The red blood cell as a novel regulator of human B-cell activation

Aberrant GPA expression and regulatory function of red blood cells in sickle cell disease

ABSTRACT

Glycophorin A (GPA), a red blood cell (RBC) surface glycoprotein, can maintain peripheral blood leukocyte quiescence through interaction with a sialic acid-binding Ig-like lectin (Siglec-9). Under inflammatory conditions such as sickle cell disease (SCD), the GPA of RBCs undergo structural changes that affect this interaction. Peripheral blood samples from patients with SCD before and after RBC transfusions were probed for neutrophil and monocyte activation markers and analyzed by fluorescence-activated cell sorting (FACS). RBCs were purified and tested by FACS for Siglec-9 binding and GPA expression, and incubated with cultured endothelial cells to evaluate their effect on barrier function. Activated leukocytes from healthy subjects (HS) were coincubated with healthy RBCs (RBCH), GPA-altered RBCs, or GPA-overexpressing (OE) cells and analyzed using FACS. Monocyte CD63 and neutrophil CD66b from patients with SCD at baseline were increased 47% and 27%, respectively, as compared with HS (P = .0017, P = .0162). After transfusion, these markers were suppressed by 22% and 17% (P = .0084, P = .0633). GPA expression in RBCSCD was 38% higher (P = .0291) with decreased Siglec-9 binding compared with RBCH (0.0266). Monocyte CD63 and neutrophil CD66b were suppressed after incubation with RBCH and GPA-OE cells, but not with GPA-altered RBCs. Endothelial barrier dysfunction after lipopolysaccharide challenge was restored fully with exposure to RBCH, but not with RBCSCD, from patients in pain crisis, or with RBCH with altered GPA. Pretransfusion RBCSCD do not effectively maintain the quiescence of leukocytes and endothelium, but quiescence is restored through RBC transfusion, likely by reestablished GPA-Siglec-9 interactions.

Tumor cell-intrinsic and tumor microenvironmental conditions co-determine signaling by the glycoimmune checkpoint receptor Siglec-7

Tumors create an immunosuppressive tumor microenvironment by altering protein expression, but also by changing their glycosylation status, like altered expression of sialoglycans. Sialoglycans are capped with sialic acid sugar residues and are recognized by Siglec immune receptors. Siglec-7 is an inhibitory immune receptor similar to PD-1, and is emerging as glycoimmune checkpoint exploited by cancer cells to evade the immune system. However, the exact cellular and molecular conditions required for Siglec-7-mediated immune cell inhibition remain largely unknown. Here, we report on the development of a chimeric Siglec-7 cell system that enables dissection of Siglec-7 signaling, rather than Siglec-7 binding. Antibody-induced clustering, sialic acid-containing polymers, and highly sialylated erythrocytes effectively induced Siglec-7 signaling, thereby validating functionality of this reporter system. Moreover, the system reveals tumor cell-dependent Siglec-7 signaling. Tumor-associated conditions important for Siglec-7 signaling were defined, such as Siglec-7 ligand expression levels, presence of the known Siglec-7 ligand CD43, and sialic acid availability for sialylation of glycans. Importantly, therapeutic targeting of the Siglec-7/sialic acid axis using a sialyltransferase inhibitor resulted in strong reduction of Siglec-7 signaling. In conclusion, using a newly established cellular tool, we defined a set of tumor-associated conditions that influence Siglec-7 signaling. Moreover, the system allows to assess the efficacy of novel cancer drugs interfering with the Siglec-7/sialic acid axis as immunotherapy to treat cancer.

Leukemia can be Effectively Early Predicted in Routine Physical Examination with the Assistance of Machine Learning Models

Objectives. The diagnosis of leukemia relies very much on the results of bone marrow examinations, which is never generally performed in routine physical examination. In many rural areas even community hospitals and primary care clinics, the lack of hematological specialist and facility does not allow a definite diagnosis of leukemia. Thus, there will be a significant benefit if machine learning (ML) models could help early predict leukemia using preliminary blood test data in a routine physical examination in community hospitals to save time before a definite diagnosis. Methods. We collected the routine physical examination data of 1230 newly diagnosed leukemia patients and 1300 healthy people. We trained and tested 3 machine learning (ML) models including linear support vector machine (LSVM), random forest (RF), and XGboost models. We not only examined the accordance between model results and statistical analysis of the input data but also examined the consistency of model accuracy scores and relative importance order of model factors with regard to different input data sets and different model arguments to check the applicability of both the models and the input data. Results. Generally, the RF and XGboost models give more identical, consistent, and robust relative importance order of factors that is also accordant with the statistical analysis, while the LSVM gives much different and nonsense orders for different inputs. Results of the RF and XGboost models show that (1) generally, the models achieve accuracy scores above 0.9, indicating effective identification of leukemia, and (2) the top three factors that contribute most to the identification of leukemia include red blood cell (RBC), hematocrit (HCT), and white blood cell (WBC), while the other factors contribute relatively less. Conclusions. This study shows a feasible case example for early identification of leukemia using routine physical examination data with the assistance of ML models, which can be conveniently, cheaply, and widely applied in community hospitals or primary care clinics to save time before definite diagnosis; however, more studies are still needed to validate the applicability of more ML models to a larger variety of input data sets.