Role of CpxR in Biofilm Development: Expression of Key Fimbrial, O-Antigen and Virulence Operons of Salmonella Enteritidis.

Salmonella Enteritidis is a non-typhoidal serovar of great public health significance worldwide. The RpoE sigma factor and CpxRA two-component system are the major regulators of the extracytoplasmic stress response. In this study, we found that the CpxR has highly significant, but opposite effects on the auto-aggregation and swarming motility of S. Enteritidis. Auto-aggregation was negatively affected in the ∆cpxR mutant, whereas the same mutant significantly out-performed its wild-type counterpart with respect to swarming motility, indicating that the CpxR plays a role in biofilm-associated phenotypes. Indeed, biofilm-related assays showed that the CpxR is of critical importance in biofilm development under both static (microtiter plate) and dynamic (flow cell) media flow conditions. In contrast, the RpoE sigma factor showed no significant role in biofilm development under dynamic conditions. Transcriptomic analysis revealed that the cpxR mutation negatively affected the constitutive expression of the operons critical for biosynthesis of O-antigen and adherence, but positively affected the expression of virulence genes critical for Salmonella-mediated endocytosis. Conversely, CpxR induced the expression of curli csgAB and fimbrial stdAC operons only during biofilm development and flagellar motAB and fliL operons exclusively during the planktonic phase, indicating a responsive biofilm-associated loop of the CpxR regulator.

Comprehensive characterization of PKHD1 mutation in human colon cancer.

INTRODUCTION: The PKHD1 (Polycystic Kidney and Hepatic Disease 1) gene is essential for producing fibrocystin or polyductin, which is crucial in various cellular functions. Mutations in PKHD1 have been found to be involved in the development and progression of colorectal cancer (CRC). Along with APC, TP53, and KRAS, PKHD1 is one of the most frequently mutated genes in CRC. PKHD1 expression is governed by the Wnt/PCP pathway, often dysregulated in CRC. Targeting this pathway, crucial for CRC progression, could unveil potential therapeutic strategies for colon cancer treatment. METHODS: This study examined an in-house dataset of 3702 colon cancer samples, analyzing mutation landscapes, clinical features, tumor mutational burden (TMB), microsatellite instability (MSI), and chromosomal instability (CIN) score. For the survival analysis of PKHD1 patients, survival data of 436 colon adenocarcinoma samples were obtained from TCGA dataset. Additionally, 433 samples from TCGA with RNA-seq data were used for the assessment of immune cell infiltration and gene set enrichment analysis. RESULTS: Polycystic Kidney and Hepatic Disease 1 mutation was detected in 424 colon cancer patients from our in-house cohort and was associated with increased TMB, higher MSI, and lower CIN score. Importantly, within the TCGA dataset, PKHD1 mutations were identified as an independent prognostic factor, not merely correlated with established prognostic biomarkers, and were associated with poorer overall survival outcomes. In terms of immune response, these mutations correlated with increased enrichment scores for 12 immune cell types, including B cell plasma, macrophages, and naive CD4+ T cells. Additionally, interferon alpha and interferon-gamma gene sets were significantly down-regulated in patients with PKHD1 mutations (FDA q-value < 0.1). CONCLUSIONS: Overall, these findings suggest that PKHD1 may be a potential biomarker for the prognosis of colon cancer and provide some insight for personalized immunotherapy.

Leveraging cfDNA fragmentomic features in a stacked ensemble model for early detection of esophageal squamous cell carcinoma.

In this study, we develop a stacked ensemble model that utilizes cell-free DNA (cfDNA) fragmentomics for the early detection of esophageal squamous cell carcinoma (ESCC). This model incorporates four distinct fragmentomics features derived from whole-genome sequencing (WGS) and advanced machine learning algorithms for robust analysis. It is validated across both an independent validation cohort and an external cohort to ensure its generalizability and effectiveness. Notably, the model maintains its robustness in low-coverage sequencing environments, demonstrating its potentials in clinical settings with limited sequencing resources. With its remarkable sensitivity and specificity, this approach promises to significantly improve the early diagnosis and management of ESCC. This study represents a substantial step forward in the application of cfDNA fragmentomics in cancer diagnostics, emphasizing the need for further research to fully establish its clinical efficacy.

Multi-Dimensional Fragmentomics Enables Early and Accurate Detection of Colorectal Cancer.

Colorectal cancer (CRC) is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage CRC detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 CRC patients and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish CRC patients from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 CRC patients and 119 controls) and a prospective cohort of 242 participants (129 CRC patients and 113 controls). The ensemble stacked model showed remarkable discriminatory power between CRC patients and controls, outperforming all base models and achieving a high area under the ROC curve (AUC) of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting CRC in the validation cohort, with sensitivity increasing as cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run tests and across various conditions in healthy individuals. In the prospective cohort, it achieved 91.47% sensitivity and 95.58% specificity. This integrated model capitalizes on the multiplex nature of cfDNA fragmentomics to achieve high sensitivity and robustness, offering significant promise for early CRC detection and broad patient benefit.