Evidence map›Paper›PMID 37205389›Full record

ArticlebioRxiv : the preprint server for biology2023

Inferring secretory and metabolic pathway activity from omic data with secCellFie.

Helen O Masson, Mojtaba Samoudi, Caressa M Robinson, Chih-Chung Kuo, Linus Weiss, Km Shams Ud Doha, Alex Campos, Vijay Tejwani, Hussain Dahodwala, Patrice Menard and 3 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Helen O MassonDepartment of Bioengineering, UC San Diego, La Jolla, CA, USA.
Mojtaba SamoudiDepartment of Pediatrics, UC San Diego, La Jolla, CA, USA.
Caressa M RobinsonDepartment of Pediatrics, UC San Diego, La Jolla, CA, USA.
Chih-Chung KuoDepartment of Bioengineering, UC San Diego, La Jolla, CA, USA.
Linus WeissDepartment of Biochemistry, Eberhard Karls University of Tübingen, Germany.
Km Shams Ud DohaProteomics Core, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.
Alex CamposProteomics Core, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.
Vijay TejwaniCollege of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA.
Hussain DahodwalaCollege of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA.
Patrice MenardNovo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
Bjorn G VoldborgNovo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
Susan T SharfsteinCollege of Nanoscale Science and Engineering, SUNY Polytechnic Institute, Albany, NY, USA.
Nathan E LewisDepartment of Bioengineering, UC San Diego, La Jolla, CA, USA.

Funding

Unraveling the mammalian secretory pathway through systems biology and algorithm developmentR35GM119850 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LEWIS, NATHAN ENOCH · 2016 to 2025
$4.3M
Illumina NovaSeq 6000 Sequencing SystemS10OD026929 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JEPSEN, KRISTEN LYNN · 2019 to 2019
$600k
ImmCellFIE: producing high-resolution snapshots of the functions of immune cellsUH2AI153029 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LEWIS, NATHAN ENOCH, ROBASKY, KIMBERLY JOAN · 2020 to 2021
$445k
NIAID NIH HHS UH2 AI153029NIGMS NIH HHS R35 GM119850NIH HHS S10 OD026929
6 · The paper itself

Abstract

Understanding protein secretion has considerable importance in the biotechnology industry and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer protein secretion capabilities from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can be used to predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

Indexed as

computational biologygenome-scale modelmetabolismomic dataSecretory pathwaysystems biology

Identifiers

PMID37205389
PMCPMC10187314

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Registered trials

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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.