Predicting fadeout versus persistence of paratuberculosis in a dairy cattle herd for management and control purposes: a modelling study

Epidemiological models enable to better understand the dynamics of infectious diseases and to assess ex-ante control strategies. For Mycobacterium avium subsp. paratuberculosis (Map), possible transmission routes have been described, but Map spread in a herd and the relative importance of the routes are currently insufficiently understood to prioritize control measures. We aim to predict early after Map introduction in a dairy cattle herd whether infection is likely to fade out or persist, when no control measures are implemented, using a modelling approach. Both vertical transmission and horizontal transmission via the ingestion of colostrum, milk, or faeces present in the contaminated environment were modelled. Calf-to-calf indirect transmission was possible. Six health states were represented: susceptible, transiently infectious, latently infected, subclinically infected, clinically affected, and resistant. The model was partially validated by comparing the simulated prevalence with field data. Housing facilities and contacts between animals were specifically considered for calves and heifers. After the introduction of one infected animal in a naive herd, fadeout occurred in 66% of the runs. When Map persisted, the prevalence of infected animals increased to 88% in 25 years. The two main transmission routes were via the farm's environment and in utero transmission. Calf-to-calf transmission was minor. Fadeout versus Map persistence could be differentiated with the number of clinically affected animals, which was rarely above one when fadeout occurred. Therefore, early detection of affected animals is crucial in preventing Map persistence in dairy herds

Twist exome capture allows for lower average sequence coverage in clinical exome sequencing

Background Exome and genome sequencing are the predominant techniques in the diagnosis and research of genetic disorders. Sufficient, uniform and reproducible/consistent sequence coverage is a main determinant for the sensitivity to detect single-nucleotide (SNVs) and copy number variants (CNVs). Here we compared the ability to obtain comprehensive exome coverage for recent exome capture kits and genome sequencing techniques. Results We compared three different widely used enrichment kits (Agilent SureSelect Human All Exon V5, Agilent SureSelect Human All Exon V7 and Twist Bioscience) as well as short-read and long-read WGS. We show that the Twist exome capture significantly improves complete coverage and coverage uniformity across coding regions compared to other exome capture kits. Twist performance is comparable to that of both short- and long-read whole genome sequencing. Additionally, we show that even at a reduced average coverage of 70× there is only minimal loss in sensitivity for SNV and CNV detection. Conclusion We conclude that exome sequencing with Twist represents a significant improvement and could be performed at lower sequence coverage compared to other exome capture techniques

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock

Why using epidemiological models to evaluate control strategies for livestock infectious diseases?

National audienceModelling is a pertinent approach: (1) to better understand and to predict pathogen spread in host populations according to the biological system characteristics under various management scenarios; and (2) to evaluate the epidemiological and the economic effectiveness of control strategies. To end with useful models, a back-and-forth between models and biological data is needed. First, building epidemiological models consists in proposing from all of the up-to-date available knowledge an integrated conceptual view of the system. This highlights which processes are well known vs. which are still of the biological assumption type. Second, observed data can be used to estimate observable parameters (such as disease-related mortality rates and production losses), whereas epidemiological models can be used to estimate unobservable parameters (such as transmission rates). Sensitivity analysis is a powerful tool to identify parameters with a major influence on model outputs, these parameters need to be precisely informed. Third, data can be used to evaluate / validate models, which in turn can help to identify potential control points of the biological system, to compare scenarios and test biological assumptions, and even (when the model has been evaluated) to predict future states of the system according to past (known) states. We illustrate such interactions between observations and models in the context of livestock infectious disease spread and control, with examples as Q fever, paratuberculosis, bovine viral diarrhea in cattle, and Salmonella carriage and the PRRS in pigs. Focus is made on the multi-scale modeling (from the within-host immune response to the infection dynamics at a regional scale), and the coupling of epidemiological and economic models to account for farmer decisions in evaluating collective control options

How to survey classical swine fever in wild boar (Sus scrofa) after the completion of oral vaccination? Chasing away the ghost of infection at different spatial scales

Additional file 1. “Disease” mapping model. This document details the hierarchical spatial Bayesian model computation

Pestes porcines : un point sur la surveillance de la PPA en 2014

National audienc

Az Alaptörvény értékei - Tudástár

IgenNemKÖFOP-2.1.1-VEKOP-15-2016-0000