Genomic expression during human myelopoiesis.
BACKGROUND
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 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.


RESULTS
Gene 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.


CONCLUSIONS
The 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.
Ferrari
Francesco
F
Department of Biomedical Sciences, University of Modena and Reggio Emilia, via G, Campi 287, 41100, Modena, Italy. ferrari.sergio@unimo.it
Bortoluzzi
Stefania
S
Coppe
Alessandro
A
Basso
Dario
D
Bicciato
Silvio
S
Zini
Roberta
R
Gemelli
Claudia
C
Danieli
Gian Antonio
GA
Ferrari
Sergio
S
D018952:Antigens, CD34 / Q000235:genetics / Q000378:metabolism; D002454:Cell Differentiation; D019070:Cell Lineage; D002877:Chromosomes, Human; D016000:Cluster Analysis; D019295:Computational Biology; D004804:Eosinophils / Q000166:cytology / Q000378:metabolism; D004900:Erythroblasts / Q000166:cytology / Q000378:metabolism; D005312:Fetal Blood / Q000166:cytology / Q000378:metabolism; D015870:Gene Expression; D020869:Gene Expression Profiling; D015894:Genome, Human; D023281:Genomics; D042381:Granulocyte Precursor Cells / Q000166:cytology / Q000378:metabolism; D006412:Hematopoietic Stem Cells / Q000166:cytology / Q000378:metabolism; D006801:Humans; D008954:Models, Biological; D009000:Monocytes / Q000166:cytology / Q000378:metabolism; D022423:Myeloid Cells / Q000166:cytology / Q000378:metabolism*; D038042:Myelopoiesis / Q000235:genetics*; D009504:Neutrophils / Q000166:cytology / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software
D018952:Antigens, CD34