Biology subjects

Vargas, R.

Publications and source records attributed to Vargas, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Variation in multiple classes of simple sequence repeats can alter drug susceptibility in Mycobacterium tuberculosis

Insertions and deletions (INDELs) in simple sequence repeats (SSRs) generate relatively high-frequency reversible genetic changes that facilitate bacterial adaptation to changing environments. Analyses of global Mycobacterium tuberculosis (Mtb) isolates indicate that many SSRs are under diversifying selection, and several of the resulting INDELs in homopolymer tracts (HTs) can increase the pathogen's fitness during exposure to host and antibiotic stresses. However, the functional impact of most variable SSRs, particularly those within more complex repeat sequences than HT, remains unclear. Here, we combine phylogenomic analysis of clinical Mtb strains from Vietnam and Peru with in vitro experimental validation of engineered strains to identify SSR INDELs that alter antibiotic susceptibility. Our findings demonstrate that INDELs across multiple SSRs of differing repeat composition are highly variable and correlate with clinical antibiotic resistance. These variants included frameshifting HT INDELs in ppe13, glpK, Rv2081c, and ppsA, and in-frame trinucleotide (triplet) SSR INDELS in ponA1, ppe53, and ppe59 that produce much more subtle changes in protein structure. Reconstruction of these INDELs in an isogenic background identified four variants that directly reduce drug potency, including a triplet SSR deletion in ppe53 that conferred intermediate resistance to isoniazid, rifampicin, and streptomycin. The clinically prevalent ppe53 CGCdel mutation shortens a polyalanine stretch adjacent to the conserved WxG domain and impairs the processing and secretion of the full-length protein. Overall, our work provides additional evidence of selective pressure across Mtb SSRs and demonstrates the significance of in-frame INDELs within triplet SSRs, highlighting their contribution to the evolution of antibiotic resistance.

microbiology↗

bioRxiv2025

The structural context of mutations in proteins predicts their effect on antibiotic resistance

In Mycobacterium tuberculosis, a prevalent and deadly pathogen, resistance to antibiotics evolves primarily through non-synonymous mutations in proteins. Sequence-based analyses are currently used to understand the genetic basis of antibiotic resistance, either via genotype-phenotype association, or via signals of convergent evolution. These methods focus on primary sequence and often neglect other biological signals such as protein structural information. We hypothesize that integrating the structural context of mutations improves the prediction of effects on function and phenotype. We curate high confidence structural annotations for the M. tuberculosis proteome from 1,371 crystallography and 2,316 AlphaFold predictions, and combine the structures with mutations from over 31,000 clinical M. tuberculosis isolates. We demonstrate that mutations in proteins known to cause resistance are clustered in 3D space, even in proteins where inactivating mutations at any position are thought to cause resistance. We develop a statistic to search the M. tuberculosis proteome for signal of clustered mutations, finding over 450 proteins that display this signal, many of which have a known relationship with antibiotic resistance. We show that a supervised classifier trained on 3D distance to known resistance sites alone has an F1 score of 94.6% at classifying mutations as resistance-conferring across proteins. This work demonstrates that protein structure provides useful information for categorizing which variants may cause antibiotic resistance, even when the majority of structures are AI-predicted.

bioinformatics↗

bioRxiv2024

Eco-Anthropological factors explaining forest patch use by 3 species of wild Atelid monkeys co-existing with a small-scale farming community in Northeastern Costa Rica, Central America

The main conservation risks for wild non-human primates (NHP) in Costa Rica, Mesoamerica, is deforestation and the allocation of lands for agriculture. This is because they result in a mosaic of forest patches that differ in size and ecological properties. NHP, being the vertebrates with the highest risk and rate of extinction, slowly adapt to this rapid environmental change, optimizing their metabolic costs to survive and reproduce. One way to balance these costs is to use forest patches depending on the benefits they provide, such as food, shelter, or social contact. To understand the possible environmental factors that predict the usage of a series of 8 connected forest patches by Ateles geoffroyi, Alouata paliatta, and Sapajus imitator we collected demographic, behavioral, climatological and other environmental data from 2018 until 2021. We used information-theoretic metrics to identify the factors that best explained the presence and behavior of the species of interest in the forest patches studied, and fit the data to a set of models built informed a priori. Using the best explanatory factors, we k-fold cross-validated 9 classifier algorithms to identify the best predictive models for the presence of the monkeys studied and their behavioral patterns given the data. Presence was highest in warmer, more humid days, especially when other groups were present in the same patch. Behavioral patterns were different in each patch; monkeys rested more often when other groups of the same species were present, and foraged more during warmer, more humid days, and smaller groups. Predictive models for the presence of the species studied, trained with the 3 best explanatory factors, reached an accuracy between 70-96%, with Gradient Boost Classifier performing the best. In contrast, behavioral patterns were more unpredictable, with the the algorithms tested only reaching between 43-51% accuracy, the AdaBoost Classifier being the best. Our findings suggest that the usage of the 8 forest patches monitored by the monkeys studied depends on patch characteristics, not related to size nor the presence of a reserve, by the presence of other NHP in the patch and the meteorological conditions. Further work on the ecological characteristics of these patches can clarify the mechanisms modulating behavioral patterns.

zoology↗

bioRxiv2023

Reply to: Contribution of carbon inputs to soil carbon accumulation cannot be neglected

In the accompanying Comment1, He et al. argue that the determinant role of microbial carbon use efficiency in global soil organic carbon (SOC) storage shown in Tao et al. (2023)2 was overestimated because carbon inputs were neglected in our data analysis while they suggest that our model-based analysis could be biased and model-dependent. Their argument is based on a different choice of independent variables in the data analysis and a sensitivity analysis of two process-based models other than that used in our study. We agree that both carbon inputs and outputs (as mediated by microbial processes) matter when predicting SOC storage - the question is their relative contributions. While we encourage further studies to examine how the evaluation of the relative importance of CUE to global SOC storage may vary with different model structures, He et al.s claims about Tao et al. (2023) need to be taken as an alternative, unproven hypothesis until empirical data support their specific parameterization. Here we show that an additional literature assessment of global data does not support He et al.s argument, in contrast to our study, and that further study on this topic is essential.

ecology↗

bioRxiv2023

Single nucleotide variation catalogue from clinical isolates mapped on tertiary and quaternary structures of ESX-1 related proteins reveals critical regions as putative Mtb therapeutic targets

Proteins encoded by the ESX-1 genes of interests are essential for full virulence in all Mycobacterium tuberculosis complex (MTBc) lineages, the pathogens with the highest mortality worldwide. Identifying critical regions in these ESX-1 related proteins could provide preventive or therapeutic targets for MTB infection, the game changer needed for tuberculosis control. We analysed a compendium of whole genome sequences of clinical MTB isolates from all lineages from >32,000 patients and identified single nucleotide variations (SNV). When mutations corresponding to all nonsynonymous SNPs were mapped on the surface of known and AlphaFold-predicted ternary protein structures, fully conserved regions emerged. Some could be assigned to known quaternary structures, whereas others could be predicted to be involved in yet-to-be-discovered interactions. Some mutants had clonally expanded (found in >1% of the isolates): these were mostly located at the surface of globular domains, remote from known intra- and inter-molecular protein-protein interactions. Fully conserved intrinsically disordered regions (IDRs) of proteins were found, suggesting that these are crucial for the pathogenicity of the MTBc. Altogether, our findings provide an evolutionary structural perspective on MTB virulence and highlight fully conserved regions of proteins as attractive vaccine antigens and drug targets. Extending this approach to other pathogens can provide a novel critical resource for the development of innovative tools for pathogen control.

genomics↗

bioRxiv2022

Common gene signature model discovery and systematic validation for TB prognosis and response to treatment

While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximated the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91-0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring. AUTHOR SUMMARYAn early diagnosis for incipient TB is a one of the key approaches to reduce global TB deaths and incidence, particularly in low and middle-income countries. However, in appreciation of TB heterogenicity at the population and individual level due to TB pathogenesis, host genetics, demographics, disease comorbidities and technical variations from sample collecting and gene profiling, the responses of the molecular gene signatures have showed to be associated with these diverse factors In this work, we develop a new computational approach that combines a network-based meta-analysis with machine-learning modeling to address the existing challenge of early incipient TB prediction against TB heterogenicity. With this new approach, we harness the power of TB heterogeneity in diverse populations and individuals during model construction by including massive datasets (57 studies in total) that allow us not only to consider different confounding variables inherited from each cohort while identifying the common gene set and building the predictive model, but also to systematically validate the model by pooling the datasets to mimic the real-world setting. This generalized predicting model provides a robust prediction of long-term TB risk estimation (>30 months to TB disease). In addition, this model also demonstrates the utility in TB treatment monitoring along with Mycobacterium tuberculosis elimination.

bioinformatics↗

bioRxiv2022

TP53 mutations in endometrial cancer contribute to clinically-relevant radiation resistance and highlight the potential for biomarker-driven radiosensitization strategies.

Endometrial cancer (EC) is the most common type of gynecologic malignancy in the United States, with over 66, 200 new cases expected in 2023. The number of mortalities per year now approximate that of ovarian cancer. Despite our ability to identify different biologic clusters of EC, we have yet to understand the functional impact of key genomic alterations associated with discrepant prognoses and exploit this knowledge for therapeutic benefit. Given this, we set out to determine how alterations in p53 signaling, as conferred my TP53 mutations, impact radiotherapy response in EC. We also explored if manipulation of this signaling pathway could be utilized as a radio-sensitization strategy in EC. Our work demonstrates that p53 signaling plays a significant role in radiotherapy response for EC and that leveraging this genomic data may allow us to exploit this pathway as a viable radiotherapeutic target in a significant number of EC cases.

cancer 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

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

Genomic sequence characteristics and the empiric accuracy of short-read sequencing

BackgroundShort-read whole genome sequencing (WGS) is a vital tool for clinical applications and basic research. Genetic divergence from the reference genome, repetitive sequences, and sequencing bias, reduce the performance of variant calling using short-read alignment, but the loss in recall and specificity has not been adequately characterized. For the clonal pathogen Mycobacterium tuberculosis (Mtb), researchers frequently exclude 10.7% of the genome believed to be repetitive and prone to erroneous variant calls. To benchmark short-read variant calling, we used 36 diverse clinical Mtb isolates dually sequenced with Illumina short-reads and PacBio long-reads. We systematically study the short-read variant calling accuracy and the influence of sequence uniqueness, reference bias, and GC content. [a] ResultsReference based Illumina variant calling had a recall [≥]89.0% and precision [≥]98.5% across parameters evaluated. The best balance between precision and recall was achieved by tuning the mapping quality (MQ) threshold, i.e. confidence of the read mapping (recall 85.8%, precision 99.1% at MQ [≥] 40). Masking repetitive sequence content is an alternative conservative approach to variant calling that maintains high precision (recall 70.2%, precision 99.6% at MQ[≥]40). Of the genomic positions typically excluded for Mtb, 68% are accurately called using Illumina WGS including 52 of the 168 PE/PPE genes (34.5%). We present a refined list of low confidence regions and examine the largest sources of variant calling error. ConclusionsOur improved approach to variant calling has broad implications for the use of WGS in the study of Mtb biology, inference of transmission in public health surveillance systems, and more generally for WGS applications in other organisms.

bioinformatics↗

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↗

bioRxiv2021

High Light and High Temperature Reduce Photosynthesis via Different Mechanisms in the C4 Model Setaria viridis

C4 plants frequently experience damaging high light (HL) and high temperature (HT) conditions in native environments, which reduce growth and yield. However, the mechanisms underlying these stress responses in C4 plants have been under-explored, especially the coordination between mesophyll (M) and bundle sheath (BS) cells. We investigated how the C4 model plant Setaria viridis responded to a four-hour HL or HT treatment at the photosynthetic, transcriptomic, and ultrastructural levels. Although we observed a comparable reduction of photosynthetic efficiency in HL- or HT-treated leaves, detailed analysis of multi-level responses revealed important differences in key pathways and M/BS specificity responding to HL and HT. We provide a systematic analysis of HL and HT responses in S. viridis, reveal different acclimation strategies to these two stresses in C4 plants, discover unique light/temperature responses in C4 plants in comparison to C3 plants, and identify potential targets to improve abiotic stress tolerance in C4 crops.

plant biology↗

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

Disruption of the gut microbiota attenuates epithelial ovarian cancer sensitivity to cisplatin therapy

Epithelial Ovarian Cancer (EOC) is the leading cause of gynecologic cancer death. Despite many patients achieving remission with first-line therapy, up to 80% of patients will recur and require additional treatment. Retrospective clinical analysis of OC patients indicates antibiotic use during chemotherapy treatment is associated with poor overall survival. We assessed whether antibiotic (ABX) therapy would impact growth of EOC and sensitivity to cisplatin in murine models. Immune competent or compromised mice were given control or ABX containing water (metronidazole, ampicillin, vancomycin, and neomycin) before being intraperitoneally injected with murine EOC cells. Stool was collected to confirm microbiome disruption and tumors were monitored, and cisplatin therapy was administered weekly until endpoint. EOC tumor-bearing mice demonstrate accelerated tumor growth and resistance to cisplatin therapy in ABX treated compared with nonABX treatment. Stool analysis indicated most gut microbial species were disrupted by ABX treatment except for ABX resistant bacteria. To test for role of the gut microbiome, cecal microbiome transplants (CMTs) of microbiota derived from ABX or nonABX treated mice were used to recolonize the microbiome of ABX treated mice. nonABX cecal microbiome was sufficient to ameliorate the chemoresistance and survival of ABX treated mice indicative of a gut derived tumor suppressor. Mechanistically, tumors from ABX treated compared to nonABX treated mice contained a high frequency of cancer stem cells that were augmented by cisplatin. These studies indicate an intact microbiome provides a gut derived tumor suppressor and maintains chemosensitivity that is disrupted by ABX treatment. SignificancePlatinum resistance is associated with poor prognosis and reduced therapeutic options for ovarian cancer patients. We identifed a tumor suppressive role of the gut microbiome that is disrupted upon antibiotic therapy.

cancer biology↗

bioRxiv2019

The Hippo pathway origin and its oncogenic alteration in evolution

The Hippo pathway is a central regulator of organ size and a key tumor suppressor via coordinating cell proliferation and death. Initially discovered in Drosophila, the Hippo pathway has been implicated as an evolutionarily conserved pathway in mammals; however, how this pathway was evolved to be functional from its origin is still largely unknown. In this study, we traced the Hippo pathway in premetazoan species, characterized the intrinsic functions of its ancestor components, and unveiled the evolutionary history of this key signaling pathway from its unicellular origin. In addition, we elucidated the paralogous gene history for the mammalian Hippo pathway components and characterized their cancer-derived somatic mutations from an evolutionary perspective. Taken together, our findings not only traced the conserved function of the Hippo pathway to its unicellular ancestor components, but also provided novel evolutionary insights into the Hippo pathway organization and oncogenic alteration.

evolutionary biology↗

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↗

bioRxiv2019

Downscaling Satellite Soil Moisture using Geomorphometry and Machine Learning

Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25-50 km grids); and large areas from regional-to-global scales have spatial information gaps. We provide an alternative approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial resolution across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991-2016). We derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations with field soil moisture observations from the North American Soil Moisture Database (n=668 locations with available data between 1991-2013; 0-5cm depth) than the original product. We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world.

ecology↗