Targeted and explorative profiling of kallikrein proteases and global proteome biology of pancreatic ductal adenocarcinoma, chronic pancreatitis, and normal pancreas highlights disease-specific proteome remodelling.

Pancreatic ductal adenocarcinoma (PDAC) represents one of the most aggressive and lethal malignancies worldwide with an urgent need for new diagnostic and therapeutic strategies. One major risk factor for PDAC is the pre-indication of chronic pancreatitis (CP), which represents highly inflammatory pancreatic tissue. Kallikreins (KLKs) are secreted serine proteases that play an important role in various cancers as components of the tumor microenvironment. Previous studies of KLKs in solid tumors largely relied on either transcriptomics or immunodetection. We present one of the first targeted mass spectrometry profiling of kallikrein proteases in PDAC, CP, and normal pancreas. We show that KLK6 and KLK10 are significantly upregulated in PDAC (n=14) but not in CP (n=7) when compared to normal pancreas (n=16), highlighting their specific intertwining with malignancy. Additional explorative proteome profiling identified 5936 proteins in our pancreatic cohort and observed disease-specific proteome rearrangements in PDAC and CP. As such, PDAC features an enriched proteome motif for extracellular matrix (ECM) and cell adhesion while there is depletion of mitochondrial energy metabolism proteins, reminiscent of the Warburg effect. Although often regarded as a PDAC hallmark, the ECM fingerprint was also observed in CP, alongside with a prototypical inflammatory proteome motif as well as with an increased wound healing process and proteolytic activity, thereby possibly illustrating tissue autolysis. Proteogenomic analysis based on publicly accessible data sources identified 112 PDAC-specific and 32 CP-specific single amino acid variants, which among others affect KRAS and ANKHD1. Our study emphasizes the diagnostic potential of kallikreins and provides novel insights into proteomic characteristics of PDAC and CP.

A Novel NAD-RNA Decapping Pathway Discovered by Synthetic Light-Up NAD-RNAs.

The complexity of the transcriptome is governed by the intricate interplay of transcription, RNA processing, translocation, and decay. In eukaryotes, the removal of the 5'-RNA cap is essential for the initiation of RNA degradation. In addition to the canonical 5'-N7-methyl guanosine cap in eukaryotes, the ubiquitous redox cofactor nicotinamide adenine dinucleotide (NAD) was identified as a new 5'-RNA cap structure in prokaryotic and eukaryotic organisms. So far, two classes of NAD-RNA decapping enzymes have been identified, namely Nudix enzymes that liberate nicotinamide mononucleotide (NMN) and DXO-enzymes that remove the entire NAD cap. Herein, we introduce 8-(furan-2-yl)-substituted NAD-capped-RNA (FurNAD-RNA) as a new research tool for the identification and characterization of novel NAD-RNA decapping enzymes. These compounds are found to be suitable for various enzymatic reactions that result in the release of a fluorescence quencher, either nicotinamide (NAM) or nicotinamide mononucleotide (NMN), from the RNA which causes a fluorescence turn-on. FurNAD-RNAs allow for real-time quantification of decapping activity, parallelization, high-throughput screening and identification of novel decapping enzymes in vitro. Using FurNAD-RNAs, we discovered that the eukaryotic glycohydrolase CD38 processes NAD-capped RNA in vitro into ADP-ribose-modified-RNA and nicotinamide and therefore might act as a decapping enzyme in vivo. The existence of multiple pathways suggests that the decapping of NAD-RNA is an important and regulated process in eukaryotes.

Hyperparameter optimization for image analysis: application to prostate tissue images and live cell data of virus-infected cells.

PURPOSE: Automated analysis of microscopy image data typically requires complex pipelines that involve multiple methods for different image analysis tasks. To achieve best results of the analysis pipelines, method-dependent hyperparameters need to be optimized. However, complex pipelines often suffer from the fact that calculation of the gradient of the loss function is analytically or computationally infeasible. Therefore, first- or higher-order optimization methods cannot be applied. METHODS: We developed a new framework for zero-order black-box hyperparameter optimization called HyperHyper, which has a modular architecture that separates hyperparameter sampling and optimization. We also developed a visualization of the loss function based on infimum projection to obtain further insights into the optimization problem. RESULTS: We applied HyperHyper in three different experiments with different imaging modalities, and evaluated in total more than 400.000 hyperparameter combinations. HyperHyper was used for optimizing two pipelines for cell nuclei segmentation in prostate tissue microscopy images and two pipelines for detection of hepatitis C virus proteins in live cell microscopy data. We evaluated the impact of separating the sampling and optimization strategy using different optimizers and employed an infimum projection for visualizing the hyperparameter space. CONCLUSIONS: The separation of sampling and optimization strategy of the proposed HyperHyper optimization framework improves the result of the investigated image analysis pipelines. Visualization of the loss function based on infimum projection enables gaining further insights on the optimization process.