Evidence map›Paper›PMID 37919720›Full record

ReviewJournal of translational medicine2023

Deciphering the molecular and cellular atlas of immune cells in septic patients with different bacterial infections.

Ping Sun, Mintian Cui, Jiongjie Jing, Fanyu Kong, Shixi Wang, Lunxian Tang, Junling Leng, Kun Chen

Open access · goldAbstract readReview
In one paragraph

Review in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.3field-weighted citation impact, top 7% 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

14 citing papers in PubMed, 20 citations in OpenAlex.

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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

8 authors at 3 institutions in 1 country.

Ping Sun *Translational Medical Center for Stem Cell Therapy, Institute for Regenerative Medicine, School of Life Sciences and Technology, Shanghai East Hospital, Tongji University, Shanghai, 200127, China.
Mintian Cui *Translational Medical Center for Stem Cell Therapy, Institute for Regenerative Medicine, School of Life Sciences and Technology, Shanghai East Hospital, Tongji University, Shanghai, 200127, China.
Jiongjie Jing *Translational Medical Center for Stem Cell Therapy, Institute for Regenerative Medicine, School of Life Sciences and Technology, Shanghai East Hospital, Tongji University, Shanghai, 200127, China.
Fanyu KongDepartment of Internal Emergency Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China.
Shixi WangTranslational Medical Center for Stem Cell Therapy, Institute for Regenerative Medicine, School of Life Sciences and Technology, Shanghai East Hospital, Tongji University, Shanghai, 200127, China.
Lunxian TangDepartment of Internal Emergency Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China.
Junling LengDepartment of Emergency, Affiliated Hospital of Yangzhou University, Yangzhou, 225000, China.
Kun ChenTranslational Medical Center for Stem Cell Therapy, Institute for Regenerative Medicine, School of Life Sciences and Technology, Shanghai East Hospital, Tongji University, Shanghai, 200127, China. chenk@tongji.edu.cn.
Shanghai East Hospital · CNTongji University · CNYangzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSepsis is a life-threatening organ dysfunction caused by abnormal immune responses to various, predominantly bacterial, infections. Different bacterial infections lead to substantial variation in disease manifestation and therapeutic strategies. However, the underlying cellular heterogeneity and mechanisms involved remain poorly understood.

methodsMultiple bulk transcriptome datasets from septic patients with 12 types of bacterial infections were integrated to identify signature genes for each infection. Signature genes were mapped onto an integrated large single-cell RNA (scRNA) dataset from septic patients, to identify subsets of cells associated with different sepsis types, and multiple omics datasets were combined to reveal the underlying molecular mechanisms. In addition, an scRNA dataset and spatial transcriptome data were used to identify signaling pathways in sepsis-related cells. Finally, molecular screening, optimization, and de novo design were conducted to identify potential targeted drugs and compounds.

resultsWe elucidated the cellular heterogeneity among septic patients with different bacterial infections. In Escherichia coli (E. coli) sepsis, 19 signature genes involved in epigenetic regulation and metabolism were identified, of which DRAM1 was demonstrated to promote autophagy and glycolysis in response to E. coli infection. DRAM1 upregulation was confirmed in an independent sepsis cohort. Further, we showed that DRAM1 could maintain survival of a pro-inflammatory monocyte subset, C10_ULK1, which induces systemic inflammation by interacting with other cell subsets via resistin and integrin signaling pathways in blood and kidney tissue, respectively. Finally, retapamulin was identified and optimized as a potential drug for treatment of E. coli sepsis targeting the signature gene, DRAM1, and inhibiting E. coli protein synthesis. Several other targeted drugs were also identified in other types of sepsis, including nystatin targeting C1QA in Neisseria sepsis and dalfopristin targeting CTSD in Streptococcus viridans sepsis.

conclusionOur study provides a comprehensive overview of the cellular heterogeneity and underlying mechanisms in septic patients with various bacterial infections, providing insights to inform development of stratified targeted therapies for sepsis.

Indexed as

Bacterial InfectionsSepsisEpigenesis, GeneticEscherichia coliHumansTranscriptomeCellular heterogeneityMulti-omicsSepsisStratified targeted therapiesVarious bacterial infections

Identifiers

PMID37919720
PMCPMC10621118
OpenAlexW4388224799

What OpenQuestion holds

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LicenceCC BY
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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.