Table 2.
Outcomes and limitations of different methods to study microbial interactions. We assigned four validation strategies to confirm microbial interaction as following: (1) Expression or activity assays (e.g., transcriptomics, proteomics, metabolomics, RT-PCR, FBA); (2) 3D structure and spatial variability; (3) Substrate specificity; and, (4) Temporal variability.
| Outcome | Limitations | Methods | Environment | Validation | Ref. a | |||
|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |||||
| Improvement in the identification of microbial community species. | Lack of mechanistic understanding of species interactions. | Combination of MALDI-TOF MS b analysis and high-throughput sequencing 16S rRNA c. | Kimchi | ✓ | O | O | ✓ | [78] |
| 16S rRNA gene sequencing. | Human oral environments | O | ✓ | O | O | [79] | ||
| Demonstration of the influence of abiotic factors on microbial community dynamics. | High computational and data requirements for reconstruction of individual metabolic models. | Metagenomics, metabolic network reconstruction and FBA d. | Anaerobic digestion microbiomes | ✓ | O | ✓ | O | [80] |
| Lack of mechanistic understanding of species interactions. | PLS-PM e | Rice soil rhizosphere | O | ✓ | O | ✓ | [81] | |
| 16S rRNA gene sequencing. | Urban and forest park soil litter layers | O | ✓ | O | ✓ | [82] | ||
| In vivo experiment of meadow steppe soil under different precipitation regimes. | Topsoil | ✓ | ✓ | O | ✓ | [83] | ||
| High computational and data requirements for reconstruction of individual metabolic models and complex wet-lab experiments required for validation. | Metabolic network reconstruction, EFM f and FBA. | Acid-sulfate-chloride springs | ✓ | O | ✓ | O | [84] | |
| Demonstration of the influence of interspecies interactions on microbial community dynamics. | Lack of mechanistic understanding of species interactions. | Co-culture of isolates, RNA-Seq g and RT-qPCR h. | Wine fermentation | ✓ | O | O | ✓ | [85] |
| qPCRi and 16S rRNA gene sequencing. | Mixed bacterial consortia | ✓ | O | O | ✓ | [86] | ||
| Improved mechanistic understanding of interspecies interactions. | Complex wet-lab experiments required for validation. | SIP j and Metagenomics. | Continuous up-flow anaerobic sludge blanket reactors | ✓ | O | ✓ | ✓ | [87] |
| Pure and co-cultures and cyclic voltammetry analysis. | Palm oil mill effluent | O | O | ✓ | ✓ | [88] | ||
| High computational and data requirements for reconstruction of individual metabolic models. | Mono- and co-culture, metabolic network reconstruction, bipartite graphs, HPLC k, CGQ l, GC-MS m; SIP. | In silicon experiments with pure and co-culture | ✓ | O | ✓ | ✓ | [89] | |
| Metabolic network reconstruction and cFBA n. | In silicon experiments pure cultures | ✓ | O | ✓ | O | [27] | ||
| Metabolic network reconstruction, evolutionary game theory and FBA. | In silicon experiments pure cultures | ✓ | O | O | O | [90] | ||
| Metagenomics, Metatranscriptomics. | Synthetic human gut | ✓ | ✓ | O | O | [5] | ||
a Ref., numbers in between brackets represent references for the different studies; b MALDI-TOF: matrix-assisted laser desorption/ionization; c rRNA: Ribosomal ribonucleic acid; d FBA: Flux Balance Analysis; e PLS-PM: Partial least squares - path model; f EFM: elementary flux mode; g RNA-Seq: Ribonucleic acid sequencing; h RT-qPCR: Real Time quantitative polymerase chain reaction; i qPCR: Quantitative polymerase chain reaction; j SIP: stable isotope probing; k HPLC: High-performance liquid chromatography; l CGQ: cell growth quantifier; m GC-MS: Gas chromatography mass spectrometry; n cFBA: Community Flux Balance Analysis.