Peroxisomes are translocated into the <i>Chlamydia</i> inclusion during infection.

<p>A- HeLa cells were infected with <i>C. trachomatis</i> L2 for 20 h. The inclusion membrane was labeled with an anti-CT813 antibody (green), peroxisomes with an anti-ALDP antibody (red) and bacterial and nuclear DNA with Hoechst (Blue). A single ApoTome x-y section is shown in the central image. The z-x projection on the top shows the peroxisome indicated by a white arrowhead in the x-y image. Scale bar: 5 µm. B- HeLa cells were transfected with cytosolic GFP (shown in blue) to illuminate the entire cell <i>except</i> for the <i>Chlamydia</i> inclusion and were infected with <i>C. trachomatis</i> L2 for 20 h. Bacteria were labeled with an anti-Hsp60 antibody (green) and peroxisomes with an anti-ALDP (red). A single ApoTome x-y section is shown in the central image. z-x and z-y projections on the top and on the right side, respectively, are centered on the peroxisome indicated by a white arrowhead. Scale bar: 5 µm. C- One optical section from the stack of images shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086196#pone.0086196.s002" target="_blank">Movie S1</a>. Cells were prepared as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086196#pone-0086196-g001" target="_blank">Figure 1</a> B; the colors are different: bacteria are in blue, peroxisomes in red, GFP in green.</p

<i>C. trachomatis</i> contain bacteria-specific plasmalogens.

<p>A- Plasmalogens that increase in infected cells. Extracted ion chromatograms of plasmalogens of control and peroxisome-deficient (“PEX19”) fibroblasts, analyzed with RP LC-MS. The peaks of plasmalogens that increased upon 24 and 48 h of infection in control cells are shown. Notation, e.g. for PE(P-32:0): PE(P = plasmalogen version of phosphatidylethanolamine; 32:0 = 32 carbons in the combined side chains and 0 double bonds in the fatty acid. B- Plasmalogens in <i>C. trachomatis</i>. Phospholipids extracted from bacteria purified on a density gradient were analyzed as in B, with or without prior acid hydrolysis. The peaks with the same masses as in Panel B are shown. C- High resolution analysis and fragmentation of the plasmalogens of Panel C by SORI ICR/FT-MS². The fragmentation pattern of PE(P-33:0) is shown as an example. D- Plasmalogen structures of <i>C. trachomatis</i>. Detected fatty acids and inferred structures for the <i>Chlamydia</i>-derived plasmalogens. S/N: signal to noise ratio.</p

Many plasmalogens decrease upon infection.

<p>A- Structure of plasmalogens. Phosphatidylethanolamine (left) and the plasmalogen version of phosphatidylethanolamine (right) are depicted. The plasmalogen variant contains a long chain alcohol in place of a fatty acid and has a double bond between the first two carbons of the alcohol (1-O-alkenyl-2-acyl-<i>sn</i>-glycero-3-phosphoethanolamine; plasmenylethanolamine). B- Example of plasmalogens abundant in the non-infected control fibroblasts. Putative plasmalogens were identified based on their predicted formula as C_xH_2x–2yNO₇P (where y = the number of double bonds in the fatty acid) after ICR-FT/MS analysis (mass error <100 parts/billion, MassTRIX <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086196#pone.0086196-Suhre1" target="_blank">[46]</a>), and on their sensitivity to acid hydrolysis. Lipid species fulfilling these criteria were either missing or severely reduced in peroxisome-deficient cells (“PEX19”) compared to control fibroblasts (CTL), confirming that they are plasmalogens. Note that, globally, plasmalogen content decreased during infection, most notably at the later time point. Identification of plasmalogens was performed on samples from a single experiment. C- Abundance of control phospholipids. Phospholipids that gave the highest signal intensity in the ICR-FT/MS (corresponding to C42H82NO8P) were arbitrarily chosen as control. D- Total phospholipids detected with ICR-FT/MS. The intensities of all annotated phospholipids (mass error <1 ppm) were summed and normalized to the total sum of intensities in the sample. The data in C and D are representative of two independent experiments.</p

Peroxisomes are not essential for <i>C. trachomatis</i> infection and development.

<p>A- Control or PEX19-deficient fibroblasts were infected with <i>C. trachomatis</i> L2 for 24 h. Infected cells were fixed and labeled with an antibody against bacterial lipopolysaccharide (LPS) coupled to FITC (green) and DNA was stained with Hoechst (blue). Scale bar: 20 µm. B- The number of infection-forming units (IFU) was determined by titrating cell lysates at 48 hrs post infection on fresh HeLa cell monolayers. The average of three experiments is shown, error bars indicate standard deviation. C- Areas of inclusions. Results for two experiments are shown (Number of inclusions measured: experiment 1: control n = 111, PEX19-deficient n = 111; experiment 2: control n = 65, PEX19-deficient n = 76).</p

Peroxisomes are close to bacteria.

<p>A- Quantitative image analysis. A green polygon representing the Region Of Interest (ROI) was drawn over an optical section from the stack of images shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086196#pone.0086196.s002" target="_blank">Movie S1</a> (left image): bacteria are in blue, peroxisomes in red, GFP in green. Peroxisomes and bacteria detected within the ROI are circled in the middle and right images, respectively. Scale bar: 2 µm. B- Quantification of the distances between intra-inclusion peroxisomes and bacteria. The minimal distances between peroxisomes and bacteria within the ROI were calculated (from three different cells with respectively 6, 13 and 14 peroxisomes each, n = 33 peroxisomes in total) and the distribution of these distances is shown in the histogram. We calculated (see Methods) that a random distribution of bacteria and peroxisomes within the ROI would result in an average distance of 1.35 µm (p = 0.05, dotted line). The observed distribution is strongly shifted to the left and supports the hypothesis of a contact, or close proximity, between intra-inclusion peroxisomes and bacteria.</p

Urinary Retinol Binding Protein Is a Marker of the Extent of Interstitial Kidney Fibrosis

<div><p>Currently, a non-invasive method to estimate the degree of interstitial fibrosis (IF) in chronic kidney disease is not available in routine. The aim of our study was to evaluate the diagnostic performance of the measurement of urinary low molecular weight (LMW) protein concentrations as a method to determine the extent of IF. The urines specimen from 162 consecutive patients who underwent renal biopsy were used in the analysis. Numerical quantification software based on the colorimetric analysis of fibrous areas was used to assess the percentage IF. Total proteinuria, albuminuria, and the urinary levels of retinol binding protein (RBP), alpha1-microglobulin (α1MG), beta 2-microglobulin (β2MG), transferrin, and IgG immunoglobulins were measured. There was a significant correlation between the degree of IF and the RBP/creatinine (creat) ratio (R2: 0.11, p<0.0001). IF was associated to a lesser extent with urinary β2MG and α1MG; however, there was no association with total proteinuria or high molecular weight (HMW) proteinuria. The correlation between IF and RBP/creat remained significant after adjustment to the estimated glomerular filtration rate, age, body mass index, α1MG, and β2MG. The specificity of the test for diagnosing a fibrosis score of >25% of the parenchyma was 95% when using a threshold of 20 mg/g creat. In conclusion, RBP appears to be a quantitative and non-invasive marker for the independent prediction of the extent of kidney IF. Because methods for the measurement of urinary RBP are available in most clinical chemistry departments, RBP measurement is appealing for implementation in the routine care of patients with chronic kidney disease.</p></div

Correlation between the estimated glomerular filtration rate and extent of interstitial fibrosis.

<p><b>A</b>. Best-fit slope of the linear regression between eGFR and extent of fibrosis. <b>B</b>, <b>C</b>, <b>D</b>. Best-fit slope of the linear regression between eGFR and extent of fibrosis in glomerular disease (B), tubular disease (C), and vascular disease (D). <b>E</b>. Histogram of the distribution of the extent of interstitial fibrosis according to the type of kidney disease. The percentages of interstitial fibrosis are expressed as the mean ± sem. *p<0.05 compared to glomerular disease, using Student's T test; the Mann-Whitney test was used for the glomerular vs. non specific lesions comparison. Glom: glomerular diseases; Tub-Int: tubular and/or interstitial diseases; Vasc: vascular diseases; Non-spe: non-specific.</p

Baseline characteristics of the study cohort (n = 162).

<p>Continuous variables are expressed as the mean ± sem; categorical variables are expressed as n (%).</p><p>Hyperlipidemia: total cholesterol >200 mg/dl and/orTriglycerides >200 mg/dl, and/or LDL cholesterol>130 mg/dl.</p><p>Cancer: Multiple myemoma ( = 13), Urogenital ( = 7), Breast ( = 2), Lung ( = 1).</p><p>Non specific: no diagnosis, moderate interstitial fibrosis and/or glomerulosclerosis.</p

Distribution of estimated glomerular filtration rates.

<p><b>A</b>. Histogram showing the distribution of the study population according to the eGFR level at inclusion. <b>B</b>. Histogram showing eGFR values according to the type of kidney disease. eGFR is expressed as the mean ± sem. *p<0.05, **p<0.01, ***p<0.001 compared to glomerular disease using Student's T test; the Mann-Whitney test was used for the glomerular vs. comparison with non-specific lesions. Glom: glomerular diseases; Tub-Int: tubular and/or interstitial diseases; Vasc: vascular diseases; Non-spe: non-specific.</p

Correlation between proteinuria and the extent of interstitial fibrosis.

<p><b>A</b>, <b>B</b>. Best-fit slope of the linear regression between RBP (A) and Alb (B) and extent of fibrosis extent.</p