In silico Design of Linear DNA for Robust Cell-Free Gene Expression.
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ABSTRACT:
SUBMITTER: Chen X
PROVIDER: S-EPMC8169995 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Cell-free gene expression systems have been valuable tools for understanding how transcription/translation can be regulated in living cells. Many studies have investigated the determining factors that affect gene expression. Here we report the effect of the length of linearized reporter DNAs encoding the firefly luciferase gene so as to exclude the influence of supercoiling. It is found that longer DNA molecules exhibit significantly greater potency in gene expression; for example, the expression level for DNA with 25.7 kbp is 1000-times higher than that for DNA of 1.7 kbp. AFM observation of the DNA conformation indicates that longer DNA takes shrunken conformation with a higher segment density in the reaction mixture for gene expression, in contrast to the stiff conformation of shorter DNA. We propose an underlying mechanism for the favorable effect of longer DNA on gene expression in terms of the enhancement of access of RNA polymerase to the shrunken conformation. It is expected that the enhancement of gene expression efficiency with a shrunken DNA conformation would also be a rather general mechanism in living cellular environments.
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Project description:Genome-wide profiling of transcription factors based on massive parallel sequencing of immunoprecipitated chromatin (ChIP-seq) requires nanogram amounts of DNA. Here we describe a high-fidelity, single-tube linear DNA amplification method (LinDA) for ChIP-seq and reChIP-seq with picogram DNA amounts obtained from a few thousand cells. This amplification technology will facilitate global analyses of transcription-factor binding and chromatin with very small cell populations, such as stem or cancer-initiating cells. In total 5 samples were generated from the F9 teratocarcinoma cell system: mRXRa-RARg-LinDA;mRXRa_100xfold-diluted_LinDA; mRXRa(1) and two others previously described in GSE30538 (mRXRa(2): !Series_sample_id=GSM757796; mRARg : !Series_sample_id=GSM757803). In addition, 14 samples were generated from the H3396 breast cancer cell system. Those samples containing the label "LinDA" has been amplified following the Linear DNA amplification method developed by Pattabhiraman et al. ((2011), Nature Methods 8, 565-67) prior library preparation for Solexa sequencing.
2011-10-11 | E-GEOD-32332 | biostudies-arrayexpress
Project description:BACKGROUND: This paper lies in the context of modeling the evolution of gene expression away from stationary states, for example in systems subject to external perturbations or during the development of an organism. We base our analysis on experimental data and proceed in a top-down approach, where we start from data on a system's transcriptome, and deduce rules and models from it without a priori knowledge. We focus here on a publicly available DNA microarray time series, representing the transcriptome of Drosophila across evolution from the embryonic to the adult stage. RESULTS: In the first step, genes were clustered on the basis of similarity of their expression profiles, measured by a translation-invariant and scale-invariant distance that proved appropriate for detecting transitions between development stages. Average profiles representing each cluster were computed and their time evolution was analyzed using coupled differential equations. A linear and several non-linear model structures involving a transcription and a degradation term were tested. The parameters were identified in three steps: determination of the strongest connections between genes, optimization of the parameters defining these connections, and elimination of the unnecessary parameters using various reduction schemes. Different solutions were compared on the basis of their abilities to reproduce the data, to keep realistic gene expression levels when extrapolated in time, to show the biologically expected robustness with respect to parameter variations, and to contain as few parameters as possible. CONCLUSIONS: We showed that the linear model did very well in reproducing the data with few parameters, but was not sufficiently robust and yielded unrealistic values upon extrapolation in time. In contrast, the non-linear models all reached the latter two objectives, but some were unable to reproduce the data. A family of non-linear models, constructed from the exponential of linear combinations of expression levels, reached all the objectives. It defined networks with a mean number of connections equal to two, when restricted to the embryonic time series, and equal to five for the full time series. These networks were compared with experimental data about gene-transcription factor and protein-protein interactions. The non-uniqueness of the solutions was discussed in the context of plasticity and cluster versus single-gene networks.
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Project description:MotivationIn the analysis of high-throughput omics data from tissue samples, estimating and accounting for cell composition have been recognized as important steps. High cost, intensive labor requirements and technical limitations hinder the cell composition quantification using cell-sorting or single-cell technologies. Computational methods for cell composition estimation are available, but they are either limited by the availability of a reference panel or suffer from low accuracy.ResultsWe introduce TOols for the Analysis of heterogeneouS Tissues TOAST/-P and TOAST/+P, two partial reference-free algorithms for estimating cell composition of heterogeneous tissues based on their gene expression profiles. TOAST/-P and TOAST/+P incorporate additional biological information, including cell-type-specific markers and prior knowledge of compositions, in the estimation procedure. Extensive simulation studies and real data analyses demonstrate that the proposed methods provide more accurate and robust cell composition estimation than existing methods.Availability and implementationThe proposed methods TOAST/-P and TOAST/+P are implemented as part of the R/Bioconductor package TOAST at https://bioconductor.org/packages/TOAST.Contactziyi.li@emory.edu or hao.wu@emory.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Genome-wide profiling of transcription factors based on massive parallel sequencing of immunoprecipitated chromatin (ChIP-seq) requires nanogram amounts of DNA. Here we describe a high-fidelity, single-tube linear DNA amplification method (LinDA) for ChIP-seq and reChIP-seq with picogram DNA amounts obtained from a few thousand cells. This amplification technology will facilitate global analyses of transcription-factor binding and chromatin with very small cell populations, such as stem or cancer-initiating cells.
2011-10-11 | GSE32332 | GEO
Project description:ObjectiveDuring routine noninvasive prenatal testing (NIPT), cell-free fetal DNA fraction is ideally derived from shallow-depth whole-genome sequencing data, preventing the need for additional experimental assays. The fraction of aligned reads to chromosome Y enables proper quantification for male fetuses, unlike for females, where advanced predictive procedures are required. This study introduces PREdict FetAl ComponEnt (PREFACE), a novel bioinformatics pipeline to establish fetal fraction in a gender-independent manner.MethodsPREFACE combines the strengths of principal component analysis and neural networks to model copy number profiles.ResultsFor sets of roughly 1100 male NIPT samples, a cross-validated Pearson correlation of 0.9 between predictions and fetal fractions according to Y chromosomal read counts was noted. PREFACE enables training with both male and unlabeled female fetuses. Using our complete cohort (nfemale = 2468, nmale = 2723), the correlation metric reached 0.94.ConclusionsAllowing individual institutions to generate optimized models sidelines between-laboratory bias, as PREFACE enables user-friendly training with a limited amount of retrospective data. In addition, our software provides the fetal fraction based on the copy number state of chromosome X. We show that these measures can predict mixed multiple pregnancies, sex chromosomal aneuploidies, and the source of observed aberrations.
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