A Protocol for the Multi-Omic Integration of Cervical Microbiota and Urine Metabolomics to Understand Human Papillomavirus (HPV)-Driven Dysbiosis.

The multi-omic integration of microbiota data with metabolomics has gained popularity. This protocol is based on a human multi-omics study, integrating cervicovaginal microbiota, HPV status and neoplasia, with urinary metabolites. Indeed, to understand the biology of the infections and to develop adequate interventions for cervical cancer prevention, studies are needed to characterize in detail the cervical microbiota and understand the systemic metabolome. This article is a detailed protocol for the multi-omic integration of cervical microbiota and urine metabolome to shed light on the systemic effects of cervical dysbioses associated with Human Papillomavirus (HPV) infections. This methods article suggests detailed sample collection and laboratory processes of metabolomics, DNA extraction for microbiota, HPV typing, and the bioinformatic analyses of the data, both to characterize the metabolome, the microbiota, and joint multi-omic analyses, useful for the development of new point-of-care diagnostic tests based on these approaches.


Introduction
A growing body of evidence, especially over the last twelve years, suggests that the composition and function of the microbiota in different human body habitats play vital roles in human development and immunity [1][2][3]. The vagina and cervix have the lowest biodiversity, with a colonized epithelium dominated by Lactobacilli [4], thus being an interface that plays a protective role between the host and the environment. The cervix serves a pivotal role as a gatekeeper to protect the upper genital tract from microbial invasion and subsequent pathology. Recent studies have indicated that changes of the cervicovaginal microbiome [5][6][7], such as bacterial vaginosis [8,9], cervical inflammation [10,11] and vaginal pH [12,13], play a role in the susceptibility to cervical Human Papillomavirus (HPV) infection and the development of cervical intraepithelial neoplasia due to the profound shifts in the relative abundances of protective bacteria [10]. Although cervical cancer is preventable mainly by vaccination, detection, and treatment of lesions, it remains an important public health problem, being the fourth and the second most common cancer in women globally and in developing countries, respectively (1). In Latin America and the Caribbean, it remains the number one cause of mortality due to malignant neoplasms among 20-to 40-year-old women (2), three times higher in Latin America than in the United States (US) (3). According to guidelines established by Healthy People [2], women that have had a Pap test in three subsequent years need to be at~90%, however, in some areas of the world such as Latin America, this does not happen, with women often lost to follow-up even before treatment occurs.
Guidelines for genital care include coming to clinics for a Pap smear for HPV typing and cervical cytology, and for those who meet the guidelines, a return for a second visit to receive colposcopy and biopsy. These are invasive and expensive methods, and the approaches explained in this article are innovative and have the potential to be useful in advancing the field. A persistent infection with high-risk human papillomavirus (hr-HPV) types is a common, but not sufficient to cause cervical cancer [14,15]. High risk (hr) HPV infection often requires lifestyle factors for cancer development, such as smoking or oral contraceptive use [14], however, the reasons for hr HPV promotion and persistence are not completely understood. A gap in our understanding of HPV infection and carcinogenesis resides in the insufficient understanding of the true microbial community structure, preventing our ability to control the microbiota for therapeutic purposes. As widely known, the cervicovaginal microbiome also plays a role in disease pathogenesis in combination with HPV, namely genital dysbiosis, a modifiable risk factor for cervical disease [3][4][5][6]. A comparison of the vaginal microbiome in women of different ethnicities within the U.S. mainland indicates different relative abundances of the four main groups of Lactobacillus and anaerobic bacteria [4]. Different studies have described that infections with HPV are associated with an increase in Atopobium vaginae and G. vaginallis [5,11]. Other Latino populations, like Mexicans, have shown Sneathia spp. as a cervical biomarker for neoplasia [7]. Not only is bacterial biota associated with HPV, but studies have also found an important role of fungal signatures such as that of Malassezia for hr HPV infections and an increase in Sporidiobolaceae and Sacharomyces in atypical squamous cells of undetermined significance (ASCUS) lesions [5,16]. Thus, non-Lactobacillus dominant communities, with a concomitant increase in pH and related to high-grade dysplasia, perturb amino acid and nucleotide metabolisms and drive inflammation and HPV infections. The combination of the microbiome with metabolome can become even more informative than classical HPV typing. Recently, microbial inventories via sequencing of 16S rRNA genes, have been complemented with metabolomics, adding a layer of information regarding the metabolic activities of the microbiome that helps elucidate the etiology of the microbiome-mediated disease [17,18]. Metabolites produced by commensal microorganisms may affect host metabolic processes, with both pathogenic and protective consequences. Multi-kingdom colonization (bacteria, Archaea, fungi, and viruses) associated with HPV infections in the vagina and cervix can produce metabolites which translocate through the host's barriers, that could influence systemic health effects in women at risk of developing cervical cancer. Indeed, recent findings suggest that cervical microbial metabolic changes may influence systemic health effects in women at risk of developing cervical cancer [18]. Recent data has been emerging on the usefulness of coupling microbiota inventories with systemic metabolomics in biofluids such as urine (that can be self-collected), thus easy to obtain and minimally invasive. Indeed, biomarkers for cervical cancer may be exfoliated as debris in urine, a kind of liquid biopsy-which could facilitate diagnostics [1]. It is, therefore, important to develop protocols to help integrate information between the microbiome, viral pathogenesis, and immunity affecting cancer progression. Additional to microbiota analyses, one could develop shotgun metagenomic methods to understand the full diversity more accurately, including Archaea and viral composition affecting the dysbiotic or disease status. Indeed, shotgun approaches are useful methods that can non-specifically detect HPVs and other highly divergent viruses (specifically DNA viruses), using rolling circle amplification (RCA) [19], or even using meta-total RNA sequencing (MeTRS) methods based on shotgun sequencing of total RNA, which could reveal the extent of RNA viruses [20] (even the novel coronavirus-SARS-CoV-2), which could lead to new methodologies to reveal the impact of the new virus on the microbiome and the immune response responsible for carcinogenesis. It is crucial to develop more microbiome/metabolome studies to understand cancer progression. The early detection and treatment of genital dysbiosis may facilitate better management of the disease [12]. As such, new quick tests that could monitor genital health based on the use of vaginal secretions and even urine could be useful. Dysbioses of the genital microbiome result in high pH due to the decrease in fermenting Lactobacillus, and create a genital microbiome/metabolome fingerprint, which in the future could be detected with different applications [21,22]. These metabolomic profiles require advanced diagnostics and point-of-care applications for which big data from microbiome and metabolome are needed. Here, we provide a unique and detailed guide for the development of multi-omic integration studies of the bacterial microbiota and urine metabolomics to understand possible biological associations with the host's urinary metabolites. This article discloses detailed materials, methods, sample processing, and data analyses. The coupling of two omic methods is a useful approach to understand the association of the human microbiome with health status and risk of disease severity, and for the development of new point-of-care testing technologies.

7.
In the Second step (solution C2), what occurs is the precipitation of non-DNA organic and inorganic material, including cell debris and proteins. Given that cervical swabs are low biomass samples, this step will be joined with inhibitor removal (solution C3). 8.
Incubate at −20 • C for 5 min. 10. Centrifuge at room temperature for 1 min at 13,000 rpm. 11. Avoiding the pellet, transfer up to, but no more than 750 µL of supernatant to a clean 2 mL tube. 12. The next step with solution 4 (rich in salt), is aimed at binding the DNA to spin filter. 13. Add 1200 µL of C4 (previously shaken) to the former supernatant and vortex for 5 s. 14. Place the spin filters in the vacuum manifold and load approximately 675 µL onto a spin filter.
Turn on the vacuum until all the supernatant flows through. Add an additional 675 µL of the supernatant to the spin filter and vacuum. 15. Load the remaining of the supernatant and repeat it. A total of three loads for each sample processed are required. 16. The next step includes washing the DNA bound to the membrane and remove contaminants. 17. Add 650 µL of cold 100% ethanol (−20 • C) to all the tubes and turn on the vacuum. 18. Add 500 µL of solution C5 to all tubes and turn on the vacuum. Carefully place the spin filter in a clean 2ml collection tube. Avoid splashing any Solution C5 onto the spin filter. It is critical to remove all traces of Solution C5 as it can interfere with many downstream DNA applications. 19. The final step is DNA elution from the membrane using sterile solution C6 (10 mM Tris) or sterile PCR water. 20. Add 60-100 µL of C6 (55 • C) to the center of the white filter membrane and let sit for 5 min.
Volume range depends on how diluted you wish the sample to be, If low biomass, lower volumes of C6 are suggested. 21. Centrifuge for 1 min at room temperature at 13,000 rpm. 22. Discard the spin filter and store DNA at −20 • C ( Figure 1).  7. In the Second step (solution C2), what occurs is the precipitation of non-DNA organic and inorganic material, including cell debris and proteins. Given that cervical swabs are low biomass samples, this step will be joined with inhibitor removal (solution C3). 8. Add 100 μL of C2 and 100 μL of C3. Vortex for 5 s. 9. Incubate at −20 °C for 5 min. 10. Centrifuge at room temperature for 1 min at 13,000 rpm. 11. Avoiding the pellet, transfer up to, but no more than 750 μL of supernatant to a clean 2 mL tube. 12. The next step with solution 4 (rich in salt), is aimed at binding the DNA to spin filter. 13. Add 1200 μL of C4 (previously shaken) to the former supernatant and vortex for 5 s.

Human Papilloma Virus Genotyping
We suggest the use of a highly sensitive short-polymerase chain reaction-fragment assay (Labo Biomedical Products, Rijswijk, The Netherlands, licensed Innogenetics technology). This assay has been used for epidemiological and vaccination studies because it has high analytical sensitivity [24,25] and other cervicovaginal microbiota related studies [5,26]. It starts with a PCR step, followed by a reverse hybridization protocol.

1.
Thaw the DNA previously extracted from clinical samples, mix by vortexing and spin down the tubes to collect all liquid at the bottom of the tube.

2.
The assay uses SPF10 primers to amplify a 65-bp fragment of the L1 open reading frame of HPV genotypes, followed by a reverse-hybridization step. The 65-bp PCR fragment assay amplifies the following common mucosal HPV genotypes: Prepare the master mix for the PCR. At first use of the PCR master mix (vial A1), it is recommended to mix the contents of the vial on a Vortex mixer and aliquot the contents in ready-to-use portions of 40 µL. This further reduces the risk of contamination. After aliquoting, store at −20 • C 4.
Prepare the tubes/96-well plates (depending on the number of samples) considering the number of the samples to process and include negative and positive controls provided in the kit.
Spin down the liquid in the tubes with the processed specimen for 15 s at 14,000 rpm in a minicentrifuge. 7.
Add (one by one) 10 µL of the supernatant of the processed sample to the appropriate vials with PCR master mix. The vials intended for the HPV negative and positive PCR controls remain closed. Cap each vial after the addition of DNA before proceeding with the next. 8.
Mix HPV positive PCR control (vial B1) on a Vortex mixer, add 10 µL to the appropriate vial, and close the vial. 9.
Use clean filter tips for each sample and mix each sample with the PCR mix by pipetting up and down a few times. In the second step, the amplified fragments undergo a line probe assay by reverse-hybridization assay performed by using the portion of the kit named RHA kit HPV SPF10-LiPA25. In this second step, the biotinylated amplicons are denatured and then hybridized with specific oligonucleotide probes immobilized as parallel lines in membrane strips. After immobilization of the oligonucleotides on the strips, these were washed, and streptavidin alkaline phosphatase (SAP) is added to bind to the biotinylated hybrid formed, yielding a purple precipitate in the strip that visually determines the specific HPV type compared to the kit-provided controls. The LiPA strips are visually inspected and interpreted following the standardized reference guide provided by the kit's manual. 12. Prewarm the vials with the hybridization Solution (HS) and the Stringent Wash Solution (SW) to at least 37 • C but must not exceed the hybridization temperature of 49 • C (all crystals should be dissolved before the opening of the vial). Currently, it is cheaper to extract good quality DNA and send directly to any company for outsourced sequencing of 16S rDNA genes.

3.
Once the facility sends the data, raw FASTQ reads can be uploaded to QIITA https://qiita.ucsd.edu/, an entirely open-source microbial study management platform [28]. It allows users to keep track of multiple studies with multiple omics data, and use stringent quality criteria PHRED scores (quality score of the nucleotides generated by automated DNA sequencing) with an ASCII offset of 33, and the user can choose the length of the reads for trimming, demultiplexing and binning using a given database (Greengenes, SILVA, or the Ribosomal Database Project-RDP). The usefulness of this platform is that it allows data to be directly made public via EBI (the European Bioinformatics Institute), a great service offered by the QIITA team at the University of California San Diego (UCSD).

4.
After the OTU table is prepared in QIITA, an alternative is to use QIIME2 for analyses or R packages such as Bioconductor [29] or the quick tool microbiomeanalyst (https://www.microbiomeanalyst.ca) for preliminary analyses [30].

5.
Reads matching chloroplast, mitochondria, and unassigned sequences should be removed as well as rare singletons. 6.
Downstream taxonomy plots, alpha and beta analyses should follow a given rarefaction level considering a minimum number of reads shared by most samples. 7.
Heatmaps can be built using the plot_heatmap function from the heatmap library [33]. 9.
Additional analyses can be plotted using a microbiomeanalyst that integrates multiple R programs [30] and also includes LEfSe (Linear discriminant analysis effect size) analyses [34]. 10. For beta diversity, samples can be visualized using pairwise Bray-Curtis distance between samples using the R package [35] with vegdist function in vegan [36]. The global differences in bacteria can be visualized with Principal Coordinates Analysis (PCoA) or non-metric multidimensional scaling (NMDS). Alternatively, beta diversity can be visualized using UniFRAC [37].

Collection of Urine Samples for Metabolomics
All reagents must be an analytical grade (HPLC). All aqueous solutions used throughout this protocol should be prepared with Milli-Q or deionized water (18.2 MΩ-cm, at 4 • ).
When possible, all procedures should be performed using glass appliances. The whole extraction needs to be performed at 4 • C since the metabolites can degrade as little as a few seconds. Mix with 800 µL of the cold methanol-water mixture (8:1 v/v) 3.
Vortex mixture for 1 min.

4.
Keep the sample at 4 • C or ice for 20 min.
De-assemble the screw cap and remove silicone septum. Perforate it using scissors and mount vials onto a rotary vacuum evaporator. (see Note 3). 7.
Remove 200 µL of the supernatant from the top phase and transfer to a glass vials, thread 8-425. 8.
Prepare reference pool quality control samples (see Note 4). 9.
Attach vials to a rotary vacuum evaporator (see Note 5). 10. Evaporate supernatants to dryness. 11. Replace caps with solid tops. 12. Put vials in desiccator and store at −80 • C for at least four weeks.

1.
Prepare the derivatization solution 1: mix 20 mg Methoxyamine hydrochloride in 1 mL pyridine using 4 mL glass vials with plastic caps solid-top, thread 13-425 (see Note 6). Briefly, vortex the obtained solution at RT until Methoxyamine hydrochloride is fully dissolved.

2.
Take out dried samples from storage and allow them to warm up to room temperature for at least 15 min before derivatization (see Note 7) and add 50 µL Derivatization Solution 1, close tightly the vials with closed caps and incubate at 37 • C for two hours.

3.
Remove samples, add 50 µL of the Derivatization Solution 2 (see Note 8) directly to the reaction mixture in the vial, close tightly the vials with closed caps and incubate for an additional 1 h at 60 • C.

4.
Transfer the reaction mixture to labeled Eppendorf vial and centrifuge at 13,000 rpm for 10 min at RT.

5.
Transfer the supernatant in the new glass vial and immediately close each sample with a solid plastic cap, thread 8-425. Store for a long time at −20 • C and for a short time at 4 • C (see Note 9). 6.
For the analysis, place a glass insert in the new glass vial and dilute each sample 1:50 with hexane prior to GC/MS analysis (see Note 10).

1.
The mass spectrometer must be tuned according to the manufacturer's manuals for optimal parameters for ion lenses, detector voltage, and other settings. Usually, this can be performed in autotune operation.

2.
Inject 1 µL of each sample including quality control samples in the GC-MS.

3.
Separate metabolites using a GC temperature ramping program. The GC oven can be programmed from 100 • C to 280 • C at a rate of 4 • C/min. The injection port temperature will be 280 • C, and helium is used as the carrier gas at a constant linear velocity of 39 cm/s. Injection mode-split (15:1).

4.
Detect metabolites by setting the ion source filament energy to 70 eV, the ion source temperature −200 • C, and the scan range (mass-to-charge, m/z) 35-600 Da.
Metabolomic Data Acquisition and Analyses

1.
Obtain the total ion chromatogram and mass chromatograms for each detected metabolite GC/MS instruments generate a single file per sample, a list of mass spectra together with their corresponding retention times (RT). These spectra are commonly shown on a chromatogram represented by RT on the horizontal axis and signal intensity on the vertical axis ( Figure 2).

2.
Identify metabolites based on the fragmentation pattern of molecular ions (characteristic fragment ions) using the GC/MS manufacturer's software equipped with NIST/EPA/NIH Mass spectral Library (NIST14) (see Note 11).
GC/MS instruments generate a single file per sample, a list of mass spectra together with their corresponding retention times (RT). These spectra are commonly shown on a chromatogram represented by RT on the horizontal axis and signal intensity on the vertical axis ( Figure 2). 2. Identify metabolites based on the fragmentation pattern of molecular ions (characteristic fragment ions) using the GC/MS manufacturer's software equipped with NIST/EPA/NIH Mass spectral Library (NIST14) (see Note 11).

1.
Statistical tests on the beta diversity can be completed via community-level differences between sample groups, assessed using the PERMANOVA test, which allows sample-sample distance to be applied to an Analyses of Variance (ANOVA)-like framework [38]. This PERMANOVA test is determined through permutations and provides strength and statistical significance on sample groupings using a Bray-Curtis distance matrix as the primary input.

2.
Analyses of Variance tests using the aov function in R (Team, 2008) must be used on the rarefied richness (alpha) values as well as for the Shannon diversity values, to find significant differences related to a given category. p-Values should be considered using an FDR of 0.05 to be considered significant. 3.
The differential abundance test for OTUs, to identify possible signatures significantly associated with the metadata categories (biomarkers), can be completed with non-parametric Wilcoxon rank sum tests that have lower false positive rates and are more robust to outliers, without needing a normal distribution assumption.

1.
Quantify each peak using the maximum peak intensity value and organize data as a "peak intensity table" using MS Excel or similar software (see Note 12). The table must include metabolite identities and peak intensity values and saved in Comma Separated Values (.csv) or Tab Delimited Text (.txt). Perform statistical analyses using free online tool MetaboAnalyst.ca [39,40] (see Note 13). 2.
Open MetaboAnalyst.ca, choose "Statistical Analysis", upload the "peak intensity table" and submit. If the table was organized correctly and saved in the required format, the "Data Integrity Check" will pass the information for further analysis. Click the "Skip" button to accept the default practice (see Note 14).
Perform data normalization to remove unwanted variations between the samples. First, perform "sample-specific normalization. Click "Proceed" (see Note 15).
For more groups, perform chemometrics analysis (Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), feature identification and cluster analysis. Detect outliers to improve downstream results (see Note 16).

Multi-Omic Integration of Microbiota and Metabolomics
The integration of metagenomics and metabolomics data could be performed using Model-based Integration of Metabolite Observations and Species Abundances 2 (MIMOSA2) freely available at borensteinlab.com/software_MIMOSA2.html [41,42]. MIMOSA2 summarizes paired microbiome-metabolome datasets to support mechanistic interpretation and hypothesis generation. MIMOSA2 also characterizes the relative capacity of community members to produce or consume metabolites based on a priori metabolic information of the activity of metabolic enzymes for each species from the KEGG database, describes how well each metabolite can be predicted by metabolic potential, and estimates how much each taxon can explain each metabolite.

1.
Prepare the output data files and save in txt format for each sample in either control or experimental groups separately.

A.
For "microbiome": From QIITA or QIIME, export the species table (BIOM) with Greengenes ID as OTU identifiers [43]. This will allow for PICRUST [44] to use the OTU table and result in a functional-gene-count matrix, telling the count of each functional-gene in each of the samples surveyed, based on the reference genome.

3.
Log in to borensteinlab.com/software_MIMOSA2.html. The following options will be used for the analysis: 4. MIMOSA2 will calculate CMP scores for each metabolite and generate a tab-delimited file for each bacterial species in each condition. Additionally, a contribution table will be generated as a tab-delimited file which will relate taxa, to KEGG metabolic pathways with its associated p-value, model slope and VarShare (represent the fraction of the variation) for each metabolite explained by the taxon in question. VarShare values could be used to build heatmaps in R, in the same format as those for microbial taxa. Finally, MIMOSA2 will identify microbial features that may underlie differences in microbial metabolite concentrations between similar communities. It answers questions such as: (1) Do the metabolites in a given dataset appear to vary depending on microbiome composition? Which ones? (2) Can differences in microbiome metabolic capabilities explain metabolite variation? (3) Which taxa, genes, and reactions appear to be playing a role in metabolite differences? [42].

1.
Methoxyamine hydrochloride is very sensitive to moisture. Storage is recommended under the vacuum at RT.

2.
Glass inserts decrease the surface area inside the vial allowing to achieve maximum sample recovery and easier sample removal.

3.
De-assemble the screw cap and remove silicone septum. Perforate it using scissors and mount the vials onto a rotary vacuum evaporator. This step is required to minimize sample cross-contamination during evaporation.

4.
Preparation of the reference pool quality control samples. Prepare a large pool sample during the preparation of sample extracts by aliquoting 100 µl from each 1 mL authentic subject sample. After a collection of a large pool sample, aliquot all for 1 mL in separate vials (thread 8-425), label all as reference quality controls and process similar to authentic subject samples. Continue as instructed in Section 3.3.7. The derivatization solution without the sample only, and empty vials, must also be included as quality controls in the analysis to ensure that identified metabolites are derived from the sample [46].
During derivatization, avoid contact with moisture, which will result in metabolite degradation. If samples are not dried, it is necessary to proceed to additional evaporation as said in Sections 3.4.9-3.4.11. 8.
Derivatization Solution 2 must be used from freshly opened vials. 9.
If an analysis is performed after samples preparation, wait two hours before injecting the first sample into the GC-MS. 10. The sample concentration must be adequately adjusted to give sufficient chromatogram, then it needs to be concentrated before injecting the sample into the gas chromatograph. The dilution of a sample (1:50) was found to be sufficient for the analysis. However, if the signal is too low, the concentration could be increased. Derivatization solution without the sample only, and empty vials, must also be included as quality controls in the analysis to ensure that identified metabolites 11. MS detects the mass of the molecular ions and the masses of the fragment ions. The database contains extensive information on many compounds including the masses of fragment ions that help to perform successful metabolite identification. In addition, to ensure a valid peak identification, data can be also processed using a deconvolution method, which is a signal processing technique that estimates the relative area corresponding to each peak when multiple peaks overlap within the same spectral region. This is especially important for low abundant metabolites that might co-elute with abundant major peaks. For general quadrupole mass spectrometers, data deconvolution by the freely available software AMDIS is recommended (http://chemdata.nist.gov/mass-spc/amdis/). 12. Some of the metabolite's so-called "missing values" will not be identified in chromatograms.
Therefore, when composing the table for the analysis, missing values should be presented as empty values or NA without quotes. 13. MetaboAnalyst is a web server designed to permit comprehensive metabolomics data analysis, visualization, and interpretation [39]. The web interfaces of MetaboAnalyst are designed to be self-explanatory. Therefore, most steps are documented on top of the corresponding pages. Available tutorials and sample data sets complement the information by providing step-by-step instructions for several most common tasks. 14. This step is strongly recommended for metabolomics datasets with a large number of variables (more than 2000 metabolites) since many of them are from baseline noises. Usually, the GC/MS analysis of brain tissues could identify less than 60 variables. 15. The normalization procedures are grouped into three categories. The sample normalization allows general-purpose adjustment for differences among your sample, which can include the protein content in the sample or intensity value of internal standard spiked into the derivatization mixture before GC/MS analysis. Data can be further transformed by "log" or "cube root" and/or scaled via "mean centering, autoscaling, Pareto scaling, and range scaling" (please see the description of each in the MetaboAnalyst program). According to [48] autoscaling and range scaling performed better than the other pretreatment methods and showed biologically sensible results in chemometrics analysis. Data transformation and scaling are two different approaches to make individual features more comparable that can be used individually or in combination. Scaling is a useful procedure when variables are of very different orders of magnitude [49]. 16. Detect outliers. This can be carried out by visual inspection of the PCA plot to identify samples-outliers outside the Hotelling T2 95% confidence ellipse [40]. Many outliers could be corrected by normalization or excluded from the analysis. In many cases, outliers are the result of operational errors during the analytical process. If those values cannot be corrected, they should be removed from the analysis, but always justified [39]. Please refer to MetaboAnalyst.ca tutorials, or FAQ to select the statistical analysis method. 17. Well-predicted metabolites were identified by the Community Metabolic Potential (CMP) scores model in groups by examining of the total pool of metabolites with positive model slope and a model p-value < 0.1. Given that MIMOSA2 is prone to false negatives because its model is approximate, it requires a weak statistical threshold (p-value < 0.1) to capture more relationships of possible interest [18,41,42]. Additional information could be found at borensteinlab.com/software_MIMOSA2.html helper.

Discussion and Conclusions
We are assisting a new stage of microbiome research, which moves beyond the typical 16S profiles listing bacteria associated to a given body site or disease, and offers the novel integration of other omics approaches, towards a better understanding of community functions in the disease, and interactions with the host. Emphasis is now given to the causality of microbiome research and the development of mechanistic understandings of the influence of microbiota, including their metabolic activities. Thus, the integration of metagenomics and metabolomics is becoming a very promising approach in the exploration of associations between the host microbiome and circulating metabolites with the aim of understanding the complex bacterial interactions with the host and their contributions to systemic health. Using urine as a liquid biopsy is gaining more popularity. Indeed, evidence suggests that biomarkers for cervical cancer may be exfoliated as debris in urine, which could facilitate diagnostics [50]. Since circulating metabolites are finally excreted through urine [51], recent studies suggest that exploring the association between urine metabolome and host microbiome (gut or even cervicovaginal) may complement sequencing-based approaches with a functional readout of the host microbiome and its interaction with host [52,53]. Indeed, a recent study coupling gut microbiome and urine metabolomics suggests that gut bacterial compositional changes could eventually be monitored and probed using urine metabolomics [52]. In addition, the application of the current protocol for functional characterization of vaginal and cervical hr HPV microbiota and urinary metabolome has identified species-contributors to the pool of several circulating urinary metabolites associated with cervical cancer development [18]. The high throughput analyses of these two datasets could be key for the development of new tools (like portable devices) to give women feasible self-testing, and to allow them to be informed of their cervicovaginal dysbiosis via vaginal or urine metabolomic screening, which could benefit low-income countries, and those with a lack of access to cervical surveillance.
Taken together, this report is a simple and easy-to follow protocol, aimed to be used as a guide for those taking on microbiota and metabolic analyses using human biospecimens, such as cervicovaginal microbiota and urine metabolomics [5,18,52]. Data integration via the use of freely available MIMOSA2 software [42] facilitates analyses providing easy export files that can be plotted in any other widely used packages such as R [54]. Generating data that combines both the microbiome and metabolomics analyses will guide the development of tools aimed at detecting dysbiosis via changes of the microbial profiles and metabolomic signatures, which will be useful for new point-of-care diagnostics.

Conflicts of Interest:
The authors declare no conflict of interest.