Metabolomics for mitochondrial and cancer studies

Metabolomics, a high-throughput global metabolite analysis, is a burgeoning field, and in recent times has shown substantial evidence to support its emerging role in cancer diagnosis, cancer recurrence, and prognosis, as well as its impact in identifying novel cancer biomarkers and developing cancer therapeutics. Newly evolving advances in disease diagnostics and therapy will further facilitate future growth in the field of metabolomics, especially in cancer, where there is a dire need for sensitive and more affordable diagnostic tools and an urgency to develop effective therapies and identify reliable biomarkers to predict accurately the response to a therapy. Here, we review the application of metabolomics in cancer and mitochondrial studies and its role in enabling the understanding of altered metabolism and malignant transformation during cancer growth and metastasis. The recent developments in the area of metabolic flux analysis may help to close the gap between clinical metabolomics research and the development of cancer metabolome. In the era of personalized medicine with more and more patient specific targeted therapies being used, we need reliable, dynamic, faster, and yet sensitive biomarkers both to track the disease and to develop and evolve therapies during the course of treatment. Recent advances in metabolomics along with the novel strategies to analyze, understand, and construct the metabolic pathways opens this window of opportunity in a very cost-effective manner.     

Opportunities for yeast metabolic engineering: Lessons from synthetic biology

Constant progress in genetic engineering has given rise to a number of promising areas of research that facilitated the expansion of industrial biotechnology. The field of metabolic engineering, which utilizes genetic tools to manipulate microbial metabolism to enhance the production of compounds of interest, has had a particularly strong impact by providing new platforms for chemical production. Recent developments in synthetic biology promise to expand the metabolic engineering toolbox further by creating novel biological components for pathway design. The present review addresses some of the recent advances in synthetic biology and how these have the potential to affect metabolic engineering in the yeast Saccharomyces cerevisiae. While S. cerevisiae for years has been a robust industrial organism and the target of multiple metabolic engineering trials, its potential for synthetic biology has remained relatively unexplored and further research in this field could strongly contribute to industrial biotechnology. This review also addresses are general considerations for pathway design, ranging from individual components to regulatory systems, overall pathway considerations and whole-organism engineering, with an emphasis on potential contributions of synthetic biology to these areas. Some examples of applications for yeast synthetic biology and metabolic engineering are also discussed.     

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.     

人工微生物混菌系统机制解析中的组学应用及进展

人工微生物混菌系统的生物工程应用价值日益受到重视,使得对于混菌系统中成员菌间的相互作用机制研究也成为近年来的一个热点.其研究结果一方面可以为现有人工混菌系统的进一步优化提供理论依据,另一方面也为全新混菌系统的人工构建提供新的思路和策略,进而促进人工微生物混菌系统未来规模化应用.基因组学、转录组学、蛋白质组学和代谢组学等...     

适应性实验室进化技术在微生物育种中的应用进展

适应性实验室进化(Adaptive laboratory evolution,ALE)技术已成为微生物学基础研究和工业微生物育种的强大工具,被广泛用来研究影响菌株表型、性能和稳定性的进化潜力以及快速获取含有有益突变的工业生产菌株。近年来,随着基因组测序技术的进步,关于微生物新陈代谢机理和动力学方面的研究变得更加广泛和深入,这也极大促进了适应性实验室进化技术的快速发展。文中主要介绍了长期、短期适应性实验室进化技术在微生物育种方面的应用实例,并总结归纳了该技术在快速高效构建优良菌株过程中的方式与作用。最后分析了目前ALE技术面临的瓶颈问题及其可能的解决方法,以期能够为该技术的未来发展提供有价值的参考依据。     

Recent advances in production of 5-aminolevulinic acid using biological strategies

5-Aminolevulinic acid (5-ALA) is the precursor for the biosynthesis of tetrapyrrole compounds and has broad applications in the medical and agricultural fields. Because of the disadvantages of chemical synthesis methods, microbial production of 5-ALA has drawn intensive attention and has been regarded as an alternative in the last years, especially with the rapid development of metabolic engineering and synthetic biology. In this mini-review, recent advances on the application and microbial production of 5-ALA using novel biological approaches (such as whole-cell enzymatic-transformation, metabolic pathway engineering and cell-free process) are described and discussed in detail. In addition, the challenges and prospects of synthetic biology are discussed.     

Metabolomic profiling reveals bacterial metabolic adaptation strategies and new metabolites

How has metabolomics helped our understanding of infectious diseases? With the threat of antimicrobial resistance to human health around the world, metabolomics has emerged as a powerful tool to comprehensively characterize metabolic pathways to identify new drug targets. However, its output is constrained to known metabolites and their metabolic pathways. Recent advances in instrumentation, methodologies, and computational mass spectrometry have accelerated the use of metabolomics to understand pathogen–host metabolic interactions. This short review discusses a selection of recent publications using metabolomics in infectious/bacterial diseases. These studies unravel the links between metabolic adaptations to environments and host metabolic responses. Moreover, they highlight the importance of enzyme function and metabolite characterization in identifying new drug targets and biomarkers, as well as precision medicine in monitoring therapeutics and diagnosing diseases.     

Metabolomics assisted metabolic network modeling and network wide analysis of metabolites in microbiology

Metabolomics is the science of qualitatively and quantitatively analyzing low molecular weight metabolites occur in a given biological system. It provides valuable information to elucidate the functional roles and relations of different metabolites in a metabolic pathway. In recent years, a large amount of research on microbial metabolomics has been conducted. It has become a useful tool for achieving highly efficient synthesis of target metabolites. At the same time, many studies have been conducted over the years in order to integrate metabolomics data into metabolic network modeling, which has yielded many exciting results. Additionally, metabolomics also shows great advantages in analyzing the relationship of metabolites network wide. Integrating metabolomics data into metabolic network construction and applying it in network wide analysis of cell metabolism would further improve our ability to control cellular metabolism and optimize the design of cell factories for the overproduction of valuable biochemicals. This review will examine recent progress in the application of metabolomics approaches in metabolic network modeling and network wide analysis of microbial cell metabolism.     

Biomarkers in renal cell carcinoma: a metabolomics approach

Since Otto Warburg, many studies have explored the unique metabolic phenotype of cancer cells highlighting the value and applicability of metabolomics in the oncology field, particularly in the development of cancer biomarkers. With respect to renal cell carcinoma (RCC), a metabolomics approach would own a great potential since urinary system is intimately connected with urine and, this biofluid, offers some advantages allowing the development of an assay suitable for use in clinical practice. Moreover, the assessment of metabolic derangements characteristics of RCC might provide a complete health assessment of this pathology, enabling the development of novel targeted therapies and even the stratification of responsive patients to specific therapeutic options improving the effectiveness of therapy. Metabolomic studies performed so far showed that the RCC metabolic signature is characterized by alterations in metabolites related to energy metabolic pathways, particularly glycolysis, amino acid and fatty acid catabolism, known to be crucial to cell proliferation. Despite some of those alterations are common to carcinogenesis, the potential role of acylcarnitines, gentisate, α-ketoglutarate and quinolinate in RCC pathophysiology has been proposed recently. The ability of metabolomics to discriminate between RCC and normal samples shows convincing evidence of its applicability in RCC management. Furthermore, the studies already carried out have not only tried to unveil the metabolic profile of RCC but also to evaluate the impact of some factors, namely technical, in RCC-metabolomics research. This type of study is pivotal in the design of metabolomics studies, helping to increase the reliability of the results. The present review updates the current knowledge on the metabolic alterations recognized in the RCC, and provides insight to the characteristics, strengths, limitations, and recent advances in RCC-metabolomics studies, always keeping in mind its potential application in clinical/health areas as a biomarker discovery tool.     

The potential of metabolomics tools in bioremediation studies

As a post-genomics tool, metabolomics is a young and vibrant field of science in its exponential growth phase. Metabolome analysis has become very popular recently, and novel techniques for acquiring and analyzing metabolomics data continue to emerge that are useful for a variety of biological studies. The bioremediation field has a lot to gain from the advances in this emerging area. Thus, this review article focuses on the potential of various experimental and conceptual approaches developed for metabolomics to be applied in bioremediation research, such as strategies for elucidation of biodegradation pathways using isotope distribution analysis and molecular connectivity analysis, the assessment of mineralization process using metabolic footprinting analysis, and the improvement of the biodegradation process via metabolic engineering. We demonstrate how the use of metabolomics tools can significantly extend and enhance the power of existing bioremediation approaches by providing a better overview of the biodegradation process.     

Recent advances in reconstructing microbial secondary metabolites biosynthesis in Aspergillus spp.

High throughput genome sequencing has revealed a multitude of potential secondary metabolites biosynthetic pathways that remain cryptic. Pathway reconstruction coupled with genetic engineering via heterologous expression enables discovery of novel compounds, elucidation of biosynthetic pathways, and optimization of product yields. Apart from Escherichia coli and yeast, fungi, especially Aspergillus spp., are well known and efficient heterologous hosts. This review summarizes recent advances in heterologous expression of microbial secondary metabolite biosynthesis in Aspergillus spp. We also discuss the technological challenges and successes in regard to heterologous host selection and DNA assembly behind the reconstruction of microbial secondary metabolite biosynthesis.     

Interactions between Helicobacter pylori infection and host metabolic homeostasis: A comprehensive review

The microbiota actively and extensively participates in the regulation of human metabolism, playing a crucial role in the development of metabolic diseases. Helicobacter pylori (H. pylori), when colonizing gastric epithelial cells, not only induces local tissue inflammation or malignant transformation but also leads to systemic and partial changes in host metabolism. These shifts can be mediated through direct contact, toxic components, or indirect immune responses. Consequently, they influence various molecular metabolic events that impact nutritional status and iron absorption in the host. Unraveling the intricate and diverse molecular interaction links between H. pylori and human metabolism modulation is essential for understanding pathogenesis mechanisms and developing targeted treatments for related diseases. However, significant challenges persist in comprehensively understanding the complex association networks among H. pylori itself, the infected host's status, the host microbiome, and the immune response. Previous metabolomics research has indicated that H. pylori infection and eradication may selectively shape the metabolite and microbial profiles of gastric lesions. Yet, it remains largely unknown how these diverse metabolic pathways, including isovaleric acid, cholesterol, fatty acids, and phospholipids, specifically modulate gastric carcinogenesis or affect the host's serum metabolism, consequently leading to the development of metabolic-associated diseases. The direct contribution of H. pylori to metabolisms still lacks conclusive evidence. In this review, we summarize recent advances in clinical evidence highlighting associations between chronic H. pylori infection and metabolic diseases, as well as its potential molecular regulatory patterns.     

Proteomics and metabolomics: the molecular make-up of toxic aromatic pollutant bioremediation

Microbial-mediated attenuation of toxic aromatic pollutants offers great potential for the restoration of contaminated environments in an ecologically acceptable manner. However, incomplete biological information regarding the regulation of growth and metabolism in many microbial communities restricts progress in the site-specific mineralization process. In the postgenomic era, recent advances in MS have allowed enormous progress in proteomics and elucidated many complex biological interactions. These research forefronts are now expanding toward the analysis of low-molecular-weight primary and secondary metabolites analysis, i.e., metabolomics. The advent of 2-DE in conjunction with MS offers a promising approach to address the molecular mechanisms of bioremediation. The two fields of proteomics and metabolomics have thus far worked separately to identify proteins and primary and secondary metabolites during bioremediation. A simultaneous study combining functional proteomics and metabolomics, i.e., proteometabolomics would create a system-wide approach to studying site-specific microorganisms during active mineralization processes. This article deals with advances in environmental proteomics and metabolomics and advocates the simultaneous study of both technologies to implement cell-free bioremediation.     

Granger causality in integrated GC–MS and LC–MS metabolomics data reveals the interface of primary and secondary metabolism

Metabolomics has emerged as a key technique of modern life sciences in recent years. Two major techniques for metabolomics in the last 10 years are gas chromatography coupled to mass spectrometry (GC–MS) and liquid chromatography coupled to mass spectrometry (LC–MS). Each platform has a specific performance detecting subsets of metabolites. GC–MS in combination with derivatisation has a preference for small polar metabolites covering primary metabolism. In contrast, reversed phase LC–MS covers large hydrophobic metabolites predominant in secondary metabolism. Here, we present an integrative metabolomics platform providing a mean to reveal the interaction of primary and secondary metabolism in plants and other organisms. The strategy combines GC–MS and LC–MS analysis of the same sample, a novel alignment tool MetMAX and a statistical toolbox COVAIN for data integration and linkage of Granger Causality with metabolic modelling. For metabolic modelling we have implemented the combined GC–LC–MS metabolomics data covariance matrix and a stoichiometric matrix of the underlying biochemical reaction network. The changes in biochemical regulation are expressed as differential Jacobian matrices. Applying the Granger causality, a subset of secondary metabolites was detected with significant correlations to primary metabolites such as sugars and amino acids. These metabolic subsets were compiled into a stoichiometric matrix N. Using N the inverse calculation of a differential Jacobian J from metabolomics data was possible. Key points of regulation at the interface of primary and secondary metabolism were identified.     

Thiamine in plants: Aspects of its metabolism and functions

Thiamine diphosphate (vitamin B₁) plays a fundamental role as an enzymatic cofactor in universal metabolic pathways including glycolysis, the pentose phosphate pathway, and the tricarboxylic acid cycle. In addition, thiamine diphosphate has recently been shown to have functions other than as a cofactor in response to abiotic and biotic stress in plants. Recently, several steps of the plant thiamine biosynthetic pathway have been characterized, and a mechanism of feedback regulation of thiamine biosynthesis via riboswitch has been unraveled. This review focuses on these most recent advances made in our understanding of thiamine metabolism and functions in plants. Phenotypes of plant mutants affected in thiamine biosynthesis are described, and genomics, proteomics, and metabolomics data that have increased further our knowledge of plant thiamine metabolic pathways and functions are summarized. Aspects of thiamine metabolism such as catabolism, salvage, and transport in plants are discussed.     

Combining Chemoinformatics with Bioinformatics: In Silico Prediction of Bacterial Flavor-Forming Pathways by a Chemical Systems Biology Approach “Reverse Pathway Engineering”

The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the “missing links” between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology.