Kynurenic acid as a biochemical factor underlying the association between Western-style diet and depression : a cross-sectional study

Consumption of a Western-style diet (WS-diet), high in saturated fat and added sugar, is associated with increased depression risk. However, the physiological mechanisms underlying the relationship requires elucidation. Diet can alter tryptophan metabolism along the kynurenine pathway (KP), potentially linking inflammation and depression. This study aimed to examine whether urinary inflammatory markers and KP metabolites differed according to WS-diet consumption and depression severity. Depression symptoms and habitual WS-diet consumption were assessed in 169 healthy adults aged 17–35 recruited from two experimental studies. Targeted metabolomics profiling of seven KP metabolites, ELISA-based assays of interleukin-6 (IL-6) and C-reactive protein (CRP) were performed using urine samples collected from the participants. Parametric tests were performed for group comparison and associations analysis. Multilevel mixed-effect modelling was applied to control for biases. Higher intake of WS-diet was associated with lower levels of neuroprotective kynurenic acid (KA; R = −0.17, p = 0.0236). There were no differences in IL-6 or CRP across diet groups (p > 0.05). Physical activity had negative associations with most KP metabolites. Mixed-effects regression analysis showed the glutamatergic inhibitor, KA, was the only biomarker to have a significant association with depression symptoms in a model adjusted for demographic and lifestyle variables: a unit increase in KA was associated with 0.21 unit decrease in Depression Anxiety and Stress Scale-21 depression score (p = 0.009). These findings suggest that urinary KA is associated with both habitual WS-diet intake, and levels of depression symptoms, independent of inflammation. Findings support the role of neuroprotection and glutamatergic modulation in depression. We propose that KA may act as endogenous glutamatergic inhibition in regulating depression severity in the absence of inflammation. Further comparison with blood-based markers will assist in validating the utility of non-invasive urine samples for measuring KP metabolites

Heterogeneous tumor microenvironment in pancreatic ductal adenocarcinoma: An emerging role of single-cell analysis

Background Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies in the world, for which the mortality is almost as high as the disease incidence and is predicted to be the second-highest cause of cancer-related deaths by 2030. These cancerous tumors consist of diversified gene expressions within the different cellular subpopulations that include neoplastic ductal cells, cancer-associated fibroblasts, and immune cells, all of which collectively facilitate cellular heterogeneity in the PDAC tumor microenvironment (TME). Active intratumoral interaction within the cell populations in TME induces the proliferation of cancerous cells, accounting for tumorigenesis and rapid metastasis. Methods This review will focus on novel findings uncovering PDAC heterogeneity in different cellular subpopulations using single-cell RNA-sequencing (scRNA-seq) and other single-cell analysis technologies. It will further explore the emerging role of single-cell technologies in assessing the role of different subpopulations of neoplastic ductal cells, cancer-associated fibroblasts, and immune cells in PDAC progression. Results and Conclusion The application of scRNA-seq in PDAC has started to unveil associations between disease progression and heterogeneity in pancreatic TME and could influence future PDAC treatment. Recent advances in scRNA-seq have uncovered comprehensive analyses of heterogeneous ecosystems present within the TME. These emerging findings underpins further need for a more in-depth understanding of intratumoral heterogeneity in the PDAC microenvironment

Heterogeneous tumor microenvironment in pancreatic ductal adenocarcinoma: An emerging role of single‐cell analysis

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies in the world, for which the mortality is almost as high as the disease incidence and is predicted to be the second‐highest cause of cancer‐related deaths by 2030. These cancerous tumors consist of diversified gene expressions within the different cellular subpopulations that include neoplastic ductal cells, cancer‐associated fibroblasts, and immune cells, all of which collectively facilitate cellular heterogeneity in the PDAC tumor microenvironment (TME). Active intratumoral interaction within the cell populations in TME induces the proliferation of cancerous cells, accounting for tumorigenesis and rapid metastasis. Methods This review will focus on novel findings uncovering PDAC heterogeneity in different cellular subpopulations using single‐cell RNA‐sequencing (scRNA‐seq) and other single‐cell analysis technologies. It will further explore the emerging role of single‐cell technologies in assessing the role of different subpopulations of neoplastic ductal cells, cancer‐associated fibroblasts, and immune cells in PDAC progression. Results and Conclusion The application of scRNA‐seq in PDAC has started to unveil associations between disease progression and heterogeneity in pancreatic TME and could influence future PDAC treatment. Recent advances in scRNA‐seq have uncovered comprehensive analyses of heterogeneous ecosystems present within the TME. These emerging findings underpins further need for a more in‐depth understanding of intratumoral heterogeneity in the PDAC microenvironment

Gene expression profiling of pancreatic ductal adenocarcinomas in response to neoadjuvant chemotherapy

Aim Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate of all major cancers. Chemotherapy is the mainstay systemic therapy for PDAC, and chemoresistance is a major clinical problem leading to therapeutic failure. This study aimed to identify key differences in gene expression profile in tumors from chemoresponsive and chemoresistant patients. Methods Archived formalin-fixed paraffin-embedded tumor tissue samples from patients treated with neoadjuvant chemotherapy were obtained during surgical resection. Specimens were macrodissected and gene expression analysis was performed. Multi- and univariate statistical analysis was performed to identify differential gene expression profile of tumors from good (0%–30% residual viable tumor [RVT]) and poor (\u3e30% RVT) chemotherapy-responders. Results Initially, unsupervised multivariate modeling was performed by principal component analysis, which demonstrated a distinct gene expression profile between good- and poor-chemotherapy responders. There were 396 genes that were significantly (p \u3c 0.05) downregulated (200 genes) or upregulated (196 genes) in tumors from good responders compared to poor responders. Further supervised multivariate analysis of significant genes by partial least square (PLS) demonstrated a highly distinct gene expression profile between good- and poor responders. A gene biomarker of panel (IL18, SPA17, CD58, PTTG1, MTBP, ABL1, SFRP1, CHRDL1, IGF1, and CFD) was selected based on PLS model, and univariate regression analysis of individual genes was performed. The identified biomarker panel demonstrated a very high ability to diagnose good-responding PDAC patients (AUROC: 0.977, sensitivity: 82.4%; specificity: 87.0%). Conclusion A distinct tumor biological profile between PDAC patients who either respond or not respond to chemotherapy was identified

Gene expression profiling of pancreatic ductal adenocarcinomas in response to neoadjuvant chemotherapy

Abstract Aim Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate of all major cancers. Chemotherapy is the mainstay systemic therapy for PDAC, and chemoresistance is a major clinical problem leading to therapeutic failure. This study aimed to identify key differences in gene expression profile in tumors from chemoresponsive and chemoresistant patients. Methods Archived formalin‐fixed paraffin‐embedded tumor tissue samples from patients treated with neoadjuvant chemotherapy were obtained during surgical resection. Specimens were macrodissected and gene expression analysis was performed. Multi‐ and univariate statistical analysis was performed to identify differential gene expression profile of tumors from good (0%–30% residual viable tumor [RVT]) and poor (>30% RVT) chemotherapy‐responders. Results Initially, unsupervised multivariate modeling was performed by principal component analysis, which demonstrated a distinct gene expression profile between good‐ and poor‐chemotherapy responders. There were 396 genes that were significantly (p < 0.05) downregulated (200 genes) or upregulated (196 genes) in tumors from good responders compared to poor responders. Further supervised multivariate analysis of significant genes by partial least square (PLS) demonstrated a highly distinct gene expression profile between good‐ and poor responders. A gene biomarker of panel (IL18, SPA17, CD58, PTTG1, MTBP, ABL1, SFRP1, CHRDL1, IGF1, and CFD) was selected based on PLS model, and univariate regression analysis of individual genes was performed. The identified biomarker panel demonstrated a very high ability to diagnose good‐responding PDAC patients (AUROC: 0.977, sensitivity: 82.4%; specificity: 87.0%). Conclusion A distinct tumor biological profile between PDAC patients who either respond or not respond to chemotherapy was identified