Microbial Dark Matter Could Revive Antibiotics
Most microbes remain out of reach, and many antibiotic-producing genes stay silent. A new review argues that combining genomics, innovative cultivation, and scalable manufacturing could turn this hidden chemistry into desperately needed new antibiotic treatments. The post Microbial Dark Matter Could Revive Antibiotics appeared first on GEN - Genetic Engineering and Biotechnology News .
Microbial organisms that have not been easily cultivated in laboratories and potentially hold undiscovered antibiotic properties pose a new hope in the fight against antimicrobial resistance. A review by researchers Ahmed Hamed and Zeinab Khalil from the University of Queensland emphasizes the importance of revisiting microbial "dark matter" and silent biosynthetic pathways within common microbes.
According to the World Health Organization, antimicrobial resistance was responsible for an estimated 1.27 million deaths globally in 2019.
Traditional screening methods tend to mine the same microorganisms, particularly Streptomyces, often rediscovering known compounds instead of discovering new structures. However, recent technological advancements, like genome-mining tools, metagenomics, and cultivation devices, have significantly expanded the search to bacteria that cannot be grown in a lab.
One such example is teixobactin, an antibiotic discovered in a soil bacterium called Eleftheria terrae, which has shown activity against multiple Gram-positive pathogens. But the problem lies in the difficulty of growing the producer organism conventionally, leading to its reliance on chemical synthesis.
Even when biosynthetic pathways have been successfully mapped, the production of natural products is still limited. For instance, only 11-32% of large-scale attempts to express these pathways in alternative microbial hosts resulted in detectable compounds. The review points out that various factors such as missing genes, insufficient precursor supply, product toxicity, and poor fermentation performance can impede development.
To overcome these challenges, bioprocess engineering techniques, like adding talc microparticles to engineered Streptomyces cultures, have shown promise in boosting production, increasing it nearly threefold.
AI algorithms can help predict which biosynthetic gene clusters are worth investigating by analyzing genomic and molecular features connected to predicted biological activity. However, experimental verification is still required to confirm these predictions. Nevertheless, researchers envision a continuous workflow that combines ecological sampling, genome and metabolome analysis, activity testing, target identification, and scalable production.
This iterative approach may pave the way for the next antibiotic breakthrough, which will depend on both finding and developing the compound.
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