Evidence map›Paper›PMID 40116894›Full record

ArticleArchives of microbiology2025

Transcriptomics with metabolomics reveals the mechanism of alkaline tolerance in Halomonas alkalicola CICC 11012s.

Ruina Liu, Geer Lin, Qi Han, Shuaicheng Mu, Shuang Liu, Su Yao, Lei Zhai

Abstract read
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In one paragraph

Article in Archives of microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Ruina LiuChina Center of Industrial Culture Collection (CICC), China National Research Institute of Food and Fermentation Industries, Beijing, People's Republic of China.
Geer LinChina Center of Industrial Culture Collection (CICC), China National Research Institute of Food and Fermentation Industries, Beijing, People's Republic of China.
Qi HanChina Center of Industrial Culture Collection (CICC), China National Research Institute of Food and Fermentation Industries, Beijing, People's Republic of China.
Shuaicheng MuChina Center of Industrial Culture Collection (CICC), China National Research Institute of Food and Fermentation Industries, Beijing, People's Republic of China.
Shuang LiuChina Center of Industrial Culture Collection (CICC), China National Research Institute of Food and Fermentation Industries, Beijing, People's Republic of China.
Su YaoChina Center of Industrial Culture Collection (CICC), China National Research Institute of Food and Fermentation Industries, Beijing, People's Republic of China.
Lei ZhaiChina Center of Industrial Culture Collection (CICC), China National Research Institute of Food and Fermentation Industries, Beijing, People's Republic of China. zhailei@china-cicc.org.

Funding

National Microbial Resource Center NO. NMRC-2024-4the Fund of Science and Technology Innovation Program of Sinolight Corporation No.ZQ2022JC-QN02
6 · The paper itself

Abstract

The potential of alkaline-tolerant bacteria as cell factories for the production of functional molecules and bulk chemicals has been increasingly recognized owing to in-depth studies of their metabolic pathways and products combined with their tolerance to alkaline environments. To further explore the cell factory potential of alkaline-tolerant bacteria, it is necessary to systematically analyze and explore the genes and metabolites related to alkaline tolerance. Halomonas alkalicola CICC 11012s is currently the strongest alkaliphile of the genus Halomonas, which can grow at pH 12.5. This study aimed to elucidate the molecular mechanisms underlying the response of H. alkalicola CICC 11012s to alkaline stress, using transcriptomic and metabolomic analyses. The expression of 259 genes and 401 metabolites was significantly altered. Important metabolic pathways included nucleotide, amino acid, and carbohydrate metabolism, as well as membrane transport. Furthermore, an integrative pathway analysis revealed that two pathways, glycine, serine, and threonine metabolism and biotin metabolism, were significantly enriched under high-alkaline conditions (pH 11.0). These findings highlight that deletion of the gene cluster tonB-exbB-exbB2-exbD significantly affects the synthesis of L-aspartate, leading to a decrease in the alkaline tolerance of H. alkalicola.

Indexed as

AlkaliesHalomonasTranscriptomeBacterial ProteinsGene Expression ProfilingGene Expression Regulation, BacterialHydrogen-Ion ConcentrationMetabolic Networks and PathwaysMetabolomeMetabolomicsAlkaliesBacterial ProteinsAlkaline toleranceHalomonas alkalicolaMetabolomeTonB transport systemTranscriptome

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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.