Evidence map›Paper›PMID 41472693›Full record

ArticleResearch square2025

Ecological determinants of altruism in prokaryote antivirus defense.

Dmitry A Biba, Kira S Makarova, Yuri I Wolf, Levi Waldron, Eugene V Koonin, Nash D Rochman

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Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Dmitry A BibaComputational Biology Branch, Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, Unite States.
Kira S MakarovaComputational Biology Branch, Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, Unite States.
Yuri I WolfComputational Biology Branch, Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, Unite States.
Levi WaldronInstitute for Implementation Science in Population Health, City University of New York School of Public Health, New York, NY, United States.
Eugene V KooninComputational Biology Branch, Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, Unite States.
Nash D RochmanComputational Biology Branch, Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, Unite States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prokaryote evolution is driven in large part by the incessant arms race with viruses. Genomic investments in antivirus defense can be coarsely classified into two categories, immune systems that abrogate virus reproduction resulting in clearance, and programmed cell death (PCD) systems. Prokaryotic defense systems are enormously diverse, as revealed by an avalanche of recent discoveries, but the basic ecological determinants of defense strategy remain poorly understood. Through mathematical modeling of defense against lytic virus infection, we identify two principal determinants of optimal defense strategy and, through comparative genomics, we test this model by measuring the genomic investment into immunity vs PCD among diverse bacteria and archaea. First, as viral pressure grows, immunity becomes the preferred defense strategy. Second, as host population size grows, PCD becomes the preferred strategy. We additionally predict that, although optimal strategy typically involves investment in both PCD and immunity, investment in immunity can also result in antagonism, increasing the likelihood that a PCD-competent cell will lyse due to infection. Together, these findings indicate that, generally, PCD is preferred at low multiplicity of infection (MOI) and immunity is preferred at high MOI. Finally, we demonstrate that PCD, which is typically considered to be an altruistic trait, is in some cases neutral and can be maintained in an unstructured population over an evolutionary timescale. Our work shows that the landscape of prokaryotic antivirus defense is substantially more complex than previously suspected.

Identifiers

PMID41472693
PMCPMC12747282

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.