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1.
FEBS J ; 289(20): 6256-6266, 2022 10.
Article in English | MEDLINE | ID: mdl-34146376

ABSTRACT

Programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) interactions are critical for dampening the immune response to both self and foreign antigens. The signaling of PD-L1 via its cytoplasmic domain, rather than through its interactions with PD-1 via the extracellular domain, has been termed PD-L1 reverse signaling. While this signaling is beneficial for cancer progression, little is understood about the consequences of PD-L1 reverse signaling in immune cells that express PD-L1 at steady state or in response to infection. Loss of PD-L1 during infection leads to unchecked T-cell proliferation and increased autoimmune T-cell responses. While the T-cell intrinsic role of PD-1 for inhibiting T-cell responses has been well explored, little to no effort has been directed at investigating the consequences of PD-L1 reverse signaling on the DCs interacting with PD-1+ T cells. We recently reported a defect in dendritic cell (DC) trafficking from the skin to the draining lymph node (LN) following immunization or infection in the absence of PD-L1. We demonstrated that a region within the cytoplasmic tail was responsible for the defect in DC trafficking. Here, we review the processes involved in DC trafficking and highlight what we know about PD-L1 expression, PD-L1 post-translational modifications, PD-L1 intracellular interactions, and PD-L1 extracellular interactions.


Subject(s)
B7-H1 Antigen , Programmed Cell Death 1 Receptor , Dendritic Cells , Lymphocyte Activation
2.
Elife ; 102021 04 12.
Article in English | MEDLINE | ID: mdl-33843587

ABSTRACT

The detection of foreign antigens in vivo has relied on fluorescent conjugation or indirect read-outs such as antigen presentation. In our studies, we found that these widely used techniques had several technical limitations that have precluded a complete picture of antigen trafficking or retention across lymph node cell types. To address these limitations, we developed a 'molecular tracking device' to follow the distribution, acquisition, and retention of antigen in the lymph node. Utilizing an antigen conjugated to a nuclease-resistant DNA tag, acting as a combined antigen-adjuvant conjugate, and single-cell mRNA sequencing, we quantified antigen abundance in the lymph node. Variable antigen levels enabled the identification of caveolar endocytosis as a mechanism of antigen acquisition or retention in lymphatic endothelial cells. Thus, these molecular tracking devices enable new approaches to study dynamic tissue dissemination of antigen-adjuvant conjugates and identify new mechanisms of antigen acquisition and retention at cellular resolution in vivo.


The lymphatic system is a network of ducts that transports fluid, proteins, and immune cells from different organs around the body. Lymph nodes provide pit stops at hundreds of points along this network where immune cells reside, and lymph fluid can be filtered and cleaned. When pathogens, such as viruses or bacteria, enter the body during an infection, fragments of their proteins can get swept into the lymph nodes. These pathogenic proteins or protein fragments activate resident immune cells and kickstart the immune response. Vaccines are designed to mimic this process by introducing isolated pathogenic proteins in a controlled way to stimulate similar immune reactions in lymph nodes. Once an infection has been cleared by the immune system, or a vaccination has triggered the immune system, most pathogenic proteins get cleared away. However, a small number of pathogenic proteins remain in the lymph nodes to enable immune cells to respond more strongly and quickly the next time they see the same pathogen. Yet it is largely unclear how much protein remains for training and how or where it is all stored. Current techniques are not sensitive or long-lived enough to accurately detect and track these small protein deposits over time. Walsh, Sheridan, Lucas, et al. have addressed this problem by developing biological tags that can be attached to the pathogenic proteins so they can be traced. These tags were designed so the body cannot easily break them down, helping them last as long as the proteins they are attached to. Walsh, Sheridan, Lucas et al. tested whether vaccinating mice with the tagged proteins allowed the proteins to be tracked. The method they used was designed to identify individual cell types based on their genetic information along with the tag. This allowed them to accurately map the complex network of cells involved in storing and retrieving archived protein fragments, as well as those involved in training new immune cells to recognize them. These results provide important insights into the protein archiving system that is involved in enhancing immune memory. This may help guide the development of new vaccination strategies that can manipulate how proteins are archived to establish more durable immune protection. The biological tags developed could also be used to track therapeutic proteins, allowing scientists to determine how long cancer drugs, antibody therapies or COVID19 anti-viral agents remain in the body. This information could then be used by doctors to plan specific and personalized treatment timetables for patients.


Subject(s)
Antigens/metabolism , Lymph Nodes/metabolism , Single-Cell Analysis , Animals , Antigen Presentation , Antigens/genetics , Antigens/immunology , Caveolae/immunology , Caveolae/metabolism , Cells, Cultured , DNA/genetics , DNA/metabolism , Dendritic Cells/immunology , Dendritic Cells/metabolism , Endocytosis , Endothelial Cells/immunology , Endothelial Cells/metabolism , Lymph Nodes/immunology , Macrophages/immunology , Macrophages/metabolism , Mice, Inbred C57BL , Mice, Transgenic , Ovalbumin/genetics , Ovalbumin/immunology , Ovalbumin/metabolism , Peptide Fragments/genetics , Peptide Fragments/immunology , Peptide Fragments/metabolism , Phosphorothioate Oligonucleotides/genetics , Phosphorothioate Oligonucleotides/metabolism , RNA, Messenger/genetics , RNA, Messenger/metabolism , Sequence Analysis, RNA , Time Factors , Tissue Distribution , Transcriptome
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