Biology subjects

Riviere, E.

Publications and source records attributed to Riviere, E..

6 recordsLinked to original sources

bioRxiv2026

Feature-based in-silico model to predict the Mycobacterium tuberculosis bedaquiline phenotype associated with Rv0678 variants

Bedaquiline resistance is emerging globally and threatens the effectiveness of the novel short all-oral regimens for rifampicin-resistant tuberculosis. Following a systematic literature review, we quantified 13 sequence, biochemical, and structural features of 62 Rv0678 missense variants reported in 136 Mycobacterium tuberculosis isolates. Using rigorous machine learning methods, we show that the strongest contributing features were the evolutionary conservation score and the shortest atomic distance to key functional sites. The final 5-feature model had good performance (ROC-AUC 0.826) and classified the bedaquiline phenotype with high accuracy [sensitivity 87.1% (95% CI, 78.3-92.6) and specificity 88.2% (95% CI, 76.6-94.5)]. Performance was lower in external validation, likely due to the measurement error introduced when using diverse phenotypic methods. missense variants on the mmpR5 protein structure and function. Integrating the five-feature in-silico in variant interpretation software could improve the prediction of the effect of Rv0678 variants and guide clinical management of rifampicin-resistant tuberculosis.

bioinformatics↗

bioRxiv2022

Predicting MmpR-based bedaquiline resistance using sequence- and structure-based features

Accurate molecular detection of resistance to bedaquiline, a core drug for the treatment of drug resistant tuberculosis, remains challenging. In this study, we investigated whether three sequence- and nine structure-based features describing the impact of Rv0678 variants on the MmpR transcriptional repressor could predict the bedaquiline phenotype of isolates containing Rv0678 variants. Paired genotypic and phenotypic data was used to train a binary random forest classifier. The mean value of the individual features was similar for resistant and susceptible variants (p[≥]0.05). Leave one out cross validation showed predictability of bedaquiline resistance from Rv0678 variants when using a binary classifier with a combination of sequence and structural features (ROC AUC = 0.766; f1 score = 0.746), but the performance was too low for clinically use. Evolutionary conservation of the affected residue was the most important individual feature (mean decrease in impurity = 0.226 {+/-} 0.003) to discriminate bedaquiline resistance from susceptibility. Prediction of bedaquiline resistance was only possible when restricting the data to missense variants, as the selected features could not be applied to Rv0678 insertions and deletions. Additionally, prediction of bedaquiline resistance was only possible when restricting the data to isolates for which the phenotype was determined using Mycobacterial Growth Indicator Tubes, suggesting possible misclassification of the bedaquiline phenotype by other methods. The results of this study suggest that structural features describing Rv0678 missense variants could be used to predict bedaquiline resistance, albeit not yet at the performance level required for clinical practice. Author summaryGuidelines by the World Health Organization indicate that antibiotic resistant tuberculosis should be treated with bedaquiline, a newly discovered antibiotic. However, resistance to bedaquiline has already emerged and is spreading rapidly. More than 500 different variants have been discovered in the bedaquiline resistance genes in Mycobacterium tuberculosis. It is not known which DNA variants specifically drive bedaquiline resistance due to insufficiency of data. Here, we used a machine learning technique to predict whether a variant in the Rv0678 gene confers bedaquiline resistance. To this end, for each variant we defined features that describe the impact of the variant on the MmpR (encoded by the Rv0678 gene) protein structure. These features were then fed into a classification algorithm to predict bedaquiline resistance. We successfully developed a bedaquiline resistance prediction model, although performance was limited. We also found that accurate predictions were only possible when restricting the data to samples that were tested on the MGIT platform, a World Health Organization endorsed method to test for bedaquiline resistance. Our study shows that predicting bedaquiline resistance from protein structural features is feasible. Furthermore, our study provides new insights into phenotypic testing platforms to assess bedaquiline resistance.

microbiology↗

bioRxiv2022

A Bayesian approach to estimate the probability of resistance to bedaquiline in the presence of a genomic variant

BackgroundBedaquiline is a core drug for treatment of rifampicin-resistant tuberculosis. Few genomic variants have been statistically associated with bedaquiline resistance. Alternative approaches for determining the genotypic-phenotypic association are needed to guide clinical care. MethodsUsing published phenotype data for variants in Rv0678, atpE, pepQ and Rv1979c genes in 756 Mycobacterium tuberculosis isolates and survey data of the opinion of 33 experts, we applied Bayesian methods to estimate the posterior probability of bedaquiline resistance and corresponding 95% credible intervals. ResultsExperts agreed on the role of Rv0678, and atpE, were uncertain about the role of pepQ and Rv1979c variants and overestimated the probability of bedaquiline resistance for most variant types, resulting in lower posterior probabilities compared to prior estimates. The posterior median probability of bedaquiline resistance was low for synonymous mutations in atpE (0.1%) and Rv0678 (3.3%), high for missense mutations in atpE (60.8%) and nonsense mutations in Rv0678 (55.1%), relatively low for missense (31.5%) mutations and frameshift (30.0%) in Rv0678 and low for missense mutations in pepQ (2.6%) and Rv1979c (2.9%), but 95% credible intervals were wide. ConclusionsBayesian probability estimates of bedaquiline resistance given the presence of a specific mutation could be useful for clinical decision-making as it presents interpretable probabilities compared to standard odds ratios. For a newly emerging variant, the probability of resistance for the variant type and gene can still be used to guide clinical decision-making. Future studies should investigate the feasibility of using Bayesian probabilities for bedaquiline resistance in clinical practice.

microbiology↗

bioRxiv2021

Predicting compound-protein interaction using hierarchical graph convolutional networks

MotivationConvolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop a computational method based on convolutional neural networks to tackle a fundamental question in drug discovery and development, i.e. the prediction of compound-protein interactions based on compound structure and protein sequence. We propose a hierarchical graph convolutional network (HGCN) to encode small molecules. The HGCN aggregates a molecule embedding from substructure embeddings, which are synthesized from atom embeddings. As small molecules usually share substructures, computing a molecule embedding from those common substructures allows us to learn better generic models. We then combined the HGCN with a one-dimensional convolutional network to construct a complete model for predicting compound-protein interactions. Furthermore we apply an explanation technique, Grad-CAM, to visualize the contribution of each amino acid into the prediction. ResultsExperiments using different datasets show the improvement of our model compared to other GCN-based methods and a sequence based method, DeepDTA, in predicting compound-protein interactions. Each prediction made by the model is also explainable and can be used to identify critical residues mediating the interaction. Availability and implementationhttps://github.com/banhdzui/cpi_hgcn.git

bioinformatics↗

bioRxiv2021

Absence of severe COVID-19 in patients with clonal mast cells activation disorders: effective anti-SARS-CoV-2 immune response.

Mast cells are key actors of innate immunity and Th2 adaptive immune response which counterbalance Th1 response, critical for anti-viral immunity. Clonal Mast Cells Activation Disorders (cMCADs) such as mastocytosis and clonal mast cells activation syndrome are characterized by an abnormal mast cells accumulation and/or activation. No data have been published on the anti-viral immune response of patients with cMCADs. The aims of the study were to collected, in a comprehensive way, outcomes of cMCADs patients who experienced a biologically-proven COVID-19 and to characterize both anti-endemic coronaviruses and specific anti-SARS-CoV-2 immune responses in these patients. Clinical follow-up and outcome data were collected prospectively for one year within the French rare disease network CEREMAST encompassing patients from all over the country. Anti-SARS-CoV-2 and anti-endemic coronaviruses specific T-cells were assessed with an enzyme-linked immunospot assay (EliSpot) and anti-SARS-CoV-2 humoral response with dosage of circulating levels of specific IgG, IgA and neutralizing antibodies. Overall, 32 cMCADs patients were identified. None of them required non-invasive or mechanical ventilation; two patients were hospitalized to receive oxygen and steroid therapy. In 21 patients, a characterization of the SARS-CoV-2-specific immune response has been performed. A majority of patients showed a high proportion of circulating SARS-CoV-2-specific interferon (IFN)-{gamma} producing T-cells and high levels of anti-Spike IgG antibodies with neutralizing activity. In addition, no defects in anti-endemic coronaviruses responses were found in patients with cMCADs compared to non-cMCADs controls. Patients with cMCADs frequently showed a spontaneous IFN-{gamma} T-cell production in absence of any stimulation that correlated with circulating basal tryptase levels, a marker of mast cells burden. These findings underscore that patients with cMCADs might be not at risk of severe COVID-19 and the spontaneous IFN-{gamma} production might explain this observation. Author SummaryMast cells are immune cells involved in many biological processes including the anti-microbial response. However, previous studies suggest that mast cells may have a detrimental role in the response against viruses such as SARS-CoV-2, responsible for COVID-19. When a mutation occurs in mast cells, it can lead to a group of diseases called clonal mast cells activation disorders (cMCADs), characterized by deregulated activation of these cells. Hence, patients with cMCADs might be more susceptible to severe COVID-19 than general population. We therefore conducted a 1-year study in France to collect data from all cMCADs patients included in the CEREMAST rare disease French network and who experienced COVID-19. Interestingly, we did not find any severe COVID-19 (i.e. requiring non-invasive or mechanical ventilation) in spite of well-known risk factors for severe COVID-19 in a part of cMCADs patients. We then have studied the immune response against SARS-CoV-2 and other endemic coronaviruses in these patients. We did not observe any abnormalities in the immune response either at the level of T and B lymphocytes. These findings underscore that these patients might not be at risk of severe COVID-19 as one might have feared.

immunology↗

bioRxiv2019

A reservoir of rituximab-resistant splenic memory B cells contributes to relapses after B-cell depletion therapy

Immune thrombocytopenia (ITP) is an autoimmune disease mediated by pathogenic antibodies directed against platelet antigens, including GPIIbIIIa. Taking advantage of spleen samples obtained from ITP patients, we characterized by multiples approaches the onset of disease relapses occurring after an initial complete response to rituximab. Analysis of splenic B cell immunoglobulin heavy chain gene repertoire at bulk level and from single anti-GPIIbIIIa B cells revealed that germinal centers were fueled by B cells originating from the ongoing lymphopoiesis, but also by rituximab-resistant memory B cells, both giving rise to anti-GPIIbIIIa plasma cells. We identified a population of splenic memory B cells that resisted rituximab through acquisition of a unique phenotype and contributed to relapses, providing a new target in B cell mediated autoimmune diseases.

immunology↗