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1.
Commun Biol ; 7(1): 172, 2024 Feb 12.
Artículo en Inglés | MEDLINE | ID: mdl-38347116

RESUMEN

The capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy of SIMPEL is demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired with SIMPEL and isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example, SIMPEL was paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package, SIMPEL extends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux.


Asunto(s)
Carbono , Metabolómica , Isótopos de Carbono/química , Espectrometría de Masas/métodos , Metabolómica/métodos
2.
Front Plant Sci ; 12: 687652, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34354723

RESUMEN

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.

3.
PeerJ ; 8: e8592, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32461821

RESUMEN

BACKGROUND: Decreasing costs make RNA sequencing technologies increasingly affordable for biologists. However, many researchers who can now afford sequencing lack access to resources necessary for downstream analysis. This means that even as algorithms to process RNA-Seq data improve, many biologists still struggle to manage the sheer volume of data produced by next generation sequencing (NGS) technologies. Scalable bioinformatics tools that exploit multiple platforms are needed to democratize bioinformatics resources in the sequencing era. This is essential for equipping many research groups in the life sciences with the tools to process the increasingly unwieldy datasets they produce. METHODS: One strategy to address this challenge is to develop a modern generation of sequence analysis tools capable of seamless data sharing and communication. Such tools will provide interoperability through offerings of interlinked resources. Systems of interlinked, scalable resources, which often incorporate cloud data storage, are broadly referred to as cyberinfrastructure. Cyberinfrastructure integrated tools will help researchers to robustly analyze large scale datasets by efficiently sharing data burdens across a distributed architecture. Additionally, interoperability will allow emerging tools to cross-adapt features of existing tools. It is important that these tools are designed to be easy to use for biologists. RESULTS: We introduce fRNAkenseq, a powered-by-CyVerse RNA sequencing analysis tool that exhibits interoperability with other resources and meets the needs of biologists for comprehensive, easy to use RNA sequencing analysis. fRNAkenseq leverages a complex set of Application Programming Interfaces (APIs) associated with the NSF-funded cyberinfrastructure project, CyVerse, to execute FASTQ-to-differential expression RNA-Seq analyses. Integrating across bioinformatics platforms, fRNAkenseq also exploits cloud integration and cross-talk with another CyVerse associated tool, CoGe. fRNAkenseq offers novel features for the biologist such as more robust and comprehensive pipelines for enrichment than those currently available by default in a single tool, whether they are cloud-based or local installation. Importantly, cross-talk with CoGe allows fRNAkenseq users to execute RNA-Seq pipelines on an inventory of 47,000 archived genomes stored in CoGe or upload their own draft genome.

4.
BMC Genomics ; 20(1): 502, 2019 Jun 17.
Artículo en Inglés | MEDLINE | ID: mdl-31208322

RESUMEN

BACKGROUND: We present results from a computational analysis developed to integrate transcriptome and metabolomic data in order to explore the heat stress response in the liver of the modern broiler chicken. Heat stress is a significant cause of productivity loss in the poultry industry, both in terms of increased livestock morbidity and its negative influence on average feed efficiency. This study focuses on the liver because it is an important regulator of metabolism, controlling many of the physiological processes impacted by prolonged heat stress. Using statistical learning methods, we identify genes and metabolites that may regulate the heat stress response in the liver and adaptations required to acclimate to prolonged heat stress. RESULTS: We describe how disparate systems such as sugar, lipid and amino acid metabolism, are coordinated during the heat stress response. CONCLUSIONS: Our findings provide more detailed context for genomic studies and generates hypotheses about dietary interventions that can mitigate the negative influence of heat stress on the poultry industry.


Asunto(s)
Perfilación de la Expresión Génica , Respuesta al Choque Térmico/genética , Hígado/metabolismo , Metabolómica , Adaptación Fisiológica/genética , Animales , Pollos , Hígado/fisiología , Masculino
5.
Metabolites ; 10(1)2019 Dec 28.
Artículo en Inglés | MEDLINE | ID: mdl-31905618

RESUMEN

Protein and oil levels measured at maturity are inversely correlated across soybean lines; however, carbon is in limited supply during maturation resulting in tradeoffs for the production of other reserves including oligosaccharides. During the late stages of seed development, the allocation of carbon for storage reserves changes. Lipid and protein levels decline while concentrations of indigestible raffinose family oligosaccharides (RFOs) increase, leading to a decreased crop value. Since the maternal source of carbon is diminished during seed maturation stages of development, carbon supplied to RFO synthesis likely comes from an internal, turned-over source and may contribute to the reduction in protein and lipid content in mature seeds. In this study, fast neutron (FN) mutagenized soybean populations with deletions in central carbon metabolic genes were examined for trends in oil, protein, sugar, and RFO accumulation leading to an altered final composition. Two lines with concurrent increases in oil and protein, by combined 10%, were identified. A delayed switch in carbon allocation towards RFO biosynthesis resulted in extended lipid accumulation and without compromising protein. Strategies for future soybean improvement using FN resources are described.

6.
PLoS One ; 13(10): e0205824, 2018.
Artículo en Inglés | MEDLINE | ID: mdl-30365526

RESUMEN

Understanding biological response to stimuli requires identifying mechanisms that coordinate changes across pathways. One of the promises of multi-omics studies is achieving this level of insight by simultaneously identifying different levels of regulation. However, computational approaches to integrate multiple types of data are lacking. An effective systems biology approach would be one that uses statistical methods to detect signatures of relevant network motifs and then builds metabolic circuits from these components to model shifting regulatory dynamics. For example, transcriptome and metabolome data complement one another in terms of their ability to describe shifts in physiology. Here, we extend a previously described linear-modeling based method used to identify single nucleotide polymorphisms (SNPs) associated with metabolic changes. We apply this strategy to link changes in sulfur, amino acid and lipid production under heat stress by relating ratios of compounds to potential precursors and regulators. This approach provides integration of multi-omics data to link previously described, discrete units of regulation into functional pathways and identifies novel biology relevant to the heat stress response, in addition to generating hypotheses.


Asunto(s)
Ciclo del Carbono , Respuesta al Choque Térmico , Metaboloma , Biología de Sistemas , Animales , Antioxidantes/química , Carbono/química , Pollos , Biología Computacional , Biblioteca de Genes , Modelos Lineales , Lípidos/química , Masculino , Polimorfismo de Nucleótido Simple , ARN/análisis , Transcriptoma
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