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Equine veterinary journal2026; doi: 10.1002/evj.70185

Vaginal host-microbe signatures linked to placental outcomes in mares.

Abstract: Ascending placentitis is a leading cause of late-term pregnancy loss in mares. Although pathogens are presumed to ascend from the caudal reproductive tract, the association between the vaginal microbiome and placentitis has not been systematically examined. Objective: To characterise microbial and host gene expression in the equine vagina during gestation and to identify taxa or transcripts that were associated with the presence of an abnormal placenta at birth. Methods: Prospective, paired observational study. Methods: Vaginal wall swabs were collected from 49 multiparous Thoroughbred mares at days 42-46 and 118-133 of gestation (first and second trimester). Pregnancies were monitored to term, and placentas were classified as normal (healthy) or abnormal. Complete data was available for 37 mares, and 13 were included in this study: normal (n = 6), abnormal (n = 5), and ascending placentitis (n = 2). Total RNA underwent deep dual RNA-sequencing. Alpha- and beta-diversity metrics, differential expression, and microbe-host correlation analyses were performed. Results: Global vaginal microbial diversity did not differ between mares with healthy or abnormal placentas. Several microbes from the phyla Actinomycetota and Pseudomonadota showed altered activity in mares with abnormal placentas. Vaginal transcriptome showed a subtle inflammatory response in the second trimester in the abnormal placenta group in the absence of clinical signs. Correlation analysis suggested an interaction between bacterial survival and virulence genes and host inflammation and apoptosis genes. Conclusions: Samples were obtained from clinical cases, limiting the availability of a complete history. Conclusions: Although overall vaginal microbial diversity was similar between outcome groups, distinct host vaginal transcriptional and microbial activity signatures distinguished mares with an abnormal placenta at birth. These findings warrant targeted investigation of the vaginal immune response and bacterial virulence factors as early biomarkers and therapeutic targets.
© 2026 The Author(s). Equine Veterinary Journal published by John Wiley & Sons Ltd on behalf of EVJ Ltd.
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Publication Date: 2026-05-10 PubMed ID: 42108693DOI: 10.1002/evj.70185Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study investigated the relationship between vaginal microbes, host gene expression, and placental health in pregnant mares.
  • Researchers aimed to identify microbial and genetic signatures in the vagina that correlate with abnormal placentas or placentitis, a major cause of pregnancy loss.

Background and Objectives

  • Ascending placentitis causes late-term pregnancy loss by infection ascending from the reproductive tract.
  • There is limited understanding of how the vaginal microbiome and host gene expression relate to placental health in horses.
  • The objective was to characterize the vaginal microbiome and host gene expression during gestation and identify associations with abnormal placental outcomes.

Methods

  • Study design: Prospective, paired observational study involving multiparous Thoroughbred mares.
  • Sample collection: Vaginal wall swabs taken at two gestational timepoints – days 42-46 (first trimester) and days 118-133 (second trimester).
  • Data from 37 mares was available but 13 were selected for detailed RNA sequencing analysis based on placental outcome:
    • Normal placenta group (6 mares)
    • Abnormal placenta group (5 mares)
    • Ascending placentitis group (2 mares)
  • Techniques used:
    • Deep dual RNA-sequencing of total RNA to profile both microbial and host gene expression.
    • Statistical analyses including alpha- and beta-diversity of microbes, differential gene expression, and correlation analyses between microbial activity and host responses.

Key Findings

  • Microbial diversity:
    • No significant difference in overall vaginal microbial diversity between mares with healthy versus abnormal placentas.
  • Microbial activity:
    • Specific microbes from phyla Actinomycetota and Pseudomonadota showed altered gene activity associated with abnormal placentas.
  • Host vaginal transcriptome:
    • Subtle inflammatory gene expression changes observed in the second trimester in mares that later had abnormal placentas, even without clinical symptoms.
  • Correlation between host and microbes:
    • Interactions noted between bacterial genes related to survival and virulence and host genes involved in inflammation and apoptosis.

Conclusions and Implications

  • While the overall vaginal microbiome diversity was similar, distinct microbial and host gene expression signatures distinguish mares with abnormal placentas.
  • The findings suggest that early changes in vaginal bacteria and host immune responses are linked to placental health.
  • This highlights potential early biomarkers for placentitis and targets for therapeutic intervention to prevent pregnancy loss.
  • Limitations include the small sample size and limited clinical history from natural cases, underlining the need for further targeted research.

Cite This Article

APA
van Heule M, Heil B, Norris JK, Gedye K, Lin X, De Spiegelaere W, Daels P, Dini P. (2026). Vaginal host-microbe signatures linked to placental outcomes in mares. Equine Vet J. https://doi.org/10.1002/evj.70185

Publication

Equine veterinary journal
ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English

Researcher Affiliations

van Heule, Machteld
  • Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, USA.
  • Department of Veterinary Medical Imaging and Small Animal Orthopaedics, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium.
Heil, Babiche
  • Matamata Veterinary Services Equine, Matamata, New Zealand.
  • Veterinary Medical Teaching Hospital, School of Veterinary Medicine, University of California-Davis, Davis, California, USA.
Norris, Jamie K
  • Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, USA.
Gedye, Kristene
  • School of Veterinary Science-Tāwharau Ora, Massey University, Palmerston North, New Zealand.
Lin, Xiaoxiao
  • Massey Genome Service, Massey University, Palmerston North, New Zealand.
De Spiegelaere, Ward
  • Department of Veterinary Medical Imaging and Small Animal Orthopaedics, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium.
Daels, Peter
  • Department of Veterinary Medical Imaging and Small Animal Orthopaedics, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium.
Dini, Pouya
  • Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, USA.

Grant Funding

  • Special Research Fund at the University of Ghent (BOF)
  • New Zealand Equine Research Foundation
  • John P. Hughes Endowment

References

This article includes 111 references
  1. Barr BS. The outcome of foals born to mares treated for placentitis.. Havemeyer Foundation Monograph Series No. 19 Proceedings of a workshop on uterine infection in mares and women: a comparative study II; 2005. p. 49–50.
  2. LeBlanc MM. Ascending placentitis in the mare: an update.. Reprod Domest Anim 2010;45:28–34.
    doi: 10.1111/j.1439-0531.2010.01633.xgoogle scholar: lookup
  3. Canisso IF, Loux SC, Lima FS. Biomarkers for placental disease in mares.. Theriogenology 2020;150:302–307.
    doi: 10.1016/j.theriogenology.2020.01.073google scholar: lookup
  4. Troedsson MHT, Miller LMJ. Equine placentitis.. Pferdeheilkd Equine Med 2016;32:49–53.
    doi: 10.21836/pem20160109google scholar: lookup
  5. Canisso IF, Ball BA, Erol E, Squires EL, Troedsson MHT. Comprehensive review on equine placentitis.. Proc Am Assoc Equine Practnrs 2015;61:490–509.
  6. Roach JM, Foote AK, Smith KC, Verheyen KL, de Mestre AM. Incidence and causes of pregnancy loss after day 70 of gestation in thoroughbreds.. Equine Vet J 2021;53:996–1003.
    doi: 10.1111/evj.13386google scholar: lookup
  7. Carrick JB, Perkins NR, Zalucki MP. Causes of abortion in Australia (2005‐2012)‐proportion of cases due to equine amnionitis and foetal loss (EAFL).. J Equine Vet Sci 2014;34:212–214.
  8. Laugier C, Foucher N, Sevin C, Leon A, Tapprest J. A 24‐year retrospective study of equine abortion in normandy (France).. J Equine Vet Sci 2011;31:116–123.
    doi: 10.1016/j.jevs.2010.12.012google scholar: lookup
  9. Giles RC, Donahue JM, Hong CB, Tuttle PA, Petrites‐Murphy MB, Poonacha KB. Causes of abortion, stillbirth, and perinatal death in horses: 3,527 cases (1986–1991).. J Am Vet Med Assoc 1993;203:1170–1175.
    doi: 10.2460/javma.1993.203.08.1170google scholar: lookup
  10. Smith KC, Blunden AS, Whitwell KE, Dunn KA, Wales AD. A survey of equine abortion, stillbirth and neonatal death in the UK from 1988 to 1997.. Equine Vet J 2003;35:496–501.
    doi: 10.2746/042516403775600578google scholar: lookup
  11. Donahue JM, Williams NM. Emergent causes of placentitis and abortion.. Vet Clin North Am Equine Pract 2000;16:443–456.
    doi: 10.1016/s0749-0739(17)30088-3google scholar: lookup
  12. Cummins C, Carrington S, Fitzpatrick E, Duggan V. Ascending placentitis in the mare: a review.. Ir Vet J 2008;61:307–313.
    doi: 10.1186/2046-0481-61-5-307google scholar: lookup
  13. Macpherson ML, Bailey CS. Treating the mare with placentitis: a clinical approach.. J Equine Vet Sci 2008;28:703–708.
    doi: 10.1016/j.jevs.2008.10.008google scholar: lookup
  14. Bailey CS, Macpherson ML, Pozor MA, Troedsson MHT, Benson S, Giguere S. Treatment efficacy of trimethoprim sulfamethoxazole, pentoxifylline and altrenogest in experimentally induced equine placentitis.. Theriogenology 2010;74:402–412.
    doi: 10.1016/j.theriogenology.2010.02.023google scholar: lookup
  15. Govaere J, Roels K, Ververs C, VandeVelde M, De Lange V, Gerits I. Ascending placentitis in the mare.. Vlaams Diergeneeskd Tijdschr 2018;87:115–126.
    doi: 10.21825/vdt.v87i3.16074google scholar: lookup
  16. Ball B, Loux SC, Dini P, Ali HE. Genomics, transcriptomics, and proteomics of normal and abnormal equine placenta: better understanding of late pregnancy function and dysfunction.. Clin Theriogenol 2020;12:333–339.
  17. Fedorka CE, Ball BA, Walker OF, Mccormick ME, Scoggin KE, Kennedy LA. Alterations of circulating biomarkers during late term pregnancy complications in the horse part I: cytokines.. J Equine Vet Sci 2021;99:103425.
    doi: 10.1016/j.jevs.2021.103425google scholar: lookup
  18. Fedorka CE, Ball BA, Wynn MAA, Mccormick ME, Scoggin KE, Esteller‐Vico A. Alterations of circulating biomarkers during late term pregnancy complications in the horse part II: steroid hormones and alpha‐fetoprotein.. J Equine Vet Sci 2021;99:103395.
    doi: 10.1016/j.jevs.2021.103395google scholar: lookup
  19. Dunlop AL, Mulle JG, Ferranti EP, Edwards S, Dunn AB, Corwin EJ. The maternal microbiome and pregnancy outcomes that impact infant health: a review.. Adv Neonatal Care 2015;15:377.
    doi: 10.1097/anc.0000000000000218google scholar: lookup
  20. Deka N, Hassan S, Seghal Kiran G, Selvin J. Insights into the role of vaginal microbiome in women's health.. J Basic Microbiol 2021;61:1–14.
    doi: 10.1002/jobm.202100421google scholar: lookup
  21. Diaz‐martínez MDC, Bernabeu A, Lledó B, Carratalá‐Munuera C, Quesada JA, Lozano FM. Impact of the vaginal and endometrial microbiome pattern on assisted reproduction outcomes.. J Clin Med 2021;10:4063.
    doi: 10.3390/jcm10184063google scholar: lookup
  22. Wee BA, Thomas M, Sweeney EL, Frentiu FD, Samios M, Ravel J. A retrospective pilot study to determine whether the reproductive tract microbiota differs between women with a history of infertility and fertile women.. Aust N Z J Obstet Gynaecol 2018;58:341–348.
    doi: 10.1111/ajo.12754google scholar: lookup
  23. Fettweis JM, Serrano MG, Brooks JP, Edwards DJ, Girerd PH, Parikh HI. The vaginal microbiome and preterm birth.. Nat Med 2019;25:1012–1021.
    doi: 10.1038/s41591-019-0450-2google scholar: lookup
  24. Aagaard K, Riehle K, Ma J, Segata N, Mistretta T‐A. A metagenomic approach to characterization of the vaginal microbiome signature in pregnancy.. PLoS One 2012;7:36466.
    doi: 10.1371/journal.pone.0036466google scholar: lookup
  25. Prince AL, Ma J, Kannan PS, Alvarez M, Gisslen T, Harris RA. The placental membrane microbiome is altered among subjects with spontaneous preterm birth with and without chorioamnionitis.. Am J Obstet Gynecol 2016;214:627.e1–627.e16.
    doi: 10.1016/j.ajog.2016.01.193google scholar: lookup
  26. Urushiyama D, Ohnishi E, Suda W, Kurakazu M, Kiyoshima C, Hirakawa T. Vaginal microbiome as a tool for prediction of chorioamnionitis in preterm labor: a pilot study.. Sci Rep 2021;11:1–10.
    doi: 10.1038/s41598-021-98587-4google scholar: lookup
  27. Loux SC, Ball BA. The proteome of fetal fluids in mares with experimentally‐induced placentitis.. Placenta 2018;64:71–78.
    doi: 10.1016/j.placenta.2018.03.004google scholar: lookup
  28. Lyle SK. Immunology of infective preterm delivery in the mare.. Equine Vet J 2014;46:661–668.
    doi: 10.1111/evj.12243google scholar: lookup
  29. Fedorka CE, Troedsson MHT. The immune response to equine ascending placentitis: a narrative review.. Theriogenology 2023;203:11–20.
    doi: 10.1016/j.theriogenology.2023.03.002google scholar: lookup
  30. Fedorka CE, Ball BA, Scoggin KE, Loux SC, Troedsson MHT, Adams AA. The feto‐maternal immune response to equine placentitis.. Am J Reprod Immunol 2019;82(5):e13179.
    doi: 10.1111/aji.13179google scholar: lookup
  31. Fedorka CE, El‐Sheikh Ali H, Scoggin KE, Loux SC, Ball BA, Troedsson MHT. Tumor necrosis factor signaling during equine placental infection leads to pro‐apoptotic and necroptotic outcomes. J Reprod Immunol 2022;152:103655.
    doi: 10.1016/j.jri.2022.103655google scholar: lookup
  32. Conley AJ. Review of the reproductive endocrinology of the pregnant and parturient mare. Theriogenology 2016;86:355–365.
    doi: 10.1016/j.theriogenology.2016.04.049google scholar: lookup
  33. Punzón‐Jiménez P, Labarta E. The impact of the female genital tract microbiome in women health and reproduction: a review. J Assist Reprod Genet 2021;38:2519–2541.
    doi: 10.1007/s10815-021-02247-5google scholar: lookup
  34. Lehtoranta L, Ala‐Jaakkola R, Laitila A, Maukonen J. Healthy vaginal microbiota and influence of probiotics across the female life span. Front Microbiol 2022;13:819958.
    doi: 10.3389/fmicb.2022.819958google scholar: lookup
  35. Ault TB, Clemmons BA, Reese ST, Dantas FG, Franco GA, Smith TPL. Bacterial taxonomic composition of the postpartum cow uterus and vagina prior to artificial insemination. J Anim Sci 2019;97:4305–4313.
    doi: 10.1093/jas/skz212google scholar: lookup
  36. Heil BA, Paccamonti DL, Sones JL. Role for the mammalian female reproductive tract microbiome in pregnancy outcomes. Physiol Genomics 2019;51:390–399.
    doi: 10.1152/physiolgenomics.00045.2019google scholar: lookup
  37. Barba M, Martínez‐Boví R, Quereda JJ, Mocé ML, Plaza‐Dávila M, Jiménez‐Trigos E. Vaginal microbiota is stable throughout the estrous cycle in Arabian mares. Animals 2020;10(11):2020.
    doi: 10.3390/ani10112020google scholar: lookup
  38. Malaluang P, Åkerholm T, Nyman G, Lindahl J, Hansson I, Morrell JM. Bacteria in the healthy equine vagina during the estrous cycle. Theriogenology 2024;213:11–18.
    doi: 10.1016/j.theriogenology.2023.09.011google scholar: lookup
  39. Petersen MR, Skive B, Christoffersen M, Lu K, Nielsen JM, Troedsson MTH. Activation of persistent Streptococcus equi subspecies zooepidemicus in mares with subclinical endometritis. Vet Microbiol 2015;179:119–125.
    doi: 10.1016/j.vetmic.2015.06.006google scholar: lookup
  40. van Heule M, Verstraete M, Norris JK, Graniczkowsa KB, Scoggin KE, Ali HES. Beyond nocardioform: transcriptionally active microbes and host responses in equine mucoid placentitis. Equine Vet J 2025;58(2):601–618.
    doi: 10.1111/evj.70112google scholar: lookup
  41. Pozor M. Equine placenta—a clinician's perspective. Part 1: normal placenta‐physiology and evaluation. Equine Vet Educ 2016;28:327–334.
    doi: 10.1111/eve.12499google scholar: lookup
  42. Quereda JJ, Barba M, Mocé ML, Gomis J, Jiménez‐Trigos E, García‐Muñoz Á. Vaginal microbiota changes during estrous cycle in dairy heifers. Front Vet Sci 2020;7:552090.
    doi: 10.3389/fvets.2020.00371google scholar: lookup
  43. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–2120.
    doi: 10.1093/bioinformatics/btu170google scholar: lookup
  44. Dobin A, Gingeras TR, Spring C, Flores R, Sampson J, Knight R. Mapping RNA‐seq with STAR. Curr Protoc Bioinformatics 2016;51:11.14.1–11.14.19.
    doi: 10.1002/0471250953.bi1114s51google scholar: lookup
  45. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019;20:1–13.
    doi: 10.1186/s13059-019-1891-0google scholar: lookup
  46. Pierce NT, Reiter T, Brooks P, Brown TC. Large‐scale sequence comparisons with sourmash. F1000Res 2019;8:1006.
    doi: 10.1101/687285google scholar: lookup
  47. Bokulich NA, Robeson MS. Bioinformatics challenges for profiling the microbiome in cancer: pitfalls and opportunities. Trends Microbiol 2024;32:1163–1166.
    doi: 10.1016/j.tim.2024.08.011google scholar: lookup
  48. McKnight DT, Huerlimann R, Bower DS, Schwarzkopf L, Alford RA, Zenger KR. microDecon: a highly accurate read‐subtraction tool for the post‐sequencing removal of contamination in metabarcoding studies. Environ DNA 2019;1:14–25.
    doi: 10.1002/edn3.11google scholar: lookup
  49. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA‐seq data with DESeq2. Genome Biol 2014;15:1–21.
    doi: 10.1186/s13059-014-0550-8google scholar: lookup
  50. Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin PR. vegan: Community Ecology Package. 2022.
  51. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 2013;8:e61217.
    doi: 10.1371/journal.pone.0061217google scholar: lookup
  52. Liu Y-X, Wen T, Chen T. amplicon: Statistics and visualization for microbiome data. 2023.
  53. Lahti L, Shetty S. Tools for microbiome analysis in R. Microbiome package version 1.23.1. Bioconductor 2017.
  54. Cao Y, Dong Q, Wang D, Zhang P, Liu Y, Niu C. microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization. Bioinformatics 2022;38:4027–4029.
    doi: 10.1093/bioinformatics/btac438google scholar: lookup
  55. Beghini F, McIver LJ, Blanco-Míguez A, Dubois L, Asnicar F, Maharjan S. Integrating taxonomic, functional, and strain‐level profiling of diverse microbial communities with bioBakery 3. Elife 2021;10:e65088.
    doi: 10.7554/elife.65088google scholar: lookup
  56. Mallick H, Rahnavard A, McIver LJ, Ma S, Zhang Y, Nguyen LH. Multivariable association discovery in population‐scale meta‐omics studies. PLoS Comput Biol 2021;17:e1009442.
    doi: 10.1371/journal.pcbi.1009442google scholar: lookup
  57. Kalbfleisch TS, Rice ES, DePriest MS, Walenz BP, Hestand MS, Vermeesch JR. Improved reference genome for the domestic horse increases assembly contiguity and composition. Commun Biol 2018;1:197–204.
    doi: 10.1038/s42003-018-0199-zgoogle scholar: lookup
  58. Janečka JE, Davis BW, Ghosh S, Paria N, Das PJ, Orlando L. Horse Y chromosome assembly displays unique evolutionary features and putative stallion fertility genes. Nat Commun 2018;9:1–15.
    doi: 10.1038/s41467-018-05290-6google scholar: lookup
  59. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014;30:923–930.
    doi: 10.1093/bioinformatics/btt656google scholar: lookup
  60. . Picard toolkit. 2019.
  61. Rohart F, Gautier B, Singh A, Lê Cao KA. mixOmics: an R package for ‘omics’ feature selection and multiple data integration.. PLoS Comput Biol 2017;13:e1005752.
    doi: 10.1371/journal.pcbi.1005752google scholar: lookup
  62. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis.. BMC Bioinformatics 2008;9:1–13.
    doi: 10.1186/1471-2105-9-559google scholar: lookup
  63. Hagey JV, Bhatnagar S, Heguy JM, Karle BM, Price PL, Meyer D. Fecal microbial communities in a large representative cohort of California dairy cows.. Front Microbiol 2019;10:1093.
    doi: 10.3389/fmicb.2019.01093google scholar: lookup
  64. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS. Metagenomic biomarker discovery and explanation.. Genome Biol 2011;12:1–18.
    doi: 10.1186/gb-2011-12-6-r60google scholar: lookup
  65. Feng Z, Cáceres NE, Sarath G, Barletta RG. Mycobacterium smegmatis L‐alanine dehydrogenase (Ald) is required for proficient utilization of alanine as a sole nitrogen source and sustained anaerobic growth.. J Bacteriol 2002;184:5001–5010.
    doi: 10.1128/jb.184.18.5001-5010.2002google scholar: lookup
  66. Hillerich B, Westpheling J. A new TetR family transcriptional regulator required for morphogenesis in Streptomyces coelicolor.. J Bacteriol 2008;190:61–67.
    doi: 10.1128/jb.01316-07google scholar: lookup
  67. Sutcliffe JG. Nucleotide sequence of the ampicillin resistance gene of Escherichia coli plasmid pBR322.. Proc Natl Acad Sci 1978;75:3737–3741.
    doi: 10.1073/pnas.75.8.3737google scholar: lookup
  68. Bradford PA. Extended‐spectrum β‐lactamases in the 21st century: characterization, epidemiology, and detection of this important resistance threat.. Clin Microbiol Rev 2001;14:933–951.
    doi: 10.1128/cmr.14.4.933-951.2001google scholar: lookup
  69. Motiejunaite R, Armalyte J, Markuckas A, Sužiedeliene E. Escherichia coli dinJ‐yafQ genes act as a toxin–antitoxin module.. FEMS Microbiol Lett 2007;268:112–119.
    doi: 10.1111/j.1574-6968.2006.00563.xgoogle scholar: lookup
  70. Elhadad D, Desai P, Rahav G, McClelland M, Gal-Mor O. Flagellin is required for host cell invasion and normal Salmonella pathogenicity Island 1 expression by Salmonella enterica serovar Paratyphi A.. Infect Immun 2015;83:3355–3368.
    doi: 10.1128/iai.00468-15google scholar: lookup
  71. Dey A, Verma AK, Chatterji D. Role of an RNA polymerase interacting protein, MsRbpA, from Mycobacterium smegmatis in phenotypic tolerance to rifampicin.. Microbiology (N Y) 2010;156:873–883.
    doi: 10.1099/mic.0.033670-0google scholar: lookup
  72. Deng J, Leijten E, Zhu Y, Nordkamp MO, Ye S, Pouw J. Multi‐omics approach identifies PI3 as a biomarker for disease severity and hyper‐keratinization in psoriasis.. J Dermatol Sci 2023;111:101–108.
    doi: 10.1016/j.jdermsci.2023.07.005google scholar: lookup
  73. Tejera P, O'Mahony DS, Owen CA, Wei Y, Wang Z, Gupta K. Functional characterization of polymorphisms in the peptidase inhibitor 3 (Elafin) gene and validation of their contribution to risk of acute respiratory distress syndrome.. Am J Respir Cell Mol Biol 2014;51:262–272.
    doi: 10.1165/rcmb.2013-0238ocgoogle scholar: lookup
  74. Chowdhury MA, Kuivaniemi H, Romero R, Edwin S, Chaiworapongsa T, Tromp G. Identification of novel functional sequence variants in the gene for peptidase inhibitor 3.. BMC Med Genet 2006;7:49.
    doi: 10.1186/1471-2350-7-49google scholar: lookup
  75. Puthia M, Ambite I, Cafaro C, Butler D, Huang Y, Lutay N. IRF7 inhibition prevents destructive innate immunity—a target for nonantibiotic therapy of bacterial infections.. Sci Transl Med 2016;8(336):336ra59.
    doi: 10.1126/scitranslmed.aaf1156google scholar: lookup
  76. Ning S, Pagano JS, Barber GN. IRF7: activation, regulation, modification and function.. Genes Immunity 2011;12:399–414.
    doi: 10.1038/gene.2011.21google scholar: lookup
  77. Chen G, Li K, Luo H, Zhao L, Jiang Y. IRF7 drives macrophages to kill bacteria and improves septic outcomes via autophagy.. JCI Insight 2025;10:e189420.
    doi: 10.1172/jci.insight.189420google scholar: lookup
  78. Xia R, Sun M, Yin J, Zhang X, Li J. Using Mendelian randomization provides genetic insights into potential targets for sepsis treatment.. Sci Rep 2024;14:8467.
    doi: 10.1038/s41598-024-58457-1google scholar: lookup
  79. Zhao C‐S, Liu S‐H, Li Z‐Y, Chen J‐Y, Xiong X‐Y. GIGYF2: a multifunctional regulator at the crossroads of gene expression, mRNA surveillance, and human disease.. Cells 2025;14:14.
    doi: 10.3390/cells14131032google scholar: lookup
  80. Zhang Z, Tang Z, Ma X, Sun K, Fan L, Fang J. TAOK1 negatively regulates IL‐17‐mediated signaling and inflammation.. Cell Mol Immunol 2018;15:794–802.
    doi: 10.1038/cmi.2017.158google scholar: lookup
  81. Ning L, Li X, Xu Y, Si Y, Zhao H, Ren Q. Multi‐omics analysis revealed that TAOK1 can be used as a prognostic marker and target in a variety of tumors, especially in cervical cancer.. Onco Targets Ther 2025;18:335–353.
    doi: 10.2147/ott.s506582google scholar: lookup
  82. Sarwar S, Ashraf S, Shafiq M, Malik A, Akhtar S, Arshad R. SEC24D gene as a biomarker in human cancers and its association with CD8+ T cell immune cell infiltration.. Am J Transl Res 2023;15:3115–3130.
  83. Hu Y, Tao SY, Deng JM, Hou ZK, Liang JQ, Huang QG. Prognostic value of NRAS gene for survival of colorectal cancer patients: a systematic review and meta‐analysis.. Asian Pac J Cancer Prev 2018;19:3001–3008.
    doi: 10.31557/apjcp.2018.19.11.3001google scholar: lookup
  84. Kwok SCM, Kumar S, Dai G. Characterization of a variant of ERGIC2 transcript.. DNA Cell Biol 2014;33:73–78.
    doi: 10.1089/dna.2013.2225google scholar: lookup
  85. Santos‐Beneit F, Barriuso‐Iglesias M, Fernández‐Martínez LT, Martínez‐Castro M, Sola‐Landa A, Rodríguez‐García A. The RNA polymerase omega factor RpoZ is regulated by PhoP and has an important role in antibiotic biosynthesis and morphological differentiation in Streptomyces coelicolor.. Appl Environ Microbiol 2011;77:7586–7594.
    doi: 10.1128/aem.00465-11google scholar: lookup
  86. Kojima I, Kasuga K, Kobayashi M, Fukasawa A, Mizuno S, Arisawa A. The rpoZ gene, encoding the RNA polymerase omega subunit, is required for antibiotic production and morphological differentiation in Streptomyces kasugaensis.. J Bacteriol 2002;184:6417–6423.
    doi: 10.1128/jb.184.23.6417-6423.2002google scholar: lookup
  87. Wei Y, Dong B, Wu X, Zhao M, Wang D, Li N. RpoZ regulates 2,4‐DAPG production and quorum sensing system in Pseudomonas fluorescens 2P24.. Front Microbiol 2023;14:1160913.
    doi: 10.3389/fmicb.2023.1160913google scholar: lookup
  88. Chatterji D, Ogawa Y, Shimada T, Ishihama A. The role of the omega subunit of RNA polymerase in expression of the relA gene in Escherichia coli.. FEMS Microbiol Lett 2007;267:51–55.
    doi: 10.1111/j.1574-6968.2006.00532.xgoogle scholar: lookup
  89. Goodenough‐Lashua DM, Garcia GA. tRNA‐guanine transglycosylase from E. coli: a ping‐pong kinetic mechanism is consistent with nucleophilic catalysis.. Bioorg Chem 2003;31:331–344.
    doi: 10.1016/s0045-2068(03)00069-5google scholar: lookup
  90. Helwak A, Turowski T, Spanos C, Tollervey D. Roles of SNORD115 and SNORD116 ncRNA clusters during neuronal differentiation.. Nat Commun 2024;15:10427.
    doi: 10.1038/s41467-024-54573-8google scholar: lookup
  91. Whalan RH, Funnell SGP, Bowler LD, Hudson MJ, Robinson A, Dowson CG. Distribution and genetic diversity of the ABC transporter lipoproteins PiuA and PiaA within Streptococcus pneumoniae and related Streptococci.. J Bacteriol 2006;188:1031–1038.
    doi: 10.1128/jb.188.3.1031-1038.2006google scholar: lookup
  92. Jomaa M, Yuste J, Paton JC, Jones C, Dougan G, Brown JS. Antibodies to the iron uptake ABC transporter lipoproteins PiaA and PiuA promote opsonophagocytosis of Streptococcus pneumoniae.. Infect Immun 2005;73:6852–6859.
    doi: 10.1128/iai.73.10.6852-6859.2005google scholar: lookup
  93. Mattei V, Murugesan S, Al Hashmi M, Mathew R, James N, Singh P. Evaluation of methods for the extraction of microbial DNA from vaginal swabs used for microbiome studies.. Front Cell Infect Microbiol 2019;9:450584.
    doi: 10.3389/fcimb.2019.00197/bibtexgoogle scholar: lookup
  94. Forney LJ, Gajer P, Williams CJ, Schneider GM, Koenig SSK, McCulle SL. Comparison of self‐collected and physician‐collected vaginal swabs for microbiome analysis.. J Clin Microbiol 2010;48:1741–1748.
    doi: 10.1128/jcm.01710-09/format/epubgoogle scholar: lookup
  95. Rodríguez‐Lázaro D, Malaluang P, Wilén E, Frosth S, Lindahl J, Hansson I. Vaginal bacteria in mares and the occurrence of antimicrobial resistance.. Microorganisms 2022;10:2204.
    doi: 10.3390/microorganisms10112204google scholar: lookup
  96. Gil‐Miranda A, Macnicol J, Orellana‐Guerrero D, Samper JC, Gomez DE. Reproductive tract microbiota of mares.. Vet Sci 2024;11:324.
    doi: 10.3390/vetsci11070324google scholar: lookup
  97. Płoneczka‐Janeczko K, Magdziarz M, Siemieniuch‐Tartanus M. The vaginal microbiome of mares on the post‐foaling day under field conditions.. Animals (Basel) 2024;14:3337.
    doi: 10.3390/ani14223337google scholar: lookup
  98. Beckers KF, Gomes VCL, Crissman KR, Liu C‐C, Schulz CJ, Childers GW. Metagenetic analysis of the pregnant microbiome in horses.. Animals 2023;13:1999.
    doi: 10.3390/ani13121999google scholar: lookup
  99. Holyoak GR, Premathilake HU, Lyman CC, Sones JL, Gunn A, Wieneke X. The healthy equine uterus harbors a distinct core microbiome plus a rich and diverse microbiome that varies with geographical location.. Sci Rep 2022;12:1–14.
    doi: 10.1038/s41598-022-18971-6google scholar: lookup
  100. Kolenbrander PE, Williams BL. Lactose‐reversible coaggregation between oral actinomycetes and Streptococcus sanguis.. Infect Immun 1981;33:95–102.
    doi: 10.1128/iai.33.1.95-102.1981google scholar: lookup
  101. Dutra De Oliveira RV, Salloume F, Bonafé S, Madalena D, Spolidorio P, Yumi Koga‐Ito C. Streptococcus mutans and Actinomyces naeslundii interaction in dual‐species biofilm.. Microorganisms 2020;8(2):194.
    doi: 10.3390/microorganisms8020194google scholar: lookup
  102. Cisar JO, Kolenbrander PE, McIntire FC. Specificity of coaggregation reactions between human oral streptococci and strains of Actinomyces viscosus or Actinomyces naeslundii.. Infect Immun 1979;24:742–752.
    doi: 10.1128/iai.24.3.742-752.1979google scholar: lookup
  103. Jakubovics NS, Gill SR, Vickerman MM, Kolenbrander PE. Role of hydrogen peroxide in competition and cooperation between Streptococcus gordonii and Actinomyces naeslundii.. FEMS Microbiol Ecol 2008;66:637–644.
    doi: 10.1111/j.1574-6941.2008.00585.xgoogle scholar: lookup
  104. Waksman SA, Schatz A, Reynolds DM. Production of antibiotic substances by actinomycetes.. Ann N Y Acad Sci 2010;1213:112–124.
    doi: 10.1111/j.1749-6632.2010.05861.xgoogle scholar: lookup
  105. Könönen E, Wade WG. Actinomyces and related organisms in human infections.. Clin Microbiol Rev 2015;28:419–442.
    doi: 10.1128/cmr.00100-14/asset/a396d7cc-fe56-4361-908b-81251736057f/assets/graphic/zcm0021525090003.jpeggoogle scholar: lookup
  106. Komiyama K, Gibbons RJ. Interbacterial adherence between Actinomyces viscosus and strains of Streptococcus pyogenes, Streptococcus agalactiae, and Pseudomonas aeruginosa.. Infect Immun 1984;44:86–90.
    doi: 10.1128/iai.44.1.86-90.1984google scholar: lookup
  107. Maulding ND, Seiler S, Pearson A, Kreusser N, Stuart J. Dual RNA‐seq analysis of SARS‐CoV‐2 correlates specific human transcriptional response pathways directly to viral expression.. BioRxiv 2021;12:2021.02.09.430517.
    doi: 10.1101/2021.02.09.430517google scholar: lookup
  108. El‐Sheikh Ali H, Boakari YL, Loux SC, DIni P, Scoggin KE, Esteller‐Vico A. Transcriptomic analysis reveals the key regulators and molecular mechanisms underlying myometrial activation during equine placentitis.. Biol Reprod 2020;102:1306–1325.
    doi: 10.1093/biolre/ioaa020google scholar: lookup
  109. El‐Sheikh Ali H, Dini P, Scoggin K, Loux S, Fedorka C, Boakari Y. Transcriptomic analysis of equine placenta reveals key regulators and pathways involved in ascending placentitis.. Biol Reprod 2021;104:638–656.
    doi: 10.1093/biolre/ioaa209google scholar: lookup
  110. El‐Sheikh Ali H, Loux SC, Kennedy L, Scoggin KE, Dini P, Fedorka CE. Transcriptomic analysis of equine chorioallantois reveals immune networks and molecular mechanisms involved in nocardioform placentitis.. Vet Res 2021;52:103.
    doi: 10.1186/s13567-021-00972-4google scholar: lookup
  111. Couto MA, Harwig SSL, Lehrer RI. Selective inhibition of microbial serine proteases by eNAP‐2, an antimicrobial peptide from equine neutrophils.. Infect Immun 1993;61:2991.
    doi: 10.1128/iai.61.7.2991-2994.1993google scholar: lookup

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