C. elegans is a powerful model for analysis of the conserved mechanisms that modulate healthy aging. In the aging nematode nervous system, neuronal death and/or detectable loss of processes are not readily apparent, but because dendrite restructuring and loss of synaptic integrity are hypothesized to contribute to human brain decline and dysfunction, we combined fluorescence microscopy and electron microscopy (EM) to screen at high resolution for nervous system changes. We report two major components of morphological change in the aging C. elegans nervous system: 1) accumulation of novel outgrowths from specific neurons, and 2) physical decline in synaptic integrity. Novel outgrowth phenotypes, including branching from the main dendrite or new growth from somata, appear at a high frequency in some aging neurons, but not all. Mitochondria are often associated with age-associated branch sites. Lowered insulin signaling confers some maintenance of ALM and PLM neuron structural integrity into old age, and both DAF-16/FOXO and heat shock factor transcription factor HSF-1 exert neuroprotective functions. hsf-1 can act cell autonomously in this capacity. EM evaluation in synapse-rich regions reveals a striking decline in synaptic vesicle numbers and a dimunition of presynaptic density size. Interestingly, old animals that maintain locomotory prowess exhibit less synaptic decline than same-age decrepit animals, suggesting that synaptic integrity correlates with locomotory healthspan. Our data reveal similarities between the aging C. elegans nervous system and mammalian brain, suggesting conserved neuronal responses to age. Dissection of neuronal aging mechanisms in C. elegans may thus influence the development of brain healthspan-extending therapies.

NRF2 is a transcription factor serving as a master regulator of the expression of many genes involved in cellular responses to oxidative and other stresses. In the absence of stress, NRF2 is constantly synthesized but maintained at low levels as it is targeted by KEAP1 for ubiquitination and proteasome-mediated degradation. NRF2 binds KEAP1 mainly through a conserved “ETGE” motif that has also been found in several other proteins, such as DPP3, which has been shown to bind KEAP1 and enhance NRF2 function upon overexpression. Here we demonstrate the interaction between endogenous DPP3 and endogenous KEAP1. We further show that the DPP3-KEAP1 interaction is strongly induced by hydrogen peroxide and that DPP3 is required for timely NRF2 induction and nuclear accumulation in the estrogen receptor (ER)-positive MCF7 breast cancer cells. Moreover, we present evidence that the binding of DPP3 to KEAP1 stabilizes the latter. Finally, we show that DPP3 is overexpressed in breast cancer and that elevated levels of DPP3 mRNA correlate with increased NRF2 downstream gene expression and poor prognosis, particularly for ER-positive breast cancer. Our studies reveal novel insights into the regulation of NRF2 and identify DPP3 and an NRF2 transcriptional signature as potential biomarkers for breast cancer prognosis and treatment.

Cancer is mainly caused by somatic genome alterations (SGAs). Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. We developed a novel tumor-specific computational framework that finds the likely causative SGAs in an individual tumor and estimates their impact on oncogenic processes, which suggests the disease mechanisms that are acting in that tumor. This information can be used to guide precision oncology. We report a tumor-specific causal inference (TCI) framework, which estimates causative SGAs by modeling causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and estimated for each tumor the SGAs that causally regulate the differentially expressed genes (DEGs) in that tumor. Overall, TCI identified 634 SGAs that are predicted to cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both the well-known and the novel candidate drivers that are predicted by TCI. TCI provides a unified framework that integrates multiple types of SGAs and molecular phenotypes to estimate which genome perturbations are causally influencing one or more molecular/cellular phenotypes in an individual tumor. By identifying major candidate drivers and revealing their functional impact in an individual tumor, TCI sheds light on the disease mechanisms of that tumor, which can serve to advance our basic knowledge of cancer biology and to support precision oncology that provides tailored treatment of individual tumors.

CReSCENT: CanceR Single Cell ExpressioN Toolkit (https://crescent.cloud), is an intuitive and scalable web portal incorporating a containerized pipeline execution engine for standardized analysis of single-cell RNA sequencing (scRNA-seq) data. While scRNA-seq data for tumour specimens are readily generated, subsequent analysis requires high-performance computing infrastructure and user expertise to build analysis pipelines and tailor interpretation for cancer biology. CReSCENT uses public data sets and preconfigured pipelines that are accessible to computational biology non-experts and are user-editable to allow optimization, comparison, and reanalysis for specific experiments. Users can also upload their own scRNA-seq data for analysis and results can be kept private or shared with other users.

We report a tumor-specific causal inference (TCI) framework, which discovers causative somatic genome alterations (SGAs) through inferring causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and identified those SGAs that causally regulate the differentially expressed genes (DEGs) within each tumor. Overall, TCI identified 634 SGAs that cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both well-known and novel candidate drivers. By identifying major candidate drivers and revealing their functional impact in a tumor, TCI shed light on disease mechanisms of each tumor, providing useful information for advancing cancer biology and precision oncology.Significance statementsCancer is mainly caused by SGAs. Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. TCI is a novel computational framework for discovering the causative SGAs and their impact on oncogenic processes, thus revealing tumor-specific disease mechanisms. This information can be used to guide precision oncology.

Estrogen regulates over a thousand genes, with an equal number of them being induced or repressed. The distinct mechanisms underlying these dual transcriptional effects remain largely unknown. We derived comprehensive views of the transcription machineries assembled at estrogen-responsive genes through integrating multiple types of genomic data. In the absence of estrogen, the majority of genes formed higher-order chromatin structures, including DNA loops tethered to protein complexes involving RNA polymerase II (Pol II), estrogen receptor alpha (ERα) and ERα-pioneer factors. Genes to be ‘repressed’ by estrogen showed active transcription at promoters and throughout the gene bodies; genes to be ‘induced’ exhibited active transcription initiation at promoters, but with transcription paused in gene bodies. In the presence of estrogen, the majority of estrogen-induced genes retained the original higher-order chromatin structures, whereas most estrogen-repressed genes underwent a chromatin reconfiguration. For estrogen-induced genes, estrogen enhances transcription elongation, potentially through recruitment of co-activators or release of co-repressors with unique roles in elongation. For estrogen-repressed genes, estrogen treatment leads to chromatin structure reconfiguration, thereby disrupting the originally transcription-efficient chromatin structures. Our in silico studies have shown that estrogen regulates gene expression, at least in part, through modifying previously assembled higher-order complexes, rather than by facilitating de novo assembly of machineries.

An important goal of cancer genomic research is to identify the driving pathways underlying disease mechanisms and the heterogeneity of cancers. It is well known that somatic genome alterations (SGAs) affecting the genes that encode the proteins within a common signaling pathway exhibit mutual exclusivity, in which these SGAs usually do not co-occur in a tumor. With some success, this characteristic has been utilized as an objective function to guide the search for driver mutations within a pathway. However, mutual exclusivity alone is not sufficient to indicate that genes affected by such SGAs are in common pathways. Here, we propose a novel, signal-oriented framework for identifying driver SGAs. First, we identify the perturbed cellular signals by mining the gene expression data. Next, we search for a set of SGA events that carries strong information with respect to such perturbed signals while exhibiting mutual exclusivity. Finally, we design and implement an efficient exact algorithm to solve an NP-hard problem encountered in our approach. We apply this framework to the ovarian and glioblastoma tumor data available at the TCGA database, and perform systematic evaluations. Our results indicate that the signal-oriented approach enhances the ability to find informative sets of driver SGAs that likely constitute signaling pathways.

Epigenetic processes play a key role in regulating gene expression. Genetic variants that disrupt chromatin‐modifying proteins are associated with a broad range of diseases, some of which have specific epigenetic patterns, such as aberrant DNA methylation (DNAm), which may be used as disease biomarkers. While much of the epigenetic research has focused on cancer, there is a paucity of resources devoted to neurodevelopmental disorders (NDDs), which include autism spectrum disorder and many rare, clinically overlapping syndromes. To address this challenge, we created EpigenCentral, a free web resource for biomedical researchers, molecular diagnostic laboratories, and clinical practitioners to perform the interactive classification and analysis of DNAm data related to NDDs. It allows users to search for known disease‐associated patterns in their DNAm data, classify genetic variants as pathogenic or benign to assist in molecular diagnostics, or analyze patterns of differential methylation in their data through a simple web form. EpigenCentral is freely available at http://epigen.ccm.sickkids.ca/.