Emergence of Multidrug-Resistant Mycobacterium Tuberculosis of the Beijing Lineage in Portugal and Guinea-Bissau: a Snapshot of Moving Clones by Whole-Genome Sequencing

The Beijing genotype comprises a highly disseminated strain type that is frequently associated with multidrug resistant (MDR) tuberculosis (TB) and increased transmissibility but, countries such as Portugal and Guinea-Bissau fall outside the regions phylogeographically associated with this specific genotype. Nevertheless, recent data shows that this genotype might be gradually emerging in these two countries as an underlying cause of primary MDR-TB. Here, we describe the emergence of Mycobacterium tuberculosis Beijing strains associated with MDR-TB in Portugal and Guinea-Bissau demonstrating the presence of the well described superclusters 100-32 and 94-32 in Portugal and Guinea-Bissau, respectively. Genome-wide analysis and comparison with a global genomic dataset of M. tuberculosis Beijing strains, revealed the presence of two genomic clusters encompassing isolates from Portugal and Guinea-Bissau, GC1 (n = 121) and GC2 (n = 39), both of which bore SNP signatures compatible with the 100-32/B0/W148 and 94-32/Central Asia Outbreak clades, respectively. Moreover, GC2 encompasses a cross-border cluster between Portugal, Guinea-Bissau and Brazil thus supporting migration-associated introduction of MDR-TB and subsequent clonal expansion at the community-level. The comparison with global Beijing datasets demonstrates the global reach of the disease and its complex dissemination across multiple countries while in parallel there are clear microevolutionary trajectories towards extensively drug resistant TB.info:eu-repo/semantics/publishedVersio

Comparison of different methods for DNA-free RNA isolation from SK-N-MC neuroblastoma

<p>Abstract</p> <p>Background</p> <p>RNA quality and quantity are important factors for ensuring the accuracy of gene expression analysis and other RNA-based downstream applications. Extraction of high quality nucleic acids is difficult from neuronal cells and brain tissues as they are particularly rich in lipids. In addition, most common RNA extraction methods are phenol-based, resulting in RNA that may be incompatible with downstream applications such as gene expression.</p> <p>Findings</p> <p>In this work, a comparative analysis of the RNA quality obtained from SK-N-MC cells was performed using six commonly used RNA isolation kits: two phenol-based kits and four non-phenol based kits. The non-phenol based kits tested AxyPrep Multisource Total RNA Miniprep, RNeasy^®Mini, EasySpin and Ilustra RNAspin Mini RNA Isolation, all performed well and resulted in the isolation of high quality RNA, as evaluated by A₂₆₀/A₂₈₀. The RNA extracted with AxyPrep Multisource Total RNA Miniprep, RNeasy^®Mini and EasySpin provided the highest RNA yields. In particular, the RNA isolated by AxyPrep Multisource Total RNA Miniprep Kit did not show any detectable genomic DNA contamination even without previous DNase treatment or after RNA direct PCR amplification using universal 18S primers.</p> <p>Conclusions</p> <p>The RNA extracted from SK-N-MC cells with AxyPrep Multisource Total RNA Miniprep Kit was superior with respect to the RNA quality and concentration. This kit does not use aggressive organic solvents and RNA free of genomic DNA was isolated without the need for DNase treatment.</p

Exploring the Mode of Action of Bioactive Compounds by Microfluidic Transcriptional Profiling in Mycobacteria

10.1371/journal.pone.0069191PLoS ONE87-POLN

Mycobacterium tuberculosis whole genome sequencing provides insights into the Manila strain and drug-resistance mutations in the Philippines.

The Philippines has a high incidence of tuberculosis disease (TB), with an increasing prevalence of multidrug-resistant Mycobacterium tuberculosis (MDR-TB) strains making its control difficult. Although the M. tuberculosis "Manila" ancient lineage 1 strain-type is thought to be prevalent in the country, with evidence of export to others, little is known about the genetic diversity of circulating strains. By whole genome sequencing (WGS) 178 isolates from the Philippines National Drug Resistance Survey, we found the majority (143/178; 80.3%) belonged to the lineage 1 Manila clade, with the minority belonging to lineages 4 (European-American; n = 33) and 2 (East Asian; n = 2). A high proportion were found to be multidrug-resistant (34/178; 19.1%), established through highly concordant laboratory drug susceptibility testing and in silico prediction methods. Some MDR-TB isolates had near identical genomic variation, providing potential evidence of transmission. By placing the Philippine isolates within a phylogeny of global M. tuberculosis (n > 17,000), we established that they are genetically similar to those observed outside the country, including a clade of Manila-like strain-types in Thailand. An analysis of the phylogeny revealed a set of ~200 SNPs that are specific for the Manila strain-type, and a subset can be used within a molecular barcode. Sixty-eight mutations known to be associated with 10 anti-TB drug resistance were identified in the Philippine strains, and all have been observed in other populations. Whilst nine putative streptomycin resistance conferring markers in gid (8) and rrs (1) genes appear to be novel and with functional consequences. Overall, this study provides an important baseline characterisation of M. tuberculosis genetic diversity for the Philippines, and will fill a gap in global datasets and aid the development of a nation-wide database for epidemiological studies and clinical decision making. Further, by establishing a molecular barcode for detecting Manila strains it will assist with the design of diagnostic tools for disease control activities

Establishment of the nasal microbiota in the first 18 months of life: Correlation with early-onset rhinitis and wheezing.

BACKGROUND: Dynamic establishment of the nasal microbiota in early life influences local mucosal immune responses and susceptibility to childhood respiratory disorders. OBJECTIVE: The aim of this case-control study was to monitor, evaluate, and compare development of the nasal microbiota of infants with rhinitis and wheeze in the first 18 months of life with those of healthy control subjects. METHODS: Anterior nasal swabs of 122 subjects belonging to the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) birth cohort were collected longitudinally over 7 time points in the first 18 months of life. Nasal microbiota signatures were analyzed by using 16S rRNA multiplexed pair-end sequencing from 3 clinical groups: (1) patients with rhinitis alone (n = 28), (2) patients with rhinitis with concomitant wheeze (n = 34), and (3) healthy control subjects (n = 60). RESULTS: Maturation of the nasal microbiome followed distinctive patterns in infants from both rhinitis groups compared with control subjects. Bacterial diversity increased over the period of 18 months of life in control infants, whereas infants with rhinitis showed a decreasing trend (P < .05). An increase in abundance of the Oxalobacteraceae family (Proteobacteria phylum) and Aerococcaceae family (Firmicutes phylum) was associated with rhinitis and concomitant wheeze (adjusted P < .01), whereas the Corynebacteriaceae family (Actinobacteria phylum) and early colonization with the Staphylococcaceae family (Firmicutes phylum; 3 weeks until 9 months) were associated with control subjects (adjusted P < .05). The only difference between the rhinitis and control groups was a reduced abundance of the Corynebacteriaceae family (adjusted P < .05). Determinants of nasal microbiota succession included sex, mode of delivery, presence of siblings, and infant care attendance. CONCLUSION: Our results support the hypothesis that the nasal microbiome is involved in development of early-onset rhinitis and wheeze in infants

Hierarchical clustering and correlations of anti-tubercular drugs.

<p>Transcriptional responses data obtained from microfluidic experiment are used for the analysis. The detailed descriptions for genes represented in this figure are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069191#pone.0069191.s003" target="_blank">Table S1</a>. (A) Hierarchical clustering via average linkage of Pearson correlations for FDA approved drugs. The individual genes are represented in y-axis and the compound treatment is in the x-axis. (B) Pearson correlations for 23 anti-tubercular drugs. Correlations were calculated between 2 independent microfluidic experiments, following median normalization to account for plate effects.</p

Transcriptional responses of <i>M. bovis</i> BCG to anti-tubercular drugs profiled using qPCR.

<p>A compendium of qPCR results can show differences in the MOA of anti-tubercular drugs. (A) Spatial grouping of drug expression profiles on a dendrogram after qPCR profiling using hierarchical clustering. From the list of drugs tested, two major clusters emerge cell wall inhibitors, and those that inhibit DNA/protein synthesis. (B) Similarity matrix of expression profiles for chemical inhibitors using qPCR profiling. The blue-red color scale shows the degree of correlation of drugs expression profiles ranging from −1 to 1 respectively. ×- depicts transcriptional responses at 2× or 0.5× the MIC₅₀, while those that have no × designation were done 1× the MIC₅₀.</p

List of investigational and FDA approved anti-tubercular drugs and their mode of action.

<p>MIC₅₀ listed for anti-tubercular investigational and SAR compounds, when available.</p

Pathway enrichment of functional classes of genes on PCR array.

<p>Pathway enrichment of functional classes of genes on PCR array.</p

Transcriptional responses to drug treatment reveal that a single gene representing logically associated gene clusters is sufficient for MoA diagnosis in <i>Mycobacterium tuberculosis</i>.

<p>Groups of drugs clustered separately based on the known mechanism of action, while SAR series of compounds with novel MoA (cyclomarin) showed a fingerprint distinct from any of the compounds tested. Periods following drug names represent duplicates. X-axis represents the 90 genes that were chosen from the microarray as representative of 90 clusters, y-axis lists the different drug treatments. i, ii, and iii, represent 3 clusters namely, cell wall inhibitor, RNA polymerase inhibitor and Cyclomarin series, respectively.</p