Evidence map›Paper›PMID 40329386›Full record

ArticleMicrobiome2025

Microbial metabolism in laboratory reared marine snow as revealed by a multi-omics approach.

Lei Hou, Zihao Zhao, Barbara Steger-Mähnert, Nianzhi Jiao, Gerhard J Herndl, Yao Zhang

Abstract read
In one paragraph

Article in Microbiome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

6 authors.

Lei Hou *State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, and College of Ocean and Earth Sciences, Xiamen University, Xiamen, China.
Zihao Zhao *State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, and College of Ocean and Earth Sciences, Xiamen University, Xiamen, China.
Barbara Steger-MähnertDepartment of Functional and Evolutionary Ecology, University of Vienna, Vienna, Austria.
Nianzhi JiaoState Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, and College of Ocean and Earth Sciences, Xiamen University, Xiamen, China. jiao@xmu.edu.cn.
Gerhard J HerndlDepartment of Functional and Evolutionary Ecology, University of Vienna, Vienna, Austria. gerhard.herndl@univie.ac.at.
Yao ZhangState Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, and College of Ocean and Earth Sciences, Xiamen University, Xiamen, China. yaozhang@xmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMarine snow represents an organic matter-rich habitat and provides substrates for diverse microbial populations in the marine ecosystem. However, the functional diversity and metabolic interactions within the microbial community inhabiting marine snow remain largely underexplored, particularly for specific metabolic pathways involved in marine snow degradation. Here, we used a multi-omics approach to explore the microbial response to laboratory-reared phytoplankton-derived marine snow.

resultsOur results demonstrated a dramatic shift in both taxonomic and functional profiles of the microbial community after the formation of phytoplankton-derived marine snow using a rolling tank system. The changes in microbial metabolic processes were more pronounced in the metaproteome than in the metagenome in response to marine snow. Fast-growing taxa within the Gammaproteobacteria were the most dominant group at both the metagenomic and metaproteomic level. These Gammaproteobacteria possessed a variety of carbohydrate-active enzymes (CAZymes) and transporters facilitating substrate cleavage and uptake, respectively. Analysis of metagenome-assembled genomes (MAGs) revealed that the response to marine snow amendment was primarily mediated by Alteromonas, Vibrio, and Thalassotalea. Among these, Alteromonas exclusively expressing auxiliary activities 2 (AA2) of the CAZyme subfamily were abundant in both the free-living (FL) and marine snow-attached (MA) microbial communities. Thus, Alteromonas likely played a pivotal role in the degradation of marine snow. The enzymes of AA2 produced by these Alteromonas MAGs are capable of detoxifying peroxide intermediates generated during the breakdown of marine snow into smaller poly- and oligomers, providing available substrates for other microorganisms within the system. In addition, Vibrio and Thalassotalea MAGs exhibited distinct responses to these hydrolysis products of marine snow in different size fractions, suggesting a distinct niche separation. Although chemotaxis proteins were found to be enriched in the proteome of all three MAGs, differences in transporter proteins were identified as the primary factor contributing to the niche separation between these two groups. Vibrio in the FL fraction predominantly utilized ATP-binding cassette transporters (ABCTs), while Thalassotalea MAGs in the MA fraction primarily employed TonB-dependent outer membrane transporters (TBDTs).

conclusionsOur findings shed light on the essential metabolic interactions within marine snow-degrading microbial consortia, which employ complementary physiological mechanisms and survival strategies to effectively scavenge marine snow. This work advances our understanding of the fate of marine snow and the role of microbes in carbon sequestration in the ocean. Video Abstract.

Indexed as

BacteriaMicrobiotaSeawaterSnowGammaproteobacteriaMetagenomeMetagenomicsMultiomicsPhytoplanktonFree-living and marine snow-attached microbesMarine snowMarine snow-degrading microbesMetagenomicsMetaproteomics

Identifiers

PMID40329386
PMCPMC12054258

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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.