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Peterson, C.

Publications and source records attributed to Peterson, C..

3 recordsLinked to original sources

Revisiting the erythroid-myeloid lineage decision - a data-driven dynamical model analysis

It is widely conjectured that the mutually antagonizing pair of transcription factors GATA1 and PU.1, deter-mines the choice between the erythroid and myeloid lineages in hematopoiesis. In theoretical approaches, this appears natural with a bistable switch driving the decision. Recent extensive binding and gene expression experiments with some focus on the triad GATA1, GATA2 and PU.1 indicate that GATA2 may be more involved in this lineage decision than previously anticipated. Here, we analyze these experimental data by modeling regulatory sub-networks with deterministic rate equations. Using network dynamical parameters determined by the data, we deduce from increasing the self-interaction bindings in silico among the triad genes that GATA2 and PU.1 exhibit non-linear behavior with one unstable and one stable state. This is in contrast to GATA1, which shows smoother behavior. We extend the network to include the downstream regulators FOG1 and CEBPA, and extract the nature of the corresponding regulatory interactions, excitatory or suppressing, between this pair and the triad by fitting to experimental gene expression time series. Based on this extended network, we simulate and explore different knockout scenarios, providing insight into the role of these regulators in the process of lineage specification, as well as predictions for future experimental validation. We address the mechanism of GATA switching as a mechanism of lineage differentiation by investigating the dynamics of FOG1 regulation by GATA2 and GATA1. Overall, this analysis strongly suggests that within this network, GATA2 is the key driver of erythroid lineage specification through its repression of PU.1, whereas GATA1 appears to be more relevant for the downstream differentiation events.

systems biology

Do Candidate Genes Affect the Brain’s White Matter Microstructure? Large-Scale Evaluation of 6,165 Diffusion MRI Scans

AbstractSusceptibility genes for psychiatric and neurological disorders - including APOE, BDNF, CLU,CNTNAP2, COMT, DISC1, DTNBP1, ErbB4, HFE, NRG1, NTKR3, and ZNF804A - have been reported to affect white matter (WM) microstructure in the healthy human brain, as assessed through diffusion tensor imaging (DTI). However, effects of single nucleotide polymorphisms (SNPs) in these genes explain only a small fraction of the overall variance and are challenging to detect reliably in single cohort studies. To date, few studies have evaluated the reproducibility of these results. As part of the ENIGMA-DTI consortium, we pooled regional fractional anisotropy (FA) measures for 6,165 subjects (CEU ancestry N=4,458) from 11 cohorts worldwide to evaluate effects of 15 candidate SNPs by examining their associations with WM microstructure. Additive association tests were conducted for each SNP. We used several meta-analytic and mega-analytic designs, and we evaluated regions of interest at multiple granularity levels. The ENIGMA-DTI protocol was able to detect single-cohort findings as originally reported. Even so, in this very large sample, no significant associations remained after multiple-testing correction for the 15 SNPs investigated. Suggestive associations (1.3x10-4 < p < 0.05, uncorrected) were found for BDNF, COMT, and ZNF804A in specific tracts. Meta-and mega-analyses revealed similar findings. Regardless of the approach, the previously reported candidate SNPs did not show significant associations with WM microstructure in this largest genetic study of DTI to date; the negative findings are likely not due to insufficient power. Genome-wide studies, involving large-scale meta-analyses, may help to discover SNPs robustly influencing WM microstructure.

neuroscience

Genetic variation and gene expression across multiple tissues and developmental stages in a non-human primate

By analyzing multi-tissue gene expression and genome-wide genetic variation data in samples from a vervet monkey pedigree, we generated a transcriptome resource and produced the first catalogue of expression quantitative trait loci (eQTLs) in a non-human primate model. This catalogue contains more genome-wide significant eQTLs, per sample, than comparable human resources, and reveals sex and age-related expression patterns. Findings include a master regulatory locus that likely plays a role in immune function, and a locus regulating hippocampal long non-coding RNAs (lncRNAs), whose expression correlates with hippocampal volume. This resource will facilitate genetic investigation of quantitative traits, including brain and behavioral phenotypes relevant to neuropsychiatric disorders.

genetics