Current Opinion in Systems Biology最新文献

While we have a solid understanding of the cell biological and biochemical control aspects of the eukaryotic cell growth and division process, much less is known about the metabolic and biosynthetic dynamics during the cell cycle. Here, we review recent discoveries made at the single-cell and population level that show that budding yeast (Saccharomyces cerevisiae) metabolism oscillates in synchrony with the cell cycle in actively dividing cells, as well as independently when the cell cycle is halted. In fact, emerging evidence suggests that the cell cycle-independent metabolic oscillations interact with elements of the cell cycle machinery via several possible mechanisms. Furthermore, recent reports indicate that different biosynthetic processes exhibit temporally changing activity patterns during the cell cycle. Thus, resources are drawn from primary metabolism in a dynamic manner, potentially giving rise to metabolic oscillations. Finally, we highlight work with mammalian cells indicating that similar metabolic dynamics might also exist in higher eukaryotes.

Aging is a major risk factor for most diseases. Pathways regulating metabolism, including nutrient sensing, energy production, and synthesis and degradation of macromolecules, have been identified as key regulators of organismal lifespan and implicated in several late-onset diseases, such as most neurodegenerative disorders. In this review, we focus on emerging evidence that links the remodeling of key organelles, namely mitochondria and lysosomes, to metabolic alterations that manifest during the aging process. We highlight data demonstrating a reciprocal interaction between organelle (dys)-function and protein homeostasis in aging. We also discuss examples of cell-type-specific metabolic alterations that can influence organ function locally and whole organism aging via inter-tissue communication. Finally, we propose how emerging methods could enable to characterize in vivo the impact of aging on organelle composition and function.

Finding a diagnosis for patients with a rare inborn metabolic disorder can be a long and difficult path. Whereas next generation sequencing is now a commonly used modality, which has significantly impacted the diagnostic yield and speed, next generation metabolic screening through untargeted metabolomics is next in line to prove its value in the diagnostic trajectory.

Untargeted metabolomics, often based on mass spectrometry platforms, is a well-established technology for the identification of novel disease markers. However, untargeted metabolomics as first line diagnostics for rare disease is now only gradually making its way into clinical practice. Most retrospective studies show that the majority of inborn metabolic disorder can be detected through untargeted metabolomics. Some diseases will still go undetected, which diagnoses are missed depends on the specific metabolomics method chosen; there is no single all-encompassing platform. Therefore, careful assessments of the opportunities and limitations are currently undertaken in prospective studies, combining untargeted metabolomics in the diagnostics setting with the current gold standard genetic and biochemical diagnostic modalities. These studies show an increased diagnostic yield when implementing untargeted metabolomics. Given the continuing technological advances, defining the optimal timing, place, and order of the various diagnostic modalities will keep on evolving in the foreseen future.

In multicellular organisms, metabolism is compartmentalized at many levels, including tissues and organs, different cell types, and subcellular compartments. Compartmentalization creates a coordinated homeostatic system where each compartment contributes to the production of energy and biomolecules that the organism needs to carry out specific metabolic tasks. Experimentally studying metabolic compartmentalization and metabolic interactions between cells and tissues in multicellular organisms is challenging at a systems level. However, recent progress in computational modeling provides an alternative approach to this problem. Here, we discuss how integrating metabolic network modeling with omics data offers an opportunity to reveal metabolic states at the level of organs, tissues and, ultimately, individual cells. We review the current status of genome-scale metabolic network models in multicellular organisms, methods to study metabolic compartmentalization in silico, and insights gained from computational analyses. We also discuss outstanding challenges and provide perspectives for the future directions of the field.

Age-related neurodegenerative diseases are a clinically unmet need with unabated prevalence around the world. Several genetic studies link these diseases with lysosomal dysfunction; however, a mechanistic understanding of how lysosomal perturbations result in neurodegeneration is unclear. Neuronopathic lysosomal storage disorders represent an attractive model for elucidating such mechanisms as they share several metabolic pathological hallmarks with common neurodegenerative diseases. This review explores how altered lipid metabolism, calcium dyshomeostasis, mitochondrial dysfunction, oxidative stress, and impaired autophagic flux contribute to cellular pathobiology in age-related neurodegeneration and neuronopathic lysosomal storage disorders. It further debates whether general lysosomal dysfunction owing to toxic substrate accumulation or extralysosomal nutrient deprivation drives these downstream processes. With increasing evidence for the latter, future studies should investigate additional lysosomal nutrients that protect against neurodegeneration using emerging subcellular ‘omics’-based technologies with the promise of identifying therapeutic targets for the treatment of neurodegenerative diseases.

The increasing amount of available high-content data in genomics, proteomics, and metabolomics has significantly improved the predictive power and model accuracy of genome-scale metabolic network models in recent years. We review recent constraint-based modeling approaches that incorporate genomics and proteomics data to form resource allocation models. Different modeling approaches to build resource allocation models and the related enzyme-constrained genome-scale metabolic models are discussed and evaluated with respect to differences regarding model features. In addition, an overview of the data required to construct, simulate and validate models for the different approaches is given, together with a list of relevant databases.

Metabolism is executed by an efficient, interconnected and ancient biochemical system, the metabolic network. Its evolutionary origins are, however, barely understood. We here discuss that because of niche adaptation, the evolutionary selection acting on the metabolic network structure distinguishes modern species and early life forms. Yet, its basic structure remained conserved over more than three billion years of diverging evolution. We speculate that this situation attributes key roles in metabolic network evolution to (i) the reaction properties of central metabolites, (ii) simple catalysts (e.g. metal ions, amino acids) whose importance remained unchanged during evolution, and (iii) the interconnectivity of the network that limits its expansion. The conservation of network structure hence implies that early life forms already used similar metabolic reaction topologies as modern species.