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Introduction
It was in the year 1663, more than three centuries ago, when Antonie van Leeuwenhoek first observed bacterial cells using his own microscope. This was the beginning of a new field of study called “microbiology”. For many years, the study of microbes remained dependent on the use of microscopes. Microbiologists, such as Ferdinand Cohn, Louis Pasteur, and Robert Koch, established microbiology as one of the important disciplines. Koch, in particular, developed solidified media using agar and, for the first time, the microbiological world understood the concept of pure cultures. Later on, it became mandatory to study bacteria in pure cultures. The majority of earlier microbiological work focused on isolating and studying bacteria in pure cultures. However, it was still not known that the majority of the bacterial population was beyond the reach of microbiologists as it was not possible to culture them using standard laboratory procedures. The evidence that a majority of the bacterial population is uncultured came from the discrepancy between the size of the population estimated through serial dilution plating and microscopy, commonly referred to as the “great plate count anomaly” (Staley and Konopka 1985). Marine ecosystems are a well-studied example of this phenomenon where only 0.01 to 0.1% of marine bacterial cells produce colonies by standard plating techniques (Kogure et al. 1979). The unculturable microorganisms have been described as the “microbial dark matter” (Bernard et al. 2018, Bowman 2018). In the twentieth century, microbiologists started to realize that bacteria are important creatures, providing enzymes, antibiotics, and other chemical compounds of use. As it was already documented that only a small fraction of bacteria could be cultivated, it was realized that an enormous wealth of compounds produced by these bacteria might be unexplored because there were no methods to trap the majority of this bacterial population. The inability of traditional microbiological methods to isolate bacteria was not the only problem. The difficulty faced by microbiologists was also at the level of bacterial species definition. Bacterial taxonomists agreed to define a genospecies based on DNA-DNA similarities of more than 70% (Schleifer and Stackebrandt 1983, Wayne et al. 1987). It is difficult to estimate the exact number of bacterial species, as the number is enormous (Dykhuizen 1998). Using DNA-DNA reassociation studies, Torsvik (Torsvik et al. 1990a, 1990b) estimated that 1 g of soil contains 4000 different bacterial genomic units.
The methods that have been developed to explore bacterial diversity are broadly classified into culture-dependent and culture-independent methods. Culture-dependent methods are the classical methods that require bacterial growth or activity, e.g., colony morphology from plate counts and community level physiological profiling (CLPP) using Biolog™ plates. Culture-independent methods were developed subsequently because of drawbacks in the traditional cultivation methods (Table I).
Pros and cons of culture-dependent and culture-independent approaches for exploring bacterial diversity.
Pros
Cons
Culture-dependent approaches
Provides knowledge about physiological or functional properties of bacteria.
Inexpensive.
Only culturable bacteria can be studied.
Favours fast growing bacteria.
Culture-independent approaches
Non-cultivable bacteria can be detected.
Many samples can be analyzed simultaneously.
Help in linking bacterial community structure and function.
Differences in DNA extraction efficiency.
Bias due to differential amplification of 16S rRNA gene.
Interpretation of bands/peaks difficult and time consuming.
Problems during sequence assembly and interpretations.
Expensive
Culture-dependent methods
Plate counts
The diversity of bacterial communities has been investigated using methods based on isolating and culturing bacteria. These techniques are selective and do not reflect the actual diversity of the bacterial community. Only a small fraction of cells can be cultured on the media known so far (Overmann et al. 2017, Steen et al. 2019). However, the paradigm that only 0.1–1% of bacteria are culturable does not hold true now (Martiny 2019). It is also not known whether the culturable bacteria are representative of the bacterial population (Torsvik et al. 1998). One of the limitations in culturing environmental bacteria is the difficulty in replicating the environmental conditions in the laboratory that certain bacteria require (Stewart 2012). Another limitation is that any departure from the original environmental parameters during cultivation can alter the community structure due to new selective conditions (Dunbar et al. 1997). Despite drawbacks, plate count is still the method of choice for isolating and studying bacteria from different habitats (Chiang et al. 2022, Djuuna et al. 2022, Schumacher et al. 2022, Zhou et al. 2022).
Community level physiological profiles (CLPP) and sole carbon source utilization (SCSU) patterns
These assays are useful for studying physiological diversity and are performed using Biolog™ plates (96-well microtiter plates) that contain different carbon sources (Garland and Mills 1991). Differences in utilization of different carbon sources yield different profiles that reflect the potential utilization of different carbon sources and help in differentiating bacterial communities. Although simple, such techniques suffer from many drawbacks. They help in assessing the metabolic diversity of only culturable bacteria and favor fast-growing bacteria. Theses technique have been used recently to study: microbial diversity in soil sown with six cultivars of rapeseed (Jezierska-Tys et al. 2021), effects of burning season and vegetation coverage on Mediterranean mixed-mesogean pine forest soils (Moya et al. 2021), bacterial community variation in Sorghum rhizosphere (Kumar et al. 2021), microbial communities contaminated with mine solid wastes (Martínez-Toledo et al. 2021), two fire-affected sclerophyll forests in the Mediterranean climate zone of Central Chile (Aponte et al. 2022), impact of eight widely consumed antibiotics on natural soil microbial communities (Pino-Otín et al. 2022), and changes in the bacterial structure in bottom sediments in Cardinal Pond, Poland (Wolińska et al. 2022).
Other approaches for isolation
Many different approaches have been used for isolating previously uncultured bacteria. One such approach is the use of gellan as a solidifying reagent. Gellan gum is a linear polysaccharide produced by Pseudomonas elodia. It consists of glucuronic acid, rhamnose, and glucose and is more stable than agar. Gellan may serve as an energy source itself, leading to an increase in the colony count. Many studies have shown that the use of gellan gum increases the number of visible bacterial colonies (Delavat et al. 2012, Rygaard et al. 2017, Stott et al. 2008, Tamaki et al. 2005, 2009). Tamaki et al. (2005) showed that under aerobic conditions, the use of gellan gum increases the viable counts by nearly 2.2 to 12.6 times as compared to agar. Stott et al. (2008) used gellan gum to study bacterial diversity of three geothermal soils in the Taupo Volcanic Zone of New Zealand. They could isolate previously uncultured species, genera, classes, and even a new phylum of bacteria. Rygaard et al. (2017) found that the use of gellan gum increased viable counts by 3- to 40-fold.
A high throughput dilution to extinction culturing (HTC) method was developed by Connon and Giovannoni (2002). In this technique, a small inoculum is added to a sterile medium in a 96-well plate, resulting in a small number of cells. Incubation is done and the plates are screened using a fluorescent stain, such as SYBR Green1. Using HTC, Connon and Giovannoni could isolate various novel microbial isolates. Four isolates could be assigned to previously uncultured marine Proteobacteria clades and were related to the clades SAR11 (α-subclass), OM43 (β-subclass), SAR92 (γ-subclass), and OM60/OM241 (γ-subclass).
Rappe et al. (2002) used HTC using pristine sea water as a medium to isolate novel SAR11 strains. Transmission electron microscopy-based size estimation of one of the isolates suggested that it was one of the smallest free-living and replicating pure cultures of bacteria known. Stingl et al. (2007) used a modified HTC method to isolate 17 new SAR11 strains from the Oregon coast (12 isolates) and Sargasso Sea (5 isolates). A number of previously uncultured organisms were also isolated from the Oregon coast, including the SAR116 group, the OCS14 clade, 2 groups of Verrucomicrobia, Bacteroidetes and uncultivated sulfur-oxidizing symbionts related to γ-proteobacteria. A recently developed method, called the ichip device, involves the cultivation of microorganisms by incubating the chips in situ (Berdi et al. 2017).
Culture-independent methods
Culture-independent methods have been developed to study the uncultivated bacterial population. These include biochemical and molecular methods. Molecular methods generally rely on PCR amplification of the 16S rRNA gene or direct sequencing of environmental DNA (e-DNA). These methods have rapidly replaced cultivation-based methods to investigate bacterial community diversity.
Fatty acid methyl ester (FAME) analysis
FAME analysis is based on the grouping of fatty acids for studying bacterial communities. It is based on the fact that signature fatty acids can differentiate between major taxonomic groups. Fatty acids are extracted directly from the environment, methylated, and analyzed through gas chromatography. Comparison of different soil types can be done using multivariate statistical analysis. This method has a number of drawbacks. Fatty acid composition is affected by factors such as temperature and nutrition. Moreover, fatty acids cannot be species-specific as the same fatty acid can be found in other species. This method has been used recently to study microbial communities in the nepheloid layers and hypoxic zones of the canary current upwelling system (Thiele et al. 2019), gut microbiome and metabolome in Helicobacter pylori patients (White et al. 2021), and the effect of residual organochlorine pesticides on microbial community (Wang et al. 2022).
GC content
GC content can be used along with culture-independent molecular methods for comparing bacterial community structure (Nusslein and Tiedje 1999). It is based on the fact that microorganisms differ in their GC content and taxonomically related groups differ by only 3–5% or may have the same GC content (Tiedje et al. 1999). Different taxonomic groups may have the same or nearly the same GC content. Although not a reliable method, it can be used to indicate changes in the bacterial community structure.
Nucleic acid reassociation and hybridization
DNA reassociation has been used for estimating bacterial diversity (Torsvik et al. 1990a, 1990b, 1996). For reassociation or hybridization, e-DNA is isolated, denatured, and reannealed. Annealing depends on the similarity between the sequences. If sequences are diverged, the rate of DNA reassociation decreases (Theron and Cloete 2000). By determining the rate of DNA reassociation, the heterogeneity in a community can be estimated. The degree of DNA similarity between the two communities can be used to draw similarity between them (Griffiths et al. 1998). Hybridization using specific probes is also used to study bacterial communities. Hybridization can be done on nucleic acids extracted directly from the environment. Dot blot hybridization is used to measure the relative abundance of certain groups of bacteria. The probes used for hybridization are generally radiolabeled, but florescent labeled probes are now more commonly used. Fluorescence in situ hybridization (FISH) uses florescent probes for detection of specific bacterial groups and has been used successfully to study the distribution of bacteria on biofilms (Schramm et al. 1996, Thurnheer et al. 2004).
DNA microarrays
A single DNA microarray contains thousands of DNA sequences that can be specific target genes (for functional diversity) or fragments that represent different species (Greene and Voordouw 2003). DNA microarrays have been used along with DNA-DNA hybridization to identify bacterial species (Cho and Tiedje 2001). A similar technique, reverse sample genome probing (RSGP), uses genome arrays to analyze microbial community structure (Voordouw et al. 1991). In this technique, labeled e-DNA is hybridized to arrays in which genomes of known microorganisms are spotted on a solid matrix. RSGP has been used to identify sulfate-reducing bacteria in oil fields (Voordouw et al. 1992), to analyze the effect of toluene and dicyclopentadiene on community composition (Shen et al. 1998), to analyze the microbial communities in soil samples that were incubated with 10% toluene (Hubert et al. 1999), and to analyze the community composition of the enriched soil samples contaminated with benzene, dicyclopentadiene, cyclopentadiene, toluene, styrene, xylenes, and naphthalene (Greene et al. 2000). DNA microarrays have been used to study microbial communities of a Gulf of Mexico coastal salt marsh (Beazley et al. 2012) and to detect pathogens in intracranial bacterial and fungal infections (Cao et al. 2018). Ballarini et al. (2013) used an oligonucleotide microarray called BactoChip for culture-independent identification of bacteria in complex communities.
PCR-based methods
PCR-based methods involve isolating e-DNA followed by amplification of the 16S rRNA gene. The PCR products are usually cloned to construct libraries (Fig. 1), which are sequenced to identify the native bacteria in environmental samples (Lal et al. 2015). PCR-based methods have certain limitations and biases that appear at the stage of sampling and storage before extraction of nucleic acids (Thies 2007). Improper soil storage can result in a change in bacterial diversity. It has been proposed that soil samples should be stored at −20°C for short-term and −80°C for long-term storage (Thies 2007). Problems are also encountered during the extraction of nucleic acids (Sipos et al. 2010, Thies 2007), including the reproducible lysis of all bacterial cells, extraction of unfragmented nucleic acids, and removal of substances such as humic acids, bacterial exopolysaccharides, and proteins that may inhibit enzyme activity during PCR and restriction digestion (Cullen and Hirsch 1998, Gelsomino et al. 1999, van Elsas et al. 1997). The lysis efficiency of bacterial cells varies with the group. If a gentle method of lysis is used, Gram-positive cells are not lysed. Harsh methods, such as bead beating can lead to shearing of DNA (Wintzingerode et al. 1997). PCR-based methods have a major drawback in that they can only detect the most abundant species present in the environmental samples (Rincon-Florez et al. 2013), mainly due to differential amplification of the 16S rRNA gene (Al-Awadhi et al. 2013).
Fig. 1.
Systematic representation of 16S rRNA clone library construction and subsequent screening and sequencing clones.
Denaturing gradient gel electrophoresis (DGGE)
DGGE is a convenient technique for analyzing the diversity of complex natural microbial populations. It is a molecular fingerprinting method that separates PCR-generated DNA products. DGGE was first described by Muyzer et al. in 1993. This technique provides information about sequence variations in a mixture of PCR fragments of identical length based on differential mobility in the acrylamide gel matrix of increasing denaturant concentration. Like other fingerprinting techniques, it also involves the isolation of e-DNA followed by PCR amplification of the 16S rRNA gene using primers with a GC clamp (a stretch of GC-rich sequences). PCR of e-DNA generates products with varying DNA sequences, but conventional separation by agarose gel electrophoresis results only in a single DNA band, which is largely non-descriptive. This limitation is overcome by DGGE as it separates PCR products based on sequence differences that result in differential denaturing characteristics of the DNA. During DGGE, PCR products encounter increasing concentrations of denaturant as they migrate through a polyacrylamide gel. In practice, the denaturants used are a fixed ratio of formamide (0–40%) and urea (0–7 M). 100% denaturing acrylamide contains 7M urea with 40% formamide (Myers et al. 1987). Another denaturant used is a constant temperature of 60°C. This technique is commonly known as TGGE (temperature gradient gel electrophoresis). However, both the techniques, TGGE and DGGE, are interchangeable and give comparable fingerprints of microbial communities (Heuer and Smalla 1997). When a PCR product is electrophoresed into a gradient of increasing denaturing conditions, it partially melts and undergoes a sharp reduction in mobility on reaching a threshold denaturant concentration. The position in the gradient where a DNA fragment melts and nearly stops migrating depends on the nucleotide sequence and the GC content. Hence, DNA with different sequences in a PCR product denatures at different denaturant concentrations, resulting in a pattern of bands (Fig. 2). The banding pattern represents the major constituents of the analyzed bacterial community (Heuer et al. 1997). Each band theoretically represents a different bacterial population present in the community. DGGE patterns can be compared with the migration of reference clones of known sequence; the major bands can be excised, reamplified, and sequenced to know their identities (Muyzer et al. 1995, Teske et al. 1996). The patterns of DGGE are more useful for direct comparison of structural diversity between different microbial communities.
Fig. 2.
Denaturing Gradient Gel Electrophoresis. The figure shows 16S rRNA gene amplification from community DNA followed by gel electrophoresis. Wells A to D contain amplified products from individual bacterial species, well E contains a mixture of amplified products from A to D, well F contains amplified product from the soil community. The conventional agarose gel electrophoresis results in a single non-descriptive band but separation on denaturing acrylamide gels results in multiple bands, each representing the dominant member of the community.
The importance of DGGE can be understood by the fact that many recent studies have used this technique to study bacterial diversity. It has recently been to study: microbial communities in vertical flow treatment wetlands (Silveira et al. 2021), bacterial community diversity from lesional and non-lesional skin of leprosy patients (Bayal et al. 2021), bacterial community composition in marine sediments from Palk Bay and Gulf of Mannar (Aravindraja et al. 2022), airborne bacterial community associated with PM2.5 under different air quality indices (Acuña et al. 2022), and diversity of cyanobacteria that colonize the roots of leafless orchids (Tsavkelova et al. 2022). DGGE has recently been used to compare the bacterial diversity in the oral cavity of people with multiple sclerosis and healthy subjects (Zangeneh et al. 2021) and microbial community structure and function in the soil rhizosphere between bacterial wilt resistant and susceptible mulberry genotypes (Dong et al. 2021). DGGE has also been used recently to study the effect of: fertilizers on soil nitrogen fixing bacteria community in a rice paddy field (Tang et al. 2021), colonization of the red imported fire ant on soil microbial communities (Travanty et al. 2022), and Asparagus racemosus starter-based rice fermented foods on intestinal microbiota (Hor et al. 2022).
Amplified ribosomal DNA restriction analysis (ARDRA)
ARDRA is another molecular technique based on PCR amplification of the 16S rRNA gene. The most important step in this technique is the selection of restriction enzymes (RE). Generally, tetracutter RE are used. The selection of the number of RE is also important. After restriction digestion of the amplified 16S rRNA gene, the restriction pattern is visualized and combined by pattern recognition and analysis software. Similarities in ARDRA patterns allow the grouping, based on numerical analysis. ARDRA fingerprints also allow the construction of a database for identification purposes. Banding patterns in diverse communities may become complex to analyze as a single species can have four to six restriction fragments (Tiedje et al. 1999). Six base cutters can be used to increase the resolution of this technique. Heyndrickx et al. (1996) gave a standardized method for ARDRA using five RE, HaeIII, DpnII, RsaI, BfaI, and Tru9I. Nicomrat and colleagues (2008) used four RE, HaeIII, HhaI, MspI, and TaqI, to explore bacterial diversity from a constructed wetland system that treats acid coal mine drainage.
ARDRA has been used recently to characterize the diversity of lactic acid bacteria from major Meekeri production areas in Sri Lanka (Adikari et al. 2021), cyanobacterial diversity from two geothermal environments of Northern Costa Rica (Brenes-Guillén et al. 2021), plant growth-promoting endophytic bacteria from Pisum sativum and Cicer arietinum (Maheshwari et al. 2022), halophilic bacteria from a former salt mine (Nosalova et al. 2022) and endophytic halotolerant bacterial isolates from haloalkaliphytes (Enquahone et al. 2022).
Single strand conformation polymorphism (SSCP)
SSCP was primarily developed and used for the detection of known or novel polymorphisms and mutations in human genes (Orita et al. 1989a, 1989b). In the absence of denaturing conditions, single-stranded DNA has a folded structure, determined by intramolecular interactions and its nucleotide sequence. The electrophoretic mobility of DNA in a gel depends on length, molecular weight, and shape. Single-stranded DNA fragments with the same size but different sequences can be separated into different bands on a polyacrylamide gel based on differences in mobility caused by their folded secondary structure (Hayashi 1991).
It is a simple and effective method for detecting minor sequence changes in PCR-amplified DNA (Sheffield et al. 1993). Many factors, such as gel matrix, temperature, fragment size, and sequence, can affect its sensitivity (Liu and Sommer 1994). This technique has several advantages. It is less laborious and does not require radioactive substrates. Reamplification and sequencing of separate bands provide useful information regarding community structure.
A variation of SSCP, called capillary electrophoresis-single strand conformation polymorphism (CE-SSCP) fingerprinting, has been developed and used by several workers (Jernigan and Hestekin 2015, Rossmann et al. 2012).
Terminal restriction fragment length polymorphism (T-RFLP)
T-RFLP is a method for analyzing variations in the length of terminal restriction fragments (T-RFs) of the 16S rRNA gene generated after restriction digestion (Dubey et al. 2014, Gupta et al. 2013, Lal et al. 2015). T-RFLP is based on variations in restriction patterns of 16S rRNA that generate a characteristic pattern of T-RFs after restriction digestion and capillary electrophoresis (Fig. 3). Methods to obtain T-RF patterns for the assessment of community structure and dynamics are more or less similar with some modifications (Blackwood et al. 2003, Dunbar et al. 2001). It involves extraction of e-DNA, PCR amplification of 16S rRNA gene using either only 5′ end labeled or both 5′ and 3′ end labeled universal or near universal primer(s), purification of PCR product, restriction digestion of purified amplicon using single or more than one RE, and finally separation of T-RFs by capillary electrophoresis using automated DNA sequencer equipped with data collection and analysis software. Digestion of amplified products is done by one or more than one, common and readily available RE. It needs a careful selection of RE to digest the amplified products that can give better resolution (Castro et al. 2005). Since different enzymes produce different community fingerprints, it is important to use at least two to four different RE to increase the accuracy.
Fig. 3.
Terminal restriction fragment length polymorphism. The capillary electrophoresis system of an automated DNA sequencer to separate the digested product is used to obtain T-RFs shown as an electropherogram.
TRFLP is a favorable technique to analyze bacterial communities and has been used in many recent investigations. It has been used to study: the impact of daily application of Lactobacillus reuteri on the indigenous skin bacterial community diversity (Frerejacques et al. 2020), variations in ammonia-oxidizing communities in a subtropical river of China (Ginawi et al. 2020), total petroleum hydrocarbons-degrading microbial community (Chen et al. 2020), changes in bacterial community structure in response to increasing concentrations of diclofenac in fed-batch reactors (Kraigher and Mandic-Mulec 2020), oral microbial flora in patients undergoing hematopoietic stem cell transplantation (Takahashi et al. 2020), duodenal microbiota of patients with pancreaticobiliary cancer (Sugimoto et al. 2021), and microbial community distribution within two boreholes located in the source area of perchloroethene (Herrero et al. 2022). T-RFLP has also been used recently to study the effects of: rifaximin on gut commensal microbiota in mice (Ferrer et al. 2021), proton pump inhibitors on the intestinal flora of low-dose aspirin users (Tsujimoto et al. 2021), Lactiplantibacillus plantarum and Lacticaseibacillus paracasei on the microbial community in children with celiac disease autoimmunity (Oscarsson et al. 2021), and banana leaf and plastic wraps on the lactic acid bacteria quantity and community composition of tempeh (Erdiansyah et al. 2022).
RISA is similar to T-RFLP but involves PCR amplification of the 16S–23S ribosomal intergenic spacer region rather than the 16S rRNA followed by separation on a polyacrylamide gel under denaturing conditions. It exploits the heterogeneity in length, which varies between 150 and 1500 bp, as well as nucleotide sequence (Fisher and Triplett 1999). In RISA, the sequence polymorphism is detected using silver stain, while in ARISA, the forward primer is fluorescently labeled and is detected automatically. RISA requires large quantities of DNA, is time-consuming, and has low resolution. ARISA is more sensitive and reduces the time of the assay. RISA has been used to analyze bacterial communities from soil (Borneman and Triplett 1997, Koroleva et al. 2021, Ranjard et al. 2000, Sigler et al. 2002), agronomic products (Ikeda et al. 2005), oral cavities (Mukherjee and Leys 2021), biofilms (De Luca et al. 2021), and outer ear (Burton et al. 2022).
Direct e-DNA sequencing: Metagenomics
New sequencing technologies are emerging at a fast pace, and now it is possible to direct sequence e-DNA (metagenome), giving rise to a new field called “metagenomics”. The classical examples of whole metagenome sequencing are acid mine drainage and the Sargasso Sea. Acid mine drainage represents one of the most extreme environments, with drainage water reaching a pH of 0–1. The microbial community forms a pink biofilm that floats on the surface of highly acidic drainage water. The biofilm is dominated by Leptospirillum species and contains F. acidarmanus at a relatively low abundance. Tyson and coworkers (2004) first constructed a 16S rRNA gene clone library and end sequenced 384 clones. The most abundant clones belonged to Leptospirillum group II. They found that 94% of the Leptospirillum group II clones were identical, and 17 minor variants were detected. Groups related to Leptospirillum group III, Sulfobacillus, and Ferroplasma, were also detected. They then cloned DNA from acid mine drainage and sequenced it with very high coverage. They were able to reconstruct nearly complete genomes of Leptospirillium group II and Ferroplasma type II. Metagenome sequencing of such an extreme environment not only deciphered the community structure but the load sharing in the community.
In another landmark metagenome sequencing project, Venter and co-workers (2004) sequenced the metagenome of a highly complex ecosystem-the Sargasso Sea. In this project, they claimed to have discovered many new genes. Six putative plasmids larger than 100 kbp in length, two plasmids of 70 to 80 kbp, and two plasmids under 10 kbp were also reconstructed. An important outcome of this project was the finding that rhodopsins are also present outside proteobacteria where they had previously been discovered. In another classical work, DeLong and co-workers (2006) used metagenome sequencing-based analysis of stratification of planktonic microbial communities in the North Pacific Subtropical Gyre. Samples were collected at different depths and a fosmid library was constructed for each depth. A vertical zonation of taxonomic groups, functional genes, and metabolic potential was found in this study.
Metagenome sequencing projects have also resulted in the reconstruction of whole genomes of uncultured organisms. Buchnera aphidicola is one such intracellular symbiont of the aphid Baizongia pistacea whose complete genome has been assembled from metagenome data (van Ham et al. 2003). Insights into the genome of this symbiont suggest a reduced genome due to symbiotic life with 544 putative genes and nine pseudogenes. In a similar attempt, Woyke and colleagues (2009) sequenced the genomes of two uncultured marine flavobacteria from the Gulf of Maine. They employed fluorescence-activated cell sorting and multiple displacement to isolate genomic DNA from flavobacterium for sequencing. Shotgun sequencing yielded 1.9 Mbp in 17 contigs and 1.5 Mbp in 21 contigs with estimated genome recoveries of about 91% and 78%, respectively.
Metagenomics is a recent field and has been used in many recent investigations to study bacterial communities. It has been used to study the role of microbial community in bioelectrokinetic remediation of tannery contaminated soil (Prakash et al. 2021), microbial diversity on the US dollars and Chinese Renminbis (Lin et al. 2021), hexabromocyclododecane degradation by soil microbial community (Li et al. 2022), sediment microbial biodiversity associated with fishing exposure (Bruce et al. 2022), antibiotic resistance genes diversity in Taihu Lake, China (Bai et al. 2022), antimicrobial resistance and virulence factor genes in an Arctic permafrost region (Kim et al. 2022), antimicrobial resistance in patients with lower respiratory tract infections (Serpa et al. 2022), microbes associated with traditional Brazilian artisanal cheeses (Kothe et al. 2022), microbial community from a municipal landfill site (Gupta et al. 2022), and microbial community responsible for bioremediation of poly-contaminated groundwater (Hauptfeld et al. 2022). Metagenomics has also been used to study the effect of Bt cotton on the soil bacterial community (Lv et al. 2022) and the different environmental conditions of soda lakes on bacterial communities (Pellegrinetti et al. 2022). Metagenomics has also been used to reconstruct genomes from Siberian permafrost. Liang et al. (2021) used metagenomics to reconstruct genomes of fossil and living microorganisms, while Sipes et al. (Sipes et al. 2021) used metagenomics to assemble eight genomes from ancient Siberian permafrost.
From who is there to what are they doing?
With advances in molecular techniques, there is a change in ecologists’ perspective. From their earlier approach to study bacterial community structure, the newer approach is to find out community function. Metagenome sequencing of acid mine drainage provides one classical example of linking community structure with function. Functionality of a bacterial community can also be characterized by the analysis of transcripts (metatranscriptomics) or proteins (metaproteomics) (Rodriguez-Valera 2004).
Metatranscriptomics involves isolating mRNA from environmental samples and its reverse transcription to form cDNA, followed by cloning and sequencing. Metatranscriptomics has been used for different habitats, such as aquatic (Poretsky et al. 2005), marine (Frias-Lopez et al. 2008, Sun et al. 2021), soil (Urich et al. 2008), human respiratory tract (Ren et al. 2021), human lungs (Han et al. 2022), human breast cancer (Hadzega et al. 2021), human feces (Tanaka et al. 2022), and wood (Nerva et al. 2022). Metatranscriptomics has been used in many recent investigations to study: the effect of bioaugmentation on microbial community and function in a continuous anaerobic process treating cow manure (Li et al. 2021), the association of human respiratory microbiota with mortality in COVID-19 (Ren et al. 2021), microbial composition in human breast cancer primary tumour tissue (Hadzega et al. 2021), iron and carbon metabolism by diverse communities in Southern Ocean (Sun et al. 2021), changes in the lung microbiota due to SARS-CoV-2 infection (Han et al. 2022), microbial communities associated with the wood of 20-year-old grapevine plants (Nerva et al. 2022), and composition of the fecal microbiota in patients with irritable bowel syndrome (Tanaka et al. 2022). Many recent investigations have used a combination of metagenomics and metatranscriptomics to: get insights into the in situ microbial communities of Challenger Deep, a hadal trench (Zhou et al. 2022), identify antimicrobial resistance gene reservoirs present in poultry, ruminant, swine GI tracts and soil (Lawther et al. 2022); study changes in microbiota in patients with chronic obstructive pulmonary disease (Yang et al. 2022), and get insight into the functional ecology of the vaginal microbiota (France et al. 2022).
Metatranscriptomics suffers from a number of drawbacks, such as difficulty in isolating RNA, short half-life of RNA, and low correlation between RNA levels and corresponding proteins, as all the RNAs do not necessarily translate into proteins due to translational repression. So, directly assessing the protein product seems to have better prospects. Though proteomics as a field emerged in the middle of the 1970s (O’ Farrell 1975), the term “metaproteomics” was proposed by Wilmes and Bond (2004). Metaproteomics involves isolation and purification of the environmental proteome and its separation by two-dimensional polyacrylamide gel electrophoresis. Highly expressed protein spots are excised and identified by MALDI-TOF with peptide sequencing. Many protocols have been developed to isolate proteins from the environment. Singleton et al. (2003) found the snap-freeze protein extraction technique (using liquid nitrogen) to be the most efficient in extracting proteins from soil samples as compared to the bead beating method. Wilmes and Bond (2004) devised a method for isolating proteome from a laboratory-scale activated sludge system optimized for enhanced biological phosphorus removal. Benndorf and colleagues (2007) used a modified method of lysis and protein separation from the soil matrix using 0.1 M NaOH and phenol.
In a classical study, Ram and colleagues (2005) used metaproteomics to characterize microbial biofilm from acid mine drainage. They detected 2033 proteins from the five most abundant species in the biofilm, which included 48% of the predicted proteins from the dominant biofilm organism, Leptospirillum group II. This study confirmed the earlier study by Tyson and coworkers (2004). Metaproteomics has been used in many recent investigations to study: functional aspects of anaerobic methanogenic microbiomes that deconstruct and utilize lignocellulose (Chirania et al. 2022), structure and function of microbial community with benzene-mineralizing, nitrate-reducing potential (Eziuzor et al. 2022), the composition of black extrinsic tooth stain in children (Hirtz et al. 2022), gut microbiota in children with autism spectrum disorder (Levi Mortera et al. 2022), and 30,000 year old fossilized woolly mammoths (Cucina et al. 2022). Many recent investigations have used a combination of metagenomics and metaproteomics to study: microbial structure and function of pre-denitrification biofilter in an urban wastewater treatment plant in China (Tian and Wang 2021) and the gut microbial community and gut barrier function in COVID-19 patients (Sun et al. 2022).
A multi-omics approach, including metagenomics, metatranscriptomics and metaproteomics, has now been used to get a deeper insight into bacterial community structure and function. Such a multi-omics approach has been used recently to discover bioactive microbial gene products in inflammatory bowel disease (Zhang et al. 2022) and to identify arsenic-methylating microorganisms in anoxic soil-derived microbial cultures (Viacava et al. 2022).
Summary
Studying microbial diversity has been the key aim of environmental microbiologists and ecologists. For this, various methods have been developed, which are classified as culture-dependent and culture-independent methods. The most promising culture independent method is whole metagenome sequencing. With tremendous growth in sequencing technologies and an increase in data output, it is now possible to get gigabases of data in a few days. This has increased the depth of sequence coverage, allowing more access to uncultivable bacterial diversity. Further improvements in sequencing technologies and multi-omics approach will surely help explore a larger fraction of previously uncultured diversity.
