Various species of Ebolavirus have caused, and are still causing, zoonotic outbreaks and public health crises in Africa. Bats have long been hypothesized to be important reservoir populations for a series of viruses such as Hendra or Marburg viruses, the severe acute respiratory syndrome coronavirus (SARS-CoV, SARS-CoV-2) as well as Ebolaviruses [2, 3]. However the ecology of disease dynamics, disease transmission, and coevolution with their natural hosts of these viruses is still poorly understood, despite their importance for predicting novel outbreaks in human or livestock populations. The evidence that bats function as sylvatic reservoirs for Ebola viruses is yet only partial. Indeed, only few serological studies demonstrated the presence of Ebolavirus antibodies in young bats [4], albeit without providing positive controls of viral detection or identifying the viral species (via PCR). There is thus an unexplained discrepancy between serological data and viral detection [2, 4]. 

In this article, Pleydell et al. [1] use a modeling approach as well as published serological and age-structure (of the bat population) data to calibrate the model simulations. The study starts with the development of an age-structured epidemiological model which includes seasonal birth pulses and waning immunity, both generating pulses of Ebolavirus transmission within a colony of African straw-coloured fruit bats (Eidolon helvum). The epidemiological dynamics of such system of ordinary differential equations can generate annual outbreaks, skipped years or multi-annual cycles up to chaotic dynamics. Therefore, the calibration of the parameters, and the definition of biologically relevant priors, are key. To this aim, the serological data are obtained from a previous study in Cameroon [5], and the age structured of the bat population (birth and mortality) from a population study in Ghana [6]. These data are integrated into the Bayesian analysis and statistical framework to fit the model and generate predictions. In a nutshell, the authors show an overlap between the data and credibility intervals generated by the calibrated model, which thus explains well the seasonality of age-structure, namely changes in pup presence, number of lactating females, or proportion of juveniles in May. The authors can estimate that 76% of adults and 39% of young bats do survive each year, and infections are expected to last one and a half weeks. The epidemiological model predicts that annual birth pulses likely generate annual disease outbreaks, so that weeks 30 to 31 of each year, are predicted to be the best period to isolate the circulating Ebolavirus in this bat population. From the model predictions, the authors estimate the probability to have missed an infectious bat among all the samples tested by PCR being approximately of one per two thousands. The disease dynamics pattern observed in the serology data, and replicated by the model, is likely driven by seasonal pulses of young susceptible bats entering the population. This seasonal birth event increases the viral transmission, resulting in the observed peak of viral prevalence. With the inclusion of immunity waning and antibody persistence, the model results illuminate therefore why previous studies have detected only few positive cases by PCR tests, in contrast to the evidence from serological data. 

 This study provides a first proof of principle that epidemiological modeling, despite its many simplifying assumptions, can be applied to wild species reservoirs of zoonotic diseases in order to optimize the design of field studies to detect viruses. Furthermore, such models can contribute to assess the probability and timing of zoonotic outbreaks in human or livestock populations. This article illustrates one of the manifold applications of mathematical theory of disease epidemiology to optimize sampling of pathogens/parasites or vaccine development and release [7, 8]. The further coupling of such models with population genetics theory and statistical inference methods (using parasite genome data) increasingly provide insights into the adaptation and evolution of parasites to human, crops and livestock populations [9, 10].

References

[1] Pleydell D.R.J., Ndong Bass I., Mba Djondzo F.A., Djomsi D.M., Kouanfack C., Peeters M., and J. Cappelle. 2023. A Bayesian analysis of birth pulse effects on the probability of detecting Ebola virus in fruit bats. bioRxiv, ver. 3 peer reviewed and recommended by Peer Community In Infections. https://doi.org/10.1101/2023.08.10.552777

[2] Caron A., Bourgarel M., Cappelle J., Liégeois F., De Nys H.M., and F. Roger. 2018. Ebola virus maintenance: if not (only) bats, what else? Viruses 10, 549. https://doi.org/10.3390/v10100549

[3] Letko M., Seifert S.N., Olival K.J., Plowright R.K., and V.J. Munster. 2020. Bat-borne virus diversity, spillover and emergence. Nature Reviews Microbiology 18, 461–471. https://doi.org/10.1038/s41579-020-0394-z

[4] Leroy E.M., Kumulungui B., Pourrut X., Rouquet P., Hassanin A., Yaba P., Délicat A., Paweska J.T., Gonzalez J.P., and R. Swanepoel. 2005. Fruit bats as reservoirs of Ebola virus. Nature 438, 575–576. https://doi.org/10.1038/438575a

[5] Djomsi D.M. et al. 2022. Dynamics of antibodies to Ebolaviruses in an Eidolon helvum bat colony in Cameroon. Viruses 14, 560. https://doi.org/10.3390/v14030560

[6] Peel A.J. et al. 2016. Bat trait, genetic and pathogen data from large-scale investigations of African fruit bats Eidolon helvum. Scientific data 3, 1–11. https://doi.org/10.1038/sdata.2016.49

[7] Nyandjo Bamen H.L., Ntaganda J.M., Tellier A. and O. Menoukeu Pamen. 2023. Impact of imperfect vaccine, vaccine trade-off and population turnover on infectious disease dynamics. Mathematics, 11(5), p.1240. https://doi.org/10.3390/math11051240

[8] Saadi N., Chi Y.L., Ghosh S., Eggo R.M., McCarthy C.V., Quaife M., Dawa J., Jit M. and A. Vassall. 2021. Models of COVID-19 vaccine prioritisation: a systematic literature search and narrative review. BMC medicine, 19, pp.1-11. https://doi.org/10.1186/s12916-021-02190-3

[9] Maerkle, H., John S., Metzger, L., STOP-HCV Consortium, Ansari, M.A., Pedergnana, V. and Tellier, A., 2023. Inference of host-pathogen interaction matrices from genome-wide polymorphism data. bioRxiv, https://doi.org/10.1101/2023.07.06.547816.

[10] Gandon S., Day T., Metcalf C.J.E. and B.T. Grenfell. 2016. Forecasting epidemiological and evolutionary dynamics of infectious diseases. Trends in ecology & evolution, 31(10), pp.776-788. https://doi.org/10.1016/j.tree.2016.07.010

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

Deciphering the virome (the set or assemblage of viruses) of the Earth, from individual organisms to entire ecosystems, has become a key priority. The first step to better understanding the impact of viruses on the ecology and functions of ecosystems is to describe their diversity. Such knowledge opens the gates to a better assessment of global nutrient cycling or of the threat that viruses represent to individual health. This explains the increasing number of pioneering studies that are currently sequencing the complete or partial genome of thousands of new viruses [1].

In their exciting study, Fritz and collaborators [2], authors sampled 209 army ants (Genus Dorylus) to investigate the virus diversity in dense forests that researchers cannot easily access. Indeed, these ants live in colonies (21 were sampled) that can move 1 km per day, covering a significant area and attacking many invertebrate and vertebrate preys.  Each sample was sequenced by a protocol called VANA sequencing and allowing the enrichment of the sample in viral sequences [3], so improving the detection of viruses present at low abundance in the ant (and more specifically in its gut for viruses infecting preys). 

Around 45,000 contigs presented homologies with bacterial, plant, invertebrate, and vertebrate infecting viruses. Half could be assigned to 56 families and 157 genera of the International Committee on Taxonomy of Viruses. Beyond this amazing harvest of new and known virus sequences using an original methodology, the results significantly improve the current frontiers of known viral taxonomy and diversity and raise exciting research tracks to expand them. 

As a preprint, several blogs or news of leading scientists and journals have already highlighted this study. For example, in the news section of Science magazine, Jon Cohen underlined the originality of the approach for virus hunting on Earth with the title “Armed with air samplers, rope tricks, and—yes—ants, virus hunters spot threats in new ways”[4]. Another example is the mention of the publication by Elisabeth Bik in her Microbiome Digest: she wrote, “An amazing read is a fresh preprint from Fritz and collaborator describing an exciting method of sampling in difficult-to-reach environments“ [5].

The paper from Fritz et al [2] thus represents a significant advance in virus ecology, as already recognized by early readers, and this is why I strongly recommend its publication in PCI Infections.

REFERENCES

1. Edgar RC, Taylor J, Lin V, Altman T, Barbera P, Meleshko D, Lohr D, Novakovsky G, Buchfink B, Al-Shayeb B, Banfield JF, de la Peña M, Korobeynikov A, Chikhi R, Babaian A (2022) Petabase-scale sequence alignment catalyses viral discovery. Nature, 602, 142–147. https://doi.org/10.1038/s41586-021-04332-2

2. Fritz M, Reggiardo B, Filloux D, Claude L, Fernandez E, Mahé F, Kraberger S, Custer JM, Becquart P, Mebaley TN, Kombila LB, Lenguiya LH, Boundenga L, Mombo IM, Maganga GD, Niama FR, Koumba J-S, Ogliastro M, Yvon M, Martin DP, Blanc S, Varsani A, Leroy E, Roumagnac P (2023) African army ants at the forefront of virome surveillance in a remote tropical forest. bioRxiv, 2022.12.13.520061, ver. 4 peer-reviewed and recommended by Peer Community in Infections. https://doi.org/10.1101/2022.12.13.520061

3. François S, Filloux D, Fernandez E, Ogliastro M, Roumagnac P (2018) Viral Metagenomics Approaches for High-Resolution Screening of Multiplexed Arthropod and Plant Viral Communities. In: Viral Metagenomics: Methods and Protocols Methods in Molecular Biology. (eds Pantaleo V, Chiumenti M), pp. 77–95. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7683-6_7

4. Cohen J (2023) Virus hunters test new surveillance tools. Science, 379, 16–17. https://doi.org/10.1126/science.adg5292

5. Ponsero A (2023) February 18th, 2023. Microbiome Digest - Bik’s Picks. https://microbiomedigest.com/2023/02/18/february-18th-2023/

Understanding the ecological and evolutionary factors underlying the spread of new fungal pathogen populations can inform the development of more effective management strategies. In plant pathology, pathogenicity is generally presented as having two components: ‘virulence’ (qualitative pathogenicity) and aggressiveness (quantitative pathogenicity). Changes in virulence in response to the deployment of new resistant varieties are a major driver of the spread of new populations (called pathotypes, or races) in modern agrosystems, and the genomic (i.e. proximal) and eco-evolutionary (i.e. ultimate) factors underlying these changes are well-documented [1,2,3]. By contrast, the role of changes in aggressiveness in the spread of pathotypes remains little known [4].

The study by Cécilia Fontyn and collaborators [5] set out to characterize changes in aggressiveness for isolates of two pathotypes of the wheat leaf rust (Puccinia triticina) that have been dominant in France during the 2005-2016 period. Isolates were genetically characterized using multilocus microsatellite typing and phenotypically characterized for three components of aggressiveness on wheat varieties: infection efficiency, latency period, and sporulation capacity. Using experiments that represent quite a remarkable amount of work and effort, Fontyn et al. showed that each dominant pathotype consisted of several genotypes, including common genotypes whose frequency changed over time. For each pathotype, the genotypes that were more common initially were replaced by a more aggressive genotype. Together, these results show that changes in the genetic composition of populations of fungal plant pathogens can be associated with, and may be caused by, changes in the quantitative components of pathogenicity. This study also illustrates how extensive, decade-long monitoring of fungal pathogen populations, such as the one conducted for wheat leaf rust in France, represents a very valuable resource for research.

REFERENCES

[1] Brown, J. K. (1994). Chance and selection in the evolution of barley mildew. Trends in Microbiology, 2(12), 470-475. https://doi.org/10.1016/0966-842x(94)90650-5

[2] Daverdin, G., Rouxel, T., Gout, L., Aubertot, J. N., Fudal, I., Meyer, M., Parlange, F., Carpezat, J., & Balesdent, M. H. (2012). Genome structure and reproductive behaviour influence the evolutionary potential of a fungal phytopathogen. PLoS Pathogens, 8(11), e1003020. https://doi.org/10.1371/journal.ppat.1003020

[3] Gladieux, P., Feurtey, A., Hood, M. E., Snirc, A., Clavel, J., Dutech, C., Roy, M., & Giraud, T. (2015). The population biology of fungal invasions.Molecular Ecology, 24(9), 1969-86. https://doi.org/10.1111/mec.13028

[4] Fontyn, C., Zippert, A. C., Delestre, G., Marcel, T. C., Suffert, F., & Goyeau, H. (2022). Is virulence phenotype evolution driven exclusively by Lr gene deployment in French Puccinia triticina populations?. Plant Pathology, 71(7), 1511-1524. https://doi.org/10.1111/ppa.13599

[5] Fontyn, C., Meyer, K. J., Boixel, A. L., Delestre, G., Piaget, E., Picard, C., Suffer, F., Marcel, T.C., & Goyeau, H. (2022). Evolution within a given virulence phenotype (pathotype) is driven by changes in aggressiveness: a case study of French wheat leaf rust populations. bioRxiv, 2022.08.29.505401, ver. 3 peer-reviewed and recommended by Peer Community in Infections.  https://doi.org/10.1101/2022.08.29.505401

One of the greatest achievements of health sciences is the eradication of infectious diseases such as smallpox that in the past imposed a severe burden on humankind, through global vaccination campaigns. Moreover, progress towards the eradication of others such as poliomyelitis, dracunculiasis, and yaws is being made.

In contrast, other infections that were previously contained are reemerging, due to several factors, including lack of access to vaccines due to geopolitical reasons, the rise of anti-vaccine movements, and the constant mobility of infected persons from the endemic sites.

One of such disease is diphtheria, caused by Corynebacterium diphtheriae and a few other related species such as C. ulcerans and C. pseudotuberculosis. Importantly, in France, diphtheria cases reported in 2022 increased 7-fold from the average of previously recorded cases per year in the previous 4 years and the situation in other European countries is similar.

Hence, as reported here, Hennart et al. (2023) developed DIPHTOSCAN, a free access bioinformatics tool with user-friendly interphase, aimed to easily identify, extract and interpret important genomic features such as the sublineage of the strain, the presence of the tox gene (as a string predictor for toxigenic disease) as well as genes coding other virulence factors such as fimbriae, and the presence of know resistant mechanisms towards antibiotics like penicillin and erythromycin currently used in the clinic to treat this infection.

The authors validated the performance of their tool with a large collection of genomes, including those obtained from the isolates of the 2022 outbreak in France, more than 1,200 other genomes isolated from France, Algeria, and Yemen, and more than 500 genomes from several countries from Europe, America, Africa, Asia, and Oceania that are available through the NCBI site.

DIPHTOSCAN will allow the rapid identification and surveillance of potentially dangerous strains such as those being tox-positive isolates and resistant to multiple drugs and/or first-line treatments and a better understanding of the epidemiology and evolution of this important reemerging disease.

Reference

Hennart M., Crestani C., Bridel S., Armatys N., Brémont S., Carmi-Leroy A., Landier A., Passet V., Fonteneau L., Vaux S., Toubiana J., Badell E. and Brisse S. (2023). A global Corynebacterium diphtheriae genomic framework sheds light on current diphtheria reemergence. bioRxiv, 2023.02.20.529124, ver 3 peer-reviewed and recommended by PCI Infections. https://doi.org/10.1101/2023.02.20.529124