258 research outputs found
Evidence against use of bacterial amino acid sequence data for construction of all-inclusive phylogenetic trees.
Effect of spin-orbit coupling on the excitation spectrum of Andreev billiards
We consider the effect of spin-orbit coupling on the low energy excitation
spectrum of an Andreev billiard (a quantum dot weakly coupled to a
superconductor), using a dynamical numerical model (the spin Andreev map).
Three effects of spin-orbit coupling are obtained in our simulations: In zero
magnetic field: (1) the narrowing of the distribution of the excitation gap;
(2) the appearance of oscillations in the average density of states. In strong
magnetic field: (3) the appearance of a peak in the average density of states
at zero energy. All three effects have been predicted by random-matrix theory.Comment: 5 pages, 4 figure
The amino acid sequence of the cytochrome c2 from the phototrophic bacterium Rhodopseudomonas globiformis
Amino acid sequences of cytochrome c-554(548) and cytochrome c' from a halophilic denitrifying bacterium of the genus Paracoccus
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Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures
Objective: Type 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome-wide association studies (GWAS) have identified > 400 independent signals that encode genetic predisposition. More than 90% of associated single-nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong transcriptional regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types that express distinct hormonal programs, so each cell type may contribute differentially to the underlying regulatory processes that modulate T2D-associated transcriptional circuits. Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to islets in bulk, produce aggregate profiles that mask important cellular and regulatory heterogeneity. Methods: We present genome-wide single-cell chromatin accessibility profiles in >1,600 cells derived from a human pancreatic islet sample using single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq). We also developed a deep learning model based on U-Net architecture to accurately predict open chromatin peak calls in rare cell populations. Results: We show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, and delta cell populations and identify cell-type-specific regulatory signatures underlying T2D. Particularly, T2D GWAS SNPs are significantly enriched in beta cell-specific and across cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. We also demonstrate, using less abundant delta cells, that deep learning models can improve signal recovery and feature reconstruction of rarer cell populations. Finally, we use co-accessibility measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS signals. Conclusions: Collectively, we identify the islet cell type of action across genetic signals of T2D predisposition and provide higher-resolution mechanistic insights into genetically encoded risk pathways. Published by Elsevier GmbH.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
Detection of regulator genes and eQTLs in gene networks
Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
figure
Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.
Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition
Functional transcription factor target discovery via compendia of binding and expression profiles
Genome-wide experiments to map the DNA-binding locations of
transcription-associated factors (TFs) have shown that the number of genes
bound by a TF far exceeds the number of possible direct target genes.
Distinguishing functional from non-functional binding is therefore a major
challenge in the study of transcriptional regulation. We hypothesized that
functional targets can be discovered by correlating binding and expression
profiles across multiple experimental conditions. To test this hypothesis, we
obtained ChIP-seq and RNA-seq data from matching cell types from the human
ENCODE resource, considered promoter-proximal and distal cumulative regulatory
models to map binding sites to genes, and used a combination of linear and
non-linear measures to correlate binding and expression data. We found that a
high degree of correlation between a gene's TF-binding and expression profiles
was significantly more predictive of the gene being differentially expressed
upon knockdown of that TF, compared to using binding sites in the cell type of
interest only. Remarkably, TF targets predicted from correlation across a
compendium of cell types were also predictive of functional targets in other
cell types. Finally, correlation across a time course of ChIP-seq and RNA-seq
experiments was also predictive of functional TF targets in that tissue.Comment: 15 pages + 8 pages supplementary material; 6 figures, 6 supplementary
figures, 5 supplementary table
Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data
Mapping gene expression as a quantitative trait using whole genome-sequencing
and transcriptome analysis allows to discover the functional consequences of
genetic variation. We developed a novel method and ultra-fast software Findr
for higly accurate causal inference between gene expression traits using
cis-regulatory DNA variations as causal anchors, which improves current methods
by taking into account hidden confounders and weak regulations. Findr
outperformed existing methods on the DREAM5 Systems Genetics challenge and on
the prediction of microRNA and transcription factor targets in human
lymphoblastoid cells, while being nearly a million times faster. Findr is
publicly available at https://github.com/lingfeiwang/findrComment: New result and method sections added. 38 pages, 4 figures, 1 table.
Supplementary: 20 pages, 10 figures, 2 table
Characterisation of the global transcriptional response to heat shock and the impact of individual genetic variation
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