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bioRxiv2021

Development and Validation of a MALDI-TOF-Based Model to Predict Extended-Spectrum Beta-Lactamase and/or Carbapenemase-Producing in Klebsiella pneumoniae Clinical Isolates

Matrix-Assisted Laser Desorption Ionization Time-Of-Flight (MALDI-TOF) Mass Spectrometry (MS) is a reference method for microbial identification and it can be used to predict Antibiotic Resistance (AR) when combined with artificial intelligence methods. However, current solutions need time-costly preprocessing steps, are difficult to reproduce due to hyperparameter tuning, are hardly interpretable, and do not pay attention to epidemiological differences inherent to data coming from different centres, which can be critical. We propose using a multi-view heterogeneous Bayesian model (KSSHIBA) for the prediction of AR using MALDI-TOF MS data together with their epidemiological differences. KSSHIBA is the first model that removes the ad-hoc preprocessing steps that work with raw MALDI-TOF data. In addition, due to its Bayesian probabilistic nature, it does not require hyperparameter tuning, provides interpretable results, and allows exploiting local epidemiological differences between data sources. To test the proposal, we used data from 402 Klebsiella pneumoniae isolates coming from two different domains and 20 different hospitals located in Spain and Portugal. KSSHIBA outperforms current state-of-the-art approaches in antibiotic susceptibility prediction, obtaining a 0.78 AUC score in Wild Type classification and a 0.90 AUC score in Extended-Spectrum Beta-Lactamases (ESBL)+Carbapenemases (CP)-producers. The proposal consistently removes the need for ad-hoc preprocessing by working with raw MALDI-TOF data, which, in turn, reduces the time needed to obtain the results of the resistance mechanism in microbiological laboratories. The proposed model implementation as well as both data domains are publicly available.

microbiology↗

bioRxiv2021

Systems-based approach for optimization of a scalable bacterial ST mapping assembly-free algorithm

Epidemiological surveillance of bacterial pathogens requires real-time data analysis with a fast turn-around, while aiming at generating two main outcomes: 1) Species level identification; and 2) Variant mapping at different levels of genotypic resolution for population-based tracking, in addition to predicting traits such as antimicrobial resistance (AMR). With the recent advances and continual dissemination of whole-genome sequencing technologies, large-scale population-based genotyping of bacterial pathogens has become possible. Since bacterial populations often present a high degree of clonality in the genomic backbone (i.e., low genetic diversity), the choice of genotyping scheme can even facilitate the understanding of ancestral relationships and can be used for prediction of co-inherited traits such as AMR. Multi-locus sequence typing (MLST) fits that purpose and can identify sequence types (ST) based on seven ubiquitous genome-scattered loci that aid in genotyping isolates beneath the species level. ST-based mapping also standardizes genotyping across laboratories and is used by laboratories worldwide. However, algorithms for inferring ST from Illumina paired-end sequencing data typically rely on genome assembly prior to classification. Genome assembly is computationally intensive and is a bottleneck for speed and scalability, which are important aspects of genomic epidemiology. The stringMLST program uses an assembly-free, kmer-based algorithm for inferring STs, which can overcome the speed and scalability bottlenecks. Here we have systematically studied the accuracy and scalability of stringMLST relative to the standard MLST program across a wide array of phylogenetically divergent Public Health-relevant bacterial pathogens. Our data shows that optimal kmer length for stringMLST is species-specific and that genome-intrinsic and -extrinsic features can affect performance and accuracy of the program. While suitable parameters could be identified for most organisms, there were a few instances where this program may not be directly deployable in its current format. More importantly, we integrated stringMLST into our freely available and scalable hierarchical-based population genomics platform, ProkEvo, and further demonstrated how the implementation facilitates automated, reproducible bacterial population analysis. The ProkEvo implementation provides a rapidly deployable genomic epidemiology tool for ST mapping along with other pan-genomic data mining strategies, while providing specific guidance on how to optimize stringMLST performance for a wide variety of bacterial pathogens.

bioinformatics↗

bioRxiv2021

Multi-virulence and phenotypic spread of Campylobacter jejuni carried by chicken meat in Brazil

Campylobacter jejuni is the most incriminated pathogen in bacterial gastroenteritis, and therefore, characteristics of its epidemiology must be continuously investigated to support possible mitigating measures. This is particularly important when evaluating representative strains of the worlds leading chicken meat exporter, Brazil. We evaluated a panel of 14 virulence genes in 359 strains of C. jejuni isolated from chilled broiler carcasses of Brazil. The genes were classified into five virulence categories (B: biofilm/motility; SS: secretion/cytotoxicity system; CI: invasion/colonization; GB: Guillain-Barre and AE: adaptation to stress). The percentage of strains with stress adaptation genes (86.07%) indicates the potential to adapt to unfavorable environmental conditions and hcp gene in 97.77%, indicates the ability to cause serious infections in humans. Genes related to GBS in 77.44% of strains are an additional concern, which must be monitored. The gene panel showed the presence of 124 virulence profiles. Individual analyzes by carcass, slaughter establishment, and municipalities where they were located showed high I.Var., of 0.82, 0.87 and 0.78, respectively. Georeferencing indicated state A as a hotspot for virulent strains. Higher levels of isolation and multi-virulence were identified in the summer, which in Brazil is hot and humid. Proteomics was able to discriminate the strains, but due to the high heterogeneity between them, it did not allow to explain their dissemination. Together, our results showed that the studied strains are a potential danger to public health and that there is an urgent need for their surveillance and the adoption of control measures, especially in state A. AUTHOR SUMMARYCampylobacter jejuni is a bacterium considered one of the main causes of foodborne illnesses and the consumption of undercooked chicken meat is one of the main sources of human infection. In Brazil, epidemiological studies of this pathogen are still scarce, when compared to countries with structured surveillance, as well as, its analysis is not required by public health agencies in any group of foods intended for human consumption. Here we investigate the epidemiology of C. jejuni strains isolated from chilled chicken carcasses in Brazil, determining virulent and multivirulent strains, by the origin of the sample and its phenotypic patterns. The strains showed a high potential for adaptation to the environment, being classified as virulent and multivirulent, with a seasonal pattern in the hottest and humid periods of the year. In state A, the strains with the highest evolutionary level were isolated, when compared to the other states in the region. We hope that this study will help to better understand the potential risks that C. jejuni poses to the population and support surveillance agencies in tracking and adopting measures to minimize the dangers that this pathogen poses to public health.

microbiology↗

bioRxiv2021

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0[≥] 3, and overall a specificity exceeding 85% and sensitivity better than 70%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification. Author summaryThe analysis of pathogen genomic data to analyze epidemics at scale is constrained by the computational cost associated with phylogenetic tree reconstruction. As a fast and efficient alternative, we employed convolutional neural networks to analyze evolutionary pairwise distance matrices as images to perform classifications of the current epidemiological situation of a growing public health sequence database. We used simulated data to train and test our model, and as validation we accurately mapped the start and end of two linked well-documented HIV-1 outbreaks in the backdrop of ongoing slower HIV spread. Thus, our new approach is efficient, accurate, scalable, and can analyze data in real time.

bioinformatics↗

bioRxiv2022

Understanding drivers of phylogenetic clustering and terminal branch lengths distribution in epidemics of Mycobacterium tuberculosis

Detecting factors associated with transmission is important to understand disease epidemics, and to design effective public health measures. Clustering and terminal branch lengths (TBL) analyses are commonly applied to genomic data sets of Mycobacterium tuberculosis (MTB) to identify sub-populations with increased transmission. Here, I used a simulation-based approach to investigate what epidemiological processes influence the results of clustering and TBL analyses, and whether difference in transmission can be detected with these methods. I simulated MTB epidemics with different dynamics (latency, infectious period, transmission rate, basic reproductive number R0, sampling proportion, and molecular clock), and found that all these factors, except the length of the infectious period and R0, affect the results of clustering and TBL distributions. I show that standard interpretations of this type of analyses ignore two main caveats: 1) clustering results and TBL depend on many factors that have nothing to do with transmission, 2) clustering results and TBL do not tell anything about whether the epidemic is stable, growing, or shrinking. An important consequence is that the optimal SNP threshold for clustering depends on the epidemiological conditions, and that sub-populations with different epidemiological characteristics should not be analyzed with the same threshold. Finally, these results suggest that different clustering rates and TBL distributions, that are found consistently between different MTB lineages, are probably due to intrinsic bacterial factors, and do not indicate necessarily differences in transmission or evolutionary success.

microbiology↗

bioRxiv2022

Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission

BackgroundInfectious disease spread in populations is controlled by individuals susceptibility (propensity to acquire infection), infectivity (propensity to pass on infection to others) and recoverability (propensity to recover/die). Estimating the effects of genetic risk factors on these host epidemiological traits can help reduce disease spread through genetic control strategies. However, the effects of previously identified disease resistance SNPs on these epidemiological traits are usually unknown. Recent advances in computational statistics make it now possible to estimate the effects of single nucleotide polymorphisms (SNPs) on these traits from longitudinal epidemic data (e.g. infection and/or recovery times of individuals or diagnostic test results). However, little is known how to optimally design disease transmission experiments or field studies to maximise the precision at which pleiotropic SNP effects estimates for susceptibility, infectivity and recoverability can be estimated. ResultsWe develop and validate analytical expressions for the precision of SNP effects estimates on the three host traits assuming a disease transmission experiment with one or more non-interacting contact groups. Maximising these leads to three distinct experimental designs, each specifying a different set of ideal SNP genotype compositions across groups: a) appropriate for a single contact-group, b) a multi-group design termed "pure", and c) a multi-group design termed "mixed", where pure and mixed refer to contact groups consisting of individuals with the same or different SNP genotypes, respectively. Precision estimates for susceptibility and recoverability were found to be less sensitive to the experimental design than infectivity. Data from multiple groups were found more informative about infectivity effects than from a single group containing the same number of individuals. Whilst the analytical expressions suggest that the multi-group pure and mixed designs estimate SNP effects with similar precision, the mixed design is preferable because it uses information from naturally occurring infections rather than those artificially induced. The same optimal design principles apply to estimating other categorical fixed effects, such as vaccinations status, helping to more effectively quantify their epidemiological impact. An online software tool SIRE-PC has been developed which calculates the precision of estimated substitution and dominance effects of a single SNP (or vaccine status) associated with all three traits depending on experimental design parameters. ConclusionsThe developed methodology and software tool can be used to aid the design of disease transmission experiments for estimating the effect of individual SNPs and other categorical variables underlying host susceptibility, infectivity and recoverability.

genetics↗

bioRxiv2022

Multiplexed strain phenotyping defines consequences of genetic diversity in Mycobacterium tuberculosis for infection and vaccination outcomes

There is growing evidence that genetic diversity in Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, contributes to the outcomes of infection and public health interventions, such as vaccination. Epidemiological studies suggest that among the phylogeographic lineages of Mtb, strains belonging to Lineage 2 (L2) are associated with concerning clinical features including hypervirulence, treatment failure, and vaccine escape. The global expansion and increasing prevalence of L2 has been attributed to the selective advantage conferred by these characteristics, yet confounding host and environmental factors make it difficult to identify the bacterial determinants driving these associations in human studies. Here, we developed a molecular barcoding strategy to facilitate high-throughput, experimental phenotyping of Mtb clinical isolates. This approach allowed us to characterize growth dynamics for a panel of genetically diverse Mtb strains during infection and after vaccination in the mouse model. We found that L2 strains exhibit distinct growth dynamics in vivo and are resistant to the immune protection conferred by Bacillus Calmette-Guerin (BCG) vaccination. The latter finding corroborates epidemiological observations and demonstrates that mycobacterial features contribute to vaccine efficacy. To investigate the genetic and biological basis of L2 strains distinctive phenotypes, we performed variant analysis, transcriptional studies, and genome-wide transposon sequencing. We identified functional genetic changes across multiple stress- and host-response pathways in a representative L2 strain that are associated with variants in regulatory genes. These adaptive changes may underlie the distinct clinical characteristics and epidemiological success of this lineage.

microbiology↗

bioRxiv2022

A mechanistic spatiotemporal model for drug resistant infections

Effective public health strategies for preventing infectious diseases require a deep understanding of transmission dynamics. However, epidemiological data alone is often insufficient for guiding public health action, as transmission patterns emerge from complex human-environment interactions. Additionally, collecting empirical data is frequently limited by cost and logistical challenges, especially in low-resource settings. Consequently, health policy increasingly relies on inference models that account for spatial and temporal dimensions, confounding factors, and uncertainty. In this work, we introduce a novel hierarchical mechanistic Bayesian model based on ecological diffusion to improve epidemiological inference for spreading pathogens. This model is specifically designed to reveal underlying spatiotemporal dynamics from empirical data, explicitly accounting for sampling bias and uncertainty. To demonstrate its effectiveness, we apply the model to simulated data of a drug-resistant pathogen and formally compare its performance to state-of-the-art epidemiological regression models. Our findings show that the model accurately identifies transmission patterns and is particularly effective at pinpointing transmission hotspots and predicting spread pathways--critical capabilities for controlling drug-resistant pathogens. While reliable monitoring remains challenging in low-resource environments, this model addresses demographic sampling bias and spatiotemporal complexities, though concentrated sampling in presumed high-risk areas can sometimes distort transmission insights. The proposed framework can inform targeted public health interventions in the early stages of resistance emergence, offering valuable guidance for resource-constrained settings.

bioinformatics↗

bioRxiv2022

Assembly and phylogeographic analysis of novel Taenia solium mitochondrial genomes reveal further differentiation between and within Asian and African-American genotypes

BackgroundTaenia solium is a parasite that hampers human health, causing taeniasis and cysticercosis. The genetic variability in its mitochondrial genome is related to the geographical origin of the specimen. Two main genotypes have been identified: The Asian and the African-American. The geographic genetic variability is expected to cause different clinical manifestations. Thus, characterizing differences between and within genotypes is crucial for completing the epidemiology of T. solium diseases. Methods/Principal FindingsHere, two Peruvian (one complete and one partial; 7,811X and 42X of coverage, respectively) and one Mexican (complete, 3,395X) T. solium mitochondrial genomes were assembled using the Chinese reference. Variant calling with respect to the reference was performed. Thirteen SNPs that involved a change in the amino acid physicochemical nature were identified. Those were present in all the assembled genomes and might be linked to differences in aerobic respiration efficiency between Latin American (African-American) and Asian genotypes. Then, phylogeographic studies were conducted using Cytochrome C oxidase subunit I and cytochrome B from these genomes and other isolates. The analysis showed that Indonesian samples are the most ancient and related to the modern T. solium ancestor of the Asian genotype. Finally, a consistent subdivision of the African-American genotype into two subgroups was found. One subgroup relates to East African countries, while the other is West Africa. The East African linage suggests a previously unnoticed influence of the Indian Ocean trade in the genetic structure of Latin America T. solium. Conclusions/SignificanceOverall, this study reports novel mitochondrial genomes valuable for further studies. New Latin American SNPs were identified and suggest metabolic differences between parasites of the Asian and African-American genotypes. Moreover, the phylogeographic analysis revealed differences within each genotype that shed light on T. soliums historical spread. Overall, the results represent an important step in completing T. solium genetic epidemiology. Author SummaryTaenia solium is a human parasite that causes taeniasis and cysticercosis. Eradicated from developed countries, they are still a public health problem in developing nations. T. solium differences in the mitochondrial genetic material depend on its geographical origin. This is expected to cause different clinical manifestations. Despite the importance of genetics to the epidemiology of T. solium diseases, few efforts have been made to assemble and compare their genomes. We aimed to help fill this knowledge gap by assembling three mitochondrial genomes from Latin America and comparing them to the Chinese reference. Additionally, two genes from the Latin American genomes and from other isolates were employed to assess T. solium genetic distribution. We found thirteen mutations with respect to the Chinese genome present in all Latin American samples, which involved a change in the amino acid physicochemical nature. Those might be causing metabolic differences between Asian and Latin American parasites that could change their affinity to specific human tissues. Moreover, we determined that Indonesian samples are the most ancient and related to the modern T. solium ancestor. Finally, we identified a previously unnoticed influence of East African countries in T. solium phylogeny, with which our assembled genomes are closely related.

genetics↗

bioRxiv2022

Distribution and frequency of salivary gland tumours: an international multicenter study

BackgroundSalivary gland tumours (SGT) are a relatively rare group of neoplasms with a wide range of histopathological appearance and clinical features. To date, most of the epidemiological studies on salivary gland tumours are limited for a variety of reason including being out of date, extrapolated from either a single centre or country studies, or investigating either major or minor glands only. MethodsThis study aimed to mitigate these shortcomings by analysing epidemiological data including demographic, anatomical location and histological diagnoses of SGT from multiple centres across the world. The analysed data included age, gender, location and histological diagnosis from fifteen centres covering the majority of the world health organisation (WHO) geographical regions between 2006 and 2019. ResultsA total of 5798 cases were analysed including 65% benign and 35% malignant tumours. A slight female predilection (54%) and peak incidence between the fourth and seventh decade for both benign and malignant tumours was observed. The majority (69%) of the SGT presented in major and 31% in the minor glands. The parotid gland was the most common location (70%) for benign and minor glands (46%) for malignant tumours. Pleomorphic adenoma (70%), and Warthins tumour (17%), were the most common benign tumours whereas mucoepidermoid carcinoma (25%) and adenoid cystic carcinoma (16%) were the most frequent malignant tumours. ConclusionsThis multicentre investigation presents the largest cohort study to date analysing salivary gland tumour data from tertiary centres scattered across the globe. These findings should serve as a baseline for future studies evaluating the epidemiological landscape of these tumours.

pathology↗

bioRxiv2022

Maximum likelihood pandemic-scale phylogenetics

Phylogenetics plays a crucial role in the interpretation of genomic data1. Phylogenetic analyses of SARS-CoV-2 genomes have allowed the detailed study of the viruss origins2, of its international3,4 and local4-9 spread, and of the emergence10 and reproductive success11 of new variants, among many applications. These analyses have been enabled by the unparalleled volumes of genome sequence data generated and employed to study and help contain the pandemic12. However, preferred model-based phylogenetic approaches including maximum likelihood and Bayesian methods, mostly based on Felsensteins pruning algorithm13,14, cannot scale to the size of the datasets from the current pandemic4,15, hampering our understanding of the viruss evolution and transmission16. We present new approaches, based on reworking Felsensteins algorithm, for likelihood-based phylogenetic analysis of epidemiological genomic datasets at unprecedented scales. We exploit near-certainty regarding ancestral genomes, and the similarities between closely related and densely sampled genomes, to greatly reduce computational demands for memory and time. Combined with new methods for searching amongst candidate evolutionary trees, this results in our MAPLE ( MAximum Parsimonious Likelihood Estimation) software giving better results than popular approaches such as FastTree 217, IQ-TREE 218, RAxML-NG19 and UShER15. Our approach therefore allows complex and accurate proba-bilistic phylogenetic analyses of millions of microbial genomes, extending the reach of genomic epidemiology. Future epidemiological datasets are likely to be even larger than those currently associated with COVID-19, and other disciplines such as metagenomics and biodiversity science are also generating huge numbers of genome sequences20-22. Our methods will permit continued use of preferred likelihood-based phylogenetic analyses.

bioinformatics↗

bioRxiv2022

Neutral vs. non-neutral genetic footprints of Plasmodium falciparum multiclonal infections

At a time when effective tools for monitoring malaria control and eradication efforts are crucial, the increasing availability of molecular data motivates their application to epidemiology. The multiplicity of infection (MOI), defined as the number of genetically distinct parasite strains co-infecting a host, is one key epidemiological parameter for evaluating malaria interventions. Estimating MOI remains a challenge for high-transmission settings where individuals typically carry multiple co-occurring infections. Several quantitative approaches have been developed to estimate MOI, including two cost-effective ones relying on molecular data: i) THE REAL McCOIL method is based on putatively neutral single nucleotide polymorphism loci, and ii) the varcoding method is a fingerprinting approach that relies on the diversity and limited repertoire overlap of the var multigene family encoding the major Plasmodium falciparum blood-stage antigen PfEMP1 and is therefore under selection. In this study, we assess the robustness of the MOI estimates generated with these two approaches by simulating P. falciparum malaria dynamics under three transmission conditions using an extension of a previously developed stochastic agent-based model. We demonstrate that these approaches are complementary and best considered across distinct transmission intensities. While varcoding can underestimate MOI, it allows robust estimation, especially under high-transmission where repertoire overlap is extremely limited from frequency-dependent selection. In contrast, THE REAL McCOIL often considerably overestimates MOI, but still provides reasonable estimates for low- and moderate-transmission. As many countries pursue malaria elimination targets, defining the most suitable approach to estimate MOI based on sample size and local transmission intensity is highly recommended for monitoring the impact of intervention programs. Author SummaryDespite control and elimination efforts, malaria continues to be a serious public health threat especially in high-transmission regions. Molecular tools for evaluating these efforts include those seeking to estimate multiplicity (or complexity) of infection (MOI), the number of genetically distinct parasite strains co-infecting a host, a key epidemiological parameter. MOI estimation remains challenging in high-transmission regions where hosts typically carry multiple co-infections by Plasmodium falciparum. THE REAL McCOIL and the varcoding are two cost-effective methods relying on distinct parts of the parasite genome, those respectively under neutrality and selection. The more recent varcoding approach relies on the var multigene family encoding for the major blood-stage antigen and contributing to a complex immune evasion strategy of the parasite. We compare the performance of the two methods by simulating disease dynamics under different transmission intensities with a stochastic agent-based model tracking infection by different parasite genomes and immune memory in individual hosts, then sampling resulting infections to estimate MOI. Although THE REAL McCOIL provides reasonable estimates for low- and moderate-transmission, varcoding allows more robust estimates especially under high-transmission. Defining the most suitable approach to estimate MOI based on local transmission intensity is highly recommended for hyper-diverse pathogens such as malaria.

evolutionary biology↗

bioRxiv2022

Estimating the prevalence of LAMA2 congenital muscular dystrophy using population genetic databases

BACKGROUNDRecessive pathogenic variants in LAMA2 resulting in complete or partial loss of laminin 2 protein cause congenital muscular dystrophy (LAMA2 CMD). The prevalence of LAMA2 CMD has been estimated by epidemiological studies to lie between 1.36 - 20 cases per million. However, prevalence estimates from epidemiological studies are vulnerable to inaccuracies owing to challenges with studying rare diseases. Population genetic databases offer an alternative method for estimating prevalence. OBJECTIVEWe aim to use population allele frequency data for reported and predicted pathogenic variants to estimate the birth prevalence of LAMA2 CMD. METHODSA list of reported pathogenic LAMA2 variants was compiled from public databases, and supplemented with predicted loss of function (LoF) variants in genome aggregation database (gnomAD). gnomAD allele frequencies for 273 reported pathogenic and predicted LoF LAMA2 variants were used to calculate disease prevalence using a Bayesian methodology. RESULTSThe world-wide birth prevalence of LAMA2 CMD was estimated to be 8.3 per million (95% confidence interval (CI) 6.27 - 10.5 per million). The prevalence estimates for each population in gnomAD varied, ranging from 1.79 per million in East Asians (95% CI 0.63 - 3.36) to 10.1 per million in Europeans (95% CI 6.74 - 13.9). These estimates were generally consistent with those from epidemiological studies, where available. CONCLUSIONSWe provide robust world-wide and population-specific birth prevalence estimates for LAMA2 CMD, including for non-European populations in which LAMA2 CMD prevalence hadnt been studied. This work will inform the design and prioritization of clinical trials for promising LAMA2 CMD treatments.

genetics↗

bioRxiv2022

Collateral impacts of pandemic COVID-19 drive the nosocomial spread of antibiotic resistance

Circulation of multidrug-resistant bacteria (MRB) in healthcare facilities is a major public health problem. These settings have been greatly impacted by the COVID-19 pandemic, notably due to surges in COVID-19 caseloads and the implementation of infection control measures. Yet collateral impacts of pandemic COVID-19 on MRB epidemiology remain poorly understood. Here, we present a dynamic transmission model in which SARS-CoV-2 and MRB co-circulate among patients and staff in a hospital population in an early pandemic context. Responses to SARS-CoV-2 outbreaks are captured mechanistically, reflecting impacts on factors relevant for MRB transmission, including contact behaviour, hand hygiene compliance, antibiotic prescribing and population structure. In a first set of simulations, broad parameter ranges are accounted for, representative of diverse bacterial species and hospital settings. On average, COVID-19 control measures coincide with MRB prevention, including fewer incident cases and fewer cumulative person-days of patient MRB colonization. However, surges in COVID-19 caseloads favour MRB transmission and lead to increased rates of antibiotic resistance, especially in the absence of concomitant control measures. In a second set of simulations, methicillin-resistant Staphylococcus aureus and extended-spectrum beta-lactamase-producing Escherichia coli are simulated in specific hospital wards and pandemic response scenarios. Antibiotic resistance dynamics are highly context-specific in these cases, and SARS-CoV-2 outbreaks significantly impact bacterial epidemiology only in facilities with high underlying risk of bacterial transmission. Crucially, antibiotic resistance burden is reduced in facilities with timelier, more effective implementation of COVID-19 control measures. This highlights the control of antibiotic resistance as an important collateral benefit of robust pandemic preparedness. Significance StatementImpacts of COVID-19 on the spread of antibiotic resistance are poorly understood. Here, an epidemiological model accounting for the simultaneous spread of SARS-CoV-2 and antibiotic-resistant bacteria is presented. The model is tailored to healthcare settings during the first wave of the COVID-19 pandemic, and accounts for hand hygiene, inter-individual contact behaviour, and other factors relevant for pathogen spread. Simulations demonstrate that public health policies enacted to slow the spread of COVID-19 also tend to limit bacterial transmission. However, surges in COVID-19 cases simultaneously select for higher rates of antibiotic resistance. Selection for resistance is thus mitigated by prompt implementation of effective COVID-19 prevention policies. This highlights the control of antibiotic resistance as an important collateral benefit of pandemic preparedness.

evolutionary biology↗

bioRxiv2022

African swine fever virus - variants on the rise

African swine fever virus (ASFV), a large and complex DNA-virus circulating between soft ticks and indigenous suids in sub-Saharan Africa, has made its way into swine populations from Europe to Asia. This virus, causing a severe haemorrhagic disease (African swine fever) with very high lethality rates in wild boar and domestic pigs, has demonstrated a remarkably high genetic stability for over 10 years. Consequently, analyses into virus evolution and molecular epidemiology often struggled to provide the genetic basis to trace outbreaks while few resources have been dedicated to genomic surveillance on whole-genome level. During its recent incursion into Germany in 2020, ASFV has unexpectedly diverged into five clearly distinguishable linages with at least ten different variants characterized by high-impact mutations never identified before. Noticeably, all new variants share a frameshift mutation in the 3 end of the DNA polymerase PolX gene O174L, suggesting a causative role as possible mutator gene. Although epidemiological modelling supported the influence of increased mutation rates, it remains unknown how fast virus evolution might progress under these circumstances. Moreover, a tailored Sanger sequencing approach allowed us, for the first time, to trace variants with genomic epidemiology to regional clusters. In conclusion, our findings suggest that this new factor has the potential to dramatically influence the course of the ASFV pandemic with unknown outcome. Therefore, our work highlights the importance of genomic surveillance of ASFV on whole-genome level, the need for high-quality sequences and calls for a closer monitoring of future phenotypic changes of ASFV.

genomics↗

bioRxiv2022

Evolution of new variants of SARS-COV-2 during the pandemic: mutation limited or selection limited?

The recent pandemic caused by SARS-Cov-2 has witnessed an evolving succession of variants of the virus. While the phenomenon of invasion by immunity evading variants is known for other viruses such as influenza, the dynamics of the ecological and evolutionary process in the succession is little known. Since during the Covid-19 pandemic, large scale epidemiological data were collected and made available in the public domain, it is possible to seek answers to a number of evolutionary questions, which will also have public health implications. We list multiple alternative hypotheses about the origin and invasion of the variants and evaluate them in the light of epidemiological data. Our analysis shows that invasion by novel variants is selection limited and not mutation limited. Further novel variants are not the necessary and sufficient causes of the repeated waves during the pandemic. Rather there is substantial overlap between the conditions leading to a wave and those favoring selection of a partial immune evading variant. This is likely to lead to an association between invasion by new variant and the rise of a new wave. But the association is not sufficiently strong and does not support a causal role of the new variant. The dynamics of interaction between epidemiological processes and selection on viral variants have many public health implications that can guide future policies for effective control of infectious epidemics.

evolutionary biology↗

bioRxiv2022

Backtracking: Improved methods for identifying the source of a deliberate release of Bacillus anthracis from the temporal and spatial distribution of cases

Reverse epidemiology is a mathematical modelling tool used to ascertain information about the source of a pathogen, given the spatial and temporal distribution of cases, hospitalisations and deaths. In the context of a deliberately released pathogen, such as Bacillus anthracis (the disease-causing organism of anthrax), this can allow responders to quickly identify the location and timing of the release, as well as other factors such as the strength of the release, and the realized wind speed and direction at release. These estimates can then be used to parameterise a predictive mechanistic model, allowing for estimation of the potential scale of the release, and to optimise the distribution of prophylaxis. In this paper we present two novel approaches to reverse epidemiology, and demonstrate their utility in responding to a simulated deliberate release of B. anthracis in ten locations in the UK and compare these to the standard grid-search approach. The two methods - a modified MCMC and a Recurrent Convolutional Neural Network - are able to identify the source location and timing of the release with significantly better accuracy compared to the grid-search approach. Further, the neural network method is able to do inference on new data significantly quicker than either the grid-search or novel MCMC methods, allowing for rapid deployment in time-sensitive outbreaks. Author summaryIn this paper we demonstrate three methods for estimating the source location and timing of a deliberate release of Bacillus anthracis based on the temporal and spatial distribution of cases. Two of our proposed methods, a modified MCMC approach and a neural network based approach, provide significant improvements over previous methods by directly addressing the problematic parameter-likelihood surface, and, in the case of the neural network approach, addressing the slow deployment speeds of existing methods. Our results represent a major step forward in the accuracy and speed of epidemiological back-calculation.

systems biology↗

bioRxiv2023

Differences in expression of tumor suppressor, innate immune, inflammasome, and potassium/gap junction channel host genes significantly predict viral reservoir size during treated HIV infection

The major barrier to an HIV cure is the persistence of infected cells that evade host immune surveillance despite effective antiretroviral therapy (ART). Most prior host genetic HIV studies have focused on identifying DNA polymorphisms (e.g., CCR5{Delta}32, MHC class I alleles) associated with viral load among untreated "elite controllers" (~1% of HIV+ individuals who are able to control virus without ART). However, there have been few studies evaluating host genetic predictors of viral control for the majority of people living with HIV (PLWH) on ART. We performed host RNA sequencing and HIV reservoir quantification (total DNA, unspliced RNA, intact DNA) from peripheral CD4+ T cells from 191 HIV+ ART-suppressed non-controllers. Multivariate models included covariates for timing of ART initiation, nadir CD4+ count, age, sex, and ancestry. Lower HIV total DNA (an estimate of the total reservoir) was associated with upregulation of tumor suppressor genes NBL1 (q=0.012) and P3H3 (q=0.012). Higher HIV unspliced RNA (an estimate of residual HIV transcription) was associated with downregulation of several host genes involving inflammasome (IL1A, CSF3, TNFAIP5, TNFAIP6, TNFAIP9, CXCL3, CXCL10) and innate immune (TLR7) signaling, as well as novel associations with potassium (KCNJ2) and gap junction (GJB2) channels, all q<0.05. Gene set enrichment analyses identified significant associations with TLR4/microbial translocation (q=0.006), IL-1{beta}/NRLP3 inflammasome (q=0.008), and IL-10 (q=0.037) signaling. HIV intact DNA (an estimate of the "replication-competent" reservoir) demonstrated trends with thrombin degradation (PLGLB1) and glucose metabolism (AGL) genes, but data were (HIV intact DNA detected in only 42% of participants). Our findings demonstrate that among treated PLWH, that inflammation, innate immune responses, bacterial translocation, and tumor suppression/cell proliferation host signaling play a key role in the maintenance of the HIV reservoir during ART. Further data are needed to validate these findings, including functional genomic studies, and expanded epidemiologic studies in female, non-European cohorts. Author SummaryAlthough lifelong HIV antiretroviral therapy (ART) suppresses virus, the major barrier to an HIV cure is the persistence of infected cells that evade host immune surveillance despite effective ART, "the HIV reservoir." HIV eradication strategies have focused on eliminating residual virus to allow for HIV remission, but HIV cure trials to date have thus far failed to show a clinically meaningful reduction in the HIV reservoir. There is an urgent need for a better understanding of the host-viral dynamics during ART suppression to identify potential novel therapeutic targets for HIV cure. This is the first epidemiologic host gene expression study to demonstrate a significant link between HIV reservoir size and several well-known immunologic pathways (e.g., IL-1{beta}, TLR7, TNF- signaling pathways), as well as novel associations with potassium and gap junction channels (Kir2.1, connexin 26). Further data are needed to validate these findings, including functional genomic studies and expanded epidemiologic studies in female, non-European cohorts.

genetics↗