Hansen, L. T. | bioRxiv Science

bioRxiv2024

Estimating gene conversion tract length and rate from PacBio HiFi data

Gene conversions are broadly defined as the transfer of genetic material from a donor to an acceptor sequence and can happen both in meiosis and mitosis. They are a subset of non-crossover events and, like crossover events, gene conversion can generate new combinations of alleles and counteract mutation load by reverting germline mutations through GC-biased gene conversion. Estimating gene conversion rate and the distribution of gene conversion tract lengths remains challenging. We present a new method for estimating tract length, rate and detection probability of non-crossover events directly in HiFi PacBio long read data. The method can be used to make inference from sequencing of gametes from a single individual. The method is unbiased even under low single nucleotide variant (SNV) densities and does not necessitate any demographic or evolutionary assumptions. We test the accuracy and robustness of our method using simulated datasets where we vary length of tracts, number of tracts, the genomic SNV density and levels of correlation between SNV density and NCO event position. Our simulations show that under low SNV densities, like those found in humans, only a minute fraction ([~]2%) of NCO events are expected to become visible as gene conversions by moving at least one SNV. We finally illustrate our method by applying it to PacBio sequencing data from human sperm.

genomics↗

bioRxiv2023

Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning

Listeria monocytogenes is a potentially severe disease-causing bacteria mainly transmitted through food. This pathogen is of great concern for public health and the food industry in particular. Many countries have implemented thorough regulations, and some have even set zero-tolerance thresholds for particular food products to minimise the risk of L. monocytogenes outbreaks. This emphasises that proper sanitation of food processing plants is of utmost importance. Consequently in recent years, there has been an increased interest in L. monocytogenes tolerance to disinfectants used in the food industry. Even though many studies are focusing on laboratory quantification of L. monocytogenes tolerance, the possibility of predictive models remains poorly studied. Within this study, we explore the prediction of tolerance and minimum inhibitory concentrations (MIC) using whole genome sequencing (WGS) and machine learning (ML). We used WGS data and MIC values to quaternary ammonium compound (QAC) disinfectants from 1649 L. monocytogenes isolates to train different ML predictors. Our study shows promising results for predicting tolerance to QAC disinfectants using WGS and machine learning. We were able to train high-performing ML classifiers to predict tolerance with balanced accuracy scores up to 0.97{+/-}0.02. For the prediction of MIC values, we were able to train ML regressors with mean squared error as low as 0.07{+/-}0.02. We also identified several new genes related to cell wall anchor domains, plasmids, and phages, putatively associated with disinfectant tolerance in L. monocytogenes. The findings of this study are a first step towards prediction of L. monocytogenes tolerance to QAC disinfectants used in the food industry. In the future, predictive models might be used to monitor disinfectant tolerance in food production and might support the conceptualisation of more nuanced sanitation programs. AUTHOR SUMMARYMicrobial contamination challenges food safety by potentially transmitting harmful microbes such as bacteria to consumers. Listeria monocytogenes is an example of such a bacteria, which is primarily transmitted through food and can cause severe diseases in at-risk groups. Fortunately, strict food safety regulations and stringent cleaning protocols are in place to prevent the transmission of Listeria monocytogenes. However, in recent years, there has been an increase in tolerance towards disinfectants used in the food industry, which can reduce their effectiveness. In this study, we used genome sequencing and phenotypic data to train machine learning models that can accurately predict whether individual Listeria monocytogenes isolates are tolerant to selected disinfectants. We were able to train models that are not only able to distinguish sensitive/tolerant isolates but also can predict different degrees of tolerance to disinfectants. Further, we were able to report a set of genes that were important for the machine learning prediction and could give information about possible tolerance mechanisms. In the future, similar predictive models might be used to guide cleaning and disinfection protocols to facilitate maximum effectiveness.

bioinformatics↗

bioRxiv2020

Characterisation and chemometric evaluation of 17 elements in ten seaweed species from Greenland

Several Greenland seaweed species have potential as foods or food ingredients, both for local consumption and export. However, knowledge regarding their content of beneficial and deleterious elements on a species specific and geographical basis is lacking. This study investigated the content of 17 elements (As, Ca, Cd, Cr, Cu, Fe, Hg, I, K, Mg, Mn, Na, Ni, P, Pb, Se and Zn) in 77 samples of ten species (Agarum clathratum, Alaria esculenta, Ascophyllum nodosum, Fucus distichus, Fucus vesiculosus, Hedophyllum nigripes, Laminaria solidungula, Palmaria palmata, Saccharina latissima and Saccharina longicruris). Element profiles differed between species but showed similar patterns within the same family. For five species, different thallus parts were investigated separately, and showed different element profiles. A geographic origin comparison of Fucus species indicated regional differences. The seaweeds investigated were especially good sources of macrominerals (K > Na > Ca > Mg) and trace minerals, such as Fe. Iodine contents were high, especially in macroalgae of the family Laminariaceae. None of the samples exceeded the EU maximum levels for Cd, Hg or Pb, but some exceeded the stricter French regulations, especially for Cd and I. In conclusion, these ten species are promising food items.

biochemistry↗