This work was supported by the Clinical Research Award of the First Affiliated Hospital of Xi&#39;an Jiaotong University, China (XJTU1AF-2018-017, XJTU1AF-CRF-2019-002), the Major Basic Research Project of Natural Science of Shaanxi Provincial Science and Technology Department (2018JM7073, 2017ZDJC-11), the Key Research and Development Project of Shaanxi Provincial Science and Technology Department (2017ZDXM-SF-068, 2019QYPY-138), the Shaanxi Provincial Collaborative Technology Innovation Project (2017XT-026, 2018XT-002), and the Medical Research Project of Xi&#39;an Social Development Guidance Plan (2017117SF/YX011-3). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We thank the colleagues in the Department of Gynecology of First Affiliated Hospital of Xi&#39;an Jiaotong University for their contributions to collecting samples.

This study was approved by the Medical Institutional Ethics Committee of the First Affiliated Hospital of Xi&#39;an Jiaotong University (No. XJTUIAF2018LSK-139). Informed consent was obtained from all enrolled patients.
1. Criteria for entering the cancer group and the control group
<ol>
	<li>For the cancer group, enroll patients who are primarily diagnosed with ovarian cancer, and after laparotomy, they are proven to have serous ovarian cancer by pathological findings.</li>...

<ol>
	<li>Manfredo Vieira, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Translocation+of+a+gut+pathobiont+drives+autoimmunity+in+mice+and+humans.">Translocation of a gut pathobiont drives autoimmunity in mice and humans.</a> Science. 359 (6380), 1156-1161 (2018).</li><li>Geller, L. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Potential+role+of+intratumor+bacteria+in+mediating+tumor+resistance+to+the+chemotherapeutic+drug+gemcitabine.">Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine.</a> Science. 357 (6356), 1156-1160 (2017).</li><li>Manfredo, V. 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D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+potential+role+of+lung+microbiota+in+lung+cancer+attributed+to+household+coal+burning+exposures.">The potential role of lung microbiota in lung cancer attributed to household coal burning exposures.</a> Environmental and Molecular Mutagenesis. 55 (8), 643-651 (2014).</li><li>Kwon, M., Seo, S. S., Kim, M. K., Lee, D. O., Lim, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Compositional+and+Functional+Differences+between+Microbiota+and+Cervical+Carcinogenesis+as+Identified+by+Shotgun+Metagenomic+Sequencing.">Compositional and Functional Differences between Microbiota and Cervical Carcinogenesis as Identified by Shotgun Metagenomic Sequencing.</a> Cancers. 11 (3), 309 (2019).</li><li>Urbaniak, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Microbiota+of+Breast+Tissue+and+Its+Association+with+Breast+Cancer.">The Microbiota of Breast Tissue and Its Association with Breast Cancer.</a> Applied and Environmental Microbiology. 82 (16), 5039-5048 (2016).</li><li>Feng, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metagenomic+and+metatranscriptomic+analysis+of+human+prostate+microbiota+from+patients+with+prostate+cancer.">Metagenomic and metatranscriptomic analysis of human prostate microbiota from patients with prostate cancer.</a> BMC Genomics. 20 (1), 146 (2019).</li><li>Walsh, D. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Postmenopause+as+a+key+factor+in+the+composition+of+the+Endometrial+Cancer+Microbiome+(ECbiome).">Postmenopause as a key factor in the composition of the Endometrial Cancer Microbiome (ECbiome).</a> Scientific Reports. 9 (1), 19213 (2019).</li><li>Walther-Antonio, M. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Potential+contribution+of+the+uterine+microbiome+in+the+development+of+endometrial+cancer.">Potential contribution of the uterine microbiome in the development of endometrial cancer.</a> Genome Medicine. 8 (1), 122 (2016).</li><li>Poore, G. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbiome+analyses+of+blood+and+tissues+suggest+cancer+diagnostic+approach.">Microbiome analyses of blood and tissues suggest cancer diagnostic approach.</a> Nature. 579 (7800), 567-574 (2020).</li><li>Bolger, A. M., Lohse, M., Usadel, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trimmomatic:+a+flexible+trimmer+for+Illumina+sequence+data.">Trimmomatic: a flexible trimmer for Illumina sequence data.</a> Bioinformatics. 30 (15), 2114-2120 (2014).</li><li>Ward, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=BugBase+predicts+organism-level+microbiome+phenotypes.">BugBase predicts organism-level microbiome phenotypes.</a> bioRxiv. , (2017).</li><li>Langille, M. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predictive+functional+profiling+of+microbial+communities+using+16S+rRNA+marker+gene+sequences.">Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences.</a> Nature Biotechnology. 31 (9), 814-821 (2013).</li><li>Langille, M. G. I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predictive+functional+profiling+of+microbial+communities+using+16S+rRNA+marker+gene+sequences.">Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences.</a> Nature Biotechnology. 31 (9), 814 (2013).</li><li>Parks, D. H., Tyson, G. W., Hugenholtz, P., Beiko, R. 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Ovarian cancer has a notable influence on women&#39;s fertility25. Most ovarian cancer patients are diagnosed at late stages, and the 5-year survival rate is less than 30%18. Confirmation of bacteria in the abdominal solid viscera, including the liver, pancreas and spleen, has been published. The existence of bacteria in the upper female reproductive tract occurs because the cervix is not enclosed2,3,<sup...

Recently, an increasing number of articles have been published that prove the existence of bacteria in abdominal solid viscera, such as the kidney, spleen, liver, and ovary1,2. Geller et al. found bacteria in pancreatic tumors, and these bacteria were resistant to gemcitabine, a chemotherapeutic drug2. S. Manfredo Vieira et al. concluded that Enterococcus gallinarum was portable to the lymph nodes, liver and spleen, and it could drive autoimmunity3.
Since the cervix plays a role as a defender, bacteria in the upper female reproductive tract, which contains the uterus, fallopian tubes, and ovaries, have been minimally researched. However, some new theories have been established in recent years. Bacteria may have access to the uterine cavity during the menstrual cycle due to changes in mucins4,5. Additionally, Zervomanolakis et al. confirmed that the uterus, together with the fallopian tubes, is a peristaltic pump controlled by the endocrine system of the ovaries, and this arrangement enables bacteria to enter the endometrium, fallopian tubes, and ovaries6.
The upper reproductive tract is no longer a mystery anymore thanks to the development of bacterial detection methods. Verstraelen et al. used a barcoded paired-end sequencing method to discover uterine bacteria by targeting at the V1-2 hypervariable region of the 16S RNA gene7. Fang et al. employed barcoded sequencing in patients with endometrial polyps and revealed the presence of diverse intrauterine bacteria8. Additionally, by using the 16S RNA gene, Miles et al. and Chen et al. found bacteria in the genital system of women who had undergone salpingo-oophorectomy and hysterectomy, respectively5,9.
Bacteria in tumor tissues have gained increasing attention in recent years. Banerjee et al. discovered that the microbiome signature differed between ovarian cancer patients and controls10. Anoxynatronum sibiricum was associated with tumor stage, and Methanosarcina vacuolata&#160;might be used to diagnose ovarian cancer11. In addition to ovarian cancer, other cancers, such as stomach, lung, prostate, breast, cervix, and endometrium, have been proven to be associated with bacteria12,13,14,15,16,17,18. Poore et al. proposed a new class of microbial-based oncology diagnostics, foreseeing early-stage cancer screening19. In this protocol, we investigated the differences between cancerous and normal ovarian tissues by comparing the composition and function of bacteria in these two tissues.
Immunohistochemistry staining and 16S rRNA gene sequencing were performed to confirm the presence of bacteria in the ovaries. The differences and predicted functions of the ovarian bacteria in cancerous and noncancerous ovarian tissues were studied. The results showed the existence of bacteria in ovarian tissues. Anoxynatronum sibiricum and Methanosarcina vacuolata were related to the stage and the diagnosis of ovarian cancer, respectively. Forty-six significantly different KEGG pathways that were present in both groups were compared.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>2200 TapeStation Software</td>
			<td>Agilgent 
			United States</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>AmpliSeq for Illumina Library Prep, Indexes, and Accessories</td>
			<td>Illumina</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Image-pro plus 7</td>
			<td>Media Cybernetics</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Leica ASP 300S</td>
			<td>Leica Biosystems Division of Leica Microsystems</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Leica EG 1150</td>
			<td>Leica Biosystems Division of Leica Microsystems</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Leica RM2235</td>
			<td>Leica Biosystems Division of Leica Microsystems</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>LPS Core monoclonal antibody, clone WN1 222-5</td>
			<td>Hycult Biotech</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Mag-Bind RxnPure Plus magnetic beads</td>
			<td>Omega Biotek</td>
			<td>M1386-00</td>
			<td></td>
		</tr>
		<tr>
			<td>Mag-Bind Universal Pathogen 96 Kit</td>
			<td>Omega Biotek</td>
			<td>M4029-01</td>
			<td></td>
		</tr>
		<tr>
			<td>MiSeq</td>
			<td>Illumina</td>
			<td>SY-410-1003</td>
			<td></td>
		</tr>
		<tr>
			<td>Silva database</td>
			<td>Max Planck Institute for Marine Microbiology and Jacobs University</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>the QuantiFluor dsDNA System</td>
			<td>Promega</td>
			<td>E2670</td>
			<td></td>
		</tr>
		<tr>
			<td>Trimmomatic</td>
			<td>Bj&#246;rn Usadel</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>ZytoChem Plus (HRP) Anti-Rabbit (DAB) Kit</td>
			<td>Zytomed Systems</td>
			<td>HRP008DAB-RB</td>
			<td></td>
		</tr>
	</tbody>
</table>

characterization and functional prediction of bacteria in ovarian tissues

The theory of a "sterile" female upper reproductive tract has been encountering increasing opposition due to advancements in bacterial detection. However, whether ovaries contain bacteria has not yet been confirmed yet. Herein, an experiment to detect bacteria in ovarian tissues was introduced. We chose ovarian cancer patients in the cancer group and noncancerous patients in the control group. 16S rRNA gene sequencing was used to differentiate bacteria in ovarian tissues from the cancer and control groups. Furthermore, we predicted the functional composition of the identified bacteria by using BugBase and PICRUSt. This method can also be used in other viscera and tissues since many organs have been proven to harbor bacteria in recent years. The presence of bacteria in viscera and tissues may help scientists evaluate cancerous and normal tissues and may be aid in the treatment of cancer.

Patients 
A total of 16 qualified patients were included in the study. The control group included 10 women with a diagnosis of benign uterine tumor (among them, 3 patients were diagnosed with uterine myoma, and 7 patients were diagnosed with uterine adenomyosis). Meanwhile, the cancer group contained 6 women with a diagnosis of serous ovarian cancer (among them, 2 patients were diagnosed with stage II, and 2 of them were diagnosed with stage III). The following characteristics showed no differences ...

Watch this Scientific Journal Video about Characterization and Functional Prediction of Bacteria in Ovarian Tissues at JoVE.com

Characterization and Functional Prediction of Bacteria in Ovarian Tissues

Immunohistochemistry staining and 16S ribosomal RNA gene (16S rRNA gene) sequencing were performed in order to discover and distinguish bacteria in cancerous and noncancerous ovarian tissues in situ. The compositional and functional differences of the bacteria were predicted by using BugBase and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt).

The theory of a "sterile" female upper reproductive tract has been encountering increasing opposition due to advancements in bacterial detection. However, ...

This protocol is designed to discover bacteria in ovarian tissues and predict their functions. Immunohistochemistry staining and 16S rRNA sequencing, were used in order to discover and distinguish bacteria in cancerous and non-cancerous ovarian tissues in situ. The compensational and the functional differences of the bacteria is predicted by using BoPs and cellular-genetic investigation of communities by reconstruction of unobserved days.Our protocol aims at extracting bacteria from ovarian tissues in situ. It can also be used to discover bacteria in tumors of any kind. The main advantages of our protocol are that it's easy and convenient, that it can be realized in almost every lab.It's also rapid to get the results. During practice in the protocol, it's important to exclude any bacteria off-site tumor tissues. Thus, all procedures in this protocol needs to be sterile.During the surgery, separate resected ovaries into roughly one centimeter thick tissue samples with a pair of new sterile tweezers. Avoid touching anything else during the whole procedure. After separation, remove the sample into a sterile tube and put it in liquid nitrogen for transmission.Preserve samples in minus 80 degrees Celsius. Fix tissues by formalin, and then embed them by paraffin. This protocol can be paused here.The tissues can be kept in room temperature for longer preservation. In order to operate further steps, cut the samples in five micrometer serial sections. Then, dry the samples.Do de-paraffin and rehydration to the samples. To retrieve antigen, a 10 minute microwave treatment in EDTA buffer is needed. Dip the samples in PBS which contains 0.3%hydrogen.Peroxidize for 20 minutes to stop endogenous peroxidase activity. Perform the antibody EMA at LPS core, at a concentration of 1 over 300. And maintain it for one night at four degrees Celsius.In order to discover the bacteria, employ a DAP substrate kit to detect existence of HRP. Basing on a manufacturer's instructions, use HRP polymer, anti-rabbit antibody. Use table concentrator to let antibody fully combine.Use PBS to eliminate uncombined antibodies. Do your chemical staining according to the manufacturer's instructions, then flush the samples under water. Do dehydration to the samples.Suit samples by signing. And enter as a sample using imaging Pro Plus Prepare library based on the manufacturer's protocol. Practice the primer pattern, which consists of the gene-specific sequences and the Illumina adapter overhang nucleotide sequences.To amplify the birth rates for V4 region of bacterial 16s on rRNA gene sequences, amplify the template from the DNA sample input by using amplicon PCR. Use Mag-Bind RxNPure Plus magnetic beads to remove the reaction mix from the PCR product. Perform indexed PCR amplification for a second time.Use Agilent 2200 TapeStation to check the library. Use QuantiFluor dsDNA System to quantify the library. With a 600 cycle, v3 standard flow cell produce approximately 100, 000 paired ends.2 times 300 base 3. Libraries are denormalized, pooled, and sequenced. The raw rates of every sample are filtrated on the basis of sequencing quality by Trimmomatic.The primer and adapter sequences should be both removed. Sequence reads with both pair and qualities lower than 25 should be shortened. Analyse the 16s rRNA by the software package QIIME.Gather sequences to form OTUs with a similarity cutoff at 97%For OTUs, the relative abundance should be calculated in each sample. Employ a Bayesian classifier, which is in the RDP training set to sort all the sequences. With given OTU, a classification which have the major coherence of the sequences is assigned to OTUs.The OTUs are aligned to Silva database, basing on a sample group information. Perform alpha diversity and the UniFrac-based principal coordinates analysis. To predict relating presentation of the characteristics of the bacteria use BugBase.Predict the functional composition of a metagenome at PICRUSt. With the usage of monitoring data and a database containing reference genomes, analyze the different functions among each group with the help of STAMP software. Use one statistic software to calculate the result.The indication of statistical significance should be set as p less than 0.05. Calculate confounding factors, which include age and parity, by Student's t-test. Calculate menopausal status, history of hypertension and diabetes by the Chi-square test.Calculate number of ovary bacteria taxa by the Mann-Whitney U test. 16 patients were enrolled into this study. BugBase was used to practice analysis of predicted metagenomes.The potentially pathogenetic and immunohistochemistry of ovaries using anti-bacterial LPS antibody. This figure shows bacterial richness and diversity in the cancer and control groups. Revealed by 16S rRNA sequencing.The figures are observed species index. Chao1 index, ACE index, Shannon index, Evenness index, Simpson index, respectively. The relative abundance of phyla and the 12 most abundant bacterial species in the ovarian samples is shown in this figure.Figure A, B, C and D.Means the relative abundance of the panel in the ovaries of the patients in the control group and the ovarian cancer group. The relative abundances of the twelve most abundant bacteria species in the ovaries of the control patients and ovarian cancer group. This is commonests, clustered using PCoA and the relative abundance of Anoxynatronum sibiricum and Methanosarcina vacuolata.Figure A shows the commonests, which were clustered using PCoA. PC1 and PC2 are plotted on x and the y-axis. The red block is equal to the sample in the ovarian cancer group.The blue circle is equal to a sample in control group. The samples from the ovarian cancer group can be separated from other samples in the control group. Figure B shows commonests which were clustered using PCoA.PC1 and PC2 are plotted on the x and y axis. The red block is equal to a sample in the ovarian cancer group. The blue solid circle is equal to a sample from the patient with uterine myoma.And the blue hollow circle is equal to a sample of a patient with uterine adenomyosis. Figure C shows the relative abundance of Anoxynatronum sibiricum. Figure D is Methanosarcina vacuolata.This figure is the BugBase analysis of predicted metagenomics. The potentially pathogenetic and oxidative stress-tolerance phenotype of the ovaries in the cancer group were stronger than that of the control group. This figure is significantly different KEGG path space between the cancer and control groups by PICRUSt analysis.When we get our samples, a pair of new sterile tweezers is needed. Pay attention to potential contamination to the samples. That bacteria off-site tumor tissues may affect the resolve greatly.Setting control group to every step is preferred. In order to reduce the effect of potential contamination.

characterization-functional-prediction-bacteria-ovarian

Research

JoVE Journal

Cancer Research

2.6K vistas.  First Affiliated Hospital of Xi’an Jiaotong University. Este protocolo está diseñado para descubrir bacterias en los tejidos ováricos y predecir sus funciones. La tinción inmunohistoquímica y la secuenciación del ARNr 16S, se utilizaron con el fin de descubrir y distinguir bacterias en tejidos ováricos cancerosos y no cancerosos in situ. Las diferencias compensatorias y funcionales de las bacterias se predicen mediante el uso de BoPs y la investigación genético-celular de las comunidades mediante la reconstrucción de días no servidos.