Project description:Environmental perturbations impact gene transcription. A subset of these transcriptional changes can be passed on to the next generation even in the absence of the initial stimulus. This phenomenon is known as transgenerational inheritance of environmental exposures (TIEE). Previous studies have mainly focused on what is transferred through the germ-line, i.e. DNA methylation, histone modifications, non-coding RNAs, etc. Nevertheless, the germ cells are not the only cells that are passed on from one generation to the next. The microbiota is also transmitted together with the host cells. In this study, we investigated the role of the gut microbiome in TIEE using Drosophila melanogaster as a model organism. We have reared flies in cold and control temperatures, 18 and 25 °C respectively, and looked at the transcriptional pattern in their offspring -grown in control condition- using RNA sequencing. To study the effect of the microbiome, we have carefully exchanged the parental feces introduced to the offspring. We observed genes responsive to thermal alteration, which have preserved their transcriptional status transgenerationally. A subset of these genes, mainly genes expressed in gut, were transcriptionally dependent on which microbiome they acquired. These findings show that the microbiota plays a previously unknown role in TIEE. Our study unveiled a new route for transmittance of environmental memories and thus represents an uncharted area to explore for researchers addressing non-genetic transgenerational inheritance.
Project description:This study presents a validated, open-source QIIME2- and R-based pipeline for 16S rRNA gene profiling using multi-amplicon sequencing. It aims to overcome the limitations of commercial, closed-source tools by offering a standardized and reproducible workflow. The pipeline was benchmarked against proprietary software using five mock communities and 12 child–caregiver fecal sample pairs, showing nearly identical microbial profiles, greater sequencing depth, and improved taxonomic resolution. High reproducibility (R = 0.99, p < 0.0001) was achieved across all datasets. Application to pediatric cancer samples revealed distinct Bifidobacterium variants in children whose microbiota closely matched their caregivers’. This highlights the pipeline’s utility in studying microbial relationships. Overall, the pipeline supports transparent, adaptable, and accurate microbiome analysis, advancing research in both clinical and experimental settings while promoting open-source solutions for reproducible science.