Biology subjects

Moustafa, A. M.

Publications and source records attributed to Moustafa, A. M..

3 recordsLinked to original sources

bioRxiv2020

Emerging SARS-CoV-2 diversity revealed by rapid whole genome sequence typing.

BackgroundDiscrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events. MethodsWe developed a tool (GNUVID) that integrates whole genome multilocus sequence typing and a supervised machine learning random forest-based classifier. We used GNUVID to assign sequence type (ST) profiles to each of 69,686 SARS-CoV-2 complete, high-quality genomes available from GISAID as of October 20th 2020. STs were then clustered into clonal complexes (CCs), and then used to train a machine learning classifier. We used this tool to detect potential introduction and exportation events, and to estimate effective viral diversity across locations and over time in 16 US states. ResultsGNUVID is a scalable tool for viral genotype classification (available at https://github.com/ahmedmagds/GNUVID) that can be used to quickly process tens of thousands of genomes. Our genotyping ST/CC analysis uncovered dynamic local changes in ST/CC prevalence and diversity with multiple replacement events in different states. We detected an average of 20.6 putative introductions and 7.5 exportations for each state. Effective viral diversity dropped in all states as shelter-in-place travel-restrictions went into effect and increased as restrictions were lifted. Interestingly, our analysis showed correlation between effective diversity and the date that state-wide mask mandates were imposed. ConclusionsOur classification tool uncovered multiple introduction and exportation events, as well as waves of expansion and replacement of SARS-CoV-2 genotypes in different states. Combined with future genomic sampling the GNUVID system could be used to track circulating viral diversity and identify emerging clones and hotspots.

genomics↗

bioRxiv2020

Structure-guided microbial targeting of antistaphylococcal prodrugs

Carboxy ester prodrugs have been widely employed as a means to increase oral absorption and potency of phosphonate antibiotics. Prodrugging can successfully mask problematic chemical features that prevent cellular uptake and can be used to target delivery of compounds to specific tissues. However, many carboxy ester promoieties are rapidly hydrolyzed by serum esterases, curbing their potential therapeutic applications. While carboxy ester-based prodrug targeting is feasible, it has seen limited use in microbes due to a paucity of information about the selectivity of microbial esterases. Here we identify the bacterial esterases, GloB and FrmB, that are required for carboxy ester prodrug activation in Staphylococcus aureus. Additionally, we determine the substrate specificities for FrmB and GloB and demonstrate the structural basis of these preferences. Finally, we establish the carboxy ester substrate specificities of human and mouse sera, which revealed several promoieties likely to be serum esterase-resistant while still being microbially labile. These studies lay the groundwork for structure-guided design of anti-staphyloccal promoieties and expand the range of molecules to target staphyloccal pathogens.

biochemistry↗

bioRxiv2020

Rapid whole genome sequence typing reveals multiple waves of SARS-CoV-2 spread

As the pandemic SARS-CoV-2 virus has spread globally its genome has diversified to an extent that distinct clones can now be recognized, tracked, and traced. Identifying clonal groups allows for assessment of geographic spread, transmission events, and identification of new or emerging strains that may be more virulent or more transmissible. Here we present a rapid, whole genome, allele-based method (GNUVID) for assigning sequence types to sequenced isolates of SARS-CoV-2 sequences. This sequence typing scheme can be updated with new genomic information extremely rapidly, making our technique continually adaptable as databases grow. We show that our method is consistent with phylogeny and recovers waves of expansion and replacement of sequence types/clonal complexes in different geographical locations. GNUVID is available as a command line application (https://github.com/ahmedmagds/GNUVID).

genomics↗