Systematic survey reveals vast diversity in archaeal type 4 filament systems

A PLOS Genetics study by Sivabalasarma and Albers uses new computational models to classify archaeal surface filaments, uncovering far greater diversity and functional range than previously catalogued.

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Researchers Shamphavi Sivabalasarma and Sonja-Verena Albers have published a systematic classification of archaeal type 4 filament (T4F) systems in PLOS Genetics, using newly generated MacSyFinder2 models to search for and categorise these cell surface structures across archaeal genomes.

All T4F systems share a minimal core — an ATPase, a platform protein, a filament-forming protein, and a class III signal peptidase — but the analysis reveals that the superfamily is far more diverse than earlier surveys suggested. Many archaeal organisms harbour more than one distinct T4F assembly machinery, and structure-based phylogenetic analysis of the variable N-terminal domains of filament-forming proteins points to extensive functional expansion across the domain of life.

T4F systems in archaea encompass structures involved in surface attachment, cell–cell interactions, and possibly DNA uptake — roles with broad relevance to understanding archaeal ecology, biofilm formation, and horizontal gene transfer. The MacSyFinder2 models produced in this study are made publicly available and provide the community with an upgraded toolkit for genome-scale T4F annotation.

The work contributes to the growing recognition that archaea, long overshadowed in cell-biology research by bacteria and eukaryotes, possess sophisticated and varied surface machineries. It is published as an open-access research article in PLOS Genetics.

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  1. Primary source PLOS Genetics · 2026-10-14
    Diversification and functional expansion of archaeal T4F machineries

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archaea type-4-filament cell-surface-structures comparative-genomics molecular-evolution macsyfinder plos-genetics
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Genetic Current is the news section of Evagene, an academic, research, and educational pedigree-modelling platform. Stories are AI-drafted summaries of items from trusted public sources, written for researchers, clinicians, educators, students, genealogists, and patients with an interest in genetics. Summaries are for educational and research purposes only and are not medical advice.

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