Insights gained from sequencing Australian non-invasive and invasive Streptococcus pyogenes isolates.

Epidemiological data have indicated that invasive infections caused by the Gram-positive cocci Streptococcus pyogenes (group A streptococcus, GAS) have increased in many Australian states over the past two decades. In July 2022, invasive GAS (iGAS) infections became nationally notifiable in Australia via public-health agencies. Surveillance for S. pyogenes infections has been sporadic within the state of New South Wales (NSW). This has led to a lack of genetic data on GAS strains in circulation, particularly for non-invasive infections, which are the leading cause of GAS's burden on the Australian healthcare system. To address this gap, we used whole-genome sequencing to analyse the genomes of 318 S. pyogenes isolates collected within two geographical regions of NSW. Invasive isolates were collected in 2007-2017, whilst non-invasive isolates were collected in 2019-2021. We found that at least 66 different emm-types were associated with clinical disease within NSW. There was no evidence of any Australian-specific clones in circulation. The M1UK variant of the emm1 global pandemic clone (M1global) has been detected in our isolates from 2013 onwards. We detected antimicrobial-resistance genes (mainly tetM, ermA or ermB genes) in less than 10 % of our 318 isolates, which were more commonly associated with non-invasive infections. Superantigen virulence gene carriage was reasonably proportionate between non-invasive and invasive infection isolates. Our study adds rich data on the genetic makeup of historical S. pyogenes infections within Australia. Ongoing surveillance of invasive and non-invasive GAS infections within NSW by whole-genome sequencing is warranted to inform on outbreaks, antimicrobial resistance and vaccine coverage.

Misleading Westerns: Common Quantification Mistakes in Western Blot Densitometry and Proposed Corrective Measures.

Densitometry data generated for Western blots are commonly used to compare protein abundance between samples. In the last decade, it has become apparent that assumptions underpinning these comparisons are often violated in studies reporting Western blot data in the literature. These violations can lead to erroneous interpretations of data and may contribute to poor reproducibility of research. We assessed the reliability of Western blot data obtained to study human myometrial tissue proteins. We ran dilution series of protein lysates to explore the linearity of densitometry data. Proteins analysed included αSMA, HSP27, ERK1/2, and GAPDH. While ideal densitometry data are directly proportional to protein abundance, our data confirm that densitometry data often deviate from this ideal, in which case they can fit nonproportional linear or hyperbolic mathematical models and can reach saturation. Nonlinear densitometry data were observed when Western blots were detected using infrared fluorescence or chemiluminescence, and under different SDS-PAGE conditions. We confirm that ghosting artefacts associated with overabundance of proteins of interest in Western blots can skew findings. We also confirm that when data to be normalised are not directly proportional to protein abundance, it is a mistake to use the normalisation technique of dividing densitometry data from the protein-of-interest with densitometry data from loading control protein(s), as this can cause the normalised data to be unusable for making comparisons. Using spiked proteins in a way that allowed us to control the total protein amount per lane, while only changing the amount of spiked proteins, we confirm that nonlinearity and saturation of densitometry data, and errors introduced from normalisation processes, can occur in routine assays that compare equal amounts of lysate. These findings apply to all Western blot studies, and we highlight quality control checks that should be performed to make Western blot data more quantitative.