Definition of erythroid cell‐positive blood transcriptome phenotypes associated with severe respiratory syncytial virus infection

Rinchai, Darawan; Altman, Matthew C.; Konza, Oceane; Hässler, Signe; Martina, Federica; Toufiq, Mohammed; Garand, Mathieu; Kabeer, Basirudeen Syed Ahamed; Palucka, Karolina; Mejías, Asunción; Ramilo, Octavio; Bedognetti, Davide; Mariotti‐Ferrandiz, Encarnita; Klatzmann, David

doi:10.1002/ctm2.244

Cited by 29 publications

(49 citation statements)

References 51 publications

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“…Recent studies revealed that extramedullary erythropoiesis and EPC expansion may also be a part of the inflammatory response [ 128 , 129 , 130 ]. A recent analysis of blood transcriptome revealed that the signature of immature erythroid cells is also associated with severe respiratory syncytial virus (RSV) infection, pharmacological immunosuppression, and late-stage cancer [ 131 ].…”

Section: Expansion Of Erythroid Progenitor Cellsmentioning

confidence: 99%

“…In tumor-bearing mice, EPCs expand during tumor progression in many extramedullary organs ( Table 6 ), predominantly the spleen, liver, and peripheral blood, as well as infiltrate tumors [ 41 , 43 , 62 ]. In humans, EPCs were detected in the spleen, TME, and peripheral blood of cancer patients [ 41 , 62 , 123 , 131 ]. In general, anemia severity correlates with the frequency of EPCs [ 41 ].…”

Section: Expansion Of Erythroid Progenitor Cellsmentioning

confidence: 99%

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Tumor Immune Evasion Induced by Dysregulation of Erythroid Progenitor Cells Development

Grzywa

Justyniarska

Nowis

et al. 2021

Cancers

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Cancer cells harness normal cells to facilitate tumor growth and metastasis. Within this complex network of interactions, the establishment and maintenance of immune evasion mechanisms are crucial for cancer progression. The escape from the immune surveillance results from multiple independent mechanisms. Recent studies revealed that besides well-described myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages (TAMs) or regulatory T-cells (Tregs), erythroid progenitor cells (EPCs) play an important role in the regulation of immune response and tumor progression. EPCs are immature erythroid cells that differentiate into oxygen-transporting red blood cells. They expand in the extramedullary sites, including the spleen, as well as infiltrate tumors. EPCs in cancer produce reactive oxygen species (ROS), transforming growth factor β (TGF-β), interleukin-10 (IL-10) and express programmed death-ligand 1 (PD-L1) and potently suppress T-cells. Thus, EPCs regulate antitumor, antiviral, and antimicrobial immunity, leading to immune suppression. Moreover, EPCs promote tumor growth by the secretion of growth factors, including artemin. The expansion of EPCs in cancer is an effect of the dysregulation of erythropoiesis, leading to the differentiation arrest and enrichment of early-stage EPCs. Therefore, anemia treatment, targeting ineffective erythropoiesis, and the promotion of EPC differentiation are promising strategies to reduce cancer-induced immunosuppression and the tumor-promoting effects of EPCs.

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Section: Expansion Of Erythroid Progenitor Cellsmentioning

confidence: 99%

Section: Expansion Of Erythroid Progenitor Cellsmentioning

confidence: 99%

Tumor Immune Evasion Induced by Dysregulation of Erythroid Progenitor Cells Development

Grzywa

Justyniarska

Nowis

et al. 2021

Cancers

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“…We have since resumed our efforts toward developing such resources, using new applications such as R/Shiny that support the interactive generation of fingerprint plots. These applications have been made available as ad hoc supplements to different publications [reference dataset for module construction: https://drinchai.shinyapps.io/dc\_gen3\_module\_analysis/ ( Altman et al , 2020 ); meta-analysis of respiratory syncytial virus blood transcriptome data: https://drinchai.shinyapps.io/RSV\_Meta\_Module\_analysis/ ( Rinchai et al , 2020a ); development of targeted Covid-19 blood transcript panels: https://drinchai.shinyapps.io/COVID\_19\_project/ ( Rinchai et al , 2020b )]. Together with making the BloodGen3Module R package available and depositing the scripts via GitHub: https://github.com/Drinchai/BloodGen3Module/ , we hope that deploying these applications via R/Shiny will guarantee their availability over the long term.…”

Section: Discussionmentioning

confidence: 99%

“…Blood transcriptome profiling involves measuring circulating leukocyte RNA abundance on a global scale. This approach has been used extensively to identify the transcriptomic changes associated with pathologies such as infection, autoimmunity, neurodegeneration, cardiovascular diseases and cancer ( Bhardwaj et al , 2020 ; Chaussabel, 2015 ; Freedman et al , 2010 ; Karsten et al , 2011 ; Mejias et al , 2014 ; Pascual et al , 2010 ; Rinchai et al , 2020a ). It has also been employed to monitor responses to vaccines and therapeutic agents ( Nakaya and Pulendran, 2015 ; Pascual et al , 2008 ).…”

Section: Introductionmentioning

confidence: 99%

BloodGen3Module: blood transcriptional module repertoire analysis and visualization using R

et al. 2021

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Motivation We previously described the construction and characterization of generic and reusable blood transcriptional module repertoires. More recently we released a third iteration (“BloodGen3” module repertoire) that comprises 382 functionally annotated gene sets (modules) and encompasses 14,168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires. Results We have developed and describe here a R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states. Availability The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module Supplementary information Supplementary data are available at Bioinformatics online.

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“…Diverse collections of omics-based experiments have the potential to help elucidate the immunology and genetics underlying the heterogeneity of the pathogenesis of sepsis [ 12 , 13 ]. In that regard, reductionist approaches have been used to successfully identify putatively novel genes/functions [ 14 , 15 , 16 , 17 ] and perform system-level reanalysis [ 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Transcriptome and Literature Mining Highlight the Differential Expression of ERLIN1 in Immune Cells during Sepsis

Huang

Toufiq

Saraiva

et al. 2021

Biology

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Sepsis results from the dysregulation of the host immune system. This highly variable disease affects 19 million people globally, and accounts for 5 million deaths annually. In transcriptomic datasets curated from public repositories, we observed a consistent upregulation (3.26–5.29 fold) of ERLIN1—a gene coding for an ER membrane prohibitin and a regulator of inositol 1, 4, 5-trisphosphate receptors and sterol regulatory element-binding proteins—under septic conditions in healthy neutrophils, monocytes, and whole blood. In vitro expression of the ERLIN1 gene and proteins was measured by stimulating the whole blood of healthy volunteers to a combination of lipopolysaccharide and peptidoglycan. Septic stimulation induced a significant increase in ERLIN1 expression; however, ERLIN1 was differentially expressed among the immune blood cell subsets. ERLIN1 was uniquely increased in whole blood neutrophils, and confirmed in the differentiated HL60 cell line. The scarcity of ERLIN1 in sepsis literature indicates a knowledge gap between the functions of ERLIN1, calcium homeostasis, and cholesterol and fatty acid biosynthesis, and sepsis. In combination with experimental data, we bring forth the hypothesis that ERLIN1 is variably modulated among immune cells in response to cellular perturbations, and has implications for ER functions and/or ER membrane protein components during sepsis.

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Definition of erythroid cell‐positive blood transcriptome phenotypes associated with severe respiratory syncytial virus infection

Cited by 29 publications

References 51 publications

Tumor Immune Evasion Induced by Dysregulation of Erythroid Progenitor Cells Development

Tumor Immune Evasion Induced by Dysregulation of Erythroid Progenitor Cells Development

BloodGen3Module: blood transcriptional module repertoire analysis and visualization using R

Transcriptome and Literature Mining Highlight the Differential Expression of ERLIN1 in Immune Cells during Sepsis

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