S100A9 induces differentiation of acute myeloid leukemia cells through TLR4

Laouedj, Malika; Tardif, Maurice; Gil, Laurine; Raquil, Marie-Astrid; Lachhab, Asmaa; Pelletier, Martin; Tessier, Philippe A.; Barabé, Frédéric

doi:10.1182/blood-2016-09-738005

Cited by 107 publications

(108 citation statements)

References 44 publications

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“…As a homodimer, A9 potently activates inflammation via Toll-like receptor 4 (TLR4). [18][19][20][21][22][23][24][25][26][27][28][29][30] As a heterocomplex with S100A8 (A8/A9, also known as calprotectin), it is antimicrobial (Figure 1a). [31][32][33][34][35][36][37][38][39][40][41][42][43][44] A9 exacerbates endotoxin-induced shock in mice.…”

Section: Introductionmentioning

confidence: 99%

Evolution of multifunctionality through a pleiotropic substitution in the innate immune protein S100A9

Harman

Loes

Warren

et al. 2020

eLife

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Multifunctional proteins are evolutionary puzzles: how do proteins evolve to satisfy multiple functional constraints? S100A9 is one such multifunctional protein. It potently amplifies inflammation via Toll-like receptor four and is antimicrobial as part of a heterocomplex with S100A8. These two functions are seemingly regulated by proteolysis: S100A9 is readily degraded, while S100A8/S100A9 is resistant. We take an evolutionary biochemical approach to show that S100A9 evolved both functions and lost proteolytic resistance from a weakly proinflammatory, proteolytically resistant amniote ancestor. We identify a historical substitution that has pleiotropic effects on S100A9 proinflammatory activity and proteolytic resistance but has little effect on S100A8/S100A9 antimicrobial activity. We thus propose that mammals evolved S100A8/S100A9 antimicrobial and S100A9 proinflammatory activities concomitantly with a proteolytic ‘timer’ to selectively regulate S100A9. This highlights how the same mutation can have pleiotropic effects on one functional state of a protein but not another, thus facilitating the evolution of multifunctionality.

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Section: Introductionmentioning

confidence: 99%

Evolution of multifunctionality through a pleiotropic substitution in the innate immune protein S100A9

Harman

Loes

Warren

et al. 2020

eLife

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“…S100A9 activity is regulated by S100A8 and higher ratios of S100A9 over S100A8 are required to induce leukemic cell differentiation [6]. In our cohort, no differences in circulating S100A9 levels were seen in MDS and SAA patients, and SAA-responders after 6 months of therapy.…”

Section: Resultsmentioning

confidence: 66%

“…Neutrophils and monocytes are sources of circulating calprotectin, representing 40% or 5% of total cytoplasmic proteins in granulocytes or monocytes, respectively [6]. In addition, platelets abundantly express S100A8/A9 [10].…”

Section: Resultsmentioning

confidence: 99%

“…S100A8 and S100A9, two components of S100 family, preferentially exist as heterodimers or heterotetramers known as calprotectin or MRP8/14 [4]. Homodimers are calcium- and zinc-binding proteins, and they can modulate HSPC and leukemic cells differentiation through the Toll-like receptor 4 (TLR4) signaling pathway [5,6]. S100A8/A9 heterodimers, which are released by granulocytes and monocytes, interact with cytoskeleton components and induce apoptosis or survival via integrin and TLR4 downstream signaling [7].…”

Section: Introductionmentioning

confidence: 99%

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Circulating S100A8 and S100A9 protein levels in plasma of patients with acquired aplastic anemia and myelodysplastic syndromes

Giudice

Kajigaya

et al. 2019

Cytokine

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The alarmin family members S100A8 and S100A9 are acute phase inflammation proteins, but they also have been proposed as biomarkers in many malignant and non-malignant diseases. In this study, circulating S100A8 and S100A9 homodimers and S100A8/A9 heterodimers in plasma were systematically investigated by ELISA in aplastic anemia (AA) and myelodysplastic syndromes (MDS). Plasma was obtained from 58 severe AA (SAA) and 30 MDS patients, and from 47 age- and sex-matched healthy donors. In 40 out of the 58 AA subjects, S100A protein levels were measured before and 6 months after immunosuppressive therapy (IST). No differences were observed in AA patients at diagnosis compared to healthy controls for circulating S100A homodimers and heterodimers. After therapy, SAA-responders showed significantly increased circulating S100A8. Non-responding patients had significantly higher levels of circulating S100A8/A9 compared to responders and healthy controls, but without variations of S100A8 and S100A9 homodimers. In MDS patients, circulating S100A8 was significantly elevated compared to those of AA and/or healthy controls. By Pearson correlation analysis of protein levels and blood counts, multiple correlations were found. However, as S100A8 and S100A9 are abundantly present in white blood cells and platelets, correlations with blood counts likely mirror the higher number of cells in the blood of some patients. In conclusion, our findings indicate that circulating S100A8 is increased in MDS but not in AA, and that may be useful to distinguish these diseases in the differential diagnosis of bone marrow failure syndromes. Clinicaltrials.gov identifiers: NCT00260689, NCT00604201, NCT01328587, NCT01623167, NCT00001620, NCT00001397.

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“…Two of these hub genes (OTX2, NEUROD1) were the conductors of key transcriptional programs in the Group 3 subtype of MBL, the most aggressive subtype of MBL 37 . The hub genes in the green-AML module encoded protein with roles in leukemogenesis and myeloid differentiation (PRAM1, RASGRP4, S100A9) or subject to recurrent alterations in infant AML (MYO1F) [42][43][44][45] . Statistical analyses supported that pediatric cancer genes were enriched among the hub genes of the six cancer-histotype specific modules (OR = 1.9 [1.2-2.9], p = 0.004).…”

Section: Scientific Reports |mentioning

confidence: 99%

Depicting the genetic architecture of pediatric cancers through an integrative gene network approach

Savary

Kim

Lespagnol³

et al. 2020

Sci Rep

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the genetic etiology of childhood cancers still remains largely unknown. it is therefore essential to develop novel strategies to unravel the spectrum of pediatric cancer genes. Statistical network modeling techniques have emerged as powerful methodologies for enabling the inference of genedisease relationship and have been performed on adult but not pediatric cancers. We performed a deep multi-layer understanding of pan-cancer transcriptome data selected from the treehouse Childhood Cancer Initiative through a co-expression network analysis. We identified six modules strongly associated with pediatric tumor histotypes that were functionally linked to developmental processes. topological analyses highlighted that pediatric cancer predisposition genes and potential therapeutic targets were central regulators of cancer-histotype specific modules. A module was related to multiple pediatric malignancies with functions involved in DnA repair and cell cycle regulation. this canonical oncogenic module gathered most of the childhood cancer predisposition genes and clinically actionable genes. in pediatric acute leukemias, the driver genes were co-expressed in a module related to epigenetic and post-transcriptional processes, suggesting a critical role of these pathways in the progression of hematologic malignancies. this integrative pan-cancer study provides a thorough characterization of pediatric tumor-associated modules and paves the way for investigating novel candidate genes involved in childhood tumorigenesis.Cancer remains the leading cause of death by disease in children of less than fourteen years of age 1 . Improving the management of pediatric cancer is essential and will benefit from more accurate diagnosis, new personalized treatment and development of specific and less damaging therapies. To face these challenges, it is necessary to unravel the complete genetic repertoire of pediatric malignancies. Recent studies have improved the understanding of the genetics of childhood cancer, but have mainly focused on depicting the germline and somatic mutational landscape of these diseases [2][3][4] .Several evidences demonstrated that the biology and genetics of pediatric cancers set them apart from adult tumors 4,5 . Childhood cancers have a 14-times lower mutation rate compared to adult tumors and mostly arise from mutations in few driver genes. Somatic alterations mostly target a handful of major genes such as CDKN2A, NOTCH1, NRAS, KRAS or TP53, and pathways disrupted by driver alterations are either common to cancer (e.g. cell cycle) or specific to pediatric cancer histotypes 4 . More than half of the driver genes are restricted to one cancer histotype and 83% of them are not shared between hematologic and solid tumors. This indicates that certain genes and pathways are exclusively dysregulated in a single type of childhood cancer. reconstructed 100 times with the same parameters on a random selection of the initial samples. The robustness was measured as the number of times a gene was assigned to the observed m...

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S100A9 induces differentiation of acute myeloid leukemia cells through TLR4

Cited by 107 publications

References 44 publications

Evolution of multifunctionality through a pleiotropic substitution in the innate immune protein S100A9

Evolution of multifunctionality through a pleiotropic substitution in the innate immune protein S100A9

Circulating S100A8 and S100A9 protein levels in plasma of patients with acquired aplastic anemia and myelodysplastic syndromes

Depicting the genetic architecture of pediatric cancers through an integrative gene network approach

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