Evidence map›Paper›PMID 36320689›Full record

ArticleChemical science2022

Collective adaptability in a replication network of minimal nucleobase sequences.

Sonia Vela-Gallego, Zulay Pardo-Botero, Cristian Moya, Andrés de la Escosura

Abstract read
In one paragraph

Article in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Sonia Vela-GallegoDepartment of Organic Chemistry, Universidad Autónoma de Madrid Campus de Cantoblanco 28049 Madrid Spain andres.delaescosura@uam.es sonia.vela@uam.es.
Zulay Pardo-BoteroIMDEA Nanociencia C/Faraday 7 28049 Madrid Spain.ORCID https://orcid.org/0000-0002-1472-4849
Cristian MoyaDepartment of Organic Chemistry, Universidad Autónoma de Madrid Campus de Cantoblanco 28049 Madrid Spain andres.delaescosura@uam.es sonia.vela@uam.es.ORCID https://orcid.org/0000-0001-5107-7751
Andrés de la EscosuraDepartment of Organic Chemistry, Universidad Autónoma de Madrid Campus de Cantoblanco 28049 Madrid Spain andres.delaescosura@uam.es sonia.vela@uam.es.ORCID https://orcid.org/0000-0002-0928-8317

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A major challenge for understanding the origins of life is to explore how replication networks can engage in an evolutionary process. Herein, we shed light on this problem by implementing a network constituted by two different types of extremely simple biological components: the amino acid cysteine and the canonical nucleobases adenine and thymine, connected through amide bonds to the cysteine amino group and oxidation of its thiol into three possible disulfides. Supramolecular and kinetic analyses revealed that both self- and mutual interactions between such dinucleobase compounds drive their assembly and replication pathways. Those pathways involving sequence complementarity led to enhanced replication rates, suggesting a potential bias for selection. The interplay of synergistic dynamics and competition between replicators was then simulated, under conditions that are not easily accessible with experiments, in an open reactor parametrized and constrained with the unprecedentedly complete experimental kinetic data obtained for our replicative network. Interestingly, the simulations show bistability, as a selective amplification of different species depending on the initial mixture composition. Overall, this network configuration can favor a collective adaptability to changes in the availability of feedstock molecules, with disulfide exchange reactions serving as 'wires' that connect the different individual auto- and cross-catalytic pathways.

Identifiers

PMID36320689
PMCPMC9491195

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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.