The existence of living organisms in the community is related to their in-depth distribution. This often marked by the high population of a number of species of the genus becomes a problematic aspect with regard to the thorough understanding of the organism’s varied or diverse habitats. In the case of microorganisms which are ubiquitous creatures, the task of determining the precise bio-strength in a given sample population is much dependant on their variation associated with diversity.
The need for a complete assessment of the diversity of microorganisms is increasing. This could be due to their beneficial and harmful effects. Beneficial effects may include microorganisms of economic importance. Harmful effects may include microorganisms that pose a threat to infectious diseases. For years investigators have been striving hard to monitor microbial diversity in order to come forward with a framework for defining them. This is linked with technical drawbacks/advancements. There have been many reports emphasizing the technical twist on microbial diversity.
In the modern scientific era, the field of Molecular biology has grown rapidly. Its contribution in deciphering the unexplored codes and resolving the key mysteries underlying nature has been tremendous especially with regard to the biological community. This was exploited to a great extent. Since living organisms are endowed with nucleic acids like DNA and RNA. These components serve as important tools in understanding molecular machinery and could help to devise a monitoring strategy. As such, researchers have initiated the methodology of rRNA screening. The main objective of the description is to perform a thorough literature review in order to highlight the information that reflects the monitoring of microbial diversity with rRNA screening as a potential tool.
Review of Literature
Dorigo, Volatier and Humbert (2005) described that molecular approaches could help in the evaluation of biodiversity of microorganisms habituated in aquatic environments. The feasibility of this strategy is due to the previous efforts and progress since twenty years (Dorigo, Volatier & Humbert, 2005). Most probably PCR based amplification of target genes situated in the operon region of the ribosomes, determining the PCR fragment changes through sequencing which enables to characterize the total fragments or length polymorphism which is based on automated ribosomal intergenic spacer analysis and terminal-restriction fragment length polymorphism, could help in the process (Dorigo, Volatier & Humbert, 2005). Earlier it was described that environmental microbiologists were successful in contributing to the molecular phylogeny associated with the identification of microorganisms without depending on cultures (Relman, D, A. 1993). This is connected to the basic features of 16S rRNA- related molecular phylogeny and the technology of nucleic acid amplification (Relman, D, A. 1993). The utilization of such culture independent approach could furnish valuable insights on the diversity and ecology of pathogenic microorganisms (Relman, D, A. 1993). This was strengthened by another report that highlighted the need of rapid identification of pathogenic bacteria (Anderson, 1994). The region primarily targeted is bacterial 16S ribosomal RNA (rRNA) gene. This is because it has potential to initiate the manufacture of rest of the gene. Hence PCR amplification of DNA could be subjected to sequencing in order to detect 16rRNAvariable regions which are specifically related to bacteria(Anderson, 1994). Later investigators have begun their research on synthesizing the rRNA-targeted nucleic acid probes, which are also not dependant on cultures. This led to assess the real composition of composition of microbial communities (Amann and Ludwig 2000).
This strategy known as Spatiotemporal quantification and Phylogenetic identification could help in undertaking reliable qualitative studies in microbial ecology (Amann and Ludwig 2000).
It is essential to know about how different strategies have been exploited by the investigators to screen and monitor the microbial diversity. The application of Oligonucleotide primers has led to the amplification of 16s rRNA genes of bacteria of the category Verrucomicrobia that are habituated in pasture soil (Farell and Janssen, 1999). This bacteria has 0.2 % of the soil DNA. Hence, in an experiment nearly 53 cloned PCR-amplified partial 16S rRNA gene fragments were obtained through the amplified ribosomal DNA restriction analysis (Farell and Janssen, 1999). This demonstrated that the 16S Rrna genes of bacteria Verrucomicrobia is essential for the primer association and the ultimate DNA extraction (Farell and Janssen,1999). Terminal restriction fragment (T-RFLP or TRF) analysis of 16S rRNA genes was used to determine the diversity of microbial communities (Dunbar, Ticknor & Kuske, 2000). The findings were used for a comparison study between two between-tree (interspace) soil environments and two pinyon rhizosphere that represent four soil communities (Dunbar, Ticknor & Kuske, 2000). This experiment revealed that that TRF analysis is a reliable strategy for understanding the relationships between bacterial community inter -associations(Dunbar, Ticknor & Kuske, 2000).Therefore, this method has implications for assessing the microbial diversity(Dunbar, Ticknor & Kuske, 2000). A similar but much more advanced method was used by the researchers to study the bacteria in two soils (Valinsky et al., 2002). This is oligonucleotide fingerprinting of rRNA genes (OFRG), an approach that enables detection of arrayed rRNA genes (rDNA) using hybridization experiments in a series by employing DNA probes (Valinsky et al., 2002). Here, analysis of 1,536 rDNA clones of bacteria living in two soils of varied habitat was done. This revealed 766 clusters which were classified into five major taxa. They include Bacillus, Actinobacteria, Proteobacteria, and two undefined assemblages (Valinsky et al., 2002)
Concomitantly, the taxa related to soil were detected and verified with cluster-specific PCR of the native soil DNA (Valinsky et al., 2002). A resolution of closely- related species was obtained because the clones exhibited sequence identities when they were present in the similar cluster (Valinsky et al., 2002). These OFRG findings were made to compare with those obtained in a denaturing gradient gel electrophoresis of the same two soils (Valinsky et al., 2002).This revealed that OFRG method is an inexpensive tool to analyze microbial communities and has beneficial ole to play in ecosystem studies, biotechnology and medicine (Valinsky et al., 2002). Hence, rRNA gene and its screening is very essential for a successful OFRG method of monitoring the microbial diversity.
In another experiment led by Valinsky and his co associates, fungal diversity was studied (Valinsky et al., 2002). Here, ORFG sorts were used to array the rRNA gene clones into taxonomic clusters involving hybridization experiments in a series, and employing single oligonucleotide probes (Valinsky et al., 2002). A kind of annealing Algorithm was simulated to create an OFRG probe targeted for fungal rDNA (Valinsky et al., 2002). 455 clusters were produced from the analysis of 1,536 fungal rDNA clones derived from soil (Valinsky et al., 2002). Nucleotide sequence analysis of clones was performed to determine the taxonomic identities produced by the OFRG experiment (Valinsky et al., 2002). The findings revealed that generated by a nucleotide sequence analysis produced clones were in agreement with the OFRG produced taxonomic identities. The clones produced represented various fungal genera thus indicating the microbial diversity (Valinsky et al., 2002). This has indicated that the microbial diversity could be assessed with the ORGF method.
The analysis of16S rRNA gene sequence was used for the detection of veterinary clinical bacterial isolates (Cai, Archambault, & Prescott, 2003). Comparison study was undertaken between conventional phenotypic identification methods and 16S rRNA gene sequencing (Cai, Archambault, & Prescott, 2003). In this context, the isolates were detected at the genus and species level (Cai, Archambault, & Prescott, 2003). The findings indicated that the reliable 16S rRNA gene-sequencing method is complete full-gene sequencing since it provides thorough information on species identification (Cai, Archambault, & Prescott, 2003).The targeting of the variable regions 1,2 and 3 of the 16S rRNA gene through sequencing could help in the detection as the potential of this approach to detect the bacteria at the genus level is same as 16S rRNA complete-gene sequencing (Cai, Archambault, & Prescott,2003). The identification of clinical pathogens through genotyping method associated with 16S rRNA gene sequence analysis has provided new insights on the field of Medical Diagnostics (Clarridge, 2004). The approach followed here is the traditional method of comparing the bacterial 16S rRNA gene sequences among strains (Clarridge, 2004).This could help in the detection of genus like mycobacteria, and could also further help in the detection of new pathogens and noncultured bacteria(Clarridge, 2004). However, 16S rRNA gene sequence analysis is falling short of reaching the laboratories due to the expensiveness and technical obstacles (Clarridge, 2004). The advent of molecular phylogenetics and gene amplification technology has become central in the identification and classification of bacteria isolated from the native habitats without relying on the culture (Lawson ,2004). With this environmental microbiology has developed to great extent where monitoring the diversity pathogenic agents of could be done easily (Lawson, 2004). The successful bacteria that were subjected characterization in this approach are the agents of bacillary angiomatosis, Tropheryma whippeli (the agent of Whipple’s disease), and gastritis inducing agent Helicobacter heilmannii (Lawson, 2004).
Further, the strategy of rRNA screening was exploited for monitoring the diversity of enteric microbiota. OFRG method was employed to define the compositions of bacterial and fungal rRNA genes isolated from small and large intestines of mice where there was limited microbial flora and no pathogens (Scupham et al. 2006). It was revealed that the highest composition of fungal rRNA sequences were related to the genera of Smittium, Entophlyctis, Neocallimastix, Fusarium, Catenomyces, Monilinia, Acremonium, Powellomyces, and Spizellomyces (Scupham et al. 2006). This has demonstrated that there is fungal diversity in the community of intestinal microbes (Scupham et al. 2006).
Next, ORGF method of producing microbial community profiles was used for analyzing a 9600 clones per experiment that also involves a novel probe set for the analysis of bacteria, enhanced statistical tool analysis and processing of data (Bent et al., 2006). This is in contrast to the original ORGF method that is used generally for 1536 clones (Bent et al., 2006). Software tool availability at the ORGF website and the overall determination of soil bacterial rRNA gene compositions were exposed to different temperature conditions (Bent et al., 2006). This could produce bacterial rRNA genes that would be in the perfect agreement with the temperature induced avocado root rot suppressiveness (Bent et al., 2006).The ultimate result was generation of 8876 bacterial rRNA gene fingerprints arranged into 5123 clusters, or operational taxonomic units (OTUs) (Bent et al., 2006). There was a positive correlation between the proportion of healthy roots and the number of clones among the OTU’s (Bent et al., 2006). The assessment of microbial diversity through OFRG was made much feasible with the association of a technique known as fluorescence in situ hybridization (FISH) (Bottari et al., 2006). Ribosomal RNA targeted oligonucleotide probes were used to detect various microorganisms in complex microbial communities (Bottari et al., 2006).
This strategy could serve as a potential tool and enables to undertake thorough studies for ecological, environmental, phylogenetic, and diagnostic studies in microbiology (Bottari et al., 2006). The use of 16S rRNA screening in association with DNA microarrays has come into light for its powerful detection and counting of microbial community in the oral cavity (Strake, 2007). This strategy employs gel-based microarrays with immobilized probes that is invented in a phylogenetic framework (Strake, 2007).
This could enable extensive microbial monitoring. Therefore, evaluating the structure of oral microbial community could lay a basis for understanding the microbial diversity of saliva and their applications in reducing the burden of diseases (Strake, 2007).This concept was strengthened by another report where much emphasis was put on Gene amplification and sequencing (Petti, 2007). This could allow the identification of emerging pathogens of bacteria and fungi and their precise classification (Petti, 2007). This approach is considered more reliable than the traditional methods as far as the unusual classification of microorganisms in immunocompromised individuals is concerned (Petti, 2007). Hence, this may indicate that clinical applications of 16Sr RNA sequencing could provide rapid discovery of broad range microorganisms (Petti, 2007).
A new strategy of using 16S rRNA sequence for monitoring the microbial diversity is its application in the form of a database known as SmartGene IDNS(Simmon, Croft & Petti CA). This software program enables rapid detection of various groups of clinical isolates (Simmon, Croft & Petti). Here, a comparison study would be performed between the SmartGene, MicroSeq databases and clinical samples of various bacteria (Simmon, Croft & Petti, 2007). This would help in the precise detection of bacteria to the genus and species level. This indicates that 16S rRNA gene sequencing is a potential tool for rapid microbe identification (Simmon, Croft & Petti, 2007).
The amplified products produced from the use of 16S rRNA primers were made to classify either by hybridization to a new high-density microarray probes that are matching to prokaryotic families (De Santis, et al., 2007). The findings from the microarray experiment have made it clear that vast number of clone-detected subfamilies have shown huge diversity in the amplification that influenced the phyla identification (De Santis, et al., 2007). Microarray has potential strategy in detecting the diversity in the samples obtained from the environment in contrast to sequencing the library of clones (De Santis, et al., 2007). Animal studies undertaken using ribosomal RNA and DNA (rRNA/rDNA) have yielded significant information. To this end, rumen microbial community was targeted using 16S/18S rRNA/rDNA to more precisely enable characterization, classification and identification (Deng et al., 2008).Techniques of fingerprinting like restriction fragment length polymorphisms (RFLP), temperature gradient gel electrophoresis (TGGE), and denaturing gradient gel electrophoresis (DGGE), could be used to explore the diversity of fungi, protozoa and bacteria in the rumen ecosystem (Deng et al., 2008).
This was strengthened by another report. With the advancements in science, finger printing methods have come into existence to better assess the microbial ecology (Tzeneva et al., 2008)The most routinely used one includes denaturing gradient gel electrophoresis (DGGE) of PCR-amplified fragments (Tzeneva et al., 2008). It enables the DNA fragments get separated as per their length and sequence (Tzeneva et al., 2008). Through DGGE, a rapid comparison of microbial communities can be performed and a compositional diversity could be studied where the bands obtained in the experiment reflect the bacterial phylotype (Tzeneva et al., 2008). A strategy of Interlaboratory comparison between terminal restriction fragment length polymorphism
(TRFLP) and full-length 16S rRNA gene sequence analysis targeted for assessing the microbial diversity was based on studying the seafloor basaltic lavas (Orcutt et al., 2009). Based on the similarity of two different DNA-based methods used in various laboratories the evaluation of microbial diversity of the same basalt samples is feasible (Orcutt et al., 2009). Hence, TRFLP could serve as a useful tool for performing a comparison study in diversity among the basalt samples, for the detection of dominant species, evaluation of evenness and richness in the microbes exhibiting low diversity (Orcutt et al., 2009). The use of 16s rRNA sequencing analysis has increased the applicability of SDS-PAGE associated with 16S rDNA sequencing and profiles of whole cell proteins (Kim et al., 2010). The analysis of SDS-PAGE targeting the Bacillus strains whole cell proteins has resulted in various patterns of banding worth suitable for reliable fingerprints. With the use of whole cell proteins, bacterial strains were confirmed by the analysis of 16S rDNA sequencing (Kim et al., 2010). Thus, the combination approach of whole cell protein SDS-PAGE and 16S rDNA sequence analysis could help in the rapid detection of Bacillus (Kim et al., 2010).
microbial diversity is a complex ecological characteristic feature. Since the creation of the nature microbes have continued to invade all corners of the living communities. This aspect has made their association rigid and deep rooted. The field of Molecular Biology has made tremendous contributions in assessing the microbial diversity through the channel of nucleic acid sequencing. 16 S rRNA sequencing has revolutionized the process of microbial monitoring in association with OFRG method. Communities related to bacteria and fungi have been explored randomly in various laboratory investigations which included both human and non human samples. Clinical applications have also been thoroughly highlighted in various reports. Hence, the utility of rRNA screening and OFRG method could serve a potential tool in the microbial diversity monitoring.
Amann, R, & Ludwig, W. (2000). ‘Ribosomal RNA-targeted nucleic acid probes for studies in microbial ecology’. FEMS Microbiol Rev,24(5),pp.555-65.
Anderson, B. (1994). ‘Broad-range polymerase chain reaction for detection and identification of bacteria’. J Fla Med Assoc, 81(12), pp.835-7.
Bent, E., Yin, B., Figueroa, A., Ye, J., Fu, Q., Liu, Z., McDonald, V., Jeske, D., Jiang, T., Borneman, J. (2006).Development of a 9600-clone procedure for oligonucleotide fingerprinting of rRNA genes: utilization to identify soil bacterial rRNA genes that correlate in abundance with the development of avocado root rot. J Microbiol Methods, 67(1), pp.171-80.
Bottari, B., Ercolini, D., Gatti, M., Neviani, E. (2006). ‘Application of FISH technology for microbiological analysis: current state and prospects’. Appl Microbiol Biotechnol, 73(3), pp.485-94
Cai, H, Archambault, M, Prescott, J, F(2003). ‘16S ribosomal RNA sequence-based identification of veterinary clinical bacteria’. J Vet Diagn Invest, 15(5), pp.465-9.
Clarridge, J,E 3rd. (2004). ‘Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases’. Clin Microbiol Rev. 17(4), pp.840-62,
Deng, W., Xi, D., Mao, H., Wanapat, M. (2008). The use of molecular techniques based on ribosomal RNA and DNA for rumen microbial ecosystem studies: a review. Mol Biol Rep,35(2),pp.265-74.
DeSantis, T,Z., Brodie, E,L., Moberg ,J,P., Zubieta, IX, Piceno, Y,M., Andersen, G,L. (2007). High-density universal 16S rRNA microarray analysis reveals broader diversity than typical clone library when sampling the environment. Microb Ecol, 53(3),pp.371-83.
Dorigo, U., Volatier, L., Humbert, J,F. (2005). ‘Molecular approaches to the assessment of biodiversity in aquatic microbial communities’. Water Res 39(11), pp.2207-18.
Dunbar, J., Ticknor, L,O., Kuske, C,R. (2000). ‘Assessment of microbial diversity in four southwestern United States soils by 16S rRNA gene terminal restriction fragment analysis.’ Appl Environ Microbiol, 66(7), pp.2943-50.
Farell and Janssen. (1999). ‘Detection of Verrucomicrobia in a pasture soil by PCR-mediated amplification of 16S rRNA genes’. Appl Environ Microbiol, 65(9), pp.4280-4.
Lawson, A.J.(2004). Discovering new pathogens: culture-resistant bacteria. Methods Mol Biol. 266, pp.305-22.
Petti, C,A. (2007). ‘Detection and identification of microorganisms by gene amplification and sequencing’. Clin Infect Dis,44(8), PP.1108-14.
Kim, T,W., Kim, Y,H., Kim, S,E., Lee, J,H., Park, C,S., Kim, H,Y. (2010). Identification and distribution of Bacillus species in doenjang by whole-cell protein patterns and 16S rRNA gene sequence analysis. J Microbiol Biotechnol, 20(8), pp.1210-4.
Orcutt ,B., Bailey, B., Staudigel, H., Tebo, B,M., Edwards, K,J. (2009). An interlaboratory comparison of 16S rRNA gene-based terminal restriction fragment length polymorphism and sequencing methods for assessing microbial diversity of seafloor basalts. Environ Microbiol, 11(7),pp.1728-35
Relman, D, A. (1993). The identification of uncultured microbial pathogens. J Infect Dis.168 (1), pp.1-8.
Scupham, A,J., Presley, L,L., Wei, B., Bent, E., Griffith, N., McPherson, M., Zhu, F., Oluwadara, O., Rao, N., Braun, J., Borneman, J. (2006). Abundant and diverse fungal microbiota in the murine intestine. Appl Environ Microbiol, 72(1),pp.793-801.
Simmon, K, E., Croft, A,C., Petti, C,A.(2006). Application of SmartGene IDNS software to partial 16S rRNA gene sequences for a diverse group of bacteria in a clinical laboratory. J Clin Microbiol, 44(12), pp.4400-6.
Starke, E,M., Smoot, J,C., Wu, J,H., Liu, W,T., Chandler, D., Stahl, D,A. (2007). ‘Saliva-based diagnostics using 16S rRNA microarrays and microfluidics’. Ann N Y Acad Sci,1098,pp.345-61.
Tzeneva, V, A., Heilig, H, G., van Vliet, W, A., Akkermans, A, D., de Vos, W, M., Smidt, H. (2008). ‘16S rRNA targeted DGGE fingerprinting of microbial communities’. Methods Mol Biol, 410, pp.335-49.
Valinsky, L., Della Vedova, G., Scupham. A,J., Alvey, S., Figueroa, A., Yin, B., Hartin, R,J., Chrobak, M., Crowley, D,E., Jiang, T., Borneman, J. (2002). ‘Analysis of bacterial community composition by oligonucleotide fingerprinting of rRNA genes’. Appl Environ Microbiol, 68 (7), pp.3243-50.
Valinsky, L., Della, Vedova, G., Jiang, T., Borneman, J. (2002). ‘Oligonucleotide fingerprinting of rRNA genes for analysis of fungal community composition’. Appl Environ Microbiol, 68(12), pp.5999-6004.