Back

Diversification and functional expansion of archaeal TFF machineries

Sivabalasarma, S.; Albers, S.

2026-04-22 microbiology
10.64898/2026.04.22.720090 bioRxiv
Show abstract

Archaea have several cell surface structures that belong to the type 4 filament (TFF) superfamily. What all have in common is the presence of core minimal assembly systems consisting of an ATPase, a platform protein, a filament forming protein and a class III signal peptidase. Here we generated novel MacSyFinder2 models to identify and classify archaeal TFF systems. Our analysis revealed a vast diversity of archaeal TFF with several members harboring one or more TFF assembly machineries. Structure-based phylogenetic analyses revealed that the variable N-terminal domain of TFF-related ATPases reflects the subsystem clustering. This indicates a diversification of the core machinery components within the archaeal secretion ATPase family driven through structural innovation. Genome-wide screening of SP-III containing proteins revealed the widespread presence of substrate binding proteins with SPIII. We hypothesize that these binding proteins with canonical SPIII cleavage sites are used to functionalize TFF machineries for efficient substrate scavenging, expanding the functional repertoire of archaeal TFF systems beyond currently characterized roles. Author SummaryThe type IV filament superfamily (TFF) comprises a broad group of surface structures that are widespread across both Archaea and Bacteria. While most well-characterized members originate from bacteria, only a limited number of archaeal TFF systems have been experimentally studied so far. Here, we expand the known diversity of archaeal TFF loci through bioinformatic and comparative genomic analyses. Our results show that these systems are far more diverse and versatile than previously appreciated, often exhibiting specialized functions. The structural diversification of the ATPase machinery likely played a key role in driving the functional diversification of TFF systems in Archaea. Overall, these findings deepen our understanding of how archaea adapt and persist in diverse environments, highlighting their surface structures as essential tools for communication, adhesion, and nutrient acquisition.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
mSystems
361 papers in training set
Top 0.4%
13.8%
2
mBio
750 papers in training set
Top 1%
13.8%
3
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 6%
9.7%
4
Nature Communications
4913 papers in training set
Top 20%
9.7%
5
microLife
19 papers in training set
Top 0.1%
3.8%
50% of probability mass above
6
PLOS Pathogens
721 papers in training set
Top 4%
3.5%
7
PLOS Genetics
756 papers in training set
Top 5%
3.5%
8
Journal of Bacteriology
190 papers in training set
Top 0.3%
3.5%
9
eLife
5422 papers in training set
Top 28%
3.5%
10
Nucleic Acids Research
1128 papers in training set
Top 8%
2.5%
11
iScience
1063 papers in training set
Top 12%
1.8%
12
Frontiers in Microbiology
375 papers in training set
Top 6%
1.6%
13
Journal of Molecular Biology
217 papers in training set
Top 2%
1.6%
14
PLOS Biology
408 papers in training set
Top 12%
1.4%
15
Molecular Biology and Evolution
488 papers in training set
Top 3%
1.4%
16
Cell Reports
1338 papers in training set
Top 27%
1.4%
17
Journal of Proteome Research
215 papers in training set
Top 1%
1.4%
18
Communications Biology
886 papers in training set
Top 13%
1.3%
19
ISME Communications
103 papers in training set
Top 2%
0.9%
20
Journal of Biological Chemistry
641 papers in training set
Top 3%
0.9%
21
Environmental Microbiology
119 papers in training set
Top 3%
0.9%
22
PLOS Computational Biology
1633 papers in training set
Top 26%
0.7%
23
Microbial Genomics
204 papers in training set
Top 2%
0.7%
24
BMC Biology
248 papers in training set
Top 5%
0.7%
25
Molecular Microbiology
66 papers in training set
Top 1.0%
0.7%
26
Science Advances
1098 papers in training set
Top 32%
0.7%
27
Microbiology Spectrum
435 papers in training set
Top 6%
0.6%
28
Microbiology
57 papers in training set
Top 1%
0.6%