Human C‐type lectin‐like CD161 is a type‐II transmembrane protein expressed on the surface of various lymphocytes across innate and adaptive immune systems. CD161+ T cells displayed enhanced ability ...to produce cytokines and were shown to be enriched in the gut. Independently of function, CD161 was used as marker of innate‐like T cells and marker of IL‐17‐producing cells. The function of CD161 is still not fully understood. In T cells, CD161 was proposed to act as co‐signalling receptor that influence T‐cell receptor‐dependent responses. However, conflicting studies were published demonstrating lack of agreement over the role of CD161 during T‐cell activation. In this review, we outline phenotypical and functional consequences of CD161 expression in T cells. We provide critical discussion over the most pressing issues including in depth evaluation of the literature concerning CD161 putative co‐signalling properties.
Celiac disease is caused by an abnormal intestinal T cell response to cereal gluten proteins. The disease has a strong human leukocyte antigen (HLA) association, and CD4
+
T cells recognizing gluten ...epitopes presented by disease-associated HLA-DQ allotypes are considered to be drivers of the disease. This paper provides an update of the currently known HLA-DQ restricted gluten T cell epitopes with their names and sequences.
Clonally related B cells infiltrate the brain, meninges, and cerebrospinal fluid of MS patients, but the mechanisms driving the B‐cell response and shaping the immunoglobulin repertoires remain ...unclear. Here, we used single‐cell full‐length RNA‐seq and BCR reconstruction to simultaneously assess the phenotypes, isotypes, constant region polymorphisms, and the paired heavy‐ and light‐chain repertoires in intrathecal B cells. We detected extensive clonal connections between the memory B cell and antibody‐secreting cell (ASC) compartments and observed clonally related cells of different isotypes including IgM/IgG1, IgG1/IgA1, IgG1/IgG2, and IgM/IgA1. There was a strong dominance of the G1m1 allotype constant region polymorphisms in ASCs, but not in memory B cells. Tightly linked to the G1m1 allotype, we found a preferential pairing of the immunoglobulin heavy‐chain variable (IGHV)4 gene family with the κ variable (IGKV)1 gene family. The IGHV4‐39 gene was most used and showed the highest frequency of pairing with IGKV1‐5 and IGKV1(D)‐33. These results link IgG constant region polymorphisms to stereotyped B‐cell responses in MS and indicate that the intrathecal B‐cell response in these patients could be directed against structurally similar epitopes.
We used single‐cell RNA‐seq to assess the transcriptome and immunoglobulin heavy‐ and light‐chain repertoires of cerebrospinal fluid B cells in MS. Patients carrying the G1m1 allotype displayed a stereotyped B‐cell response utilizing the IGHV4 and IGKV1 gene families. This may indicate that the B cells are targeting similar epitopes across different patients.
C‐type lectin‐like CD161, a class II transmembrane protein, is a surface receptor expressed by NK cells and T cells. In coeliac disease, CD161 was expressed more frequently on gluten‐reactive CD4 + T ...cells compared to other memory CD4 + T cells isolated from the same tissue compartment. CD161 is a putative co‐signalling molecule that was proposed to act as co‐stimulatory receptor in the context of signalling through TCR, but contradicting results were published. In order to understand the role of CD161 in gluten‐reactive CD4 + T cells, we combined T cell stimulation assays or T cell proliferation assays with ligation of CD161 and intracellular cytokine staining. We found that CD161 ligation provided neither co‐stimulatory nor co‐inhibitory signals to modulate proliferation and IFN‐γ or IL‐21 production by gluten‐reactive CD4 + T cell clones. Thus, we suggest that CD161 does not function as a co‐signalling receptor in the context of gluten‐reactive CD4 + T cells.
Abstract
Nearly 350 IgG-based therapeutics are approved for clinical use or are under development for many diseases lacking adequate treatment options. These include molecularly engineered ...biologicals comprising the IgG Fc-domain fused to various effector molecules (so-called Fc-fusion proteins) that confer the advantages of IgG, including binding to the neonatal Fc receptor (FcRn) to facilitate in vivo stability, and the therapeutic benefit of the specific effector functions. Advances in IgG structure-function relationships and an understanding of FcRn biology have provided therapeutic opportunities for previously unapproachable diseases. This article discusses approved Fc-fusion therapeutics, novel Fc-fusion proteins and FcRn-dependent delivery approaches in development, and how engineering of the FcRn-Fc interaction can generate longer-lasting and more effective therapeutics.
Innate lymphoid cells (ILCs) are important for tissue immune homeostasis, and are thoroughly characterized in mice and humans. Here, we have performed in‐depth characterization of rat ILCs. Rat ILCs ...were identified based on differential expression of transcription factors and lack of lineage markers. ILC3s represented the major ILC population of the small intestine, while ILC2s were infrequent but most prominent in liver and adipose tissue. Two major subsets of group 1 ILCs were defined. Lineage–T‐bet+Eomes+ cells were identified as conventional NK cells, while lineage–T‐bet+Eomes– cells were identified as the probable rat counterpart of ILC1s based on their selective expression of the ILC marker CD200R. Rat ILC1s were particularly abundant in liver and intestinal tissues, and were functionally similar to NK cells. Single‐cell transcriptomics of spleen and liver cells confirmed the main division of NK cells and ILC1‐like cells, and demonstrated Granzyme A as an additional ILC1 marker. We further report differential distributions of NK cells and ILCs along the small and large intestines, and the association of certain bacterial taxa to frequencies of ILCs. In conclusion, we provide a framework for future studies of ILCs in diverse rat experimental models, and novel data on the potential interplay between commensals and intestinal ILCs.
Rat ILCs are here defined and characterized across multiple tissues, and we have defined the borders between rat NK cells and ILC1s via scRNA‐Seq. Variations of intestinal ILC frequencies in individual rats were mapped against composition of the commensal microbiota.
Celiac disease is caused by an abnormal intestinal T-cell response to gluten proteins of wheat, barley and rye. Over the last few years, a number of gluten T-cell epitopes restricted by celiac ...disease associated HLA-DQ molecules have been characterized. In this work, we give an overview of these epitopes and suggest a comprehensive, new nomenclature.
We created a TCR transgenic mouse with CD4+ T cells recognizing the immunodominant DQ2.5‐glia‐ω2 gluten epitope. We show that these cells respond to deamidated gluten feed in vivo and compare them to ...previously published α2‐ and γ1‐specific mice. These mice may help enlighten key aspects of celiac disease pathogenesis.
Locating diseases precisely from medical images, like ultrasonic and CT images, have been one of the most challenging problems in medical image analysis. In recent years, the vigorous development of ...deep learning models have greatly improved the accuracy in disease location on medical images. However, there are few artificial intelligent methods for identifying cholelithiasis and classifying gallstones on CT images, since no open source CT images dataset of cholelithiasis and gallstones is available for training the models and verifying their performance. In this paper, we build up the first medical image dataset of cholelithiasis by collecting 223846 CT images with gallstone of 1369 patients. With these CT images, a neural network is trained to "pick up" CT images of high quality as training set, and then a novel Yolo neural network, named Yolov3-arch neural network, is proposed to identify cholelithiasis and classify gallstones on CT images. Identification and classification accuracies are obtained by 10-fold cross-validations. It is obtained that our Yolov3-arch model is with average accuracy 92.7% in identifying granular gallstones and average accuracy 80.3% in identifying muddy gallstones. This achieves 3.5% and 8% improvements in identifying granular and muddy gallstones to general Yolo v3 model, respectively. Also, the average cholelithiasis identifying accuracy is improved to 86.50% from 80.75%. Meanwhile, our method can reduce the misdiagnosis rate of negative samples by the object detection model.
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