Biology subjects

Diambra, L.

Publications and source records attributed to Diambra, L..

6 recordsLinked to original sources

bioRxiv2026

Sexual dimorphism in the homeostasis of the gut microbiome-immune axis

The gut microbiome is a key regulator of host immunity; however, microbial factors defining basal immune homeostasis remain poorly understood. While large-scale multi-omics have mapped microbial influences on stimulated immune responses, the fundamental dialogue maintaining resting inflammatory tone is often obscured by systemic confounders. In this study, we demonstrate that failing to account for sex leads to a Simpsons paradox. Using a multivariate framework, we reveal a profound sexual dimorphism in the gut-immune homeostatic axis. In women not using oral contraceptives, we identified consistent homeostatic anchors, including Bacteroides ovatus, Faecalitalea cylindroides, and Firmicutes bacterium CAG:83, which correlate negatively with TNF- and IL-1{beta} levels. This regulatory network is virtually abolished in women using oral contraceptives, revealing an associative silencing where exogenous synthetic hormones uncouple the microbiome from host immune sentinels. Furthermore, taxa such as Butyricimonas virosa exhibit sign reversals in cytokine associations depending on hormonal context, highlighting the plasticity of microbial immunomodulation. Our findings establish that basal immune homeostasis is a sex-specific construct and underscore the need for endocrine stratification to accurately decipher the regulatory architecture.

immunology↗

bioRxiv2024

Unraveling tumor heterogeneity: Quantitative insights from scRNA-seq analysis in breast cancer subtypes

Tumors are complex systems characterized by genetic, transcriptomic, phenotypic, and microenvironmental variations. The complexity of this heterogeneity plays a crucial role in metastasis, tumor progression, and recurrence. In this work, we utilized publicly available single-cell transcriptomics data from human breast cancer samples (ER+, HER2+, and triple-negative) to evaluate key concepts pertinent to cancer biology. Quantitative assessments included measures based on copy number alterations (CNAs), entropy, transcriptomic heterogeneity, and different protein-protein interaction networks (PPINs). We found that entropy and PPIN activity related to the cell cycle delineate cell clusters with notably elevated mitotic activity, particularly elevated in aggressive breast cancer subtypes. Additionally, CNA distributions differentiate between ER+ and HER2+/TN subtypes. Further, we identified positive correlations among the CNA score, entropy, and the activities of PPINs associated with the cell cycle, as well as basal and mesenchymal cell lines. These scores reveal associations with tumor characteristics, reflecting the known malignancy spectrum across breast cancer subtypes. By bridging the gap between existing literature and a comprehensive quantitative approach, we present a novel framework for quantifying cancer traits from scRNA-seq data by establishing several scores. This approach highlights the potential for deeper insights into tumor biology compared to conventional marker-based approaches.

bioinformatics↗

bioRxiv2022

ORIGINS: a protein network-based approach to quantify cell pluripotency from scRNA-seq data

Trajectory inference is a common application of scRNA-seq data. However, it is often necessary to previously determine the origin of the trajectories, the stem or progenitor cells. In this work, we propose a computational tool to quantify pluripotency from single cell transcriptomics data. This approach uses the protein-protein interaction (PPI) network associated with the differentiation process as a scaffold and the gene expression matrix to calculate a score that we call differentiation activity. This score reflects how active the differentiation network is for each cell. We benchmark the performance of our algorithm with two previously published tools, LandSCENT [1] and CytoTRACE [2], for four data sets: breast, colon, hematopoietic and lung. We show that our algorithm is more efficient than LandSCENT and requires less RAM memory than the other programs. We also illustrate a complete workflow from the count matrix to trajectory inference using the breast data set. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/491232v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1fd432forg.highwire.dtl.DTLVardef@45cdf0org.highwire.dtl.DTLVardef@d35b31org.highwire.dtl.DTLVardef@80702_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

SARS-CoV-2 codon usage bias downregulates host expressed genes with similar codon usage

Severe acute respiratory syndrome is quickly spreading throughout the world and was declared as a pandemic by the World Health Organisation (WHO). The pathogenic agent is a new coronavirus (SARS-CoV-2) that infects pulmonary cells with great effectiveness. In this study we focus on the codon composition for the viral proteins synthesis and its relationship with the proteins synthesis of the host. Our analysis reveals that SARS-CoV-2 preferred codons have poor representation of G or C nucleotides in the third position, a characteristic which could conduct to an unbalance in the tRNAs pools of the infected cells with serious implications in host protein synthesis. By integrating this observation with proteomic data from infected cells, we observe a reduced translation rate of host proteins associated with highly expressed genes, and that they share the codon usage bias of the virus. The functional analysis of these genes suggests that this mechanism of epistasis contributes to understand some deleterious collateral effect as result of the viral replication. In this manner, our finding contribute to the understanding of the SARS-CoV-2 pathogeny and could be useful for the design of a vaccine based on the live attenuated strategy.

genomics↗

bioRxiv2019

Alternating regimes of motion in cell motility models

Cellular movement is a complex dynamic process, resulting from the interaction of multiple elements at the intra and extra-cellular levels. This epiphenomenon presents a variety of behaviors, which can include normal and anomalous diffusion or collective migration. In some cases cells can get neighborhood information through chemical or mechanical cues. A unified understanding about how such information can influence the dynamics of cell movement is still lacking. In order to improve our comprehension of cell migration we consider a cellular Potts model where cells move actively in the direction of a driving field. The intensity of this driving field is constant, while its orientation can evolves according to two alternative dynamics based on the Ornstein-Uhlenbeck process. In the first case, the next orientation of the driving field depends on the previous direction of the field. In the second case, the direction update considers the mean orientation performed by the cell in previous steps. Thus, the latter update rule mimics the ability of cells to perceive the environment, avoiding obstacles and thus increasing the cellular displacement. Our results indicate that both dynamics introduce temporal and spatial correlations in cell velocity in a friction coefficient and cell density dependent manner. Furthermore, we observe alternating regimes in the mean square displacement, with normal and anomalous diffusion. The crossovers between superdiffusive and diffusive regimes, are strongly affected by both the driving field dynamics and cell-cell interactions. In this sense, when cell polarization update grants information about the previous cellular displacement decreases the duration of the diffusive regime, in particular for high density cultures.

biophysics↗

bioRxiv2018

Gene target discovery with network analysis in Toxoplasma gondii

Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogens biology. In this work, we propose a system biology approach to analyze transcriptomic data of the parasite Toxoplasma gondii. By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of the parasite were embedded into the dynamics of a gene network model. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 339 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small subnetwork module that controls the parasites life cycle. These new findings can contribute to understand of parasite pathogenesis.

systems biology↗