Back

Active RNA synthesis patterns nuclear condensates

Banani, S. F.; Goychuk, A.; Natarajan, P.; Zheng, M. M.; Dall'Agnese, G.; Henninger, J. E.; Kardar, M.; Young, R. A.; Chakraborty, A. K.

2024-10-13 biophysics
10.1101/2024.10.12.614958 bioRxiv
Show abstract

Biomolecular condensates are membraneless compartments that organize biochemical processes in cells. In contrast to well-understood mechanisms describing how condensates form and dissolve, the principles underlying condensate patterning - including their size, number and spacing in the cell - remain largely unknown. We hypothesized that RNA, a key regulator of condensate formation and dissolution, influences condensate patterning. Using nucleolar fibrillar centers (FCs) as a model condensate, we found that inhibiting ribosomal RNA synthesis significantly alters the patterning of FCs. Physical theory and experimental observations support a model whereby active RNA synthesis generates a non-equilibrium state that arrests condensate coarsening and thus contributes to condensate patterning. Altering FC condensate patterning by expression of the FC component TCOF1 impairs ribosomal RNA processing, linking condensate patterning to biological function. These results reveal how non-equilibrium states driven by active chemical processes regulate condensate patterning, which is important for cellular biochemistry and function.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 8%
18.8%
Similar papers in this journal
2
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 2%
15.3%
Similar papers in this journal
3
eLife
● 5828 papers in training set
Top 6%
12.9%
Similar papers in this journal
4
Nature Physics
○ 45 papers in training set
Top 0.1%
8.0%
Similar papers in this journal
50% of probability mass above
5
Biophysical Journal
○ 631 papers in training set
Top 1%
6.3%
Similar papers in this journal
6
iScience
○ 1154 papers in training set
Top 4%
4.1%
7
Science Advances
● 1243 papers in training set
Top 10%
3.3%
8
Cell Reports
○ 1498 papers in training set
Top 11%
3.3%
9
Nucleic Acids Research
◐ 1281 papers in training set
Top 5%
3.3%
10
PLOS Computational Biology
● 1863 papers in training set
Top 12%
2.4%
11
Communications Biology
○ 993 papers in training set
Top 13%
1.8%
12
Scientific Reports
○ 3612 papers in training set
Top 51%
1.8%
13
Molecular Biology of the Cell
● 311 papers in training set
Top 2%
1.1%
14
Nature Cell Biology
○ 118 papers in training set
Top 3%
1.1%
15
Nature Chemistry
○ 42 papers in training set
Top 0.9%
0.9%
16
Journal of The Royal Society Interface
● 235 papers in training set
Top 4%
0.9%
17
The Journal of Physical Chemistry B
● 167 papers in training set
Top 2%
0.9%
18
The Journal of Chemical Physics
● 56 papers in training set
Top 0.5%
0.9%
19
Molecular Cell
○ 350 papers in training set
Top 5%
0.9%
20
PRX Life
● 42 papers in training set
Top 1.0%
0.6%
21
Nature Materials
○ 28 papers in training set
Top 0.6%
0.6%
22
Nature Chemical Biology
○ 119 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.