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ABSTRACT The intimate correlation of chronic kidney disease (CKD) with structural alteration in gut microbiota or metabolite profile has been documented in a growing body of studies. Nevertheless, a paucity of demonstrated knowledge regarding the impact and underlying mechanism of gut microbiota or metabolite on occurrence or progression of CKD is unclarified thus far. In this study, a liquid chromatography coupled-mass spectrometry and long-read sequencing were applied to identify gut metabolites and microbiome with statistically-discriminative abundance in diabetic CKD patients (n = 39), hypertensive CKD patients (n = 26), or CKD patients without comorbidity (n = 40) compared to those of healthy participants (n = 60). The association between CKD-related species and metabolite was evaluated by using zero-inflated negative binomial (ZINB) regression. The predictive utility of identified operational taxonomic units (OTUs), metabolite, or species-metabolite association toward the diagnosis of incident chronic kidney disease with distinct pathogenic factor was assessed using the random forest regression model and the receiver operating characteristic (ROC) curve. The results of statistical analyses indicated alterations in the relative abundances of 26 OTUs and 41 metabolites that were specifically relevant to each CKD-patient group. The random forest regression model with only species, metabolites, or its association differentially distinguished the hypertensive, diabetic CKD patients, or enrolled CKD patients without comorbidity from the healthy participants. IMPORTANCE Gut dysbiosis-altered metabolite association exhibits specific and convincing utility to differentiate CKD associated with distinct pathogenic factor. These results present the validity of pathogenesis-associated markers across healthy participants and high-risk population toward the early screening, prevention, diagnosis, or personalized treatment of CKD.

ABSTRACT Drug resistance in Mycobacterium tuberculosis (MTB) has long been a serious health issue worldwide. Most drug-resistant MTB isolates were identified due to treatment failure or in clinical examinations 3~6 months postinfection. In this study, we propose a whole-genome sequencing (WGS) pipeline via the Nanopore MinION platform to facilitate the efficacy of phenotypic identification of clinical isolates. We used the Nanopore MinION platform to perform WGS of clinical MTB isolates, including susceptible (n = 30) and rifampin- (RIF) or rifabutin (RFB)-resistant isolates (n = 20) according to results of a susceptibility test. Nonsynonymous variants within the rpoB gene associated with RIF resistance were identified using the WGS analytical pipeline. In total, 131 variants within the rpoB gene in RIF-resistant isolates were identified. The presence of the emergent Asp531Gly or His445Gln was first identified to be associated with the rifampin and rifabutin resistance signatures of clinical isolates. The results of the minimum inhibitory concentration (MIC) test further indicated that the Ser450Leu or the mutant within the rifampin resistance-determining region (RRDR)-associated rifabutin-resistant signature was diminished in the presence of novel mutants, including Phe669Val, Leu206Ile, or Met148Leu, identified in this study. IMPORTANCE Current approaches to diagnose drug-resistant MTB are time-consuming, consequently leading to inefficient intervention or further disease transmission. In this study, we curated lists of coding variants associated with differential rifampin and rifabutin resistant signatures using a single molecule real-time (SMRT) sequencing platform with a shorter hands-on time. Accordingly, the emerging WGS pipeline constitutes a potential platform for efficacious and accurate diagnosis of drug-resistant MTB isolates.