Novel Biocatalysts from Uncultivated Microbes - Essay Example

?Novel Biocatalysts from Uncultivated Microbes Biocatalysts or enzymes are protein compounds that have essential roles in biological systems. From a biological viewpoint, the most important function of enzymes is to hasten or catalyze chemical reactions by lowering the activation energy (Nelson, Cox, & Lehninger, 2004). This trait is also utilized in the production of industrial goods. These biocatalysts are becoming more popular and preferred over chemical catalysts in conversion reactions because they are environmentally friendly, more economical, and have cleaner action. In many industries, biocatalysts are replacing chemical catalysts for production and conversion processes. Among the industries that benefit from biocatalysts are the food industry, laundry detergent manufacture, pharmaceutical industry, and chemical production. Microorganisms like bacteria are the main source of these biocatalysts. Traditionally pure bacterial cultures are maintained, and allowed to express the biocatalysts or enzymes, which are then purified from the cultures. However, with the increased array and variability of industrial products, the need for cleaner technologies, and decreasing emphasis on the use of chemical catalysts, the need for more novel biocatalysts also increased. Worth mentioning are industrial enzymes that are produced from hyperthermophilic microorganisms or those that can survive high temperatures like those that can be found in certain marine environments (Uria, Fawzya, & Chasanah, 2005). The discovery, isolation, and study of potentially important biocatalysts from microorganisms have been conducted on cultivated or cultured microorganisms. From a mixture of organisms, pure cultures are grown from where biocatalysts are extracted. This has been the norm for many years. However, not all microorganisms can be grown in culture. This is due to the fact that some microorganism have special and still unknown cultural and nutritional requirements. The culture media, which is normally agar, is not suitable for many many microorganisms of potential commercial value, because these microbes thrive in temperatures that are above the melting point of agar. Microbial organisms that are culturable account for only a very small fraction of the total microbial diversity, and most industrial biocatalysts are sourced from this culturable fraction (Amann, Ludwig, & Schleifer, 1995; Wilmes, et al., 2009). However, there is a large, untapped potential of unculturable microorganisms to produce more useful and unique biocatalysts for other processes that are not catalyzed by the current crop of biocatalysts. Characterization of bacterial species was based on phylogenetic markers, most used of these are 16S rRNA. However, this was not enough to provide genome sequence information. The advent of metagenomics, or the DNA sequence-based and functional analysis of the total microbial genome present in a mixture, provided the ability to explore the vast diversity and unique properties of microorganisms (Riesenfeld, Schloss, & Handelsman, 2004). Metagenomics does not require the cultivation of microorganisms, instead the DNA is extracted from the mixture of microorganisms collected from a certain environment. The DNA is then subjected to different genomic analysis, and the sequence data can be stored in databases for reference or for manipulations in genetic transformation. Metagenomics provides a rapid means for the discovery of new genes, biocatalysts and pathways (Uria, Fawzya, & Chasanah, 2005). Industrial applications require that biocatalysts are more efficient, and these has led to the creation of engineered enzymes with desired properties such as stability, activity in specific environments and substrate selectivity. Biocatalyst engineering approaches are site-directed mutagenesis and random mutagenesis (Uria, Fawzya, & Chasanah, 2005). Steps in Metagenomics towards the Discovery of Novel Biocatalysts Several initial steps are employed towards the discovery and development of biocatalyst using metagenomics (Figure 1) (Handelsman, 2004). First, samples are collected from the natural environment of the microorganisms. Environments that have been of particular interest for gene and biocatalyst mining are marine (Uria, Fawzya, & Chasanah, 2005), soil (Daniel, 2005) and wastewater (Lammle, et al., 2007). The genomic DNA is then extracted and purified following established protocols. The heterologous DNA fragments in the mixture are ligated into vector or plasmid DNA, and further transformed into E. coli host cells. The length of the genomic fragment will determine the choice of vectors, which can be plasmids (less than 10 kb DNA), cosmids, (25–35 kb), fosmids (25–40 kb), or bacterial artificial chromosomes, BACs (100–200 kb). E. coli is the preferred host because it lacks genes for restriction and homologous recombination. This property is useful in cloning foreign genes. In addition, highly efficient E. coli competent cells are commercially available (reviewed in Uchiyama & Miyazaki, 2009). Transformed E. coli colonies or cells that carry the DNA inserts constitute the metagenomic library. From the library, DNA can be multiplied, purified, sequenced, and analyzed. The clones can be screened for phylogenetic markers like recA or 16SrRNA and by hybridization with known genes, or via multiplex PCR. Functional analysis can be performed by expressing the proteins in an expression vector, and checking for activity. This accumulation of desired clones and DNA sequences lead to novel biotechnical applications and an analysis and understanding of functional roles of microbes in the environment (Streit & Schmitz, 2004). For each step involved in the metagenomic studies, several issues, and decisions are considered relevant (Kowalchuk, et al., 2007). Considerations include the biodiversity present in the environment, the amount of microbial biomass present, microbial sample maintenance, integrity, and pooling. It is important to select the proper environment in order to obtain target enzymes. For example, if the desired biocatalyst is for high temperature process, then the search should be done in high temperature environments. Another criterion for DNA extraction is that the gene, which encodes the activity of interest, has to be present in sufficient, and preferably, high amounts. Secondly, the extraction and preparation methods of DNA should be appropriate in capturing intact genes. The genes must be detectable through genetic or phenotypic expression. Shotgun analysis or the mass sequencing of genomes is now part of routine laboratory procedures for obtaining DNA sequences. The bigger challenge is the reconstruction of the full sequences or whole genomes from a very large number of data coming from a mixture of microorganisms. Downstream analysis and exploitation of the metagenomic library includes high throughput sequence analysis, annotation, and recording. Figure 1. Schematic representation for the construction of metagenomic libraries from environmental samples. Figure is adapted from Handelsman (2004). Degenerate primers may be utilized to search for gene homologues, which can result in isolation of the actual gene product present in the library. Gene sequences can be inserted in expression vectors and can be over-expressed for identifying their activities or products. Enzyme activities can be manipulated, and optimized by engineering modifications into the original gene sequence (Figure 2) (Kowalchuk et al., 2007). Figure 2. Experimental steps utilized for studying and exploiting function and activity of genes from environmental metagenomes (Kowalchuk, et al., 2007) Biocatalyst discovery is aided in large by screening the metagenomic clones for function, which is a direct way to identify and obtain genes with the desired function (Uchiyama & Miyazaki, 2009). However, the main problem with this approach is that it is possible to obtain insufficient expression of unknown gene sequences in E. coli, the host of choice, despite the fact that many genes of different species have been successfully expressed in E. coli. Sequence-based screening, which can be performed easily with PCR, or nucleic acid hybridization, has limited value in finding novel genes because the sequences identified are limited to those that have are only homologues of the probe sequences. Several factors affect the chance of identifying a gene: size of target gene, assay technique, and efficiency of gene expression. Several workers have devised means to overcome these problems. Lammle and co-workers designed a plasmid vector that contained a dual-orientation promoter that increased the chances of transcribing the inserts (Lammle, et al., 2007). Assays for enzyme activities are usually conducted on agar plates that contain media with the specific substrate. Visually scored, most assays have low sensitivity due faint color or low zone formation. Agar plate screening has been improved by using cell lysates. In this method, clones are multiplied on 96-well plates, and cell lysates are prepared. This method was applied by Suenaga, Ohnuki, and Miyazaki (2007) when they screened the metagenomic DNA collected from sludge for estradiol dioxygenases. Using the cell lysates, several novel estradiol dioxygenases were identified. The cell lysate method has also been used for identifying genes with resistance to heavy metals (Mirete, de Figueras, & Gonzalez-Pastor, 2007) and antibiotics (Mori, et al., 2008). The identification of important genes and biocatalysts is also aided by reporter assays that can link biological events to expression of reporter genes like green protein fluorescence protein and antibiotic resistance proteins. Selection of more sensitive reporter gene benefits the identification of novel enzymes. Sample Products Derived from the Metagenomic Data Metagenomics was instrumental in the development of novel pharmaceutical and biotechnological products. However, only a small group of enzymes are searched for in metagenomic libraries of non-cultivated microbes (Streit & Schmitz, 2004). These include lipases and esterases, because these enzymes are highly selective, remain active even in organic solvents and do not need co-factors (Jaeger & Eggert, 2002). Oxidoreductases with high selectivity for specific enantiomers have also been identified by metagenome mining (Knietsch, et al., 2003). The oxidoreductases are useful in preparing carbonyl compounds, amino acids, and alcohols, which normally require difficult preparative methods. Other enzymes that have been identified with metagenomic analysis are polysaccharide-modifying enzymes, proteases, nitrilases, vitamins and novel therapeutic molecules, particularly type I and type II polyketide synthases (?-ketoacyl synthetases), which are key genes involved in polyketide antibiotics synthesis. The shotgun gene sequencing technique was used by Courtois and co-workers who reported in 2003 of the construction and screening of 5000 clones which was “shotgunned” from an environmental DNA library. The gene fragments of DNA from microbes directly derived from soil were inserted into an E. coli-Streptomyces lividans shuttle cosmid vector. Diversity, genetic content, and heterologous gene expression were analyzed in both expression hosts. Primers that targeted the conserved region of polyketide synthase I genes were used to screen pools of clones. Using this approach, it was found that the metagenomic library was highly diverse, with phylogenetic analysis showing that the microorganisms sampled have not been described previously. After the library was screened by PCR for type I polyketide synthase genes, and after new molecules were expressed from live colonies and cell extracts, new polyketide synthase genes were ientified in at least eight clones, and a minimum of five additional clones were confirmed using high-pressure liquid chromatography analysis to produce heterologous molecules. The work confirmed that genes for natural products can be captured using the shotgun technology, and that larger inserts in libraries, use of multiple hosts for expression and pre-screening can enhance the detection of useful and novel metabolites (Courtois, et al., 2003). Figure 3. Some antibiotics that were discovered from metagenomic libraries. Figure adapted from Handelsman (2004). The engineering of methyl halide transferases is a good example of how metagenomic data can be utilized in industry. Methyl halides constitute another group of molecules that can be produced more efficiently using biocatalysts compared to chemical catalysts. Methyl halides are agricultural fumigants that are produced naturally by plants and microorganisms. However, the production volume from these sources is very low and thus, cannot be utilized on an industrial scale. The enzyme single methyl halide transferase (MHT) transfers the methyl group from S-adenosyl methionine (SAM) to a halide ion to produce methyl halide. In 2009, Bayer and co-workers chemically synthesized the 89 putative MHT genes from all the organisms that were published in the NCBI sequence database. The clones were inserted and screened in E. coli to identify the production rates of CH3Cl, CH3Br, and CH3I. Selection for the highest MHT activity was done, and the subsequent engineering of the gene into yeast Saccharomyces cerevisiae resulted in increased productivity of 190 mg/ L-1h-1 from glucose and sucrose. Methyl halide production of the engineered S. cerevisiae in co-culture with Actinotalea fermentans, which has celluloytic activity, was possible from the crop biomass of switchgrass, corn stover, poplar and sugarcane bagasse (Bayer, et al., 2009). Conclusion Metagenomics has become a very essential tool in understanding the genomes of unculturable and uncultivable microorganisms and has opened up endless possibilities for understanding and exploiting this rich natural resource group. Metagenomics has accelerated the rate of discovering new biocatalysts, products, and processes. Understanding microbial community structure and function provide possible solutions to industrial, medical, and agricultural problems. To access these solutions, methods for library construction and functional analysis should be improved. Computing and data management should be advanced in order to facilitate the proper handling, annotation, assembly, and storage of metagenomic data. 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