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BackgroundDecompensated cirrhosis patients are more prone to bacterial infections. Myeloid-derived suppressor cells (MDSCs) expand in sepsis patients and disrupt immune cell functions. Granulocyte-macrophage colony-stimulating factor (GM-CSF) therapy helps in restoring immune cell functions and resolving infections. Its role in MDSC modulation in cirrhosis with sepsis is not well understood.MethodsA total of 164 decompensated cirrhotic—62 without (w/o), 72 with sepsis, and 30 with sepsis treated with GM-CSF—and 15 healthy were studied. High-dimensional flow cytometry was performed to analyze MDSCs, monocytes, neutrophils, CD4 T cells, and Tregs at admission and on days 3 and day 7. Ex vivo co-cultured MDSCs with T cells were assessed for proliferation and apoptosis of T cells and differentiation to Tregs. Plasma factors and mRNA levels were analyzed by cytokine-bead assay and qRT-PCR.ResultsFrequencies of MDSCs and Tregs were significantly increased (p = 0.011 and p = 0.02) with decreased CD4 T cells (p = 0.01) in sepsis than w/o sepsis and healthy controls (HCs) (p = 0.000, p = 0.07, and p = 0.01) at day 0 and day 7. In sepsis patients, MDSCs had increased IL-10, Arg1, and iNOS mRNA levels (p = 0.016, p = 0.043, and p = 0.045). Ex vivo co-cultured MDSCs with T cells drove T-cell apoptosis (p = 0.03, p = 0.03) with decreased T-cell proliferation and enhanced FOXP3+ expression (p = 0.044 and p = 0.043) in sepsis compared to w/o sepsis at day 0. Moreover, blocking the MDSCs with inhibitors suppressed FOXP3 expression. GM-CSF treatment in sepsis patients significantly decreased MDSCs and FOXP3+ Tregs but increased CD4 T-cell functionality and improved survival.ConclusionMDSCs have an immunosuppressive function by expanding FOXP3+ Tregs and inhibiting CD4+ T-cell proliferation in sepsis. GM-CSF treatment suppressed MDSCs, improved T-cell functionality, and reduced Tregs in circulation.

SIRT7 belongs to the family of “NAD+ dependent deacetylases” called Sirtuins. In the present work we report a novel role of SIRT7 in regulating cellular polarity. SIRT7 overexpression in immortalized mouse fibroblasts (NIH3T3) induced epithelial transition. This transition was accompanied by typical N- to E- cadherin transition, stabilization of β-catenin, and the downregulation of transcription factors responsible for maintenance of mesenchymal phenotype (Snail, Slug, and Zeb1). Interestingly, a subpopulation of cells overexpressing SIRT7 exhibited an intermediate stage between mesenchymal and epithelial characters. Transformed epithelial cells showed a loss of heterochromatisation as evidenced by a loss of HP1α and H3K9 dimethylation staining. In conclusion, we report a role of SIRT7 in mesenchymal cells, which may have implications for health and disease.

T-cell specificity to differentiate between self and non-self relies on T-cell receptor (TCR) recognition of peptides presented by the Major Histocompatibility Complex (MHC). Investigations into the three-dimensional (3D) structures of peptide:MHC (pMHC) complexes have provided valuable insights of MHC functions. Given the limited availability of experimental pMHC structures and considerable diversity of peptides and MHC alleles, it calls for the development of efficient and reliable computational approaches for modeling pMHC structures. Here we present an update of PANDORA and the systematic evaluation of its performance in modelling 3D structures of pMHC class II complexes (pMHC-II), which play a key role in the cancer immune response. PANDORA is a modelling software that can build low-energy models in a few minutes by restraining peptide residues inside the MHC-II binding groove. We benchmarked PANDORA on 136 experimentally determined pMHC-II structures covering 44 unique αβ chain pairs. Our pipeline achieves a median backbone Ligand-Root Mean Squared Deviation (L-RMSD) of 0.42 Å on the binding core and 0.88 Å on the whole peptide for the benchmark dataset. We incorporated software improvements to make PANDORA a pan-allele framework and improved the user interface and software quality. Its computational efficiency allows enriching the wealth of pMHC binding affinity and mass spectrometry data with 3D models. These models can be used as a starting point for molecular dynamics simulations or structure-boosted deep learning algorithms to identify MHC-binding peptides. PANDORA is available as a Python package through Conda or as a source installation at https://github.com/X-lab-3D/PANDORA.

Predicting pathogenicity of missense variants in molecular diagnostics remains a challenge despite the available wealth of data, such as evolutionary information, and the wealth of tools to integrate that data. We describe DeepRank-Mut, a configurable framework designed to extract and learn from physicochemically relevant features of amino acids surrounding missense variants in 3D space. For each variant, various atomic and residue-level features are extracted from its structural environment, including sequence conservation scores of the surrounding amino acids, and stored in multi-channel 3D voxel grids which are then used to train a 3D convolutional neural network (3D-CNN). The resultant model gives a probabilistic estimate of whether a given input variant is disease-causing or benign. We find that the performance of our 3D-CNN model, on independent test datasets, is comparable to other widely used resources which also combine sequence and structural features. Based on the 10-fold cross-validation experiments, we achieve an average accuracy of 0.77 on the independent test datasets. We discuss the contribution of the variant neighborhood in the model’s predictive power, in addition to the impact of individual features on the model’s performance. Two key features: evolutionary information of residues in the variant neighborhood and their solvent accessibilities were observed to influence the predictions. We also highlight how predictions are impacted by the underlying disease mechanisms of missense mutations and offer insights into understanding these to improve pathogenicity predictions. Our study presents aspects to take into consideration when adopting deep learning approaches for protein structure-guided pathogenicity predictions.