Current Opinion in Systems Biology最新文献

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.

Cellular burden limits the applications of bacterial synthetic biology. Experimental approaches for burden minimisation have recently become available. Tools to identify construct design with low footprint on the host include capacity monitors that quantify cellular capacity, high-throughput approaches and cell-free systems for construct prototyping. Orthogonal ribosomes and feedback controllers are instead useful to seek control of resource allocation and lower burden. Other approaches include genome reduction to increase the available resource pool and synthetic addiction to couple cell fitness and product accumulation. However, controlling the cellular response to exogenous expression is still a challenge, and more tools are needed to widen the applications of synthetic biology. Further effort that combines novel evolutionary data with burden-aware tools can set the foundation to increase the stability and robustness of future genetic systems.

Metabolism is still often regarded as a set of canonical reactions, identical in all organisms, yet that is far from correct. Metabolism and the metabolic networks required for cellular functions vary dramatically even within species. This diversity is also present in bacterial pathogens. This mini-review explores the role of metabolic convergence and divergence in shaping the metabolic network of Mycobacterium tuberculosis and its ability to survive in the host. With the help of a few selected examples, we aim to illustrate the magnitude of changes observed in M. tuberculosis metabolic network.