Back

Optimized Plaque Assay for Detecting Chronically Infecting Viruses of Haloarchaea

Navok, S.; Cohen, L.; Ron, E. Z.; Gophna, U.

2024-11-13 microbiology
10.1101/2024.11.12.619373 bioRxiv
Show abstract

Archaeal viruses predominantly exhibit a chronic lifestyle, where viral particles are released without causing host cell death. Therefore, conventional plaque assays, which are well-suited for identifying lytic viruses, usually fail to detect chronically infecting viruses due to their non-lytic nature. To address this limitation, we developed an optimized plaque assay protocol for detecting chronically infecting viruses of haloarchaea, particularly species within the Haloferax genus. By enhancing viral diffusibility and infectivity through adjustments in agar concentration and lowering incubation temperature, this modified protocol improved plaque formation, enabling the detection of viruses that cause mild growth delays. We demonstrate successful plaque formation for two chronically infecting viruses, Haloferax volcanii pleomorphic virus 1 (HFPV-1) and lemon-shaped virus 48N (LSV-48N), on representative Haloferax strains and highlight the potential for isolating novel viruses from environmental samples. This improved plaque assay is an effective method for the detection and quantification of chronically infecting archaeal viruses, providing a new tool for discovering new viral families in extreme environments.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Applied and Environmental Microbiology
● 339 papers in training set
Top 0.2%
18.5%
Similar papers in this journal
2
Frontiers in Microbiology
○ 427 papers in training set
Top 1%
7.9%
Similar papers in this journal
3
Scientific Reports
○ 3612 papers in training set
Top 10%
6.7%
Similar papers in this journal
4
Viruses
○ 332 papers in training set
Top 0.7%
6.2%
Similar papers in this journal
5
ISME Communications
◐ 120 papers in training set
Top 0.5%
4.8%
Similar papers in this journal
6
mSphere
● 302 papers in training set
Top 1%
4.4%
7
Access Microbiology
● 25 papers in training set
Top 0.1%
4.0%
50% of probability mass above
8
Microbiology Spectrum
● 469 papers in training set
Top 4%
4.0%
9
PLOS ONE
● 5266 papers in training set
Top 34%
3.5%
10
Pathogens
○ 56 papers in training set
Top 0.2%
2.6%
11
Virology Journal
○ 32 papers in training set
Top 0.2%
2.4%
12
Journal of General Virology
● 53 papers in training set
Top 0.4%
2.1%
13
Virology
○ 61 papers in training set
Top 0.6%
1.9%
14
Environmental Science & Technology
● 64 papers in training set
Top 0.7%
1.7%
15
mSystems
● 394 papers in training set
Top 5%
1.3%
16
Virus Evolution
◐ 155 papers in training set
Top 1%
1.1%
17
PeerJ
◐ 308 papers in training set
Top 8%
1.1%
18
Microorganisms
○ 106 papers in training set
Top 2%
1.1%
19
MicrobiologyOpen
○ 24 papers in training set
Top 0.3%
1.1%
20
Environmental Microbiology
○ 133 papers in training set
Top 2%
1.1%
21
Journal of Applied Microbiology
◐ 20 papers in training set
Top 0.7%
1.0%
22
The ISME Journal
◐ 228 papers in training set
Top 3%
0.9%
23
Microbiology
● 65 papers in training set
Top 2%
0.8%
24
Environmental Science & Technology Letters
● 21 papers in training set
Top 0.3%
0.6%
25
Journal of Virology
● 499 papers in training set
Top 3%
0.6%
26
Journal of Medical Virology
○ 140 papers in training set
Top 3%
0.6%
27
Environmental Microbiology Reports
○ 31 papers in training set
Top 1%
0.6%
28
Microbiome
○ 154 papers in training set
Top 3%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.