Topology and Computational Performance of Attractor Neural Networks

To explore the relation between network structure and function, we studied the computational performance of Hopfield-type attractor neural nets with regular lattice, random, small-world and scale-free topologies. The random net is the most efficient for storage and retrieval of patterns by the entire network. However, in the scale-free case retrieval errors are not distributed uniformly: the portion of a pattern encoded by the subset of highly connected nodes is more robust and efficiently recognized than the rest of the pattern. The scale-free network thus achieves a very strong partial recognition. Implications for brain function and social dynamics are suggestive.Comment: 2 figures included. Submitted to Phys. Rev. Letter

Correlated alterations in genome organization, histone methylation, and DNA-lamin A/C interactions in Hutchinson-Gilford progeria syndrome

Hutchinson-Gilford progeria syndrome (HGPS) is a premature aging disease that is frequently caused by a de novo point mutation at position 1824 in LMNA. This mutation activates a cryptic splice donor site in exon 11, and leads to an in-frame deletion within the prelamin A mRNA and the production of a dominant negative lamin A protein, known as progerin. Here we show that primary HGPS skin fibroblasts experience genome-wide correlated alterations in patterns of H3K27me3 deposition, DNA-lamin A/C associations, and, at late passages, genome-wide loss of spatial compartmentalization of active and inactive chromatin domains. We further demonstrate that the H3K27me3 changes associate with gene expression alterations in HGPS cells. Our results support a model that the accumulation of progerin in the nuclear lamina leads to altered H3K27me3 marks in heterochromatin, possibly through the down-regulation of EZH2, and disrupts heterochromatin-lamina interactions. These changes may result in transcriptional misregulation and eventually trigger the global loss of spatial chromatin compartmentalization in late passage HGPS fibroblasts

BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues

Abstract Background Bisulfite sequencing is widely employed to study the role of DNA methylation in disease; however, the data suffer from biases due to coverage depth variability. Imputation of methylation values at low-coverage sites may mitigate these biases while also identifying important genomic features associated with predictive power. Results Here we describe BoostMe, a method for imputing low-quality DNA methylation estimates within whole-genome bisulfite sequencing (WGBS) data. BoostMe uses a gradient boosting algorithm, XGBoost, and leverages information from multiple samples for prediction. We find that BoostMe outperforms existing algorithms in speed and accuracy when applied to WGBS of human tissues. Furthermore, we show that imputation improves concordance between WGBS and the MethylationEPIC array at low WGBS depth, suggesting improved WGBS accuracy after imputation. Conclusions Our findings support the use of BoostMe as a preprocessing step for WGBS analysis.https://deepblue.lib.umich.edu/bitstream/2027.42/143848/1/12864_2018_Article_4766.pd

Recurrent de novo point mutations in lamin A cause Hutchinson-Gilford progeria syndrome

Hutchinson-Gilford progeria syndrome (HGPS) is a rare genetic disorder characterized by features reminiscent of marked premature ageing(1,2). Here, we present evidence of mutations in lamin A (LMNA) as the cause of this disorder. The HGPS gene was initially localized to chromosome 1q by observing two cases of uniparental isodisomy of 1q - the inheritance of both copies of this material from one parent - and one case with a 6-megabase paternal interstitial deletion. Sequencing of LMNA, located in this interval and previously implicated in several other heritable disorders(3,4), revealed that 18 out of 20 classical cases of HGPS harboured an identical de novo ( that is, newly arisen and not inherited) single-base substitution, G608G( GGC > GGT), within exon 11. One additional case was identified with a different substitution within the same codon. Both of these mutations result in activation of a cryptic splice site within exon 11, resulting in production of a protein product that deletes 50 amino acids near the carboxy terminus. Immunofluorescence of HGPS fibroblasts with antibodies directed against lamin A revealed that many cells show visible abnormalities of the nuclear membrane. The discovery of the molecular basis of this disease may shed light on the general phenomenon of human ageing.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62684/1/nature01629.pd

Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.

Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that DL methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies

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]

Everolimus rescues multiple cellular defects in laminopathy-patient fibroblasts

LMNA encodes the A-type lamins that are part of the nuclear scaffold. Mutations in LMNA can cause a variety of disorders called laminopathies, including Hutchinson-Gilford progeria syndrome (HGPS), atypical Werner syndrome, and Emery-Dreifuss muscular dystrophy. Previous work has shown that treatment of HGPS cells with the mTOR inhibitor rapamycin or with the rapamycin analog everolimus corrects several of the phenotypes seen at the cellular level—at least in part by increasing autophagy and reducing the amount of progerin, the toxic form of lamin A that is overproduced in HGPS patients. Since other laminopathies also result in production of abnormal and potentially toxic lamin proteins, we hypothesized that everolimus would also be beneficial in those disorders. To test this, we applied everolimus to fibroblast cell lines from six laminopathy patients, each with a different mutation in LMNA. Everolimus treatment increased proliferative ability and delayed senescence in all cell lines. In several cell lines, we observed that with treatment, there is a significant improvement in nuclear blebbing, which is a cellular hallmark of HGPS and other lamin disorders. These preclinical results suggest that everolimus might have clinical benefit for multiple laminopathy syndromes

Genetic regulatory signatures underlying islet gene expression and type 2 diabetes

The majority of genetic variants associated with type 2 diabetes (T2D) are located outside of genes in noncoding regions that may regulate gene expression in disease-relevant tissues, like pancreatic islets. Here, we present the largest integrated analysis to date of high-resolution, high-throughput human islet molecular profiling data to characterize the genome (DNA), epigenome (DNA packaging), and transcriptome (gene expression). We find that T2D genetic variants are enriched in regions of the genome where transcription Regulatory Factor X (RFX) is predicted to bind in an islet-specific manner. Genetic variants that increase T2D risk are predicted to disrupt RFX binding, providing a molecular mechanism to explain how the genome can influence the epigenome, modulating gene expression and ultimately T2D risk

Preliminary conceptual design of commercial geopressured geothermal fuel plants

Previous feasibility studies Bechtel (1975), TRW (1975) for electric power generation utilizing geothermal resources have tended to focus primarily on the power plant and have neglected the fuel production and effluent disposal facilities. The Dow Chemical USA study (1974) for the Governor's Energy Advisory Council, State of Texas, placed equal emphasis on the power plant and the fuel plant. The study reported in Chapter II and in what follows in this chapter, also places equal emphasis on the two types of facilities. It is important that the fuel plant, the well field, the fuel processing plant, and the effluent disposal facility be the subject of a preliminary conceptual design and costing activity so that economic and net energetics analysis can be performed. The activity also serves to assess technological maturity of the fuel plant and to identify technical problems requiring further study. The resource considered was the model resource outlined in Sectio B, Chapter II. Fuel plants were outlined for three power generation plants: single-stage flash steam, two-stage flash steam, and propane secondary working fluid plant

Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle

We integrate comeasured gene expression and DNA methylation (DNAme) in 265 human skeletal muscle biopsies from the FUSION study with >7 million genetic variants and eight physiological traits: height, waist, weight, waist-hip ratio, body mass index, fasting serum insulin, fasting plasma glucose, and type 2 diabetes. We find hundreds of genes and DNAme sites associated with fasting insulin, waist, and body mass index, as well as thousands of DNAme sites associated with gene expression (eQTM). We find that controlling for heterogeneity in tissue/muscle fiber type reduces the number of physiological trait associations, and that long-range eQTMs (>1 Mb) are reduced when controlling for tissue/muscle fiber type or latent factors. We map genetic regulators (quantitative trait loci; QTLs) of expression (eQTLs) and DNAme (mQTLs). Using Mendelian randomization (MR) and mediation techniques, we leverage these genetic maps to predict 213 causal relationships between expression and DNAme, approximately two-thirds of which predict methylation to causally influence expression. We use MR to integrate FUSION mQTLs, FUSION eQTLs, and GTEx eQTLs for 48 tissues with genetic associations for 534 diseases and quantitative traits. We identify hundreds of genes and thousands of DNAme sites that may drive the reported disease/quantitative trait genetic associations. We identify 300 gene expression MR associations that are present in both FUSION and GTEx skeletal muscle and that show stronger evidence of MR association in skeletal muscle than other tissues, which may partially reflect differences in power across tissues. As one example, we find that increased RXRA muscle expression may decrease lean tissue mass.Peer reviewe