UBQLN2 is one of a family of proteins implicated in ubiquitin-dependent protein quality control and integrally tied to human neurodegenerative disease. Whereas wild-type UBQLN2 accumulates in intraneuronal deposits in several common age-related neurodegenerative diseases, mutations in the gene encoding this protein result in X-linked amyotrophic lateral sclerosis/frontotemporal dementia associated with TDP43 accumulation. Using in vitro protein analysis, longitudinal fluorescence imaging and cellular, neuronal, and transgenic mouse models, we establish that UBQLN2 is intrinsically prone to self-assemble into higher-order complexes, including liquid-like droplets and amyloid aggregates. UBQLN2 self-assembly and solubility are reciprocally modulated by the protein’s ubiquitin-like and ubiquitin-associated domains. Moreover, a pathogenic UBQLN2 missense mutation impairs droplet dynamics and favors amyloid-like aggregation associated with neurotoxicity. These data emphasize the critical link between UBQLN2’s role in ubiquitin-dependent pathways and its propensity to self-assemble and aggregate in neurodegenerative diseases.

Several metabolites serve as substrates for histone modifications and communicate changes in the metabolic environment to the epigenome. Technologies such as metabolomics and proteomics have allowed us to reconstruct the interactions between metabolic pathways and histones. These technologies have shed light on how nutrient availability can have a dramatic effect on various histone modifications. This metabolism–epigenome cross talk plays a fundamental role in development, immune function, and diseases like cancer. Yet, major challenges remain in understanding the interactions between cellular metabolism and the epigenome. How the levels and fluxes of various metabolites impact epigenetic marks is still unclear. Discussed herein are recent applications and the potential of systems biology methods such as flux tracing and metabolic modeling to address these challenges and to uncover new metabolic–epigenetic interactions. These systems approaches can ultimately help elucidate how nutrients shape the epigenome of microbes and mammalian cells.

Cis-regulatory elements control gene expression and are dynamic in their structure, reflecting changes to the composition of diverse effector proteins over time. Here we sought to connect the structural changes at cis-regulatory elements to alterations in cellular fate and function. To do this we developed PRINT, a computational method that uses deep learning to correct sequence bias in chromatin accessibility data and identifies multi-scale footprints of DNA-protein interactions. We find that multi-scale footprints enable more accurate inference of TF and nucleosome binding. Using PRINT with single-cell multi-omics, we discover wide-spread changes to the structure and function of candidate cis-regulatory elements (cCREs) across hematopoiesis, wherein nucleosomes slide, expose DNA for TF binding, and promote gene expression. Activity segmentation using the co-variance across cell states identifies "sub-cCREs" as modular cCRE subunits of regulatory DNA. We apply this single-cell and PRINT approach to characterize the age-associated alterations to cCREs within hematopoietic stem cells (HSCs). Remarkably, we find a spectrum of aging alterations among HSCs corresponding to a global gain of sub-cCRE activity while preserving cCRE accessibility. Collectively, we reveal the functional importance of cCRE structure across cell states, highlighting changes to gene regulation at single-cell and single-base-pair resolution.