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

Association of Timing of Plasma Transfusion With Adverse Maternal Outcomes in Women With Persistent Postpartum Hemorrhage

IMPORTANCE Early plasma transfusion for women with severe postpartum hemorrhage (PPH) is recommended to prevent coagulopathy. However, there is no comparative, quantitative evidence on the association of early plasma transfusion with maternal outcomes. OBJECTIVE To compare the incidence of adverse maternal outcomes among women who received plasma during the first 60 minutes of persistent PPH vs women who did not receive plasma for similarly severe persistent PPH. DESIGN, SETTING, AND PARTICIPANTS This multicenter cohort study used a consecutive sample of women with persistent PPH, defined as PPH refractory to first-line measures to control bleeding, between January 1, 2011, and January 1, 2013. Time-dependent propensity score matching was used to select women who received plasma during the first 60 minutes of persistent PPH and match each of them with a woman who had shown the same severity and received the same treatment of PPH but who had not received plasma at the moment of matching. Transfusions were not guided by coagulation tests. Statistical analysis was performed from June 2018 to June 2019. EXPOSURES Transfusion of plasma during the first 60 minutes of persistent PPH vs no or later plasma transfusion. MAIN OUTCOMES AND MEASURES Incidence of adverse maternal outcomes, defined as a composite of death, hysterectomy, or arterial embolization. RESULTS This study included 1216 women (mean [SD] age, 31.6 [5.0] years) with persistent PPH, of whom 932 (76.6%) delivered vaginally and 780 (64.1%) had PPH caused by uterine atony. Seven women (0.6%) died because of PPH, 62 women (5.1%) had a hysterectomy, and 159 women (13.1%) had arterial embolizations. Among women who received plasma during the first 60 minutes of persistent PPH, 114 women could be matched with a comparable woman who had not received plasma at the moment of matching. The incidence of adverse maternal outcomes was similar between the women, with adverse outcomes recorded in 24 women (21.2%) who received early plasma transfusion and 23 women (19.9%) who did not receive early plasma transfusion (odds ratio, 1.09; 95% CI, 0.57-2.09). Results of sensitivity analyses were comparable to the primary results. CONCLUSIONS AND RELEVANCE In this cohort study, initiation of plasma transfusion during the first 60 minutes of persistent PPH was not associated with adverse maternal outcomes compared with no or later plasma transfusion, independent of severity of PPH

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

Aberrant Receptor-Mediated Endocytosis of Schistosoma mansoni Glycoproteins on Host Lipoproteins

BACKGROUND: Bilharzia is one of the major parasitic infections affecting the public health and socioeconomic circumstances in (sub) tropical areas. Its causative agents are schistosomes. Since these worms remain in their host for decades, they have developed mechanisms to evade or resist the immune system. Like several other parasites, their surface membranes are coated with a protective layer of glycoproteins that are anchored by a lipid modification. METHODS AND FINDINGS: We studied the release of glycosyl-phosphatidylinositol (GPI)-anchored proteins of S. mansoni and found them in the circulation associated with host lipoprotein particles. Host cells endocytosed schistosomal GPI-anchored proteins via their lipoprotein receptor pathway, resulting in disturbed lysosome morphology. In patients suffering from chronic schistosomiasis, antibodies attacked the parasite GPI-anchored glycoproteins that were associated with the patients' own lipoprotein particles. These immunocomplexes were endocytosed by cells carrying an immunoglobulin-Fc receptor, leading to clearance of lipoproteins by the immune system. As a consequence, neutral lipids accumulated in neutrophils of infected hamsters and in human neutrophils incubated with patient serum, and this accumulation was associated with apoptosis and reduced neutrophil viability. Also, Trypanosoma brucei, the parasite that causes sleeping sickness, released its major GPI-anchored glycoprotein VSG221 on lipoprotein particles, demonstrating that this process is generalizable to other pathogens/parasites. CONCLUSIONS: Transfer of parasite antigens to host cells via host lipoproteins disrupts lipid homeostasis in immune cells, promotes neutrophil apoptosis, may result in aberrant antigen presentation in host cells, and thus cause an inefficient immune response against the pathogen

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

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

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