Longoria, K. D. | Search bioRxiv

bioRxiv2025

Perinatal Shifts in Fecal-Derived Metabolites and Associations with Postpartum Depression

IntroductionResearch on maternal depression is largely limited to static, blood-derived biomarkers in the postpartum period and mechanistic targets derived from populations outside the physiological contexts of pregnancy and postpartum, resulting in critical gaps in understanding context-specific mechanisms underlying this debilitating condition. ObjectivesTo examine temporal shifts in the maternal gut metabolome and associations between pregnancy-specific shifts and postpartum depression (PPD). MethodsWe applied untargeted metabolomics (UPLC-MS/MS) to fecal samples collected from participants (N= 25) enrolled in the Maternal and Infant NutriTion (MINT) study. Random forest analysis was used to identify key pathways and metabolites contributing to temporal shifts. Fold change analysis and paired t-tests were used to quantify the magnitude and significance of metabolite changes. Associations between pregnancy-specific changes and PPD (Edinburgh Postnatal Depression Scale at 6 weeks) were identified using Pearsons correlation. ResultsLipid, amino acid, and xenobiotic metabolism emerged as core pathways driving temporal changes in the maternal gut metabolome. The most pronounced shifts occurred from 35 weeks gestation to postpartum, with 55 metabolites significantly altered compared to 24 from 24-to 35 weeks gestation and 26 from 24 weeks gestation to postpartum. Of the 29 metabolites associated with PPD; 68.9% were metabolic intermediates, primarily involved in lipid and amino acid metabolism (58.6%). ConclusionsThis study provides some of the first evidence of temporal shifts in the maternal gut metabolome and associations with PPD, highlighting the importance of lipid and amino acid metabolism and laying the groundwork for future multi-omics research.

systems biology↗

bioRxiv2023

Comparison of commercial DNA extraction kits for whole metagenome sequencing of human oral, vaginal, and rectal microbiome samples

IntroductionAdvancements in DNA extraction and sequencing technologies have been fundamental in deciphering the significance of the microbiome related to human health and pathology. Whole metagenome shotgun sequencing (WMS) is gaining popularity in use compared to its predecessor (i.e., amplicon-based approaches). However, like amplicon-based approaches, WMS is subject to bias from DNA extraction methods that can compromise the integrity of sequencing and subsequent findings. The purpose of this study was to evaluate systematic differences among four commercially available DNA extraction kits frequently used for WMS analysis of the microbiome. MethodsOral, vaginal, and rectal swabs were collected in replicates of four by a healthcare provider from five participants and randomized to one of four DNA extraction kits. Two extraction blanks and three replicate mock community samples were also extracted using each extraction kit. WMS was completed with NovaSeq 6000 for all samples. Sequencing and microbial communities were analyzed using nonmetric multidimensional scaling and compositional bias analysis. ResultsExtraction kits differentially biased the percentage of reads attributed to microbial taxa across samples and body sites. The PowerSoil Pro kit performed best in approximating expected proportions of mock communities. While HostZERO was biased against gram-negative bacteria, the kit outperformed other kits in extracting fungal DNA. In clinical samples, HostZERO yielded a smaller fraction of reads assigned to Homo sapiens across sites and had a higher fraction of reads assigned to bacterial taxa compared to other kits. However, HostZERO appears to bias representation of microbial communities and demonstrated the most dispersion by site, particularly for vaginal and rectal samples. ConclusionsSystematic differences exist among four frequently referenced DNA extraction kits when used for WMS analysis of the human microbiome. Consideration of such differences in study design and data interpretation is imperative to safeguard the integrity of microbiome research and reproducibility of results.

microbiology↗