Karpievitch, Y. V. | bioRxiv Science

bioRxiv2024

Conservation of gene expression patterns between the amniotic epithelium at birth and a newborn's nasal epithelium

BackgroundAmniotic epithelial cells are foetal-derived stem cells, capable of differentiating into all three germ layers, including mature epithelial cell populations. However, the conservation between amniotic epithelium and other epithelial tissues has not been sufficiently explored. We hypothesised that the amniotic epithelium might serve as a surrogate tissue source for investigating transcriptional profiles in the respiratory epithelium of the newborn. We compared gene expression profiles and weighted gene co-expression network structure in paired amniotic and newborn nasal epithelial samples from 85 participants in the Airway Epithelium Respiratory Illnesses and Allergy (AERIAL) birth cohort. ResultsIn total, 11,867 genes (79.7%) were commonly expressed in both amniotic and nasal epithelium, with uniquely expressed genes (2,563 and 458, respectively) enriched for biological functions related to each tissues specialist functions (e.g. developmental programs and ciliated cells, respectively). We observed a strong overlap in weighted gene co-expression network structure between both tissues, with ten co-expression modules identified in consensus network analysis. Genes commonly expressed in both tissues and/or found in the consensus network modules were enriched for biologically relevant gene signatures and pathway terms related to airway function. We also observed significant overlap in gene expression and network structure between the amniotic epithelium and published datasets of epithelial samples from the lower airway and other epithelial tissues including skin and oesophagus, suggesting a global epithelial signature. ConclusionsOverall, we observed significant overlap in gene expression and network structure between paired amniotic and nasal epithelial samples, supporting the potential of the amnion as a non-invasive and abundant tissue surrogate. Observed differences between tissues were related to each tissues specialist functions, which remains an important consideration when assessing their overlap. Future studies aimed at investigating amnion-based biomarkers for respiratory exposures in utero and disease outcomes in childhood are needed to extend these results towards clinical translation.

molecular biology↗

bioRxiv2017

HOME: A histogram based machine learning approach for effective identification of differentially methylated regions

BackgroundThe development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate.\n\nResultsWe present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organisms dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME.\n\nConclusionHOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation.

bioinformatics↗