Biology subjects

Freschi, L.

Publications and source records attributed to Freschi, L..

4 recordsLinked to original sources

bioRxiv2018

Genome wide association with quantitative resistance phenotypes in Mycobacterium tuberculosis reveals novel resistance genes and regulatory regions

Drug resistance is threatening attempts at tuberculosis epidemic control. Molecular diagnostics for drug resistance that rely on the detection of resistance-related mutations could expedite patient care and accelerate progress in TB eradication. We performed minimum inhibitory concentration testing for 12 anti-TB drugs together with Illumina whole genome sequencing on 1452 clinical Mycobacterium tuberculosis (MTB) isolates. We then used a linear mixed model to evaluate genome wide associations between mutations in MTB genes or noncoding regions and drug resistance, followed by validation of our findings in an independent dataset of 792 patient isolates. Novel associations at 13 genomic loci were confirmed in the validation set, with 2 involving noncoding regions. We found promoter mutations to have smaller average effects on resistance levels than gene body mutations in genes where both can contribute to resistance. Enabled by a quantitative measure of resistance, we estimated the heritability of the resistance phenotype to 11 anti-TB drugs and identify a lower than expected contribution from known resistance genes. We also report the proportion of variation in resistance levels explained by the novel loci identified here. This study highlights the complexity of the genomic mechanisms associated with the MTB resistance phenotype, including the relatively large number of potentially causative or compensatory loci, and emphasizes the contribution of the noncoding portion of the genome.

evolutionary biology↗

bioRxiv2018

Genotypic clustering does not imply recent tuberculosis transmission in a high prevalence setting: A genomic epidemiology study in Lima, Peru

BackgroundWhole genome sequencing (WGS) can elucidate Mycobacterium tuberculosis (Mtb) transmission patterns but more data is needed to guide its use in high-burden settings. In a household-based transmissibility study of 4,000 TB patients in Lima, Peru, we identified a large MIRU-VNTR Mtb cluster with a range of resistance phenotypes and studied host and bacterial factors contributing to its spread.\n\nMethodsWGS was performed on 61 of 148 isolates in the cluster. We compared transmission link inference using epidemiological or genomic data with and without the inclusion of controversial variants, and estimated the dates of emergence of the cluster and antimicrobial drug resistance acquisition events by generating a time-calibrated phylogeny. We validated our findings in genomic data from an outbreak of 325 TB cases in London. Using a larger set of 12,032 public Mtb genomes, we determined bacterial factors characterizing this cluster and under positive selection in other Mtb lineages.\n\nFindingsFour isolates were distantly related and the remaining 57 isolates diverged ca. 1968 (95% HPD: 1945-1985). Isoniazid resistance arose once, whereas rifampicin resistance emerged subsequently at least three times. Amplification of other drug resistance occurred as recently as within the last year of sampling. High quality PE/PPE variants and indels added information for transmission inference. We identified five cluster-defining SNPs, including esxV S23L to be potentially contributing to transmissibility.\n\nInterpretationClusters defined by MIRU-VNTR typing, could be circulating for decades in a high-burden setting. WGS allows for an improved understanding of transmission, as well as bacterial resistance and fitness factors.\n\nFundingThe study was funded by the National Institutes of Health (Peru Epi study U19-AI076217 and K01-ES026835 to MRF). The funding sources had no role in any aspect of the study, manuscript or decision to submit it for publication.\n\nResearch in contextO_ST_ABSEvidence before this studyC_ST_ABSUse of whole genome sequencing (WGS) to study tuberculosis (TB) transmission has proven to have higher resolution that traditional typing methods in low-burden settings. The implications of its use in high-burden settings are not well understood.\n\nAdded value of this studyUsing WGS, we found that TB clusters defined by traditional typing methods may be circulating for several decades. Genomic regions typically excluded from WGS analysis contain large amount of genetic variation that may affect interpretation of transmission events. We also identified five bacterial mutations that may contribute to transmission fitness.\n\nImplications of all the available evidenceAdded value of WGS for understanding TB transmission may be even higher in high-burden vs. low-burden settings. Methods integrating variants found in polymorphic sites and insertions and deletions are likely to have higher resolution. Several host and bacterial factors may be responsible for higher transmissibility that can be targets of intervention to interrupt TB transmission in communities.

microbiology↗

bioRxiv2018

Deep Learning Predicts Tuberculosis Drug Resistance Status from Whole-Genome Sequencing Data

BackgroundThe diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinical Mycobacterium tuberculosis isolates promises to circumvent the long wait times and limited scope of conventional phenotypic antimicrobial susceptibility, but gaps remain for predicting phenotype accurately from genotypic data.\n\nMethods and FindingsUsing targeted or whole genome sequencing and conventional drug resistance phenotyping data from 3,601 Mycobacterium tuberculosis strains, 1,228 of which were multidrug resistant, we investigated the use of machine learning to predict phenotypic drug resistance to 10 anti-tuberculosis drugs. The final model, a multitask wide and deep neural network (MD-WDNN), achieved improved high predictive performance: the average AUCs were 0.979 for first-line drugs and 0.936 for second-line drugs during repeated cross-validation. On an independent validation set, the MD-WDNN showed average AUCs, sensitivities, and specificities, respectively, of 0.937, 87.9%, and 92.7% for first-line drugs and 0.891, 82.0% and 90.1% for second-line drugs. In addition to being able to learn from samples that have only been partially phenotyped, our proposed multidrug architecture shares information across different anti-tuberculosis drugs and genes to provide a more accurate phenotypic prediction. We use t-distributed Stochastic Neighbor Embedding (t-SNE) visualization and feature importance analyses to examine inter-drug similarities.\n\nConclusionsMachine learning is capable of accurately predicting resistant status using genomic information and holds promise in bringing sequencing technologies closer to the bedside.

bioinformatics↗

bioRxiv2018

SELF-PRUNING affects auxin responses synergistically with the cyclophilin A DIAGEOTROPICA in tomato

SummaryThe antiflorigenic signal SELF-PRUNING, which controls growth habit, exerts its effects through auxin transport, signaling and metabolism in tomato.\n\nAbstractThe SELF PRUNING (SP) gene is a key regulator of growth habit in tomato (Solanum lycopersicum). It is an ortholog of TERMINAL FLOWER 1, a phosphatidyl-ethanolamine binding protein with anti-florigenic activity in Arabidopsis thaliana. A spontaneous loss-of-function sp mutation has been bred into a large number of industrial tomato cultivars, as it produces a suite of pleiotropic effects that are favorable for mechanical harvesting, including determinate growth habit, short plant stature and simultaneous fruit ripening. However, the physiological basis for these phenotypic differences has not been thoroughly explained. Here, we show that the sp mutation alters polar auxin transport as well as auxin responses such gravitropic curvature and elongation of excised hypocotyl segments. We further demonstrate that free auxin levels and auxin-regulated gene expression patterns are altered in sp, with epistatic effects of diageotropica, a mutation in a cyclophilin A protein-encoding gene. Our results indicate that SP impacts growth habit in tomato, at least in part, via changes in auxin transport and responsiveness. These findings hint at novel targets that could be manipulated in the control of growth habit and productivity.

plant biology↗