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Shay, J.

Publications and source records attributed to Shay, J..

4 recordsLinked to original sources

A Distinct Mode of Chromosome-End Genome Instability Revealed by Circulome Characterization and Analyses

Genome instability is a central hallmark of cancer and it is characterized largely from sequence: mutation, copy-number change and rearrangement scored against a linear reference. The genome has additional degrees of freedom, such as the physical form (topology) of the DNA itself. A specific mode of localized genome dynamics changes both dimensions (sequence and topology) at once: defined chromosomal regions are also represented as extrachromosomal circular DNA (eccDNA). The biogenesis of eccDNA is a feature of many cancers, supplying the tumor with genetic elements that can participate in phenotypic selection, and supplying the observer with a window into the genomic instability landscape. Genome-wide maps of where these circles arise have not been available at scale or at high resolution. Building such maps requires physical selection of circular species, because most sequenced reads from unselected DNA derive from unrearranged sequence, conflating the signal from dynamic rearrangements with that from bulk chromosomal DNA. Here we combine biophysical and biochemical isolation of circular DNA with next-generation sequencing, applying it to a panel of cancer cell lines and to patient tumors, with non-cancerous cells and adjacent normal tissues as controls. The resulting genome-wide maps of the circular-element repertoire, the Circulome, reveal a consistent and previously unreported enrichment of these elements at particular chromosome ends. The circles map to regions extending well past the telomeres, reaching varying distances up to tens of megabases inward. This activity is accompanied by corroborating rearrangements within the linear genome, consistent with regions of chromosomal instability. Together these features define a spatially localized mode of instability in cancer, which we term Erosion at Distal Genomic Ends (EDGE). We formulate a classification problem to discriminate bladder tumor from matched normal tissue, finding that the EDGE signal outperforms the mean of all interior chromosomal regions, for each chromosome. These findings define a distinct spatial signature of cancer chromosome instability detectable through Circulome analysis, and connect early models of region-specific instability to the human cancer genome.

genomics↗

Interpretable deep learning framework towards understanding molecular changes in human brains with Alzheimer's disease: implication for microglia activation and sex differences in AD

INTRODUCTIONThe objective of this study is to characterize the dysregulation of gene expression in AD affected brain tissues through an interpretable deep learning framework. METHODSWe trained multi-layer perceptron models for the classification of neuropathologically confirmed AD vs. controls using transcriptomic data from three brain regions of ROSMAP study. The disease spectrum was then modeled as a progressive trajectory. SHAP value was derived to explain model predictions and identify significantly implicated genes for subsequent gene co-expression network analysis. RESULTSThe models achieved excellent performance in classification and prediction in two external datasets from Mayo RNA-seq cohort and Mount Sinai Brain Bank cohort. SHAP explainer revealed common and specific transcriptomic signatures from different brain regions. DISCUSSIONWe identified common gene signatures among different brain regions in microglia and sex specific modules in neurons implicated in AD. This work paves the way for utilizing artificial intelligence approaches in studying AD at the molecular level. Research-in-ContextO_LISystematic review: Postmortem brain transcriptomes have been analyzed to study the molecular changes associated with Alzheimers disease, usually by a direct contrast approach such as differential gene expression analysis. Nuanced gene regulatory networks thus cannot be easily pinpointed from convoluted data such as those from bulk-tissue profiling. We applied a novel interpretable deep learning approach to dissect the RNA-seq data collected from three different brain regions of a large clinical cohort and identified significant genes for network analysis implicated for AD. C_LIO_LIInterpretation: Our models successfully predicted neuropathological and clinical traits in both internal and external validations. We corroborated known microglial biology in addition to revealing novel sex chromosome-linked gene contributing to sex dimorphism in AD. C_LIO_LIFuture directions: The framework could have broad utility for interpreting multi-omic data such as those from single-cell profiling, to advance our understanding of molecular mechanisms of complex human disease such as AD. C_LI HighlightsO_LIWe applied novel interpretable deep learning methods to postmortem brain transcriptomes from three different brain regions C_LIO_LIWe interpreted the models to identify genes most strongly implicated in AD C_LIO_LINetwork analysis corroborated known microglial biology and revealed novel sex specific transcriptional factors associated with neuronal loss in AD C_LI

neuroscience↗

Single-cell analysis of human lung epithelia reveals concomitant expression of the SARS-CoV-2 receptor ACE2 with multiple virus receptors and scavengers in alveolar type II cells

The novel coronavirus SARS-CoV-2 was identified as the causative agent of the ongoing pandemic COVID 19. COVID-19-associated deaths are mainly attributed to severe pneumonia and respiratory failure. Recent work demonstrated that SARS-CoV-2 binds to angiotensin converting enzyme 2 (ACE2) in the lung. To better understand ACE2 abundance and expression patterns in the lung we interrogated our in-house single-cell RNA-sequencing dataset containing 70,085 EPCAM+ lung epithelial cells from paired normal and lung adenocarcinoma tissues. Transcriptomic analysis revealed a diverse repertoire of airway lineages that included alveolar type I and II, bronchioalveolar, club/secretory, quiescent and proliferating basal, ciliated and malignant cells as well as rare populations such as ionocytes. While the fraction of lung epithelial cells expressing ACE2 was low (1.7% overall), alveolar type II (AT2, 2.2% ACE2+) cells exhibited highest levels of ACE2 expression among all cell subsets. Further analysis of the AT2 compartment (n = 27,235 cells) revealed a number of genes co-expressed with ACE2 that are important for lung pathobiology including those associated with chronic obstructive pulmonary disease (COPD; HHIP), pneumonia and infection (FGG and C4BPA) as well as malarial/bacterial (CD36) and viral (DMBT1) scavenging which, for the most part, were increased in smoker versus light or non-smoker cells. Notably, DMBT1 was highly expressed in AT2 cells relative to other lung epithelial subsets and its expression positively correlated with ACE2. We describe a population of ACE2-positive AT2 cells that co-express pathogen (including viral) receptors (e.g. DMBT1) with crucial roles in host defense thus comprising plausible phenotypic targets for treatment of COVID-19.

genomics↗

T-loops Refold after Telomerase Extension and Unfold Again at Late S/G2 for C-strand Fill-in

Telomeres are the structures that protect the ends of each chromosome and prevent them from being recognized as broken DNA in need of repair. During human fetal development telomere length is ~15 kb and is maintained by the ribonucleoprotein telomerase. In neonates telomerase becomes spliced into inactive forms in most tissues. Afterwards, lagging strand synthesis fails to copy the very end and telomeres become progressively shorter as cells divide. Part of the mechanism for avoiding the DNA damage machinery is by forming a t-loop structure that has no free end. The telomeric 3 overhang is inserted into the duplex DNA where it displaces the G-rich strand, forming a D-loop. It is unknown whether this D-loop is the size of the overhang, or whether branch migration results in a larger D-loop. Telomere t-loops must unfold for replication and telomerase extension during S phase. It is also unknown whether t-loops remain unfolded after replication until C-strand fill-in at late S/G2, or if they refold after replication and then unfold again for fill-in at late S/G2. We have developed a biochemical t-loop assay to address these issues, and demonstrate that most t-loops persist throughout S phase both before and after their replication. HighlightsO_LIT-loops unfold twice during each S-phase C_LIO_LIVirtually all telomeres are packaged into t-loops C_LIO_LILarge amounts of t-loops can easily be purified without specialized materials C_LI

molecular biology↗