High-throughput sequencing (HTS) has revealed an incredible diversity of microorganisms in ecosystems and is also changing the monitoring of macroorganism biodiversity (Deiner et al. 2017; Piper et al. 2019).  

The diagnostic of plant pathogens and the identification of pests is gradually integrating the use of these techniques, but there are still obstacles. Most of them are related to the reliability of these analyses, which have long been considered insufficient because of their dependence on a succession of sophisticated operations involving parameters that are sometimes difficult to adapt to complex matrices or certain diagnostic contexts. The need to validate HTS approaches is gradually being highlighted in recent work but remains poorly documented (Bester et al. 2022).

In this paper, a large community of experts presents and discusses the key steps for optimal control of HTS performance and reliability in a diagnostic context (Massart et al. 2022). It also addresses the issue of costs. The article provides recommendations that closely combine the quality control requirements commonly used in conventional diagnostics with newer or HTS-specific control elements and concepts that are not yet widely used. It discusses the value of these for the use of the various techniques currently covered by the terms "High Throughput Sequencing" in diagnostic activities. The elements presented are intended to limit false positive or false negative results but will also optimise the interpretation of contentious results close to the limits of analytical sensitivity or unexpected results, both of which appear to be frequent when using HTS.

Furthermore, the need for risk analysis, verification and validation of methods is well illustrated with numerous examples for each of the steps considered crucial to ensure reliable use of HTS. The clear contextualisation of the proposals made by the authors complements and clarifies the need for user expertise according to the experimental objectives. Some unanswered questions that will require further development and validation are also presented.

This article should benefit a large audience including researchers with some level of expertise in HTS but unfamiliar with the recent concepts of controls common in the diagnostic world as well as scientists with strong diagnostic expertise but less at ease with the numerous and complex procedures associated with HTS.

References

Bester R, Steyn C, Breytenbach JHJ, de Bruyn R, Cook G, Maree HJ (2022) Reproducibility and Sensitivity of High-Throughput Sequencing (HTS)-Based Detection of Citrus Tristeza Virus and Three Citrus Viroids. Plants, 11, 1939. https://doi.org/10.3390/plants11151939

Deiner K, Bik HM, Mächler E, Seymour M, Lacoursière-Roussel A, Altermatt F, Creer S, Bista I, Lodge DM, de Vere N, Pfrender ME, Bernatchez L (2017) Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Molecular Ecology, 26, 5872–5895. https://doi.org/10.1111/mec.14350

Massart, S et al. (2022) Guidelines for the reliable use of high throughput sequencing technologies to detect plant pathogens and pests. Zenodo, 6637519, ver. 3  peer-reviewed and recommended by Peer Community in Infections. https://doi.org/10.5281/zenodo.6637519

Piper AM, Batovska J, Cogan NOI, Weiss J, Cunningham JP, Rodoni BC, Blacket MJ (2019) Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance. GigaScience, 8, giz092. https://doi.org/10.1093/gigascience/giz092

Mycobacterium bovis, also called M. tuberculosis var. bovis, is a bacterium belonging to the M. tuberculosis complex (i.e., MTBC) and which can cause through zoonotic transmission another form of human tuberculosis (Tb). It is above all the agent of bovine tuberculosis (i.e., bTb) which affects not only cattle (wild or farmed) but also a large diversity of other wild mammals worldwide. An increasing number of infected animal cases are being discovered in many regions of the world, thus raising the problem of tuberculosis transmission, including to humans, more complex than previously thought. Efforts have been made in terms of vaccination or culling of populations of host carrier species, such as the badger for example, however leading to consequences of greater dispersion of the infectious agent. M. bovis shows a more or less significant capacity to persist outside its hosts, particularly in the environment under certain abiotic and biotic conditions. This bacillus can be transmitted and spread in many ways, including through aerosol, mucus and sputum, urine and feces, by direct contact with infected animals, their dead bodies or rather via their excreta or by inhalation of aerosols, depending on the host species concerned.

In this paper, Calenge and his collaborators (Callenge et al. 2024) benefited from a national surveillance program on M. bovis cases in wild species, set up in 2011 in France, i.e., Sylvatub, for detecting and monitoring M. bovis infection in European badger (Meles meles) populations. Sylvatub is a participatory program involving both national and local stakeholder systems in order to determine changes in bTb infection levels in domestic and wild animal species. This original work had two aims: to describe spatial disease dynamics in the three clusters under scrutiny using a complex Bayesian model; and to develop indicators for the monitoring of the M. bovis infection by stakeholders and decision-makers of the program. This paper is timely and very comprehensive.

In this cogent study, the authors illustrate this point by using epidemiological surveillance to obtain large amounts of data (which is generally lacking in human epidemiology, but more dramatically lacking in animal epidemiology) and a highly sophisticated biostatistical analysis (Callenge et al. 2024). It is in itself a demonstration of the current capabilities of population dynamics applied to infectious disease situations, in this case animal, in the rapidly developing discipline of disease ecology and evolution. One of the aims of the study is to propose statistical models that can be used by the different stakeholders in charge, for instance, of wildlife conservation or the regional or State veterinary services to assess disease risk in the most affected regions.

References

Assel AKHMETOVA​, Jimena GUERRERO​, Paul McADAM, Liliana CM SALVADOR​, Joseph CRISPELL​, John LAVERY​, Eleanor PRESHO​, Rowland R KAO​, Roman BIEK​, Fraser MENZIES​, Nigel TRIMBLE​, Roland HARWOOD​, P Theo PEPLER, Katarina ORAVCOVA​, Jordon GRAHAM​, Robin SKUCE​, Louis DU PLESSIS​, Suzan THOMPSON​, Lorraine WRIGHT​, Andrew W BYRNE​, Adrian R ALLEN. 2023. Genomic epidemiology of Mycobacterium bovis infection in sympatric badger and cattle populations in Northern Ireland. Microbial Genomics 9: mgen001023. https://doi.org/10.1099/mgen.0.001023

Roman BIEK, Anthony O’HARE, David WRIGHT, Tom MALLON, Carl McCORMICK, Richard J ORTON, Stanley McDOWELL, Hannah TREWBY, Robin A SKUCE, Rowland R KAO. 2012. Whole genome sequencing reveals local transmission patterns of Mycobacterium bovis in sympatric cattle and badger populations. PLoS Pathogens 8: e1003008. https://doi.org/10.1371/journal.ppat.1003008

Clément CALENGE, Ariane PAYNE, Edouard REVEILLAUD, Céline RICHOMME, Sébastien GIRARD, Stephanie DESVAUX. 2024. Assessing the dynamics of Mycobacterium bovis infection in three French badger populations. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community In Infections. https://doi.org/10.1101/2023.05.31.543041

Marc CHOISY, Pejman ROHANI. 2006. Harvesting can increase severity of wildlife disease epidemics. Proceedings of the Royal Society, London, Ser. B 273: 2025-2034. https://doi.org/10.1098/rspb.2006.3554

Shannon C DUFFY, Sreenidhi SRINIVASAN, Megan A SCHILLING, Tod STUBER, Sarah N DANCHUK, Joy S MICHAEL, Manigandan VENKATESAN, Nitish BANSAL, Sushila MAAN, Naresh JINDAL, Deepika CHAUDHARY, Premanshu DANDAPAT, Robab KATANI, Shubhada CHOTHE, Maroudam VEERASAMI, Suelee ROBBE-AUSTERMAN, Nicholas JULEFF, Vivek KAPUR, Marcel A BEHR. 2020. Reconsidering Mycobacterium bovis as a proxy for zoonotic tuberculosis: a molecular epidemiological surveillance study. Lancet Microbe 1: e66-e73. https://doi.org/10.1016/S2666-5247(20)30038-0

Jean-François GUEGAN. 2019. The nature of ecology of infectious disease. The Lancet Infectious Diseases 19. https://doi.org/10.1016/s1473-3099(19)30529-8

Brandon H HAYES, Timothée VERGNE, Mathieu ANDRAUD, Nicolas ROSE. 2023. Mathematical modeling at the livestock-wildlife interface: scoping review of drivers of disease transmission between species. Frontiers in Veterinary Science 10: 1225446. https://doi.org/10.3389/fvets.2023.1225446

David KING, Tim ROPER, Douglas YOUNG, Mark EJ WOOLHOUSE, Dan COLLINS, Paul WOOD. 2007. Bovine tuberculosis in cattle and badgers. Report to Secretary of State about tuberculosis in cattle and badgers. London, UK. https://www.bovinetb.info/docs/RBCT_david_%20king_report.pdf  

Robert MM SMITH , Francis DROBNIEWSKI, Andrea GIBSON, John DE MONTAGUE, Margaret N LOGAN, David HUNT, Glyn HEWINSON, Roland L SALMON, Brian O’NEILL. 2004. Mycobacterium bovis Infection, United Kingdom. Emerging Infectious Diseases 10: 539-541. https://doi.org/10.3201/eid1003.020819