Inhibitors of bacterial immune systems: discovery, mechanisms and applications

Mayo-Muñoz, David; Pinilla-Redondo, Rafael; Camara-Wilpert, Sarah; Birkholz, Nils; Fineran, Peter C.

doi:10.1038/s41576-023-00676-9

“…That such defense of bacteria is effective was confirmed through large virus-bacteria interaction matrices, revealing sparse lytic interactions 3,7–10 . However, these matrices also suggested that closely related viruses can overcome host defenses by various mechanisms such as point mutations or anti-defense genes 11–13 . Indeed, comparative genomics of largely identical viruses often demonstrates synteny interrupted by regions of high turnover of diverse genes that are predominantly short and lack annotation 14 .…”

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning reveals a diverse arsenal of anti-defenses in bacterial viruses

Lopatina,

Ferenc,

Bartlau

et al. 2024

Preprint

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Antagonistic interactions with viruses are an important driver of the ecology and evolution of bacteria, and associating genetic signatures to these interactions is of fundamental importance to predict viral infection success. Recent studies have highlighted that bacteria possess a large, rapidly changing arsenal of defense genes and that viruses can neutralize at least some of these genes with matching anti-defenses. However, a broadly applicable approach for discovering the genetic underpinnings of such interactions is missing since typically used methods such as comparative genomics are limited by the rampant horizontal gene transfer and poor annotation of viral and bacterial genes. Here we show that genes that allow the viruses to overcome bacterial defenses can be systematically identified using an interpretable machine-learning approach even when using diverse bacteria-virus infection data. To verify the predictions, we experimentally characterized eight previously unknown anti-defense proteins in viruses specific for Vibrio bacteria and showed that they counteract a wide range of bacterial immune systems, including AbiH, AbiU, Septu, DRT, CBASS, and Retron. The power of our computational approach is highlighted by the identification of anti-defense proteins that inhibit non-homologous defense systems, which we verify for Retron and AbiH. We suggest that the computational prediction based on experimental interactions offers a promising avenue to unravel the genetic mechanisms of co-evolution between bacteria and their viruses.

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“…That such defense of bacteria is effective was confirmed through large virusbacteria interaction matrices, revealing sparse lytic interactions 3,[7][8][9][10] . However, these matrices also suggested that closely related viruses can overcome host defenses by various mechanisms such as point mutations or anti-defense genes [11][12][13] . Indeed, comparative genomics of largely identical viruses often demonstrates synteny interrupted by regions of high turnover of diverse genes that are predominantly short and lack annotation 14 .…”

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning reveals a diverse arsenal of anti-defenses in 1 bacterial viruses

Polz,

Lopatina,

Ferenc

et al. 2024

Preprint

0

View full text Add to dashboard Cite

Antagonistic interactions with viruses are an important driver of the ecology and evolution of bacteria, and associating genetic signatures to these interactions is of fundamental importance to predict viral infection success. Recent studies have highlighted that bacteria possess a large, rapidly changing arsenal of defense genes and that viruses can neutralize at least some of these genes with matching anti-defenses. However, a broadly applicable approach for discovering the genetic underpinnings of such interactions is missing since typically used methods such as comparative genomics are limited by the rampant horizontal gene transfer and poor annotation of viral and bacterial genes. Here we show that genes that allow the viruses to overcome bacterial defenses can be systematically identified using an interpretable machine-learning approach even when using diverse bacteria-virus infection data. To verify the predictions, we experimentally characterized eight previously unknown anti-defense proteins in viruses specific for Vibrio bacteria and showed that they counteract a wide range of bacterial immune systems, including AbiH, AbiU, Septu, DRT, CBASS, and Retron. The power of our computational approach is highlighted by the identification of anti-defense proteins that inhibit non-homologous defense systems, which we verify for Retron and AbiH. We suggest that the computational prediction based on experimental interactions offers a promising avenue to unravel the genetic mechanisms of co-evolution between bacteria and their viruses.

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Phage Power: Harnessing Nature’s Arsenal in the Battle Against Microbial Threats for Sustainable Healthcare

Jyotirmayee,

Khanda,

Verma

2024

Emerging Paradigms for Antibiotic-Resistant Infections: Beyond the Pill

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Inhibitors of bacterial immune systems: discovery, mechanisms and applications

Cited by 21 publications

References 190 publications

Interpretable machine learning reveals a diverse arsenal of anti-defenses in bacterial viruses

Interpretable machine learning reveals a diverse arsenal of anti-defenses in bacterial viruses

Interpretable machine learning reveals a diverse arsenal of anti-defenses in 1 bacterial viruses

Phage Power: Harnessing Nature’s Arsenal in the Battle Against Microbial Threats for Sustainable Healthcare

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