TABLE 2.
Examples of microbial studies used omics technologies.
| Omics strategies | Approach | Study objective | Drug/ compound | Pathogen | Reference |
|---|---|---|---|---|---|
| Genomics | Single-cell sequencing | Evaluate human microbiota | — | Microbiota of a healthy oral subject | Campbell et al. (2013) |
| Genomics | Single-cell sequencing | Identify bacteria that affect disease susceptibility and severity | — | Intestinal microbiome from 11 patients with inflammatory bowel disease | Palm et al. (2014) |
| Genomics and metagenomics | Single-cell sequencing + Shotgun sequencing | Evaluate the genomes of SAR86 marine bacterial lineage | — | SAR86 from seawater | Dupont et al. (2012) |
| Metagenomics | Shotgun sequencing | Assess health risk of antimicrobial resistance genes (ARGs) | — | 1,921 gut microbiome genomes from 59 healthy stool donors | Zhang et al. (2021) |
| Metagenomics | Shotgun sequencing | Investigate the rates and targets of horizontal gene transfer (HGT) across thousands of bacterial strains | — | Samples were collected from 15 human populations spanning a range of industrialization | Groussin et al. (2021) |
| Transcriptomics | RNA-Seq | Analyze the regulation of adaptive resistance upon adaptation to disparate toxins | Ampicillin, tetracycline, n-butanol | E. coli | Erickson et al. (2017) |
| Transcriptomics | Microarray | Identify molecular mechanism of Licochalcone A | Licochalcone A from Glycyrrhiza inflata | S. aureus | Shen et al. (2015) |
| Transcriptomics, metabolomics, lipidomics and lipid A profiling data | Genome-scale metabolic modelling | Analyze bacterial metabolic changes at the systems levels | Polymyxins | P. aeruginosa | Zhu et al. (2018) |
| Proteomics | nanoLC-MS/MS | Analyze bacterial phosphoproteomic changes of prokaryotes for drug resistance | - | A. baumannii, H. pylori, K. pneumoniae, V. vulnificus, A. platensis, M. taiwanensis, T. thermophilus, M. mazei, M. portucalensis | Lai et al. (2017) |
| Proteomics | MS and 2D-DIGE | Identify changes in subproteome | Piperacillin/ tazobactam | E. coli | dos Santos et al. (2010) |
| Proteomics | 2DE and iTRAQ | Investigate the mechanism of Plumbagin | Plumbagin | B. subtilis | Reddy et al. (2015) |
| Metabolomics and proteomics | Computational model | Identify the biomarkers to predict patient outcomes and guide therapeutic development | - | S. aureus | Wozniak et al. (2020) |
| Metabolomics | HPLC with MS | identify metabolic changes of bacteria | Methicillin, ampicillin, kanamycin, norfloxacin | Two isogenic S. aureus strains | Schelli et al. (2017) |
Nano LC-MS/MS, nanoscale liquid chromatography coupled to tandem mass spectrometry; MS, mass spectrometry; 2D-DIGE, two-dimensional difference gel electrophoresis; 2DE, two-dimensional electrophoresis; iTRAQ, isobaric tag for relative and absolute quantification; HPLC, high performance liquid chromatography.