Monocytes are short-lived cells and undergo spontaneous apoptosis every day. Inflammatory responses may induce dramatic up-regulation of monocyte survival and differentiation. When monocytes are recruited to an area of infection they may differentiate into macrophages. In different microenvironments macrophages polarize into two types. The M1 or classically activated macrophages are characterized by the high ability to produce pro-inflammatory cytokines and the production of NO through the induced synthesis of iNOS. The M2 or alternatively activated macrophages are divided into 3 subtypes, M2 a, b and c, and they have anti-inflammatory properties. Mediators of M1 macrophage TLR-dependent polarization include transcription factors such as NF-κB, AP-1, PU.1, CCAAT/enhancer-binding protein α (C/EBP-α), STAT1 as well as interferon regulatory factor 5 (IRF5), while the transcription factors which promote M2 activation include IRF4, C/EBP-β, Krüppel-like factor 4 (KLF4), STAT6 and PPARγ receptor.
Background The COVID-19 pandemic, which has a prominent social and economic impact worldwide, shows a largely unexplained male bias for the severity and mortality of the disease. Loss of chromosome Y (LOY) is a risk factor candidate in COVID-19 due to its prior association with many chronic age-related diseases, and its impact on immune gene transcription. Methods Publicly available scRNA-seq data of PBMC samples derived from male patients critically ill with COVID-19 were reanalyzed, and LOY status was added to the annotated cells. We further studied LOY in whole blood for 211 COVID-19 patients treated at intensive care units (ICU) from the first and second waves of the pandemic. Of these, 139 patients were subject to cell sorting for LOY analysis in granulocytes, low-density neutrophils (LDNs), monocytes, and PBMCs. Results Reanalysis of available scRNA-seq data revealed LDNs and monocytes as the cell types most affected by LOY. Subsequently, DNA analysis indicated that 46%, 32%, and 29% of critically ill patients showed LOY above 5% cut-off in LDNs, granulocytes, and monocytes, respectively. Hence, the myeloid lineage that is crucial for the development of severe COVID-19 phenotype is affected by LOY. Moreover, LOY correlated with increasing WHO score (median difference 1.59%, 95% HDI 0.46% to 2.71%, p=0.025), death during ICU treatment (median difference 1.46%, 95% HDI 0.47% to 2.43%, p=0.0036), and history of vessel disease (median difference 2.16%, 95% HDI 0.74% to 3.7%, p=0.004), among other variables. In 16 recovered patients, sampled during ICU stay and 93–143 days later, LOY decreased significantly in whole blood and PBMCs. Furthermore, the number of LDNs at the recovery stage decreased dramatically (median difference 76.4 per 10,000 cell sorting events, 95% HDI 55.5 to 104, p=6e−11). Conclusions We present a link between LOY and an acute, life-threatening infectious disease. Furthermore, this study highlights LOY as the most prominent clonal mutation affecting the myeloid cell lineage during emergency myelopoiesis. The correlation between LOY level and COVID-19 severity might suggest that this mutation affects the functions of monocytes and neutrophils, which could have consequences for male innate immunity.
The study aimed to analyze the CD14(bright) CD16(+) and CD14(dim) CD16(+) monocyte subsets in juvenile-onset complication-free diabetes mellitus type 1 in the context of their association with microvascular complications. 61 children with type 1 diabetes and 30 healthy individuals were enrolled in a study. CD14(bright) CD16(+) and CD14(dim) CD16(+) monocytes were quantified in peripheral blood by means of flow cytometry. At the time of sampling blood glucose concentration was taken along with biochemical measurement of renal function, CRP and glycosylated hemoglobin. The Spearman's correlations were used to compare the relationship between CD16(+) monocyte subsets and the clinical parameters that can predict the development of microangiopathies. The flow cytometric analysis of monocyte subsets in peripheral blood of analyzed subjects revealed that the numbers of CD14(bright) CD16(+) and CD14(dim) CD16(+) monocytes were significantly higher in patients with type 1 diabetes than in the healthy individuals. As to the relationship between CD16(+) monocyte subsets and the clinical parameters that can predict development of microangiopathies, it was shown that both CD16(+) subsets were associated with increased risk of retinopathy development, defined as retinopathy development value. Elevated levels of intermediate CD14(bright) CD16(+) and non-classical CD14(dim) CD16(+) monocytes predict development of diabetic retinopathy in patients with type 1 diabetes.
The progress in translational cancer research relies on access to well-characterized samples from a representative number of patients and controls. The rationale behind our biobanking are explorations of post-zygotic pathogenic gene variants, especially in non-tumoral tissue, which might predispose to cancers. The targeted diagnoses are carcinomas of the breast (via mastectomy or breast conserving surgery), colon and rectum, prostate, and urinary bladder (via cystectomy or transurethral resection), exocrine pancreatic carcinoma as well as metastases of colorectal cancer to the liver. The choice was based on the high incidence of these cancers and/or frequent fatal outcome. We also collect age-matched normal controls. Our still ongoing collection originates from five clinical centers and after nearly 2-year cooperation reached 1711 patients and controls, yielding a total of 23226 independent samples, with an average of 74 donors and 1010 samples collected per month. The predominant diagnosis is breast carcinoma, with 933 donors, followed by colorectal carcinoma (383 donors), prostate carcinoma (221 donors), bladder carcinoma (81 donors), exocrine pancreatic carcinoma (15 donors) and metachronous colorectal cancer metastases to liver (14 donors). Forty percent of the total sample count originates from macroscopically healthy cancer-neighboring tissue, while contribution from tumors is 12%, which adds to the uniqueness of our collection for cancer predisposition studies. Moreover, we developed two program packages, enabling registration of patients, clinical data and samples at the participating hospitals as well as the central system of sample/data management at coordinating center. The approach used by us may serve as a model for dispersed biobanking from multiple satellite hospitals. Our biobanking resource ought to stimulate research into genetic mechanisms underlying the development of common cancers. It will allow all available “-omics” approaches on DNA-, RNA-, protein- and tissue levels to be applied. The collected samples can be made available to other research groups.
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