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Light chain-associated amyloidosis is characterized by the extracellular deposition of amyloid fibrils in abdominothoracic organs, skin, soft tissue, and peripheral nerves. Phagocytic cells of the innate immune system appear to be ineffective at clearing the material; however, human light chain amyloid extract, injected subcutaneously into mice, is rapidly cleared in a process that requires neutrophil activity. To better elucidate the phagocytosis of light chain fibrils, a potential method of cell-mediated dissolution, amyloid-like fibrils were labeled with the pH-sensitive dye pHrodo red and a near infrared fluorophore. After injecting this material subcutaneously in mice, optical imaging was used to quantitatively monitor phagocytosis and dissolution of fibrils concurrently. Histologic evaluation of the residual fibril masses revealed the presence of CD68+, F4/80+, ionized calcium binding adaptor molecule 1- macrophages containing Congo red-stained fibrils as well as neutrophil-associated proteins with no evidence of intact neutrophils. These data suggest an early infiltration of neutrophils, followed by extensive phagocytosis of the light chain fibrils by macrophages, leading to dissolution of the mass. Optical imaging of this novel murine model, coupled with histologic evaluation, can be used to study the cellular mechanisms underlying dissolution of synthetic amyloid-like fibrils and human amyloid extracts. In addition, it may serve as a test bed to evaluate investigational opsonizing agents that might serve as therapeutic agents for light chain-associated amyloidosis.
Pubmed ID: 30735627
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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