Biology subjects

Chammas, R.

Publications and source records attributed to Chammas, R..

4 recordsLinked to original sources

bioRxiv2026

Multi-scale Transcriptional Characterization of HPV16 and HPV18 in Cervical Cancer: Host Pathway Alterations and Links With Viral Gene Expression Programs

High-risk HPV is central to cervical carcinogenesis, but whether individual genotypes drive distinct tumor biology or converge on a shared oncogenic state remains unclear. Profiling viral and host gene expression across two independent cohorts (n = 93; 76 HPV16, 17 HPV18), we show that HPV16- and HPV18-associated cervical cancers differ in host transcriptional programs. A 55-gene signature discriminated the two genotypes, and 293 genes were concordantly differentially expressed across cohorts. Pathway and transcription-factor activity inference organized these differences along genotype-specific axes: HPV16 tumors were enriched for epithelial polarity and RTK-RAS-MAPK signatures, with elevated inferred CDX2 regulon activity, a transcription factor not normally active in cervical epithelium, suggesting an aberrant lineage-identity program not previously linked to this genotype. HPV18 tumors instead showed metabolic reprogramming and inflammatory signaling (STAT3, HIF1A), alongside reduced CASP8 and increased FDX1 expression, a pattern consistent with sensitization to immunogenic cell death. Both genotypes also organized along a continuum of early-to-late viral gene expression correlating with distinct host pathways: extracellular-matrix and translation-related in HPV16, interferon/antiviral and cell-cycle/mitochondrial in HPV18, indicating that viral transcriptional state associates with host biology beyond genotype alone. These results point to candidate genotype-specific vulnerabilities that merit functional validation.

cancer biology↗

bioRxiv2025

Spatially Organized DNA-templated Silver Nanoclusters as Potent Antimicrobial Agents for ESKAPE Infections

Antibiotic-resistant bacteria cause more than one million deaths annually worldwide. The rapid evolution and gene exchange among pathogens often render new antibiotic drugs ineffective soon after deployment, underscoring the urgent need for alternative therapeutic strategies. Nanoscale silver is well known for its innate bacteriostatic and bactericidal activity but typically requires high concentrations for efficacy that causes toxicities and limits broader clinical applications. To address this, we introduce programmable self-assembling DNA scaffolds that template, stabilize, and spatially organize multiple copies of monodisperse silver nanoclusters (DNA-AgNCs). These assemblies enhance antimicrobial potency of formulations while also exhibiting intrinsic fluorescence, providing dual functionality for therapeutic and bioimaging applications. Detailed characterization identified DNA-AgNC scaffolds with improved stability and enhanced activity against clinically relevant antibiotic-resistant planktonic ESKAPE pathogens. We also revealed the DNA-AgNC constructs that significantly lowered intracellular infections of human bone cells with Staphylococcus aureus. Collectively, the results highlight spatially organized DNA-AgNCs as a promising modular platform for next-generation antibacterial therapy and real-time bioimaging.

biochemistry↗

bioRxiv2019

A stochastic spatial model for heterogeneity in cancergrowth

Establishing a quantitative understanding of tumor heterogeneity, a major feature arising from the evolutionary processes taking place within the tumor microenvironment, is an important challenge for cancer biologists. Recently established experimental techniques enabled summarizing the variety of tumor cell phenotypes in proliferative or migratory. In the former, cells mostly proliferate and rarely migrate, while the opposite happens with cells having the latter phenotype, a \"go-and-grow\" description of heterogeneity. In this manuscript we present a discrete time Markov chain to simulate the spatial evolution of a tumor which heterogeneity is described by cells having those two phenotypes. The cell density curves have two qualitatively distinct temporal regimes, as they recover the Gompertz curve widely used for tumor growth description, and a bi-phasic growth which temporal shape resembles the tumor growth dynamics under influence of immunoediting. We also show how our representation of heterogeneity gives rise to variable spatial patterning even when the tumors have similar size and dynamics.\n\nAuthor summaryWe present a spatial stochastic model to represent the growth of a tumor as a structure having cells of two phenotypes: one whose cells have division as their predominant transition, and another where cells are mostly migrating. The migratory phenotype results from a transformation of the proliferative. Our proposition is based on the assumption that a tumor grows initially within a limited region while its cells are capable of acquire nutrients. During that phase, the cancer cells start changing their phenotype because of stress in their microenvironment and exhaustion of nutrients that lead them to become more migratory and capable of generating metastasis. Our model enables us to recover the usual dynamics observed in tumor growth such as a logistic-like curve, called Gompertz model, widely observed, or the bi-phasic growth observed characterized by equilibrium phase interspersed between two growth regimes. Our approach also enable us to understand the internal spatial and temporal structure of the two sub-populations and can be useful to investigate the phenomena underpinning heterogeneous tumor growth, a feature that helps on the design of treatment strategies based on mitigating heterogeneity related drug resistance.

systems biology↗

bioRxiv2017

Stochastic model of contact inhibition and the proliferation of melanoma in situ

Contact inhibition is a central feature orchestrating cell proliferation in culture experiments; its loss is associated with malignant transformation and tumorigenesis. We performed a co-culture experiment with human metastatic melanoma cell line (SKMEL-147) and immortalized keratinocyte cells (HaCaT). After 8 days a spatial pattern was detected, characterized by the formation of clusters of melanoma cells surrounded by keratinocytes constraining their proliferation. In addition, we observed that the proportion of melanoma cells within the total population has increased. To explain our results we propose a spatial stochastic model (following a philosophy of the Widom-Rowlinson model from Statistical Physics and Molecular Chemistry) which considers cell proliferation, death, migration, and cell-to-cell interaction through contact inhibition. Our numerical simulations demonstrate that loss of contact inhibition is a sufficient mechanism, appropriate for an explanation of the increase in the proportion of tumor cells and generation of spatial patterns established in the conducted experiments.

cancer biology↗