NAAS Score 2020

                   5.36

UserOnline

Free counters!

Previous Next

Application of Metagenomics in Livestock Improvement

Amiya Ranjan Sahu Nibedita Nayak Rajesh Kumar Sahu Jatin Kumar Sahoo Avinash Kumar
Vol 7(8), 30-38
DOI- http://dx.doi.org/10.5455/ijlr.20170423033727

Metagenomics is the direct genetic analysis of genomes contained with an environmental sample of entire communities of organisms which comprises both genomic technologies and bioinformatics tools. At first cloning of environmental DNA is done, and then followed by screening for functional expression. Sequencing is done by fosmid, cosmid and bacterial artificial chromosome derived metagenomic studies, Sanger sequencing derived shotgun metagenomic studies and next-generation sequencing derived shotgun metagenomic studies. Broadly metagenomics focuses the metatranscriptomics and metaproteiomics giving genetic information of novel biocatalysts or enzymes, genetic linkage and phylogenetic study of uncultured organisms and evolutionary profiles of community function and structure. The steps followed in functional metagenomic study are experimental design, sample processing, sequencing technology, assembly, binning, annotation, statistical analysis, data storage and sharing of data. The development of sequence based metagenomics has substantially reduces the cost of processing and quite rapid in action. Analysis of species populations and their interaction, results that both species composition and interaction changes over time and response to environmental stimuli. Metagenomic data and metadata can be leveraged towards designing low and high throughput experiments defining the roles of genes and microorganisms in the establishment of a dynamic microbial community. Metagenomics is useful in identification of the complex consortium of bacteria, protozoa, archeae, fungi etc. and interaction among them results in better feed degradation and productivity of animals. It helps in formulating the feed ingredients consisting of probiotics and in immunomodulation both in animals and poultry.


Keywords : Livestock species Metagenome Microbiota Ruminal Enzymes

Introduction

Gene to Metagenome

Gene is a locus of DNA encodes the functional RNA and protein product which is called as molecular unit of heredity. It is the functional and physical unit of heredity that passes from parents to offspring indicating the phenotypic traits of the individual. The complete set of DNA including all of its genes present in a haploid set of chromosome is called as the genome of an organism. The genetic materials of an organism comprise both the coding and non-protein information. The term genome was coined by Hans Winker in the year 1920. The presence of DNA molecule in different position of a cell indicates the chromosomal or extra chromosomal identity like nuclear genome (nuclear DNA), mitochondrial genome, chloroplast genome, non-chromosomal genetic elements like virus, plasmids, transposable elements etc. PCR-RAPD is the best and dominant method for study of population structure without any prior knowledge (Sharma et al., 2004). Then single nucleotide polymorphism predominantly used for variability study through genotyping by PCR-RFLP or Tetra-primer Automated Refractory Mutation System-PCR, which helps in association study with economically important traits (Sahu et al., 2017). The recent concept proceeded as metagenome which is literally referred as “beyond single genome”. The term metagenome indicates the collective genomes of all members of a community having pool of microorganisms. There are a number of bacterial species ranging from 150 to 800 types which are estimated of about 1013 to 1014 numbers, i.e. more than the own body cells of an organism. Metagenomics is the direct genetic analysis of genomes contained with an environmental sample that is free from any cultural tests. It is also called as the community genomics due to the isolation and analysis of total DNA from a population of microbial community without any prior cultivation (Chen and Petcher, 2005). Metagenomics is allowing us to increase the resolution of picture in a microbial biome.

Historical Background

Direct cloning from genomic DNA was done by Pace (1997). The term metagenomics was first used by Handelsman et al. (1998). Metagenomic study of rumen was started in 2004. Singh et al. (2012) had started metagenomic study of buffalo rumen at Anand Agricultural University, Anand, India. From 2004 till date several novel fibrolytic enzymes have obtained from rumen metagenome and many works have been carried out. Metagenomics is to investigate microbes in their natural environments, surpassing culturing difficulties, understanding the host interactions, discover DNA variation and genotyping, understanding about the structure and function of the cell, understanding metabolism of different livestock and poultry species and to study microbial population.

Metagenomics Study

16s DNA libraries-Microbial Diversity Analysis

16s rRNA gene is universal in the bacteria. So the relationship can be measured in all bacteria by phylogenetic analysis of 16s rRNA. It is used as a biomarker for microbial ecology studies due to very short (1542 nucleotide bases), and quick and cheaper sequencing. There are nine hypervariable regions (V1–V9) gives sequence diversity for species identification. Hypervariable regions are conserved stretches enabling PCR amplification of target sequence using universal Primers (Lu et al., 2000). Some advantageous are recognition of bacteria with unusual phenotypic profiles, rare bacterial identification, slow growing bacterial study, uncultivated bacteria study and culture negative infections.

DNA Library Construction

For novel biotechnological applications.

Quorum Sensing

It is the regulation of gene expression in response to fluctuations in cell population density. Quorum sensing bacteria produce and release chemical signal molecules called auto-inducers that increase in concentration as a function of cell density. Gram positive and gram negative bacteria use quorum sensing communication circuits to regulate a diverse array of physiological activities like symbiosis, virulence, competence, conjugation, antibiotic production, motility sporulation and biofilm formation (Dickschat, 2010).

Mobile Metagenomics

Mobile metagenome is the total pool of mobile genetic elements associated with a bacterial community. Mobile genetic element is a discrete genetic unit capable of mediating its own transfer between distinct DNA molecules. Antibiotics constitute core line of defense against infectious disease caused by bacteria. But antibiotic resistance due to resistance gene pools shared among pathogens. These resistance genes are associated with mobile genetic elements such as plasmid, integrons and transposons, bacteriophase, insertion sequences and conjugate transposons.

Methodology of Metagenomics Study

Sampling

It is the first and most crucial step which includes representative of all cells present in the sample to give extracted DNA. Samples can be obtained from any sources like biopsy materials, ground water samples, rumen or gut ingredients etc.

DNA Extraction

DNA is extracted by any standardized method after fractionation or selective lysis to obtain minimal host DNA. After that selective filtration, centrifugation or flow cytometry is done to enrich the target fraction. Then random hexamers are used to amplify the target DNA where some problems like reagent contamination, chimera formation or sequence bias in amplification may occur that should be prevented.

DNA Sequencing

Sanger’s method of sequencing is the gold standard for the sequencing due to the merits like low error rate, long read length (>700 bp) and large insert sizes which leads better assembly of shotgun data. But it has disadvantages like labour intensive cloning process and overall costs per giga base. The most recent technique like next generation sequencing overcomes these difficulties due to the shorter run time and multiplexing along with the greater read length in low costs. The next generation sequencing method utilizes emulsion polymerase chain reaction or solid surface PCR amplification by immobilizing random DNA fragments (Morozova, 2008).

Assembly

It is required to obtain the longer genomic contains from short read fragments to recover the genome of uncultured organisms. It is mainly focus to obtain full length CDs for subsequent characterization rather than functional description of the community.

Binning

Sorting DNA sequences into groups that might represent an individual genome or genomes from closely related organisms is called binning. Many statistical software’s like MEGAN, MGRAST etc. are required for binning the data (Huson et al., 2007).

Annotation

The processes like features prediction and functional annotation are done to modify the large contigs produced by the assembly. Entire community is used for annotation having unassembled reads and short contigs.

Statistical Analysis Using Bioinformatics Tools

MEGAN analysis of metagenomic data is used to compute and explore the taxonomical content of the data set (Huson et al., 2007). For rRNA identification a BLAST similarity search for the longer cluster representative is performed against the M5RNA database, GreenGenes, Silva-SSU, Silva-LSU and European ribosomal RNA database (Meyer et al., 2008).

Data Storage and Data Sharing

Minimum information of any sequence checklists are done to store data, but costs of sequencing continues to drop where as costs of analysis and storage space remains more.

Application in Livestock Improvement

  1. Enhancement of production by improving digestibility of cellulosic and ligno-cellulosic feed ingredients.
  2. Enzymes and other microbial population identification that can be used as probiotics.
  3. Disease resistance and use of certain strains for drug of choice.
  4. Interaction between genotype and environmental study leads better control over management.

Rumen Microbial Eco-System

It is the very complex consortium of bacteria, protozoa, archeae, fungi and bacteriophages, where the interaction among them results in better feed degradation. Most of these microbes (85-90%) are uncultivable due to unique rumen fermentation. Fiber degradation is not optimal. Genetic and ecological manipulation of rumen fermentation has many constraints as given below-

  1. Poor understanding of ecological factors (Krause et al., 2007).
  2. Lack of reliable transformation system (Wallace and Lahlou-Kassi, 1995).
  3. Lack of knowledge of functional genomics framework within which fiber digestion operates (Nene et al., 2000).

Effect of Metagenomics in Ruminants

  1. They help in H2 transactions.
  2. Fatty acid production from non nitrogenous element.
  3. Hydrolysis of foods due to better fibre degradation.
  4. Ruminal microflora helps in biofilm formation.
  5. They have proper adhesion mechanism.
  6. Microorganisms in rumen help in immunomodulation which ultimately leads disease resistance.

There are 268 gigabases of metagenomic DNA from fiber adherent microbes of cow rumen has been identified (Hess et al., 2011). Total 27,755 putative carbohydrate-active genes were identified and 90 candidate proteins expressed of which 57% were enzymatically active. They were also identified and assembled 15 uncultured microbial genomes. In another study, three fiber-adherent microbiomes of different animals fed the same diet and one pooled liquid sample were studied, which constitute a total of 510 unique OTUs (Brulc et al., 2009). A majority of sequences (64%) belonged to 59 OTUs, which were shared between all libraries. A number of carbohydrate active enzymes were studied where total glycosyl hydrolyses were most active and pectate lyase were least active.

Chicken Gut Microbiota

Chickens are most efficient converter of feed to lean meat although their feed is often of low digestible. The chicken intestinal bacteria, which outnumber host cells up to ten times provide benefit to the host. The gut microbiota has an important role in poultry health and production, by influencing digestion and nutrient absorption, intestinal morphology and defense of host against infection. Gut microbiota positively influence the host’s gastrointestinal development, biochemistry, immunology, physiology and play an important role of pathogenesis of intestinal disease since they are believed to protect against colonization of the intestine by pathogens and to stimulate the immune response to chicken. Recent molecular studies targeting the bacterial DNA in poultry guts have yielded more detailed insight in to the composition of microbial community (Lan et al., 2002). Different microbiota was detected in intestinal gut and caecum (Stanley et al., 2012). Good perfoming birds’ microbiome appears to be enriched with genes associated with sulphur assimilation and flagella motility (Singh et al., 2013)

Metagenomics and Disease Resistance

Indigenous breeds of animals and chicken breeds are more resistance to bacteria specifically Salmonella reduction by competitive exclusion (Zhang et al., 2007). Thirabunyanon et al. (2009) found a novel probiotics strain of Bacillu subtilis having inhibitory activity against Salmonella enteritidis infection. Lactobacillus jansenni which has been used as immunobiotic also found in extensively reared CARI Nirbheek. Scupham et al. (2010) reported that Megamonas hypermegalewhich belongs to phylum Firmicutes is a beneficial bacterium and associated with suppression of Campylobacter.

Identification of Novel Enzymes by Metagenomics

Three lipases derived from dairy rumen microflora had different substrate specificity and good thermal stability. A novel endo-β-1, 4-xylanase was recently identified which has two catalytic domains of family GH43 and two CBMs of family IV (Zhao et al., 2010). Laccase are identified from reindeer rumen by metagenomic approaches able to degrade lignin (Pope et al., 2010). The details of identified enzymes from various reports are given in Table 1.

Table 1: Enzymes identified in metagenomics study using Function-based Sanger’s sequencing technique

Enzyme Name Sample Source Reference
Xylanase Holstein cows rumen Zhao et al., 2010
Endoglucanase; Exoglucanase;

β-glucosidase

Contents of buffalo rumen Duan et al., 2009
Endoglucanase; β-glucosidase Forest soil, elephant dung, cow rumen Wang et al., 2009
Mannanase-xylanase-glucanase Cow rumen fluid Palackal et al., 2007
Esterase Arctic soil Yu et al., 2011
Lipase Dairy cow rumen Zhao et al., 2009
Laccase Svalbard reindeer Pope et al., 2010

Limitations

Despite the great strides made by metagenomics researchers, significant technical hurdles remain due to too much data. The complexity of sample environments often makes purification of DNA challenging as in the case of extracting DNA from an acidic environment in a mine, excessive shearing limited insert sizes to 3-4 kb. Sample contamination and chimeric clone sequences can also be a problem. In the case of one oceanic metagenome study, the presence of DNA from a freshwater bacterium suggests that the sample might have been contaminated. Problems can also arise because sequence similarities in distinct species can lead to errors in assembly. Perhaps the greatest challenge of metagenomics is attempting to sequence the genomes of underrepresented species (i.e. comprising less than 1% of the microbial community). Such cases require the sequencing of gigabases of DNA for adequate coverage which is outside the reach of sequencing technology in the present time.

Conclusions

The new methods and technologies are used day by day which can reduce the costs of sequencing and can explore microbial diversity of unknown ecosystems in a better way. Research on rumen and gut microbial diversity of different livestock and poultry species may help in identifying the probiotics communities that have both growth promoting and immunity boosting properties. The analysis of gut microbial diversity would help to find out the strains that are contributing to the adaptability of animals to the native conditions. This is the best way to reveal the phylogenetic and evolutionary relationships of modern species with the native and ancestors of livestock and poultry.

References

  1. Brulc JMAntonopoulos DAMiller MEWilson MKYannarell ACDinsdale EAEdwards REFrank EDEmerson JBWacklin PCoutinho PMHenrissat BNelson KE and White BA. 2009. Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. In: Proc. National Acad. Sci., USA, 106 (6): 1948-53.
  2. Chen K and Pachter L. 2005. Bioinformatics for whole genome shotgun sequencing of microbial communities. PLoS Comput. Biol., 1: 24.
  3. Dickschat JS (2010) Quorum sensing and bacterial biofilms. Nat Prod Rep 27: 343−369.
  4. Duan RY, Wang XA, Tu YB, Huang MY and Wang C. 2009. Recruitment pattern of tree populations along an altitudinal gradient: Larix chinensis Beissn in Qinling Mountains (China). Pol. J. Ecol., 57: 453-461.
  5. Handelsman J, Rondon MR, Brady SF, Clardy J, Goodman RM. 1998. Molecular biological access to the chemistry of unknown soil microbes: A new frontier for natural products. Chem. Biol., 5 (10): 245-249.
  6. Hess M, Sczyrba A, Egan R, Kim TW, Chokhawala H, Schroth G, Lou S, Clrk DS, Chen F, Zhang T, Mackle RL, Pennacchio LA, Tringe SG, Visel A, Woyke T, Wang Z and Rubin EM. 2011. Metagenomic discovery of biomass degrading genes and genomes from cow rumen. Science, 331 (6016): 463-467.
  7. Huson DH, Auch AF, Qi J and Schuster SC. 2007. MEGAN analysis of metagenomic data. Genome Res., 17: 377–386.
  8. Lan GTZhu LP and Zhang W. 2002. Discovery of MHC restriction in antigen recognition of T cells. Zhongguo Yi Xue Ke Xue Yuan Xue Bao, 24(5): 539-41.
  9. Lu JJ, Perng CL, Lee SY and Wan CC. 2000. Use of PCR with universal primers and restriction endonuclease digestions for detection and identification of common bacterial pathogens in cerebrospinal fluidJ. Clin. Microbiol., 38 (6): 2076-2080.
  10. Meyer F, Paarmann D, D’Souza M, Olson R, Glass EM, Kubal M, Paczian T, Rodriguez A, Stevens R, Wilke A, Wilkening J  and Edwards RA. 2008. The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics9: 386.
  11. Morozova, O. and Marra, M.A., 2008. Applications of next-generation sequencing technologies in functional genomics. Genomics92(5), pp.255-264.
  12. Nene, V., Morzaria, S., Baker, L., Odonyo, A., Zerbini, E. and Bishop, R., 2000. Genomics research: Prospects for improving livestock productivity. In In Agricultural Biotechnology and the.
  13. Pace NR. 1997. A molecular view of microbial diversity and the biosphere. Science, 276: 734-740.
  14. Palackal N, Lyon CS, ZAidi S, Luginbhul P, Dupree P, Goubet F. 2007. A multifunctional hybrid glycosyl hydrolases discovered in an uncultured microbial consortium from ruminant gut. Appl. microbiol. Biotechnol., 74: 113-124.
  15. Pope PB, Denman SE, Jones M, Tringe SG and Barry K. 2010. Adaption to herbivore by the Tammar wallaby includes bacterial and glycoside hydrolases profiles different to other herbivores. Proc. Natl. Acad. Sci., USA, 107: 14793-14798.
  16. Sahu AR, Jeichitra V, Rajendran R and Raja A. 2017. Polymorphism in exon 3 of myostatin (MSTN) gene and its association with growth traits in Indian sheep breeds. Small. Rumin. Res., 149: 81-84.
  17. Sahu AR, Jeichitra V, Rajendran R and Raja A. 2017. Polymorphism of growth hormone receptor (GHR) gene in Nilagiri sheep. Trop. Anim. Health Prod., 49: 281-285.
  18. Scupham AJ, Jones JA, Rettedal EA and Weber TE. 2010. Antibiotic manipulation of intestinal microbiota to identify microbes associated with campylobacter jejuni exclusion in poultry. Appl. Environ. Microbiol., 74 (24): 8026–8032
  19. Singh M, Ahir VB, Tripathi AK, Ramani UV, Sajnani M, Koringa PG, Jakhesara S, Pandya PR, Rank DN, Murty DS, Kothari RK and Joshi CG. 2012. Metagenomic analysis of Surti buffalo (Bubalus bubalis) rumen: a preliminary study Krishna. Mol. Biol. Rep., 39: 4841–4848
  20. Singh P, Karimi A, Devendra K, Waldroup PW. 2013. Influence of penicillin on microbial diversity of the cecal microbiota in broiler chickens. Poult. Sci., 92: 272–276.
  21. Sharma AK, Bhushan B, Kumar S, Kumar P, Sharma A and Kumar S. 2004. Molecular characterization of Rathi and Tharparkar indigenous cattle (Bos indicus) breeds by RAPD-PCR. Asia-Australas. J. Anim. Sci., 17 (9): 1204-1209.
  22. Stanley D, Keyburn AL, Denman SE and Moore RJ. 2012. Changes in the caecal microflora of chickens following Clostridium perfringens challenge to induce necrotic enteritis. Vet. Microbiol., 159: 155–162.
  23. Thirabunyanon M, Boonprasom P and Niamsup P. 2009. Probiotic potential of lactic acid bacteria isolated from fermented dairy milks on anti-proliferation of colon cancer cells. Biotechnol. Letters, 31: 571–576.
  24. Wallace R.J. and Lahlou-Kassi A. 1995. Rumen Ecology Research Planning. Proceedings of a Workshop Held at ILRI, Addis Ababa, Ethiopia, 13–18 March 1995. ILRI (International Livestock Research Institute), Nairobi, Kenya. 270 pp
  25. Wang W, Zhang P and Liu X. 2009. Short read DNA fragment anchoring algorithm. BMC Bioinformatics, 10 (1): 17.
  26. Yu EY, Kwon MA, Lee M, Oh JY, Choi JE and Lee JY. 2011. Isolation and characterization of cold-active family VIII esterases from an arctic soil metagenome. Appl. Microbiol. Biotechnol., 90: 573-581.
  27. Zhang G, Ma L and Doyle MP. 2007. Potential Competitive Exclusion Bacteria from Poultry Inhibitory to Campylobacter Jejuni and Salmonella. J. Food Prot., 70 (4): 867-873.
  28. Zhao S, wang J, Bu D, Liu K, Zhu Y and Dong Z. 2010. Novel glycoside hydrolases identified by screening a Chineese Holstein dairy cow rumen-derived metagenome library. Appl. Environ. Microbiol., 76: 6701-6705.
Full Text Read : 1755 Downloads : 372
Previous Next

Open Access Policy

Close