Evidence map›Paper›PMID 36450286›Full record

ArticleCell systems2022

Allelic correlation is a marker of trade-offs between barriers to transmission of expression variability and signal responsiveness in genetic networks.

Ryan H Boe, Vinay Ayyappan, Lea Schuh, Arjun Raj

Abstract read
In one paragraph

Article in Cell systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. bioRxiv : the preprint server for biology · 2024
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ryan H BoeGenetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Vinay AyyappanDepartment of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.
Lea SchuhInstitute of AI for Health, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany; Department of Mathematics, Technical University of Munich, Garching 85748, Germany.
Arjun RajDepartment of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: arjunrajlab@gmail.com.

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007170 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI BRASS, LAWRENCE F · 1985 to 2022
$54.0M
Targeted Combination Therapy for MelanomaP50CA174523 · NCI · WISTAR INSTITUTE · PI HERLYN, MEENHARD F · 2014 to 2018
$11.1M
Training Program in Computational GenomicsT32HG000046 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI JUNHYONG KIM, Mingyao Li · 1999 to 2026
$9.5M
Engineering and Imaging 3D genome structure-function dynamics across time scalesU01DK127405 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI BLOBEL, GERD A, PHILLIPS-CREMINS, JENNIFER ELIZABETH · 2020 to 2024
$5.7M
Decoding the bridges and barriers to cellular reprogramming and lineage identityR01GM137425 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI JAIN, RAJAN, RAJ, ARJUN · 2019 to 2023
$5.6M
A plasticity and reprogramming paradigm for therapy resistance at the single cell levelU01CA227550 · NCI · UNIVERSITY OF PENNSYLVANIA · PI RADHAKRISHNAN, RAVI, RAJ, ARJUN · 2018 to 2022
$3.3M
Understanding and Overcoming Resistance to BRAF/MEK Kinase Inhibitors in MelanomaR01CA238237 · NCI · UNIVERSITY OF PENNSYLVANIA · PI HERLYN, MEENHARD F, RAJ, ARJUN · 2020 to 2024
$2.9M
NCI NIH HHS P50 CA174523NCI NIH HHS R01 CA238237NCI NIH HHS U01 CA227550NHGRI NIH HHS T32 HG000046NIDDK NIH HHS U01 DK127405NIGMS NIH HHS R01 GM137425NIGMS NIH HHS T32 GM007170
6 · The paper itself

Abstract

Genetic networks should respond to signals but prevent the transmission of spontaneous fluctuations. Limited data from mammalian cells suggest that noise transmission is uncommon, but systematic claims about noise transmission have been limited by the inability to directly measure it. Here, we build a mathematical framework modeling allelic correlation and noise transmission, showing that allelic correlation and noise transmission correspond across model parameters and network architectures. Limiting noise transmission comes with the trade-off of being unresponsive to signals, and within responsive regimes, there is a further trade-off between response time and basal noise transmission. Analysis of allele-specific single-cell RNA-sequencing data revealed that genes encoding upstream factors in signaling pathways and cell-type-specific factors have higher allelic correlation than downstream factors, suggesting they are more subject to regulation. Overall, our findings suggest that some noise transmission must result from signal responsiveness, but it can be minimized by trading off for a slower response. A record of this paper's transparent peer review process is included in the supplemental information.

Indexed as

Gene Regulatory NetworksSignal TransductionAllelesAnimalsMammalsallelic correlationnetwork modelingnoise transmissionsignal processingtranscriptional noise

Identifiers

PMID36450286
PMCPMC9811561

What OpenQuestion holds

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Registered trials

None linked

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.