AGR2, an Endoplasmic Reticulum Protein, Is Secreted into the Gastrointestinal Mucus

The MUC2 mucin is the major constituent of the two mucus layers in colon. Mice lacking the disulfide isomerase-like protein Agr2 have been shown to be more susceptible to colon inflammation. The Agr2(-/-) mice have less filled goblet cells and were now shown to have a poorly developed inner colon mucus layer. We could not show AGR2 covalently bound to recombinant MUC2 N- and C-termini as have previously been suggested. We found relatively high concentrations of Agr2 in secreted mucus throughout the murine gastrointestinal tract, suggesting that Agr2 may play extracellular roles. In tissue culture (CHO-K1) cells, AGR2 is normally not secreted. Replacement of the single Cys in AGR2 with Ser (C81S) allowed secretion, suggesting that modification of this Cys might provide a mechanism for circumventing the KTEL endoplasmic reticulum retention signal. In conclusion, these results suggest that AGR2 has both intracellular and extracellular effects in the intestine

Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-ω auto-Abs in children

We found that 19 (10.4%) of 183 unvaccinated children hospitalized for COVID-19 pneumonia had autoantibodies (auto-Abs) neutralizing type I IFNs (IFN-alpha 2 in 10 patients: IFN-alpha 2 only in three, IFN-alpha 2 plus IFN-omega in five, and IFN-alpha 2, IFN-omega plus IFN-beta in two; IFN-omega only in nine patients). Seven children (3.8%) had Abs neutralizing at least 10 ng/ml of one IFN, whereas the other 12 (6.6%) had Abs neutralizing only 100 pg/ml. The auto-Abs neutralized both unglycosylated and glycosylated IFNs. We also detected auto-Abs neutralizing 100 pg/ml IFN-alpha 2 in 4 of 2,267 uninfected children (0.2%) and auto-Abs neutralizing IFN-omega in 45 children (2%). The odds ratios (ORs) for life-threatening COVID-19 pneumonia were, therefore, higher for auto-Abs neutralizing IFN-alpha 2 only (OR [95% CI] = 67.6 [5.7-9,196.6]) than for auto-Abs neutralizing IFN-. only (OR [95% CI] = 2.6 [1.2-5.3]). ORs were also higher for auto-Abs neutralizing high concentrations (OR [95% CI] = 12.9 [4.6-35.9]) than for those neutralizing low concentrations (OR [95% CI] = 5.5 [3.1-9.6]) of IFN-omega and/or IFN-alpha 2

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Influence of xenobiotic contaminants on landfill soil microbial activity and diversity

6 páginas, 3 figuras y 3 tablasLandfills are often the final recipient of a range of environmentally important contaminants such as hydrocarbons, polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs). In this study the influence of these contaminants on microbial activity and diversity was assessed in a municipal solid waste (MSW) landfill placed in Torrejón de Ardoz (Madrid, Spain). Soil samples were collected from four selected areas (T2, T2B, T8 and T9) in which the amount of total hydrocarbons, PAHs and PCBs were measured. Soil biomass, substrate induced respiration (SIR) and physiological profiles of soil samples were also determined and used as indicators of total microbial activity. Highest concentration of total hydrocarbons was detected in T2 and T9 samples, with both PCBs and benzopyrene being detected in T9 sample. Results corresponding to microbial estimation (viable bacteria and fungi, and SIR) and microbiological enzyme activities showed that highest values corresponded to areas with the lowest concentration of hydrocarbons (T2B and T8). It is noticeable that in such areas was detected the lowest concentration of the pollutants PAHs and PCBs. A negative significant correlation between soil hydrocarbons concentration and SIR, total bacteria and fungi counts and most of the enzyme activities determined was established. DGGE analysis was also carried out to determine the microbial communities’ structure in the soil samples, establishing different profiles of Bacteria and Archaea communities in each analysed area. Through the statistical analysis a significant negative correlation was only found for Bacteria domain when Shannon index and hydrocarbon concentration were correlated. In addition, a bacterial 16S rRNA gene based clone library was prepared from each soil. From the clones analysed in the samples, the majority corresponded to Proteobacteria, followed by Acidobacteria and Actinobacteria. It is important to remark that the most polluted sample (T9) showed the lowest microbial diversity only formed by six phyla being Proteobacteria and Acidobacteria the most representative.This work has been supported by the Comunidad de Madrid EIADES project (S-0505/AMB/0296) and the CICYT project (CGL2006-12730-C03-02).Peer reviewe

Dynamic changes in mucus thickness and ion secretion during Citrobacter rodentium infection and clearance.

Citrobacter rodentium is an attaching and effacing pathogen used as a murine model for enteropathogenic Escherichia coli. The mucus layers are a complex matrix of molecules, and mucus swelling, hydration and permeability are affected by many factors, including ion composition. Here, we used the C. rodentium model to investigate mucus dynamics during infection. By measuring the mucus layer thickness in tissue explants during infection, we demonstrated that the thickness changes dynamically during the course of infection and that its thickest stage coincides with the start of a decrease of bacterial density at day 14 after infection. Although quantitative PCR analysis demonstrated that mucin mRNA increases during early infection, the increased mucus layer thickness late in infection was not explained by increased mRNA levels. Proteomic analysis of mucus did not demonstrate the appearance of additional mucins, but revealed an increased number of proteins involved in defense responses. Ussing chamber-based electrical measurements demonstrated that ion secretion was dynamically altered during the infection phases. Furthermore, the bicarbonate ion channel Bestrophin-2 mRNA nominally increased, whereas the Cftr mRNA decreased during the late infection clearance phase. Microscopy of Muc2 immunostained tissues suggested that the inner striated mucus layer present in the healthy colon was scarce during the time point of most severe infection (10 days post infection), but then expanded, albeit with a less structured appearance, during the expulsion phase. Together with previously published literature, the data implies a model for clearance where a change in secretion allows reformation of the mucus layer, displacing the pathogen to the outer mucus layer, where it is then outcompeted by the returning commensal flora. In conclusion, mucus and ion secretion are dynamically altered during the C. rodentium infection cycle

Change in membrane current (ΔIm) after stimulation with forskolin and carbachol with and without pre-treatment with the CFTR inhibitor GlyH-101.

<p>(mean±SEM, n = 6). As the Caco-2 cells did not respond to carbachol, the ability of GlyH-101 to inhibit the response was not analyzed in this cell line.</p

The basal electrochemical properties of the distal colonic explants measured in Ussing chambers during <i>C. rodentium</i> infection.

<p>Baseline PD (A), Im (B) and Rp (C) of distal colonic tissue during <i>C. rodentium</i> infection. Statistics: ANOVA, Dunnet’s post hoc test, * = p<0.05, ** = p<0.01.</p

mRNA levels of mucins and some related proteins in distal colon during <i>C. rodentium</i> infection.

<p>Muc1 (A), Muc2 (B), Muc4 (C), Muc13 (D), Clca-3 (E), Cftr (F), and Best-2 (G). Expression data were normalized against the Hprt-1 housekeeping gene. Fold changes were calculated using ΔΔCT with mean of CT from three uninfected mice as control.</p

<i>C. rodentium</i> density, colitis score and mucus thickness and growth during infection.

<p>A: Colony forming units (CFU) of <i>C. rodentium</i> were analyzed in fecal pellets collected from individual mice. Compared to non-infected, all time points had an increased amount of <i>C. rodentium</i> (p<0.05). The total amount of luminal bacteria in the distal colon was scored in DAPI stained sections: Score 3 = same density as non-infected animals, 2 = medium density, 1 = low density and 0 = no bacteria detected (*p<0.05). Statistics: ANOVA, Dunnet’s post hoc test. B: The thickness of mucus layer changed during infection with lowest thickness between day 4 to 10 and the highest at day 14 post infection, whereas goblet cells depletion and colitis increased to the highest score at day 10 and 14 post infection, respectively. C: The thickness of the inner mucus layer changed during the course of infection, and reached its highest thickness at the start of the decrease in fecal <i>C. rodentium</i> density (CFU/gram feces). The distal colon explant was mounted and the thickness measured with a scaled micropipette, p<0.05 # vs day 14 and 19, * vs day 0. C: The <i>ex vivo</i> growth of the adherent mucus layer measured for the first 15 minutes after mounting the distal colon tissue. No significant differences were observed between the different time points (n = 5–12). Statistics: ANOVA, Tukey’s post hoc test.</p

List of primers for SyberGreen based RT-PCR.

<p>List of primers for SyberGreen based RT-PCR.</p