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Biology subjects

Boyd, O.

Publications and source records attributed to Boyd, O..

2 recordsLinked to original sources

The impact of cooking and burial on proteins: a characterisation of experimental foodcrusts and ceramics

Foodcrusts have received relatively little attention in the burgeoning field of proteomic analysis of ancient cuisine. We remain ignorant of how cooking and burial impacts protein survival, and crucially, the extent to which the extractome reflects the composition of input ingredients. Therefore, through experimental analogues we explore the extent of protein survival in unburied and buried foodcrusts and ceramics using typical Mesolithic ingredients (red deer, Atlantic salmon and sweet chestnut). We then explore a number of physiochemical properties theorised to aid protein preservation. The results reveal that proteins were much more likely to be detected in foodcrusts than ceramics using the methodology employed, input ingredient strongly influences protein preservation, and that degradation is not universal nor linear between proteins, indicating that multiple protein physiochemical properties are at play. While certain properties such as hydrophobicity apparently aid protein preservation, none single-handedly explain why particular proteins/peptides survive in buried foodcrusts: this complex interplay requires further investigation. The findings demonstrate that proteins indicative of the input ingredient can be identifiable in foodcrust, but that the full proteome is unlikely to preserve. While this shows promise for the survival of proteins in archaeological foodcrust, further research is needed to accurately interpret foodcrust extractomes.

biochemistry↗

Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England

Genetic drift in infectious disease transmission results from randomness of transmission and host recovery or death. The strength of genetic drift for SARS-CoV-2 transmission is expected to be high due to high levels of superspreading, and this is expected to substantially impact disease epidemiology and evolution. However, we dont yet have an understanding of how genetic drift changes over time or across locations. Furthermore, noise that results from data collection can potentially confound estimates of genetic drift. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. Our method is highly scalable to increasingly large genomic datasets, which overcomes a limitation in commonly used phylogenetic methods. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium and separately infer the strength of genetic drift for pre-B.1.177, B.1.177, Alpha, and Delta. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude, which cannot be explained by literature values of superspreading. Our estimates of genetic drift will be informative for parameterizing evolutionary models and studying potential mechanisms for increased drift. Author SummaryThe transmission of pathogens like SARS-CoV-2 is strongly affected by chance effects in the contact process between infected and susceptible individuals, collectively referred to as random genetic drift. We have an incomplete understanding of how genetic drift changes across time and locations. To address this gap, we developed a computational method that infers the strength of genetic drift from time series genomic data that corrects for non-biological noise and is computationally scalable to the large numbers of sequences available for SARS-CoV-2, overcoming a major challenge of existing methods. Using this method, we quantified the strength of genetic drift for SARS-CoV-2 transmission in England throughout time and across locations. These estimates constrain potential mechanisms and help parameterize models of SARS-CoV-2 evolution. More generally, the computational scalability of our method will become more important as increasingly large genomic datasets become more common.

evolutionary biology↗