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Falconer, C.

Publications and source records attributed to Falconer, C..

2 recordsLinked to original sources

Burkholderia pseudomallei clinical isolates are highly susceptible in vitro to cefiderocol, a novel siderophore cephalosporin

Cefiderocol is a novel cephalosporin designed to treat multidrug resistant Gram-negative infections. By forming a chelated complex with ferric iron, cefiderocol is transported into the periplasmic space via bacterial iron transport systems and primarily binds to penicillin-binding protein 3 (PBP3) to inhibit peptidoglycan synthesis. This mode of action results in cefiderocol having greater in vitro activity against many Gram-negative bacilli than currently used carbapenems, {beta}-lactam/{beta}-lactamase inhibitor combinations, and cephalosporins. Thus, we investigated the in vitro activity of cefiderocol (S-649266) against a total of 271 clinical isolates of Burkholderia pseudomallei from Australia. The collection was comprised of primary isolates (92.3%) and subsequent isolates (7.7%). Minimum inhibitory concentrations (MIC) of cefiderocol ranged from [≤]0.03 to 32 mg/L, where the MIC90 was 1 mg/L and 16 mg/L for primary and subsequent isolates, respectively. Based upon non-species specific (Gram-negative bacilli) clinical breakpoints for cefiderocol (MIC [≤] 4 mg/L), twelve isolates (4.4%) would be classified as non-susceptible. Further testing for co-resistance to meropenem, ceftazidime, trimethoprim-sulfamethoxazole, amoxicillin-clavulanate and doxycycline was performed on a subset of isolates with elevated cefiderocol MICs ([≥]2 mg/L, 4.8%) and 84.6% of these isolates exhibited resistance to at least one of these antimicrobials. Cefiderocol was found to be highly active in vitro against B. pseudomallei primary clinical isolates. This novel compound shows great potential for the treatment of melioidosis in endemic countries and should be explored further.

microbiology

Genotype-free demultiplexing of pooled single-cell RNA-seq

A variety of experimental and computational methods have been developed to demultiplex samples from pooled individuals in a single-cell RNA sequencing (scRNA-Seq) experiment which either require adding information (such as hashtag barcodes) or measuring information (such as genotypes) prior to pooling. We introduce scSplit which utilises genetic differences inferred from scRNA-Seq data alone to demultiplex pooled samples. scSplit also extracts a minimal set of high confidence presence/absence genotypes in each cluster which can be used to map clusters to original samples. Using a range of simulated, merged individual-sample as well as pooled multi-individual scRNA-Seq datasets, we show that scSplit is highly accurate and concordant with demuxlet predictions. Furthermore, scSplit predictions are highly consistent with the known truth in cell-hashing dataset. We also show that multiplexed-scRNA-Seq can be used to reduce batch effects caused by technical biases. scSplit is ideally suited to samples for which external genome-wide genotype data cannot be obtained (for example non-model organisms), or for which it is impossible to obtain unmixed samples directly, such as mixtures of genetically distinct tumour cells, or mixed infections. scSplit is available at: https://github.com/jon-xu/scSplit

bioinformatics