Pregnancy alters many physiological systems, including the maternal gut microbiota. Diet is a key regulator of this system and can alter the host immune system to promote inflammation. Multiple perinatal disorders have been associated with inflammation, maternal metabolic alterations, and gut microbial dysbiosis, including gestational diabetes mellitus, pre-eclampsia, preterm birth, and mood disorders. However, the effects of high-inflammatory diets on the gut microbiota during pregnancy have yet to be fully explored. We aimed to address this gap using a system-based approach to characterize associations among dietary inflammatory potential, a measure of diet quality, and the gut microbiome during pregnancy. Forty-seven pregnant persons were recruited prior to 16 weeks of gestation. Participants completed a food frequency questionnaire (FFQ) and provided fecal samples. Dietary inflammatory potential was assessed using the Dietary Inflammatory Index (DII) from the FFQ data. Fecal samples were analyzed using 16S rRNA amplicon sequencing. Differential taxon abundances with respect to the DII score were identified, and the microbial metabolic potential was predicted using PICRUSt2. Inflammatory diets were associated with decreased vitamin and mineral intake and a dysbiotic gut microbiota structure and predicted metabolism. Gut microbial compositional differences revealed a decrease in short-chain fatty acid producers such as Faecalibacterium, and an increase in predicted vitamin B12 synthesis, methylglyoxal detoxification, galactose metabolism, and multidrug efflux systems in pregnant individuals with increased DII scores. Dietary inflammatory potential was associated with a reduction in the consumption of vitamins and minerals and predicted gut microbiota metabolic dysregulation.

Streptococcus pyogenes , or Group A Streptococus (GAS), a significant human pathogen, employs quorum sensing (QS) systems to coordinate its behavior and genetic regulation in order to enhance survival. Our previous research established that one such QS system, the Rgg2/3 system, can suppress macrophage NFκB activity and production of pro-inflammatory cytokines. Yet, the scope of suppression and the mechanism by which it occurs remains unknown. In this study, we used transcriptomic and phosphoproteomic approaches to address these unanswered questions. We found QS-ON GAS broadly suppressed most inflammatory transcriptional pathways including those of NFκB, type I and type II interferon responses, and intracellular stress responses. Yet, we found no alternative transcriptional programs were activated after QS-ON GAS infection. Additionally, phosphoproteomics showed no disruption in typical inflammatory pathways such as those related to NFκB and MAPK activation, which was confirmed by western blotting and translocation assays. Instead, the proteomic data highlighted a potential role for epigenetic mechanisms of inflammatory regulation. To determine if epigenetic regulation was involved in QS-mediated immunomodulation, DNA methylation was measured and studies were performed inhibiting various histone and chromatin modifiers. These studies also showed no dijerence between QS-ON compared with QS-OFF infected macrophages. These findings expand our understanding of QS-mediated suppression and of GAS virulence strategies that appear to employ unusual methods of restricting inflammation. Uncovering this mechanism will ojer invaluable insight into GAS, itself, as well as understudied immunological pathways. Importance: Streptococcus pyogenes is a ubiquitous pathogen that causes over 600 million infections every year and 500 thousand to 1 million fatalities. While in developed countries it is generally known to cause mild conditions such as pharyngitis, it can also manifest as severe infections such as necrotizing fasciitis, septic arthritis, and lead to post-infectious sequelae including rheumatic heart disease and glomerulonephritis. Elucidating new mechanisms of virulence in this organism, including how it evades and suppresses immune responses can be critical in understanding its pathogenicity, epidemiology, and identification of novel treatment avenues in this era of multi-drug-resistant bacteria. In this study, we characterize the broad spectrum by which GAS modulates the host innate immune response and begin to uncover host pathways that bacteria can use or inhibit for its survival.

Background: Pregnancy alters many physiological systems, including the maternal gut microbiota. Diet is a key regulator of this system and can alter the host immune system to promote inflammation. Multiple perinatal disorders have been associated with inflammation, maternal metabolic alterations, and gut microbial dysbiosis, including gestational diabetes mellitus, preeclampsia, preterm birth, and mood disorders. However, the effects of high inflammatory diets on the gut microbiota during pregnancy have yet to be fully explored. Objective: To use a systems-based approach to characterize associations among dietary inflammatory potential, a measure of diet quality, and the gut microbiome during pregnancy. Methods: Forty-nine pregnant persons were recruited prior to 16 weeks of gestation. Participants completed a food frequency questionnaire (FFQ) and provided fecal samples. Dietary inflammatory potential was assessed using the Dietary Inflammatory Index (DII) from FFQ data. Fecal samples were analyzed using 16S rRNA amplicon sequencing. Differential taxon abundance with respect to DII score were identified, and microbial metabolic potential was predicted using PICRUSt2. Results: Inflammatory diets were associated with decreased vitamin and mineral intake and dysbiotic gut microbiota structure and predicted metabolism. Gut microbial compositional differences revealed a decrease in short chain fatty acid producers such as Faecalibacterium, and an increase in predicted vitamin B12 synthesis, methylglyoxal detoxification, galactose metabolism and multi drug efflux systems in pregnant individuals with increased DII scores. Conclusions: Dietary inflammatory potential was associated with a reduction in the consumption of vitamins & minerals and predicted gut microbiota metabolic dysregulation.

Importance: Perinatal depression (PND) affects 10-20% of pregnant women, with significant racial disparities in prevalence, screening, and treatment. Neighborhood-level factors significantly influence PND risk, particularly among women of color, but current machine learning models using electronic medical records (EMRs) rarely incorporate neighborhood characteristics. Objective: To determine whether integrating neighborhood-level information with EMRs improves fairness in PND prediction while identifying key neighborhood factors influencing model bias across racial/ethnic groups. Design, Setting, and Participants: Study of 6,137 pregnant women who received care at a large urban academic hospital from 2010-2019, comprising 58% Non-Hispanic Black (NHB), 10% Non-Hispanic White (NHW), and 28% Hispanic (H) individuals, with depression status determined by PHQ-9 scores. Exposures: 125 neighborhood-level factors from Chicago Health Atlas merged with 61 EMR features based on residential location. Main Outcomes and Measures: Model performance (ROCAUC, PRAUC) and fairness metrics (disparate impact, equal opportunity difference, equalized odds). Feature importance analyzed using Shapley values and the impact of each neighborhood factor on model bias were evaluated. Results Models integrating neighborhood-level measures showed moderate predictive performance (ROCAUC: NHB 55%, NHW 57%, H 58%) while significantly improving fairness metrics compared to EMR-only models (p<0.05). Factors, such as suicide mortality rate and neighborhood safety rate, helped reduce bias. NHB women showed stronger correlations between PND risk factors and neighborhood variables compared to other groups. Most neighborhood factors had differential impacts across racial/ethnic groups, increasing bias for NHB women while reducing it for Hispanic women. Conclusions and Relevance: Incorporating neighborhood-level information enhances fairness in PND prediction while maintaining predictive capability. The differential impact of neighborhood factors across racial/ethnic groups highlights the importance of considering neighborhood context in clinical risk assessment to reduce disparities in prenatal depression care.

Microbial communities are dynamic structures that continually adapt to their surrounding environment. Such communities play pivotal roles in countless ecosystems from environmental to human health. Perturbations of these community structures have been implicated in disease processes such as Crohn’s disease and cancer. Disturbances to existing ecosystems often occur over time, making it essential to have robust methods for detecting longitudinal alterations in microbial interactions as they develop. Existing methods for identifying temporal microbial community alterations have focused on abundance alterations in individual taxa, rather than relationships between the taxa, known as microbial interactions. Identifying these interactions overtime provides a fuller understanding of how the microbial ecosystem changes as a whole. To fill this gap, we have developed a pipeline that handles the complicated nature of repeated compositional count data, LIMÓN – Longitudinal Individual Microbial Omics Networks. This novel statistical approach addresses key challenges of modeling temporal and microbial data including overdispersion, zero-inflated count data, compositionality, repeated measure design sample covariates over time, and identification of individualized or sample specific networks. This approach allows users to denoise covariate effects from their data, return networks per time point, identify interaction changes between each time point, and return individual networks and network characteristics per sample/time point. In doing so, LIMÓN provides a platform to identify the relationship between network interactions and sample features of interest over time. Here we show LIMÓN, in simulation studies, can accurately remove covariate effects, render sample specific networks, and better recover underlying network edges from covariate confounded data. Analysis of a longitudinal infant microbiome and diet dataset illustrates LIMÓN’s novel utility to identify key microbial interactions related to diet type across time. AUTHOR SUMMARY: Microbes (bacteria, fungi etc.) are integral components of many ecosystems, from the environment to the human body, where they can shift between healthy and disease states. Microbes do not exist alone but in rich diverse communities. Yet, many current methods used to study microbe alterations in disease focus on changes in individual microbes rather than how the entire community adapts. To better understand how microbial communities shift, we developed an open-source tool called LIMÓN, which allows users study how these relationships shift over time. By leveraging robust statistical techniques, LIMÓN can account for the complexities of the data, such as covariates and differences between individual samples. This approach helps us uncover important patterns in how microbes interact in various conditions. In this example, we applied LIMÓN to data from infants who were fed three different diets during the first year of life and identify specific microbial interactions related to diet that change overtime. This work broadens the scope for exploring microbial ecosystem dynamics in health and nature, offering a more comprehensive perspective vs traditional method used.

Mental health disorders, such as depression and anxiety, are highly prevalent during pregnancy and postpartum and can have severe consequences on maternal morbidity and mortality, and on infant neurodevelopment. Yet the neurobiological mechanisms of these disorders are underexplored. Critically absent are longitudinal studies of mental health and brain alterations across the entire perinatal period using electroencephalography (EEG). In this study, a racially, ethnically, and socioeconomically diverse perinatal cohort (n=61, 45% Non-Hispanic Black, 32% Hispanic, 65% public insurance) was followed bimonthly from early pregnancy through postpartum. At each visit, participants completed an 8-minute closed-eye resting state EEG recording and self-reported validated questionnaires to assess depression and anxiety symptoms. Linear regression models showed EEG-measured relative delta waves were negatively associated with both depression and anxiety severity in the first trimester and postpartum. Alpha waves were positively associated with elevated symptoms of anxiety at postpartum. Early pregnancy alpha and delta waves were also associated with late third trimester pregnancy and postpartum depression and anxiety. This study is the first to report that specific brain waves are associated with mood and anxiety disorders during perinatal periods characterized by large reproductive hormonal fluctuations. This work is an initial step to understanding how changes in dynamic brain activity during the perinatal period may be used as possible unbiased markers of perinatal mood and anxiety disorders.