Cho, K.-C. | Search bioRxiv

bioRxiv2024

Assessing Long-Term Stored Tissues for Multi-Omics Data Quality and Proteogenomics Suitability

As research into the complexities of cancer biology deepens, the integration of multi-omics analyses has emerged as a powerful approach to unravel the complex molecular basis of cancers. However, challenges related to sample availability, including size, collection procedures, and storage duration, hinder the broad application of this methodology. Despite these limitations, there is a growing interest in exploring the potential of archived samples to expand the scope of multi-omics research. Our study aims to investigate the impact of storage duration on the measurment in genomic, transcriptomic, and proteomic profiles of archived samples, demonstrating their viability for advancing our understanding of cancer biology. To comprehensively address these trends and limitations, we systematically examined archived samples collected over a decade, focusing on their transcriptomic, proteomic, and phosphoproteomic attributes. Analysis revealed intricate patterns and dynamic shifts, especially in long-term transcriptomic data, with observed declines in read counts related to protein coding and gene coverage. However, these changes did not compromise the fundamental gene expression landscape. Proteomic result also demonstrated that storage period did not significantly influence proteomic measurement. Comparisons of housekeeping gene (HKG) and housekeeping protein (HKP) expressions unveiled consistent transcriptomic levels across samples, while distinctive proteomic disparities between tumor and normal tissues. In conclusion, the challenges posed by limited sample availability in multi-omics studies can be partially overcome through the strategic integration of archived samples. While technical shifts were evident in certain aspects of transcriptomic data, core gene expression patterns remained robust, and the functionality of essential transcription factors (TFs) and kinases remained unaffected. These findings underscore the potential of archived samples as valuable resources for multi-omics research, providing a broader landscape for investigating cancer biology and paving the way for more comprehensive insights into this intricate field.

cancer biology↗

bioRxiv2017

Associations between sexual habits, menstrual hygiene practices, demographics and the vaginal microbiome as revealed by Bayesian network analysis

The vaginal microbiome plays an influential role in several disease states in reproductive age women, including bacterial vaginosis (BV). While demographic characteristics are associated with differences in vaginal microbiome community structure, little is known about the influence of sexual and hygiene habits. Furthermore, associations between the vaginal microbiome and risk symptoms of bacterial vaginosis have not been fully elucidated. Using Bayesian network (BN) analysis of 16S rRNA gene sequence results, demographic and extensive questionnaire data, we describe both novel and previously documented associations between habits of women and their vaginal microbiome. The BN analysis approach shows promise in uncovering complex associations between disparate data types. Our findings based on this approach support published associations between specific microbiome members (e.g., Eggerthella, Gardnerella, Dialister, Sneathia and Ruminococcaceae), the Nugent score (a BV diagnostic) and vaginal pH (a risk symptom of BV). Additionally, we found that several microbiome members were directly connected to other risk symptoms of BV (such as vaginal discharge, odor, itch, irritation, and yeast infection) including L. jensenii, Corynebacteria, and Proteobacteria. No direct connections were found between the Nugent Score and risk symptoms of BV other than pH, indicating that the Nugent Score may not be the most useful criteria for assessment of clinical BV. We also found that demographics (i.e., age, ethnicity, previous pregnancy) were associated with the presence/absence of specific vaginal microbes. The resulting BN revealed several as-yet undocumented associations between birth control usage, menstrual hygiene practices and specific microbiome members. Many of these complex relationships were not identified using common analytical methods, i.e., ordination and PERMANOVA. While these associations require confirmatory follow-up study, our findings strongly suggest that future studies of the vaginal microbiome and vaginal pathologies should include detailed surveys of participants sanitary, sexual and birth control habits, as these can act as confounders in the relationship between the microbiome and disease. Although the BN approach is powerful in revealing complex associations within multidimensional datasets, the need in some cases to discretize the data for use in BN analysis can result in loss of information. Future research is required to alleviate such limitations in constructing BN networks. Large sample sizes are also required in order to allow for the incorporation of a large number of variables (nodes) into the BN, particularly when studying associations between metadata and the microbiome. We believe that this approach is of great value, complementing other methods, to further our understanding of complex associations characteristic of microbiome research.

microbiology↗