A KLK6 Activity-Based Probe Reveals a Role for KLK6 Activity in Pancreatic Cancer Cell Invasion

Extracellular proteolysis in cancer: Proteases, substrates, and mechanisms in tumor progression and metastasis

A vast ensemble of extracellular proteins influences the development and progression of cancer, shaped and reshaped by a complex network of extracellular proteases. These proteases, belonging to the distinct classes of metalloproteases, serine proteases, cysteine proteases, and aspartic proteases, play a critical role in cancer. They often become dysregulated in cancer, with increases in pathological protease activity frequently driven by the loss of normal latency controls, diminished regulation by endogenous protease inhibitors, and changes in localization. Dysregulated proteases accelerate tumor progression and metastasis by degrading protein barriers within the extracellular matrix (ECM), stimulating tumor growth, reactivating dormant tumor cells, facilitating tumor cell escape from immune surveillance, and shifting stromal cells toward cancer-promoting behaviors through the precise proteolysis of specific substrates to alter their functions. These crucial substrates include ECM proteins and proteoglycans, soluble proteins secreted by tumor and stromal cells, and extracellular domains of cell surface proteins, including membrane receptors and adhesion proteins. The complexity of the extracellular protease web presents a significant challenge to untangle. Nevertheless, technological strides in proteomics, chemical biology, and the development of new probes and reagents are enabling progress and advancing our understanding of the pivotal importance of extracellular proteolysis in cancer.

Practical Synthesis of Chiral α-Aminophosphonates with Weak Bonding Organocatalysis at ppm Loading

α-Aminophosphonic acids as an important class of bioisosteres of α-amino acids demonstrate various biologically important activities. We report here the development of a highly enantioselective isomerization of α-iminophosphonates enabled by an extraordinarily efficient organocatalyst. This organocatalyst afforded a total turnover number (TON) of 20,000-1,000,000 for a wide range of α-alkyl iminophosphonates. Even at a parts-per-million (ppm) loading, this catalyst achieved a complete reaction in greater than 93% enantiomeric excess (ee). Computational studies revealed that this small-molecule catalyst achieved enzyme-like efficiency via a network of weak bonding interactions that effectively preorganized the substrate and catalyst toward a transition-state-like complex. Considering the substrate tolerance, catalytic efficiency, and mechanism, this organocatalyst could be regarded as a small-molecule isomerase.

Covalent activity-based probes for imaging of serine proteases

Serine proteases are one of the largest mechanistic classes of proteases. They regulate a plethora of biochemical pathways inside and outside the cell. Aberrant serine protease activity leads to a wide variety of human diseases. Reagents to visualize these activities can be used to gain insight into the biological roles of serine proteases. Moreover, they may find future use for the detection of serine proteases as biomarkers. In this review, we discuss small molecule tools to image serine protease activity. Specifically, we outline different covalent activity-based probes and their selectivity against various serine protease targets. We also describe their application in several imaging methods.

Modeling of new markers for the diagnosis and prognosis of pancreatic cancer based on the transition from inflammation to cancer

Background

Pancreatic adenocarcinoma (PAAD) is a lethal disease with a poor prognosis. Genes involved in acute pancreatitis (AP) or chronic pancreatitis (CP) might be important for PAAD development. This study sought to identify potential PAAD diagnosis markers and to establish a PAAD prognosis prediction model based on AP- and CP-related genes.

Methods

The significantly differentially expressed genes in both AP or CP and PAAD were obtained by a bioinformatics analysis. A risk-score model for predicting survival was constructed based on The Cancer Genome Atlas (TCGA) data and validated using an International Cancer Genome Consortium (ICGC) cohort. Protein expression and the effects of the genes in the risk models were validated by immunohistochemistry, or Cell Counting Kit-8 (CCK-8) and transwell assays. The study sample data included six AP tissue samples and five normal pancreatic tissue samples, six CP tissue samples and six normal pancreatic tissue samples from the Gene Expression Omnibus (GEO) expression profiling microarrays GSE109227 and GSE41418 data sets, respectively, and fragments per kilobase per million mapped fragments (FPKM) data from four normal controls and 150 PAAD cases from TCGA database, and 182 cancer patient samples with complete survival prognostic data from the ICGC database.

Results

In total, 508 significantly differentially expressed genes were found in both AP or CP and PAAD. Trefoil factor 2 (TFF2), tubulointerstitial nephritis antigen (TINAG), trefoil factor 1 (TFF1), aquaporin 5 (AQP5), SAM pointed domain containing ETS transcription factor (SPDEF), anterior gradient protein 2 (AGR2), apolipoprotein B messenger RNA editing enzyme catalytic subunit 1 (APOBEC1), kallikrein-related peptidase 6 (KLK6), dopa decarboxylase (DDC), mucin 13 (MUC13), claudin 18 (CLDN18), annexin A10 (ANXA10), and tetraspanin 1 (TSPAN1) were found to be present in PAAD and had the largest fold change. A risk-score model, comprising 19 genes, was constructed for prognostic prediction. A high-risk score indicated a poor prognosis. TINAG, DDC, SPDEF, and APOBEC1 proteins were increased in PAAD, while TINAG and DDC were correlated with the pathologic grade. Decreased TINAG, APOBEC1, transmembrane protein 94 (TMEM94), and kelch like family member 36 (KLHL36) expression inhibited PAAD cell proliferation, while decreased SPDEF, TMEM94, and KLHL36 expression significantly inhibited PAAD cell migration.

Conclusions

The AP and CP co-related genes were significantly correlated with PAAD. TINAG, DDC, SPDEF, and APOBEC1 could serve as new PAAD predictors. The risk model developed in this study could be used to predict the prognosis of PAAD patients.

KLK7, KLK10, and KLK11 in Papillary Thyroid Cancer: Bioinformatic Analysis and Experimental Validation

Despite many studies on papillary thyroid carcinoma (PTC) in the past few decades, some critical and significant genes remain undiscovered. To explore genes that may play crucial roles in PTC, a detailed analysis of the expression levels, mutations, and clinical significance of Kallikrein-related peptidases (KLKs) family genes in PTC was undertaken to provide new targets for the precise treatment of the disease. A comprehensive analysis of KLK family genes was performed using various online tools, such as GEPIA, Kaplan-Meier Plotter, LinkedOmics, GSCA, TIMER, and Cluego. KLK7, KLK10, and KLK11 were critical factors of KLK family genes. Then, functional assays were carried out on KLK7/10/11 to determine their proliferation, migration, and invasion capabilities in PTC. The mRNA expression levels of KLK7, KLK10, KLK11, and KLK13 were significantly elevated in thyroid carcinoma, while KLK1, KLK2, KLK3 and KLK4 mRNA levels were decreased compared to normal tissues. Correlations between KLK2/7-12/15 expression levels and tumor stage were also observed in thyroid carcinoma. Survival analysis demonstrated that KLK4/5/7/9-12/14 was associated with overall survival in patients with thyroid cancer. Not only were KLK genes strongly associated with cancer-related pathways, but also KLK7/10/11 was associated with immune-cell infiltration. Finally, silencing KLK7/10/11 impaired human papillary thyroid carcinoma cells' growth, migration ability, and invasiveness. The increased expression of KLK7, KLK10, and KLK11 may serve as molecular markers to identify PTC patients. KLK7, KLK10, and KLK11 could be potential prognostic indicators and targets for precision therapy against PTC.

Photodynamic Stromal Depletion in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest solid malignancies, with a five-year survival of less than 10%. The resistance of the disease and the associated lack of therapeutic response is attributed primarily to its dense, fibrotic stroma, which acts as a barrier to drug perfusion and permits tumour survival and invasion. As clinical trials of chemotherapy (CT), radiotherapy (RT), and targeted agents have not been successful, improving the survival rate in unresectable PDAC remains an urgent clinical need. Photodynamic stromal depletion (PSD) is a recent approach that uses visible or near-infrared light to destroy the desmoplastic tissue. Preclinical evidence suggests this can resensitise tumour cells to subsequent therapies whilst averting the tumorigenic effects of tumour-stromal cell interactions. So far, the pre-clinical studies have suggested that PDT can successfully mediate the destruction of various stromal elements without increasing the aggressiveness of the tumour. However, the complexity of this interplay, including the combined tumour promoting and suppressing effects, poses unknowns for the clinical application of photodynamic stromal depletion in PDAC.

Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors

Kallikrein 6 (KLK6) is an attractive drug target for the treatment of neurological diseases and for various cancers. Herein, we explore the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding (ΔG_bind) for a series of 49 inhibitors of KLK6. We found that the performance of the methods varied strongly with the tested system. For only one of the three KLK6 datasets, the docking scores obtained with rDock were in good agreement (R² ≥ 0.5) with experimental values of ΔG_bind. A similar result was obtained with MM/GBSA (using the ff14SB force field) calculations based on single minimized structures. Improved binding affinity predictions were obtained with the free energy perturbation (FEP) method, with an overall MUE and RMSE of 0.53 and 0.68 kcal/mol, respectively. Furthermore, in a simulation of a real-world drug discovery project, FEP was able to rank the most potent compounds at the top of the list. These results indicate that FEP can be a promising tool for the structure-based optimization of KLK6 inhibitors.

Targeting Protein-Protein Interfaces with Peptides: The Contribution of Chemical Combinatorial Peptide Library Approaches

Protein-protein interfaces play fundamental roles in the molecular mechanisms underlying pathophysiological pathways and are important targets for the design of compounds of therapeutic interest. However, the identification of binding sites on protein surfaces and the development of modulators of protein-protein interactions still represent a major challenge due to their highly dynamic and extensive interfacial areas. Over the years, multiple strategies including structural, computational, and combinatorial approaches have been developed to characterize PPI and to date, several successful examples of small molecules, antibodies, peptides, and aptamers able to modulate these interfaces have been determined. Notably, peptides are a particularly useful tool for inhibiting PPIs due to their exquisite potency, specificity, and selectivity. Here, after an overview of PPIs and of the commonly used approaches to identify and characterize them, we describe and evaluate the impact of chemical peptide libraries in medicinal chemistry with a special focus on the results achieved through recent applications of this methodology. Finally, we also discuss the role that this methodology can have in the framework of the opportunities, and challenges that the application of new predictive approaches based on artificial intelligence is generating in structural biology.