| Literature DB >> 34828362 |
Jakub Lach1,2, Paulina Jęcz1, Dominik Strapagiel2, Agnieszka Matera-Witkiewicz3, Paweł Stączek1.
Abstract
Halophiles, the salt-loving organisms, have been investigated for at least a hundred years. They are found in all three domains of life, namely Archaea, Bacteria, and Eukarya, and occur in saline and hypersaline environments worldwide. They are already a valuable source of various biomolecules for biotechnological, pharmaceutical, cosmetological and industrial applications. In the present era of multidrug-resistant bacteria, cancer expansion, and extreme environmental pollution, the demand for new, effective compounds is higher and more urgent than ever before. Thus, the unique metabolism of halophilic microorganisms, their low nutritional requirements and their ability to adapt to harsh conditions (high salinity, high pressure and UV radiation, low oxygen concentration, hydrophobic conditions, extreme temperatures and pH, toxic compounds and heavy metals) make them promising candidates as a fruitful source of bioactive compounds. The main aim of this review is to highlight the nucleic acid sequencing experimental strategies used in halophile studies in concert with the presentation of recent examples of bioproducts and functions discovered in silico in the halophile's genomes. We point out methodological gaps and solutions based on in silico methods that are helpful in the identification of valuable bioproducts synthesized by halophiles. We also show the potential of an increasing number of publicly available genomic and metagenomic data for halophilic organisms that can be analysed to identify such new bioproducts and their producers.Entities:
Keywords: biodiversity; bioinformatics; biomolecules; genome mining; halophiles; hypersaline environments; metagenomics
Mesh:
Substances:
Year: 2021 PMID: 34828362 PMCID: PMC8619533 DOI: 10.3390/genes12111756
Source DB: PubMed Journal: Genes (Basel) ISSN: 2073-4425 Impact factor: 4.096
Strengths and weaknesses of methods used in the in silico identification of bioproducts.
| Target | Class of Methods | Bioinformatic Tool | Advantages | Disadvantages |
|---|---|---|---|---|
| Biosynthetic Gene Clusters | Rule-based BGCs identification | antiSMASH [ | High number of identified BGC classes (71 in antiSMASH 6.0 version) | Requires high-quality assemblies |
| PRISM [ | ||||
| SMURF [ | ||||
| BAGEL [ | ||||
| Rule-independent BGCs identification | eSNaPD [ | Can be used for highly fragmented data (e.g., metagenomes) | Less specific for known BGCs then rule-based methods | |
| NaPDos [ | ||||
| EvoMining [ | ||||
| ClusterFinder [ | ||||
| Halophilic enzymes | Classic alignment based approach | Expasy Enzyme [ | The user is not limited by a predefined set of databases and comparison parameters | Requires a combination of tools for the best effect |
| Pfam [ | ||||
| BRENDA [ | ||||
| KEGG [ | ||||
| BLAST [ | ||||
| Automated pipelines | Anastasia [ | Provides high reproducibility | Limited customization options | |
| MCIC [ | ||||
| FINDER [ | ||||
| Ribosomally produced AMP | Classic alignment-based approach | DRAMP [ | The user is not limited by a predefined set of databases and comparison parameters | Requires a combination of tools for the best effect |
| CAMPR [ | ||||
| LAMP [ | ||||
| APD [ | ||||
| Automated pipelines | Macrel [ | Provides high reproducibility | Limited databases for individual tools | |
| AMAP [ | ||||
| AmPEP [ |