In vivo visualization and quantification of collecting lymphatic vessel contractility using near-infrared imaging

Techniques to image lymphatic vessel function in either animal models or in the clinic are limited. In particular, imaging methods that can provide robust outcome measures for collecting lymphatic vessel function are sorely needed. In this study, we aimed to develop a method to visualize and quantify collecting lymphatic vessel function in mice, and to establish an in vivo system for evaluation of contractile agonists and antagonists using near-infrared fluorescence imaging. The flank collecting lymphatic vessel in mice was exposed using a surgical technique and a near-infrared tracer was infused into the inguinal lymph node. Collecting lymphatic vessel contractility and valve function could be easily visualized after the infusion. A diameter tracking method was established and the diameter of the vessel was found to closely correlate to near-infrared fluorescence signal. Phasic contractility measures of frequency and amplitude were established using an automated algorithm. The methods were validated by tracking the vessel response to topical application of a contractile agonist, prostaglandin F2α, and by demonstrating the potential of the technique for non-invasive evaluation of modifiers of lymphatic function. These new methods will enable high-resolution imaging and quantification of collecting lymphatic vessel function in animal models and may have future clinical applications

Regulation of lymphangiogenesis in the diaphragm by macrophages and VEGFR-3 signaling.

Lymphatic vessels play important roles in fluid drainage and in immune responses, as well as in pathological processes including cancer progression and inflammation. While the molecular regulation of the earliest lymphatic vessel differentiation and development has been investigated in much detail, less is known about the control and timing of lymphatic vessel maturation in different organs, which often occurs postnatally. We investigated the time course of lymphatic vessel development on the pleural side of the diaphragmatic muscle in mice, the so-called submesothelial initial diaphragmatic lymphatic plexus. We found that this lymphatic network develops largely after birth and that it can serve as a reliable and easily quantifiable model to study physiological lymphangiogenesis in vivo. Lymphangiogenic growth in this tissue was highly dependent on vascular endothelial growth factor receptor (VEGFR)-3 signaling, whereas VEGFR-1 and -2 signaling was dispensable. During diaphragm development, macrophages appeared first in a linearly arranged pattern, followed by ingrowth of lymphatic vessels along these patterned lines. Surprisingly, ablation of macrophages in colony-stimulating factor-1 receptor (Csf1r)-deficient mice and by treatment with a CSF-1R-blocking antibody did not inhibit the general lymphatic vessel development in the diaphragm but specifically promoted branch formation of lymphatic sprouts. In agreement with these findings, incubation of cultured lymphatic endothelial cells with conditioned medium from P7 diaphragmatic macrophages significantly reduced LEC sprouting. These results indicate that the postnatal diaphragm provides a suitable model for studies of physiological lymphangiogenic growth and maturation, and for the identification of modulators of lymphatic vessel growth

Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity

<div><p>The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any <i>a priori</i> knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for <i>in silico</i> predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal distant modulation of the binding specificity at P2 for some HLA-I alleles by residues in the HLA-I binding site but outside of the B-pocket and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and <i>in vitro</i> binding assays.</p></div

Ranking of the neo-antigens identified in four melanoma samples [17,20].

<p>Column 2 shows the mutated neo-antigens (the mutated residue is highlighted in bold). Column 5 shows the ranking based on our predictions (i.e. number of peptide to be tested to find this neo-antigen). Columns 6 to 8 show the ranking based on NetMHC [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.ref008" target="_blank">8</a>], NetMHCpan [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.ref012" target="_blank">12</a>] and NetMHCstabpan [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.ref036" target="_blank">36</a>], respectively. The last column shows the total number of neo-antigen candidates (i.e., all possible 9- and 10-mers encompassing all missense mutations).</p

Comparison between motifs predicted by our algorithm and known motifs.

<p><b>A</b>: Comparison with IEDB motifs for 44 HLA-I binding motifs identified with the fully unsupervised approach. Alleles without previously documented ligands are highlighted in red. For HLA-B56:01, the three known ligands are shown. <b>B</b>: Comparison with motifs obtained from mono-allelic cell lines [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.ref031" target="_blank">31</a>]. <b>C</b>: Motif identified with the semi-supervised approach.</p

Analysis of newly identified HLA-I motifs.

<p><b>A</b>: Structural view of two different HLA-I alleles with N90 as in HLA-A02:20 (PDB: 2BVQ [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.ref040" target="_blank">40</a>], pink sidechains) or K90 as in HLA-A02:01 (PDB: 2BNR [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.ref041" target="_blank">41</a>], green sidechains). For clarity, the α₁ helix has been truncated. <b>B</b>: B pocket residues’ conservation across HLA-I alleles displaying preference for histidine at P2. The last line shows the sequence of HLA-B14:02, which does not show histidine preference at P2 (see motif in <b>C</b>), but has the same B pocket as HLA-B15:18. The last column shows amino acids at position 97, which is not part of the B pocket. <b>C</b>: Structural view of HLA-B14:02 in complex with a peptide with arginine at P2 (PDB: 3BVN [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.ref042" target="_blank">42</a>]). Residues not conserved between HLA-B15:18 and HLA-B14:02 are displayed in orange. None of them are making direct contact with the arginine residue at P2. <b>D</b>: Stability values (half-lives) obtained for peptides with H or R at P2 for both HLA-B14:02 wt and W97R mutant. NB stands for no binding. Dashed lines indicate lower bounds for half-lives values. Residue numbering follows the one used in most X-ray structures in the PDB.</p

General pipeline for HLA-I motif identification and annotation, and training of predictors.

<p>High accuracy HLA peptidomics data were first generated for 10 samples and collected from publicly available data for 40 other samples. In each sample motifs were identified using on our recent mixture model algorithm [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.ref024" target="_blank">24</a>]. Motifs were then annotated to their respective allele based on co-occurrence of alleles across samples (e.g., first HLA-A24:02, then HLA-A01:01 and HLA-C06:02, see also Fig B in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.s001" target="_blank">S1 Supporting Information</a> for another example). Finally all peptides assigned to each motif were pooled together to train our new HLA-I ligand predictor for each HLA-I allele (MixMHCpred v1.0).</p

Comparison between our predictor (MixMHCpred1.0) and existing tools.

<p><b>A</b>: Fraction of the true positives among the top 1% predictions (PP1%) for the naturally presented HLA-I ligand identified in mono-allelic cell lines, with 99-fold excess of decoy peptides. Of note, PP1% is equivalent to the both Precision and Recall, since the number of actual positives is the same as the number of predicted positives. <b>B-C</b>: Graphical representation of results in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.t001" target="_blank">Table 1</a> and Table C in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005725#pcbi.1005725.s001" target="_blank">S1 Supporting Information</a>. Panel B shows the AUC values and panel C the fraction of neo-antigens predicted in the top 1% of predictions (which typically corresponds to what is experimentally tested for immunogenicity). <b>D-E</b>: Predictions of Cancer Testis Antigens from the CTDatabase. Panel D shows AUC values and panel E shows PP1%. Truncated y-axes are explicitly indicated.</p

Correlation between amino acids frequencies at positions P4 to P7 in our HLA peptidomics data and in the human proteome.

<p>Correlation between amino acids frequencies at positions P4 to P7 in our HLA peptidomics data and in the human proteome.</p

Impaired Aryl Hydrocarbon Receptor Ligand Production by the Gut Microbiota Is a Key Factor in Metabolic Syndrome

In pressInternational audienceThe extent to which microbiota alterations define or influence the outcome of metabolic diseases is still unclear, but the byproducts of microbiota metabolism are known to have an important role in mediating the host-microbiota interaction. Here, we identify that in both pre-clinical and clinical settings, metabolic syndrome is associated with the reduced capacity of the microbiota to metabolize tryptophan into derivatives that are able to activate the aryl hydrocarbon receptor. This alteration is not merely an effect of the disease as supplementation with AhR agonist or a Lactobacillus strain, with a high AhR ligand-production capacity, leads to improvement of both dietary- and genetic-induced metabolic impairments, particularly glucose dysmetabolism and liver steatosis, through improvement of intestinal barrier function and secretion of the incretin hormone GLP-1. These results highlight the role of gut microbiota-derived metabolites as a biomarker and as a basis for novel preventative or therapeutic interventions for metabolic disorders