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

Mosquitoes are first vector of pathogen worldwide and transmit several arbovirus, most of them leading to major outbreaks (1). Chikungunya virus (CHIKV) is a perfect example of the “explosive type” of arbovirus, as observed in La Réunion Island in 2005-2006 (2-6) and also in the outbreak of 2007 in Italy (7), both vectorized by Ae. albopictus. Being able to better understand CHIKV intra-vector dynamics is still of major interest since not all chikungunya strain are explosive ones (8). 

In this study (9), the authors have evaluated the vector competence of a local strain of Aedes albopictus (collected in Lyon, France) for CHIKV. They evaluated infection, dissemination and transmission dynamics of CHIKV using different dose of virus in individual mosquitoes from day 2 to day 20 post exposure, by titration and quantification of CHIKV RNA load in the saliva. As highlighted by both reviewers, the most innovative idea in this study was the use of three different oral doses trying to span human viraemia detected in two published studies (10-11), doses that were estimated through their model of human CHIKV viremia in the blood.  They have found that CHIKV dissemination from the Ae. albopictus midgut depends on the interaction between time post-exposure and virus dose (already highlighted by other international publications).  Then their results were implemented in the agent-based model nosoi to estimate the epidemic potential of CHIKV in a French population of Ae. albopictus, using realistic vectorial capacity parameters.

To conclude, the authors have discussed the importance of other parameters that could influence vector competence as mosquito microbiota and temperature, parameters that need also to be estimated in local mosquito population to improve the risk assessment through modelling.  

As pointed out by both reviewers, this is a nice study, well written and easy to read. These results allow filling in another gap of our understanding of CHIKV intra-vector dynamics and highlight the epidemic potential of CHIKV upon transmission by Aedes albopictus in mainland France. For all these reasons, I chose to recommend this article for Peer Community In Infections.

References

1.       Marine Viglietta, Rachel Bellone, Adrien Albert Blisnick, Anna-Bella Failloux. (2021). Vector Specificity of Arbovirus Transmission. Front Microbiol Dec 9;12:773211. https://doi.org/10.3389/fmicb.2021.773211

2.       Schuffenecker I, Iteman I, Michault A, Murri S, Frangeul L, Vaney M-C, Lavenir R, Pardigon N, Reynes J-M, Pettinelli F, Biscornet L, Diancourt L, Michel S, Duquerroy S, Guigon G, Frenkiel M-P, Bréhin A-C, Cubito N, Desprès P, Kunst F, Rey FA, Zeller H, Brisse S. (2006). Genome Microevolution of Chikungunya viruses Causing the Indian Ocean Outbreak. 2006. PLoS Medicine, 3, e263. https://doi.org/10.1371/journal.pmed.0030263

3.       Bonilauri P, Bellini R, Calzolari M, Angelini R, Venturi L, Fallacara F, Cordioli P, 687 Angelini P, Venturelli C, Merialdi G, Dottori M. (2008). Chikungunya Virus in Aedes albopictus, Italy. Emerging Infectious 689 Diseases, 14, 852–854. https://doi.org/10.3201/eid1405.071144

4.       Pagès F, Peyrefitte CN, Mve MT, Jarjaval F, Brisse S, Iteman I, Gravier P, Tolou H, Nkoghe D, Grandadam M. (2009). Aedes albopictus Mosquito: The Main Vector of the 2007 Chikungunya Outbreak in Gabon. PLoS ONE, 4, e4691. https://doi.org/10.1371/journal.pone.0004691

5.       Paupy C, Kassa FK, Caron M, Nkoghé D, Leroy EM (2012) A Chikungunya Outbreak Associated with the Vector Aedes albopictus in Remote Villages of Gabon. Vector-Borne and Zoonotic Diseases, 12, 167–169. https://doi.org/10.1089/vbz.2011.0736

6.       Mombouli J-V, Bitsindou P, Elion DOA, Grolla A, Feldmann H, Niama FR, Parra H-J, Munster VJ. (2013). Chikungunya Virus Infection, Brazzaville, Republic of Congo, 2011. Emerging Infectious Diseases, 19, 1542–1543. https://doi.org/10.3201/eid1909.130451

7.       Venturi G, Luca MD, Fortuna C, Remoli ME, Riccardo F, Severini F, Toma L, Manso MD, Benedetti E, Caporali MG, Amendola A, Fiorentini C, Liberato CD, Giammattei R, Romi R, Pezzotti P, Rezza G, Rizzo C. (2017). Detection of a chikungunya outbreak in Central Italy, August to September 2017. Eurosurveillance, 22, 17–00646. https://doi.org/10.2807/1560-7917.es.2017.22.39.17-00646

8.       de Lima Cavalcanti, T.Y.V.; Pereira, M.R.; de Paula, S.O.; Franca, R.F.d.O. (2022). A Review on Chikungunya Virus Epidemiology, Pathogenesis and Current Vaccine Development. Viruses 2022, 14, 969. https://doi.org/10.3390/v14050969

9.       Barbara Viginier, Lucie Cappuccio, Celine Garnier, Edwige Martin, Carine Maisse, Claire Valiente Moro, Guillaume Minard, Albin Fontaine, Sebastian Lequime, Maxime Ratinier, Frederick Arnaud, Vincent Raquin. (2023). Chikungunya intra-vector dynamics in Aedes albopictus from Lyon (France) upon exposure to a human viremia-like dose range reveals vector barrier permissiveness and supports local epidemic potential. medRxiv, ver.3, peer-reviewed and recommended by Peer Community In Infections. https://doi.org/10.1101/2022.11.06.22281997

10.     Appassakij H, Khuntikij P, Kemapunmanus M, Wutthanarungsan R, Silpapojakul K (2013) Viremic profiles in CHIKV-infected cases. Transfusion, 53, 2567–2574. https://doi.org/10.1111/j.1537-2995.2012.03960.x

11.     Riswari SF, Ma’roef CN, Djauhari H, Kosasih H, Perkasa A, Yudhaputri FA, Artika IM, Williams M, Ven A van der, Myint KS, Alisjahbana B, Ledermann JP, Powers AM, Jaya UA (2015) Study of viremic profile in febrile specimens of chikungunya in Bandung, Indonesia. Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology, 74, 61–5. https://doi.org/10.1016/j.jcv.2015.11.017

Trypanosoma lewisi is an atypical trypanosome species. Transmitted by fleas, it has a high prevalence and worldwide distribution in small mammals, especially rats [1]. Although not typically thought to infect humans, there has been a number of reports of human infections by T. lewisi in Asia including a case of a fatal infection in an infant [2]. The fact that the parasite is resistant to lysis by normal human serum [3] suggests that many people, especially immunocompromised individuals, may be at risk from zoonotic infections by this pathogen, particularly in regions where there is close contact with T. lewisi-infected rat fleas. Indeed, it is also possible that cryptic T. lewisi infections exist but have hitherto gone undetected. Such asymptomatic infections have been detected for a number of parasitic infections including the related parasite T. b. gambiense [4]. 
 
Despite the fact that T. lewisi parasites pose a risk to human health, very little is known about their population structure, reproductive mode, population size or dispersal. In the article [5], Ségard et al. presented the first attempt at examining the population structure of the parasite. They developed microsatellite markers and used them to analyse a small set of samples from West Africa and Southeast Asia. Although the number of microsatellite markers is not very high and they encountered problems of PCR amplification especially of the southeast Asian samples, they did provide preliminary data that hints at a clonal population structure with rare recombination and suggests population subdivisions occurring at a scale that is equal, and probably smaller than a neighborhood of several houses with a short generation time. These are very interesting preliminary findings that will need to be validated using a larger cohort with more markers or by whole genome sequencing.
 

References

[1] Hoare CA (1972) The trypanosomes of mammals. A zoological monograph. The trypanosomes of mammals. A zoological monograph.

[2] Truc P, Büscher P, Cuny G, Gonzatti MI, Jannin J, Joshi P, Juyal P, Lun Z-R, Mattioli R, Pays E, Simarro PP, Teixeira MMG, Touratier L, Vincendeau P, Desquesnes M (2013) Atypical Human Infections by Animal Trypanosomes. PLOS Neglected Tropical Diseases, 7, e2256. https://doi.org/10.1371/journal.pntd.0002256

[3] Lun Z-R, Wen Y-Z, Uzureau P, Lecordier L, Lai D-H, Lan Y-G, Desquesnes M, Geng G-Q, Yang T-B, Zhou W-L, Jannin JG, Simarro PP, Truc P, Vincendeau P, Pays E (2015) Resistance to normal human serum reveals Trypanosoma lewisi as an underestimated human pathogen. Molecular and Biochemical Parasitology, 199, 58–61. https://doi.org/10.1016/j.molbiopara.2015.03.007

[4] Büscher P, Bart J-M, Boelaert M, Bucheton B, Cecchi G, Chitnis N, Courtin D, Figueiredo LM, Franco J-R, Grébaut P, Hasker E, Ilboudo H, Jamonneau V, Koffi M, Lejon V, MacLeod A, Masumu J, Matovu E, Mattioli R, Noyes H, Picado A, Rock KS, Rotureau B, Simo G, Thévenon S, Trindade S, Truc P, Reet NV (2018) Do Cryptic Reservoirs Threaten Gambiense-Sleeping Sickness Elimination? Trends in Parasitology, 34, 197–207. https://doi.org/10.1016/j.pt.2017.11.008

[5] Ségard A, Roméro A, Ravel S, Truc P, Gauthier D, Gauthier P, Dossou H-J, Sylvestre B, Houéménou G, Morand S, Chaisiri K, Noûs C, De Meeûs T (2022) Development of nine microsatellite loci for Trypanosoma lewisi, a potential human pathogen in Western Africa and South-East Asia, and preliminary population genetics analyses. Zenodo, 6460010, ver. 3 peer-reviewed and recommended by Peer Community in Infections. https://doi.org/10.5281/zenodo.6460010

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

Structural variations (SVs) play a key role in viral evolution, and therefore they are also important for infection dynamics. However, the contribution of structural variations to the evolution of double-stranded viruses is limited. This knowledge can help to understand the population dynamics and might be crucial for the future development of viral attenuated vaccines.

In this study, Fuandila et al (1) use the Cyprinid herpesvirus 3 (CyHV-3), commonly known as koi herpesvirus (KHV), to investigate the variability and contribution of structural variations (SV) for viral evolution after 99 passages in vitro. This virus, with the largest genome among herperviruses, causes a lethal infection in common carp and koi associated with mortalities up to 95% (2). Interestingly, KHV infections are caused by haplotype mixtures, which possibly are a source of genome diversification, but make genomic comparisons more difficult.

The authors have used ultra-deep long-read sequencing of two passages, P78 and P99, which were previously described to have differences in virulence. They have found a surprisingly high and wide distribution of SVs along the genome, which were enriched in inversion and deletion events and that often led to defective viral genomes. Although it is known that these defective viral genomes negatively impact viral replication, their implications for virus persistence are still unclear.

Subsequently, the authors concentrated on the virulence-relevant region ORF150, which was found to be different in P78 (deletion in 100% of the reads) and P99 (reference-like haplotype). To understand this loss and gain of full ORF150, they searched for SV turn-over in 10 intermediate passages. This analysis revealed that by passage 10 deleted and inverted (attenuated) haplotypes had already appeared, steadily increased frequency until P78, and then completely disappeared between P78 and P99. This is a striking result that raises new questions as to how this clearance occurs, which is really important as these reversions may result in undesirable increases in virulence of live-attenuated vaccines.

We recommend this preprint because its use of ultra-deep long-read sequencing has permitted to better understand the role of SV diversity and dynamics in viral evolution. This study shows an unexpectedly high number of structural variations, revealing a novel source of virus diversification and confirming the different mixtures of haplotypes in different passages, including the gain of function. This research provides basic knowledge for the future design of live-attenuated vaccines, to prevent the reversion to virulent viruses. 

References

(1)  Fuandila NN, Gosselin-Grenet A-S, Tilak M-K, Bergmann SM, Escoubas J-M, Klafack S, Lusiastuti AM, Yuhana M, Fiston-Lavier A-S, Avarre J-C, Cherif E (2022) Structural variation turnovers and defective genomes: key drivers for the in vitro evolution of the large double-stranded DNA koi herpesvirus (KHV). bioRxiv, 2022.03.10.483410, ver. 4 peer-reviewed and recommended by Peer Community in Infections. https://doi.org/10.1101/2022.03.10.483410

(2)  Sunarto A, McColl KA, Crane MStJ, Sumiati T, Hyatt AD, Barnes AC, Walker PJ. Isolation and characterization of koi herpesvirus (KHV) from Indonesia: identification of a new genetic lineage. Journal of Fish Diseases, 34, 87-101. https://doi.org/10.1111/j.1365-2761.2010.01216.x