PTPN23 binds the dynein adaptor BICD1 and is required for endocytic sorting of neurotrophin receptors

Signalling by target-derived neurotrophins is essential for the correct development of the nervous system and its maintenance throughout life. Several aspects concerning the lifecycle of neurotrophins and their receptors have been characterised over the years, including formation of signalling-competent ligand-receptor complexes, their endocytosis and trafficking. However, the molecular mechanisms directing the sorting of activated neurotrophin receptors are still elusive. Previously, our laboratory identified Bicaudal-D1 (BICD1), a dynein motor adaptor, as a key factor for lysosomal degradation of brain-derived neurotrophic factor (BDNF) -activated TrkB and p75NTR in motor neurons. Here, using a proteomic approach, we identified protein tyrosine phosphatase, non-receptor type 23 (PTPN23), a member of the endosomal sorting complexes required for transport (ESCRT) machinery, in the BICD1 interactome. Molecular mapping revealed that PTPN23 is not a canonical BICD1 cargo; instead, PTPN23 binds the N-terminus of BICD1, which is also essential for the recruitment of cytoplasmic dynein. In line with the BICD1 knockdown phenotype, loss of PTPN23 leads to increased accumulation of BDNF-activated p75NTR and TrkB in swollen vacuole-like compartments, suggesting that neuronal PTPN23 is a novel regulator of the endocytic sorting of neurotrophin receptors

Virus Genotype-Dependent Transcriptional Alterations in Lipid Metabolism and Inflammation Pathways in the Hepatitis C Virus-infected Liver

Despite advances in antiviral therapy, molecular drivers of Hepatitis C Virus (HCV)-related liver disease remain poorly characterised. Chronic infection with HCV genotypes (1 and 3) differ in presentation of liver steatosis and virological response to therapies, both to interferon and direct acting antivirals. To understand what drives these clinically important differences, liver expression profiles of patients with HCV Genotype 1 or 3 infection (n = 26 and 33), alcoholic liver disease (n = 8), and no liver disease (n = 10) were analysed using transcriptome-wide microarrays. In progressive liver disease, HCV genotype was the major contributor to altered liver gene expression with 2151 genes differentially expressed >1.5-fold between HCV Genotype 1 and 3. In contrast, only 6 genes were altered between the HCV genotypes in advanced liver disease. Induction of lipogenic, lipolytic, and interferon stimulated gene pathways were enriched in Genotype 1 injury whilst a broad range of immune-associated pathways were associated with Genotype 3 injury. The results are consistent with greater lipid turnover in HCV Genotype 1 patients. Moreover, the lower activity in inflammatory pathways associated with HCV genotype 1 is consistent with relative resistance to interferon-based therapy. This data provides a molecular framework to explain the clinical manifestations of HCV-associated liver disease

Wplyw temperatury na przezywalnosc paleczek Salmonella w osadach sciekowych

Badania wykazały, że najdłużej przeżywały pałeczki S. senftenberg w temperaturze 4°C. Dzienny czas przeżycia określony na podstawie równań prostych regresji był zbliżony dla obydwóch rodzajów osadów i wynosił od 28,13 do 28,33. W temperaturze 20°C pałeczki były izolowane przez 24,87 dni w osadzie po fermentacji kwaśnej, natomiast w osadzie fermentacji metanowej izolowano je 26,13 dni. Szybkość obumierania komórek Salmonella w temperaturze 30°C wynosiła-0,74 log/dzień (osad po fermentacji kwaśnej) i -0,87 log/dzień (osad po fermentacji metanowej), natomiast w temperaturze 40°C tempo eliminacji pałeczek Salmonella wynosiło odpowiednio -1,39 i -1,57 log/dzień, przy wysoko istotnych współczynnikach korelacji.The research indicates that the rods of S. senftenberg at 4°C survived for the longest time. Daily survival time determined on the basis of regression line equations was similar for both types of sludge and varied from 28.13 to 28.33. At 20°C the rods were isolated for 24.87 days in the sludge after acid fermentation, while in the sludge of methane fermentation they were isolated for 26.13 days. Elimination rate of Salmonella cells at 30°C amounted to -0.74 log/day (sludge after acid fermentation) and -0.87 log/day (sludge after methane fermentation), while at 40°C the elimination rate of Salmonella rods amounted to -1.39 and -1.57 log/day, respectively, with highly significant correlation coefficients

Accumulation of Deleterious Passenger Mutations Is Associated with the Progression of Hepatocellular Carcinoma

<div><p>In hepatocellular carcinoma (HCC), somatic genome-wide DNA mutations are numerous, universal and heterogeneous. Some of these somatic mutations are drivers of the malignant process but the vast majority are passenger mutations. These passenger mutations can be deleterious to individual protein function but are tolerated by the cell or are offset by a survival advantage conferred by driver mutations. It is unknown if these somatic deleterious passenger mutations (DPMs) develop in the precancerous state of cirrhosis or if it is confined to HCC. Therefore, we studied four whole-exome sequencing datasets, including patients with non-cirrhotic liver (n = 12), cirrhosis without HCC (n = 6) and paired HCC with surrounding non-HCC liver (n = 74 paired samples), to identify DPMs. After filtering out putative germline mutations, we identified 187±22 DPMs per non-diseased tissue. DPMs number was associated with liver disease progressing to HCC, independent of the number of exonic mutations. Tumours contained significantly more DPMs compared to paired non-tumour tissue (258–293 per HCC exome). Cirrhosis- and HCC-associated DPMs do not occur predominantly in specific genes, chromosomes or biological pathways and the effect on tumour biology is presently unknown. Importantly, for the first time we have shown a significant increase in DPMs with HCC.</p></div

Frequency distribution of DPMs.

<p>A frequency distribution of the genes containing DPMs in 1000G and WES 1 (A), WES 2 (B), WES 3 (C), and WES 4 (D) shows that most are unique to a given patient. Each gene containing a DPM was grouped based on the number of patients in which that gene contained a DPM (x-axis).</p

Absolute number of exonic variants and mutation subtypes in 1000G, liver injury, cirrhosis and HCC.

<p>The exonic variants in each of the 5 datasets were enumerated (A and B) and then subdivided into 5 groups (missense, frameshift ins/del, stop-gain/-loss and non-frameshift ins/del) (C and D, expressed as a percentage of all somatic exonic mutations). 1000G and WES 1 (A and C) contain unpaired samples, while WES 2–4 (B and D) are composed of paired tumour and non-tumour samples taken from the same individual. Data are expressed as median (interquartile range). * p<0.05, ** p<0.01, *** p<0.001 and **** p<0.0001, Mann-Whitney U test (1000G and WES 1) or Wilcoxon matched-pairs signed-rank test (WES 2–4). NC-non-cirrhosis; C-cirrhosis; NT-non-tumour; T-tumour.</p

Bioinformatics analysis pipeline.

<p>Each resultant data file is indicated by a sloped rectangle and each process represented by a square rectangle. Our pipeline contains 3 stages: alignment and calibration; variant calling and filtering; and variants annotation and filtration of putative germline mutations.</p

Hypothetical model of HCC progression.

<p>HCC progression is presented here as multiple waves of driver sweeps within hepatocyte subclones. The equilibrium between DPM accumulation and negative selection on the hepatocyte subclones are shown in the top row. A schematic model of the liver (with each circle representing a hepatocyte and the colour gradient representing the DPM load within each hepatocyte) is shown in the centre row. The average DPM load for the tissue is depicted in the bottom row.</p