👤 Svenja Ebeling

Single Particle Tracking (SPT) is a powerful technique for elucidating the dynamic behaviours of macromolecules within live cells. However, SPT's application to subcellular environments is hampered by the error-proneness of tracking at high particle velocities and densities and the lack of tools to assess trajectory reliability. Here, we introduce FidlTrack, a methodology that benchmarks and improves SPT fidelity. It contains three modules: a parameter optimiser that uses synthetic ground truth SPT data to determine the fidelity-maximising experimental and tracking settings; Structure-aware tracking, that exploits the information provided by organelle structures to constrain particle tracking algorithms; And a tracking quality evaluator that detects, quantifies and removes error-prone ambiguous track segments. Together these tools allow the rational design of SPT experiments, resolving the motion in tight and convoluted organelles, and provide up to 2-fold enrichment in accurate data. We showcase FidlTrack's utility for reliably tracking proteins in the cytosol, mitochondria and endoplasmic reticulum (ER). Further, we demonstrate its efficacy by analysing ER protein dynamics at exit sites, resolving BACE1 amyloidogenic cleavage of the amyloid precursor protein and characterising the spatiotemporal binding dynamics of an ER-targeted intrabody. FidlTrack is provided as a universal open-access platform that can be incorporated into any SPT pipeline. Show less

We screened variants on an exome-focused genotyping array in >300,000 participants (replication in >280,000 participants) and identified 444 independent variants in 250 loci significantly associated with total cholesterol (TC), high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), and/or triglycerides (TG). At two loci (JAK2 and A1CF), experimental analysis in mice showed lipid changes consistent with the human data. We also found that: (i) beta-thalassemia trait carriers displayed lower TC and were protected from coronary artery disease (CAD); (ii) excluding the CETP locus, there was not a predictable relationship between plasma HDL-C and risk for age-related macular degeneration; (iii) only some mechanisms of lowering LDL-C appeared to increase risk for type 2 diabetes (T2D); and (iv) TG-lowering alleles involved in hepatic production of TG-rich lipoproteins (TM6SF2 and PNPLA3) tracked with higher liver fat, higher risk for T2D, and lower risk for CAD, whereas TG-lowering alleles involved in peripheral lipolysis (LPL and ANGPTL4) had no effect on liver fat but decreased risks for both T2D and CAD. Show less

We aimed to determine differentially expressed genes relevant to orbital inflammation and orbital fat expansion in thyroid orbitopathy (TO) using microarray gene profiling in a case-control study. Human orbital adipose samples were obtained from individuals with active TO (n = 12), inactive TO (n = 21), and normal controls (n = 21). Gene expression profiles were examined using microarray analysis and were compared between active and inactive TO, and between active TO and normal controls. Top ranked differentially expressed genes were validated by real-time RT-PCR in an additional eight active TO, 13 inactive TO, and 11 normal controls and correlated with gene set enrichment analysis (GSEA) and molecular pathways analysis. Seven hundred twenty-one probes (683 genes) and 806 probes (735 genes) were significantly differentially expressed in comparing active to inactive TO and in comparing active TO to healthy controls, respectively. All selected genes were confirmed to be differentially expressed by real-time RT-PCR. Multiple top ranked genes in active versus inactive TO comparison are overrepresented by immune and inflammatory response genes. They include defensins (DEFA1, DEFA1B, DEFA3), which were overexpressed by 3.05- to 4.14-fold and TIMD4 by 4.20-fold. Markers for adipogenesis were overexpressed including SCD, FADS1, and SCDP1. Gene set enrichment analysis revealed dysregulation of epigenetic signatures, T-cell activation, Th1 differentiation, defensin pathway, cell adhesion, cytoskeleton organization, apoptosis, cell cycling, and lipid metabolism in active TO. Active TO is characterized by upregulation of genes involved in cell-mediated immune, innate immune, and inflammatory response and enhanced orbital adipogenesis. TIMD4, DEFA1, DEFA1B, and DEFA3 genes may be involved in the innate immune-mediated orbital inflammation in TO. Epigenetic mechanisms may play a role in the pathogenesis of TO. Show less