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Morais, M. C. C.

Publications and source records attributed to Morais, M. C. C..

3 recordsLinked to original sources

HTLV-1-Induced Neuroimmunome Correlates with Disease Progression and Severity

Human T-cell lymphotropic virus type 1 (HTLV-1) is a retrovirus that infects approximately 5-10 million people worldwide. While most individuals remain asymptomatic, a significant subset develops debilitating neuroinflammatory or malignant disorders, including adult T-cell leukemia/lymphoma (ATL) and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). To unravel the systemic molecular mechanisms underlying HTLV-1 pathogenesis, we employed a multi-dimensional systems biology approach, integrating bulk transcriptomic data from total PBMCs (n = 200) with single-cell RNA sequencing (scRNA-seq) from 233,093 peripheral blood mononuclear cells (PBMCs). Our analysis revealed a consistent and clinically relevant neuroimmune signature within leukocytes, termed the neuroimmunome, comprising a set of differentially expressed genes shared across the nervous and immune systems. Through dimensionality reduction and machine learning techniques, such as PCA, gradient boosting, and MANOVA with bootstrapping, we identified potential biomarkers predictive of HTLV-1-driven leukemogenesis, which were subsequently validated across ATL, HAM/TSP, and asymptomatic cohorts via flow cytometry. Notably, expression levels of proteins such as ATF4 and SKIL were strongly correlated with proviral load, suggesting that sustained neuroimmune dysregulation may contribute to disease progression. These findings highlight a previously underappreciated neuroimmunological layer, redefining HTLV-1-associated disease as a condition deeply rooted in neuroimmune network disruption within leukocytes and offering potential novel targets.

molecular biology↗

Some lessons and perspectives for applications of stochastic models in biological and cancer research

Randomness is an unavoidable feature of inner cellular environment and its effects propagate to higher levels of living matter organization such as cells, tissues, and organisms. Approaching those systems experimentally to understand their dynamics is a complex task because of the plethora of compounds interacting in a web that combines intra and inter level elements such that a coordinate behavior come up. Such a characteristic points to the necessity of establishing principles that help on the description, categorization, classification, and the prediction of the behavior of biological systems. The theoretical machinery already available, or the ones to be discovered motivated by biological problems, can play an important role on that quest. Here we exemplify the applicability of theoretical tools by discussing some biological problems that we have approached mathematically: fluctuations in gene expression and cell proliferation in the context of loss of contact inhibition. We discuss the methods that we have employed aiming to provide the reader with a phenomenological, biologically motivated, perspective of the use of theoretical methods. Furthermore, we discuss some of our conclusions after employing our approach and some research perspectives that they motivate.

systems biology↗

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↗