Machine learning predicts accurately mycobacterium tuberculosis drug resistance from whole genome sequencing data

Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making. Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradientboosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without “co-occurrent resistance” markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard. Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for thirdline drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other largescale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation. Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation

Machine learning predicts accurately mycobacterium tuberculosis drug resistance from whole genome sequencing data

Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making. Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradient-boosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without “co-occurrent resistance” markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard. Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for third-line drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other large-scale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation. Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation

Cellular and humoral immune responses and protection against schistosomes induced by a radiation-attenuated vaccine in chimpanzees

The radiation-attenuated Schistosoma mansoni vaccine is highly effective in rodents and primates but has never been tested in humans, primarily for safety reasons. To strengthen its status as a paradigm for a human recombinant antigen vaccine, we have undertaken a small-scale vaccination and challenge experiment in chimpanzees (Pan troglodytes). Immunological, clinical, and parasitological parameters were measured in three animals after multiple vaccinations, together with three controls, during the acute and chronic stages of challenge infection up to chemotherapeutic cure. Vaccination induced a strong in vitro proliferative response and early gamma interferon production, but type 2 cytokines were dominant by the time of challenge. The controls showed little response to challenge infection before the acute stage of the disease, initiated by egg deposition. In contrast, the responses of vaccinated animals were muted throughout the challenge period. Vaccination also induced parasite-specific immunoglobulin M (IgM) and IgG, which reached high levels at the time of challenge, while in control animals levels did not rise markedly before egg deposition. The protective effects of vaccination were manifested as an amelioration of acute disease and overall morbidity, revealed by differences in gamma-glutamyl transferase level, leukocytosis, eosinophilia, and hematocrit. Moreover, vaccinated chimpanzees had a 46% lower level of circulating cathodic antigen and a 38% reduction in fecal egg output, compared to controls, during the chronic phase of infection

Multiple Statistical Analysis Techniques Corroborate Intratumor Heterogeneity in Imaging Mass Spectrometry Datasets of Myxofibrosarcoma

MALDI mass spectrometry can generate profiles that contain hundreds of biomolecular ions directly from tissue. Spatially-correlated analysis, MALDI imaging MS, can simultaneously reveal how each of these biomolecular ions varies in clinical tissue samples. The use of statistical data analysis tools to identify regions containing correlated mass spectrometry profiles is referred to as imaging MS-based molecular histology because of its ability to annotate tissues solely on the basis of the imaging MS data. Several reports have indicated that imaging MS-based molecular histology may be able to complement established histological and histochemical techniques by distinguishing between pathologies with overlapping/identical morphologies and revealing biomolecular intratumor heterogeneity. A data analysis pipeline that identifies regions of imaging MS datasets with correlated mass spectrometry profiles could lead to the development of novel methods for improved diagnosis (differentiating subgroups within distinct histological groups) and annotating the spatio-chemical makeup of tumors. Here it is demonstrated that highlighting the regions within imaging MS datasets whose mass spectrometry profiles were found to be correlated by five independent multivariate methods provides a consistently accurate summary of the spatio-chemical heterogeneity. The corroboration provided by using multiple multivariate methods, efficiently applied in an automated routine, provides assurance that the identified regions are indeed characterized by distinct mass spectrometry profiles, a crucial requirement for its development as a complementary histological tool. When simultaneously applied to imaging MS datasets from multiple patient samples of intermediate-grade myxofibrosarcoma, a heterogeneous soft tissue sarcoma, nodules with mass spectrometry profiles found to be distinct by five different multivariate methods were detected within morphologically identical regions of all patient tissue samples. To aid the further development of imaging MS based molecular histology as a complementary histological tool the Matlab code of the agreement analysis, instructions and a reduced dataset are included as supporting information

Evaluation of Urine CCA Assays for Detection of Schistosoma mansoni Infection in Western Kenya

Although accurate assessment of the prevalence of Schistosoma mansoni is important for the design and evaluation of control programs, the most widely used tools for diagnosis are limited by suboptimal sensitivity, slow turn-around-time, or inability to distinguish current from former infections. Recently, two tests that detect circulating cathodic antigen (CCA) in urine of patients with schistosomiasis became commercially available. As part of a larger study on schistosomiasis prevalence in young children, we evaluated the performance and diagnostic accuracy of these tests—the carbon test strip designed for use in the laboratory and the cassette format test intended for field use. In comparison to 6 Kato-Katz exams, the carbon and cassette CCA tests had sensitivities of 88.4% and 94.2% and specificities of 70.9% and 59.4%, respectively. However, because of the known limitations of the Kato-Katz assay, we also utilized latent class analysis (LCA) incorporating the CCA, Kato-Katz, and schistosome-specific antibody results to determine their sensitivities and specificities. The laboratory-based CCA test had a sensitivity of 91.7% and a specificity of 89.4% by LCA while the cassette test had a sensitivity of 96.3% and a specificity of 74.7%. The intensity of the reaction in both urine CCA tests reflected stool egg burden and their performance was not affected by the presence of soil transmitted helminth infections. Our results suggest that urine-based assays for CCA may be valuable in screening for S. mansoni infections

Proteomic Analysis of the Dysferlin Protein Complex Unveils Its Importance for Sarcolemmal Maintenance and Integrity

Dysferlin is critical for repair of muscle membranes after damage. Mutations in dysferlin lead to a progressive muscular dystrophy. Recent studies suggest additional roles for dysferlin. We set out to study dysferlin's protein-protein interactions to obtain comprehensive knowledge of dysferlin functionalities in a myogenic context. We developed a robust and reproducible method to isolate dysferlin protein complexes from cells and tissue. We analyzed the composition of these complexes in cultured myoblasts, myotubes and skeletal muscle tissue by mass spectrometry and subsequently inferred potential protein functions through bioinformatics analyses. Our data confirm previously reported interactions and support a function for dysferlin as a vesicle trafficking protein. In addition novel potential functionalities were uncovered, including phagocytosis and focal adhesion. Our data reveal that the dysferlin protein complex has a dynamic composition as a function of myogenic differentiation. We provide additional experimental evidence and show dysferlin localization to, and interaction with the focal adhesion protein vinculin at the sarcolemma. Finally, our studies reveal evidence for cross-talk between dysferlin and its protein family member myoferlin. Together our analyses show that dysferlin is not only a membrane repair protein but also important for muscle membrane maintenance and integrity

Glycan labeling strategies and their use in identification and quantification

Most methods for the analysis of oligosaccharides from biological sources require a glycan derivatization step: glycans may be derivatized to introduce a chromophore or fluorophore, facilitating detection after chromatographic or electrophoretic separation. Derivatization can also be applied to link charged or hydrophobic groups at the reducing end to enhance glycan separation and mass-spectrometric detection. Moreover, derivatization steps such as permethylation aim at stabilizing sialic acid residues, enhancing mass-spectrometric sensitivity, and supporting detailed structural characterization by (tandem) mass spectrometry. Finally, many glycan labels serve as a linker for oligosaccharide attachment to surfaces or carrier proteins, thereby allowing interaction studies with carbohydrate-binding proteins. In this review, various aspects of glycan labeling, separation, and detection strategies are discussed

Swimming physiology of European silver eels (Anguilla anguilla L.): energetic costs and effects on sexual maturation and reproduction

The European eel migrates 5,000–6,000 km to the Sargasso Sea to reproduce. Because they venture into the ocean in a pre-pubertal state and reproduce after swimming for months, a strong interaction between swimming and sexual maturation is expected. Many swimming trials have been performed in 22 swim tunnels to elucidate their performance and the impact on maturation. European eels are able to swim long distances at a cost of 10–12 mg fat/km which is 4–6 times more efficient than salmonids. The total energy costs of reproduction correspond to 67% of the fat stores. During long distance swimming, the body composition stays the same showing that energy consumption calculations cannot be based on fat alone but need to be compensated for protein oxidation. The optimal swimming speed is 0.61–0.67 m s−1, which is ~60% higher than the generally assumed cruise speed of 0.4 m s−1 and implies that female eels may reach the Sargasso Sea within 3.5 months instead of the assumed 6 months. Swimming trials showed lipid deposition and oocyte growth, which are the first steps of sexual maturation. To investigate effects of oceanic migration on maturation, we simulated group-wise migration in a large swim-gutter with seawater. These trials showed suppressed gonadotropin expression and vitellogenesis in females, while in contrast continued sexual maturation was observed in silver males. The induction of lipid deposition in the oocytes and the inhibition of vitellogenesis by swimming in females suggest a natural sequence of events quite different from artificial maturation protocols

Identifying Proteins in Zebrafish Embryos Using Spectral Libraries Generated from Dissected Adult Organs and Tissues

Proteomic