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At least 1,621 records · Page 90Linked to original sources

bioRxiv2021

Environmentally-induced mdig is a major contributor to the severity of COVID-19 through fostering expression of SARS-CoV-2 receptor NRPs and glycan metabolism

The novel {beta}-coronavirus, SARS-CoV-2, the causative agent of coronavirus disease 2019 (COVID-19), has infected more than 101 million people and resulted in 2.2 million death worldwide. Recent epidemiological studies suggested that some environmental factors, such as air pollution, might be the important contributors to the mortality of COVID-19. However, how environmental exposure enhances the severity of COVID-19 remains to be fully understood. In the present report, we provide evidence showing that mdig, a previously reported environmentally-induced oncogene that antagonizes repressive trimethylation of histone proteins, is a master regulator for SARS-CoV-2 receptors neuropilin-1 (NRP1) and NRP2, cathepsins, glycan metabolism and inflammation, key determinants for viral infection and cytokine storm of the patients. Depletion of mdig in bronchial epithelial cells by CRISPR-Cas-9 gene editing resulted in a decreased expression of NRP1, NRP2, cathepsins, and genes involved in protein glycosylation and inflammation, largely due to a substantial enrichment of lysine 9 and/or lysine 27 trimethylation of histone H3 (H3K9me3/H3K27me3) on these genes as determined by ChIP-seq. These data, accordingly, suggest that mdig is a key mediator for the severity of COVID-19 in response to environmental exposure and targeting mdig may be one of the effective strategies in ameliorating the symptom and reducing the mortality of COVID-19.

cell biology↗

bioRxiv2021

Who infects Whom? - Reconstructing infection chains of Mycobacterium avium ssp. paratuberculosis in an endemically infected dairy herd by use of genomic data

Recent evidence of circulation of multiple strains within herds and mixed infections of cows marks the beginning of a rethink of our knowledge on Mycobacterium avium ssp. paratuberculosis (MAP) epidemiology. Strain typing opens new ways to investigate MAP transmission. This work presents a method for reconstructing infection chains in a setting of endemic Johnes disease on a well-managed dairy farm. By linking genomic data with demographic field data, strain-specific differences in spreading patterns could be quantified for a densely sampled dairy herd. Mixed infections of dairy cows with MAP are common, and some strains spread more successfully. Infected cows remain susceptible for co-infections with other MAP genotypes. The model suggested that cows acquired infection from 1-4 other cows and spread infection to 0-17 individuals. Reconstructed infection chains supported the hypothesis that high shedding animals that started to shed at an early age and showed a progressive infection pattern represented a greater risk for spreading MAP. Transmission of more than one genotype between animals was recorded. In this farm with a good MAP control management program, adult-to-adult contact was proposed as the most important transmission route to explain the reconstructed networks. For each isolate, at least one more likely ancestor could be inferred. Our study results help to capture underlying transmission processes and to understand the challenges of tracing MAP spread within a herd. Only the combination of precise longitudinal field data and bacterial strain type information made it possible to trace infection in such detail.

genomics↗

bioRxiv2021

The promiscuous and highly mobile resistome of a superbug.

Antimicrobial resistance (AR) is a major global threat to public health. Understanding the population dynamics of AR is critical to restrain and control this issue. However, no study has provided a global picture of the resistome of Acinetobacter baumannii, a very important nosocomial pathogen. Here we analyze 1450+ genomes (covering > 40 countries and > 4 decades) to infer the global population dynamics of the resistome of this species. We show that gene flow and horizontal transfer have driven the dissemination of AR genes in A. baumannii. We found considerable variation in AR gene content across lineages. Although the individual AR gene histories have been affected by recombination, the AR gene content has been shaped by the phylogeny. Furthermore, many AR genes have been transferred to other well-known pathogens, such as Pseudomonas aeruginosa or Klebsiella pneumoniae. Finally, despite using this massive data set, we were not able to sample the whole diversity of AR genes, which suggests that this species has an open resistome. Ours results highlight the high mobilization risk of AR genes between important pathogens. On a broader perspective, this study gives a framework for an emerging perspective (resistome-centric) on the genome epidemiology (and surveillance) of bacterial pathogens.

microbiology↗

bioRxiv2021

Follow-up of a hospital cohort during the first 3,530 suspected cases of COVID-19 in Sao Jose do Rio Preto, Sao Paulo, Brazil

IntroductionIn a global context, COVID-19 is the most significant health threat in the present days, evidenced by the fact that, in just over four months, SARS-CoV-2 has spread to 171 countries, reaching a Pandemic status. Most patients with COVID-19 have a mild course of the disease. However, approximately 20% develop severe illness with a high mortality rate which is associated with age, comorbidities, and immunosuppression. Epidemiological studies are used to reveal the extent of viral spread in homes, communities, and hospitals. Thus, preventive and control measures can be established by the authorities. ObjectiveIn this study, patients with suspect COVID-19 symptoms who search for hospital care at the city of Sao Jose do Rio Preto (Sao Paulo, Brazil) were monitored, in order to identify the first case of this new disease in the region. In the first two months (March and April), more than 3000 individuals looked for the public and private health system with suspected respiratory symptoms, but only 164 (8.4%) were COVID-19 confirmed. ResultsFrom those, males (56.1%) and patients of the age distribution of 16-59 (91.2%), with diarrhea (22.2%), runny nose (25%), altered taste (15.9%), and anosmia (11.6%) presented statistical significance, although none comorbidities were related with COVID-19 occurrence. The odds ratio analysis supports this finding. Days of onset of symptoms are positively associated with whit viral load, and the same happens with the occurrence of symptoms (dyspnea and low saturation).

molecular biology↗

bioRxiv2021

The putative causal effect of type 2 diabetes in risk of cataract: a Mendelian randomization study in East Asian

BackgroundThe epidemiological association between type 2 diabetes and cataract has been well-established. However, it remains unclear whether the two diseases share a genetic basis, and if so, whether this reflects a causal relationship. MethodsWe utilized East Asian population-based genome-wide association studies (GWAS) summary statistics of type 2 diabetes (Ncase=36,614, Ncontrol=155,150) and cataract (Ncase=24,622, Ncontrol=187,831) to comprehensively investigate the shared genetics between the two diseases. We performed 1. linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics ({rho}-HESS) to estimate the genetic correlation and local genetic correlation between type 2 diabetes and cataract; 2. multiple Mendelian randomization (MR) analyses to infer the putative causality between type 2 diabetes and cataract; and 3. Summary-data-based Mendelian randomization (SMR) to identify candidate risk genes underling the causality. ResultsWe observed a strong genetic correlation (rg=0.58; p-value=5.60x10-6) between type 2 diabetes and cataract. Both {rho}-HESS and multiple MR methods consistently showed a putative causal effect of type 2 diabetes on cataract, with estimated liability-scale MR odds ratios (ORs) at around 1.10 (95% confidence interval [CI] ranging from 1.06 to 1.17). In contrast, no evidence supports a causal effect of cataract on type 2 diabetes. SMR analysis identified two novel genes MIR4453HG ({beta}SMR=-0.34, p-value=6.41x10-8) and KCNK17 ({beta}SMR=-0.07, p-value=2.49x10-10), whose expression levels were likely involved in the putative causality of type 2 diabetes on cataract. ConclusionsOur results provided robust evidence supporting a causal effect of type 2 diabetes on the risk of cataract in East Asians, and posed new paths on guiding prevention and early-stage diagnosis of cataract in type 2 diabetes patients. Key MessagesO_LIWe utilized genome-wide association studies of type 2 diabetes and cataract in a large Japanese population-based cohort and find a strong genetic overlap underlying the two diseases. C_LIO_LIWe performed multiple Mendelian randomization models and consistently disclosed a putative causal effect of type 2 diabetes on the development of cataract. C_LIO_LIWe revealed two candidate genes MIR4453HG and KCNK17 whose expression levelss are likely relevant to the causality between type 2 diabetes and cataract. C_LIO_LIOur study provided theoretical fundament at the genetic level for improving early diagnosis, prevention and treatment of cataract in type 2 diabetes patients in clinical practice C_LI

genetics↗

bioRxiv2021

Identifying the genetic basis and molecular mechanisms underlying phenotypic correlation between complex human traits using a gene-based approach

Phenotypic correlations between complex human traits have long been observed based on epidemiological studies. However, the genetic basis and underlying mechanisms are largely unknown. Here we developed a gene-based approach to measure genetic overlap between a pair of traits and to delineate the shared genes/pathways, through three steps: 1) translating SNP-phenotype association profile to gene-phenotype association profile by integrating GWAS with eQTL data using a newly developed algorithm called Sherlock-II; 2) measuring the genetic overlap between a pair of traits by a normalized distance and the associated p value between the two gene-phenotype association profiles; 3) delineating genes/pathways involved. Application of this approach to a set of GWAS data covering 59 human traits detected significant overlap between many known and unexpected pairs of traits; a significant fraction of them are not detectable by SNP based genetic similarity measures. Examples include Cancer and Alzheimers Disease (AD), Rheumatoid Arthritis and Crohns disease, and Longevity and Fasting glucose. Functional analysis revealed specific genes/pathways shared by these pairs. For example, Cancer and AD are co-associated with genes involved in hypoxia response and P53/apoptosis pathways, suggesting specific mechanisms underlying the inverse correlation between them. Our approach can detect yet unknown relationships between complex traits and generate mechanistic hypotheses and has the potential to improve diagnosis and treatment by transferring knowledge from one disease to another.

genetics↗

bioRxiv2021

Capsule protects against intracellular killing and enables vascular endothelial cell translocation during invasive pneumococcal disease

Streptococcus pneumoniae (Spn) is a leading cause of invasive disease. Chief among its virulence determinants is capsular polysaccharide which protects the bacterium from phagocytosis. While 100 antigenically distinct capsule types are produced by Spn, i.e. serotypes, only 20-30 are commonly associated with invasive disease. A frequency that suggests serotypespecific properties of the capsule influence virulence. Herein, we show capsule has strong antioxidant properties. Moreover, that this property promotes invasive disease by protecting Spn taken up by vascular endothelial cells during bacteremia from endosome-killing and enhancing the translocation rate into organs. Crucially, isogenic capsule-switch mutants of Spn varied considerably in their resistance to H2O2-killing in culture and measured levels correlated positively with intracellular survival rates in vitro, organ invasion rates in vivo, and epidemiologically-established human attack rates for the corresponding serotype. The amount of capsule produced and specific biochemical features of a serotype, such as acetylation, also influenced Spn resistance to oxidative stress. Autolysin-mediated shedding was also found to be necessary, indicating that capsule worked as a distal sink for reactive oxygen species. Our results outline a new role for capsular polysaccharide, as an intracellular antioxidant. They help to explain why certain serotypes of Spn have greater propensity for human disease.

microbiology↗

bioRxiv2021

Cuban history of CRF19 recombinant subtype of HIV-1

CRF19 is a recombinant form of HIV-1 subtypes D, A1 and G, which was first sampled in Cuba in 1999, but was already present there in 1980s. CRF19 was reported almost uniquely in Cuba, where it accounts for ~25% of new HIV-positive patients and causes rapid progression to AIDS (~ 3 years). We analyzed a large data set comprising ~ 350 pol and env sequences sampled in Cuba over the last 15 years and ~ 350 from Los Alamos database. This data set contained both CRF19 (~ 315), and A1, D and G sequences. We performed and combined analyses for the three A1, G and D regions, using fast maximum likelihood approaches, including: (1) phylogeny reconstruction, (2) spatio-temporal analysis of the virus spread, and ancestral character reconstruction for (3) transmission mode and (4) drug resistance mutations (DRMs). This allowed us to acquire new insights on the CRF19 origin and transmission patterns. We showed that CRF19 recombined between 1966 and 1977, most likely in Cuban community stationed in Congo region. We further investigated CRF19 spread on the Cuban province level, and discovered that the epidemic started in 1970s, most probably in Villa Clara, that it was at first carried by heterosexual transmissions, and then quickly spread in the 1980s within the "men having sex with men" (MSM) community, with multiple transmissions back to heterosexuals. The analysis of the transmission patterns of common DRMs showed mostly acquired drug resistance rather than transmitted one. Our results show a very early introduction of CRF19 in Cuba, which could explain its local epidemiological success. Ignited by a major founder event, the epidemic then followed a similar pattern as other subtypes and CRFs in Cuba. The reason for the short time to AIDS remains to be understood and requires specific surveillance, in Cuba and elsewhere. Author summaryCRF19 is a recombinant form of HIV-1, which causes rapid progression to AIDS (~ 3 years versus 5 - 10 years for other subtypes and CRFs). CRF19 is reported almost uniquely in Cuba, where it is highly prevalent (~ 25%) among newly detected HIV-1 patients. In this study, we found that CRF19 most likely recombined around the 1970s in the Cuban community that was stationed in Democratic Republic of the Congo and Angola at that time. It was introduced very early into the Cuban province of Villa Clara, from where it had several introductions to La Habana in the 1980s and then further spread to other Cuban provinces. The CRF19 epidemic most probably started with heterosexual transmissions, followed in the 1980s by multiple introductions into "men having sex with men" (MSM) community, followed by multiple transmissions back to heterosexuals (often females). The early introduction of CRF19 into Cuba most likely explains its success, not observed in other parts of the world. However, importantly, its rapid progression to AIDS makes it crucial to survey CRF19 sub-epidemics not only in Cuba, but also in other parts of the world having regular exchanges with Cuba.

bioinformatics↗

bioRxiv2021

Predicting Dengue Fever in Brazilian Cities

Dengue Fever is an increasingly serious public health concern both in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we develop a model to predict the number of Dengue Fever cases in Brazilian cities one month ahead. We compare different machine learning approaches as well as different sets of input features based on epidemiological and meteorological data. We find that different models work best in different cities, and a random forests model trained on data of historical Dengue cases performs best overall. It produces lower aggregate errors than a seasonal naive baseline model, Gradient Boosting Regression, feed-forward Neural Networks, and Support Vector Regression. Predictions on an unseen test set are on average within 11.5 cases for the median city. Mean absolute errors on the hold-out test set are reduced to 10.8 for the median city when selecting the optimal combination of algorithm and input features for each city individually.

bioinformatics↗

bioRxiv2021

ESC - a comprehensive resource for SARS-CoV-2 immune escape variants

Ever since the breakout of COVID-19 disease, ceaseless genomic research to inspect the epidemiology and evolution of the pathogen has been undertaken globally. Large scale viral genome sequencing and analysis have uncovered the functional impact of numerous genetic variants in disease pathogenesis and transmission. Emerging evidence of mutations in spike protein domains escaping antibody neutralization is reported. We have built a database with precise collation of manually curated variants in SARS-CoV-2 from literature with potential escape mechanisms from a range of neutralizing antibodies. This comprehensive repository encompasses a total of 5258 variants accounting for 2068 unique variants tested against 230 antibodies, patient convalescent plasma and vaccine breakthrough events. This resource enables the user to gain access to an extensive annotation of SARS-CoV-2 escape variants which would contribute to exploring and understanding the underlying mechanisms of immune response against the pathogen. The resource is available at http://clingen.igib.res.in/esc/ GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/431922v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@12bd86forg.highwire.dtl.DTLVardef@18e7279org.highwire.dtl.DTLVardef@fd97f8org.highwire.dtl.DTLVardef@104434b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Antigenic waves of virus-immune co-evolution

The evolution of many microbes and pathogens, including circulating viruses such as seasonal influenza, is driven by immune pressure from the host population. In turn, the immune systems of infected populations get updated, chasing viruses even further away. Quantitatively understanding how these dynamics result in observed patterns of rapid pathogen and immune adaptation is instrumental to epidemiological and evolutionary forecasting. Here we present a mathematical theory of co-evolution between immune systems and viruses in a finite-dimensional antigenic space, which describes the cross-reactivity of viral strains and immune systems primed by previous infections. We show the emergence of an antigenic wave that is pushed forward and canalized by cross-reactivity. We obtain analytical results for shape, speed, and angular diffusion of the wave. In particular, we show that viral-immune co-evolution generates a new emergent timescale, the persistence time of the waves direction in antigenic space, which can be much longer than the coalescence time of the viral population. We compare these dynamics to the observed antigenic turnover of influenza strains, and we discuss how the dimensionality of antigenic space impacts on the predictability of the evolutionary dynamics. Our results provide a concrete and tractable framework to describe pathogen-host co-evolution.

evolutionary biology↗

bioRxiv2021

Reanalysis of deep-sequencing data from Austria points towards a small SARS-COV-2 transmission bottleneck on the order of one to three virions

An early analysis of SARS-CoV-2 deep-sequencing data that combined epidemiological and genetic data to characterize the transmission dynamics of the virus in and beyond Austria concluded that the size of the viruss transmission bottleneck was large - on the order of 1000 virions. We performed new computational analyses using these deep-sequenced samples from Austria. Our analyses included characterization of transmission bottleneck sizes across a range of variant calling thresholds and examination of patterns of shared low-frequency variants between transmission pairs in cases where de novo genetic variation was present in the recipient. From these analyses, among others, we found that SARS-CoV-2 transmission bottlenecks are instead likely to be very tight, on the order of 1-3 virions. These findings have important consequences for understanding how SARS-CoV-2 evolves between hosts and the processes shaping genetic variation observed at the population level.

evolutionary biology↗

bioRxiv2021

Flanker: a tool for comparative genomics of gene flanking regions

Analysing the flanking sequences surrounding genes of interest is often highly relevant to understanding the role of mobile genetic elements (MGEs) in horizontal gene transfer, particular for antimicrobial resistance genes. Here, we present Flanker, a Python package which performs alignment-free clustering of gene flanking sequences in a consistent format, allowing investigation of MGEs without prior knowledge of their structure. These clusters, known as flank patterns, are based on Mash distances, allowing for easy comparison of similarity across sequences. Additionally, Flanker can be flexibly parameterised to finetune outputs by characterising upstream and downstream regions separately and investigating variable lengths of flanking sequence. We apply Flanker to two recent datasets describing plasmid-associated carriage of important carbapenemase genes (blaOXA-48 and blaKPC-2/3) and show that it successfully identifies distinct clusters of flank patterns, including both known and previously uncharacterised structural variants. For example, Flanker identified four Tn4401 profiles that could not be sufficiently characterised using TETyper or MobileElementFinder, demonstrating the utility of Flanker for flanking gene characterisation. Similarly, using a large (n=226) European isolate dataset, we confirm findings from a previous smaller study demonstrating association between Tn1999.2 and blaOXA-48 upregulation and demonstrate 17 flank patterns (compared to the 5 previously identified). More generally the demonstration in this study that flank patterns are associated with to geographical regions and antibiotic susceptibility phenotypes suggests that they may be useful as epidemiological markers. Flanker is freely available under an MIT license at https://github.com/wtmatlock/flanker. Data SummaryNCBI accession numbers for all sequencing data used in this study is provided in Supplementary Table 1. The analysis performed in this manuscript can be reproduced in a binder environment provided on the Flanker Github page (https://github.com/wtmatlock/flanker).

bioinformatics↗

bioRxiv2021

Machine learning to predict the source of campylobacteriosis using whole genome data

Campylobacteriosis is among the worlds most common foodborne illnesses, caused predominantly by the bacterium Campylobacter jejuni. Effective interventions require determination of the infection source which is challenging as transmission occurs via multiple sources such as contaminated meat, poultry, and drinking water. Strain variation has allowed source tracking based upon allelic variation in multi-locus sequence typing (MLST) genes allowing isolates from infected individuals to be attributed to specific animal or environmental reservoirs. However, the accuracy of probabilistic attribution models has been limited by the ability to differentiate isolates based upon just 7 MLST genes. Here, we broaden the input data spectrum to include core genome MLST (cgMLST) and whole genome sequences (WGS), and implement multiple machine learning algorithms, allowing more accurate source attribution. We increase attribution accuracy from 64% using the standard iSource population genetic approach to 71% for MLST, 85% for cgMLST and 78% for kmerized WGS data using machine learning. To gain insight beyond the source model prediction, we use Bayesian inference to analyse the relative affinity of C. jejuni strains to infect humans and identified potential differences, in source-human transmission ability among clonally related isolates in the most common disease causing lineage (ST-21 clonal complex). Providing generalizable computationally efficient methods, based upon machine learning and population genetics, we provide a scalable approach to global disease surveillance that can continuously incorporate novel samples for source attribution and identify fine-scale variation in transmission potential. Author summaryC. jejuni are the most common cause of food-borne bacterial gastroenteritis but the relative contribution of different sources are incompletely understood. We traced the origin of human C. jejuni infections using machine learning algorithms that compare the DNA sequences of bacteria sampled from infected people, contaminated chickens, cattle, sheep, wild birds and the environment. This approach achieved improvement in accuracy of source attribution by 33% over existing methods that use only a subset of genes within the genome and provided evidence for the relative contribution of different infection sources. Sometimes even very similar bacteria showed differences, demonstrating the value of basing analyses on the entire genome when developing this algorithm that can be used for understanding the global epidemiology and other important bacterial infections.

bioinformatics↗

bioRxiv2021

A large-scale transcriptome-wide association study (TWAS) of ten blood cell phenotypes reveals complexities of TWAS fine-mapping

Hematological measures are important intermediate clinical phenotypes for many acute and chronic diseases. Hematological measures are highly heritable, and although genome-wide association studies (GWAS) have identified thousands of loci containing trait-associated variants, the causal genes underlying these associations are often uncertain. To better understand the underlying genetic regulatory mechanisms, we performed a transcriptome-wide association study (TWAS) using PrediXcan to systematically investigate the association between genetically-predicted gene expression and hematological measures in 54,542 individuals of European ancestry from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. We found 239 significant gene-trait associations with hematological measures. Among this set of 239 associations, we replicated 71 at p < 0.05 with same direction of effect for the blood cell trait in a meta-analysis of TWAS results consisting of up to 35,900 European ancestry individuals from the Womens Health Initiative (WHI), the Atherosclerosis Risk in Communities Study (ARIC), and BioMe Biobank. We further attempted to refine this list of candidate genes by performing conditional analyses, adjusting for individual variants previously associated with these hematological measures, and performed further fine-mapping of TWAS loci. To assist with the interpretation of TWAS findings, we designed an R Shiny application to interactively visualize TWAS results, one genomic locus at a time, by integrating our TWAS results with additional genetic data sources (GWAS, TWAS from other gene expression reference panels, conditional analyses, known GWAS variants, etc.). Our results and R Shiny application highlight frequently overlooked challenges with TWAS and illustrate the complexity of TWAS fine-mapping efforts. Author SummaryTranscriptome-wide association studies (TWAS) have shown great promise in furthering our understanding of the genetic regulatory mechanisms underlying complex trait variation. However, interpreting TWAS results can be incredibly complex, especially in large-scale analyses where hundreds of signals appear throughout the genome, with multiple genes often identified in a single chromosomal region. Our research demonstrates this complexity through real data examples from our analysis of hematological traits, and we provide a useful web application to visualize TWAS results in a broadly approachable format. Together, our results and web application illustrate the importance of interpreting TWAS studies in context and highlight the need to carefully examine results in a region-wide context to draw reasonable conclusions and formulate mechanistic hypotheses.

genetics↗

bioRxiv2021

Validation of the Early Warning and Response System (EWARS) for dengue outbreaks: Evidence from the national vector control program in Mexico

BackgroundDuring 2017, twenty health districts (locations) in Mexico implemented a dengue outbreak early warning and response system (EWARS) that uses epidemiological, meteorological and entomological variables (alarm indicators) to predict dengue outbreaks and triggers early response activities. Eleven of these districts were analyzed as they presented reliable information. Nine districts presented outbreak alarms but without subsequent outbreaks ("non-outbreak districts") and two presented after the alarms dengue outbreaks ("outbreak districts"). This study is concerned with i) if the alarms without outbreaks were false alarms or if the control services had established effective response activities averting an outbreak and ii) if vector control activities can mitigate or even avert dengue outbreaks. MethodsFive components of dengue outbreak response (larval control, entomological studies with water container interventions, focal spraying, indoor residual spraying, space spraying) were quantitatively analyzed across two groups ("outbreak districts" and "non-outbreak districts"). ResultsThe average coverage of vector control and responses were higher in non-outbreak districts and across all five components. In the "outbreak districts" the response activities started late and were of much lower intensity compared to "non-outbreak districts". District vector control teams demonstrated diverse compliance with local guidlines for initial, early and late responses to outbreak alarms which could explain the different outcomes observed following the outbreak alarms. Conclusionfindings from this study plausibly demonstrates important operational scenarios when succeeding or failing alarms signals generated by EWARS at national level. This study presents evidence warranting for further investigation into the effectiveness and cost-effectiveness of EWARS using gold-standard designs.

scientific communication and education↗

bioRxiv2021

A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota

Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. The observational character of prospective cohort data and the intricate characteristics of microbiome data make it, however, challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health.

microbiology↗

bioRxiv2021

Development of a chemiluminescence immunoassay to accurately detect African swine fever virus antibodies in serum

The outbreak of African swine fever (ASF) has caused significant economic losses to animal husbandry worldwide. Currently, there is no effective vaccine or treatment available to control the disease, and therefore, efficient disease control is dependent on early detection and diagnosis of ASF virus (ASFV). In this study, a chemiluminescent immunoassay (CLIA) was developed using the ASFV protein p54 as a serum diagnostic antigen and an anti-p54 monoclonal antibody. After optimizing the working parameters of the CLIA, the sensitivity of the established CLIA was 1:128, ASFV-specific serum antibody was identified, and there was no cross-reaction with other swine virus antibodies. After testing 49 clinical serum samples, the consistency rate between the CLIA and the World Organization for Animal Health (OIE) recommended commercial kit was 100%. Thus, this CLIA had a high degree of specificity, sensitivity, and reliability, and could be used as a rapid detection method for epidemiological investigations of ASFV infection.

microbiology↗