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Characterization and Functional Prediction of Bacteria in Ovarian Tissues
Chapters
Summary October 23rd, 2021
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).
Transcript
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.
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