Biology subjects

Freschi, L.

Publications and source records attributed to Freschi, L..

17 recordsLinked to original sources

bioRxiv2025

Shedding light on cuticle formation: phytochrome B and downstream signaling events controlling cuticle deposition in tomato fruits

The cuticle is a hydrophobic barrier limiting water loss and pathogen entry. Although light influences cuticle formation, the underlying mechanisms remain poorly understood, particularly in fruits. Here, we show that fruits from tomato (Solanum lycopersicum) plants grown under far-red-enriched light conditions displayed increased cuticle load due to the up-regulation of cutin and wax biosynthetic genes. Both tomato PHYB-encoding genes (SlPHYB1 and SlPHYB2) were implicated as negative regulators of fruit cuticle formation, as revealed by the thinner cuticles, reduced abundance of cutin monomers and cuticular waxes, and downregulation of cuticle-related genes detected in fruits from transgenic plants overexpressing constitutively active alleles of SlPHYB1/B2 (SlYHB1OE and SlYHB2OE). The impaired cuticle of SlYHB1/B2OE fruits resulted in increased susceptibility to Botrytis cinerea, despite their markedly enhanced flavonoid accumulation in the peel. PHYTOCHROME-INTERACTING FACTORs (SlPIF3, SlPIF4) and the B-box protein SlBBX28 were identified as downstream factors of SlPHYB1/B2-mediated regulation of cuticle deposition, synergistically activating the expression of cuticle-related genes. Additionally, SlPHYB1/B2 signaling also downregulated multiple central carbon metabolism-related genes in the fruit peel, likely reducing the pool of activated fatty acids (FAs) feeding into cutin and wax biosynthetic pathways. Together, our results uncover a novel regulatory layer of light signaling in fleshy fruits, revealing that, besides controlling fruit nutritional traits, the PHYB-PIF-BBX regulatory module also shapes the fruit cuticle formation. HighlightsPhytochrome-dependent light signaling regulates cuticle deposition in tomato fruits by controlling downstream transcription factors and the supply of precursors feeding into cutin/wax biosynthetic pathways, thereby affecting postharvest water loss and pathogen susceptibility.

plant biology↗

bioRxiv2022

The miR156-targeted SlSBP15 represses tomato shoot branching via modulating auxin transport and interacting with GOBLET and BRANCHED1b

The microRNA156 (miR156)/SQUAMOSA PROMOTER-BINDING PROTEIN-LIKE (SPL/SBP) regulatory hub is highly conserved among phylogenetically distinct species, but how it interconnects multiple pathways to converge to common integrators controlling shoot architecture is still unclear. Here, we demonstrated that the miR156/SlSBP15 hub modulates tomato shoot branching (SB) by connecting phytohormones with important genetic pathways regulating both axillary bud (AB) development and outgrowth. We verified that plants overexpressing the miR156 (156-OE plants) display high SB, whereas plants overexpressing a miR156-resistant SlSBP15 alelle (rSBP15 plants) display arrested SB and are able to partially restore the wild-type (WT) phenotype in156-OE background. Although rSBP15 plants showed ABs smaller than MT, its activation is dependent on shoot apex-derived auxin transport inhibition. Additionally, hormonal measurements reveal that IAA and ABA concentrations were lower in 156-OE and higher in rSBP15-OE plants. SlSBP15 regulates AB development and outgrowth by inhibiting auxin transport and the activity of GOBLET (GOB), and by interacting with BRANCHED1b (SlBRC1b) at the protein level to control abscisic acid (ABA) levels within ABs. Our data provide a new mechanism by which the miR156/SPL/SBP hub regulates SB, and suggest that SlSBP15 has potential applications in improving tomato architecture.

developmental biology↗

bioRxiv2022

The discovery of genome-wide mutational dependence in naturally evolving populations

BackgroundEvolutionary pressures on bacterial pathogens can result in phenotypic change including increased virulence, drug resistance, and transmissibility. Understanding the evolution of these phenotypes in nature and the multiple genetic changes needed has historically been difficult due to sparse and contemporaneous sampling. A complete picture of the evolutionary routes frequently travelled by pathogens would allow us to better understand bacterial biology and potentially forecast pathogen population shifts. MethodsIn this work, we develop a phylogeny-based method to assess evolutionary dependency between mutations. We apply our method to a dataset of 31,428 Mycobacterium tuberculosis complex (MTBC) genomes, a globally prevalent bacterial pathogen with increasing levels of antibiotic resistance. ResultsWe find evolutionary dependency within simultaneously- and sequentially-acquired variation, and identify that genes with dependent sites are enriched in antibiotic resistance and antigenic function. We discover 20 mutations that potentiate the development of antibiotic resistance and 1,003 dependencies that evolve as a consequence antibiotic resistance. Varying by antibiotic, between 9% and 80% of resistant strains harbor a dependent mutation acquired after a resistance-conferring variant. We demonstrate that mutational dependence can not only improve prediction of phenotype (e.g. antibiotic resistance), but can also detect sequential environmental pressures on the pathogen (e.g. the pressures imposed by sequential antibiotic exposure during the course of standard multi-antibiotic treatment). Taken together, our results demonstrate the feasibility and utility of detecting dependent events in the evolution of natural populations. Data and code available at: https://github.com/farhat-lab/DependentMutations

genomics↗

bioRxiv2022

Phase variation as a major mechanism of adaptation in Mycobacterium tuberculosis complex

Phase variation induced by insertions and deletions (INDELs) in genomic homopolymeric tracts (HT) can silence and regulate genes in pathogenic bacteria but this process is not characterized in MTBC adaptation. We leverage 31,428 diverse clinical isolates to identify genomic regions including phase-variants under positive selection. Of 87,651 INDEL events that emerge repeatedly across the phylogeny, 12.4% are phase-variants within HTs (0.02% of the genome by length). We estimated the in-vitro frameshift rate in a neutral HT at 100x the neutral substitution rate at 1.1 x 10-5 frameshifts/HT/year. Using neutral evolution simulations, we identified 4,098 substitutions and 45 phase-variants to be putatively adaptive to MTBC (P<0.002). We experimentally confirm that a putatively adaptive phase-variant alters the expression of espA, a critical mediator of ESX-1 dependent virulence. Our evidence supports a new hypothesis that phase variation in the ESX-1 system of MTBC can act as a toggle between antigenicity and survival in the host.

microbiology↗

bioRxiv2021

Auxin-driven ecophysiological diversification of leaves in domesticated tomato

Heterobaric leaves have bundle sheath extensions (BSEs) that compartmentalise the parenchyma, whereas homobaric leaves do not. The presence of BSEs affects leaf hydraulics and photosynthetic rate. The tomato (Solanum lycopersicum) obscuravenosa (obv) mutant lacks BSEs. Here we identify the obv gene and the causative mutation, a non-synonymous amino acid change that disrupts a C2H2 zinc finger motif in a putative transcription factor. This mutation exists as a rare polymorphism in the natural range of wild tomatoes, but has increased in frequency in domesticated tomatoes, suggesting that the latter diversified into heterobaric and homobaric leaf types. The obv mutant displays reduced vein density, leaf hydraulic conductance and photosynthetic assimilation rate. We show that these and other effects on plant development, including changes in leaf insertion angle, leaf margin serration, minor vein density and fruit shape, are controlled by OBV via changes in auxin signalling. Loss of function of the transcriptional regulator AUXIN RESPONSE FACTOR (ARF4) also results in defective BSE development, revealing an additional component of a novel genetic module controlling aspects of leaf development important for ecological adaptation and subject to breeding selection. One sentence summarydistribution of tomato heterobaric and homobaric leaves is controlled by a single-nucleotide polymorphism in an auxin-related transcription factor

plant biology↗

bioRxiv2021

A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis

Long diagnostic wait times hinder international efforts to address multi-drug resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution. However, generalizability and clinical adoption have been limited in part by a lack of interpretability and verifiability, especially in deep learning methods. Here, we present a deep convolutional neural network (CNN) that predicts the antibiotic resistance phenotypes of M. tuberculosis isolates. The CNN performs with state-of-the-art levels of predictive accuracy. Evaluation of salient sequence features permits biologically meaningful interpretation and validation of the CNNs predictions, with promising repercussions for functional variant discovery, clinical applicability, and translation to phenotype prediction in other organisms.

genomics↗

bioRxiv2021

The role of epistasis in amikacin, kanamycin, bedaquiline, and clofazimine resistance in Mycobacterium tuberculosis complex

Antibiotic resistance among bacterial pathogens poses a major global health threat. M. tuberculosis complex (MTBC) is estimated to have the highest resistance rates of any pathogen globally. Given the slow growth rate and the need for a biosafety level 3 laboratory, the only realistic avenue to scale up drug-susceptibility testing (DST) for this pathogen is to rely on genotypic techniques. This raises the fundamental question of whether a mutation is a reliable surrogate for phenotypic resistance or whether the presence of a second mutation can completely counteract its effect, resulting in major diagnostic errors (i.e. systematic false resistance results). To date, such epistatic interactions have only been reported for streptomycin that is now rarely used. By analyzing more than 31,000 MTBC genomes, we demonstrated that eis C-14T promoter mutation, which is interrogated by several genotypic DST assays endorsed by the World Health Organization, cannot confer resistance to amikacin and kanamycin if it coincides with loss-of-function (LoF) mutations in the coding region of eis. To our knowledge, this represents the first definitive example of antibiotic reversion in MTBC. Moreover, we raise the possibility that mmpR (Rv0678) mutations are not valid markers of resistance to bedaquiline and clofazimine if these coincide with LoF mutation in the efflux pump encoded by mmpS5 (Rv0677c) and mmpL5 (Rv0676c).

genomics↗

bioRxiv2021

GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning

IntroductionMultidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens. MethodsWe present Translational Genomics platform for Tuberculosis (GenTB), a web-based application to predict antibiotic resistance from next-generation sequence data. The user can choose between two potential predictors, a Random Forest (RF) classifier and a Wide and Deep Neural Network (WDNN) to predict phenotypic resistance to 13 and 10 anti-tuberculosis drugs, respectively. We benchmark GenTBs predictive performance along with leading TB resistance prediction tools (Mykrobe and TB-Profiler) using a ground truth dataset of 20,408 isolates with laboratory-based drug susceptibility data. ResultsAll four tools reliably predicted resistance to first-line tuberculosis drugs but had varying performance for second-line drugs. The mean sensitivities for GenTB-RF and GenTB-WDNN across the nine shared drugs was 77.6% (95% CI 76.6 - 78.5%) and 75.4% (95% CI 74.5 - 76.4%) respectively, and marginally higher than the sensitivities of TB-Profiler at 74.4% (95% CI 73.4 - 75.3%) and Mykrobe at 71.9% (95% CI 70.9 - 72.9%). The higher sensitivities were at an expense of [≤]1.5% lower specificity: Mykrobe 97.6% (95% CI 97.5 - 97.7%), TB-Profiler 96.9% (95% CI 96.7 to 97.0%), GenTB-WDNN 96.2% (95% CI 96.0 to 96.4%), and GenTB-RF 96.1% (95% CI 96.0 to 96.3%). Genotypic resistance sensitivity was 11% and 9% lower for isoniazid and rifampicin respectively, on isolates sequenced at low depth (<10x across 95% of the genome) emphasizing the need to quality control input sequence data before prediction. We discuss differences between tools in reporting results to the user including variants underlying the resistance calls and any novel or indeterminate variants ConclusionGenTB is an easy-to-use online tool to rapidly and accurately predict resistance to anti-tuberculosis drugs. GenTB can be accessed online at https://gentb.hms.harvard.edu, and the source code is available at https://github.com/farhat-lab/gentb-site.

bioinformatics↗

bioRxiv2020

Population structure, biogeography and transmissibility of Mycobacterium tuberculosis

Mycobacterium tuberculosis is a clonal pathogen proposed to have co-evolved with its human host for millennia, yet our understanding of its genomic diversity and biogeography remains incomplete. Here we use a combination of phylogenetics and dimensionality reduction to reevaluate the population structure of M. tuberculosis, providing the first in-depth analysis of the ancient East African Indian Lineage 1 and the modern Central Asian Lineage 3 and expanding our understanding of Lineages 2 and 4. We assess sub-lineages using genomic sequences from 4,939 pan-susceptible strains and find 30 new genetically distinct clades that we validate in a dataset of 4,645 independent isolates. We characterize sub-lineage geographic distributions and demonstrate a consistent geographically restricted and unrestricted pattern for 20 groups, including three groups of Lineage 1. We assess the transmissibility of the four major lineages by examining the distribution of terminal branch lengths across the M. tuberculosis phylogeny and identify evidence supporting higher transmissibility in Lineages 2 and 4 than 3 and 1 on a global scale. We define a robust expanded barcode of 95 single nucleotide substitutions (SNS) that allows for the rapid identification of 69 Mtb sub-lineages and 26 additional internal groups. Our results paint a higher resolution picture of the Mtb phylogeny and biogeography.

microbiology↗

bioRxiv2020

PHYTOCHROMES B1/B2 ARE MAJOR REGULATORS OF RIPENING-ASSOCIATED EPIGENOME REPROGRAMMING IN TOMATO FRUITS

Phytochrome-mediated light and temperature perception has been shown to be a major regulator of fruit development. Furthermore, chromatin remodelling via DNA demethylation has been described as a crucial mechanism behind the fruit ripening process; however, the molecular basis underlying the triggering of this epigenetic modification remains largely unknown. Here, through integrative analyses of the methylome, siRNAome and transcriptome of tomato fruits from phyA and phyB1B2 null mutants, we report that PHYB1 and PHYB2 control genome-wide DNA methylation during fruit development from green towards ripe stages. The experimental evidence indicates that PHYB1B2 signal transduction is mediated by a gene expression network involving chromatin organization factors (DNA methylases/demethylases, histone-modifying enzymes and remodelling factors) and transcriptional regulators leading in the altered mRNA profile of photosynthetic and ripening-associated genes. This new level of understanding provides insights into the orchestration of epigenetic mechanisms in response to environmental cues affecting agronomical traits.

plant biology↗

bioRxiv2020

Transcriptome analysis of three Agave fiber-producing cultivars suitable for biochemicals and biofuels production in semiarid regions

O_LIAgaves, which have been grown commercially for fiber or alcoholic beverages, are emerging as a candidate crop for biochemicals and biofuels production in semiarid regions because of their high productivity in low rainfall areas, drought tolerance, and low lignin content. C_LIO_LIIn this work, we present the transcriptomic atlas of Agave sisalana, Agave fourcroydes, and agave hybrid 11648 (A. amaniensis x A. angustifolia) under prolonged drought in field conditions. Leaf, stem, and root tissues were sequenced, and gene expression profiles were correlated with biomass composition, enzymatic hydrolysis of cell wall carbohydrates, histochemical analysis, and non-structural carbohydrates content. C_LIO_LIDifferences in biomass accessibility were attributed to either lignin content or lignin composition, possibly through modification of s/g ratio promoted by changes in Caffeic Acid 3-O-Methyltransferase (COMT) transcript abundance. Unlike most plants, the most highly expressed transcripts do not encode photosynthetic proteins, but rather involved in stress response. Although the three cultivars presented quantitative differences in global gene expression, they activated a highly overlapping set of genes. The main molecular strategies employed by agave to cope with high-temperature and drought seem to consist in overexpressing HSP and LEA, as well as promoting raffinose accumulation as an osmolyte. C_LIO_LIIn conclusion, our data provide vital new genetic information for the study of Agave species and provide new insights into cell wall architecture, recalcitrance, and resistance to abiotic stresses for these species. C_LI

plant biology↗

bioRxiv2019

Tuberculosis resistance acquisition in space and time: an analysis of globally diverse M. tuberculosis whole genome sequences

BackgroundMycobacterium tuberculosis (MTB) whole genome sequencing data can provide insights into temporal and geographic trends in resistance acquisition and inform public health interventions.\n\nMethodsWe curated a set of clinical MTB isolates with high quality sequencing and culture-based drug susceptibility data spanning four lineages and more than 20 countries. We constructed geographic and lineage specific MTB phylogenies and used Bayesian molecular dating to infer the most-recent-common-susceptible-ancestor age for 4,869 instances of resistance to 10 drugs.\n\nFindingsOf 8,550 isolates curated, 6,099 from 15 countries met criteria for molecular dating. The number of independent resistance acquisition events was lower than the number of resistant isolates across all countries, suggesting ongoing transmission of drug resistance. Ancestral age distributions supported the presence of old resistance, [≥]20 years prior, in the majority of countries. A consistent order of resistance acquisition was observed globally starting with resistance to isoniazid, but resistance ancestral age varied by country. We found a direct correlation between country wealth and resistance age (R2= 0.47, P-value= 0.014). Amplification of fluoroquinolone and second-line injectable resistance among multidrug-resistant isolates is estimated to have occurred very recently (median ancestral age 4.7 years IQR 1.9-9.8 prior to sample collection). We found the sensitivity of commercial molecular diagnostics for second-line resistance to vary significantly by country (P-value <0.0003)\n\nInterpretationOur results highlight that both resistance transmission and amplification are contributing to disease burden globally but are variable by country. The observation that wealthier nations are more likely to have old resistance suggests that programmatic improvements can reduce resistance amplification, but that fit resistant strains can circulate for decades subsequently.\n\nFundingThis work was supported by the NIH BD2K grant K01 ES026835, a Harvard Institute of Global Health Burke Fellowship (MF), Boston Childrens Hospital OFD/BTREC/CTREC Faculty Career Development Fellowship and Bushrod H. Campbell and Adah F. Hall Charity Fund/Charles A. King Trust Postdoctoral Fellowship (AD).\n\nResearch in contextO_ST_ABSEvidence before this studyC_ST_ABSAcquisition and spread of drug-resistance by Mycobacterium tuberculosis (MTB) varies across countries. Local factors driving evolution of drug resistance in MTB are not well studied.\n\nAdded value of this studyWe applied molecular dating to 6,099 global MTB patient isolates and found the order of resistance acquisition to be consistent across the countries examined, i.e. acquisition of isoniazid resistance first followed by rifampicin and streptomycin followed by resistance to other drugs. In all countries with data available there was evidence for transmission of resistant strains from patient-to-patient and in the majority for extended periods of time (>20 years).\n\nCountries with lower gross wealth indices were found to have more recent resistance acquisition to the drug rifampicin. Based on the resistance patterns identified in our study we estimate that commercial diagnostic tests vary considerably in sensitivity for second-line resistance diagnosis by country.\n\nImplications of all available evidenceThe longevity of resistant MTB in many parts of the world emphasizes its fitness for transmission and its continued threat to public health. The association between country wealth and recent resistance acquisition emphasizes the need for continued investment in TB care delivery and surveillance programs. Geographically relevant diagnostics that take into account a countrys unique distribution of resistance are necessary.

bioinformatics↗

bioRxiv2019

In-host population dynamics of M. tuberculosis during treatment failure

BackgroundTuberculosis (TB) is a leading cause of death globally from an infectious agent. Understanding the population dynamics of TBs causative agent Mycobacterium tuberculosis (Mtb) in-host is vital for understanding the efficacy of antibiotic treatment. Here we use longitudinally collected clinical Mtb isolates that underwent Whole-Genome Sequencing (WGS) from the sputa of 307 subjects to investigate Mtb diversity during the course of active TB disease. Methods and findingsWe excluded cases suspected of reinfection or contamination to analyze data from 200 subjects, 167 of which met microbiological criteria for delayed culture conversion, treatment failure or relapse. Using technical and biological replicate samples, we defined an allele frequency threshold attributable to in-host evolution. Of the 167 subjects with unsuccessful treatment outcome, 27 (16%) developed new resistance mutations between sampling with 20/27 (74%) occurring in patients with pre-existing antibiotic resistance. Low abundance resistance variants at a purity of [≥]19% in the first isolate predicts fixation of these variants in the subsequent sample with 27.0% sensitivity and 95.8% specificity. We identify significant in-host variation in seven genes associated with antibiotic resistance and twenty other genes, including metabolic genes and genes known to modulate host innate immunity by interacting with TLR2. We confirm Rv0095c, Rv1944c, PPE18, PPE54 and PPE60 to be under positive selection by assessing phylogenetic convergence across a global and genetically diverse independent sample of 20,352 isolates. ConclusionsOur large sample provides a comprehensive picture of the mutational dynamics in-host during active TB disease. We demonstrate a framework to study temporal changes in Mtb population diversity using average depth WGS data. We show that minor variants can be used to inform antibiotic treatment regimens in patients with TB. Furthermore, we detect a signature of positive selection in-host, possibly stemming from innate immune pressure and informing our understanding of host-pathogen interactions.

genomics↗

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↗