Back
 ABB  Vol.12 No.5 , May 2021
Functional Metagenomics from the Rumen Environment—A Review
Abstract: The rumen microbiome plays an essential role in ruminant physiology, nutrition and pathology as well as host immunity. A better understanding of rumen microbial processes and identification of which populations are responsible for specific functions within the rumen microbiome will lead to better management and sustainable utilization of the available feed base while maintaining a low environmental impact. Recent advance in the culture independent method of microbiology such as metagenomics, unravels potentially the rumen microbial process. There are two basic types of metagenomics studies: Sequence-based and function-based metagenomics. Sequence-based metagenomics involves sequencing and analysis of DNA from environmental samples. Its purpose is to assemble genomes, identify genes, find complete metabolic pathways, and compare organisms of different communities. Whereas functional metagenomics is the study of the collective genome of a microbial community by expressing it in a foreign host usually Escherichia coli (E. coli). It is a promising approach unearthing novel enzymes even from yet to culture rumen microbiota. Further advances in the screening techniques promise vast opportunities to rumen microbiologists, and animal nutritionist. The identification of novel enzyme through functional metagenomics consists of three parts: rumen sample collection; DNA library construction and screening of individual clone. Functional metagenomics was successfully applied to identify different antibiotics, hydrolytic enzymes, antibiotic resistance genes, and many other functions; moreover, it allowed characterization of genes encoding enzymes with a particular activity, which represents completely novel sequence. There are a number of outputs from functionally screened rumen product such as carbohydrate active enzymes (CAZymes) that can break down plant cell walls. Company involved commercialization of metagenomics research such as Syngenta, Genencor International, BRAIN etc., has produced many biological molecules of commercial interest. The aim of this paper is to elucidate functional metagenomics, from rumen environment and its potential for commercial purpose.

1. Introduction

Unlike monogastric, the forestomach in the ruminants is divided into four compartments, i.e. rumen, reticulum, omasum and abomasum or true stomach or glandular stomach [1]. The Ruminants forestomach allows colonization of countless numbers of microbes [2], which are collectively called the rumen microbiome [3]. Out of these groups, bacteria and protozoa predominate the microbial biomass [4]. The rumen microbiome plays an essential role in ruminant physiology, nutrition and pathology as well as host immunity [5]. Rumen microorganisms are able to modulate nutrient absorption and may be among the major determinants of nutrient utilization efficiency and detoxifying plant secondary compounds [6] [7]. However, they are also responsible for methane production [8]. Diet composition and dry matter intake are responsible for altering the rumen microbiome composition [9]. Rumen is one of the most underutilized microbial ecosystems that produce an array of enzymes for digestion and utilization of different plant constituents [4], e.g. Lignocellulolytic enzymes from synergistic relationship of rumen microbiome extract energy from the fiber feed and support digestion of the host. This yields volatile fatty acids (VFAs, acetate, butyrate, propionate), formic acid, H2, CO2, and CH4 [10].

The major obstacles hindering our understanding of the structure and function of the rumen microbiome are that only approximately 15% of rumen bacteria appear to be culturable [11], which highlights the importance of molecular biology approaches to sidestep this limitation and study the rumen system in total [12].

Recent developments in the study of gut microbial communities (microbiomes) through genomics and metagenomics are revolutionizing our understanding of the functions of the ecosystem and the interactions among their members and the host animal [8]. Metagenomics is a fast growing and diverse field within environmental biology directed at obtaining knowledge on genomes of environmental microbes, without prior cultivation, as well as of entire microbial communities. Other terms are also used to describe this: environmental genomics, eco-genomics, community genomics, and mega-genomics [13].

Two approaches have been commonly used for exploring the rich genetic resource provided by rumen microbiome: high throughput screening of cloned expression libraries made from rumen metagenome DNA for gene products of interest (functional metagenomics) and sequencing based characterization of the aggregate collection of genomes and genes present in rumen microbial communities, at both DNA (metagenomics) and RNA levels (meta-transcript omics) [5].

The term “functional metagenomics”, in a broad sense, is meant to reflect a connection between the identity of a microbe, or a community, uncovered via metagenomics and their respective function(s) in the environment [13]. By combining different approaches, investigation at a functional level (e.g. cellulose degradation, hydrogen metabolism) rather than a phylogenetic one is more pragmatic approach to uncover new protein [8] [14]. That helps to better assign function, role and significance to differences in microbial community structure.

Functional screening technology was first applied to rumen materials to mine novel enzymes in 2005 [15]. Despite the accumulated body of information, there is still an incomplete understanding of the functioning and ecology of the rumen microbiome and its behavior from yet to be culture microbes. For instance, the complete mechanism of plant polysaccharide degradation, the quintessential rumen function, is not yet elucidated [16]. So, this manuscript is intended to highlight functional metagenomics approach from the rumen environment.

2. Microbes Involved in Various Rumen Functions

The rumen harbor very complex consortium of bacteria, protozoa, archaea, fungi and bacteriophages, where the interaction among them results in better feed degradation [17]. According to Woese’s classification all microbes in the rumen ecosystem can be distinguished into three domains: Bacteria (bacteria), Archaea (methanogens), and Eucarya (protozoa and fungi) [18]. Table 1 illustrates the physical, chemical, and microbiological characteristics of rumen ecosystem.

3. Methodology

3.1. Rumen Functional Metagenomics Methodology

The Rumen microbiome functional metagenomics protocol consists of three parts: Environment (rumen fluid) sample collection; DNA library construction and isolate or screening of individual clones. A DNA library consists of random fragments of DNA (genes from rumen metagenome) insert into a circular DNA vectors called e.g. Plasmid/fosmids. These circular vectors are then put into a microbe usually E. coli, so they can be replicated during the microbe’s life cycle. The next step is to isolate or screen individual clones following particular function (e.g. antibiotic resistance).

3.2. Rumen Samples Collection Method

The standardization of collection and processing methods for rumen samples is

Table 1. Physical, chemical, and microbiological characteristics of rumen ecosystem.

Source: [19].

crucial to reduce the level of errors that may affect the analysis and interpretation of the data [20]. Table 2 illustrates different rumen sampling and processing techniques.

3.3. Rumen Microbiome DNA Extraction Methods

Sampling and DNA extraction methods result in adequate yields of microbial DNA that also accurately represents the microbial community are crucial [22]. Environmental samples DNA fragments size varied in a range between less than 10 kb and more than 400 kb, depending on the sample and the mechanical, chemical, or enzymatic protocols used for the DNA extraction [23]. Different Author evaluated the phylogeny of rumen microbes using different rumen microbiome DNA extraction methods. Table 3, illustrate the different DNA extraction evaluation method and its output.

Table 2. Rumen sampling and processing techniques.

Table 3. DNA extraction evaluation method and its output.

4. Constructing Functional Metagenomics Libraries

The metagenomics for different enzyme discovery involves creating of a metagenomics library from rumen sample and screening the library clones for specific enzymes [27]. Early studies on rumen microorganisms depended on retrieval of genes from libraries of genomic DNA via functional screening or, lately, via PCR amplification of genes and their homologs [28]. Procedurally, clone library construction involves obtaining a DNA or RNA extract from a mixed microbial community of interest, such as rumen sample. Ribosomal RNA Gene is then amplified using PCR or RT-PCR. Amplicons are purified and inserted into a vector such as plasmid containing antibiotic resistance genes [29]. Expression library then screened for a target reaction with a specific substrate [30] [31]. The clone in the expression vectors followed by activity-based screening has endless possibilities of unlocking concealed potential in uncultured microbial world [32].

There are two distinct strategies taken in metagenomics, according to the primary goal. First, large insert libraries (cosmid, fosmid, or bacterial artificial chromosomes (pBACs)) are constructed for archiving and sequence homology screening purposes: to capture the largest amount of the available genetic resources available in the sample and archive it for further studies/interrogation. Second, small insert expression libraries, especially those made in lambda phage vectors, are constructed for activity screening [14].

The DNA extraction procedure and size sorting using denaturing gradient gel electrophoresis is a critical step when constructing large-insert libraries (e.g., fosmid, cosmid) and small insert expression libraries (e.g., those in lambda phage and plasmid vectors) [14]. The choice of a vector depends largely on the length of the inserts. Plasmids are suitable for cloning smaller than 10-kb DNA fragments, and cosmids (25 - 35 kb), fosmids (25 - 40 kb), or BACs (100 - 200 kb) can be used to clone larger fragments [33]. Among these vectors, plasmids have high copy numbers and strong vector-borne promoters. Nevertheless, these apparent merits do not improve the hit rate significantly [33]. Cosmid or fosmid based libraries are often preferred due to their large and consistent insert size and high cloning efficiency [27].

For the construction of a library, most researchers use E .coli as a surrogate host [33]. In most such cases, the host for the cloned DNA has been the workhorse of the molecular geneticist [34]. Various types of E. coli strains are available as highly efficient competent cells from commercial sources.

However, most function-based approaches for metagenomic screening are hindered by the biased and insufficient expression in E. coli due to transcription-translation machinery of E. coli is not compatible with the expression of genes harvested from environmental microbes. This can result in a very low proportion of positive clones being obtained from one round of screening of metagenomic libraries (in some cases less than 0.01%) [35]. There is an urgent need to develop a greater range of alternative hosts with good expression of foreign genes of metagenomic origins [36]. Furthermore, technical challenge in library construction such as insufficient amount and length of the extracted DNA Parks and Graham [37], inefficient transcription of target genes as well as improper assembly of the corresponding enzymes [38] and DNA shearing [39].

Development of new host systems using microbes, namely, Streptomyces spp., thermus ther-mophilus, Sulfolobus solfataricus and Proteobacteria [40] [41] [42] [43], have widened the choice of host and compatible enzyme assay systems. E. coli, owing to its ease of transformation and being the best genetically characterized bacterium, has been the choice host for heterologous gene expression in metagenomic studies [32].

5. Screening Strategies to Obtain Metagenome Derived Biocatalysts

Functional screening has become an increasingly important field for discovering novel biomolecules for applications in biotechnology and medicine [5]. Most activity screenings of metagenomics libraries are based on the cultivation of metagenomics clones on indicator plates allowing analysis of defined enzyme activities via bio catalytic conversion of an indicator substrate that leads to the formation of a clear or colored halo surrounding the “positive” colony [44]. Several parameters are important for successful screening of metagenomics libraries, such as the abundance of the gene in the library, the average insert size, the host-vector system, the use of an adequate host organism that is able to express the target gene, the assay method, the efficiency of heterologous gene expression in a surrogate host and the throughput of screening methods is relatively low [5] [33] [45]. Table 4, illustrate recent examples of functional screening strategies employed to obtain metagenome-derived biocatalysts.

With synchronized advances in the HTS (high throughput screening) methods and the choice of transformation systems with wide available range of hosts for heterologous gene expression, it is now possible to screen up to 50,000 clones per second or over one billion clones per day [46]. Functional screening technology was first applied to rumen materials to mine novel enzymes in 2005 [15].

Table 4. Recent examples of functional screening strategies employed to obtain metagenome derived biocatalysts.

Note: AZCL, azurine-cross-linked; GMD, gel micro-droplet; FACS, fluorescence-activated cell sorting; ARS, agriculture research service. GMD: the microfluidic gel microdroplets. Source: [36].

5.1. Agar Plate Screening Method

It is the oldest method of screening, in which many have used it as a state of the art hydrolytic enzyme screening methodology. This method for functional metagenomics screening gives a simple and straightforward approach to identify novel enzymes that function under diverse conditions. Novel hydrolytic enzyme such as lipases, esterases, cellulases, proteases, laccases, glycosylases, nitrilases, and dehalogenases, have been identified using this method [47]. The agar plate screening method helps to pin point genes responsible for the resistance to the toxic elements such as antibiotic, extreme salt concentration, extreme pH, and heavy metals [37]. And the assay are based on the production of a chromophore or fluorophore in colonies incubated with a chromogenic or fluorogenic substrate, with the throughput of 103 - 106 clones per day [48] [49]. The method has successfully isolate large number of unique enzymes from various environments [37].

5.2. Microarray Based Screening

The idea behind DNA microarray-based approach is, screening metagenomics libraries for the presence of selected genes. It efficiently spot a genomic target region [50]. Unlike agar plate, DNA micro array is sequence-based screening method of metagenomics library [51]. The protocol includes identification biological photoreceptors based on a homology search in already sequenced, annotated genomes. The similarity of novel DNA sequences to already identified genes encoding for functional proteins is the basis for microarrays approach [52]. The use of microarrays to profile libraries offers an effective approach for characterizing many clones rapidly [51]. This format is referred to as a metagenome microarray (MGA) [14]. However, the difficulty and limitation of this approach is related to achieving high hybridization efficiency and that the target genes derived from conserved regions of already known protein families reduce our chances for obtaining fundamentally new proteins [35].

5.3. Microtiter Plate Screening

Microtiter plates approach involves incubation of bacterial culture with enzyme substrate in the microwells [32]. The use of microtiter plate assays is a conventional and straightforward high-throughput approach to protein library screening [53]. The method provides high-throughput at minimal expenses in time, money and work effort [54]. The protein property of interest can be directly or indirectly measured in the microtiter plate, most commonly via spectrophotometry or fluorometry [53]. With the occurrence of substrate conversion, in microtiter plates a visual signal emerges, such as color or fluorescence, which is used to identify colonies expressing an enzyme with desirable properties [36].

5.4. Fluorescence Activated Cell Sorting (FACS) Base Screening

Fluorescence-activated cell sorting (FACS) is an emerging technology having a powerful tool for screening enzyme libraries due to its high sensitivity and its ability to analyze as many as 108 mutants per day [55]. FACS enables the identification of biological activity within a single cell based on cell size, shape, and fluorescence [56]. FACS have many advantages: 1) it deposits single events into a variety of vessels quickly and accurately; 2) the laminar flow fluidics of FACS prevents disruption of cells during sorting and 3) the contamination is limited because of the small volume of each droplet [57]. FACS can easily couple to a number of different high-throughput screening methods due to its powerful cell sorting capacity such as droplet sorting and reporter-based screening [36]. Recently, this system incorporates a laser with multiple wavelength capabilities screen up to 50,000 clones per second, or over one billion clones per day [58].

5.5. Microfluidics Based Screening

Microfluidic base screening platform has equipped with high-throughput screening technology which give an advantage over the other method due to its suitability for cell based assay, low analysis cost, and easy handling pico liter volumes of liquids [59]. The method allows high-throughput screening with rapid analysis of thousands of chemical, biochemical, genetic or pharmacological tests in parallel [60]. Micro droplets are produced in large numbers at speeds of thousands of droplets per second and a single droplet functions as a reaction chamber. Cells, enzyme variants, substrates and products are confined in the picoliter volume of the droplets, where reactions take place [61]. Subsequently, the droplets are sorted according to fluorescence or color of the product. The coupling of microfluidics with FACS results in the ultrahigh-throughput screening of metagenomic libraries. However, the major bottleneck of such technique is the detection method, which is mostly limited to fluorescent signal. In the future, other detection methods, such as mass spectrometry, nuclear magnetic resonance (NMR) and colorimetric assay, may be combined with microfluidic devices to accelerate the discovery of novel biocatalysts or other genes with important functions in the microbiota [36].

6. Functionally Screened Rumen Product

Rumen associated functionally screened microbiome product and application of highly active enzymes for commercial applications will provide a new dimension in agroindustry’s and also decreasing the methane release into atmosphere [28] [62]. Rumen microorganisms produce a series of enzymes known as carbohydrate active enzymes (CAZymes) that can break down plant cell walls. There are four types of CAZymes that are distinguished based on protein sequence, gene sequence, and structural similarities: glycoside hydrolases (GHs), glycosyltransferases (GTs), polysaccharide lyases (PLs), and carbohydrate esterases (CEs); these CAZymes cooperatively contribute to dietary cellulose, hemicellulose, and pectin deconstruction [63] [64]. Some of the CAZy family such as GH3 (b-glucosidase), CE6 (esterase) from cow and GH (Cellodextrinase) from Buffalo were obtained using functional metagenomics approach [65] [66] [67]. Some of the Author that characterize CAZymes includes Hess et al., [68], identified 27,755 putative cow rumen carbohydrate active genes and expressed 90 candidate proteins, of which (51) 57% were enzymatically active against cellulosic substrates. Cheng et al., [69] functionally screened high temperature resistant and pH tolerance industrial relevance novel esterase and xylanases from cow rumen metagenomics. Zhao et al., [70], screened substrate specific and good thermal stable three lipases derived from dairy rumen microflora. Pope et al., [71] also identified laccase from reindeer rumen by metagenomic approaches which able to degrade lignin. Wichmann et al., [72], identified 80 unique antibiotic enzymes together with a novel clade of chloramphenicol acetyl-transferases from cow manure. Thirabunyanon et al., [73], found a novel probiotics strain of Bacillu subtilis having inhibitory activity against Salmonella enteritidis infection. Some of identified enzymes together with screening method from various reports are given in Table 5.

Advances in functional metagenomics have paved industry with an unprecedented chance to bring biomolecules of metagenomic origin into a commercial success. There are a number of companies involved commercialization of metagenomic research such as Diversa Corp, BASF, DSM, Syngenta, Genencor International, and BRAIN AG have commercialized many biological molecules of commercial interest Table 6 [74].

Table 5. Metagenome studies on rumen enzymes.

Source: [4].

Table 6. Commercialization of metagenomics technologies.

N.D. no details available or products still under development. Source: [74].

Currently, the major laboratories working in the area of rumen metagenomics include DOE Joint Genome Institute-Genome Technology, USA; USDA, USA; INRA, France; CSIRO, Australia; and AgResearch, New Zealand, and Agricultural University India [4].

7. Conclusion

Functional metagenomics screening technology is the powerful tool of future research arena with a potential of mining environmentally as well as commercially important biocatalyst. Rumen sample collection, DNA library construction and screening of the clone are the standard procedure for rumen functional metagenomics work. Screening technology ranges from the old technique such as Agar plate to high throughput advanced technology such as mass spectrometry, nuclear magnetic resonance (NMR) and colorimetric assay. The four rumen environment origin enzymes (CAZymes) have a significant contribution to agro industry and pharmaceutical Company.

Cite this paper: Bekele, W. , Zegeye, A. , Simachew, A. and Assefa, G. (2021) Functional Metagenomics from the Rumen Environment—A Review. Advances in Bioscience and Biotechnology, 12, 125-141. doi: 10.4236/abb.2021.125009.
References

[1]   Agarwal, N., Kamara, D.N. and Chaudhary, L.C. (2015) Rumen Microbial Ecosystem of Domesticated Ruminants. In: Puniya, A., Singh, R. and Kamra, D., Eds., Rumen Microbiology: From Evolution to Revolution, Springer, New Delhi, 17-30.
https://doi.org/10.1007/978-81-322-2401-3_2

[2]   Stevens, C.E. and Hume, I.D. (1998) Contributions of Microbes in Vertebrate Gastrointestinal Tract to Production and Conservation of Nutrients. Physiological Reviews, 78, 393-427.
https://doi.org/10.1152/physrev.1998.78.2.393

[3]   Hobson, P.N. and Stewart, C.S. (1997) The Rumen Microbial Ecosystem. 1st Edition, Springer, Dordrecht.
https://doi.org/10.1007/978-94-009-1453-7

[4]   Goel, G., Dagar, S.S., Raghav, M. and Bansal, S. (2015) Rumen: An Underutilised Niche for Industrially Important Enzymes. In: Puniya, A., Singh, R. and Kamra, D., Eds., Rumen Microbiology: From Evolution to Revolution, Springer, New Delhi, 247-263.
https://doi.org/10.1007/978-81-322-2401-3_17

[5]   Li, R.W. (2015) Rumen Metagenomics. In: Puniya, A., Singh, R. and Kamra, D., Eds., Rumen Microbiology: From Evolution to Revolution, Springer, New Delhi, 223-245.
https://doi.org/10.1007/978-81-322-2401-3_16

[6]   John Wallace, R. (2008) Gut Microbiology—Broad Genetic Diversity yet Specific Metabolic Niches. Animal, 2, 661-668.
https://doi.org/10.1017/S1751731108001687

[7]   Jami, E., White, B.A. and Mizrahi, I. (2014) Potential Role of the Bovine Rumen Microbiome in Modulating Milk Composition and Feed Efficiency. PLoS ONE, 9, e85423.
https://doi.org/10.1371/journal.pone.0085423

[8]   Morgavi, D.P., Kelly, W.J., Janssen, P.H. and Attwood, G.T. (2013) Rumen Microbial (Meta)Genomics and Its Application to Ruminant Production. Animal, 7, 184-201.
https://doi.org/10.1017/S1751731112000419

[9]   McCann, J.C., Wickersham, T.A. and Loor, J.J. (2014) High-Throughput Methods Redefine the Rumen Microbiome and Its Relationship with Nutrition and Metabolism. Bioinformatics and Biology Insights, 8, 109-125.
https://doi.org/10.4137%2FBBI.S15389

[10]   Krause, D., Denman, A.E. and Mackie, R.I. (2003) Opportunities to Improve Fiber Degradation in the Rumen: Microbiology, Ecology, and Genomics. FEMS Microbiology Reviews, 27, 663-693.
https://doi.org/10.1016/S0168-6445(03)00072-X

[11]   Edwards, J.E., McEwan, N.R., Travis, A.J. and Wallace, R.J. (2004) 16S rDNA Library-Based Analysis of Ruminal Bacterial Diversity. Antonie van Leeuwenhoek, 86, 263-281.
https://doi.org/10.1023/B:ANTO.0000047942.69033.24

[12]   Teather, R.M. (2001) Community Genomics—The Key to the Rumen? In: Beauchemin, K.A. and Crews, D.H.J., Eds., Advances in Beef Cattle Science, Livestock Sciences Section, Lethbridge Research Centre, Agriculture and Agri-Food Canada (AAFC), Lethbridge, 228-233.

[13]   Chistoserdova, L. (2009) Functional Metagenomics: Recent Advances and Future Challenges. Biotechnology and Genetic Engineering Reviews, 26, 335-352.
https://doi.org/10.5661/bger-26-335

[14]   Vieites, J.M., Guazzaroni, ME., Beloqui, A., Golyshin, P.N. and Ferrer, M. (2010) Molecular Methods to Study Complex Microbial Communities. In: Streit, W. and Daniel, R., Eds., Metagenomics, Vol. 668, Humana Press, Totowa, 1-37.
https://doi.org/10.1007/978-1-60761-823-2_1

[15]   Ferrer, M., Golyshina, O.V., Chernikova, T.N., Khachane, A.N., Reyes-Duarte, D., Santos, V.A.P.M.D., Strompl, C., Elborough, K., Jarvis, G., Neef, A., Yakimov, M.M., Timmis, K.N. and Golyshin, P.N. (2005) Novel Hydrolase Diversity Retrieved from a Metagenome Library of bovIne Rumen Microflora. Environmental Microbiology, 7, 1996-2010.
https://doi.org/10.1111/j.1462-2920.2005.00920.x

[16]   Wilson, D.B. (2011) Microbial Diversity of Cellulose Hydrolysis. Current Opinion in Microbiology, 14, 259-263.
https://doi.org/10.1016/j.mib.2011.04.004

[17]   Sahu, A., Nayak, N., Sahu, R. and Kumar, J. (2017) Application of Metagenomics in Livestock Improvement. International Journal of Livestock Research, 7, 30-38.
https://doi.org/10.5455/ijlr.20170423033727

[18]   Woese, C.R., Kandler, O. and Wheelis, M.L. (1990) Towards a Natural System of Organism: Proposal for the Domain Archaea, Bacteria, and Eucarya. Proceedings of the National Academy of Sciences of the United States of America, 87, 4576-4579.
https://doi.org/10.1073/pnas.87.12.4576

[19]   Mackie, R.I., McSweeney, C.S. and Aminov, R.I. (2013) Rumen. In: eLS. John Wiley & Sons, Ltd., Chichester.

[20]   Martinez-Fernandez, G., Denman, S.E. and McSweeney, C.S. (2019) Sample Processing Methods Impacts on Rumen Microbiome. Frontiers in Microbiology, 10, Article No. 861.
https://doi.org/10.3389/fmicb.2019.00861

[21]   Jaeyong, S., Hyuck, C., Jin, Y.J., Seul, L., Hyun, J.L., Youlchang, B., Sang, Y.J. and Minseok, K. (2018) Effects of Sampling Techniques and Sites on Rumen Microbiome and Fermentation Parameters in Hanwoo Steers. Journal of Microbiology and Biotechnology, 28, 1700-1705.
https://doi.org/10.4014/jmb.1803.03002

[22]   Henderson, G., Cox, F., Kittelmann, S., Miri, V.H., Zethof, M., Noel, S.J., Waghorn, G.C. and Janssen, P.H. (2013) Effect of DNA Extraction Methods and Sampling Techniques on the Apparent Structure of Cow and Sheep Rumen Microbial Communities. PLoS ONE, 8, e74787.
https://doi.org/10.1371/journal.pone.0074787

[23]   Hélène, B., Franck, P., Van Tran Van, Nathalie, L., Renaud, N., Timothy, M. and Pascal, S. (2005) High Molecular Weight DNA Recovery from Soils Prerequisite for Biotechnological Metagenomic Library Construction. Journal of Microbiological Methods, 62, 1-11.
https://doi.org/10.1016/j.mimet.2005.01.003

[24]   Fliegerova, K., Tapio, I., Bonin, A., Mrazek, J., Callegari, M.L., Bani, P. and Wallace, R.J. (2014) Effect of DNA Extraction and Sample Preservation Method on Rumen Bacterial Population. Anaerobe, 29, 80-84.
https://doi.org/10.1016/j.anaerobe.2013.09.015

[25]   Vaidya, J.D., van den Bogert, B., Edwards, J.E., Boekhorst, J., van Gastelen, S., Saccenti, E., Plugge, C.M. and Smidt, H. (2018) The Effect of DNA Extraction Methods on Observed Microbial Communities from Fibrous and Liquid Rumen Fractions of Dairy Cows. Frontiers in Microbiology, 9, Article No. 92.
https://doi.org/10.3389/fmicb.2018.00092

[26]   Chen, Y.B., Lan, D.L., Tang, C., Yang, X.N. and Li J. (2015) Effect of DNA Extraction Method on the Apparent Structure of Yak Rumen Microbial Communities as Reveled by 16S rDNA Sequencing. Polish Journal of Microbiology, 64, 29-36.
https://doi.org/10.33073/pjm-2015-004

[27]   Lam, K.N., Cheng, J., Engel, K., Neufeld, J.D. and Charles, T.C. (2015) Current and Future Resources for Functional Metagenomics. Frontiers in Microbiology, 6, Article No. 1196.
https://doi.org/10.3389/fmicb.2015.01196
https://www.frontiersin.org/

[28]   Kothari, R.K., Nathani, N.M., Mootapally, C., Rank, J.K., Gosai, H.B., Dave, B.P. and Joshi, C.G. (2018) Comprehensive Exploration of the Rumen Microbial Ecosystem with Advancements in Metagenomics. In: Nagarajan, M., Ed., Metagenomics, Academic Press, Waltham, 215-229.
https://doi.org/10.1016/B978-0-08-102268-9.00011-2

[29]   Leigh, M.B., Taylor, L. and Neufeld, J.D. (2010) Clone Libraries of Ribosomal RNA Gene Sequences for Characterization of Bacterial and Fungal Communities. In: Timmis, K.N., Ed., Handbook of Hydrocarbon and Lipid Microbiology, Springer, Berlin, Heidelberg, 3969-3993.
https://doi.org/10.1007/978-3-540-77587-4_310

[30]   Lorenz, P. and Eck, J. (2005) Metagenomics and Industrial Applications. Nature Reviews Microbiology, 3, 510-516.
https://doi.org/10.1038/nrmicro1161

[31]   Simon, C. and Daniel, R. (2010) Metagenomic Analyses: Past and Future Trends. Applied and Environmental Microbiology, 77, 1153-1161.
https://doi.org/10.1128/AEM.02345-10

[32]   Satish, K., Kishore, K.K., Bharat, B. and Manoj, P.B. (2015) Metagenomics: Retrospect and Prospects in High Throughput Age: A Review. Biotechnology Research International, 2015, Article ID: 121735.
https://doi.org/10.1155/2015/121735

[33]   Uchiyama, T. and Miyazaki K. (2009) Functional Metagenomics for Enzyme Discovery: Challenges to Efficient Screening. Current Opinion in Biotechnology, 20, 616-622.
https://doi.org/10.1016/j.copbio.2009.09.010

[34]   Wexler, M. and Johnston, A.W.B. (2010) Wide Host-Range Cloning for Functional Metagenomics. In: Streit, W. and Daniel, R., Eds., Metagenomics: Methods and Protocols, Vol. 668, Humana Press, Totowa, 77-96.
https://doi.org/10.1007/978-1-60761-823-2_5

[35]   Gabor, E., Liebeton, K., Niehaus, F., Eck, J. and Lorenz P. (2007) Updating the Metagenomics Toolbox. Biotechnology Journal, 2, 201-206.
https://doi.org/10.1002/biot.200600250

[36]   Tanyaradzwa, R.N. and Houjin, Z. (2018) Recent Advances in Function-Based Metagenomics Screening. Genomics Proteomics Bioinformatics, 16, 405-415.
https://doi.org/10.1016/j.gpb.2018.01.002

[37]   Parkers, R.J. and Graham, F.L. (1997) A Helper Dependent System for Adenovirus Vector Production Helps Define a Lower Limit for Efficient DNA Packaging. Journal of Virology, 71, 3293-3298.
https://doi.org/10.1128/JVI.71.4.3293-3298.1997

[38]   Katzke, N., Knapp, A., Loeschcke, A., Drepper, T. and Jaeger, K.E. (2017) Novel Tools for the Functional Expression of Metagenomic DNA. In: Streit, W. and Daniel, R., Eds., Metagenomics, Vol. 1539, Humana Press, New York, 159-196.
https://doi.org/10.1007/978-1-4939-6691-2_10

[39]   Zhou, J., Bruns M.A. and Tiedje, J.M. (1996) DNA Recovery from Soils of Diverse Composition. Applied and Environmental Microbiology, 62, 316-322.
https://doi.org/10.1128/AEM.62.2.316-322.1996

[40]   Albers, S.V., Jonuscheit, M. and Dinkelaker, S. (2006) Production of Recombinant and Tagged Proteins in the Hyperthermophilic Archaeon Sulfolobus solfataricus. Applied and Environmental Microbiology, 72, 102-111.
https://doi.org/10.1128/AEM.72.1.102-111.2006

[41]   Angelov, A., Mientus, M., Liebl, S. and Liebl, W. (2009) A Two-Host Fosmid System for Functional Screening of (Meta)Genomic Libraries from Extreme Thermophiles. Systematic and Applied Microbiology, 32, 177-185.
https://doi.org/10.1016/j.syapm.2008.01.003

[42]   Craig, J.W., Chang, F.-Y., Kim, J.H., Obiajulu, S.C. and Brady, S.F. (2010) Expanding Small-Molecule Functional Metagenomics through Parallel Screening of Broad-Host-Range Cosmid Environmental DNA Libraries in Diverse Proteobacteria. Applied and Environmental Microbiology, 76, 1633-1641.
https://doi.org/10.1128/AEM.02169-09

[43]   Wang, G.-Y.-S., Graziani, E., Waters, B., Pan, W., Li, X., McDermott, J., Meurer, G., Saxena, G., Andersen, R.J. and Davies, J. (2000) Novel Natural Products from Soil DNA Libraries in a Streptomycete Host. Organic Letters, 2, 2401-2404.
https://doi.org/10.1021/ol005860z

[44]   Steele, H.L., Jaeger, K.E., Daniel, R. and Streit, W.R. (2009) Advances in Recovery of Novel Biocatalysts from Metagenomes. Journal of Molecular Microbiology and Biotechnology, 16, 25-37.
https://doi.org/10.1159/000142892

[45]   Handelsman, J. (2004) Uncultured Soil Bacteria Are a Reservoir of New Antibiotic Resistance Genes. Environmental Microbiology, 6, 981-989.
https://doi.org/10.1111/j.1462-2920.2004.00664.x

[46]   Wenzel, S.C. and Müller, R. (2005) Recent Developments towards the Heterologous Expression of Complex Bacterial Natural Product Biosynthetic Pathways. Current Opinion in Biotechnology, 16, 594-606.
https://doi.org/10.1016/j.copbio.2005.10.001

[47]   Popovic, A., Tchigvintsev, A., Tran, H., Chernikova, T.N., Golyshina, O.V., Yakimov, M.M., Golyshin, P.N. and Yakunin. (2015) Metagenomics as a Tool for Enzyme Discovery: Hydrolytic Enzymes from Marine Related Metagenomes. In: Krogan, N.J. and Babu, M., Eds., Prokaryotic Systems Biology, Vol. 883, Springer, Cham, 1-20.
https://doi.org/10.1007/978-3-319-23603-2_1

[48]   Turner, N.J. (2003) Directed Evolution of Enzymes for Applied Biocatalysis. Trends in Biotechnology, 21, 474-478.
https://doi.org/10.1016/j.tibtech.2003.09.001

[49]   Geddie, M.L., Rowe, L.A., Alexander, O.B. and Matsumura, I. (2004) High Throughput Microplate Screens for Directed Protein Evolution. Methods in Enzymology, 388, 134-145.
https://doi.org/10.1016/S0076-6879(04)88012-1

[50]   Sachse, K., Hotzel, H., Slickers, P., Ellinger, T. and Ehricht, R. (2005) DNA Microarray-Based Detection and Identification of Chlamydia and Chlamydophila spp. Molecular and Cellular Probes, 19, 41-50.
https://doi.org/10.1016/j.mcp.2004.09.005

[51]   Park, S.J., Kang, C.H., Chae, J.C. and Rhee, S.K. (2008) Metagenome Microarray for Screening of Fosmid Clones Containing Specific Genes. FEMS Microbiology Letters, 284, 28-34.
https://doi.org/10.1111/j.1574-6968.2008.01180.x

[52]   Pathak, G.P. and Gärtner, W. (2010) Detection and Isolation of Selected Genes of Interest from Metagenomic Libraries by a DNA Microarray Approach. In: Streit, W.R. and Rolf, D., Eds., Metagenomics: Methods and Protocols, Vol. 668, Humana Press, Totowa, 299-312.
https://doi.org/10.1007/978-1-60761-823-2_21

[53]   Cirino, P.C. and Qian, S. (2013) Chapter 2—Protein Engineering as an Enabling Tool for Synthetic Biology. In: Zhao, H., Ed., Synthetic Biology, Academic Press, Waltham, 23-42.
https://doi.org/10.1016/B978-0-12-394430-6.00002-9

[54]   Kensy, F., Engelbrecht, C. and Büchs J. (2009) Scale-up from Microtiter Plate to Laboratory Fermenter: Evaluation by Online Monitoring Techniques of Growth and Protein Expression in Escherichia coli and Hansenula polymorpha Fermentations. Microbial Cell Factories, 8, Article No. 68.
https://doi.org/10.1186/1475-2859-8-68

[55]   Yang, G. and Withers, S.G. (2009) Ultrahigh-Throughput FACS-Based Screening for Directed Enzyme Evolution. ChemBioChem, 10, 2704-2715.
https://doi.org/10.1002/cbic.200900384

[56]   Van Rossum, T., Kengen, S.W. and van der Oost, J. (2013) Reporter Based Screening and Selection of Enzymes. The FEBS Journal, 280, 2979-2996.
https://doi.org/10.1111/febs.12281

[57]   Kodzius, R. and Gojobori T. (2016) Single Cell Technologies in Environmental Omics. Gene, 576, 701-707.
https://doi.org/10.1016/j.gene.2015.10.031

[58]   Robertson, D.E. and Steer, B.A. (2004) Recent Progress in Biocatalyst Discovery and Optimization. Current Opinion in Chemical Biology, 8, 141-149.
https://doi.org/10.1016/j.cbpa.2004.02.010

[59]   Dittrich, P.S. and Manz, A. (2006) Lab-on-a-Chip: Microfluidics in Drug Discovery. Nature Reviews Drug Discovery, 5, 210-218.
https://doi.org/10.1038/nrd1985

[60]   Guansheng, Du., Qun Fang., Jaap, M.J. and den Toonder. (2016) Microfluidics for Cell-Based High Throughput Screening Platforms—A Review. Analytica Chimica Acta, 903, 36-50.
https://doi.org/10.1016/j.aca.2015.11.023

[61]   Leemhuis, H., Kelly, R.M. and Dijkhuizen, L. (2009) Directed Evolution of Enzymes: Library Screening Strategies. IUBMB Life, 61, 222-228.
https://doi.org/10.1002/iub.165

[62]   Choudhury, P.K., Salem, A.Z.M., Jena, R., Kumar, S., Singh, R. and Puniya, A.K. (2015) Rumen Microbiology: An Overview. In: Puniya, A., Singh, R. and Kamra, D., Eds., Rumen Microbiology: From Evolution to Revolution, Springer, New Delhi, 3-16.
https://doi.org/10.1007/978-81-322-2401-3_1

[63]   Kumar, S. and Pitta, D.W. (2015) Revolution in Rumen Microbiology. In: Puniya, A., Sinh, R. and Kamra, D., Eds., Rumen Microbiology: From Evolution to Revolution, Springer, New Delhi, 357-379.
https://doi.org/10.1007/978-81-322-2401-3_24

[64]   Kala, A., Kamra, D.N., Kumar, A., Agarwal, N., Chaudhary, L.C. and Joshi, C.G. (2017) Impact of Levels of Total Digestible Nutrients on Microbiome, Enzyme Profile and Degradation of Feeds in Buffalo Rumen. PLoS ONE, 12, e0172051.
https://doi.org/10.1371/journal.pone.0172051

[65]   Wang, F., Li, F., Chen, G. and Liu, W. (2009) Isolation and Characterization of Novel Cellulase Genes from Uncultured Microorganisms in Different Environmental Niches. Microbiological Research, 164, 650-657.
https://doi.org/10.1016/j.micres.2008.12.002

[66]   Lopez-Cortes, N., Reyes-Duarte, D., Beloqui, A., Polaina, J., Ghazi, I., Golyshina, O.V., Ballesteros, A., Golyshin, P.N. and Ferrer M. (2007) Catalytic Role of Conserved HQGE Motif in the CE6 Carbohydrate Esterase Family. FEBS Letters, 581, 4657-4662.
https://doi.org/10.1016/j.febslet.2007.08.060

[67]   Duan, C.J., Xian, L., Zhao, G.C., Feng, Y., Pang, H., Bai, X.L., Tang, J.L., Ma, Q.S. and Feng, J.X. (2009) Isolation and Partial Characterization of Novel Genes Encoding Acidic Cellulases from Metagenomes of Buffalo Rumens. Journal of Applied Microbiology, 107, 245-256.
https://doi.org/10.1111/j.1365-2672.2009.04202.x

[68]   Hess, M., Sczyrba, A., Egan, R., Kim, T.W., Chokhawala, H., et al. (2011) Metagenomic Discovery of Biomass Degrading Genes and Genomes from Cow Rumen. Science, 331, 463-467.
https://doi.org/10.1126/science.1200387

[69]   Cheng, G., Hu, Y., Yin, Y., Yang, X., Xiang, C., Wang, B., et al. (2012) Functional Screening of Antibiotic Resistance Genes from Human Gut Microbiota Reveals a Novel Gene Fusion. FEMS Microbiology Letters, 336, 11-16.
https://doi.org/10.1111/j.1574-6968.2012.02647.x

[70]   Zhao, S., Wang, J., Bu, D., Liu, K., Zhu, Y. and Dong Z. (2010) Novel Glycoside Hydrolases Identified by Screening a Chinese Holstein Dairy Cow Rumen-Derived Metagenome Library. Applied and Environmental Microbiology, 76, 6701-6705.
https://doi.org/10.1128/AEM.00361-10

[71]   Pope, P.B., Denman, S.E., Jones, M., Tringe, S.G., Barry, K., et al. (2010) Adaptation to Herbivory by the Tammar walla by Includes Bacterial and Glycoside Hydrolase Profiles Different from Other Herbivores. Proceedings of the National Academy of Sciences of the United States of America, 107, 14793-14798.
https://doi.org/10.1073/pnas.1005297107

[72]   Wichmann, F., Udikovic-Kolic, N., Andrew, S. and Handelsman, J. (2014) Diverse Antibiotic Resistance Genes in Dairy Cow Manure. mBio, 5, e01017-13.
https://doi.org/10.1128/mBio.01017-13

[73]   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. Biotechnology Letters, 31, 571-576.
https://doi.org/10.1007/s10529-008-9902-3

[74]   Cowan, D., Meyer, Q., Staford, W., Muyanga, S., Cameron, R. and Wittwer, P. (2005) Metagenomic Gene Discovery: Past, Present and Future. Trends in Biotechnology, 23, 321-329.
https://doi.org/10.1016/j.tibtech.2005.04.001

 
 
Top