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.