell subsets were quantified in serial blood samples collected throughout pregnancy. Cell signaling-based Elastic Net, a regularized regression method adapted from the elastic net algorithm, was developed to infer and prospectively validate a predictive model of interrelated immune events that accurately captures the chronology of pregnancy. Model components highlighted existing knowledge and revealed previously unreported biology, including a critical role for the interleukin-2-dependent STAT5ab signaling pathway in modulating T cell function during pregnancy. These findings unravel the precise timing of immunological events occurring during a term pregnancy and provide the analytical framework to identify immunological deviations implicated in pregnancy-related pathologies

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

Motivation Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. Results We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified. Availability and implementation Datasets and scripts for reproduction of results are available through: Https://nalab.stanford.edu/multiomics-pregnancy/

Effect of aspirin on cancer incidence and mortality in older adults.

BACKGROUND: ASPirin in Reducing Events in the Elderly (ASPREE), a randomized double-blind placebo-controlled trial (RCT) of daily low-dose aspirin (100 mg) in older adults, showed an increase in all-cause mortality, primarily due to cancer. In contrast prior RCTs, mainly involving younger individuals, demonstrated a delayed cancer benefit with aspirin. We now report a detailed analysis of cancer incidence and mortality. METHODS: 19,114 Australian and U.S. community-dwelling participants aged 70+ years (U.S. minorities 65+ years) without cardiovascular disease, dementia or physical disability were randomized and followed for a median of 4.7 years. Fatal and non-fatal cancer events, a prespecified secondary endpoint, were adjudicated based on clinical records. RESULTS: 981 cancer events occurred in the aspirin and 952 in the placebo groups. There was no statistically significant difference between groups for all incident cancers (HR = 1.04, 95% CI = 0.95 to 1.14), hematological cancer (HR = 0.98, 95% CI = 0.73 to 1.30), or all solid cancers (HR = 1.05, 95% CI = 0.95 to 1.15), including by specific tumor type. However, aspirin was associated with an increased risk of incident cancer that had metastasized (HR = 1.19, 95% CI = 1.00 to 1.43) or was stage 4 at diagnosis (HR = 1.22, 95% CI = 1.02 to 1.45), and with higher risk of death for cancers that presented at stages 3 (HR = 2.11, 95% CI = 1.03 to 4.33) or 4 (HR = 1.31, 95% CI = 1.04 to 1.64). CONCLUSIONS: In older adults, aspirin treatment had an adverse effect on later stages of cancer evolution. These findings suggest that in older persons, aspirin may accelerate the progression of cancer and thus, suggest caution with its use in this age group