Tag: myeloid cells

Identification of SGK1 as a candidate regulator associated with myeloid cell senescence in ME/CFS: An integrative transcriptomic and experimental study

Abstract:

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex illness lacking objective diagnostic biomarkers and clear pathophysiological understanding. Patients exhibit immune dysregulation and chronic inflammation reminiscent of aging phenotypes, yet the role of myeloid cell senescence in this disease remains unexplored.

Methods: A stepwise integrative transcriptomic and computational strategy was employed. Differentially expressed genes were identified from the bulk transcriptomic dataset GSE14577. Weighted gene co-expression network analysis was performed to identify key modules correlated with the ME/CFS phenotype. Candidate genes were obtained by intersecting these modules with aging-related genes from the GenAge database. Core hub genes were further selected using LASSO regression and random forest machine learning algorithms. Cell type-specific expression was mapped using single-cell RNA sequencing data GSE214283, and functional validation was conducted via siRNA knockdown in a THP-1 cell senescence model.

Results: Integrative analysis identified HMGCR and SGK1 as core hub genes. Functional enrichment analysis revealed that candidate genes were enriched in pathways related to cellular senescence and inflammation. Single-cell transcriptomics analysis suggested that SGK1 expression was enriched in myeloid cell populations, including monocytes and neutrophils, in individuals with ME/CFS, whereas HMGCR showed a broader expression pattern. Single-gene GSEA indicated that SGK1 was associated with autophagy and lysosomal function. In vitro experiments showed that SGK1 expression was significantly upregulated in D-galactose-induced senescent THP-1 cells, and SGK1 knockdown markedly reduced the mRNA expression levels of senescence markers p16, p21, and p53.

Conclusion: This study identifies SGK1 as a candidate molecule associated with myeloid cell senescence-related alterations in ME/CFS. Integrated transcriptomic, single-cell, and experimental analyses suggest a potential link between SGK1 expression and aging-associated immune alterations. However, further validation in primary ME/CFS-derived immune cells and clinical cohorts is required to determine the biological significance and clinical relevance of SGK1.

Source: Jin Y, Qin R, Li W, Liu J, Chen X. Identification of SGK1 as a candidate regulator associated with myeloid cell senescence in ME/CFS: An integrative transcriptomic and experimental study. PLoS One. 2026 Oct 16;21(10):e0359082. doi: 10.1371/journal.pone.0359082. PMID: 42886747. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0359082 (Full text)

A multiomics recovery factor predicts long COVID in the IMPACC study

Abstract:

BACKGROUND: Following SARS-CoV-2 infection, approximately 10%–35% of patients with COVID-19 experience long COVID (LC), in which debilitating symptoms persist for at least 3 months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities.

METHODS: We utilized machine learning methods on biologic analytes provided over 12 months after hospital discharge from more than 500 patients with COVID-19 in the IMPACC cohort to identify a multiomics “recovery factor,” trained on patient-reported physical function survey scores. Immune profiling data included PBMC transcriptomics, serum O-link and plasma proteomics, plasma metabolomics, and blood mass cytometry by time of flight (CyTOF) protein levels. Recovery factor scores were tested for association with LC, disease severity, clinical parameters, and immune subset frequencies. Enrichment analyses identified biologic pathways associated with recovery factor scores.

RESULTS: Participants with LC had lower recovery factor scores compared with recovered participants. Recovery factor scores predicted LC as early as hospital admission, irrespective of acute COVID-19 severity. Biologic characterization revealed increased inflammatory mediators, elevated signatures of heme metabolism, and decreased androgenic steroids as predictive and ongoing biomarkers of LC. Lower recovery factor scores were associated with reduced lymphocyte and increased myeloid cell frequencies. The observed signatures are consistent with persistent inflammation driving anemia and stress erythropoiesis as major biologic underpinnings of LC.

CONCLUSION: The multiomics recovery factor identifies patients at risk of LC early after SARS-CoV-2 infection and reveals LC biomarkers and potential treatment targets.

TRIAL REGISTRATION: ClinicalTrials.gov NCT04378777.

FUNDING:

National Institute of Allergy and Infectious Diseases (NIAID), NIH (3U01AI167892-03S2, 3U01AI167892-01S2, 5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI057229-18, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07S1, 3U19AI089992-09, 3U19AI128913-03, and 5T32DA018926-1, 3U19AI1289130, U19AI128913-04S1, R01AI122220); NIH (UM1TR004528); and National Science Foundation (NSF) (DMS2310836).

Source: Gabernet G, Maciuch J, Gygi JP, Moore JF, Hoch A, Syphurs C, Chu T, Doni Jayavelu N, Corry DB, Kheradmand F, Baden LR, Sekaly RP, McComsey GA, Haddad EK, Cairns CB, Rouphael N, Fernandez-Sesma A, Simon V, Metcalf JP, Agudelo Higuita NI, Hough CL, Messer WB, Davis MM, Nadeau KC, Pulendran B, Kraft M, Bime C, Reed EF, Schaenman J, Erle DJ, Calfee CS, Atkinson MA, Brakenridge SC, Melamed E, Shaw AC, Hafler DA, Augustine AD, Becker PM, Ozonoff A, Bosinger SE, Eckalbar W, Maecker HT, Kim-Schulze S, Steen H, Krammer F, Westendorf K; IMPACC Network; Peters B, Fourati S, Altman MC, Levy O, Smolen KK, Montgomery RR, Diray-Arce J, Kleinstein SH, Guan L, Ehrlich LI. A multiomics recovery factor predicts long COVID in the IMPACC study. J Clin Invest. 2025 Sep 9;135(21):e193698. doi: 10.1172/JCI193698. PMID: 40924481; PMCID: PMC12582403. https://pmc.ncbi.nlm.nih.gov/articles/PMC12582403/ (Full text)

Causal relationship between immune cells and post-viral fatigue syndrome: a Mendelian randomization study

Abstract:

Background: Accumulating evidence has hinted at a correlation between immune cells and post-viral fatigue syndrome (PVFS). However, it is still ambiguous whether these associations indicate a causal connection.

Objective: To elucidate the potential causal link between immune cells and PVFS, we performed a two-sample Mendelian randomization (MR) study.

Methods: We obtained summary data on PVFS cases (Ncase = 195) and controls (Ncontrol = 382,198) from the FinnGen consortium. Additionally, we retrieved comprehensive statistical information on 731 immune cell features. Our analysis encompassed both forward and reverse MR approaches. To ensure the reliability and validity of our findings, we conducted rigorous sensitivity analyses, addressing issues of robustness and heterogeneity.

Result: Our study presents compelling evidence of a probable causal link between immune cells and PVFS. Notably, we have pinpointed 28 distinct types of immune cell traits that potentially exhibit a causal association with PVFS. Among a pool of 7 31 immune cell traits, we identified 28 immune cell types that exhibited a potential causal association with PVFS. These included 9 B cells, 1 conventional dendritic cell (cDC), 1 maturation stage of T cell, 3 myeloid cells, 9 T, B, NK, and monocyte cells (TBNK), and 5 regulatory T cells (Treg).

Conclusion: Through genetic analyses, our study has unveiled profound causal connections between specific types of immune cells and PVFS, offering valuable guidance for forthcoming clinical investigations.

Source: Wang Z, Bai Z, Sun Y. Causal relationship between immune cells and post-viral fatigue syndrome: a Mendelian randomization study. Virol J. 2025 May 30;22(1):171. doi: 10.1186/s12985-025-02809-4. PMID: 40448142; PMCID: PMC12124062. https://pmc.ncbi.nlm.nih.gov/articles/PMC12124062/ (Full text)