Breaking the genetic silence of bacteria to synthesize antibiotics that escape resistance | To research

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New antibiotics could be discovered in the future by deciphering “silent” genetic codes in bacteria that manufacture unknown natural products. The technique uses computers to predict the chemical structures of suspected antibiotic natural products, which can then be synthesized, providing a much-needed pathway for new drugs to address the current global public health crisis of antibiotic resistance.

An estimated 1.2 million people die each year from drug-resistant infections. The resistance is expected to kill 300 million people by 2050 and cost the global economy $100 trillion (£80 billion). There is therefore a pressing need to discover drugs that fight pathogens in new ways that bypass existing resistance mechanisms.

Many antibiotics currently used in the clinic are derived from bacteria. It is believed that bacteria evolved the ability to produce antibiotics as part of an arms race against other bacteria competing for limited resources. As such, their genetics offer a potentially rich vein of potent antibiotic compounds, each encoded by unique clusters of two or more genes.

Sequencing of the bacterial genome has identified many clusters of biosynthetic genes that code for natural products, and many are thought to be antibiotics. But knowing exactly what these genes do has been a challenge because too often bacteria grown in the lab simply cannot be coerced into producing all the compounds they are genetically capable of making, resulting in many sequences of groups of genes remain “silent”.

In an effort to break this silence and find new antibiotics, Sean BradyThe laboratory at Rockerfeller University in the United States has now developed a “no biology” technique that exploits libraries of sequenced bacterial genomes to identify and manufacture promising compounds, called synthetic-bioinformatic natural products, or syn- BNP.

“Instead of decoding genetic instructions using natural biological processes, bioinformatics algorithms are used to predict the chemical structures produced by bacteria, and then chemical synthesis is used to construct these potential antibiotics,” says Brady.

The team began by searching 10,000 bacterial genomes for a gene coding for a lipopeptide enzyme, commonly seen in gene clusters producing known lipopeptide antibiotics. Finding 3426 gene clusters with this gene, the researchers then constructed an evolutionary tree to identify all clusters that were not associated with known lipopeptides. This led them to focus on a distinct group of lipopeptide genes in the genome of Paenibacillus mucilaginosus.

Bioinformatics algorithms then predicted eight potential compounds produced by the gene cluster, and the team synthesized them chemically. When they tested them for their antibacterial activity, the researchers found that one of the new structures, which they called cilagicin, was able to kill multidrug-resistant Gram-positive bacteria.

Modified molecules

Further experiments revealed a lack of detectable resistance, likely due to the fact that cilagicine has two distinct molecular targets that kill bacteria by disrupting cell wall construction, making it more difficult for resistance to evolve. However, when cilagicine was tested in infected mice, it became apparent that the drug lost its antibacterial potency in the presence of serum, likely because it binds unproductively to serum proteins. The team solved this problem by modifying the lipid component of cilagicine with biphenyl to produce an analogue, cilagicine BP.

“Advances in genomics and chemistry have allowed scientists to dig deeper than ever to uncover these powerful antibiotic weapons, like the Brady group did with cilagicine,” says ian seple who studies the chemical synthesis of antibiotics at the University of California, San Francisco. “The resulting modified cilagicine BP is a promising starting point for a new class of antibiotics. Typically, multidisciplinary approaches like these play a major role in the discovery of next-generation antibiotic candidates,” adds Seiple.

“This is an interesting approach that has led to the successful discovery of new antibiotics active against Gram-positive bacteria,” comments Laura Piddockscientific director of Global Partnership for Antibiotic Research and Development. “I hope the authors now apply it to discover new antibiotics active against Gram-negative bacterial species, pathogens that the World Health Organization has identified as a key priority for new treatments.”

Previous work by Brady’s team used the syn-BNP approach to overcome the resistance of Gram-negative bacteria to the antibiotic colistin – an antibiotic of last resort – another lipopeptide antibiotic. The technique revealed an improved, natural analogue of colistin called macolacin, which the researchers further optimized. The approach isn’t limited to antibiotic discovery either, with Brady’s lab recently reporting the discovery of a powerful anti-cancer compound.

“That’s just the tip of the iceberg,” Brady says. “The number of sequenced silent biosynthetic gene clusters already far exceeds the number of characterized natural products. We hope that the syn-BNP method can be used to uncover an increasingly diverse collection of new bioactive molecules inspired by this vast and rapidly growing pool of silent biosynthetic gene clusters.

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