Biology subjects

Valcarcel, A.

Publications and source records attributed to Valcarcel, A..

4 recordsLinked to original sources

bioRxiv2023

Mycobacterium tuberculosis-Macrophage Interactome: Molecular Network Structure and Resilience against Antibiotics

Resistance to several antibiotics against Mycobacterium tuberculosis is a serious problem to be solved worldwide. In the present work, we made the statistical analysis of the gene regulatory network of Mycobacterium tuberculosis and of the Mycobacterium tuberculosis-macrophage interactome to find the probable cause of this resistance. The results from this analysis show that both the gene regulatory network and the interactoma have a hierarchical free scale modular structure that assures a high degree of resilience of these networks against external perturbations. In particular, the interactome is a complex hybrid network that results from the formation of novel links between the Mycobacterium tuberculosis and macrophage proteins and from the modification of the previously existing links between the native macrophage proteins, which give rise to novel negative and positive feedback loops that modify the dynamical behavior of the interactome and protect the mycobacterium against the attack with antibiotics by taking control of the macrophage immune response and apoptosis. The statistical analysis of the interactome shows that the highly connected mycobacterium proteins inhA, ahpC, kasA, katG and rpsL exert this control by creating new links with the host proteins FAS and NF-{kappa}B. These new hybrid circuits embedded in the hierarchical scale-free modular molecular structure of the interactome produce its high resistance to external perturbations like antibiotics. As consequence, the present work proposes the hypothesis that Mycobacterium tuberculosis antibiotic resistance in vivo during chronic tuberculosis is only a particular case of a more complex problem that is the interactome resilience against antibiotics. Thus, new strategies of drug design are necessary to shatter the complex structure of the Mycobacterium tuberculosis-macrophage interactome.

systems biology↗

bioRxiv2022

gMCStool: automated network-based tool to search for metabolic vulnerabilities in cancer

The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gMCStool, a freely accessible and online tool that allows us to carry out this task in a simple, efficient and intuitive environment. gMCStool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of thousands of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. Based on RNA-seq data, gMCStool extends and improves our previously developed algorithms to predict, visualize and analyze metabolic essential genes in cancer, demonstrating a superior performance than competing algorithms in both accuracy and computational performance. A detailed illustration of gMCStool is presented for multiple myeloma (MM), an incurable hematological malignancy. gMCStool could identify a synthetic lethal that explains the dependency on CTP Synthase 1 (CTPS1) in a sub-group of MM patients. We provide in vitro experimental evidence that supports this hypothesis, which opens a new research area to treat MM.

systems biology↗

bioRxiv2019

TAPAS: A Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis

Total brain white matter lesion (WML) volume is the most widely established magnetic resonance imaging (MRI) outcome measure in studies of multiple sclerosis (MS). To estimate WML volume, there are a number of automatic segmentation methods, yet, manual delineation remains the gold standard approach. These approaches often yield a probability map to which a threshold is applied to create lesion segmentation masks. Unfortunately, few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. In this study, we propose and validate an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense WMLs. Using multimodal MRI, the proposed method applies an automatic segmentation algorithm to obtain probability maps. We obtain the true subject-specific threshold that maximizes Sorensen-Dice Similarity Coefficient (DSC). Then the subject-specific thresholds are modeled on a naive estimate of volume using a general additive model. Applying this model, we predict a subject-specific threshold in data not used for training. We ran a Monte Carlo-resampled split-sample cross-validation (100 validation sets) using two data sets: the first obtained from the Johns Hopkins Hospital (JHH) on a Philips 3 Tesla (3T) scanner (n = 94) and a second collected at the Brigham and Womens Hospital (BWH) using a Siemens 3T scanner (n = 40). By means of the proposed automated technique, in the JHH data, we found an average reduction in subject-level absolute error of 0.1 mL per one mL increase in manual volume. Using Bland-Altman analysis, we found that volumetric bias associated with group-level thresholding is mitigated when applying TAPAS. The BWH data showed similar absolute error estimates using group-level thresholding or TAPAS likely since Bland-Altman analyses indicate no systematic biases associated with group or TAPAS volume estimates. The current study presents the first validated fully automated method for subject-specific threshold prediction to segment brain lesions.

neuroscience↗

bioRxiv2017

MIMoSA: A Method for Inter-Modal Segmentation Analysis

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis. While these lesions have been studied for over two decades using MRI technology, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WML are based on a single imaging modality, recent advances have used multimodal techniques for identifying WML. Complementary imaging modalities emphasize different tissue properties, which can help identify and characterize interrelated features of lesions. However, prior work has ignored relationships between imaging modalities, which may be informative in this clinical context. To harness the coherent changes in these measurements, we utilized inter-modal coupling regression (IMCo) to estimate the covariance structure across modalities. We then used a local logistic regression, MIMoSA, which leverages new covariance features from IMCo regression as well as the mean structure of each imaging modality in order to model the probability that any voxel is part of a lesion. Finally, we introduced a novel thresholding algorithm to fully automate the estimation of the probability maps to generate fully automated segmentations masks for 94 subjects. To evaluate the performance of the automated segmentations generated using MIMoSA we compared results with gold standard manual segmentations. We demonstrate the superiority of MIMoSA to other automated segmentation techniques by comparing it to the OASIS algorithm as well as LesionTOADS. MIMoSA resulted in statistically significant improvement in lesion segmentation.

neuroscience↗