Semin Reprod Med 2023; 41(05): 125-143
DOI: 10.1055/s-0044-1779025
Review Article

Exploring the Microbiome in Human Reproductive Tract: High-Throughput Methods for the Taxonomic Characterization of Microorganisms

1   Department of Biochemistry and Molecular Biology, Faculty of Sciences, University of Granada, Granada, Spain
,
2   Bioinformatics Unit, Institute of Parasitology and Biomedicine “López-Neyra” (IPBLN), CSIC, Granada, Spain
,
2   Bioinformatics Unit, Institute of Parasitology and Biomedicine “López-Neyra” (IPBLN), CSIC, Granada, Spain
,
1   Department of Biochemistry and Molecular Biology, Faculty of Sciences, University of Granada, Granada, Spain
,
2   Bioinformatics Unit, Institute of Parasitology and Biomedicine “López-Neyra” (IPBLN), CSIC, Granada, Spain
› Institutsangaben
Funding This study was funded by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) and the European Regional Development Fund (FEDER) through MCIN/AEI/10.13039/501100011033 and ERFD's “A way of making Europe projects”. Endo-Map (PID2021-127280OB-I00) and ROSY (CNS2022-135999). L.C.T.-C. was also recipient of a postdoctoral fellowship from the Regional Government of Andalusian (POSTDOC_21_00394). J.L.R. was recipient of a technical support staff grant from the Ministry of Science and Innovation of the Spanish Government (PTA2021-019864-I). N.M.M. was also recipient of FPU19/01638 funded by MCIN/AEI/10.13039/501100011033 and by ESF Investing in your future. E.A.-L. was recipient of a postdoctoral fellowship from the Regional Government of Andalusian (POSTDOC_20_00541).

Abstract

Microorganisms are important due to their widespread presence and multifaceted roles across various domains of life, ecology, and industries. In humans, they underlie the proper functioning of multiple systems crucial to well-being, including immunological and metabolic functions. Emerging research addressing the presence and roles of microorganisms within human reproduction is increasingly relevant. Studies implementing new methodologies (e.g., to investigate vaginal, uterine, and semen microenvironments) can now provide relevant insights into fertility, reproductive health, or pregnancy outcomes. In that sense, cutting-edge sequencing techniques, as well as others such as meta-metabolomics, culturomics, and meta-proteomics, are becoming more popular and accessible worldwide, allowing the characterization of microbiomes at unprecedented resolution. However, they frequently involve rather complex laboratory protocols and bioinformatics analyses, for which researchers may lack the required expertise. A suitable pipeline would successfully enable both taxonomic classification and functional profiling of the microbiome, providing easy-to-understand biological interpretations. However, the selection of an appropriate methodology would be crucial, as it directly impacts the reproducibility, accuracy, and quality of the results and observations. This review focuses on the different current microbiome-related techniques in the context of human reproduction, encompassing niches like vagina, endometrium, and seminal fluid. The most standard and reliable methods are 16S rRNA gene sequencing, metagenomics, and meta-transcriptomics, together with complementary approaches including meta-proteomics, meta-metabolomics, and culturomics. Finally, we also offer case examples and general recommendations about the most appropriate methods and workflows and discuss strengths and shortcomings for each technique.

* Both authors contributed equally to this work.




Publikationsverlauf

Artikel online veröffentlicht:
06. Februar 2024

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  • References

  • 1 Integrative HMP (iHMP) Research Network Consortium. The Integrative Human Microbiome Project. Nature 2019; 569 (7758) 641-648
  • 2 Castellanos N, Diez GG, Antúnez-Almagro C. et al. A critical mutualism–competition interplay underlies the loss of microbial diversity in sedentary lifestyle. Front Microbiol 2020; 10: 3142
  • 3 Houttu V, Boulund U, Nicolaou M. et al. Physical activity and dietary composition relate to differences in gut microbial patterns in a multi-ethnic cohort - the HELIUS study. Metabolites 2021; 11 (12) 858
  • 4 Shivani S, Kao CY, Chattopadhyay A. et al. Uremic toxin-producing bacteroides species prevail in the gut microbiota of Taiwanese CKD patients: an analysis using the new Taiwan microbiome baseline. Front Cell Infect Microbiol 2022; 12: 726256
  • 5 Ursell LK, Metcalf JL, Parfrey LW, Knight R. Defining the human microbiome. Nutr Rev 2012; 70 (Suppl 1, Suppl 1): S38-S44
  • 6 Qin J, Li R, Raes J. et al; MetaHIT Consortium. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010; 464 (7285) 59-65
  • 7 Barton W, Penney NC, Cronin O. et al. The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level. Gut 2018; 67 (04) 625-633
  • 8 García-López R, Pérez-Brocal V, Moya A. Beyond cells - the virome in the human holobiont. Microb Cell 2019; 6 (09) 373-396
  • 9 Parizadeh M, Arrieta MC. The global human gut microbiome: genes, lifestyles, and diet. Trends Mol Med 2023; 29 (10) 789-801
  • 10 Sola-Leyva A, Andrés-León E, Molina NM. et al. Mapping the entire functionally active endometrial microbiota. Hum Reprod 2021; 36 (04) 1021-1031
  • 11 Chen X, Lu Y, Chen T, Li R. The female vaginal microbiome in health and bacterial vaginosis. Front Cell Infect Microbiol 2021; 11: 631972
  • 12 Ravel J, Moreno I, Simón C. Bacterial vaginosis and its association with infertility, endometritis, and pelvic inflammatory disease. Am J Obstet Gynecol 2021; 224 (03) 251-257
  • 13 Venneri MA, Franceschini E, Sciarra F, Rosato E, D'Ettorre G, Lenzi A. Human genital tracts microbiota: dysbiosis crucial for infertility. J Endocrinol Invest 2022; 45 (06) 1151-1160
  • 14 Reid G, Brigidi P, Burton JP. et al. Microbes central to human reproduction. Am J Reprod Immunol 2015; 73 (01) 1-11
  • 15 Haggerty CL, Totten PA, Tang G. et al. Identification of novel microbes associated with pelvic inflammatory disease and infertility. Sex Transm Infect 2016; 92 (06) 441-446
  • 16 Kroon SJ, Ravel J, Huston WM. Cervicovaginal microbiota, women's health, and reproductive outcomes. Fertil Steril 2018; 110 (03) 327-336
  • 17 Vitale SG, Ferrari F, Ciebiera M. et al. The role of genital tract microbiome in fertility: a systematic review. Int J Mol Sci 2021; 23 (01) 180
  • 18 Miller EA, Beasley DE, Dunn RR, Archie EA. Lactobacilli dominance and vaginal pH: Why is the human vaginal microbiome unique?. Front Microbiol 2016; 7: 1936
  • 19 Ravel J, Gajer P, Abdo Z. et al. Vaginal microbiome of reproductive-age women. Proc Natl Acad Sci U S A 2011; 108 (Suppl 1, Suppl 1): 4680-4687
  • 20 Altmäe S, Franasiak JM, Mändar R. The seminal microbiome in health and disease. Nat Rev Urol 2019; 16 (12) 703-721
  • 21 Tomaiuolo R, Veneruso I, Cariati F, D'Argenio V. Microbiota and human reproduction: the case of male infertility. High Throughput 2020; 9 (02) 10
  • 22 Weng SL, Chiu CM, Lin FM. et al. Bacterial communities in semen from men of infertile couples: metagenomic sequencing reveals relationships of seminal microbiota to semen quality. PLoS One 2014; 9 (10) e110152
  • 23 Schulz F, Eloe-Fadrosh EA, Bowers RM. et al. Towards a balanced view of the bacterial tree of life. Microbiome 2017; 5 (01) 140
  • 24 Boivin J, Bunting L, Collins JA, Nygren KG. International estimates of infertility prevalence and treatment-seeking: potential need and demand for infertility medical care. Hum Reprod 2007; 22 (06) 1506-1512
  • 25 Aron-Wisnewsky J, Clément K. The gut microbiome, diet, and links to cardiometabolic and chronic disorders. Nat Rev Nephrol 2016; 12 (03) 169-181
  • 26 Wilkins D, Tong X, Leung MHY, Mason CE, Lee PKH. Diurnal variation in the human skin microbiome affects accuracy of forensic microbiome matching. Microbiome 2021; 9 (01) 129
  • 27 Stamper CE, Hoisington AJ, Gomez OM. et al. The microbiome of the built environment and human behavior: implications for emotional health and well-being in postmodern western societies. Int Rev Neurobiol 2016; 131: 289-323
  • 28 Ramos C, Gibson GR, Walton GE, Magistro D, Kinnear W, Hunter K. Systematic review of the effects of exercise and physical activity on the gut microbiome of older adults. Nutrients 2022; 14 (03) 674
  • 29 Shanahan F, Ghosh TS, O'Toole PW. Human microbiome variance is underestimated. Curr Opin Microbiol 2023; 73: 102288
  • 30 Molina NM, Sola-Leyva A, Haahr T. et al. Analysing endometrial microbiome: methodological considerations and recommendations for good practice. Hum Reprod 2021; 36 (04) 859-879
  • 31 Molina NM, Sola-Leyva A, Saez-Lara MJ. et al. New opportunities for endometrial health by modifying uterine microbial composition: present or future?. Biomolecules 2020; 10 (04) 593
  • 32 Altmäe S, Rienzi L. Endometrial microbiome: new hope, or hype?. Reprod Biomed Online 2021; 42 (06) 1051-1052
  • 33 Lev-Sagie A, Goldman-Wohl D, Cohen Y. et al. Vaginal microbiome transplantation in women with intractable bacterial vaginosis. Nat Med 2019; 25 (10) 1500-1504
  • 34 López-Aladid R, Fernández-Barat L, Alcaraz-Serrano V. et al. Determining the most accurate 16S rRNA hypervariable region for taxonomic identification from respiratory samples. Sci Rep 2023; 13 (01) 3974
  • 35 Chakravorty S, Helb D, Burday M, Connell N, Alland D. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J Microbiol Methods 2007; 69 (02) 330-339
  • 36 Hu H, Kristensen JM, Herbold CW. et al. Global abundance patterns, diversity, and ecology of Patescibacteria in wastewater treatment plants. bioRxiv 2023 https://doi.org/10.1101/2023.10.25.562895
  • 37 Klindworth A, Pruesse E, Schweer T. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res 2013; 41 (01) e1
  • 38 Abellan-Schneyder I, Matchado MS, Reitmeier S. et al. Primer, pipelines, parameters: issues in 16S rRNA gene sequencing. MSphere 2021; 6 (01) 01202-01220
  • 39 Fasesan D, Dawkins K, Ramirez R. et al. Analysis of a tropical warm spring microbiota using 16S rRNA metabarcoding. Adv Microbiol 2020; 10 (04) 145-165
  • 40 Guo J, Starr D, Guo H. Classification and review of free PCR primer design software. Bioinformatics 2021; 36 (22-23): 5263-5268
  • 41 Bukin YS, Galachyants YP, Morozov IV, Bukin SV, Zakharenko AS, Zemskaya TI. The effect of 16S rRNA region choice on bacterial community metabarcoding results. Sci Data 2019; 6 (01) 190007
  • 42 de Muinck EJ, Trosvik P, Gilfillan GD, Hov JR, Sundaram AYM. A novel ultra high-throughput 16S rRNA gene amplicon sequencing library preparation method for the Illumina HiSeq platform. Microbiome 2017; 5 (01) 68
  • 43 Graspeuntner S, Loeper N, Künzel S, Baines JF, Rupp J. Selection of validated hypervariable regions is crucial in 16S-based microbiota studies of the female genital tract. Sci Rep 2018; 8 (01) 9678
  • 44 Shaffer JP, Nothias LF, Thompson LR. et al; Earth Microbiome Project 500 (EMP500) Consortium. Standardized multi-omics of Earth's microbiomes reveals microbial and metabolite diversity. Nat Microbiol 2022; 7 (12) 2128-2150
  • 45 Nayfach S, Pollard KS. Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol 2015; 16 (01) 51
  • 46 Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 2016; 13 (07) 581-583
  • 47 Bolyen E, Rideout JR, Dillon MR. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 2019; 37 (08) 852-857
  • 48 Schloss PD. Reintroducing mothur: 10 years later. Appl Environ Microbiol 2020; 86 (02) e02343-19
  • 49 Edgar RC. Accuracy of microbial community diversity estimated by closed- and open-reference OTUs. PeerJ 2017; 5: e3889
  • 50 Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20 (01) 257
  • 51 Cole JR, Chai B, Marsh TL. et al; Ribosomal Database Project. The Ribosomal Database Project (RDP-II): previewing a new autoaligner that allows regular updates and the new prokaryotic taxonomy. Nucleic Acids Res 2003; 31 (01) 442-443
  • 52 Quast C, Pruesse E, Yilmaz P. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 2013; 41 (Database issue): D590-D596
  • 53 DeSantis TZ, Hugenholtz P, Larsen N. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 2006; 72 (07) 5069-5072
  • 54 Sayers EW, Bolton EE, Brister JR. et al. Database resources of the national center for biotechnology information. Nucleic Acids Res 2022; 50 (D1): D20-D26
  • 55 Ma B, France MT, Crabtree J. et al. A comprehensive non-redundant gene catalog reveals extensive within-community intraspecies diversity in the human vagina. Nat Commun 2020; 11 (01) 940
  • 56 Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis 2015; 26: 27663
  • 57 Oksanen J, Kindt R, Legendre P. et al. The vegan package. Community Ecology Package 2007; 10 (631) 719
  • 58 Paulson JN, Talukder H, Corrada Bravo H. Longitudinal differential abundance analysis of microbial marker-gene surveys using smoothing splines. BioRxiv 2017 https://doi.org/10.1101/099457
  • 59 McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 2013; 8 (04) e61217
  • 60 Wen T, Niu G, Chen T, Shen Q, Yuan J, Liu YX. The best practice for microbiome analysis using R. Protein Cell 2023; 14 (10) 713-725
  • 61 Liu YX, Qin Y, Chen T. et al. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell 2021; 12 (05) 315-330
  • 62 Toole DR, Zhao J, Martens-Habbena W, Strauss SL. Bacterial functional prediction tools detect but underestimate metabolic diversity compared to shotgun metagenomics in southwest Florida soils. Appl Soil Ecol 2021; 168: 104129
  • 63 Willis AD. Rarefaction, alpha diversity, and statistics. Front Microbiol 2019; 10: 2407
  • 64 Lemos LN, Fulthorpe RR, Triplett EW, Roesch LF. Rethinking microbial diversity analysis in the high throughput sequencing era. J Microbiol Methods 2011; 86 (01) 42-51
  • 65 Magurran AE. Measuring biological diversity. Curr Biol 2021; 31 (19) R1174-R1177
  • 66 Faith DP. The role of the phylogenetic diversity measure, PD, in bio-informatics: getting the definition right. Evol Bioinform Online 2007; 2: 277-283
  • 67 Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J 2017; 11 (12) 2639-2643
  • 68 Lozupone CA, Hamady M, Kelley ST, Knight R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl Environ Microbiol 2007; 73 (05) 1576-1585
  • 69 Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 2005; 71 (12) 8228-8235
  • 70 Durazzi F, Sala C, Castellani G, Manfreda G, Remondini D, De Cesare A. Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Sci Rep 2021; 11 (01) 3030
  • 71 Altmäe S, Molina NM, Sola-Leyva A. Omission of non-poly(A) viral transcripts from the tissue level atlas of the healthy human virome. BMC Biol 2020; 18 (01) 179
  • 72 Segerman B. The most frequently used sequencing technologies and assembly methods in different time segments of the bacterial surveillance and RefSeq genome databases. Front Cell Infect Microbiol 2020; 10: 527102
  • 73 Logsdon GA, Vollger MR, Eichler EE. Long-read human genome sequencing and its applications. Nat Rev Genet 2020; 21 (10) 597-614
  • 74 Athanasopoulou K, Boti MA, Adamopoulos PG, Skourou PC, Scorilas A. Third-generation sequencing: the spearhead towards the radical transformation of modern genomics. Life (Basel) 2021; 12 (01) 30
  • 75 Meslier V, Quinquis B, Da Silva K. et al. Benchmarking second and third-generation sequencing platforms for microbial metagenomics. Sci Data 2022; 9 (01) 694
  • 76 Goodwin S, McPherson JD, McCombie WR. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 2016; 17 (06) 333-351
  • 77 Orellana LH, Krüger K, Sidhu C, Amann R. Comparing genomes recovered from time-series metagenomes using long- and short-read sequencing technologies. Microbiome 2023; 11 (01) 105
  • 78 Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol 2017; 35 (09) 833-844
  • 79 Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 2011; 17 (01) 10-12
  • 80 Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30 (15) 2114-2120
  • 81 Andrés-León E, Rojas AM. miARma-Seq, a comprehensive pipeline for the simultaneous study and integration of miRNA and mRNA expression data. Methods 2019; 152: 31-40
  • 82 Andrés-León E, Núñez-Torres R, Rojas AM. miARma-Seq: a comprehensive tool for miRNA, mRNA and circRNA analysis. Sci Rep 2016; 6: 25749
  • 83 Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9 (04) 357-359
  • 84 Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. 2013
  • 85 Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res 2017; 27 (05) 824-834
  • 86 Peng Y, Leung HC, Yiu SM, Chin FY. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 2012; 28 (11) 1420-1428
  • 87 Truong DT, Franzosa EA, Tickle TL. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods 2015; 12 (10) 902-903
  • 88 Blanco-Míguez A, Beghini F, Cumbo F. et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat Biotechnol 2023; 41 (11) 1633-1644
  • 89 Beghini F, McIver LJ, Blanco-Míguez A. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife 2021; 10: e65088
  • 90 Santiago-Rodriguez TM, Garoutte A, Adams E. et al. Metagenomic information recovery from human stool samples is influenced by sequencing depth and profiling method. Genes (Basel) 2020; 11 (11) 1380
  • 91 Aizpurua O, Dunn RR, Hansen LH, Gilbert MTP, Alberdi A. Field and laboratory guidelines for reliable bioinformatic and statistical analysis of bacterial shotgun metagenomic data. Crit Rev Biotechnol 2023; DOI: 10.1080/07388551.2023.2254933.
  • 92 Tremblay J, Schreiber L, Greer CW. High-resolution shotgun metagenomics: the more data, the better?. Brief Bioinform 2022; 23 (06) bbac443
  • 93 Ojala T, Häkkinen AE, Kankuri E, Kankainen M. Current concepts, advances, and challenges in deciphering the human microbiota with metatranscriptomics. Trends Genet 2023; 39 (09) 686-702
  • 94 Ojala T, Kankuri E, Kankainen M. Understanding human health through metatranscriptomics. Trends Mol Med 2023; 29 (05) 376-389
  • 95 Shakya M, Lo C-C, Chain PSG. Advances and challenges in metatranscriptomic analysis. Front Genet 2019; 10: 904
  • 96 Aguiar-Pulido V, Huang W, Suarez-Ulloa V, Cickovski T, Mathee K, Narasimhan G. Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis: supplementary issue: bioinformatics methods and applications for big metagenomics data. Evol Bioinform Online 2016; 12 (01) S36436
  • 97 Wikström T, Abrahamsson S, Bengtsson-Palme J. et al. Microbial and human transcriptome in vaginal fluid at midgestation: association with spontaneous preterm delivery. Clin Transl Med 2022; 12 (09) e1023
  • 98 Deng ZL, Gottschick C, Bhuju S, Masur C, Abels C, Wagner-Döbler I. Metatranscriptome analysis of the vaginal microbiota reveals potential mechanisms for protection against metronidazole in bacterial vaginosis. MSphere 2018; 3 (03) e00262-18
  • 99 Medina-Bastidas D, Camacho-Arroyo I, García-Gómez E. Current findings in endometrial microbiome: impact on uterine diseases. Reproduction 2022; 163 (05) R81-R96
  • 100 Subramanian A, Tamayo P, Mootha VK. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005; 102 (43) 15545-15550
  • 101 Caspi R, Billington R, Ferrer L. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 2016; 44 ( D1): D471-D480
  • 102 Tan A, Murugapiran S, Mikalauskas A. et al. Rational probe design for efficient rRNA depletion and improved metatranscriptomic analysis of human microbiomes. BMC Microbiol 2023; 23 (01) 299
  • 103 Gihawi A, Ge Y, Lu J. et al. Major data analysis errors invalidate cancer microbiome findings. bioRxiv 2023 https://doi.org/10.1128/mbio.01607-23
  • 104 Gihawi A, Cardenas R, Hurst R, Brewer DS. Quality control in metagenomics data. Methods Mol Biol 2023; 2649: 21-54
  • 105 Hempel CA, Wright N, Harvie J, Hleap JS, Adamowicz SJ, Steinke D. Metagenomics versus total RNA sequencing: most accurate data-processing tools, microbial identification accuracy and perspectives for ecological assessments. Nucleic Acids Res 2022; 50 (16) 9279-9293
  • 106 Kopylova E, Noé L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 2012; 28 (24) 3211-3217
  • 107 Dobin A, Davis CA, Schlesinger F. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013; 29 (01) 15-21
  • 108 Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res 2007; 17 (03) 377-386
  • 109 Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015; 31 (10) 1674-1676
  • 110 Zhang Y, Thompson KN, Huttenhower C, Franzosa EA. Statistical approaches for differential expression analysis in metatranscriptomics. Bioinformatics 2021; 37 (Suppl. 01) i34-i41
  • 111 Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010; 26 (01) 139-140
  • 112 Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15 (12) 550
  • 113 Nearing JT, Douglas GM, Hayes MG. et al. Microbiome differential abundance methods produce different results across 38 datasets. Nat Commun 2022; 13 (01) 342
  • 114 Knight R, Vrbanac A, Taylor BC. et al. Best practices for analysing microbiomes. Nat Rev Microbiol 2018; 16 (07) 410-422
  • 115 Terrón-Camero LC, Gordillo-González F, Salas-Espejo E, Andrés-León E. Comparison of metagenomics and metatranscriptomics tools: a guide to making the right choice. Genes (Basel) 2022; 13 (12) 2280
  • 116 Lu J, Rincon N, Wood DE. et al. Metagenome analysis using the Kraken software suite. Nat Protoc 2022; 17 (12) 2815-2839
  • 117 Jagtap P, Mehta S, Sajulga R. et al, Metatranscriptomics analysis using microbiome RNA-seq data. 2023 Available at: https://training.galaxyproject.org/training-material/topics/metagenomics/tutorials/metatranscriptomics/tutorial.html . Accessed January 19, 2024
  • 118 Arıkan M, Muth T. Integrated multi-omics analyses of microbial communities: a review of the current state and future directions. Mol Omics 2023; 19 (08) 607-623
  • 119 Stewart FJ, Ottesen EA, DeLong EF. Development and quantitative analyses of a universal rRNA-subtraction protocol for microbial metatranscriptomics. ISME J 2010; 4 (07) 896-907
  • 120 Monleon-Getino T, Frias-Lopez J. A priori estimation of sequencing effort in complex microbial metatranscriptomes. Ecol Evol 2020; 10 (23) 13382-13394
  • 121 Li C, Gu Y, He Q. et al. Integrated analysis of microbiome and transcriptome data reveals the interplay between commensal bacteria and fibrin degradation in endometrial cancer. Front Cell Infect Microbiol 2021; 11: 748558
  • 122 France MT, Fu L, Rutt L. et al. Insight into the ecology of vaginal bacteria through integrative analyses of metagenomic and metatranscriptomic data. Genome Biol 2022; 23 (01) 66
  • 123 Wilmes P, Bond PL. Metaproteomics: studying functional gene expression in microbial ecosystems. Trends Microbiol 2006; 14 (02) 92-97
  • 124 Zhang X, Figeys D. Perspective and guidelines for metaproteomics in microbiome studies. J Proteome Res 2019; 18 (06) 2370-2380
  • 125 Marcus K, Lelong C, Rabilloud T. What room for two-dimensional gel-based proteomics in a shotgun proteomics world?. Proteomes 2020; 8 (03) 17
  • 126 Kunath BJ, Minniti G, Skaugen M. et al. Metaproteomics: sample preparation and methodological considerations. Adv Exp Med Biol 2019; 1073: 187-215
  • 127 Lee EM, Srinivasan S, Purvine SO. et al. Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome. mSystems 2023; 8 (04) e0067822
  • 128 Issa Isaac N, Philippe D, Nicholas A, Raoult D, Eric C. Metaproteomics of the human gut microbiota: challenges and contributions to other OMICS. Clin Mass Spectrom 2019; 14 (Pt A): 18-30
  • 129 Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol 2016; 428 (04) 726-731
  • 130 Farr Zuend C, Tobin NH, Vera T. et al. Pregnancy associates with alterations to the host and microbial proteome in vaginal mucosa. Am J Reprod Immunol 2020; 83 (06) e13235
  • 131 Mesuere B, Van der Jeugt F, Willems T. et al. High-throughput metaproteomics data analysis with Unipept: a tutorial. J Proteomics 2018; 171: 11-22
  • 132 Muthubharathi BC, Gowripriya T, Balamurugan K. Metabolomics: small molecules that matter more. Mol Omics 2021; 17 (02) 210-229
  • 133 Bhosle A, Wang Y, Franzosa EA, Huttenhower C. Progress and opportunities in microbial community metabolomics. Curr Opin Microbiol 2022; 70: 102195
  • 134 Ye D, Li X, Shen J, Xia X. Microbial metabolomics: from novel technologies to diversified applications. Trends Analyt Chem 2022; 148: 116540
  • 135 Gonzalez-Covarrubias V, Martínez-Martínez E, Del Bosque-Plata L. The potential of metabolomics in biomedical applications. Metabolites 2022; 12 (02) 194
  • 136 Jimenez NR, Maarsingh JD, Łaniewski P, Herbst-Kralovetz MM. Commensal lactobacilli metabolically contribute to cervical epithelial homeostasis in a species-specific manner. MSphere 2023; 8 (01) e0045222
  • 137 Correia GD, Marchesi JR, MacIntyre DA. Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods. Curr Opin Microbiol 2023; 73: 102292
  • 138 Simintiras CA, Dhakal P, Ranjit C, Fitzgerald HC, Balboula AZ, Spencer TE. Capture and metabolomic analysis of the human endometrial epithelial organoid secretome. Proc Natl Acad Sci U S A 2021; 118 (15) e2026804118
  • 139 Oliver A, LaMere B, Weihe C. et al. Cervicovaginal microbiome composition is associated with metabolic profiles in healthy pregnancy. MBio 2020; 11 (04) e01851-20
  • 140 Severgnini M, Morselli S, Camboni T. et al. A deep look at the vaginal environment during pregnancy and puerperium. Front Cell Infect Microbiol 2022; 12: 838405
  • 141 Marangoni A, Laghi L, Zagonari S. et al. New insights into vaginal environment during pregnancy. Front Mol Biosci 2021; 8: 656844
  • 142 Kindschuh WF, Baldini F, Liu MC. et al. Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome. Nat Microbiol 2023; 8 (02) 246-259
  • 143 Horrocks V, Hind CK, Wand ME. et al. Nuclear magnetic resonance metabolomics of symbioses between bacterial vaginosis-associated bacteria. MSphere 2022; 7 (03) e0016622
  • 144 Ortiz CN, Torres-Reverón A, Appleyard CB. Metabolomics in endometriosis: challenges and perspectives for future studies. Reprod Fertil 2021; 2 (02) R35-R50
  • 145 Fu M, Zhang X, Liang Y, Lin S, Qian W, Fan S. Alterations in vaginal microbiota and associated metabolome in women with recurrent implantation failure. MBio 2020; 11 (03) 03242-19
  • 146 Bokulich NA, Łaniewski P, Adamov A, Chase DM, Caporaso JG, Herbst-Kralovetz MM. Multi-omics data integration reveals metabolome as the top predictor of the cervicovaginal microenvironment. PLOS Comput Biol 2022; 18 (02) e1009876
  • 147 Blaurock J, Baumann S, Grunewald S, Schiller J, Engel KM. Metabolomics of human semen: a review of different analytical methods to unravel biomarkers for male fertility disorders. Int J Mol Sci 2022; 23 (16) 9031
  • 148 Wishart DS, Cheng LL, Copié V. et al. NMR and metabolomics—a roadmap for the future. Metabolites 2022; 12 (08) 678
  • 149 Alseekh S, Aharoni A, Brotman Y. et al. Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nat Methods 2021; 18 (07) 747-756
  • 150 Ashrafian H, Sounderajah V, Glen R. et al. Metabolomics: the stethoscope for the twenty-first century. Med Princ Pract 2021; 30 (04) 301-310
  • 151 Chen Y, Li EM, Xu LY. Guide to metabolomics analysis: a bioinformatics workflow. Metabolites 2022; 12 (04) 357
  • 152 Sun J, Xia Y. Pretreating and normalizing metabolomics data for statistical analysis. Genes Dis 2023 Available at: https://doi.org/10.1016/j.gendis.2023.04.018 . Accessed January 19, 2024
  • 153 Bauermeister A, Mannochio-Russo H, Costa-Lotufo LV, Jarmusch AK, Dorrestein PC. Mass spectrometry-based metabolomics in microbiome investigations. Nat Rev Microbiol 2022; 20 (03) 143-160
  • 154 Han S, Van Treuren W, Fischer CR. et al. A metabolomics pipeline for the mechanistic interrogation of the gut microbiome. Nature 2021; 595 (7867) 415-420
  • 155 Guo J, Yu H, Xing S, Huan T. Addressing big data challenges in mass spectrometry-based metabolomics. Chem Commun (Camb) 2022; 58 (72) 9979-9990
  • 156 Troják M, Hecht H, Skoryk M. Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms). 2023
  • 157 Petera M, Martin J-F, Le Corguillé G. Mass spectrometry: LC-MS preprocessing with XCMS. 2023
  • 158 O'Donnell VB, Schebb NH, Milne GL. et al. Failure to apply standard limit-of-detection or limit-of-quantitation criteria to specialized pro-resolving mediator analysis incorrectly characterizes their presence in biological samples. Nat Commun 2023; 14 (01) 7172
  • 159 Lagier J-C, Armougom F, Million M. et al. Microbial culturomics: paradigm shift in the human gut microbiome study. Clin Microbiol Infect 2012; 18 (12) 1185-1193
  • 160 Vanstokstraeten R, Callewaert E, Blotwijk S. et al. Comparing vaginal and endometrial microbiota using culturomics: proof of concept. Int J Mol Sci 2023; 24 (06) 5947
  • 161 Diop K, Fall NS, Levasseur A. et al. Characterisation of the vaginal microbiota using culturomics and metagenomics suggests transplantation of gut microbiota into the vagina during bacterial vaginosis. 2020; DOI: 10.21203/rs.3.rs-63079/v1.
  • 162 Dubourg G, Morand A, Mekhalif F. et al. Deciphering the urinary microbiota repertoire by culturomics reveals mostly anaerobic bacteria from the gut. Front Microbiol 2020; 11: 513305
  • 163 Vanstokstraeten R, Mackens S, Callewaert E. et al. Culturomics to investigate the endometrial microbiome: proof-of-concept. Int J Mol Sci 2022; 23 (20) 12212
  • 164 Krog MC, Madsen ME, Bliddal S. et al. The microbiome in reproductive health: protocol for a systems biology approach using a prospective, observational study design. Hum Reprod Open 2022; 2022 (02) hoac015
  • 165 Tarazona S, Balzano-Nogueira L, Gómez-Cabrero D. et al. Harmonization of quality metrics and power calculation in multi-omic studies. Nat Commun 2020; 11 (01) 3092
  • 166 Syed H, Otto GW, Kelberman D, Bacchelli C, Beales PL. MOPower: an R-shiny application for the simulation and power calculation of multi-omics studies. bioRxiv 2021 https://doi.org/10.1101/2021.12.19.473339
  • 167 Narayanasamy S, Jarosz Y, Muller EE. et al. IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses. Genome Biol 2016; 17 (01) 260
  • 168 Ma Y, Liu L, Ma Y, Zhang S. HONMF: integration analysis of multi-omics microbiome data via matrix factorization and hypergraph. Bioinformatics 2023; 39 (06) btad335
  • 169 Argelaguet R, Velten B, Arnol D. et al. Multi-omics factor analysis - a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol 2018; 14 (06) e8124
  • 170 Muñoz-Benavent M, Hartkopf F, Van Den Bossche T. et al. gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms. NAR Genom Bioinform 2020; 2 (03) lqaa058
  • 171 Luo W, Pant G, Bhavnasi YK, Blanchard Jr SG, Brouwer C. Pathview Web: user friendly pathway visualization and data integration. Nucleic Acids Res 2017; 45 (W1): W501-W508
  • 172 Fettweis JM, Serrano MG, Brooks JP. et al. The vaginal microbiome and preterm birth. Nat Med 2019; 25 (06) 1012-1021
  • 173 Lozano FM, Lledó B, Morales R. et al. Characterization of the endometrial microbiome in patients with recurrent implantation failure. Microorganisms 2023; 11 (03) 741
  • 174 Reschini M, Benaglia L, Ceriotti F. et al. Endometrial microbiome: sampling, assessment, and possible impact on embryo implantation. Sci Rep 2022; 12 (01) 8467
  • 175 Zeng Z, Wang N, Sui L. et al. Characteristics and potential diagnostic ability of vaginal microflora in patients with aerobic vaginitis using 16S Ribosomal RNA sequencing. Diagn Microbiol Infect Dis 2023; 105 (01) 115806
  • 176 Kumar S, Kumari N, Talukdar D. et al; GARBH-Ini Study Group. The vaginal microbial signatures of preterm birth delivery in Indian women. Front Cell Infect Microbiol 2021; 11: 622474
  • 177 Tirone C, Paladini A, De Maio F. et al. The relationship between maternal and neonatal microbiota in spontaneous preterm birth: a pilot study. Front Pediatr 2022; 10: 909962
  • 178 Guang Y, Shen X, Tan Y. et al. Systematic analysis of microbiota in pregnant Chinese women and its association with miscarriage. Ann Transl Med 2022; 10 (20) 1099
  • 179 Raimondi S, Candeliere F, Amaretti A. et al. Vaginal and anal microbiome during Chlamydia trachomatis infections. Pathogens 2021; 10 (10) 1347
  • 180 Chen Q, Zhang X, Hu Q, Zhang W, Xie Y, Wei W. The alteration of intrauterine microbiota in chronic endometritis patients based on 16S rRNA sequencing analysis. Ann Clin Microbiol Antimicrob 2023; 22 (01) 4
  • 181 Khan KN, Fujishita A, Muto H. et al. Levofloxacin or gonadotropin releasing hormone agonist treatment decreases intrauterine microbial colonization in human endometriosis. Eur J Obstet Gynecol Reprod Biol 2021; 264: 103-116
  • 182 Chen P, Chen P, Guo Y, Fang C, Li T. Interaction between chronic endometritis caused endometrial microbiota disorder and endometrial immune environment change in recurrent implantation failure. Front Immunol 2021; 12: 748447
  • 183 Bukharin OV, Perunova NB, Ivanova EV. et al. Semen microbiota and cytokines of healthy and infertile men. Asian J Androl 2022; 24 (04) 353-358
  • 184 Yao Y, Qiu XJ, Wang DS. et al. Semen microbiota in normal and leukocytospermic males. Asian J Androl 2022; 24 (04) 398-405
  • 185 Chen P, Li Y, Zhu X. et al. Interaction between host and microbes in the semen of patients with idiopathic nonobstructive azoospermia. Microbiol Spectr 2023; 11 (01) e0436522
  • 186 Gachet C, Prat M, Burucoa C, Grivard P, Pichon M. Spermatic microbiome characteristics in infertile patients: impact on sperm count, mobility, and morphology. J Clin Med 2022; 11 (06) 1505
  • 187 Tuominen H, Rautava J, Kero K, Syrjänen S, Collado MC, Rautava S. HPV infection and bacterial microbiota in the semen from healthy men. BMC Infect Dis 2021; 21 (01) 373
  • 188 Cao T, Wang S, Pan Y. et al. Characterization of the semen, gut, and urine microbiota in patients with different semen abnormalities. Front Microbiol 2023; 14: 1182320
  • 189 Lundy SD, Sangwan N, Parekh NV. et al. Functional and taxonomic dysbiosis of the gut, urine, and semen microbiomes in male infertility. Eur Urol 2021; 79 (06) 826-836
  • 190 Garcia-Segura S, Del Rey J, Closa L. et al. Seminal microbiota of idiopathic infertile patients and its relationship with sperm DNA integrity. Front Cell Dev Biol 2022; 10: 937157
  • 191 Manzoor A, Amir S, Gul F. et al. Characterization of the gastrointestinal and reproductive tract microbiota in fertile and infertile Pakistani couples. Biology (Basel) 2021; 11 (01) 40
  • 192 Molina NM, Plaza-Díaz J, Vilchez-Vargas R. et al. Assessing the testicular sperm microbiome: a low-biomass site with abundant contamination. Reprod Biomed Online 2021; 43 (03) 523-531
  • 193 Krog MC, Hugerth LW, Fransson E. et al. The healthy female microbiome across body sites: effect of hormonal contraceptives and the menstrual cycle. Hum Reprod 2022; 37 (07) 1525-1543
  • 194 da Costa AC, Moron AF, Forney LJ. et al. Identification of bacteriophages in the vagina of pregnant women: a descriptive study. BJOG 2021; 128 (06) 976-982
  • 195 Li F, Chen C, Wei W. et al. The metagenome of the female upper reproductive tract. Gigascience 2018; 7 (10) giy107
  • 196 Goltsman DSA, Sun CL, Proctor DM. et al. Metagenomic analysis with strain-level resolution reveals fine-scale variation in the human pregnancy microbiome. Genome Res 2018; 28 (10) 1467-1480
  • 197 Ferretti P, Pasolli E, Tett A. et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe 2018; 24 (01) 133-145.e5
  • 198 Serrano MG, Parikh HI, Brooks JP. et al. Racioethnic diversity in the dynamics of the vaginal microbiome during pregnancy. Nat Med 2019; 25 (06) 1001-1011
  • 199 Aderaldo J, Teixeira DT, Torres MG. et al. A shotgun metagenomic mining approach of human semen microbiome. Research Square 2022
  • 200 Macklaim JM, Fernandes AD, Di Bella JM, Hammond JA, Reid G, Gloor GB. Comparative meta-RNA-seq of the vaginal microbiota and differential expression by Lactobacillus iners in health and dysbiosis. Microbiome 2013; 1 (01) 12
  • 201 Cho WK, Jo Y, Jeong S. De novo assembly and annotation of the vaginal metatranscriptome associated with bacterial vaginosis. Int J Mol Sci 2022; 23 (03) 1621
  • 202 Corral-Vazquez C, Blanco J, Aiese Cigliano R, Zaida S, Vidal F, Anton E. A transcriptomic insight into the human sperm microbiome through next-generation sequencing. Syst Biol Reprod Med 2023; 69 (03) 188-195
  • 203 Hulstaert E, Morlion A, Avila Cobos F. et al. Charting extracellular transcriptomes in the human biofluid RNA atlas. Cell Rep 2020; 33 (13) 108552
  • 204 Cariati F, Carotenuto C, Bagnulo F. et al. Endometrial microbiota profile in in-vitro fertilization (IVF) patients by culturomics-based analysis. Front Endocrinol (Lausanne) 2023; 14: 1204729
  • 205 Liu L, Chen Y, Chen JL. et al. Integrated metagenomics and metabolomics analysis of third-trimester pregnant women with premature membrane rupture: a pilot study. Ann Transl Med 2021; 9 (23) 1724
  • 206 Jean S. et al. Multi-omic microbiome profiles in the female reproductive tract in early pregnancy. Infect Microbes Dis 2019; 1 (02) 49-60
  • 207 Yeoman CJ, Thomas SM, Miller ME. et al. A multi-omic systems-based approach reveals metabolic markers of bacterial vaginosis and insight into the disease. PLoS One 2013; 8 (02) e56111