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BACKGROUND: Molecular diagnostics are considered the most promising route to achieving rapid, universal drug susceptibility testing for Mycobacterium tuberculosiscomplex (MTBC). We aimed to generate a WHO endorsed catalogue of mutations to serve as a global standard for interpreting molecular information for drug resistance prediction. METHODS: A candidate gene approach was used to identify mutations as associated with resistance, or consistent with susceptibility, for 13 WHO endorsed anti-tuberculosis drugs. 38,215 MTBC isolates with paired whole-genome sequencing and phenotypic drug susceptibility testing data were amassed from 45 countries. For each mutation, a contingency table of binary phenotypes and presence or absence of the mutation computed positive predictive value, and Fisher's exact tests generated odds ratios and Benjamini-Hochberg corrected p-values. Mutations were graded as Associated with Resistance if present in at least 5 isolates, if the odds ratio was >1 with a statistically significant corrected p-value, and if the lower bound of the 95% confidence interval on the positive predictive value for phenotypic resistance was >25%. A series of expert rules were applied for final confidence grading of each mutation. FINDINGS: 15,667 associations were computed for 13,211 unique mutations linked to one or more drugs. 1,149/15,667 (7·3%) mutations were classified as associated with phenotypic resistance and 107/15,667 (0·7%) were deemed consistent with susceptibility. For rifampicin, isoniazid, ethambutol, fluoroquinolones, and streptomycin, the mutations' pooled sensitivity was >80%. Specificity was over 95% for all drugs except ethionamide (91·4%), moxifloxacin (91·6%) and ethambutol (93·3%). Only two resistance mutations were classified for bedaquiline, delamanid, clofazimine, and linezolid as prevalence of phenotypic resistance was low for these drugs. INTERPRETATION: This first WHO endorsed catalogue of molecular targets for MTBC drug susceptibility

The genetics of autosomal recessive intellectual disability (ARID) has mainly been studied in consanguineous families, however, founder populations may also be of interest to study intellectual disability (ID) and the contribution of ARID. Here, we used a genotype-driven approach to study the genetic landscape of ID in the founder population of Finland. A total of 39 families with syndromic and non-syndromic ID were analyzed using exome sequencing, which revealed a variant in a known ID gene in 27 families. Notably, 75% of these variants in known ID genes were de novo or suspected de novo (64% autosomal dominant; 11% X-linked) and 25% were inherited (14% autosomal recessive; 7% X-linked; and 4% autosomal dominant). A dual molecular diagnosis was suggested in two families (5%). Via additional analysis and molecular testing, we identified three cases with an abnormal molecular karyotype, including chr21q22.12q22.2 uniparental disomy with a mosaic interstitial 2.7 Mb deletion covering DYRK1A and KCNJ6. Overall, a pathogenic or likely pathogenic variant was identified in 64% (25/39) of the families. Last, we report an alternate inheritance model for 3 known ID genes (UBA7, DDX47, DHX58) and discuss potential candidate genes for ID, including SYPL1 and ERGIC3 with homozygous founder variants and de novo variants in POLR2F and DNAH3. In summary, similar to other European populations, de novo variants were the most common variants underlying ID in the studied Finnish population, with limited contribution of ARID to ID etiology, though mainly driven by founder and potential founder variation in the latter case.

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 analysing very large datasets 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: (i) it is fast no matter how large the sample size and (ii) 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 M. 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.

Motivation: Timely identification of Mycobacterium tuberculosis (MTB) resistance to existing drugs is vital to decrease mortality and prevent the amplification of existing antibiotic resistance. Machine learning methods have been widely applied for timely predicting resistance of MTB given a specific drug and identifying resistance markers. However, they have been not validated on a large cohort of MTB samples from multi-centers across the world in terms of resistance prediction and resistance marker identification. Several machine learning classifiers and linear dimension reduction techniques were developed and compared for a cohort of 13 402 isolates collected from 16 countries across 6 continents and tested 11 drugs. Results: Compared to conventional molecular diagnostic test, area under curve of the best machine learning classifier increased for all drugs especially by 23.11%, 15.22% and 10.14% for pyrazinamide, ciprofloxacin and ofloxacin, respectively (P < 0.01). Logistic regression and gradient tree boosting found to perform better than other techniques. Moreover, logistic regression/gradient tree boosting with a sparse principal component analysis/non-negative matrix factorization step compared with the classifier alone enhanced the best performance in terms of F1-score by 12.54%, 4.61%, 7.45% and 9.58% for amikacin, moxifloxacin, ofloxacin and capreomycin, respectively, as well increasing area under curve for amikacin and capreomycin. Results provided a comprehensive comparison of various techniques and confirmed the application of machine learning for better prediction of the large diverse tuberculosis data. Furthermore, mutation ranking showed the possibility of finding new resistance/susceptible markers.

Item does not contain fulltext, RATIONALE: The development of molecular diagnostics that detect both the presence of Mycobacterium tuberculosis in clinical samples and drug resistance-conferring mutations promises to revolutionize patient care and interrupt transmission by ensuring early diagnosis. However, these tools require the identification of genetic determinants of resistance to the full range of antituberculosis drugs. OBJECTIVES: To determine the optimal molecular approach needed, we sought to create a comprehensive catalog of resistance mutations and assess their sensitivity and specificity in diagnosing drug resistance. METHODS: We developed and validated molecular inversion probes for DNA capture and deep sequencing of 28 drug-resistance loci in M. tuberculosis. We used the probes for targeted sequencing of a geographically diverse set of 1,397 clinical M. tuberculosis isolates with known drug resistance phenotypes. We identified a minimal set of mutations to predict resistance to first- and second-line antituberculosis drugs and validated our predictions in an independent dataset. We constructed and piloted a web-based database that provides public access to the sequence data and prediction tool. MEASUREMENTS AND MAIN RESULTS: The predicted resistance to rifampicin and isoniazid exceeded 90% sensitivity and specificity but was lower for other drugs. The number of mutations needed to diagnose resistance is large, and for the 13 drugs studied it was 238 across 18 genetic loci. CONCLUSIONS: These data suggest that a comprehensive M. tuberculosis drug resistance diagnostic will need to allow for a high dimension of mutation detection. They also support the hypothesis that currently unknown genetic determinants, potentially discoverable by whole-genome sequencing, encode resistance to second-line tuberculosis drugs.

As air traffic grows, annoyance produced by aircraft noise will grow unless new aircraft produce no objectionable noise outside airport boundaries. Such ultra-quiet aircraft must be of revolutionary design, having unconventional planforms and most likely with propulsion systems highly integrated with the airframe. Sophisticated source and propagation modeling will be required to properly account for effects of the airframe on noise generation, reflection, scattering, and radiation. It is tempting to say that since all the effects are included in the Navier-Stokes equations, time-accurate CFD can provide all the answers. Unfortunately, the computational time required to solve a full aircraft noise problem will be prohibitive for many years to come. On the other hand, closed form solutions are not available for such complicated problems. Therefore, a hybrid approach is recommended in which analysis is taken as far as possible without omitting relevant physics or geometry. Three examples are given of recently reported work in broadband noise prediction, ducted fan noise propagation and radiation, and noise prediction for complex three-dimensional jets., JAXA Special Publication, 宇宙航空研究開発機構特別資料