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Monitoring of switches in heterochromatin-induced silencing shows incomplete establishment and developmental instabilities.

ABSTRACT: Position effect variegation (PEV) in Drosophila results from new juxtapositions of euchromatic and heterochromatic chromosomal regions, and manifests as striking bimodal patterns of gene expression. The semirandom patterns of PEV, reflecting clonal relationships between cells, have been interpreted as gene-expression states that are set in development and thereafter maintained without change through subsequent cell divisions. The rate of instability of PEV is almost entirely unexplored beyond the final expression of the modified gene; thus the origin of the expressivity and patterns of PEV remain unexplained. Many properties of PEV are not predicted from currently accepted biochemical and theoretical models. In this work we investigate the time at which expressivity of silencing is set, and find that it is determined before heterochromatin exists. We employ a mathematical simulation and a corroborating experimental approach to monitor switching (i.e., gains and losses of silencing) through development. In contrast to current views, we find that gene silencing is incompletely set early in embryogenesis, but nevertheless is repeatedly lost and gained in individual cells throughout development. Our data support an alternative to locus-specific "epigenetic" silencing at variegating gene promoters that more fully accounts for the final patterns of PEV.

SUBMITTER: Bughio F

PROVIDER: S-EPMC6778184 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Monitoring of switches in heterochromatin-induced silencing shows incomplete establishment and developmental instabilities.

Bughio Farah F Huckell Gary R GR Maggert Keith A KA

Proceedings of the National Academy of Sciences of the United States of America 20190916 40

Position effect variegation (PEV) in <i>Drosophila</i> results from new juxtapositions of euchromatic and heterochromatic chromosomal regions, and manifests as striking bimodal patterns of gene expression. The semirandom patterns of PEV, reflecting clonal relationships between cells, have been interpreted as gene-expression states that are set in development and thereafter maintained without change through subsequent cell divisions. The rate of instability of PEV is almost entirely unexplored be ...[more]

PMID: 31527269

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Project description:Over the past decade, optical spectroscopy has been employed in combination with multivariate chemometric models to investigate a wide variety of diseases and pathological conditions, primarily due to its excellent chemical specificity and lack of sample preparation requirements. Despite promising results in several proof-of-concept studies, its translation to the clinical setting has often been hindered by inadequate accuracy of the conventional spectroscopic models. To address this issue and the possibility of curved (nonlinear) effects in the relationship between the concentrations of the analyte of interest and the mixture spectra (due to fluctuations in sample and environmental conditions), support vector machine-based least-squares nonlinear regression (LS-SVR) has been recently proposed. In this paper, we investigate the robustness of this methodology to noise-induced instabilities and present an analytical formula for estimating modeling precision as a function of measurement noise and model parameters. This formalism can be readily used to evaluate uncertainty in information extracted from spectroscopic measurements, particularly important for rapid-acquisition biomedical applications. Subsequently, using field data (Raman spectra) acquired from a glucose clamping study on an animal model subject, we perform the first systematic investigation of the relative effect of additive interference components (namely, noise in prediction spectra, calibration spectra, and calibration concentrations) on the prediction error of nonlinear spectroscopic models. Our results show that the LS-SVR method gives more accurate results and is substantially more robust to additive noise when compared with conventional regression methods such as partial least-squares regression (PLS), when careful selection of the LS-SVR model parameters are performed. We anticipate that these results will be useful for uncertainty estimation in similar biomedical applications where the precision of measurements and its response to noise in the data set is as important, if not more so, than the generic accuracy level.

Dataset Information

Monitoring of switches in heterochromatin-induced silencing shows incomplete establishment and developmental instabilities.

Publications

Monitoring of switches in heterochromatin-induced silencing shows incomplete establishment and developmental instabilities.

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