Glycyrrhizic acid inhibits myeloid differentiation of hematopoietic stem cells by binding S100 calcium binding protein A8 to improve cognition in aged mice

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Abstract

Background

Glycyrrhizic acid (GA), a saponin compound often used as a flavoring agent, can elicit anti-inflammatory and anti-tumor effects, and alleviate aging. However, the specific mechanism by which GA alters immune cell populations to produce these beneficial effects is currently unclear.

Results

In this study, we systematically analyzed single-cell sequencing data of peripheral blood mononuclear cells from young mice, aged mice, and GA-treated aged mice. Our in vivo results show that GA reduced senescence-induced increases in macrophages and neutrophils, and increased numbers of lymphoid lineage subpopulations specifically reduced by senescence. In vitro, GA significantly promoted differentiation of Lin ⁻CD117 ⁺ hematopoietic stem cells toward lymphoid lineages, especially CD8 ⁺ T cells. Moreover, GA inhibited differentiation of CD4 ⁺ T cells and myeloid (CD11b ⁺) cells by binding to S100 calcium-binding protein 8 (S100A8) protein. Overexpression of S100A8 in Lin ⁻ CD117 ⁺ hematopoietic stem cells enhanced cognition in aged mice and the immune reconstitution of severely immunodeficient B-NDG (NOD.CB17-Prkdcscid/l2rgtm1/Bcgen) mice.

Conclusions

Collectively, GA exerts anti-aging effects by binding to S100A8 to remodel the immune system of aged mice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12979-023-00337-9.

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Most cited references 40

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Fast gapped-read alignment with Bowtie 2.

Ben Langmead, Steven L Salzberg (2022)

As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

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HISAT: a fast spliced aligner with low memory requirements.

Daehwan Kim, Ben Langmead, Steven L Salzberg (2018)

HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

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Is Open Access

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Bo Li, Colin Dewey (2011)

Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

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Author and article information

Contributors

Hongbin Li: lihb@shzu.edu.cn

Hailiang Liu: hailiang_1111@tongji.edu.cn

Journal

Journal ID (nlm-ta): Immun Ageing

Journal ID (iso-abbrev): Immun Ageing

Title: Immunity & Ageing : I & A

Publisher: BioMed Central (London )

ISSN (Electronic): 1742-4933

Publication date (Electronic): 11 March 2023

Publication date PMC-release: 11 March 2023

Publication date Collection: 2023

Volume: 20

Electronic Location Identifier: 12

Affiliations

[1 ]GRID grid.411680.a, ISNI 0000 0001 0514 4044, Key Laboratory of Xinjiang Phytomedicine Resource and Utilization of Ministry of Education, College of Life Sciences, , Shihezi University, ; Shihezi, 832003 China

[2 ]GRID grid.452753.2, ISNI 0000 0004 1799 2798, Institute for Regenerative Medicine, , Shanghai East Hospital, Tongji University School of Medicine, ; Shanghai, 200123 China

[3 ]China Colored-Cotton (Group) Company, Ltd, Urumqi, 830000 Xinjiang China

[4 ]GRID grid.263817.9, ISNI 0000 0004 1773 1790, Institute of Advanced Biotechnology, , Southern University of Science and Technology, ; Shenzhen, 518055 China

Article

Publisher ID: 337

DOI: 10.1186/s12979-023-00337-9

PMC ID: 10007777

PubMed ID: 36906583

SO-VID: d351e8db-c81b-40a6-8e40-6b00f01149fa

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 29 November 2022

Date accepted : 3 March 2023

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100012166, National Key Research and Development Program of China;

Award ID: 2020YFC2002800

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 82271593

Custom metadata

ScienceOpen disciplines: Immunology

Keywords: glycyrrhizic acid (pubchem cid:14,982),hematopoietic stem cells,myeloid lineage,s100a8,cognitive level

Data availability:

ScienceOpen disciplines: Immunology

Keywords: glycyrrhizic acid (pubchem cid:14,982), hematopoietic stem cells, myeloid lineage, s100a8, cognitive level

Glycyrrhizic acid inhibits myeloid differentiation of hematopoietic stem cells by binding S100 calcium binding protein A8 to improve cognition in aged mice

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Abstract

Background

Results

Conclusions

Supplementary Information

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Gamma Delta T cells

Most cited references 40

Fast gapped-read alignment with Bowtie 2.

HISAT: a fast spliced aligner with low memory requirements.

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

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