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

The gut of vertebrates is a host for hundreds or thousands of different species of microorganisms named the gut microbiome. This latter may differ greatly in natural environments between individuals, populations and species (1). The vertebrate gut microbiome plays key roles in host fitness through functions including nutrient acquisition, immunity and defense against infectious agents. While bank voles are small mammals potentially reservoirs of a large number of infectious agents, questions about the links between their gut microbiome and the presence of pathogens are scarcely addressed. 

In this study, Bouilloud et al. (2) used complementary analyses of community and microbial ecology to (i) assess the variability of gut bacteriome diversity and composition in wild populations of the bank vole Myodes glareolus collected in four different sites in Eastern France and (ii) evaluate the three-way interactions between the gut bacteriota, the gastro-intestinal helminths and pathogenic bacteria detected in the spleen. Authors identified important variations of the gut bacteriota composition and diversity among bank voles mainly explained by sampling localities. They found positive correlations between the specific richness of both the gut bacteria and the helminth community, as well as between the composition of these two communities, even when accounting for the influence of geographical distance. The helminths Aonchotheca murissylvatici, Heligmosomum mixtum and the bacteria Bartonella sp were the main taxa associated with the whole gut bacteria composition. Besides, changes in relative abundance of particular gut bacterial taxa were specifically associated with other helminths (Mastophorus muris, Catenotaenia henttoneni, Paranoplocephala omphalodes and Trichuris arvicolae) or pathogenic bacteria. Infections with Neoehrlichia mikurensis, Orientia sp, Rickettsia sp and P. omphalodes were especially associated with lower relative abundance of members of the family Erysipelotrichaceae (Firmicutes), while coinfections with higher number of bacterial infections were associated with lower relative abundance of members of the Bacteroidales family (Bacteroidetes). 

As pointed out by both reviewers, this study represents a significant advance in the field. I would like to commend the authors for this enormous work. The amount of data, analyses and results is considerable which has sometimes complicated the understanding of the story at the beginning of the evaluation process. Thanks to constructive scientific interactions with both reviewers through the two rounds of evaluation, the authors have efficiently addressed the reviewer's concerns and improved the manuscript, making this great story easier to read. The innovative results of this study emphasize the complex interlinkages between gut bacteriome and infections in wild animal populations and I strongly recommend this article for publication In Peer Community Infections. 

References

(1) Vujkovic-Cvijin I, Sklar J, Jiang L, Natarajan L, Knight R, Belkaid Y (2020) Host variables confound gut microbiota studies of human disease. Nature, 587, 448–454. https://doi.org/10.1038/s41586-020-2881-9

(2) Bouilloud M, Galan M, Dubois A, Diagne C, Marianneau P, Roche B, Charbonnel N (2023) Three-way relationships between gut microbiota, helminth assemblages and bacterial infections in wild rodent populations. biorxiv, 2022.05.23.493084, ver. 2 peer-reviewed and recommended by Peer Community in Infections. https://doi.org/10.1101/2022.05.23.493084

Zoonotic pathogens, those transmitted from animals to humans, constitute a major public health risk with high associated global economic costs. Diseases associated with these pathogens represent more than 60% of emerging infectious diseases and predominantly originate in wildlife (1). Over the last decades, the emergence and re-emergence of zoonotic pathogens have led to an increasing number of epidemics, as illustrated by the current Covid-19 pandemic. There is ample evidence that human impact on native ecosystems such as deforestation, agricultural development, and urbanization, is linked to spillover of pathogens from animals to humans (2). However, research and calls to action have mainly focused on the importance of surveillance and prevention of zoonotic emergences along landscape interfaces, with special emphasis on tropical forests and agroecosystems, and studies and reviews pointing out the zoonotic risk associated with cities are scarce.  Additionally, cities are sometimes wrongly seen as one homogeneous ecosystem, almost exclusively human, with a Northern hemisphere-biased perception of what a city is, which fails to take into account the ecological and socio-economic diversities that can constitute an urban area.

Here, Dobigny and Morand (3) aim to draw attention to the importance of urban ecosystems in zoonotic risk and advocate that further attention should be paid to urban, peri-urban and suburban areas. In this well-organized and well-documented review, the authors show, using updated literature, that cities are places where massive contacts occur between wildlife, domestic animals, and human inhabitants (thus constituting spillover opportunities), and that it is even likely that human and wildlife contact in urban centers is more prevalent than in wild areas, perhaps contrary to intuition. Indeed, cities currently constitute the most important environment of human life and are places for millions of close interactions between humans and animals, including pets and domestic animals, wild animals through the intrusion of wild urban-adapted species (e.g., some bat, rodent, or bird species among others), manipulation and consumption of wildlife meat, and the existence of wildlife meat markets, which all constitute a major risk for zoonotic spillover. In cities, lab escapees of zoonotic pathogens also exist, and trends of adaptation to urban ecological conditions of many vectors of primary health importance is also a concern. The authors further argue that cities are predominant places for both epidemic amplification of human-human transmitted pathogens, because they are places with high human densities and population growth, and for dissemination of reservoirs, vectors and pathogens, as they are transport hubs. Dobigny & Morand further predict, likely correctly, that cities may be important places for pathogen evolution.  Finally, they propose actions and recommendations to limit the risk of zoonotic spillover events from urban ecosystems and future directions for research aiming at assessing this risk. 

The reviewers found the manuscript well-organized and presented, timely, and bringing novel contributions to the field of zoonotic emergence. I strongly recommend this article, which should benefit a large audience, particularly in the context of the current Covid-19 pandemics and the ongoing One Health initiatives aiming at preventing future zoonotic disease emergence (4). 

References

(1) Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, Daszak P (2008) Global trends in emerging infectious diseases. Nature, 451, 990–993. https://doi.org/10.1038/nature06536

(2) White RJ, Razgour O (2020) Emerging zoonotic diseases originating in mammals: a systematic review of effects of anthropogenic land-use change. Mammal Review, 50, 336–352. https://doi.org/10.1111/mam.12201

(3) Dobigny G, Morand S (2022) Zoonotic emergence at the animal-environment-human interface: the forgotten urban socio-ecosystems. Zenodo, 6444776, ver. 3 peer-reviewed and recommended by Peer Community in Infections. https://doi.org/10.5281/zenodo.6444776

(4) Morand S, Lajaunie C (2021) Biodiversity and COVID-19: A report and a long road ahead to avoid another pandemic. One Earth, 4, 920–923. https://doi.org/10.1016/j.oneear.2021.06.007

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

Worldwide, ticks and tick-borne diseases are a persistent example of problems at the One Health interface between humans, wildlife, and environment (1, 2). The management and prevention of ticks and tick-borne diseases require a better understanding of host, tick and pathogen interactions and thus get a better view of the tick-borne pathosystems.

In this study (3), the tick-borne pathosystem included three component species: first a seabird host, the Yellow-legged gull (YLG - Larus michahellis, Laridae), second a soft nidicolous tick (Ornithodoros maritimus, Argasidae, syn. Alectorobius maritimus) known to infest this host and third a blood parasite (Babesia sp. YLG, Piroplasmidae). In this pathosystem, authors investigated the role of the soft tick, Ornithodoros maritimus, as a potential vector of Babesia sp. YLG. They analyzed the transmission of Babesia sp. YLG by collecting different tick life stages from YLG nests during 4 consecutive years on the islet of Carteau (Gulf of Fos, Camargue, France). Ticks were dissected and organs were analyzed separately to detect the presence of Babesia sp DNA and to evaluate different transmission pathways.

While the authors detected Babesia sp. YLG DNA in the salivary glands of nymphs, females and males, this result reveals a strong suspicion of transmission of the parasite by the soft tick. Babesia sp. YLG DNA was also found in tick ovaries, which could indicate possible transovarial transmission. Finally, the authors detected Babesia sp. YLG DNA in several male testes and in endospermatophores, and notably in a parasite-free female (uninfected ovaries and salivary glands). These last results raise the interesting possibility of sexual transmission from infected males to uninfected females.

As pointed out by both reviewers, this is a nice study, well written and easy to read. All the results are new and allow to better understand the role of the soft tick, Ornithodoros maritimus, as a potential vector of Babesia sp. YLG. They finally question about the degree to which the parasite can be maintained locally by ticks and the epidemiological consequences of infection for both O. maritimus and its avian host. For all these reasons, I chose to recommend this article for Peer Community In Infections.

References

1. Dantas-Torres et al (2012). Ticks and tick-borne diseases: a One Health perspective. Trends Parasitol. 28:437. https://doi.org/10.1016/j.pt.2012.07.003 
2. Johnson N et al (2022). One Health Approach to Tick and Tick-Borne Disease Surveillance in the United Kingdom. Int J Environ Res Public Health. 19:5833. https://doi.org/10.3390/ijerph19105833
3. Bonsergent C, Vittecoq M, Leray C, Jouglin M, Buysse M, McCoy KD, Malandrin L. A soft tick vector of Babesia sp. YLG in Yellow-legged gull (Larus michahellis) nests. bioRxiv, 2023.03.24.534071, ver. 3 peer-reviewed and recommended by Peer Community in Infections. https://doi.org/10.1101/2023.03.24.534071