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Handzlik, J. E.

Publications and source records attributed to Handzlik, J. E..

3 recordsLinked to original sources

The differentiation of myeloid progenitors is effected by cascading waves of coordinated gene expression that remodel cellular physiology in a characteristic sequence

The differentiation of hematopoietic progenitors into specialized types requires the transmittal of information from a few external or internal regulators to the thousands of genes that produce a cell types characteristic phenotypes. While the main signaling pathways, transcription factors, and the genes eliciting the terminal phenotypes are known, how information flows from a few regulators to thousands of genes to change the state of the cell remains to be fleshed out. To profile this information transfer process, we sampled the differentiation of the PUER myeloid cell line into macrophages and neutrophils at 29 time points over seven days. There is extensive transient regulation; the number of transcripts modulated in time is twice the number differentially expressed between endpoints. Differentiation is marked by two sharp transitions, at [~] 8h and [~] 80h, when transcriptomic state changes suddenly. We utilized non-negative matrix factorization to identify behaviors, characteristic temporal patterns of gene expression, and to classify transcripts by behavior. Only 10 distinct behaviors are sufficient to recapitulate the expression of [~]36,000 transcripts with high fidelity. Gene expression in most of the behaviors occurs in pulses of varying initiation times and durations. This implies that information transfer during differentiation occurs in cascading waves of gene expression culminating in the permanent turning on of certain genes after [~] 80h. Each behavior is enriched in specific biological processes, so that physiological remodeling proceeds in a characteristic order--signal transduction, translation and mRNA processing, metabolism, and, ultimately, myeloid phenotypic processes. The sharp transition at 8h corresponds to the completion of transcriptional and translational remodeling and the initiation of metabolic remodeling; the one at 80h corresponds to the elicitation of myeloid phenotypes. Our analysis shows that differentiation relies upon a series of transient, rapid, and complex gene regulatory events and highlights the importance of profiling it at a high temporal resolution. Author summaryThe maturation of hematopoietic progenitor cells into differentiated cell types occurs over a period of about a week. This process requires the progenitors to respond to external signals by changing the expression of thousands of genes to elicit the required phenotypes. We profiled how information is transferred from a few upstream regulators to thousands of genes by measuring genome-wide gene expression at high temporal resolution during white-blood cell differentiation. We show that the information transfer occurs in cascading waves, some as short as 8 hours and others lasting for 3 days, in which thousands of genes change expression coordinately. The physiological processes remodeled in each successive wave follow a characteristic order, starting with signal transduction pathways, followed by translation and mRNA processing, then metabolism, and culminating in the production of innate immunity phenotypes. Maturation is also punctuated with two sharp transitions, when genome-wide expression changes suddenly, associated with the initiation of metabolic remodeling and the production of terminal phenotypes. Our analysis shows that a complete description of differentiation requires the characterization of transient changes and not just those observable at the endpoints.

developmental biology↗

Data-driven modeling predicts gene regulatory network dynamics during the differentiation of multipotential progenitors

Cellular differentiation during hematopoiesis is guided by gene regulatory networks (GRNs) thought to be organized as a hierarchy of bistable switches, with antagonism between Gata1 and PU.1 driving red- and white-blood cell differentiation. We utilized high temporal-resolution gene-expression data from in vitro erythrocyte-neutrophil differentiation and a predictive data-driven dynamical modeling framework to learn the architecture and dynamics of gene regulation in a comprehensive twelve-gene GRN. The inferred genetic architecture is densely interconnected rather than hierarchical. The analysis of model dynamics revealed that neutrophil differentiation is driven by C/EBP and Gfi1 rather than PU.1. This prediction is supported by the sequence of gene up-regulation in an independent mouse bone marrow scRNA-Seq dataset. These results imply that neutrophil differentiation is not driven by the Gata1-PU.1 switch but by neutrophil-specific genes instead. More generally, this work shows that data-driven dynamical modeling can uncover the causality of developmental events that might otherwise be obscured.

systems biology↗

Manatee: detection and quantification of small ncRNAs from next-generation sequencing data

Small non-coding RNAs (sncRNAs) play important roles in health and disease. Next Generation Sequencing technologies are considered as the most powerful and versatile methodologies to explore small RNA (sRNA) transcriptomes in diverse experimental and clinical studies. Small RNA-Seq data analysis proved to be challenging due to non-unique genomic origin, short length and abundant post-transcriptional modifications of sRNA species. Here we present Manatee, an algorithm for quantification of sRNA classes and detection of uncharacterized expressed non-coding loci. Manatee adopts a novel approach for abundance estimation of genomic reads that combines sRNA annotation with reliable alignment density information and extensive reads salvation. Comparison of Manatee against state-of-the-art implementations using real/simulated data sets demonstrates its superior accuracy in quantification of diverse sRNA classes providing at the same time insights about unannotated expressed loci. It is user-friendly, easily embeddable in pipelines and provides a simplified output suitable for direct usage in downstream analyses and functional studies.

bioinformatics↗