Evidence map›Paper›PMID 36711573›Full record

ArticlebioRxiv : the preprint server for biology2023

Discovering dynamic plant enzyme complexes in yeast for novel alkaloid pathway identification from a medicinal plant kratom.

Yinan Wu, Chang Liu, Franklin L Gong, Sijin Li

Open access · greenAbstract readPreprint
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, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Yinan WuRobert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, 14853, USA.
Chang LiuRobert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, 14853, USA.
Franklin L GongRobert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, 14853, USA.
Sijin LiRobert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, 14853, USA.
Cornell University · US

Funding

Training future leaders at the chemistry-biology interfaceT32GM138826 · NIGMS · CORNELL UNIVERSITY · PI Jeremy Baskin · 2021 to 2026
$2.6M
Simultaneous Treatment of Viral and Bacterial Otitis Media Using Plant Natural ProductsR21DC019206 · NIDCD · CORNELL UNIVERSITY · PI LI, SIJIN · 2021 to 2023
$441k
NIDCD NIH HHS R21 DC019206NIGMS NIH HHS T32 GM138826
6 · The paper itself

Abstract

Discovering natural product biosynthetic pathways from medicinal plants is challenging and laborious, largely due to the complexity of the transcriptomics-driven pathway prediction process. Here we developed a novel approach that captures the protein-level connections between enzymes for pathway discovery with improved accuracy. We proved that heterologous protein-protein interaction screening in yeast enabled the efficient discovery of both dynamic plant enzyme complexes and the pathways they organize. This approach discovered complexes and pathways in the monoterpene indole alkaloid metabolism of a medicinal plant, kratom with high success rate. Screening using a strictosidine β-D-glucosidase (MsSGD1) against 19 medium-chain dehydrogenase/reductases (MsMDRs) identified five MsSGD1-MsMDR complexes. Three out of the five interacting MsMDRs were then proven functional, while the remaining 14 non-interacting candidates did not show obvious activities. The work discovered three branched pathways by combining transcriptomics, metabolomics, and heterologous PPI screening and demonstrated a new plant pathway discovery strategy.

Identifiers

PMID36711573
PMCPMC9882157
OpenAlexW4317207695

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.