Evaluation of Semiautomated IS6110-Based Restriction Fragment Length Polymorphism Typing for Mycobacterium tuberculosis in a High-Burden Setting.

The manual IS6110-based restriction fragment length polymorphism (RFLP) typing method is highly discriminatory; however, it is laborious and technically demanding, and data exchange remains a challenge. In an effort to improve IS6110-based RFLP to make it a faster format, DuPont Molecular Diagnostics recently introduced the IS6110-PvuII kit for semiautomated typing of Mycobacterium tuberculosis using the RiboPrinter microbial characterization system. This study aimed to evaluate the semiautomated RFLP typing against the standard manual method. A total of 112 isolates collected between 2013 and 2014 were included. All isolates were genotyped using manual and semiautomated RFLP typing methods. Clustering rates and discriminatory indexes were compared between methods. The overall performance of semiautomated RFLP compared to manual typing was excellent, with high discriminatory index (0.990 versus 0.995, respectively) and similar numbers of unique profiles (72 versus 74, respectively), numbers of clustered isolates (33 versus 31, respectively), cluster sizes (2 to 6 and 2 to 5 isolates, respectively), and clustering rates (21.9% and 17.1%, respectively). The semiautomated RFLP system is technically simple and significantly faster than the manual RFLP method (8 h versus 5 days). The analysis is fully automated and generates easily manageable databases of standardized fingerprints that can be easily exchanged between laboratories. Based on its high-throughput processing with minimal human effort, the semiautomated RFLP can be a very useful tool as a first-line method for routine typing of M. tuberculosis isolates, especially where Beijing strains are highly prevalent, followed by manual RFLP typing if resolution is not achieved, thereby saving time and labor.

GCN2 in Viral Defence and the Subversive Tactics Employed by Viruses.

The recent SARS-CoV-2 pandemic and associated COVID19 disease illustrates the important role of viral defence mechanisms in ensuring survival and recovery of the host or patient. Viruses absolutely depend on the host's protein synthesis machinery to replicate, meaning that impeding translation is a powerful way to counteract viruses. One major approach used by cells to obstruct protein synthesis is to phosphorylate the alpha subunit of eukaryotic translation initiation factor 2 (eIF2α). Mammals possess four different eIF2α-kinases: PKR, HRI, PEK/PERK, and GCN2. While PKR is currently considered the principal eIF2α-kinase involved in viral defence, the other eIF2α-kinases have also been found to play significant roles. Unsurprisingly, viruses have developed mechanisms to counteract the actions of eIF2α-kinases, or even to exploit them to their benefit. While some of these virulence factors are specific to one eIF2α-kinase, such as GCN2, others target all eIF2α-kinases. This review critically evaluates the current knowledge of viral mechanisms targeting the eIF2α-kinase GCN2. A detailed and in-depth understanding of the molecular mechanisms by which viruses evade host defence mechanisms will help to inform the development of powerful anti-viral measures.

Identification and control for the effects of bioinformatic globin depletion on human RNA-seq differential expression analysis.

When profiling blood samples by RNA-sequencing (RNA-seq), RNA from haemoglobin (Hgb) can account for up to 70% of the transcriptome. Due to considerations of sequencing depth and power to detect biological variation, Hgb RNA is typically depleted prior to sequencing by hybridisation-based methods; an alternative approach is to deplete reads arising from Hgb RNA bioinformatically. In the present study, we compared the impact of these two approaches on the outcome of differential gene expression analysis performed using RNA-seq data from 58 human tuberculosis (TB) patient or contact whole blood samples-29 globin kit-depleted and 29 matched non-depleted-a subset of which were taken at TB diagnosis and at six months post-TB treatment from the same patient. Bioinformatic depletion of Hgb genes from the non-depleted samples (bioinformatic-depleted) substantially reduced library sizes (median = 57.24%) and fewer long non-coding, micro, small nuclear and small nucleolar RNAs were captured in these libraries. Profiling published TB gene signatures across all samples revealed inferior correlation between kit-depleted and bioinformatic-depleted pairs when the proportion of reads mapping to Hgb genes was higher in the non-depleted sample, particularly at the TB diagnosis time point. A set of putative "globin-fingerprint" genes were identified by directly comparing kit-depleted and bioinformatic-depleted samples at each timepoint. Two TB treatment response signatures were also shown to have decreased differential performance when comparing samples at TB diagnosis to six months post-TB treatment when profiled on the bioinformatic-depleted samples compared with their kit-depleted counterparts. These results demonstrate that failure to deplete Hgb RNA prior to sequencing has a negative impact on the sensitivity to detect disease-relevant gene expression changes even when bioinformatic removal is performed.