Abstract
BackgroundHuman myelopoiesis is an exciting biological model for cellular differentiation since it represents a plastic process where multipotent stem cells gradually limit their differentiation potential, generating different precursor cells which finally evolve into distinct terminally differentiated cells. This study aimed at investigating the genomic expression during myeloid differentiation through a computational approach that integrates gene expression profiles with functional information and genome organization.ResultsGene expression data from 24 experiments for 8 different cell types of the human myelopoietic lineage were used to generate an integrated myelopoiesis dataset of 9,425 genes, each reliably associated to a unique genomic position and chromosomal coordinate. Lists of genes constitutively expressed or silent during myelopoiesis and of genes differentially expressed in commitment phase of myelopoiesis were first identified using a classical data analysis procedure. Then, the genomic distribution of myelopoiesis genes was investigated integrating transcriptional and functional characteristics of genes. This approach allowed identifying specific chromosomal regions significantly highly or weakly expressed, and clusters of differentially expressed genes and of transcripts related to specific functional modules.ConclusionThe analysis of genomic expression during human myelopoiesis using an integrative computational approach allowed discovering important relationships between genomic position, biological function and expression patterns and highlighting chromatin domains, including genes with coordinated expression and lineage-specific functions.
Highlights
Human myelopoiesis is an exciting biological model for cellular differentiation since it represents a plastic process where multipotent stem cells gradually limit their differentiation potential, generating different precursor cells which evolve into distinct terminally differentiated cells
Chromosomes 16, 19, 21 and 22 show high expression indexes across diverse cell types, whereas, chromosomes 4, 13 and 18 have low expression indexes. Following these indirect indications of a possible correlation among genomic position of genes and their expression pattern during myelopoiesis, we investigated the positional clustering of genes similar for expression characteristics in the considered cell types, as well as the existence of chromosomal regions homogeneous in expression behavior
This work presents a genomic approach applied to the analysis of gene expression profiles during myeloid differentiation, which substantiated the existence of relationships between genomic position, biological function and expression patterns of genes
Summary
Human myelopoiesis is an exciting biological model for cellular differentiation since it represents a plastic process where multipotent stem cells gradually limit their differentiation potential, generating different precursor cells which evolve into distinct terminally differentiated cells. Different bioinformatic methods were developed to identify chromosomal regions of increased or decreased expression from transcriptional data [13,14,15,16,17,18,19,20] and to find significant local enrichment of specific features in genomes [21] Some of these methodologies based on transcriptome mapping were successfully used to study the correlation between expression and position of genes in tumours or in other diseases, proving to be effective both in identifying chromosomal aberrations in cancers [15,16,17,18,20] and in discovering novel genes potentially involved in tumorigenicity [22] and other disorders [23,24]
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