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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2023

Performance of amplicon and capture based next-generation sequencing approaches for the epidemiological surveillance of Omicron SARS-CoV-2 and other variants of concern.

To control the SARS-CoV-2 pandemic, healthcare systems have focused on ramping up their capacity for epidemiological surveillance through viral whole genome sequencing. In this paper, we tested the performance of two protocols of SARS-CoV-2 nucleic acid enrichment, an amplicon enrichment using different versions of the ARTIC primer panel and a hybrid-capture method using KAPA RNA Hypercap. We focused on the challenge of the Omicron variant sequencing, the advantages of automated library preparation and the influence of the bioinformatic analysis in the final consensus sequence. All 94 samples were sequenced using Illumina iSeq 100 and analysed with two bioinformatic pipelines: a custom-made pipeline and an Illumina-owned pipeline. We were unsuccessful in sequencing six samples using the capture enrichment due to low reads. On the other hand, amplicon dropout and mispriming caused the loss of mutation G21987A and the erroneous addition of mutation T15521A respectively using amplicon enrichment. Overall, we found high sequence agreement regardless of method of enrichment, bioinformatic pipeline or the use of automation for library preparation in eight different SARS-CoV-2 variants. Automation and the use of a simple app for bioinformatic analysis can simplify the genotyping process, making it available for more diagnostic facilities and increasing global vigilance.

microbiology↗
bioRxiv2023

Genomic epidemiology of European Aspergillus fumigatus causing COVID-19-associated pulmonary aspergillosis in Europe

The opportunistic fungus Aspergillus fumigatus has been found to cause coinfections in patients with severe SARS-CoV-2 virus infection, leading to COVID-19-associated pulmonary aspergillosis (CAPA). The CAPA all-cause mortality rate is approximately 50% and may be complicated by azole-resistance. Genomic epidemiology can help shed light on the genetics of A. fumigatus causing CAPA including the prevalence of alleles that are associated with azole-resistance. Here, a population genomic analysis of 21 CAPA isolates from four European countries is presented. The CAPA isolates were compared with A. fumigatus from a wider population of 167 non-CAPA clinical isolates and 73 environmental isolates. Bioinformatic analysis and antifungal susceptibility testing were performed to quantify resistance and identify possible genetically-encoded azole-resistant mechanisms. Phylogenetic analysis of the 21 CAPA isolates showed a lack of genetic distinction from the wider A. fumigatus population, with isolates distributed within two distinct clades (A and B), with the majority of the CAPA isolates in clade B (71.4%). The prevalence of phenotypic azole-resistance in CAPA was 14.3% (n=3/21); all three CAPA isolates contained a known resistance-associated cyp51A polymorphism. CAPA isolates are drawn from the wider A. fumigatus population rather than forming a unique genetic background showing that COVID-19 patients are susceptible to the entire A. fumigatus population. However, the relatively high prevalence of azole-resistance alleles that we document poses a threat to treatment success rates, warranting enhanced detection and surveillance of A. fumigatus genotypes in these patients. Furthermore, potential changes to antifungal first-line treatment guidelines may be needed to improve patient outcomes.

microbiology↗
bioRxiv2023

Dietaryindex: A User-Friendly and Versatile R Package for Standardizing Dietary Pattern Analysis in Epidemiological and Clinical Studies

BackgroundFew standardized and open-source tools exist for calculating dietary pattern indexes from dietary intake data in epidemiological and clinical studies. Miscalculations of dietary indexes, with suspected erroneous findings, are occasionally noted in the literature. ObjectiveThe primary aim is to develop and validate dietaryindex, a user-friendly and versatile R package that standardizes the calculation of dietary indexes. MethodsDietaryindex utilizes a two-step process: an initial calculation of serving size for each food and nutrient category, followed by the calculation of individual dietary indexes. It includes generic functions that accept any preprocessed serving sizes of food groups and nutrients, with the standard serving sizes defined according to the methodologies used in well-known prospective cohort studies. For ease of use, dietaryindex also offers one-step functions that directly reference common datasets and tools, including the National Health and Nutrition Examination Survey (NHANES) and Block Food Frequency Questionnaire, eliminating the need for data preprocessing. At least two independent researchers validated the serving size definitions and scoring algorithms of dietaryindex. ResultsDietaryindex can calculate multiple dietary indexes of high interest in research, including Healthy Eating Index (HEI) - 2020, Alternative Healthy Eating Index 2010, Dietary Approaches to Stop Hypertension Index, Alternate Mediterranean Diet Score, Dietary Inflammatory Index, American Cancer Society 2020 dietary index, and Planetary Health Diet Index from the EAT-Lancet Commission. In our validation process, dietaryindex demonstrated full accuracy (100%) in all generic functions with two-decimal rounding precision in comparison to hand-calculated results. Similarly, using NHANES 2017-2018 data and ASA24 and DHQ3 example data, the HEI2015 outputs from dietaryindex aligned (99.95% - 100%) with results using the SAS codes from the National Cancer Institute. ConclusionsDietaryindex is a user-friendly, versatile, and validated informatics tool for standardized dietary index calculations. We have open-sourced all the validation files and codes with detailed tutorials on GitHub (https://github.com/jamesjiadazhan/dietaryindex).

bioinformatics↗
bioRxiv2023

Genomic epidemiology and antifungal-resistant characterization of Candida auris, Colombia, 2016-2021

Since 2016 in Colombia, ongoing transmission of Candida auris has been reported in multiple cities. Here, we provide an updated description of C. auris genomic epidemiology and the dynamics of antifungal resistance in Colombia. We sequenced 99 isolates from C. auris cases with collection dates ranging from June 2016 to January 2021; the resulting sequences coupled with 103 previously generated sequences from C. auris cases were described in a phylogenetic analysis. All C. auris cases were of clade IV. Of the 182 isolates with antifungal susceptibility data, 67 (37%) were resistant to fluconazole, and 39 (21%) were resistant to amphotericin B. Isolates predominately clustered by country except for 16 isolates from Bogota, Colombia, which grouped with isolates from Venezuela. The largest cluster (N=166 isolates) contained two subgroups. The first subgroup contained 26 isolates, mainly from Cesar; of these 85% (N=22) were resistant to fluconazole. The second subgroup consisted of 47 isolates from the north coast; of these, 81% (N=38) were resistant to amphotericin B. Mutations in the ERG11 and TAC1B genes were identified in fluconazole-resistant isolates, and two amino acid substitutions in PSK76257-(FLO8) and PSK74852 genes were associated with higher minimum inhibitory concentration values for amphotericin B. This work may help identify mechanisms conferring azole and amphotericin B resistance in C. auris. Overall, C. auris cases from different geographic locations in Colombia exhibited high genetic relatedness, suggesting continued transmission between cities since 2016. These findings also suggest a lack of or minimal introductions of different clades of C. auris into Colombia. IMPORTANCECandida auris is an emerging fungus that presents a serious global health threat and has caused multiple outbreaks in Colombia. This work discusses the likelihood of introductions and local transmission of C. auris and provides an updated description of the molecular mechanisms associated with antifungal resistance in Colombia. Efforts like this tracking genomic variation provide information about the evolving C. auris burden that could help guide public health strategies to control C. auris spread.

genomics↗
bioRxiv2023

Phenological and epidemiological impacts of climate change on peach production

Agricultural food security is threatened by climate change impacts, which can distress crop growth and favor the spread of infectious diseases. Here, we examined the synergism of the two potentially most disruptive causes of future yield failure in peach production: the effects of global climate change on fruit growth and on the spread of fungal diseases. Coupling a phenological and epidemiological model across the French continental territory, we provided projections of yield losses for four peach cultivars (early, mid-early, mid-late, and late) in the XXI century under different climate change scenarios. Global warming is expected to impair fruit phenology with blooming failure events in the south-western part of the country. This will be less extreme under the more moderate emission scenario, even though sporadic blooming failures will still occur. In contrast, future warmer and drier conditions will decrease brown rot-induced yield loss in the historical locations devoted to peach cultivation. To adapt to these changes, the benefits of shifting peach production sites to new suitable areas are evaluated. Thanks to this strategy, the peach national yield could still be fulfilled even under the most extreme climate change scenario. Comprehensive mathematical frameworks, that concomitantly consider the climatic effects on the plant hosts and on their pathogens, are required to provide reliable future predictions of crop yields and to inform control and adaptation strategies to guarantee food security under global warming.

ecology↗
bioRxiv2023

A community-curated, global atlas of Bacillus cereus sensu lato genomes for epidemiological surveillance

The ability to cause foodborne illness, anthrax, and other infections has been attributed to numerous lineages within Bacillus cereus sensu lato (s.l.). However, existing pathogen surveillance databases facilitate dangerous pathogen misidentifications when applied to B. cereus s.l., potentially hindering outbreak or bioterrorism attack response efforts. To address this, we developed BTyperDB (www.btyper.app), an atlas of B. cereus s.l. genomes with standardized, community-curated metadata. BTyperDB aggregates all publicly available B. cereus s.l. genomes (including >2,600 previously unassembled genomes) with novel genomes donated by laboratories around the world, nearly doubling the number of publicly available B. cereus s.l. genomes. To showcase its utility for pathogen surveillance, we use BTyperDB to identify emerging anthrax toxin- and capsule-harboring lineages. Overall, our study provides insight into the epidemiology of an under-studied group of emerging pathogens and highlights the benefits of inclusive, community-driven metadata FAIRification efforts.

microbiology↗
bioRxiv2024

Whole Genome Sequencing of DENV-2 isolated from Aedes aegypti mosquitoes in Esmeraldas, Ecuador. Genomic epidemiology of genotype III Southern Asian-American in the country.

Ecuador is a tropical country reporting Dengue virus (DENV) outbreaks with areas of hyperendemic viral transmission. Entomo-virological surveillance and monitoring effort conducted in the Northwestern border province of Esmeraldas in April 2022, five pools of female Aedes aegypti mosquitoes from a rural community tested positive for DENV serotype 2 by RT-qPCR. One pool was sequenced by Illumina MiSeq, and it corresponded to genotype III Southern Asian-American. Comparison with other genomes revealed genetic similarity to a human DENV genome sequenced in 2021, also from Esmeraldas. Potential introduction events to the country could have originated from Colombia, considering the vicinity of the collection sites to the neighboring country and high human movement. The inclusion of genomic information complements entomo-virological surveillance, providing valuable insights into genetic variants. This contribution enhances our understanding of Dengue virus (DENV) epidemiology in rural areas and guides evidence-based decisions for surveillance and interventions. Author SummaryIn this study, the complete genetic information of the dengue virus isolated from the mosquito vector Aedes aegypti was characterized using molecular biology, sequencing, and bioinformatics analysis. The isolation of the virus from the vector has contributed significantly to our understanding of the transmission dynamics of the disease and its genetic relationship with human cases. Identifying the serotypes and genotypes circulating in Ecuador has enabled us to comprehend the evolution of the virus from 2014 to 2023. Therefore, surveillance of genetic variability would aid in adapting precise prevention and control strategies to the genetic variants present in a specific geographical area. Entomo-virological surveillance is essential for assessing changes in risk and the impact of control measures.

genomics↗
bioRxiv2024

Global genomic epidemiology of blaGES-5 carbapenemase-associated integrons

Antimicrobial resistance (AMR) gene cassettes comprise an AMR gene flanked by short recombination sites (attI x attC or attC x attC). Integrons are genetic elements able to capture, excise, and shuffle these cassettes, providing adaptation on demand, and can be found on both chromosomes and plasmids. Understanding the patterns of integron diversity may help to understand the epidemiology of AMR genes. As a case study, we examined the clinical resistance gene blaGES-5, an integron-associated class A carbapenemase first reported in Greece in 2004 and since observed worldwide, which to our knowledge has not been the subject of a previous global analysis. Using a dataset comprising all NCBI contigs containing blaGES-5 (n = 431), we developed a pangenome graph-based workflow to characterise and cluster the diversity of blaGES-5 -associated integrons. We demonstrate that blaGES-5-associated integrons on plasmids are different to those on chromosomes. Chromosomal integrons were almost all identified in P. aeruginosa ST235, with a consistent gene cassette content and order. We observed instances where insertion sequence IS110 disrupted attC sites, which might immobilise the gene cassettes and explain the conserved integron structure despite the presence of intI1 integrase promoters, which would typically facilitate capture or excision and rearrangement. The plasmid-associated integrons were more diverse in their gene cassette content and order, which could be an indication of greater integrase activity and shuffling of integrons on plasmids.

genomics↗
bioRxiv2024

Global diversity and evolution of Salmonella Panama, an understudied serovar causing gastrointestinal and invasive disease worldwide: a genomic epidemiology study

BackgroundNontyphoidal Salmonella (NTS) is a globally important bacterial pathogen, typically associated with foodborne gastrointestinal infection. Some NTS serovars can also colonise normally sterile sites in humans to cause invasive NTS (iNTS) disease. One understudied Salmonella enterica serovar which is responsible for a significant number of cases of iNTS disease is Panama. Despite global dissemination, numerous outbreaks, and a reported association with iNTS disease, S. enterica serovar Panama (S. Panama) has not been investigated in detail. MethodsUsing combined epidemiological and whole genome sequencing data we analysed 836 S. Panama genomes derived from historical collections, national surveillance datasets, and publicly available data. The collection represents all inhabited continents and includes isolates collected between 1931 and 2019. Maximum likelihood and Bayesian phylodynamic approaches were used to determine population structure & evolutionary history, and to infer geo-temporal dissemination. A combination of different bioinformatic approaches utilising short-read and long-read data were used to characterise geographic and clade-specific trends in antimicrobial resistance (AMR), and genetic markers for invasiveness. FindingsWe identified the presence of multiple geographically linked S. Panama clades, and regional trends in antimicrobial resistance profiles. Most isolates were pan-susceptible to antibiotics and belonged to clades circulating in the United States of America, Latin America, and the Caribbean. Multidrug resistant (MDR) isolates in our collection belonged to two phylogenetic clades circulating in Europe and Asia/Oceania, which exhibited the highest invasiveness indices based on the conservation of 196 extra-intestinal predictor genes. InterpretationThis first large-scale phylogenetic analysis of S. Panama revealed important information about population structure, AMR, global ecology, and genetic markers of invasiveness of the identified genomic subtypes. Our findings provide an important baseline for understanding S. Panama infection in the future. The presence of MDR clades with an elevated invasiveness index should be monitored by ongoing surveillance as such clades may pose an increased public health risk. FundingThe project was supported by both a Global Challenges Research Fund (GCRF) data & resources grant BBS/OS/GC/000009D and the BBSRC Core Capability Grant to the Earlham Institute BB/CCG1720/1. JCDH received a Wellcome Trust Investigator award (grant number 222528/Z/21/Z). CVP was supported by the John Lennon Memorial Scholarship from the University of Liverpool and a Fee Bursary Award from the Institute of Integrative Biology at the University of Liverpool. FXW was awarded the Institut Pasteur; Sante publique France; the Fondation Le Roch-Les Mousquetaires; and the French governments Investissement dAvenir programme, Laboratoire dExcellence "Integrative Biology of Emerging Infectious Diseases" (grant number ANR-10-LABX-62-IBEID). MDS was supported by a BBSRC grant (KSB: BB/V009184/1). RJBengtsson was supported by an MRC grant (KSB: MR/R020787/1). RJBennett was supported by a BBSRC DTP (BB/M011186/1). DJI was supported by a National Health and Medical Research Council (NHMRC) Emerging Leadership Fellowship (GNT1195210). BPH was supported by NHMRC Leadership Fellowship (GNT1196103). KSB and EVR are affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Gastrointestinal Infections at University of Liverpool in partnership with the United Kingdom Health Security Agency, in collaboration with University of Warwick. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health and Social Care or UKHSA. MAC is affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) (NIHR200892) in Genomics and Enabling Data at University of Warwick in partnership with the UK Health Security Agency (UKHSA), in collaboration with Universities of Cambridge and Oxford. MAC is based at UKHSA. The views expressed are those of the author(s) and not necessarily those of the NIHR, the Department of Health and Social Care or the UK Health Security Agency. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. No payment was received by any pharmaceutical company or other agency to write this article. The authors were not precluded from accessing data in the study, and accept responsibility to submit for publication. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSalmonella Panama has consistently been reported as a frequently isolated Salmonella serovar in national surveillance datasets, causing both sporadic cases and larger outbreaks. However, this picture has been masked due to most of the focus being placed on the top two serovars associated with nontyphoidal Salmonella (NTS) and invasive NTS disease: Typhimurium and Enteritidis. Previous works on S. Panama have determined transmission to include human faeces and breast milk, as well as non-human sources such as environmental reservoirs and animals (reptiles and pigs used for food). In contrast to most of the Salmonella serovars causing gastroenteritis, S. Panama has also been associated with a range of systemic infections including septicaemia, meningitis, and osteomyelitis, mostly affecting infants. The patterns of antimicrobial resistance of this pathogen were not well known, with studies reporting a mixture of different antimicrobial resistance profiles. Added value of this studyHere we conducted a large-scale study of 836 globally relevant S. Panama isolates to understand global population structure and disease ecology. The S. Panama genomes used in this study were sourced from a combination of historical collections (including the first ever isolated strain of this serovar dating from 1931), national surveillance datasets, and publicly available data. Some of these isolates had been linked to travel, allowing an inferred location for understanding population structure. Overall, this assembled S. Panama collection spanned a range of 88 years and covered 45 countries and regions of all inhabited continents. We applied bacterial phylodynamic approaches to determine population structure, evolutionary history, and clade-specific trends of invasiveness and antimicrobial resistance correlated with geo-temporal parameters. Implications of all the available evidenceThis study revealed the population structure and global ecology of S. Panama, indicating that multidrug resistant S. Panama circulates in European and Asian regions, and presents an increased number of genetic markers for extra-intestinal invasiveness. The transmission and expansion of antimicrobial-resistant S. Panama strains presented in this study highlight the significance of supporting control and monitoring efforts from an international perspective.

genomics↗
bioRxiv2024

Epidemiological inference at the threshold of data availability: an influenza A(H1N2)v spillover event in the United Kingdom

Viruses which infect animals regularly spill over into the human population, but individual events may lead to anything from a single case to a novel pandemic. Rapidly gaining an understanding of a spillover event is critical to calibrating a public health response. We here propose a novel method, using likelihood free rejection sampling to evaluate the properties of an outbreak of swine-origin influenza A(H1N2)v in the United Kingdom, detected in November 2023. From the limited data available we generate historical estimates of the probability that the outbreak had died out in the days following the detection of the first case. Our method suggests that the outbreak could have been said to be over with 95% certainty between 19 and 29 days after the first case was detected, depending upon the probability of a case being detected. We further estimate the number of undetected cases conditional upon the outbreak still being live, the epidemiological parameter R0, and the date on which the spillover event itself occurred. Our method requires minimal data to be effective. While our calculations were performed after the event, the real-time application of our method has potential value for public health responses to cases of emerging viral infection.

microbiology↗
bioRxiv2024

Genomic epidemiology of Plasmodium knowlesi reveals putative genetic drivers of adaptation in Malaysia.

Sabah, Malaysia, has amongst the highest burden of human Plasmodium knowlesi infection in the country, associated with increasing encroachment on the parasites macaque host habitat. However, the genomic make-up of P. knowlesi in Sabah was previously poorly understood. To inform on local patterns of transmission and putative adaptive drivers, we conduct population-level genetic analyses of P. knowlesi human infections using 52 new whole genomes from Sabah, Malaysia, in combination with publicly available data. We identify the emergence of distinct geographical subpopulations within the macaque-associated clusters using IBD-based connectivity analysis. Secondly, we report on introgression events between the clusters, which may be linked to differentiation of the subpopulations, and that overlap genes critical for survival in human and mosquito hosts. Using village-level locations from P. knowlesi infections, we also identify associations between several introgressed regions and both intact forest perimeter-area ratio and mosquito vector habitat suitability. Our findings provide further evidence of the complex role of changing ecosystems and sympatric macaque hosts in Malaysia driving distinct genetic changes seen in P. knowlesi populations. Future expanded analyses of evolving P. knowlesi genetics and environmental drivers of transmission will be important to guide public health surveillance and control strategies. Author SummaryThe zoonotic P. knowlesi parasite is an emerging, yet understudied, cause of malaria in Southeast Asia. Sabah, Malaysia, has amongst the highest burden of human P. knowlesi infection in the country, however, the region is currently understudied. Thus, we produced a collection of high-quality P. knowlesi genomes from Sabah, and in combination with publicly available data, performed an extensive population genetics analysis. Our work contributes novel insights for Plasmodium knowlesi population genetics and genetic epidemiology.

genomics↗
bioRxiv2024

Transient evolutionary epidemiology of viral adaptation and lethal mutagenesis

Beneficial mutations drive the within-host adaptation of viral populations and can prolong the duration of host infection. Yet, most mutations are not adaptive and the increase of the mean fitness of viral populations is hampered by deleterious and lethal mutations. Because of this ambivalent role of mutations, it is unclear if a higher mutation rate boosts or slows down viral adaptation. Here we study the interplay between selection, mutation, genetic drift and within-host dynamics of viral populations. We obtain good approximations for the transient evolutionary epidemiology of viral adaptation under the assumption that the mutation rate is high and the effects of non-lethal mutations remain small. We use this theoretical framework to discuss the feasibility of lethal mutagenesis to treat viral infections in the light of quantitative predictions we obtained for the critical mutation rates of a range of different viruses.

evolutionary biology↗
bioRxiv2024

Sero-epidemiological investigation and risk factors associated with camelpox in pastoral areas of Somali region, eastern Ethiopia: a cross-sectional study

Camelpox is a highly significant viral disease that has a major economic impact on camels in Ethiopia. However, the epidemiology of the disease in the country, particularly in Somali region, is currently not well understood. The objective of this study was to estimate the prevalence of camelpox and identify associated risk factors in order to implement effective disease control measures in the study area. A cross-sectional design was employed from January 2023 to July 2023, involving a sample of 374 camels from 75 households in two districts and six peasant associations (PAs). Serum samples were tested using a competitive enzyme immunoassay (c-ELISA) to detect camelpox-specific antibodies. The overall seroprevalence of camelpox infection in the study area was found to be 16.0%. We administered structured questionnaires to camel owners to gather additional information on potential risk factors. Variables such as age, sex, and seasonal patterns were found to have a significant association with camelpox seropositivity. Female camels exhibited 3.2 times higher odds of infection compared to male camels, while young dromedaries aged between 6 months and 4 years were found to have a 2.3 times higher risk of infection than adults, indicating susceptibility to the age factor. Furthermore, the risk of infection was found to be 26 times higher during the rainy season than to the dry period. Thus, by identifying contributing factors, effective preventative measures, such as an appropriate vaccination strategy, can be developed to reduce the spread of camelpox and the associated economic losses. This study provides valuable insights for disease control and management practices.

microbiology↗
bioRxiv2024

The Untapped Potential of Tree Size in Reconstructing Evolutionary and Epidemiological Dynamics

A phylogenetic tree has three types of attributes: size, shape (topology), and branch lengths. Phylody-namic studies are often motivated by questions regarding the size of clades, nevertheless, nearly all of the inference methods only make use of the other two attributes. In this paper, we ask whether there is additional information if we consider tree size more explicitly in phylodynamic inference methods. To address this question, we first needed to be able to compute the expected tree size distribution under a specified phylodynamic model; perhaps surprisingly, there is not a general method for doing so -- it is known what this is under a Yule or constant rate birth-death model but not for the more complicated scenarios researchers are often interested in. We present three different solutions to this problem: using i) the deterministic limit; ii) master equations; and iii) an ensemble moment approximation. Using simulations, we evaluate the accuracy of these three approaches under a variety of scenarios and alternative measures of tree size (i.e., sampling through time or only at the present; sampling ancestors or not). We then use the most accurate measures for the situation, to investigate the added informational content of tree size. We find that for two critical phylodynamic questions -- i) is diversification diversity dependent? and, ii) can we distinguish between alternative diversification scenarios? -- knowing the expected tree size distribution under the specified scenario provides insights that could not be gleaned from considering the expected shape and branch lengths alone. The contribution of this paper is both a novel set of methods for computing tree size distributions and a path forward for richer phylodynamic inference into the evolutionary and epidemiological processes that shape lineage trees.

evolutionary biology↗
bioRxiv2024

Applying rearrangement distances to enable plasmid epidemiology with pling

Plasmids are a key vector of antibiotic resistance, but the current bioinformatics toolkit is not well suited to tracking them. The rapid structural changes seen in plasmid genomes present considerable challenges to evolutionary and epidemiological analysis. Typical approaches are either low resolution (replicon typing) or use shared k-mer content to define a genetic distance. However this distance can both overestimate plasmid relatedness by ignoring rearrangements, and underestimate by over-penalising gene gain/loss. Therefore a model is needed which captures the key components of how plasmid genomes evolve structurally - through gene/block gain or loss, and rearrangement. A secondary requirement is to prevent promiscuous transposable elements (TEs) leading to over-clustering of unrelated plasmids. We choose the "Double Cut and Join Indel" model, in which plasmids are studied at a coarse level, as a sequence of signed integers (representing genes or aligned blocks), and the distance between two plasmids is the minimum number of rearrangement events or indels needed to transform one into the other. We show how this gives much more meaningful distances between plasmids. We introduce a software workflow pling (https://github.com/iqbal-lab-org/pling), which uses the DCJ-Indel model, to calculate distances between plasmids and then cluster them. In our approach, we combine containment distances and DCJ-Indel distances to build a TE-aware plasmid network. We demonstrate superior performance and interpretability to other plasmid clustering tools on the "Russian Doll" dataset and a hospital transmission dataset. Impact statementStudying plasmid transmission is a necessary component of understanding antibiotic resistance spread, but identifying recently related plasmids is difficult and often requires manual curation. Pling simplifies this by leveraging a combination of containment distances and rearrangement distances to cluster plasmids. The outcome are clusters of recently related plasmids with a clear backbone and relatively large core genomes, in contrast to other tools which sometimes overcluster. Additionally the network constructed by pling provides a framework with which to spot evolutionary events, such as potential fusions of plasmids and spread of transposable elements. Data summarySupplementary information and figures are available as an additional PDF. The tool presented in this paper is available under https://github.com/iqbal-lab-org/pling. Additional computational analysis and scripts are described and provided under https://github.com/babayagaofficial/pling_paper_analyses. The sequence data used can be found under BioProject no. PRJNA246471 in the National Center for Biotechnology Information for the "Russian doll" dataset (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA246471), and under Project no. PRJEB31034 in European Nucleotide Archive for the "Addenbrookes" dataset (https://www.ebi.ac.uk/ena/browser/view/PRJEB30134). All other genome sequences used were sourced from PLSDB (https://ccb-microbe.cs.uni-saarland.de/plsdb/), and lists of accession numbers can be found in the additional analysis github.

bioinformatics↗
bioRxiv2024

Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning

Wastewater-based epidemiology (WBE) is a powerful tool for monitoring community disease occurrence, but current methods for bacterial detection suffer from limited scalability, the need for a priori knowledge of the target organism, and the high degree of genetic similarity between different strains of the same species. Here, we show that surface-enhanced Raman spectroscopy (SERS) can be a scalable, label-free method for detection of bacteria in wastewater. We preferentially enhance Raman signal from bacteria in wastewater using positively-charged plasmonic gold nanorods (AuNRs) that electrostatically bind to the bacterial surface. Transmission cryoelectron microscopy (cryoEM) confirms that AuNRs bind selectively to bacteria in this wastewater matrix. We spike the bacterial species Staphylococcus epidermidis, Staphylococcus aureus, Serratia marcescens, and Escerichia coli and AuNRs into filter-sterilized wastewater, varying the AuNR concentration to achieve maximum signal across all pathogens. We then collect 540 spectra from each species, and train a machine learning (ML) model to identify bacterial species in wastewater. For bacterial concentrations of 109 cells/mL, we achieve an accuracy exceeding 85%. We also demonstrate that this system is effective at environmentally-realistic bacterial concentrations, with a limit of bacterial detection of 104 cells/mL. These results are a key first step toward a label-free, high-throughput platform for bacterial WBE.

bioengineering↗
bioRxiv2024

Effects of host heterogeneity on epidemiological dynamics are mediated by how host infectiousness is determined

Host heterogeneities in susceptibility and infectiousness can affect parasite transmission, but standard analyses typically consider a limited range of epidemiological scenarios. We propose two contrasting phenomenological scenarios that capture a wide range of processes by which host infectiousness is determined, and explore how these scenarios affect the impact of host heterogeneities on parasite transmission. Specifically, we contrast Recipient dependence (RD), where a hosts infectiousness is a fixed characteristic of the recipient host being infected, with Donor dependence (DD), where a hosts infectiousness is determined by the donor host that infected them. Contrasting Susceptible-Infected models of these two phenomenological scenarios show that under RD, R0 is driven by population-level covariance between susceptibility and infectiousness, but for DD scenarios it is driven by the maximum infectiousness present in the population. Our results show that these different scenarios, which capture different ways by which host infectiousness is determined, should be considered explicitly when exploring the consequences of host heterogeneity on transmission.

ecology↗
bioRxiv2024

Improving inference in wastewater-based epidemiology by modelling the statistical features of digital PCR

Digital polymerase chain reaction (dPCR) is a powerful technique for quantifying gene targets in environmental samples, with various applications such as biodiversity monitoring and wastewater-based epidemiology. However, statistical analyses of environmental dPCR data often assume, explicitly or implicitly, that concentration measurements have a normal or log-normal error structure, which does not reflect the underlying partitioning statistics of dPCR. Using simulations and real-world environmental data, we show that (log-)normality assumptions are violated for dPCR measurements, leading to inaccurate estimates of gene concentrations and underlying biological processes. To enable reliable analyses of environmental dPCR data, we present a dPCR-specific likelihood model that accounts for concentration-dependent measurement noise and non-detects as characteristic of dPCR assays. We demonstrate that this approach overcomes biases in inference from environmental data, such as estimating free-eDNA decay in seawater or pathogen transmission from wastewater monitoring. Our method is implemented in the R packages "dPCRfit" for regression analyses and "EpiSewer" for wastewater surveillance.

bioinformatics↗