Alternatively activated macrophages promote pancreatic fibrosis in chronic pancreatitis.

Chronic pancreatitis (CP) is a progressive and irreversible inflammatory and fibrotic disease with no cure. Unlike acute pancreatitis (AP), we find that alternatively activated macrophages (AAMs) are dominant in mouse and human CP. AAMs are dependent on interleukin (IL)-4 and IL-13 signalling, and we show that mice lacking IL-4Rα, myeloid-specific IL-4Rα and IL-4/IL-13 were less susceptible to pancreatic fibrosis. Furthermore, we demonstrate that mouse and human pancreatic stellate cells (PSCs) are a source of IL-4/IL-13. Notably, we show that pharmacologic inhibition of IL-4/IL-13 in human ex vivo studies as well as in established mouse CP decreases pancreatic AAMs and fibrosis. We identify a critical role for macrophages in pancreatic fibrosis and in turn PSCs as important inducers of macrophage-alternative activation. Our study challenges and identifies pathways involved in crosstalk between macrophages and PSCs that can be targeted to reverse or halt pancreatic fibrosis progression

In Silico Derivation of HLA-Specific Alloreactivity Potential from Whole Exome Sequencing of Stem Cell Transplant Donors and Recipients: Understanding the Quantitative Immuno-biology of Allogeneic Transplantation

Donor T cell mediated graft vs. host effects may result from the aggregate alloreactivity to minor histocompatibility antigens (mHA) presented by the HLA in each donor-recipient pair (DRP) undergoing stem cell transplantation (SCT). Whole exome sequencing has demonstrated extensive nucleotide sequence variation in HLA-matched DRP. Non-synonymous single nucleotide polymorphisms (nsSNPs) in the GVH direction (polymorphisms present in recipient and absent in donor) were identified in 4 HLA-matched related and 5 unrelated DRP. The nucleotide sequence flanking each SNP was obtained utilizing the ANNOVAR software package. All possible nonameric-peptides encoded by the non-synonymous SNP were then interrogated in-silico for their likelihood to be presented by the HLA class I molecules in individual DRP, using the Immune-Epitope Database (IEDB) SMM algorithm. The IEDB-SMM algorithm predicted a median 18,396 peptides/DRP which bound HLA with an IC50 of <500nM, and 2254 peptides/DRP with an IC50 of <50nM. Unrelated donors generally had higher numbers of peptides presented by the HLA. A similarly large library of presented peptides was identified when the data was interrogated using the Net MHCPan algorithm. These peptides were uniformly distributed in the various organ systems. The bioinformatic algorithm presented here demonstrates that there may be a high level of minor histocompatibility antigen variation in HLA-matched individuals, constituting an HLA-specific alloreactivity potential. These data provide a possible explanation for how relatively minor adjustments in GVHD prophylaxis yield relatively similar outcomes in HLA matched and mismatched SCT recipients.Comment: Abstract: 235, Words: 6422, Figures: 7, Tables: 3, Supplementary figures: 2, Supplementary tables:

Interpretable deep learning models for the inference and classification of LHC data

The Shower Deconstruction methodology is pivotal in distinguishing signal and background jets, leveraging the detailed information from perturbative parton showers. Rooted in the Neyman-Pearson lemma, this method is theoretically designed to differentiate between signal and background processes optimally in high-energy physics experiments. A key challenge, however, arises from the combinatorial growth associated with increasing jet constituents, which hampers its computational feasibility. We address this by demonstrating that the likelihood derived from comparing the most probable signal and background shower histories is equally effective for discrimination as the conventional approach of summing over all potential histories in top quark versus Quantum Chromodynamics (QCD) scenarios. We propose a novel approach by conceptualising the identification of the most probable shower history as a Markov Decision Process (MDP). Utilising a sophisticated modular point-transformer architecture, our method efficiently learns the optimal policy for this task. The developed neural agent excels in constructing the most likely shower history and demonstrates robust generalisation capabilities on unencountered test data. Remarkably, our approach mitigates the complexity inherent in the inference process, achieving a linear scaling relationship with the number of jet constituents. This offers a computationally viable and theoretically sound method for signal-background differentiation, paving the way for more effective data analysis in particle physics.Comment: Extended discussion in section 2.1. Results unchanged. Matches journal versio