Developmental programming in human umbilical cord vein endothelial cells following fetal growth restriction

Background: Fetal growth restriction (FGR) is associated with an increased susceptibility for various noncommunicable diseases in adulthood, including cardiovascular and renal disease. During FGR, reduced uteroplacental blood flow, oxygen and nutrient supply to the fetus are hypothesized to detrimentally influence cardiovascular and renal programming. This study examined whether developmental programming profiles, especially related to the cardiovascular and renal system, differ in human umbilical vein endothelial cells (HUVECs) collected from pregnancies complicated by placental insufficiency-induced FGR compared to normal growth pregnancies. Our approach, involving transcriptomic profiling by RNA-sequencing and gene set enrichment analysis focused on cardiovascular and renal gene sets and targeted DNA methylation assays, contributes to the identification of targets underlying long-term cardiovascular and renal diseases. Results: Gene set enrichment analysis showed several downregulated gene sets, most of them involved in immune or inflammatory pathways or cell cycle pathways. seven of the 22 significantly upregulated gene sets related to kidney development and four gene sets involved with cardiovascular health and function were downregulated in FGR (n = 11) versus control (n = 8). Transcriptomic profiling by RNA-sequencing revealed downregulated expression of LGALS1, FPR3 and NRM and upregulation of lincRNA RP5-855F14.1 in FGR compared to controls. DNA methylation was similar for LGALS1 between study groups, but relative hypomethylation of FPR3 and hypermethylation of NRM were present in FGR, especially in male offspring. Absolute differences in methylation were, however, small. Conclusion: This study showed upregulation of gene sets related to renal development in HUVECs collected from pregnancies complicated by FGR compared to control donors. The differentially expressed gene sets related to cardiovascular function and health might be in line with the downregulated expression of NRM and upregulated expression of lincRNA RP5-855F14.1 in FG

Degenerate T-cell Recognition of Peptides on MHC Molecules Creates Large Holes in the T-cell Repertoire

The cellular immune system screens peptides presented by host cells on MHC molecules to assess if the cells are infected. In this study we examined whether the presented peptides contain enough information for a proper self/nonself assessment by comparing the presented human (self) and bacterial or viral (nonself) peptides on a large number of MHC molecules. For all MHC molecules tested, only a small fraction of the presented nonself peptides from 174 species of bacteria and 1000 viral proteomes (0.2%) is shown to be identical to a presented self peptide. Next, we use available data on T-cell receptor-peptide-MHC interactions to estimate how well T-cells distinguish between similar peptides. The recognition of a peptide-MHC by the T-cell receptor is flexible, and as a result, about one-third of the presented nonself peptides is expected to be indistinguishable (by T-cells) from presented self peptides. This suggests that T-cells are expected to remain tolerant for a large fraction of the presented nonself peptides, which provides an explanation for the “holes in the T-cell repertoire” that are found for a large fraction of foreign epitopes. Additionally, this overlap with self increases the need for efficient self tolerance, as many self-similar nonself peptides could initiate an autoimmune response. Degenerate recognition of peptide-MHC-I complexes by T-cells thus creates large and potentially dangerous overlaps between self and nonself

The self/nonself overlap of immunogenic versus non-immunogenic pMHCs.

<p>For immunogenic or non-immunogenic HIV-1 peptides presented on HLA-A*0201 determined by Frankild et al. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002412#pcbi.1002412-Frankild1" target="_blank">[9]</a>, for immunogenic and non-immunogenic vaccinia-derived peptides determined by Assarsson et al. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002412#pcbi.1002412-Assarsson1" target="_blank">[10]</a>, for immunogenic and non-immunogenic HIV-1 peptides on non-HLA-A*0201 determined by Perez et al. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002412#pcbi.1002412-Perez1" target="_blank">[37]</a> and for immunogenic and non-immunogenic pMHCs sampled from the IEDB on HLA-A*0201 (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002412#s4" target="_blank">Methods</a> for selection criteria applied to all four data sets), the presence of a self/nonself overlap was determined with the degenerate T-cell recognition model. For all sets of peptides, the immunogenic peptides have less overlaps with self, the significance of this association was tested using a Chi-square test, the p-value is reported in the last column.</p

Summary of all the average self/nonself overlaps obtained using peptides predicted to be presented on HLA molecules.

<p>Overlaps were determined using all positions of the peptide (P1–9), the non-anchor positions (P1 and P3–8) or the middle positions between the anchors (P3–8). Further, overlaps were determined as exact, i.e. every position should be identical, or as degenerate, i.e. with 1 or 2 substitutions being allowed to mimic T-cell recognition (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002412#s4" target="_blank">Methods</a>). Finally, overlaps with 100% or (a randomly chosen) 50% of the human proteome are shown. Self/nonself overlaps indicated with a star (*) are shown per HLA molecule in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002412#pcbi-1002412-g002" target="_blank">Figure 2</a>.</p

Self/nonself overlaps of peptides presented on different HLA molecules.

<p>In A, the exact overlap of the complete peptide (positions 1–9). In B, the exact overlap of the middle positions of the peptide (positions 3–8) that are assumed to be in contact with the TCR. In C, the degenerate overlap of positions 3–8, i.e. a cross-reactive T-cell overlap. In all cases, the left and right figures show the self/nonself overlaps determined using a scaled or fixed MHC binding threshold, respectively (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002412#s4" target="_blank">Methods</a>). HLA molecules that have been described to have a GC-positive, GC-negative or GC-neutral preference <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002412#pcbi.1002412-Calis1" target="_blank">[1]</a> are colored green, red and black, respectively. HLA molecules with additional anchors (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002412#s4" target="_blank">Methods</a>) are indicated with a plus-sign.</p

Viral and bacterial self/nonself overlaps for peptides of different lengths.

<p>The chance that a bacterial or viral peptide overlaps with a peptide in the human proteome is shown as open and closed circles for bacteria and viruses, respectively. Stars indicate the self/nonself overlaps with shuffled bacterial (open stars) or viral (closed stars) proteins. For all peptides of 5 amino acids or longer, the overlap of unshuffled viruses and bacteria is significantly smaller than the shuffled (representing the expected) overlap (Ranksums test: p0.05).</p

Role of peptide processing predictions in T cell epitope identification : contribution of different prediction programs

Proteolysis is the general term to describe the process of protein degradation into peptides. Proteasomes are the main actors in cellular proteolysis, and their activity can be measured in in vitro digestion experiments. However, in vivo proteolysis can be different than what is measured in these experiments if other proteases participate or if proteasomal activity is different in vivo. The in vivo proteolysis can be measured only indirectly, by the analysis of peptides presented on MHC-I molecules. MHC-I presented peptides are protected from further degradation, thus enabling an indirect view on the underlying in vivo proteolysis. The ligands presented on different MHC-I molecules enable different views on this process; in combination, they might give a complete picture. Based on in vitro proteasome-only digestions and MHC-I ligand data, different proteolysis predictors have been developed. With new in vitro digestion and MHC-I ligand data sets, we benchmarked how well these predictors capture in vitro proteasome-only activity and in vivo whole-cell proteolysis, respectively. Even though the in vitro proteasome digestion patterns were best captured by methods trained on such data (ProteaSMM and NetChop 20S), the in vivo whole-cell proteolysis was best predicted by a method trained on MHC-I ligand data (NetChop Cterm). Follow-up analysis showed that the likely source of this difference is the activity from proteases other than the proteasome, such as TPPII. This non-proteasomal in vivo activity is captured by NetChop Cterm and should be taken into account in MHC-I ligand predictions

Developmental programming in human umbilical cord vein endothelial cells following fetal growth restriction

BACKGROUND: Fetal growth restriction (FGR) is associated with an increased susceptibility for various noncommunicable diseases in adulthood, including cardiovascular and renal disease. During FGR, reduced uteroplacental blood flow, oxygen and nutrient supply to the fetus are hypothesized to detrimentally influence cardiovascular and renal programming. This study examined whether developmental programming profiles, especially related to the cardiovascular and renal system, differ in human umbilical vein endothelial cells (HUVECs) collected from pregnancies complicated by placental insufficiency-induced FGR compared to normal growth pregnancies. Our approach, involving transcriptomic profiling by RNA-sequencing and gene set enrichment analysis focused on cardiovascular and renal gene sets and targeted DNA methylation assays, contributes to the identification of targets underlying long-term cardiovascular and renal diseases.RESULTS: Gene set enrichment analysis showed several downregulated gene sets, most of them involved in immune or inflammatory pathways or cell cycle pathways. seven of the 22 significantly upregulated gene sets related to kidney development and four gene sets involved with cardiovascular health and function were downregulated in FGR (n = 11) versus control (n = 8). Transcriptomic profiling by RNA-sequencing revealed downregulated expression of LGALS1, FPR3 and NRM and upregulation of lincRNA RP5-855F14.1 in FGR compared to controls. DNA methylation was similar for LGALS1 between study groups, but relative hypomethylation of FPR3 and hypermethylation of NRM were present in FGR, especially in male offspring. Absolute differences in methylation were, however, small.CONCLUSION: This study showed upregulation of gene sets related to renal development in HUVECs collected from pregnancies complicated by FGR compared to control donors. The differentially expressed gene sets related to cardiovascular function and health might be in line with the downregulated expression of NRM and upregulated expression of lincRNA RP5-855F14.1 in FGR samples; NRM is involved in cardiac remodeling, and lincRNAs are correlated with cardiovascular diseases. Future studies should elucidate whether the downregulated LGALS1 and FPR3 expressions in FGR are angiogenesis-modulating regulators leading to placental insufficiency-induced FGR or whether the expression of these genes can be used as a biomarker for increased cardiovascular risk. Altered DNA methylation might partly underlie FPR3 and NRM differential gene expression differences in a sex-dependent manner.</p