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The visceral endoderm is a polarized epithelial monolayer necessary for

The cross-talk between blood proteins, immune cells, and brain function involves complex mechanisms. Plasma protein C1 inhibitor (C1INH) is an inhibitor of vascular inflammation that is induced by activation of the kallikrein-kinin system (KKS) and the complement system. Knockout of C1INH was previously correlated with peripheral vascular permeability via the bradykinin pathway, yet there was no evidence of its correlation with blood-brain barrier (BBB) integrity and brain function. In order to understand the effect of plasma C1INH on brain pathology via the vascular system, we knocked down circulating C1INH in wild-type (WT) mice using an antisense oligonucleotide (ASO), without affecting C1INH expression in peripheral immune cells or the brain, and examined brain pathology. Long-term elimination of endogenous C1INH in the plasma induced the activation of the KKS and peritoneal macrophages but did not activate the complement system. Bradykinin pathway proteins were elevated in the periphery and the brain, resulting in hypotension. BBB permeability, extravasation of plasma proteins into the brain parenchyma, activation of glial cells, and elevation of pro-inflammatory response mediators were detected. Furthermore, infiltrating innate immune cells were observed entering the brain through the lateral ventricle walls and the neurovascular unit. Mice showed normal locomotion function, yet cognition was impaired and depressive-like behavior was evident. In conclusion, our results highlight the important role of regulated plasma C1INH as it acts as a gatekeeper to the brain via the neurovascular system. Thus, manipulation of C1INH in neurovascular disorders might be therapeutically beneficial.

MYC2 is well known as a positive regulator for abscisic acid (ABA)

To define the cell populations that drive joint inflammation in rheumatoid arthritis (RA), we applied single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA sequencing (RNA-seq) and flow cytometry to Tcells, B cells, monocytes, and fibroblasts from 51 samples of synovial tissue from patients with RA or osteoarthritis (OA). Utilizing an integrated strategy based on canonical correlation analysis of 5,265 scRNA-seq profiles, we identified 18 unique cell populations. Combining mass cytometry and transcriptomics revealed cell states expanded in RA synovia: THY1(CD90)(+)HLA-DRA(hi) sublining fibroblasts, IL1B(+) pro-inflammatory monocytes, ITGAX(+)TBX21(+) autoimmune-associated B cells and PDCD1(+) peripheral helper T (T-PH) cells and follicular helper T (T-FH) cells. We defined distinct subsets of CD8(+)Tcells characterized by GZMIK(+), GZMB(+), and GNLY(+) phenotypes. We mapped inflammatory mediators to their source cell populations; for example, we attributed 1L6 expression to THYl(+)HLA-DRA(hi )fibroblasts and IL1B production to pro-inflammatory monocytes. These populations are potentially key mediators of RA pathogenesis.

Linked Article: Albrecht et al. Br J Dermatol 2019; 180:749-55.

Eukaryotic elongation factor 2 kinase (eEF-2K), an atypical

Cytoplasmic dynein is the most complex cytoskeletal motor protein and is

Objective: Nighttime wakening with asthma symptoms is a key to assessment and therapy decisions, with no gold standard objective measure. The study aims were to (1) determine the feasibility, (2) explore equivalence, and (3) test concordance of a consumer-based accelerometer with standard actigraphy for measurement of sleep patterns in women with asthma as an adjunct to self-report. Methods: Panel study design of women with poorly controlled asthma from a university-affiliated primary care clinic system was used. We assessed sensitivity and specificity, equivalence and concordance of sleep time, sleep efficiency, and wake counts between the consumer-based accelerometer Fitbit Charge (TM) and Actigraph wGT3X+. We linked data between devices for comparison both automatically by 24-hour period and manually by sleep segment. Results: Analysis included 424 938 minutes, 738 nights, and 833 unique sleep segments from 47 women. The fitness tracker demonstrated 97% sensitivity and 40% specificity to identify sleep. Between device equivalence for total sleep time (15 and 42-minute threshold) was demonstrated by sleep segment. Concordance improved for wake counts and sleep efficiency when adjusting for a linear trend. Conclusions: There were important differences in total sleep time, efficiency, and wake count measures when comparing individual sleep segments versus 24-hour measures of sleep. Fitbit overestimates sleep efficiency and underestimates wake counts in this population compared to actigraphy. Low levels of systematic bias indicate the potential for raw measurements from the devices to achieve equivalence and concordance with additional processing, algorithm modification, and modeling. Fitness trackers offer an accessible and inexpensive method to quantify sleep patterns in the home environment as an adjunct to subjective reports, and require further informatics development.