A small number of high-burden countries account for the majority of tuberculosis cases worldwide. Detailed data are lacking from these regions. To explore the evolutionary history of M. tuberculosis in China — the third highest TB burden country — we analyzed a countrywide collection of 4,578 isolates. Little genetic diversity was detected within the large M. tuberculosis population in China, with 99.4% of the bacterial population belonging to lineage 2 and three sublineages of lineage 4. The deeply rooted phylogenetic positions and geographic restriction of these four genotypes indicate that their populations expanded in situ following a small number of introductions to China. Coalescent analyses suggest that these bacterial sub-populations emerged in China around 1,000 years ago, expanded in parallel from the 12th century onward, and the whole population peaked in the late 18th century. More recently, sublineage L2.3, which is indigenous to China and exhibited relatively high transmissibility and extensive global dissemination, came to dominate the population dynamics of M. tuberculosis in China. Our results indicate that historical expansion of four M. tuberculosis strains shaped the current TB epidemic in China, and highlight the long-term genetic continuity of the indigenous M. tuberculosis population.

We have devised a novel amplification strategy based on isothermal strand-displacement polymerization reaction, which was termed multiple cross displacement amplification (MCDA). The approach employed a set of ten specially designed primers spanning ten distinct regions of target sequence and was preceded at a constant temperature (61–65 °C). At the assay temperature, the double-stranded DNAs were at dynamic reaction environment of primer-template hybrid, thus the high concentration of primers annealed to the template strands without a denaturing step to initiate the synthesis. For the subsequent isothermal amplification step, a series of primer binding and extension events yielded several single-stranded DNAs and single-stranded single stem-loop DNA structures. Then, these DNA products enabled the strand-displacement reaction to enter into the exponential amplification. Three mainstream methods, including colorimetric indicators, agarose gel electrophoresis and real-time turbidity, were selected for monitoring the MCDA reaction. Moreover, the practical application of the MCDA assay was successfully evaluated by detecting the target pathogen nucleic acid in pork samples, which offered advantages on quick results, modest equipment requirements, easiness in operation, and high specificity and sensitivity. Here we expounded the basic MCDA mechanism and also provided details on an alternative (Single-MCDA assay, S-MCDA) to MCDA technique.

Motivation Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. Availability and implementation The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php. Supplementary information Supplementary data are available at Bioinformatics online.

Loop-mediated isothermal amplification (LAMP) is restricted to detecting a single target, limiting the usefulness of this method. To achieve multiplex LAMP-based detection, we developed a novel approach we called the multiple endonuclease restriction real-time-LAMP assay. In this system, the LAMP forward or backward inner primers contain 5' end short sequences that are recognized by the restriction endonuclease Nb.BsrDI, and the new forward or backward inner primers were modified at the 5' end with a fluorophore and in the middle with a dark quencher. Nb.BsrDI digests the newly synthesized double-stranded terminal sequences (5' end short sequences and their complementary sequences), which releases the quenching, resulting in a gain of signal. The assay permitted real-time detection of single or multiple target sequences in a single tube, and the positive results can be obtained in as short as 12 minutes. The novel methodology is highly efficient and specific, detecting down to 250 fg of DNA per reaction of Listeria DNA tested, and was successful in evaluating raw meat samples. The multiple endonuclease restriction real-time-LAMP technology, which is an extension of LAMP to accommodate robust, target-specific, and multiplex detection, provides a molecular diagnostic tool with less detection time and high sensitivity and specificity compared with those of LAMP and quantitative real-time PCR.

The dN/dS ratio provides evidence of adaptation or functional constraint in protein-coding genes by quantifying the relative excess or deficit of amino acid-replacing versus silent nucleotide variation. Inexpensive sequencing promises a better understanding of parameters, such as dN/dS, but analyzing very large data sets poses a major statistical challenge. Here, I introduce genomegaMap for estimating within-species genome-wide variation in dN/dS, and I apply it to 3,979 genes across 10,209 tuberculosis genomes to characterize the selection pressures shaping this global pathogen. GenomegaMap is a phylogeny-free method that addresses two major problems with existing approaches: 1) It is fast no matter how large the sample size and 2) it is robust to recombination, which causes phylogenetic methods to report artefactual signals of adaptation. GenomegaMap uses population genetics theory to approximate the distribution of allele frequencies under general, parent-dependent mutation models. Coalescent simulations show that substitution parameters are well estimated even when genomegaMap’s simplifying assumption of independence among sites is violated. I demonstrate the ability of genomegaMap to detect genuine signatures of selection at antimicrobial resistance-conferring substitutions in Mycobacterium tuberculosis and describe a novel signature of selection in the cold-shock DEAD-box protein A gene deaD/csdA. The genomegaMap approach helps accelerate the exploitation of big data for gaining new insights into evolution within species.