7.16A: Detecting Uncultured Microorganisms - Biology

7.16A: Detecting Uncultured Microorganisms - Biology

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Using metagenomics, the microbial constituents of the world can be identified by culturing each individual species.


Recognize the methods used to detect uncultured microorganisms

Key Points

  • Many organisms that can cause diseases are not culturable, so a unicellular culture cannot be obtained.
  • Initial studies of microbial fauna showed that they are more diverse than expected and contained many microbes that were not culturable.
  • Advances in molecular biological techniques allow the sequencing of all or at least many of the genomes of microbes found in a sample.

Key Terms

  • shotgun sequencing: A DNA sequencing technique in which a large number of small fragments of a long DNA strand are generated at random, sequenced, and reassembled to form a sequence of the original strand.
  • culturable: Able to be cultured (grown in a suitable environment).
  • high-throughput sequencing: Technologies that parallelize the sequencing process, producing thousands or millions of sequences at once.

Identification of bacteria in the laboratory is particularly relevant in medicine, where the correct treatment is determined by the bacterial species causing an infection. Consequently, the need to identify human pathogens was a major impetus for the development of techniques to identify bacteria.

Early studies have shown that the microbial life around us in the air, sea, and soil is very diverse and only a small fraction of the species are known. One limitation of identifying human pathogens or conventional sequencing begins with a culture of identical cells as a source of DNA. However, early metagenomic studies revealed that there are probably large groups of microorganisms in many environments that cannot be cultured and thus cannot be sequenced. These early studies focused on 16S ribosomal RNA sequences which are relatively short, often conserved within a species, and generally different between species. Many 16S rRNA sequences have been found which do not belong to any known cultured species, indicating that there are numerous non-isolated organisms out there. These surveys of ribosomal RNA (rRNA) genes taken directly from the environment revealed that cultivation based methods find less than 1% of the bacterial and archaeal species in a sample.

The discovery of such diversity led to the field of metagenomics, which is the study of metagenomes, genetic material recovered directly from environmental samples. Rather than culturing a microbe, this approach takes a sample and identifies the different species in it by sequencing all the species simultaneously. However, recovery of DNA sequences longer than a few thousand base pairs from environmental samples was very difficult until recent advances in molecular biological techniques. More specifically, the construction of libraries in bacterial artificial chromosomes (BACs) provided better vectors for molecular cloning.

Advances in bioinformatics, refinements of DNA amplification, and the proliferation of computational power have greatly aided the analysis of DNA sequences recovered from environmental samples. These advances have allowed the adaptation of shotgun sequencing to metagenomic samples. The approach, used to sequence many cultured microorganisms and the human genome, randomly shears DNA, sequences many short sequences, and reconstructs them into a consensus sequence.

Shotgun sequencing and screens of clone libraries reveal genes present in environmental samples. This can be helpful in understanding the ecology of a community, particularly if multiple samples are compared to each other. This was further followed by high-throughput sequencing which did the same process as the shotgun sequencing but at a much bigger scale in terms of the amount of DNA that could sequenced from one sample. This provides information both on which organisms are present and what metabolic processes are possible in the community. Using metagenomics, and the resultant sequencing of uncultured microbes, metagenomics has the potential to advance knowledge in a wide variety of fields. It can also be applied to solve practical challenges in medicine, engineering, agriculture, and sustainability.

Catabolism and interactions of uncultured organisms shaped by eco-thermodynamics in methanogenic bioprocesses

Current understanding of the carbon cycle in methanogenic environments involves trophic interactions such as interspecies H2 transfer between organotrophs and methanogens. However, many metabolic processes are thermodynamically sensitive to H2 accumulation and can be inhibited by H2 produced from co-occurring metabolisms. Strategies for driving thermodynamically competing metabolisms in methanogenic environments remain unexplored.


To uncover how anaerobes combat this H2 conflict in situ, we employ metagenomics and metatranscriptomics to revisit a model ecosystem that has inspired many foundational discoveries in anaerobic ecology—methanogenic bioreactors. Through analysis of 17 anaerobic digesters, we recovered 1343 high-quality metagenome-assembled genomes and corresponding gene expression profiles for uncultured lineages spanning 66 phyla and reconstructed their metabolic capacities. We discovered that diverse uncultured populations can drive H2-sensitive metabolisms through (i) metabolic coupling with concurrent H2-tolerant catabolism, (ii) forgoing H2 generation in favor of interspecies transfer of formate and electrons (cytochrome- and pili-mediated) to avoid thermodynamic conflict, and (iii) integration of low-concentration O2 metabolism as an ancillary thermodynamics-enhancing electron sink. Archaeal populations support these processes through unique methanogenic metabolisms—highly favorable H2 oxidation driven by methyl-reducing methanogenesis and tripartite uptake of formate, electrons, and acetate.


Integration of omics and eco-thermodynamics revealed overlooked behavior and interactions of uncultured organisms, including coupling favorable and unfavorable metabolisms, shifting from H2 to formate transfer, respiring low-concentration O2, performing direct interspecies electron transfer, and interacting with high H2-affinity methanogenesis. These findings shed light on how microorganisms overcome a critical obstacle in methanogenic carbon cycles we had hitherto disregarded and provide foundational insight into anaerobic microbial ecology.


Publicly available sequence databases of the small subunit ribosomal RNA gene, also known as 16S rRNA in bacteria and archaea, are growing rapidly, and the number of entries currently exceeds 4 million. However, a unified classification and nomenclature framework for all bacteria and archaea does not yet exist. In this Analysis article, we propose rational taxonomic boundaries for high taxa of bacteria and archaea on the basis of 16S rRNA gene sequence identities and suggest a rationale for the circumscription of uncultured taxa that is compatible with the taxonomy of cultured bacteria and archaea. Our analyses show that only nearly complete 16S rRNA sequences give accurate measures of taxonomic diversity. In addition, our analyses suggest that most of the 16S rRNA sequences of the high taxa will be discovered in environmental surveys by the end of the current decade.

New perspective on uncultured bacterial phylogenetic division OP11

Organisms belonging to the OP11 candidate phylogenetic division of Bacteria have been detected only in rRNA-based sequence surveys of environmental samples. Preliminary studies indicated that such organisms represented by the sequences are abundant and widespread in nature and highly diverse phylogenetically. In order to document more thoroughly the phylogenetic breadth and environmental distribution of this diverse group of organisms, we conducted further molecular analyses on environmental DNAs. Using PCR techniques and primers directed toward each of the five described subdivisions of OP11, we surveyed 17 environmental DNAs and analyzed rRNA gene sequences in 27 clonal libraries from 14 environments. Ninety-nine new and unique sequences were determined completely, and approximately 200 additional clones were subjected to partial sequencing. Extensive phylogenetic comparisons of the new sequences to those representing other bacterial divisions further resolved the phylogeny of the bacterial candidate division OP11 and identified two new candidate bacterial divisions, OP11-derived 1 (OD1) and Sulphur River 1 (SR1). The widespread environmental distribution of representatives of the bacterial divisions OD1, OP11, and SR1 suggests potentially conspicuous biogeochemical roles for these organisms in their respective environments. The information on environmental distribution offers clues for attempts to culture landmark representatives of these novel bacterial divisions, and the sequences are specific molecular signatures that provide for their identification in other contexts.


Bootstrap consensus tree showing the…

Bootstrap consensus tree showing the well-supported phylogenetic relationships for the bacterial divisions OP11,…

Sulfide-driven denitrification: detecting active microorganisms in fed-batch enrichment cultures by DNA stable isotope probing

A microbial community was enriched in the anoxic compartment of a pilot-scale bioreactor that was operated for 180 days, fed with sewage and designed for organic matter, nitrogen and sulfide removal by coupling anaerobic digestion, nitrification and mixotrophic denitrification. Denitrification occurred with endogenous electron donors, mainly sulfide and residual organic matter, coming from the anaerobic compartment. The microorganisms involved in denitrification with sulfide as electron donor were identified by DNA-stable isotope probing with [U- 13 C]-labelled CO2 and NaHCO3. Complete denitrification occurred every two days, and the applied NO3 − /S 2− ratio was 1.6. Bacteria belonging to the Sulfurimonas denitrificans was identified as a chemoautotrophic denitrifier, and those related to Georgfuchisa toluolica, Geothrix fermentans and Ferritrophicum radicicola were most probably associated with heterotrophic denitrification using endogenous cells and/or intermediate metabolites. This study showed that DNA-SIP was a suitable technique to identify the active microbiota involved in sulfide-driven denitrification in a complex environment, which may contribute to improve design and operation of bioreactors aiming for carbon–nitrogen-sulfur removal.


Culture-independent molecular studies have suggested a relationship between gut microbiota and inflammatory bowel disease 7, 11 , allergies 8, 10 and obesity 9, 12 . Because the functions of many genes present in sequence databases are undefined or incorrectly annotated, metagenomic data alone does not represent all cultivated gut bacteria. Cultivation of hitherto uncultured bacteria, which comprise over 70% of gut bacteria 4 , will expand our capabilities to perform more comprehensive microbiological studies (e.g., analysis of bioactivity of individual species and studies using isolates in gnotobiotic animals). These studies will provide more detailed information about the relationships between gut microbiota and human diseases. Therefore, developing culture methods that overcome every limitation of conventional culture methods is critical to advancing microbial research.

Cocultivation with other bacteria using a membrane filter can result in supply of various unknown growth factors that are critically important for cell growth 25 .

Compared with 1.5% agar medium, soft agar contributes to maintaining ideal growth conditions because molecules diffuse faster in the latter. For example, methylene blue dye diffuses faster in 0.4% agar medium than in 1.5% agar (data not shown). Therefore, growth factors provided by a supporter strain can reach the target strain faster in soft agar than in solid plate medium. Further, metabolic products and secreted substances can be removed more quickly, which reduces metabolite-induced inhibition of growth and activity of bacteria 26 . Moreover, bacteria in soft agar medium can form single colonies and are easier to isolate.

This system is simple and can be easily and economically applied to conventional instrumentation and various culture conditions (e.g., aerobic and anaerobic cultures).

The nutrient composition of TYG medium is simpler than that of conventional culture media nutrient-rich media can induce metabolic imbalance and kill a proportion of bacteria 27 . In contrast, the most conventional culture media (e.g., EG medium) used to isolate gut bacteria contain multiple sugars, beef extract, blood, vitamins and fatty acids to promote cell growth 19, 28, 29 . However, the small intestine absorbs most nutrients from digested food, the remaining nutrients accounting for only approximately 6–7% of the large intestine's contents. Therefore, the actual nutritional conditions provided by digested food in the human gut are likely inadequate for gut microbiota to thrive without symbiosis, mutualism, commensalism, or a combination of all of these. For example, over 70% of colony counts of a 14 day stool culture on EG agar were obtained after 2 days of incubation. Although this medium accelerates the growth of certain bacteria, it may artificially supplement the substances originally provided by other bacteria in the human gut. Thus, undetected interbacterial interactions of isolates from EG agar are likely attributable to replenished substances required for symbiosis.

In contrast, colony counts in TYG agar continued to increase over 14 days. Gaps in bacterial growth occur reportedly in symbiotic cross-feeding studies of Bifidobacterium species and butyric acid-producing bacteria 17, 18 . In a medium containing fructo-oligosaccharides as the sole energy source, Bifidobacterium strains function as substrate utilizers, grow first, and are then replaced by butyrate-producing bacteria that degrade substrates, metabolites or both (e.g., lactate). Considering these reports and the transition of colony counts on EG and TYG plates, we surmise that the discrepancy in bacterial growth time detected here may indicate interbacterial interactions similar to the cross-feeding observed between strains that grow quickly or slowly on TYG agar. Specifically, the less complex nutrient composition of TYG agar may facilitate detection of interbacterial interactions and prevent bacterial death caused by metabolic imbalance.

To assess this hypothesis, we cocultivated neighboring colonies on TYG agar plates. Using filter cocultivation, Parabacteroides sp. BL157, which is difficult to subculture, grew and was isolated. Further, we detected interactions between Parabacteroides sp. BL157 and K. pneumoniae BL175 as well as between Sutterella sp. BL252 and B. fragilis BL539. Specifically, we found that the growth of Parabacteroides sp. BL157 was accelerated by the growth of neighboring K. pneumoniae BL175 and that the growth of B. fragilis BL539 partially depended on the presence of Sutterella sp. BL252.

The growth of Parabacteroides sp. BL157 and B. fragilis BL539 was supported by K. pneumoniae BL175 and Sutterella sp. BL252 however, the details of the interaction between Parabacteroides sp. BL157 and K. pneumoniae BL175, and between B. fragilis BL539 and Sutterella sp. BL252 are not definitive a further study is required. Certain interbacterial interactions are mediated through bacterial metabolites. For example, supernatants from a Bacillus strain accelerate the growth of S. thermophilum 15 . Further, supernatants from Sphingomonas spp. support the growth of Catellibacterium nectariphilum 30 . Moreover, Porphyromonas gingivalis grows in the oral cavity while utilizing succinic acid generated by Treponema denticola, and T. denticola utilizes isobutyric acid generated by P. gingivalis 31 . Here a Parabacteroides sp. BL157/B. fragilis BL539 colony grew near but did not overlap a K. pneumoniae BL175/Sutterella sp. BL252 colony, suggesting that they communicate through substance(s) generated by supporting strains. Moreover, it has been reported that both B. fragilis and K. pneumoniae produce autoinducer-2, an interspecific signaling molecule that is related to quorum sensing and formation of biofilm colonies 32, 33 . Therefore, interspecific signaling may also be related to the interbacterial interactions detected in these studies.

Phascolarctobacterium sp. BL377, which was initially colonized in this study, cannot be subcultured in monoculture in the presence of a highly growth-stimulating medium (i.e., EG medium). Here we found that B. dorei BL376 and other Bacteroides strains supported the growth of Phascolarctobacterium sp. BL377. The Bacteroides strains examined in this study were found to generate succinic acid as a metabolite (data not shown). In addition, some of Phascolarctobacterium species reportedly utilize succinic acid 34, 35 . Therefore, common substance(s) generated by the Bacteroides species studied here (e.g., succinate) may also influence the growth of BL377.

Although complex interbacterial communication among gut bacteria has been suggested by a genomic survey that did not involve culture 16 , there is little information concerning the multiple growth dependence of viable gut bacteria. In the present study, growth of B. fragilis, which promoted the growth of Phascolarctobacterium sp. BL377, was stimulated by Sutterella sp. BL252, suggesting that multistep interbacterial interactions of viable gut bacteria occurred in the coculture system described here.

Although the name of isolates in these studies were selected based on homology of 16 S rRNA sequences, the homology of 16 S rRNA gene sequences suggests that strains BL157, BL252 and BL377 likely represent novel genus or species. Thus, the name of these isolates should be changed to denote novel species. Thus, the proposition to systematically designate these isolates as novel species is in progress.

Using the cocultivation technique designed here, we succeeded in isolating several growth-dependent gut bacteria. However, this technique requires further optimization, possibly using multilayer culture, high-throughput multi-well culture, and replicating the complex ecosystem of the gut. A clue to solving these issues may be found in other innovative culturing methods 21-24 . Thus, a combination of this filter system and other advanced methods will result in a powerful approach to cultivation and to understanding the ecology of hitherto uncultured bacteria.


Author contributions: Y.H. and V.K.S. contributed equally to this work Y.H., Y.S., A.T., M.H., and M.O. designed research Y.H. and A.T. performed research Y.H., V.K.S., T.P., S.N., and A.T. analyzed data and Y.H., T.D.T., T.K., M.H., and M.O. wrote the paper.

The authors declare no conflict of interest.

Data deposition: The sequences reported in this paper have been deposited in the DNA Data Bank of Japan [accession nos. AP009510 (chromosome), AP009511–3 (plasmids), and AB360878–AB360905 (others)].

2. Materials and Methods

2.1 Sampling Procedure

The sampling site, the Diyarbakır region, is located at the boundary of the Anatolian plate and the Middle Eastern oil region in south-eastern Turkey. A total of 20 crude oil samples (B1, B6, B8, B14, B23, B32, B56, GK8, GS6, GS15, M3, K2, K3, K32, K35, K44, S4, S15, Y18 and Y30) consisting of an oil/water mixture were collected from the production wells of Diyarbakır oil fields (Figure 1). These wells produced oils withdrawn from the oil sandstone deposits (depths from 1600 m to 2620 m, API gravity from 24.3° to 42.3°, water content around 94%, an average pH of 7.0 and salinity from 2966 mg l −1 to 26,961 mg l −1 ). The samples were aseptically taken at the wellhead and put into sterile 500 ml serum bottles sealed with rubber stoppers and aluminium caps. The samples were shipped at ambient temperature. Upon arrival at the laboratory, the samples were immediately analysed. All samples were treated within 48 h after collection. Decantation was used to separate produced water from the oil/water mixture.

Fig. 1.

Sampling locations in Diyarbakır region. Produced water samples were collected from 20 different oil wells © Maphill / Creative Commons Attribution-NoDerivatives (CC BY-ND)

2.2 DNA Extraction

Bacteria in the produced water samples were collected by filtration over 0.20 μm pore size polyamide filters (Sartolon ® , Sartorius AG, Germany). Genomic DNA was extracted with the UltraClean ® Microbial DNA isolation kit (MO BIO Laboratories Inc, USA) according to the manufacturer’s protocol.

2.3 Polymerase Chain Reaction Amplification

Extracted DNA was used as the template for PCR amplification of partial 16S rRNA fragments. Primer pair consisting of 341F with a GC clamp and 907R was used for DGGE analysis (26). A 40-base GC clamp was used to prevent complete denaturation of the fragment during DGGE (27).

Due to the low DNA yield, a two-step PCR strategy was used. At the first step, a real-time PCR (quantitative PCR, qPCR) approach was applied to the produced water samples. The reaction mixture in a final volume of 22.5 μl contained 0.2 μl of each primer, 12.5 μl iQ TM SYBR ® Green Supermix (Bio-Rad Laboratories Inc, USA), 9.6 μl RNase-Free Water (Qiagen, Germany) and 0.5 μl DNA template. qPCR was performed in iCycler iQ TM Real-Time PCR Detection System (Bio-Rad Laboratories Inc, USA) using the following conditions: 5 min at 95°C 40 cycles of 95°C for 30 s, 57°C for 40 s, 72°C for 40 s and 80°C for 25 s and a final 72°C for 10 min. In the qPCR method, after each cycle, a signal was formed. By observing the signals for each sample, PCR products could be detected. The reaction was terminated when the desired amount of product was reached. At the second step, a conventional PCR approach was applied to the qPCR products. Reaction mixture in a final volume of 25 μl contained 0.2 μl of each primer, 12.5 μl Taq PCR Master Mix (Qiagen, Germany), 9.6 μl RNase-Free Water (Qiagen, Germany) and 0.5 μl DNA template. The PCR was performed in TGradient thermocycler (Biometra, Germany) using the following conditions: 5 min at 95°C 12 cycles of 95°C for 30 s, 57°C for 40 s and 72°C for 40 s and a final 74°C for 30 min.

2.4 Denaturing Gradient Gel Electrophoresis

The DCode TM system (Bio-Rad Laboratories, USA) was used for DGGE analysis. 25 μl of each PCR product (200–300 ng) were loaded onto 6% polyacrylamide gels (w/v) containing gradients of 20% to 70% denaturants (urea/formamide). The gels were run for 16 h at 100 V and 60°C in 1× Tris-acetate-EDTA buffer. After completion of electrophoresis, the gels were stained with SYBR ® Gold Nucleic Acid Gel Stain (Invitrogen TM , Thermo Fisher Scientific, USA) for 20 min, visualised and photographed. Selected predominant DGGE bands were excised, eluted in 40 μl of 1× Tris buffer (pH 8) for 2 d at 4°C and re-amplified with 25 cycles as described above. Reaction mixture in a final volume of 25 μl contained 0.125 μl of primer 341F, 0.125 μl of primer 907R, 12.5 μl of Taq PCR Master Mix, 9.75 μl of ultra-pure water and 0.5 μl of template. The PCR products were quantified on a 1.5% (w/v) agarose gel and then sequenced by Macrogen Inc (Seoul, South Korea).

2.5 Comparative Sequence Analysis

The resulting sequences were first aligned and edited using CodonCode Aligner software (CodonCode Corp, USA). Then they were compared to sequences stored in the database GenBank ® using the National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST ® ) (28, 29). All obtained partial 16S rRNA gene sequences were deposited in GenBank ® database under the following accession numbers: KF720792 - KF720796, KF720798, KF720801 - KF720802, KF720804, KF720806 - KF720808, KF720810 - KF720811, KF720814, KF720818, KF720820, KF720823, KF720825 - KF720826, KF720828, KF720830 - KF720832, KF720839, KF720844, KF720852, KF720855, KF720858, KF720872, KF720877, KF720882 - KF720884, KF720886 - KF720889, KF720891, KF720893 - KF720894, KF720896 and KF720903.


We thank Dr. Nikolai Chernych for his technical assistance during the isolation and purification of metagenomics DNA. We also thank the Department of Energy Joint Genome Institute for sequencing the metagenomes.


CDV and GM were supported by the ERC Advanced Grant PARASOL (no. 322551). A-SA and RG were supported by the research grant 17-04828S from the Grant Agency of the Czech Republic. MM was supported by the Czech Academy of Sciences (Postdoc program PPPLZ application number L200961651). DYS was supported by the SIAM/Gravitation Program (Dutch Ministry of Education and Science, grant 24002002) and by the Russian Science Foundation (grant 16–14-00121). Sequencing was performed by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, as part of the Community Sequencing Program (contract no. DE-AC02- 05CH11231).

Availability of data and materials

The raw sequence reads of the five metagenomes have been deposited to the NCBI Sequence Read Archive (see Additional file 1: Table S6 for accession numbers and read and contig statistics). The final 871 MAGs described in this paper have been deposited as Whole Genome Shotgun projects at DDBJ/EMBL/GenBank, and accession numbers are listed in Additional file 4 (BioProject ID PRJNA434545). All versions described in this paper are version XXXX01000000. The cleaned and dereplicated amplicon sequence datasets are available in FigShare ( Maximum likelihood trees based on the concatenated alignment of 16 ribosomal proteins, basis for Figs. 2 and 3, in newick format (.tre file) and complementary datasets (used to plot completeness, contamination, genome recovery size, G + C mol% and RPKG in iTOL), as well as K number assignments for the predicted proteins of all MAGs (KEGG-orthologues, Ghost Koala) and the fully annotated CPR MAGs supporting the conclusions of this article are also available in FigShare (

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