Evidence map›Paper›PMID 30575376›Full record

ArticleBiochemistry2019

Display of Single-Chain Insulin-like Peptides on a Yeast Surface.

Mi-Young Jeong, Jared Rutter, Danny Hung-Chieh Chou

Open access · greenAbstract read
In one paragraph

Article in Biochemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.5field-weighted citation impact, top 10% of its field
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

8 citing papers in PubMed, 20 citations in OpenAlex.

  1. New Horizons: Next-Generation Insulin Analogues: Structural Principles and Clinical Goals.The Journal of clinical endocrinology and metabolism · 2022
    Review
  2. Article
  3. Frontiers in bioengineering and biotechnology · 2022
    Review
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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

3 authors at 2 institutions in 1 country.

Mi-Young Jeong
Jared Rutter
Danny Hung-Chieh ChouORCID 0000-0001-9110-614X
University of Utah · USHoward Hughes Medical Institute · US

Funding

STUDIES ON INSULIN RECEPTOR ISO FORMSR35GM125001 · NIGMS · UNIVERSITY OF UTAH · PI CHOU, DANNY HUNG-CHIEH · 2017 to 2021
$2.0M
NIGMS NIH HHS R35 GM125001
6 · The paper itself

Abstract

Insulin and insulin-like peptides play a pivotal role in a wide variety of cellular and physiological events, including energy storage, proliferation, aging, and differentiation. Variants of insulin and insulin-like peptides may therefore be probes for studying the insulin signaling pathway and therapeutic candidates for treating metabolic diseases. Here, we report a method for genetically displaying single-chain insulin-like peptides on the surface of Saccharomyces cerevisiae strain DY1632. Using a previously reported single-chain insulin analogue, SCI-57, as a model, we demonstrate that nearly 70% of yeast binds to insulin receptor (IR), suggesting that SCI-57 is folded correctly and maintains its IR binding property. Furthermore, the interaction between displayed SCI-57 and IR can be weakened using increasing concentrations of native insulin as a soluble competitor, suggesting that the interaction is insulin-dependent. We further applied this methodology to three other single-chain insulin analogues with various lengths and confirmed their interactions with IR. In summary, we successfully displayed a number of insulin-like peptides on a yeast surface and demonstrated insulin-dependent interactions with IR. This method may, therefore, be used for construction of libraries of insulin-like peptides to select for chemical probes or therapeutic molecules.

Indexed as

Cell Surface Display TechniquesFlow CytometryHumansInsulinMicroscopy, FluorescencePeptidesReceptor, InsulinRecombinant ProteinsSaccharomyces cerevisiaeInsulinPeptidesReceptor, InsulinRecombinant Proteins

Identifiers

PMID30575376
PMCPMC6669910
OpenAlexW2906221588

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.