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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.

Oral and fecal microbial biomarkers have previously been associated with cardiometabolic (CM) risk, however, no comprehensive attempt has been made to explore this association in minority populations or across different geographic regions. We characterized gut- and oral-associated microbiota and CM risk in 655 participants of African-origin, aged 25-45, from Ghana, South Africa, Jamaica, and the United States (US). CM risk was classified using the CM risk cut-points for elevated waist circumference, elevated blood pressure and elevated fasted blood glucose, low high-density lipoprotein (HDL), and elevated triglycerides. Gut-associated bacterial alpha diversity negatively correlated with elevated blood pressure and elevated fasted blood glucose. Similarly, gut bacterial beta diversity was also significantly differentiated by waist circumference, blood pressure, triglyceridemia and HDL-cholesterolemia. Notably, differences in inter- and intra-personal gut microbial diversity were geographic-region specific. Participants meeting the cut-points for 3 out of the 5 CM risk factors were significantly more enriched with Lachnospiraceae, and were significantly depleted of Clostridiaceae, Peptostreptococcaceae, and Prevotella. The predicted relative proportions of the genes involved in the pathways for lipopolysaccharides (LPS) and butyrate synthesis were also significantly differentiated by the CM risk phenotype, whereby genes involved in the butyrate synthesis via lysine, glutarate and 4-aminobutyrate/succinate pathways and LPS synthesis pathway were enriched in participants with greater CM risk. Furthermore, inter-individual oral microbiota diversity was also significantly associated with the CM risk factors, and oral-associated Streptococcus, Prevotella, and Veillonella were enriched in participants with 3 out of the 5 CM risk factors. We demonstrate that in a diverse cohort of African-origin adults, CM risk is significantly associated with reduced microbial diversity, and the enrichment of specific bacterial taxa and predicted functional traits in both gut and oral environments. As well as providing new insights into the associations between the gut and oral microbiota and CM risk, this study also highlights the potential for novel therapeutic discoveries which target the oral and gut microbiota in CM risk.

We compared the gut microbial populations in 100 women, from rural Ghana and urban US [50% lean (BMI

Multiple aspects of the local extracellular environment profoundly affect cell phenotype and function. Physical and chemical cues in the environment trigger intracellular signaling cascades that ultimately activate transcription factors (TFs) - powerful regulators of the cell phenotype. TRACER (TRanscriptional Activity CEll aRrays) was employed for large-scale, dynamic quantification of TF activity in human fibroblasts cultured on hydrogels with a controlled elastic modulus and integrin ligand density. We identified three groups of TFs: responders to alterations in ligand density alone, substrate stiffness or both. Dynamic networks of regulatory TFs were constructed computationally and revealed distinct TF activity levels, directionality (i.e., activation or inhibition), and dynamics for adhesive and mechanical cues. Moreover, TRACER networks predicted conserved hubs of TF activity across multiple cell types, which are significantly altered in clinical fibrotic tissues. Our approach captures the distinct and overlapping effects of adhesive and mechanical stimuli, identifying conserved signaling mechanisms in normal and disease states.

Live-cell assays to measure cellular function performed within 3D cultures have the potential to elucidate the underlying processes behind disease progression and tissue formation. Cells cultured in 3D interact and remodel their microenvironment and can develop into complex structures. We have developed a transcription factor (TF) activity array that uses bioluminescence imaging (BLI) of lentiviral delivered luminescent reporter constructs that allows for the non-invasive imaging of TF activity in both 2D and 3D culture. Imaging can be applied repeatedly throughout culture to capture dynamic TF activity, though appropriate normalization is necessary. We investigated in-well normalization using Gaussia or Renilla luciferase, and external well normalization using firefly luciferase. Gaussia and Renilla luciferase were each unable to provide consistent normalization for long-term measurement of TF activity. However, external well normalization provided low variability and accounted for changes in cellular dynamics. Using external normalization, dynamic TF activities were quantified for five TFs. The array captured expected changes in TF activity to stimuli, however the array also provided dynamic profiles within 2D and 3D that have not been previously characterized. The development of the technology to dynamically track TF activity within cells cultured in both 2D and 3D can provide greater understanding of complex cellular processes. Biotechnol. Bioeng. 2013; 110: 563-572. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Background: Extracellular activation of signal transduction pathways and their downstream target transcription factors (TFs) are critical regulators of cellular processes and tissue development. The intracellular signaling network is complex, and techniques that quantify the activities of numerous pathways and connect their activities to the resulting phenotype would identify the signals and mechanisms regulating tissue development. The ability to investigate tissue development should capture the dynamic pathway activity and requires an environment that supports cellular organization into structures that mimic in vivo phenotypes. Taken together, our objective was to develop cellular arrays for dynamic, large-scale quantification of TF activity as cells organized into spherical structures within 3D culture. Methodology/Principal Findings: TF-specific and normalization reporter constructs were delivered in parallel to a cellular array containing a well-established breast cancer cell line cultured in Matrigel. Bioluminescence imaging provided a rapid, non-invasive, and sensitive method to quantify luciferase levels, and was applied repeatedly on each sample to monitor dynamic activity. Arrays measuring 28 TFs identified up to 19 active, with 13 factors changing significantly over time. Stimulation of cells with b-estradiol or activin A resulted in differential TF activity profiles evolving from initial stimulation of the ligand. Many TFs changed as expected based on previous reports, yet arrays were able to replicate these results in a single experiment. Additionally, arrays identified TFs that had not previously been linked with activin A. Conclusions/Significance: This system provides a method for large-scale, non-invasive, and dynamic quantification of signaling pathway activity as cells organize into structures. The arrays may find utility for investigating mechanisms regulating normal and abnormal tissue growth, biomaterial design, or as a platform for screening therapeutics. [ABSTRACT FROM AUTHOR]