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Play it loud : instruments of rock & roll
Play It Loud celebrates the musical instruments that gave rock and roll its signature sound-from Louis Jordan's alto saxophone and John Lennon's Rickenbacker to the drum set owned by Metallica's Lars Ulrich, Lady Gaga's keytar, and beyond. Seven engrossing essays by veteran music journalists and scholars discuss the technical developments that fostered rock's seductive riffs and driving rhythms, the thrilling innovations musicians have devised to achieve unique effects, and the visual impact their instruments have had. Abundant photographs depict rock's most iconic instruments-including Jerry Lee Lewis's baby grand piano, Chuck Berry's Gibson ES-350T guitar, Bootsy Collins's star-shaped bass, Keith Moon's drum set, and the white Stratocaster Jimi Hendrix played at Woodstock-as works of art in their own right. Produced in collaboration with the Rock & Roll Hall of Fame, this astounding book goes behind the music to offer a rare and in-depth look at the instruments that inspired the musicians and made possible the songs we know and love. Exhibition: The Metropolitan Museum of Art, New York, USA (01.04-15.09.2019); The Rock & Roll Hall of Fame, Cleveland, USA (20.11.2019-13.09.2020). -- Book jacket.
Book
Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays
We use high-density single nucleotide polymorphism (SNP) genotyping microarrays to demonstrate the ability to accurately and robustly determine whether individuals are in a complex genomic DNA mixture. We first develop a theoretical framework for detecting an individual's presence within a mixture, then show, through simulations, the limits associated with our method, and finally demonstrate experimentally the identification of the presence of genomic DNA of specific individuals within a series of highly complex genomic mixtures, including mixtures where an individual contributes less than 0.1% of the total genomic DNA. These findings shift the perceived utility of SNPs for identifying individual trace contributors within a forensics mixture, and suggest future research efforts into assessing the viability of previously sub-optimal DNA sources due to sample contamination. These findings also suggest that composite statistics across cohorts, such as allele frequency or genotype counts, do not mask identity within genome-wide association studies. The implications of these findings are discussed.
Journal Article
Small cell carcinoma of the ovary, hypercalcemic type, displays frequent inactivating germline and somatic mutations in SMARCA4
Jeffrey Trent, David Huntsman and colleagues identify the SWI/SNF chromatin-remodeling gene
SMARCA4
as commonly mutated in small cell carcinoma of the ovary, hypercalcemic type (SCCOHT). Their results implicate SMARCA4 as a crucial factor in the oncogenesis of SCCOHT, a rare but highly malignant cancer.
Small cell carcinoma of the ovary of hypercalcemic type (SCCOHT) is an extremely rare, aggressive cancer affecting children and young women. We identified germline and somatic inactivating mutations in the SWI/SNF chromatin-remodeling gene
SMARCA4
in 69% (9/13) of SCCOHT cases in addition to SMARCA4 protein loss in 82% (14/17) of SCCOHT tumors but in only 0.4% (2/485) of other primary ovarian tumors. These data implicate
SMARCA4
in SCCOHT oncogenesis.
Journal Article
Integrated Genomic Characterization Reveals Novel, Therapeutically Relevant Drug Targets in FGFR and EGFR Pathways in Sporadic Intrahepatic Cholangiocarcinoma
Advanced cholangiocarcinoma continues to harbor a difficult prognosis and therapeutic options have been limited. During the course of a clinical trial of whole genomic sequencing seeking druggable targets, we examined six patients with advanced cholangiocarcinoma. Integrated genome-wide and whole transcriptome sequence analyses were performed on tumors from six patients with advanced, sporadic intrahepatic cholangiocarcinoma (SIC) to identify potential therapeutically actionable events. Among the somatic events captured in our analysis, we uncovered two novel therapeutically relevant genomic contexts that when acted upon, resulted in preliminary evidence of anti-tumor activity. Genome-wide structural analysis of sequence data revealed recurrent translocation events involving the FGFR2 locus in three of six assessed patients. These observations and supporting evidence triggered the use of FGFR inhibitors in these patients. In one example, preliminary anti-tumor activity of pazopanib (in vitro FGFR2 IC50≈350 nM) was noted in a patient with an FGFR2-TACC3 fusion. After progression on pazopanib, the same patient also had stable disease on ponatinib, a pan-FGFR inhibitor (in vitro, FGFR2 IC50≈8 nM). In an independent non-FGFR2 translocation patient, exome and transcriptome analysis revealed an allele specific somatic nonsense mutation (E384X) in ERRFI1, a direct negative regulator of EGFR activation. Rapid and robust disease regression was noted in this ERRFI1 inactivated tumor when treated with erlotinib, an EGFR kinase inhibitor. FGFR2 fusions and ERRFI mutations may represent novel targets in sporadic intrahepatic cholangiocarcinoma and trials should be characterized in larger cohorts of patients with these aberrations.
Journal Article
Multi-modality machine learning predicting Parkinson’s disease
Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson’s disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug–gene interactions. We performed automated ML on multimodal data from the Parkinson’s progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson’s Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.
Journal Article
Resolving spatial subclonal genomic heterogeneity of loss of heterozygosity and extrachromosomal DNA in gliomas
Mapping the spatial organization of DNA-level somatic copy number changes in tumors can provide insight to understanding higher-level molecular and cellular processes that drive pathogenesis. We describe an integrated framework of spatial transcriptomics, tumor/normal DNA sequencing, and bulk RNA sequencing to identify shared and distinct characteristics of an initial cohort of eleven gliomas of varied pathology and a replication cohort of six high-grade glioblastomas. We identify focally amplified extrachromosomal DNA (ecDNA) in four of the eleven initial gliomas, with subclonal tumor heterogeneity in two
EGFR
-amplified grade IV glioblastomas. In a
TP53
-mutated glioblastoma, we detect a subclone with
EGFR
amplification on ecDNA coupled to chromosome 17 loss of heterozygosity. To validate subclonal somatic aneuploidy and copy number alterations associated with ecDNA double minutes, we examine the replication cohort, identifying
MDM2/MDM4
ecDNA subclones in two glioblastomas. The spatial heterogeneity of
EGFR
and p53 inactivation underscores the role of ecDNA in enabling rapid oncogene amplification and enhancing tumor adaptability under selective pressure.
Currently, there is limited knowledge about the spatial heterogeneity of glioma-driving molecular events. Here, the authors employ a multiomics approach to characterize the spatial transcriptomic heterogeneity of various types of gliomas and identify spatially distinct tumor subclones with genomic plasticity driven by mutations on extrachromosomal DNA.
Journal Article
Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort
A genome-wide, whole brain approach to investigate genetic effects on neuroimaging phenotypes for identifying quantitative trait loci is described. The Alzheimer's Disease Neuroimaging Initiative 1.5 T MRI and genetic dataset was investigated using voxel-based morphometry (VBM) and FreeSurfer parcellation followed by genome-wide association studies (GWAS). One hundred forty-two measures of grey matter (GM) density, volume, and cortical thickness were extracted from baseline scans. GWAS, using PLINK, were performed on each phenotype using quality-controlled genotype and scan data including 530,992 of 620,903 single nucleotide polymorphisms (SNPs) and 733 of 818 participants (175 AD, 354 amnestic mild cognitive impairment, MCI, and 204 healthy controls, HC). Hierarchical clustering and heat maps were used to analyze the GWAS results and associations are reported at two significance thresholds (p<10−7 and p<10−6). As expected, SNPs in the APOE and TOMM40 genes were confirmed as markers strongly associated with multiple brain regions. Other top SNPs were proximal to the EPHA4, TP63 and NXPH1 genes. Detailed image analyses of rs6463843 (flanking NXPH1) revealed reduced global and regional GM density across diagnostic groups in TT relative to GG homozygotes. Interaction analysis indicated that AD patients homozygous for the T allele showed differential vulnerability to right hippocampal GM density loss. NXPH1 codes for a protein implicated in promotion of adhesion between dendrites and axons, a key factor in synaptic integrity, the loss of which is a hallmark of AD. A genome-wide, whole brain search strategy has the potential to reveal novel candidate genes and loci warranting further investigation and replication.
Journal Article
Small RNA Deep Sequencing Identifies a Unique miRNA Signature Released in Serum Exosomes in a Mouse Model of Sjögren's Syndrome
Sjögren's Syndrome (SS) is an autoimmune disease characterized by lymphocytic infiltration and loss of function of moisture-producing exocrine glands as well as systemic inflammation. SS diagnosis is cumbersome, subjective and complicated by manifestation of symptoms that overlap with those of other rheumatic and ocular diseases. Definitive diagnosis averages 4-5 years and this delay may lead to irreversible tissue damage. Thus, there is an urgent need for diagnostic biomarkers for earlier detection of SS. Extracellular vesicles called exosomes carry functional small non-coding RNAs which play a critical role in maintaining cellular homeostasis via transcriptional and translational regulation of mRNA. Alterations in levels of specific exosomal miRNAs may be predictive of disease status. Here, we have assessed serum exosomal RNA using next generation sequencing in a discovery cohort of the NOD mouse, a model of early-intermediate SS, to identify dysregulated miRNAs that may be indicative of SS. We found five miRNAs upregulated in serum exosomes of NOD mice with an adjusted
< 0.05-miRNA-127-3p, miRNA-409-3p, miRNA-410-3p, miRNA-541-5p, and miRNA-540-5p. miRNAs 127-3p and 541-5p were also statistically significantly upregulated in a validation cohort of NOD mice. Pathway analysis and existing literature indicates that differential expression of these miRNAs may dysregulate pathways involved in inflammation. Future studies will apply these findings in a human cohort to understand how they are correlated with manifestations of SS as well as understanding their functional role in systemic autoimmunity specific to SS.
Journal Article
A survey of genetic human cortical gene expression
It is widely assumed that genetic differences in gene expression underpin much of the difference among individuals and many of the quantitative traits of interest to geneticists. Despite this, there has been little work on genetic variability in human gene expression and almost none in the human brain, because tools for assessing this genetic variability have not been available. Now, with whole-genome SNP genotyping arrays and whole-transcriptome expression arrays, such experiments have become feasible. We have carried out whole-genome genotyping and expression analysis on a series of 193 neuropathologically normal human brain samples using the Affymetrix GeneChip Human Mapping 500K Array Set and Illumina HumanRefseq-8 Expression BeadChip platforms. Here we present data showing that 58% of the transcriptome is cortically expressed in at least 5% of our samples and that of these cortically expressed transcripts, 21% have expression profiles that correlate with their genotype. These genetic-expression effects should be useful in determining the underlying biology of associations with common diseases of the human brain and in guiding the analysis of the genomic regions involved in the control of normal gene expression.
Journal Article
Autism and Increased Paternal Age Related Changes in Global Levels of Gene Expression Regulation
A causal role of mutations in multiple general transcription factors in neurodevelopmental disorders including autism suggested that alterations in global levels of gene expression regulation might also relate to disease risk in sporadic cases of autism. This premise can be tested by evaluating for changes in the overall distribution of gene expression levels. For instance, in mice, variability in hippocampal-dependent behaviors was associated with variability in the pattern of the overall distribution of gene expression levels, as assessed by variance in the distribution of gene expression levels in the hippocampus. We hypothesized that a similar change in variance might be found in children with autism. Gene expression microarrays covering greater than 47,000 unique RNA transcripts were done on RNA from peripheral blood lymphocytes (PBL) of children with autism (n = 82) and controls (n = 64). Variance in the distribution of gene expression levels from each microarray was compared between groups of children. Also tested was whether a risk factor for autism, increased paternal age, was associated with variance. A decrease in the variance in the distribution of gene expression levels in PBL was associated with the diagnosis of autism and a risk factor for autism, increased paternal age. Traditional approaches to microarray analysis of gene expression suggested a possible mechanism for decreased variance in gene expression. Gene expression pathways involved in transcriptional regulation were down-regulated in the blood of children with autism and children of older fathers. Thus, results from global and gene specific approaches to studying microarray data were complimentary and supported the hypothesis that alterations at the global level of gene expression regulation are related to autism and increased paternal age. Global regulation of transcription, thus, represents a possible point of convergence for multiple etiologies of autism and other neurodevelopmental disorders.
Journal Article