bioRxiv Science⌕ Search

Biology subjects

Ramazzotti, M.

Publications and source records attributed to Ramazzotti, M..

3 recordsLinked to original sources
bioRxiv2020

Significant and conflicting correlation of IL-9 with Prevotella and Bacteroides in human colorectal cancer

BackgroundColorectal cancer (CRC) is a widespread disease that represents an example of chronic inflammation-associated tumor. In fact, the immune system, besides protecting the host from developing tumors, can support the CRC progression. In this scenario, the gut microbiota (GM) is essential to modulate immune responses and a dysbiotic condition can favor chronic/abnormal immune activation that support the tumor growth. GM can elicit the production of cytokines, influencing the immunostimulatory or immunosuppressive reactions, such as the tendency to mount Th1, Th17, Tregs or Th9 responses that play different roles towards colon cancer. Paradigmatic is the role of IL-9 that can both promote tumor progression in hematological malignancies and inhibit tumorigenesis in solid cancers. Therefore, to investigate the microbiota-immunity axis in CRC patients is crucial to well understand the cancer development with positive relapses in prevention and treatment. AimThe cellular and molecular characterization of the immune response and the evaluation of GM composition in healthy and tumor mucosa, focusing on the correlation between cytokines profile and GM signature. MethodsWe collected tumoral (CRC) and healthy (CRC-S) mucosa samples of 45 CRC patients. For each sample, we characterized the Tissue Infiltrating Lymphocytes (TIL)s subset profile and the GM composition. In addition, in 14 CRC patients, we evaluated the CRC and CRC-S molecular inflammatory response (26 cytokines/chemokines) and we correlated this profile with GM composition using the Dirichlet Multinomial Regression. ResultsThe analysis of T cells subsets distribution showed that CRC samples displayed higher percentages of Th17, Th2, Tregs, Tc17, Tc1/Tc17, and Tcreg, compared to CRC-S. Notably, also the number of Th9 was higher, even if not significantly, in CRC tissue compared to healthy one. In addition, we found that MIP-1, IL-1{beta}, IL-2, IP-10, IL-6, IL-8, IL-17A, IFN-{gamma}, TNF-, MCP-1, IL-1, P-selectin and IL-9 were significantly increased in CRC compared to CRC-S. Moreover, the GM analysis revealed that CRC samples had significantly higher levels of Fusobacteria, Proteobacteria, Fusobacterium, Ruminococcus2 (Lachnospiraceae family) and Ruminococcus (Ruminococcaceae family) than CRC-S. Finally, we found that the abundance of Prevotella spp in CRC samples was negatively correlated with IL-17A and positively with IL-9. In addition, the abundance of Bacteroides and Escherichia/Shigella species in CRC samples showed a negative association with IL-9 and IP-10 respectively. ConclusionsOur data show a clear dissimilarity of inflammatory profile and GM composition between the tumor and the adjacent healthy tissue, displaying the generation of a peculiar CRC microenvironment. Interestingly, relating the tissue cytokine profile with the GM composition, we confirmed the presence of a bidirectional crosstalk between the immune response and the hosts commensal microorganisms; in detail, we documented for the first time that Prevotella spp. and Bacteroides spp. are correlated (positively and negatively, respectively) with the IL-9, whose role in CRC development is still debated.

immunology↗
bioRxiv2020

Identification of variable sites in Sars-CoV-2 and their abundance profiles in time

The SARS-CoV-2 pandemic that we are currently experiencing is exerting a massive toll both in human lives and economic impact. One of the challenges we must face is to try to understand if and how different variants of the virus emerge and change their frequency in time. Such information can be extremely valuable as it may indicate shifts in aggressiveness, and it could provide useful information to trace the spread of the virus in the population. In this work we identified and traced over time 7 amino acid variants that are present with high frequency in Italy and Europe, but that were absent or present at very low frequencies during the first stages of the epidemic in China and the initial reports in Europe. The analysis of these variants helps defining 6 phylogenetic clades that are currently spreading throughout the world with changes in frequency that are sometimes very fast and dramatic. In the absence of conclusive data at the time of writing, we discuss whether the spread of the variants may be due to a prominent founder effect or if it indicates an adaptive advantage.

bioinformatics↗
bioRxiv2017

Functional pathways for metabolic network-based data analysis: the MetPath algorithm

Analyzing biological data using pathways helps identify trends in data tied to the function of a network. A large number of pathway-based analysis tools have been developed toward this goal. These pathways are often manually curated and thus associations are subject to the biases of the curator. A potentially attractive alternative is to define pathways based on the inherent functionality and connectivity of the network itself. Within metabolism, functionality is defined by the production and consumption of metabolites, and connectivity by metabolites participating in reactions through common enzymes. In this work, we present an algorithm, termed MetPath, that calculates pathways for production and consumption of metabolites. We show how these pathways have attractive properties, such as the ability to integrate multiple data types and weight contribution of genes within the pathway by their functional contribution to metabolite production/consumption. Pathways calculated in this manner are condition-specific and thus are custom tailored to the system of interest, in contrast to curated pathways. We find that these pathways predict gene expression correlation better compared to manually-curated pathways. Additionally, we find that these pathways can be used to understand gene expression changes between growth conditions and between cell types. This work serves to better understand the functional pathway structure underlying cell metabolism and helps to enable systems analyses of high-throughput data.

systems biology↗