Genomic analyses on part of Escherichia coli's chromosome had suggested the existence of a GATC regulated network. This has recently been confirmed through a transcriptome analysis. Two hypotheses about the molecular control mechanism have been proposed-(i) the GATC network regulation is caused by the presence of GATC clusters within the coding sequences; the regulation is the direct consequence of the clusters' hemi-methylation and therefore their elevated melting temperature, (ii) the regulation is caused by the presence of GATCs in the non-coding 500 bp upstream regions of the affected genes; it is the consequence of an interaction with a regulatory protein like Fnr or CAP. An analysis of the transcriptome data has not allowed us to decide between the two hypotheses. We have therefore taken a classic genomic approach, analyzing the statistical distribution of GATC along the chromosome, using a realistic model of the chromosome as theoretical reference. We observe no particular distribution of GATC in the non-coding upstream regions; however, we confirm the presence of GATC clusters within the genes. In order to verify that the particular distribution observed in E. coli is not a statistical artefact, but has a physiological role, we have carried out the same analysis on Salmonella, making the hypothesis that the genes containing a GATC clusters should be largely the same in the two bacteria. This has been indeed observed, showing that the genes containing a GATC cluster are part of a regulation network. The present is a case study, which demonstrates that the analysis of transcriptome data does not always permit to identify the primary cause of a phenomenon observed; on the other hand, a classic genomic approach linked with a comparative study of related genomes may allow this identification.
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http://dx.doi.org/10.1016/j.compbiolchem.2003.12.004 | DOI Listing |
Brief Bioinform
November 2024
Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, United States.
Pathway analysis plays a critical role in bioinformatics, enabling researchers to identify biological pathways associated with various conditions by analyzing gene expression data. However, the rise of large, multi-center datasets has highlighted limitations in traditional methods like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), which struggle with low signal-to-noise ratios (SNR) and large sample sizes. To tackle these challenges, we use a deep learning-based classification method, Gene PointNet, and a novel $P$-value computation approach leveraging the confusion matrix to address pathway analysis tasks.
View Article and Find Full Text PDFSTAR Protoc
January 2025
Department of Statistics, University of Georgia, 310 Herty Drive, Athens, GA 30602, USA. Electronic address:
Spatial transcriptomics enhances our understanding of cellular organization by mapping gene expression data to precise tissue locations. Here, we present a protocol for using weighted ensemble method for spatial transcriptomics (WEST), which uses ensemble techniques to boost the robustness and accuracy of existing algorithms. We describe steps for preprocessing data, obtaining embeddings from individual algorithms, and ensemble integrating all embeddings as a similarity matrix.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom.
Many machine learning techniques have been used to construct gene regulatory networks (GRNs) through precision matrix that considers conditional independence among genes, and finally produces sparse version of GRNs. This construction can be improved using the auxiliary information like gene expression profile of the related species or gene markers. To reach out this goal, we apply a generalized linear model (GLM) in first step and later a penalized maximum likelihood to construct the gene regulatory network using Glasso technique for the residuals of a multi-level multivariate GLM among the gene expressions of one species as a multi-levels response variable and the gene expression of related species as a multivariate covariates.
View Article and Find Full Text PDFDrug Deliv Transl Res
January 2025
Kinimmune, Inc. St. Louis, 63141, Missouri, USA.
PD-L1/PD-1 checkpoint inhibitors (CPIs) are mainstream agents for cancer immunotherapy, but the prognosis is unsatisfactory in solid tumor patients lacking preexisting T-cell reactivity. Adjunct therapy strategies including the intratumoral administration of immunostimulants aim to address this limitation. CpG oligodeoxynucleotides (ODNs), TLR9 agonists that can potentiate adaptive immunity, have been widely investigated to tackle PD-L1/PD-1 resistance, but clinical success has been hindered by inconsistent efficacy and immune-related toxicities caused by systemic exposure.
View Article and Find Full Text PDFDiscov Oncol
January 2025
School of Medicine, Anhui University of Science & Technology, Huainan, China.
Background: Lung adenocarcinoma is one of the most common malignant tumors worldwide. Its complex molecular mechanisms and high tumor heterogeneity pose significant challenges for clinical treatment. The manganese ion metabolism family plays a crucial role in various biological processes, and the abnormal expression of the NUDT3 gene in multiple cancers has drawn considerable attention.
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