In utero colonization or deposition of beneficial microorganisms and their by-products likely occurs through various mechanisms, such as hematogenous spread or ascension from the reproductive tract. With high-throughput sequencing techniques, the identification of microbial components in first-pass neonatal meconium has been achieved. While these components are low-biomass and often not abundant enough to culture, the presence of microbial DNA signatures may promote fetal immune tolerance or epigenetic regulation prior to birth. The aim of this study was to investigate the maternal source of the neonatal first-pass meconium microbiome. Maternal vaginal and anal samples collected from twenty-one maternal–infant dyad pairs were compared via principal component analysis to first-pass neonatal meconium microbial compositions. Results demonstrated the greatest degree of similarity between the maternal gut microbiome in the second and third trimesters and vaginal microbiome samples across pregnancy, suggesting that the maternal gut microbiota may translocate to the fetal gut during pregnancy. This study sheds light on the origin and timing of the potential transfer of maternal microbial species to offspring in utero.

Background: Vaginal Lactobacillus predominance is associated with improved vaginal health and reduced pregnancy complications. Little is known about how dietary quality may improve vaginal microbial composition or about dietary interventions that may promote Lactobacillus abundance. To understand the host factors affecting vaginal microbiota during pregnancy in a multi-ethnic cohort in Hawai`i. We hypothesize that better diet quality improves vaginal microbial composition, as represented by Lactobacillus abundance and depletion of anaerobic organisms. Methods: We compared comprehensive diet quality, as measured by the Healthy Eating Index-2015 (HEI-2015), to taxonomic classifications of bacteria present within the vagina. Participants of the four predominant ethnic groups in Hawai’i (Japanese, Filipino, Non-Hispanic White and Native Hawaiian) completed Quantitative Food Frequency Questionnaires (QFFQs) and collected vaginal swabs during each trimester. 16s rRNA amplicon sequencing (V2–V9 regions) was performed on vaginal samples. HEI-2015 scores and macro- and micronutrient intake were compared with the predominant species present using the Mann–Whitney-U test, PERMANOVA, and the Pearson correlation coefficient. A mixed-effects logistics regression model was used to predict the depletion of Lactobacillus species while accounting for confounding demographic factors. Results: Matched, longitudinal data for 40 participants demonstrated three predominant Lactobacillus species: L. crispatus, L. iners, and L. gasseri, with another subset of samples with anaerobic abundance. (Primarily, Atopobium vaginae, Prevotella, and Gardnerella vaginalis.) Non-Hispanic White participants had lower amounts of Lactobacillus iners compared to other racial and ethnic groups. HEI scores correlated with the chao index and observed species number primarily in the first trimester (r = 0.25, p < 0.05). Greater carbohydrate intake was associated with a higher abundance of L. crispatus, while lower carbohydrate intake trended towards more L. iners (0.056) and anaerobic species. Conclusions: Increased carbohydrate consumption and improved diet quality may be associated with beneficial vaginal microbial composition in pregnancy.

Despite significant progress in understanding the molecular basis of colorectal cancer (CRC), the precise mechanisms driving its development and progression remain poorly defined. This gap limits the identification of novel therapeutic targets and the development of effective early detection methods. N6-methyladenosine (m6A) has merged as a key role in CRC pathogenesis. But research on mRNA methylation in CRC remains sparse. We aimed to address the roles of m6A modifications in CRC and to understand how mRNA methylation contributes to CRC development and progression. We obtained a comprehensive mapping of altered m6 peaks within mRNAs using Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq) from 34 CRC samples and 34 adjacent normal tissue samples and dissect molecular mechanisms of identified key CRC gene(s). By integrating these data, we identified differentially expressed mRNAs with altered m6A levels. Further analysis identified 119 overlapping peaks with both significantly altered RNA methylation and expression levels. Finally, we analyzed the relationship between m6A-regulated gene expression and Immune infiltrates using CRC patient-derived PBMC samples. Additionally, we established a subcutaneous xenograft tumor model to explore the role of SIM2 in CRC progression. Our comprehensive analysis of 68 fresh-frozen CRC samples identified 119 overlapping m6A peaks across 77 genes, classified based on m6A and mRNA expression changes. Survival analyses revealed a signature of m6A-modified genes with prognostic potential. These methylated genes were significant associated with immune cell profiles in the tumor microenvironment and immune checkpoints regulation, highlighting m6A as a promising immunotherapeutic target for CRC. SIM2 emerged as a key candidate, exhibiting elevated m6A and RNA expression levels in tumors and Macrophage M2 cells. NTMT1 (a writer) and YTHDF1 (a reader) were identified as pivotal regulators of m6A modifications in the 5'-UTR of SIM2 mRNA, emphasizing their role in CRC progression. Silencing methylated SIM2 significantly suppressed tumor growth, suggesting its potential as a therapeutic target. Our integrative analyses provide a valuable resource for unraveling the molecular landscape of CRC. These finding offer new insight for advancing diagnostic precision and refining prognostic and therapeutic strategies for CRC.

This study describes the development of a resource module that is part of a learning platform named ‘NIGMS Sandbox for Cloud-based Learning’ https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module is designed to facilitate interactive learning of whole-genome bisulfite sequencing (WGBS) data analysis utilizing cloud-based tools in Google Cloud Platform, such as Cloud Storage, Vertex AI notebooks and Google Batch. WGBS is a powerful technique that can provide comprehensive insights into DNA methylation patterns at single cytosine resolution, essential for understanding epigenetic regulation across the genome. The designed learning module first provides step-by-step tutorials that guide learners through two main stages of WGBS data analysis, preprocessing and the identification of differentially methylated regions. And then, it provides a streamlined workflow and demonstrates how to effectively use it for large datasets given the power of cloud infrastructure. The integration of these interconnected submodules progressively deepens the user’s understanding of the WGBS analysis process along with the use of cloud resources. Through this module, we can enhance the accessibility and adoption of cloud computing in epigenomic research, speeding up the advancements in the related field and beyond. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious and pathogenic coronavirus that emerged in late 2019 and caused a pandemic of respiratory illness termed as coronavirus disease 2019 (COVID-19). Cancer patients are more susceptible to SARS-CoV-2 infection. The treatment of cancer patients infected with SARS-CoV-2 is more complicated, and the patients are at risk of poor prognosis compared to other populations. Patients infected with SARS-CoV-2 are prone to rapid development of acute respiratory distress syndrome (ARDS) of which pulmonary fibrosis (PF) is considered a sequelae. Both ARDS and PF are factors that contribute to poor prognosis in COVID-19 patients. However, the molecular mechanisms among COVID-19, ARDS and PF in COVID-19 patients with cancer are not well-understood. In this study, the common differentially expressed genes (DEGs) between COVID-19 patients with and without cancer were identified. Based on the common DEGs, a series of analyses were performed, including Gene Ontology (GO) and pathway analysis, protein–protein interaction (PPI) network construction and hub gene extraction, transcription factor (TF)–DEG regulatory network construction, TF–DEG–miRNA coregulatory network construction and drug molecule identification. The candidate drug molecules (e.g., Tamibarotene CTD 00002527) obtained by this study might be helpful for effective therapeutic targets in COVID-19 patients with cancer. In addition, the common DEGs among ARDS, PF and COVID-19 patients with and without cancer are TNFSF10 and IFITM2. These two genes may serve as potential therapeutic targets in the treatment of COVID-19 patients with cancer. Changes in the expression levels of TNFSF10 and IFITM2 in CD14+/CD16+ monocytes may affect the immune response of COVID-19 patients. Specifically, changes in the expression level of TNFSF10 in monocytes can be considered as an immune signature in COVID-19 patients with hematologic cancer. Targeting N⁶-methyladenosine (m6A) pathways (e.g., METTL3/SERPINA1 axis) to restrict SARS-CoV-2 reproduction has therapeutic potential for COVID-19 patients.