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Journal of Drug Delivery and Therapeutics

Open Access to Pharmaceutical and Medical Research

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Open Access   Full Text Article                                                                                                                                                Research Article 

Metagenomics Analysis of Breast Cancer to Study Bacterial Diversity

Muskan Jain and Ruchi Yadav* 

Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, UP, India

 

 

INTRODUCTION

Metagenomics is the inspection of hereditary material recuperated straightforwardly from environmental instance¹. This field may also be alluded to as natural genomics, Eco genomics or community genomics. Metagenomics gives a culture independent perception about microbial communities². It has come out as a powerful core since most microorganisms can’t grow in a pure culture and that culturing can’t capture that full spectrum of microbial variegation³.

Metagenomics provides the powerful tool of research and study about prokaryotes and viruses that are existing in the adjoining through the analysis of their DNA of microbes⁴. It can not only recognize the microbes but also gives perception into the practical and metabolic part of the microbes⁵. There are also many biotechnological applications of metagenomics as it gives the great impact on the industrial production. Metagenomics has also been useful in finding the diseases that are caused by microorganism⁶. Shotgun metagenomics is used in the discovery and diagnosis of pathogens in clinical samples. For RNA viruses, firstly RNA is isolated from the sample which is converted to cDNA. Besides bacterial pathogen and viruses, metagenomics also helps in the detection of parasitic infection⁷.

Metagenomics also helps to determine gut microbial species it gives insight into the development of probiotics⁸. Observation of homosapiens associated bacterial communities permits to establish ways to modulate them, to optimize human health⁹. Metagenome alludes to an assortment of complete hereditary material of a blended community of microbes, for example, natural metagenome, human metagenome, and so on it contains genomes of both cultivatable and uncultivatable microorganisms¹⁰. Metagenomics is an atomic apparatus used to investigate the blended genomic materials separated from ecological examples, which gives definite data of species variety and abundancy¹¹.

Breast cancer is the swelling structures in the breast cells. After skin cancer, breast cancer is the most broadly accepted malignancy analysed in ladies in the United States. Breast cancer can happen in both males and females, yet it's undeniably more normal in ladies¹². Indications of mammary glands cancer may involve a ball or lumps in the breast, an alteration of breast shape, scratching of the skin, fluid coming out from areola, a recently reversed areola, or a red or textured fix of skin. In those with removed lay out of the illness, there can be bone agony, swollen lymph hubs, windedness, or yellow skin¹³. Breast cancer most likely takes place in cells from the covering of milk conduits and the lobules that stockpile these pipes with milk. Cancers taking place in the pipes are referred to as ductal carcinomas, weather those take place from lobules are referred to as lobular carcinomas¹⁴. 

An edge to high throughput sequencing is that this procedure doesn’t  need cloning the DNA prior to sequencing, removing one of the main bases and bottlenecks in natural sampling¹⁵. The earliest metagenomic studies took place using high-throughput sequencing used highly parallel 454 pyrosequencing¹⁶. Three additional technologies commonly put into environmental sampling are the Ion Torrent Personal Genome Machine, the Illumina MiSeq or HiSeq and the Applied Biosystems Solid system. These technologies for sequencing DNA produce shorter fragments than Sanger sequencing; Ion Torrent PGM System and 454 pyrosequencing typically produces ~400 bp reads, Illumina MiSeq produces 400-700bp reads (guarded on in case paired end options are used), and Solid generate 25–75 bp reads ¹⁷.

The information created by metagenomics tests are both huge and innately loud, containing divided information addressing upwards of 10,000 species¹⁸. The sequencing of the cow rumen metagenome produced 279 gb, or 279 billion base sets of nucleotide succession data, while the human gut microbe’s quality list recognized 3.3 million qualities amassed from 567.7 gigabases of grouping data¹⁹. Many tools have been created to integrate metadata and sequence data, permitting downstream comparative examination of different datasets using several ecological indices²⁰.

Comparative interpretation between metagenomes can give additional perception into the purpose of complex microbes and their functions in host²¹. Pairwise or multiple comparison between metagenome might be made at the level of sequence composition, taxonomic diversity, or functional complement²². Comparisons of population infrastructure and phylogenetic diversity can be made based on 16S and other phylogenetic marker genes, in the case of low-diversity communities it can be done by genome reconstruction from the metagenomic datasets²³. 

Metagenomic sequencing is especially valuable in the analysis of viral communities and the best way to get to the hereditary range of the viral local area from an environmental example is through metagenomics²⁴. As viruses come up short on a common all-inclusive phylogenetic marker (as 16S RNA for microscopic organisms and archaea, and 18S RNA for eukaryote), Viral metagenomes (likewise called viromes) should hence give increasingly more data about viral variety and development²⁵. Advances in bioinformatics, refinements of DNA enhancement, and the expansion of computational force have significantly helped the investigation of DNA successions recuperated from ecological examples, permitting the transformation of shotgun sequencing to metagenomic samples²⁶.

MATERIALS AND METHODS

Metagenomic data retrieval: Metagenomic data of breast cancer was retrieved from ENA database (European Nucleotide Database) https://www.ebi.ac.uk/ena/browser/home with the accession number ERP107332 and project Id PRJEB25419 https://www.ebi.ac.uk/ena/browser/view/PRJEB25419. Four FASTQ format files were downloaded for two different breast cancer metagenomics sample as shown in table 1. In this experiment Illumina HiSeq 2000 paired end sequencing was done of Breast cancer metagenome. For current research these two samples files were selected for metagenomics analysis of breast cancer samples.

 

 

Table 1: Table of study accession, sample accession, experiment accession, run accession and sample name.

S. No	Sample Accession	Run Accession	Sample name	SRA files: FTP
1	SAMEA104667884	ERR2368791	Breast Cancer Metagenomics	1.ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR236/001/ERR2368791/ERR2368791_1.fastq.gz 2.ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR236/001/ERR2368791/ERR2368791_2.fastq.gz
2	SAMEA104667885	ERR2368792	Breast Cancer Metagenomics	1.ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR236/002/ERR2368792/ERR2368792_1.fastq.gz 2.ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR236/002/ERR2368792/ERR2368792_2.fastq.gz

 

 

 

Metagenomics tools used analysis: Galaxy server (https://usegalaxy.org/) and metagenomics tools at Galaxy server (https://metagenomics.usegalaxy.eu/) were used for metagenomics analysis and annotation of samples data. FastQC tool²⁷ was used for quality control analysis and identification of statistical parameters and reads information in each file. Trimmomatic²⁸ tool was used for trimming fastq file to filter the adapter sequence and to increase the quality of raw files so that it can be used for further analysis.

Kraken 2²⁹ tool was used for taxonomic analysis of all samples and bacterial community along with their abundance was predicted. Further Convert Kraken³⁰ tool was used to convert taxonomic file generated from Kraken 2 tool into Krona compatible file so that it can be used in Krona tool³¹ for visualization of bacterial diversity in all the sample files taken into consideration.

 

.

Figure 1: Steps used for metagenomics analysis and tools used for each step has been shown in brackets. These tools were used form metagenomics galaxy server.

 

 

Figure 1 shows the steps and tools used for metagenomics analysis. First step is quality control. This process is performed for the accuracy, completeness, relevancy, validity, timeliness of the data. 

Second step is trimming of data it executes a wide range of useful trimming tasks for illumina paired end and single ended data. To perform the trimming process of data search Trimmomatic tool from the galaxy tool panel. FastQC output file was used to perform trimmomatic tool. 

Third step is taxonomic prediction which was done using Kraken 2 tool it is a taxonomic sequence classifier that gives taxonomic labels to small DNA reads. Kraken 2 tool analyses the k-mers within a read and aligns the taxonomic database with these query k-mers. Output of trimmomatic tool was used as an input in Kraken 2 tool.  Convert Kraken tool was used to create taxonomic abundance file that can used further for visualization purpose. This tool converts Kraken metagenomic classifier to the full portrayal of NCBI taxonomy. Kraken 2 output file was used as input in Convert Kraken tool.

Lastly, Krona pie chart tool was used for visualization of metagenomic result in a zoomable pie-chart. Result of convert kraken tool was used as input file in krona tool.

RESULTS AND DISCUSSION

Quality control of all sample’s files used for this research as mentioned in table 1 was done using FASTQC tool . Table 2 shows the comparison of total number sequence and GC content in all four samples that was generated from quality control analysis. 

Table 2: Sample title, total sequence, and GC content for all samples.

S.N.	Sample Title	Total seq.	%GC
1	ERR2368791_1_fastq_gz	747728	42
2	ERR2368791_2_fastq_gz	7472728	42
3	ERR2368792_1_fastq_gz	7893195	43
4	ERR2368792_2_fastq_gz	7893195	43

 

FASTQC generates different statistical graphs that can be used to analyse the quality of each sample file. Quality control for all sample file was done but FASTQC result of first file that is ERR2368791_1_fastq_gz has been shown her demonstration purpose. Figure 2 shows the Per base sequence quality graph more the graph is in green region means sequence is of good quality.

 

Figure 2: Per base sequence quality graph of ERR2368791_1_fastq_gz

Figure 3: Per sequence quality scores

Figure 4:  per sequence GC content

 

 

Figure 3 shows the Per sequence quality scores that is used to analyze the quality of reads sequence in each Fastq file. Average quality of reads in fastq file is calculated according to Phred score.  Figure 4 shows the per sequence GC content in sample file (red line) in comparison with standard GC content (blue line). GC content graph of ERR2368791_1_fastq_gz shows that file can be further used for metagenomics analysis.

Kraken2 which is a taxonomic sequence classifier that gives taxonomic labels to small DNA reads. This tool  aligns each k-mer in Kraken's genomic library to lowest common ancestor (LCA) during a taxonomic data analysis in comparison with all genomes that contain that k-mer³². The set of LCA taxa that correspond to the k-mers of reads in sample files are then analysed to make one taxonomic label for the reads³³.  It classifies each sequence in a single line of output this file have five tab-delimited fields that are “C/U” which is depicting the whether the sequence is classified or not, sequence ID, taxonomic ID, length of sequence in base pairs, space-delimited list showing LCA mapping of each k-mer in sequence. 

Convert Kraken tool convert output of KRAKEN 2 into table format. This table  includes Read name, Tax ID field, and appended 22 columns having taxonomic ranks from Super kingdom to Subspecies.

Visualization of taxonomy files by Krona tool

Krona visualization tool renders results of a metagenomic profiling as a zoomable pie chart. It allows hierarchical data, here taxonomic levels, to be explored with zooming, multi-layered pie charts.

 

Figure 5: Result of KRONA pie chart for the paired data ERR2368791_1 and  ERR2368791_2

 

Figure 5 shows all the bacteria present in ERR2368791_1 and ERR2368791_2 breast cancer metagenomics samples. A different colour in the figure shows the different bacteria and also their ratio in the sample.   Species which are shown here with different ratios like 3% of Cichlomorphae, 2% of Nematocera, 4% of pyronematacae, 2% of streptophyta, 89% of Actinidiae.

 

 

Figure 6: Result of KRONA pie chart for the paired data  ERR2368792_1 and  ERR2368792_2

 

Figure 6 shows all the bacteria present in the paired data  of ERR2368792_1 and  ERR2368792_2.  Species which are shown in this figure are 4% of Nematocera, 2% of Brachycera, 3% of Galegeae, 2% of Nelumbo nucifera, 22% of Bifidobacterium cuniculi, 2% of Human endogenous virus, 2% of Murine leukemia, 41% of Pyronemetaceae, 15% of Cichlomorphae and so on.

 

Figure 7: Combined result of KRONA pie chart for the all the paired ERR2368791_1, ERR2368791_2 and ERR2368792_1 and  ERR2368792_2 for comparison

 

 

Figure 7 shows the combined taxonomic profile in all samples ERR2368791_1, ERR2368791_2 and ERR2368792_1 and   ERR2368792_2.  Comparative analysis between all sample files was done.   Species which are shown in this figure 7 are 4% of Nematocera, 2% of Brachycera, 3% of Galegeae, 2% of Nelumbo nucifera, 22% of Bifidobacterium cuniculi, 2% of Human endogenous virus, 2% of Murine leukemia, 41% of Pyronemetaceae, 15% of Cichlomorphae, 1% of Proteobacteria, 2% of Sordariales.

Comparative metagenomics analysis identifies the microbes present in breast cancer data these are Actinobacteria, Bifidobacterium cuniculi, Murnine leukemia virus, Human endogenous retrovirus, Pezizomycotina

Actinobacteria are a group of Gram-positive bacteria which has high guanine and cytosine content in their DNA, which can be terrestrial or aquatic. Although  they are single celled like bacteria, they don’t  have particular  cell wall, but they generate  a mycelium which is nonseptate and more slender³⁴. 

Bifidobacterium cuniculi is a class of gram-positive, nonmotile, frequently fanned anaerobic microscopic organisms. They are universal occupants of the gastrointestinal parcel, vagina and mouth (B. dentium) of well evolved creatures, including people. Bifidobacteria are one of the significant genera of microorganisms that make up the gastrointestinal parcel microbiota in vertebrates. Some bifidobacteria are utilized as probiotics³⁵.

Murnine leukemia virus (MLVs or MuLVs) are retroviruses named for their capacity to cause disease in murine (mouse) has. Some MLVs may taint different vertebrates. MLVs incorporate both exogenous and endogenous infections. Duplicating MLVs have a positive sense, single-abandoned RNA (ssRNA) genome that repeats through a DNA halfway by means of the cycle of opposite record³⁶. 

Human endogenous retrovirus these are endogenous viral components in the genome that intently look like and can be gotten from retroviruses. They are plentiful in the genomes of jawed vertebrates, and they contain up to 5–8% of the human genome³⁷.

Pezizomycotina make up the vast majority of the Ascomycota growths and incorporate most lichenized parasites as well. Pezizomycotina contains the filamentous ascomycetes and is a region of the Ascomycota (growths that structure their spores in a sac-like ascus). It is pretty much inseparable from the more established taxon Euascomycota. These growths recreate by splitting instead of sprouting and this region incorporates practically all the ascus organisms that have fruiting bodies noticeable to the unaided eye³⁸.

CONCLUSION

Metagenomics study of breast cancer was done to predict the bacterial diversity in the samples using computational methods. Complete metagenomics analysis was done to stud the taxonomic and functional classification metagenomes in breast cancer data. Metagenomics analysis identifies the microbes in breast cancer data these are Actinobacteria, Bifidobacterium cuniculi, Murnine leukemia virus, Human endogenous retrovirus, Pezizomycotina . Current study signifies the important microorganisms that are present in breast cancer samples. These results can be used understanding the function and relation of these bacteria with the breast cancer development and progression. Detailed study is required to establish the association of microbes in breast cancer that can be further used for drug development. 

Acknowledgment

We would like to acknowledge Amity Institute of biotechnology, Amity University Uttar Pradesh, Lucknow campus for providing us facilities to conducting this study. This research project is not funded by any specific grant from funding agencies in the public, commercial, or non-profit sectors.

Conflict of Interest

Authors do not have any conflict of interests.

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