Background: Currently, a comprehensive method for exploration of transcriptional regulation has not been well established. We explored a novel pipeline to analyze transcriptional regulation using co-analysis of RNA sequencing (RNA-seq), assay for transposase-accessible chromatin using sequencing (ATAC-seq), and chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq).
Methods: The G protein-coupled receptors (GPCRs) possibly associated with macrophages were further filtered using a reduced-Cox regression model. ATAC-seq profiles were used to map the chromatin accessibility of the GPRC5B promoter region. Pearson analysis was performed to identify the transcription factor (TF) whose expression was correlated with open chromatin regions of GPRC5B promoter. ChIP-seq profiles were obtained to confirm the physical binding of GATA4 and its predicted binding regions. For verification, quantitative polymerase chain reaction (qPCR) and multidimensional database validations were performed.
Results: The reduced-Cox regression model revealed the prognostic value of GPRC5B. A novel pipeline for TF exploration was proposed. With our novel pipeline, we first identified chr16:19884686-19885185 as a reproducible open chromatin region in the GPRC5B promoter. Thereafter, we confirmed the correlation between GATA4 expression and the accessibility of this region, confirmed its physical binding, and proved how its overexpression could regulate GPRC5B. GPRC5B was significantly downregulated in colon adenocarcinoma (COAD) as seen in 28 patient samples. The correlation between GPRC5B and macrophages in COAD was validated using multiple databases.
Conclusion: GPRC5B, correlated with macrophages, was a key GPCR affecting COAD prognosis. Further, with our novel pipeline, TF GATA4 was identified as a direct upstream of GPRC5B. This study proposed a novel pipeline for TF exploration and provided a theoretical basis for COAD therapy.
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http://dx.doi.org/10.3389/fimmu.2021.741634 | DOI Listing |
Sci Adv
January 2025
Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Designing binders to target undruggable proteins presents a formidable challenge in drug discovery. In this work, we provide an algorithmic framework to design short, target-binding linear peptides, requiring only the amino acid sequence of the target protein. To do this, we propose a process to generate naturalistic peptide candidates through Gaussian perturbation of the peptidic latent space of the ESM-2 protein language model and subsequently screen these novel sequences for target-selective interaction activity via a contrastive language-image pretraining (CLIP)-based contrastive learning architecture.
View Article and Find Full Text PDFJ Ultrasound
January 2025
, Costa Contina street n. 19, 66054, Vasto, Chieti, Italy.
Aim: o point out how novel analysis tools of AI can make sense of the data acquired during OL and OC diagnosis and treatment in an effort to help improve and standardize the patient pathway for these disease.
Material And Methods: ultilizing programmed detection of heterogeneus OL and OC habitats through radiomics and correlate to imaging based tumor grading plus a literature review.
Results: new analysis pipelines have been generated for integrating imaging and patient demographic data and identify new multi-omic biomarkers of response prediction and tumour grading using cutting-edge artificial intelligence (AI) in OL and OC.
Plast Reconstr Surg
January 2025
Division of Plastic Surgery, Mayo Clinic; Rochester, MN.
Introduction: Quantitative neuromorphometry analysis of the peripheral nerve is paramount to nerve regeneration research. However, this technique relies upon accurate segmentation and determination of myelin and axonal area. Manual histological analysis methods are time- consuming, and subject to error and bias.
View Article and Find Full Text PDFJ Proteome Res
January 2025
Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, 69120 Heidelberg, Germany.
The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, different algorithms come with different strengths and weaknesses and choosing the appropriate algorithm poses a challenge for the user. Here we introduce PeptideForest, a semisupervised machine learning approach that integrates the assignments of multiple algorithms to train a random forest classifier to alleviate that issue.
View Article and Find Full Text PDFHeliyon
January 2025
Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy.
Background: The modern approach to treating rectal cancer, which involves total mesorectal excision directed by imaging assessments, has significantly enhanced patient outcomes. However, locally recurrent rectal cancer (LRRC) continues to be a significant clinical issue. Identifying LRRC through imaging is complex, due to the mismatch between fibrosis and inflammatory pelvic tissue.
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