Biology subjects

Carels, N.

Publications and source records attributed to Carels, N..

5 recordsLinked to original sources

bioRxiv2025

Identifying potential therapeutic targets for T-cell acute lymphoblastic leukemia using malignant networks and topological analysis

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive and heterogeneous disease requiring new therapeutic targets. We identified overexpressed genes in T-ALL cell lines, built subnetworks using the IntAct interactome, and evaluated the topological role of each gene. An attack strategy, removing one gene at a time, was applied with eleven network measures and persistent homology. A positive control group of essential genes for T-ALL tumorigenesis was included. Clustering, largest connected component, and especially Betti 1 effectively distinguished control genes from others, showing that topological metrics are valuable for target identification. Notably, NPM1 emerged as a key gene for maintaining network integrity, promoting proliferation, and ensuring survival, highlighting its potential as a promising therapeutic target in T-ALL. Significance of the studyThis study proposes an innovative approach to finding therapeutic targets for T-ALL based on oncogenic protein-protein interaction networks and the identification of the most effective analysis metrics following network attacks. These findings can be broadly applied to any neoplasm, seeking more personalized and efficient therapy.

cancer biology↗

bioRxiv2023

OPTIMIZING THERAPEUTIC TARGETS FOR BREAST CANCER USING BOOLEAN NETWORK MODELS

Studying gene regulatory networks associated with cancer provides valuable insights for therapeutic purposes, given that cancer is fundamentally a genetic disease. However, as the number of genes in the system increases, the complexity arising from the interconnections between network components grows exponentially. In this study, using Boolean logic to adjust the existing relationships between network components has facilitated simplifying the modeling process, enabling the generation of attractors that represent cell phenotypes based on breast cancer RNA-seq data. A key therapeutic objective is to guide cells, through targeted interventions, to transition from the current cancer attractor to a physiologically distinct attractor unrelated to cancer. To achieve this, we developed a computational method that identifies network nodes whose inhibition can facilitate the desired transition from one tumor attractor to another associated with apoptosis, leveraging transcriptomic data from cell lines. To validate the model, we utilized previously published in vitro experiments where the downregulation of specific proteins resulted in cell growth arrest and death of a breast cancer cell line. The method proposed in this manuscript combines diverse data sources, conducts structural network analysis, and incorporates relevant biological knowledge on apoptosis in cancer cells. This comprehensive approach aims to identify potential targets of significance for personalized medicine.

cancer biology↗

bioRxiv2023

Statistical characterization of the dynamics of Glioblastoma Multiforme subtypes through parameters estimation of the epigenetic landscape

Glioblastoma Multiforme (GBM), a brain tumor distinguished for its aggressive nature, presents heterogeneity that stems from multifaceted mechanisms, such as genetic mutations, epigenetic modifications, genomic instability, and selective pressures. We reason that the aggressiveness of GBM enables even a limited dataset to serve as a representative sample within the domain of cancer attractors, allowing our sample to reflect a representative trajectory within the attractors domain. Utilizing single-cell RNA sequencing data, we proceed with a detailed analysis of GBMs cellular landscape. Rooted in characteristics observed in stochastic systems, we considered factors like genomic instability to introduce a level of noise or unpredictability, thereby characterizing the cancer dynamics through stochastic fixed points. These fixed points, derived from centroids obtained through various clustering methods, were rigorously verified for method sensitivity. This methodological foundation, wherein sample and time averages are equivalent, assigns paramount interpretative value to the data cluster centroids, aiding both in parameter fitting and subsequent stochastic simulations. This scenario is supported by the compelling correlation found between centroids of experimental and simulated datasets. The use of stochastic modeling to compute the Waddington landscape enriched our analysis of GBMs cellular heterogeneity and provided a visual framework for validating the centroids and standard deviations as accurate characterizations of potential cancer attractors. Specifically, this approach allowed us to assess the compatibility between data dispersion and the corresponding basin of attraction, thereby bridging molecular-level variations and systems-level dynamics. We also examined the stability and transitions between these attractors, revealing a potential interplay between subtypes and potentially uncovering factors that drive cancer recurrence and progression. By connecting molecular mechanisms related to cancer heterogeneity with statistical properties of gene expression dynamics we expect to set the stage for the development of potential diagnostic tools and pave the way for personalized therapeutic interventions.

systems biology↗

bioRxiv2021

SARS-CoV-2 proteins bind heme and hemoglobin

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has led to a global crisis that included collapsing healthcare systems and shut-down communities, producing considerable economic burden. Despite the number of effective vaccines quickly implemented, the emergence of new variants is a primary concern. The scientific community undertook a rapid response to better study this new virus. However, critical questions about viral protein-protein interactions and mechanisms of its physiopathology are still unclear. Although severe COVID-19 was associated with hematological dysfunctions, scarce experimental data were produced about iron dysmetabolism and the viral proteins possible interaction with hemoglobin (Hb) chains. This work demonstrates the binding of SARS-CoV-2 proteins to hemin and Hb using a multimethodological approach. In silico analysis indicated binding motifs between a cavity in the viral nucleoprotein and hemoglobins porphyrin coordination region. Different hemin binding capacities of mock and SARS-CoV-2-infected culture extracts were noticed using gel electrophoresis and TMB staining. Hemin-binding proteins were isolated from SARS-CoV-2-infected cells by affinity chromatography and identified by shotgun proteomics, indicating that structural (nucleoprotein, spike, and membrane protein) and non-structural (Nsp3 and Nsp7) viral proteins interact with hemin. In vitro analyses of virus adsorption to host cells and viral replication studies in Vero cells demonstrated inhibitory activities - at different levels - by hemin, protoporphyrin IX (PpIX) Hb. Strikingly, free Hb at 1M suppressed viral replication (99 %), and its interaction with SARS-CoV-2 was localized to the RBD region of the Spike protein. The findings showed clear evidence of new avenues to disrupt viral replication and understand virus physiopathology that warrants further investigation.

biochemistry↗

bioRxiv2020

Atazanavir inhibits SARS-CoV-2 replication and pro-inflammatory cytokine production

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is already responsible for far more deaths than previous pathogenic coronaviruses (CoVs) from 2002 and 2012. The identification of clinically approved drugs to be repurposed to combat 2019 CoV disease (COVID-19) would allow the rapid implementation of potentially life-saving procedures. The major protease (Mpro) of SARS-CoV-2 is considered a promising target, based on previous results from related CoVs with lopinavir (LPV), an HIV protease inhibitor. However, limited evidence exists for other clinically approved antiretroviral protease inhibitors, such as atazanavir (ATV). ATV is of high interest because of its bioavailability within the respiratory tract. Our results show that ATV could dock in the active site of SARS-CoV-2 Mpro, with greater strength than LPV. ATV blocked Mpro activity. We confirmed that ATV inhibits SARS-CoV-2 replication, alone or in combination with ritonavir (RTV) in Vero cells, human pulmonary epithelial cell line and primary monocytes, impairing virus-induced enhancement of IL-6 and TNF- levels. Together, our data strongly suggest that ATV and ATV/RTV should be considered among the candidate repurposed drugs undergoing clinical trials in the fight against COVID-19.

microbiology↗