Aim: I sought to determine trauma-specific transcriptomic signatures for septic sub-cohorts.
Methods: In retrospective large-scale data analysis, I applied (old and new methods), including lagged correlation between transcripts and clinical subtype counts (by integrating over 800 samples from trauma patients).
Results: Focussing on novel pathways and correlation methods we revealed (persistently down-regulated) ribosomal genes and changed time profiles of metabolic enzyme precursors /transcripts. Candidates associated to insulin signalling, including HK3, hinted towards “metabolic syndrome”. Correlation analysis yielded robust results for LCN2 and LTF (r>0.9), but only moderate associations to subtype counts (e.g. top-performing r (Eosinophil, IL5RA)>0.6).
Discussion: Gene Centred Normalisation Reduces Ambiguity and Improves Interpretation.
Table of Contents
1. THEORY
1.1 Normalization.
1.2 Comparison of two groups of samples.
1.3 Signal Log Ratio Algorithm.
1.4 Correlation (r)
1.5 Log2-transformation.
1.6 Intensity ratio.
1.7 Hypothesis pair.
1.8 Threshold for p-value.
1.9 Fold change
1.10 Time series.
1.11 Microarray preparation
1.12 Probe preparation, hybridization and imaging.
1.13 Low level information analysis
2. INTRODUCTION
2.1 SIRS, SEPSIS AND SEPTIC SHOCK.
2.2 Related Background.
2.3 .CEL File Description.
2.4 Gene Expression Omnibus (GEO)
2.5 KEGG.
3. MATERIALS & METHODS
3.1 Data.
3.2 Data Analysis.
3.3 Clustering.
3.4 Enrichment tests.
3.5 Lagged Correlation.
3.6 Additional Information.
4. RESULTS.
4.1 Differentially Expressed Genes.
4.2 Clustering:
4.3 Regulation of some important genes.
4.3.1 HLA-DMB & LCN2.
4.3.2 Correlation of LCN 2and LTF.
4.3.3 SLC4A1 & IL5RA.
4.4 Gender Linked Genes:
4.5 Gene Set Enrichment Analysis (GSEA)
4.5.1 Kegg Mapper.
4.5.2 Glycolysis Gluconeogenesis.
4.5.3 Ribosome.
4.6 Toll Like Receptors Signaling Pathway and Heatmap.
5. DISCUSSION.
6. REFERENCES.
7. SUPPLEMENTARY.
Research Objectives and Themes
This thesis aims to determine trauma-specific transcriptomic signatures for septic sub-cohorts through a comprehensive retrospective large-scale data analysis, integrating clinical variables and evaluating both established and novel computational approaches.
- Transcriptomic analysis of trauma and sepsis patients
- Integration of clinical variables with genomic data
- Comparison of statistical methods (e.g., lagged correlation)
- Identification of gene sets and pathways involved in sepsis (e.g., Glycolysis, Ribosome)
- Validation of gene expression regulation (e.g., LCN2, HLA-DMB)
Excerpt from the Thesis
4.3.1 HLA-DMB & LCN2
HLA-DMB belongs to the HLA (Human Leukocyte Antigen) class II beta chain paralogues. This class II molecule is a heterodimer consisting of an alpha (DMA) and a beta (DMB) chain, both anchored in the membrane. DM plays a central role in the peptide loading of MHC class II molecules by helping to release the CLIP (class II-associated invariant chain peptide) molecule from the peptide binding site. Class II molecules are expressed in antigen presenting cells (APC: B lymphocytes, dendritic cells, macrophages). [38]
LCN2 (Lipocalin-2) also known as oncogene 24p3 or Neutrophil Gelatinase-Associated Lipocalin (NGAL). LCN2 is an iron-trafficking protein involved in multiple processes such as apoptosis, innate immunity and renal development. The binding of NGAL to bacterial siderophores is important in the innate immune response to bacterial infection. Upon encountering invading bacteria the toll-like receptors on immune cells stimulate the synthesis and secretion of NGAL. Secreted NGAL then limits bacterial growth by sequestering iron-containing siderophores. LCN2 also functions as growth factor. Originally, NGAL was isolated from a supernatant of activated human neutrophils.[41] Lack of LCN2 expression has been possibly linked to acne could be caused due to lack of gene expression, which possibly can be correct with Isotretinoin.[42,43].
Summary of Chapters
1. THEORY: This chapter provides the theoretical foundation for microarray data analysis, covering techniques such as normalization, fold change calculation, and statistical hypothesis testing.
2. INTRODUCTION: The introduction defines the clinical context of trauma and sepsis, explaining the terminology and the relevance of transcriptomic data in understanding disease progression.
3. MATERIALS & METHODS: This section details the data sources, preprocessing steps, clustering methods, and advanced statistical tools like lagged correlation used in the study.
4. RESULTS: This chapter presents the analytical findings, highlighting differentially expressed genes, gender-linked genes, and pathway enrichments in trauma patients.
5. DISCUSSION: The discussion evaluates the experimental results, interprets the biological significance of the identified pathways, and addresses the limitations of the current dataset.
6. REFERENCES: This section lists the academic literature and data sources consulted throughout the research process.
7. SUPPLEMENTARY: This appendix provides additional data visualizations and pathway details related to specific E. coli infections and metabolic processes.
Keywords
Bioinformatics, Transcriptomics, Sepsis, Trauma, Genomic Storm, Microarray, Normalization, KEGG, Glycolysis, Ribosome, HLA-DMB, LCN2, Gene Set Enrichment Analysis, Lagged Correlation, Innate Immunity
Frequently Asked Questions
What is the primary focus of this research?
The research focuses on the reanalysis of genomic transcriptomic data from trauma patients to identify specific molecular signatures associated with septic sub-cohorts.
What are the central themes of this thesis?
The central themes include the computational analysis of large-scale gene expression datasets, the impact of sepsis on metabolic and immune-related pathways, and the integration of clinical variables to improve diagnostic interpretation.
What is the primary research goal?
The primary goal is to determine trauma-specific transcriptomic signatures and understand how genes like LCN2 and HLA-DMB are regulated during the progression of sepsis.
Which scientific methods are utilized?
The study employs a range of methods including R-based statistical analysis, the RMA normalization method, hierarchical clustering, and Gene Set Enrichment Analysis (GSEA).
What topics are covered in the main body?
The main body covers the theoretical background of microarray analysis, detailed methodology, results concerning differentially expressed genes, and in-depth investigations of metabolic pathways such as Glycolysis and the Ribosome pathway.
Which keywords best characterize this work?
Key terms include Bioinformatics, Transcriptomics, Sepsis, Trauma, Genomic Storm, and Microarray analysis.
How does the author approach the problem of data ambiguity?
The author emphasizes that gene-centred normalisation reduces ambiguity in the data, leading to improved interpretation of gene expression profiles.
What is the significance of the findings regarding LCN2 and HLA-DMB?
These genes were identified as highly regulated in the data, with LCN2 showing significant upregulation and HLA-DMB showing consistent downregulation, which was further confirmed in validation datasets.
- Quote paper
- Deepak Tanwar (Author), 2014, Comprehensive Reanalysis of Genomic Storm (Transcriptomic) Data, Integrating Clinical Varibles and Utilizing New and Old Approaches, Munich, GRIN Verlag, https://www.grin.com/document/284986
