Omics-Driven Biotechnology for Industrial Applications

Amer, Bashar; Baidoo, Edward E. K.

doi:10.3389/fbioe.2021.613307

Cited by 65 publications

(40 citation statements)

References 208 publications

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“…Several molecular mechanisms, including the oxidative stress stimulation and nutrient-sensing pathways, have been proposed to explain the role of CR for improving health and extending life [ 3 ]. Recent advances in science and technology have allowed omics-based (e.g., transcriptomics, proteomics, metabolomics) deep investigation of the molecular mechanism of CR [ 4 , 5 ]. However, it is still unclear how CR delays the aging process and the surge of age-related diseases.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Gene Expression Profiles of Rat Tissues after Mild and Short-Term Calorie Restrictions

et al. 2021

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Many studies have shown the beneficial effects of calorie restriction (CR) on rodents’ aging; however, the molecular mechanism explaining these beneficial effects is still not fully understood. Previously, we conducted transcriptomic analysis on rat liver with short-term and mild-to-moderate CR to elucidate its early response to such diet. Here, we expanded transcriptome analysis to muscle, adipose tissue, intestine, and brain and compared the gene expression profiles of these multiple organs and of our previous dataset. Several altered gene expressions were found, some of which known to be related to CR. Notably, the commonly regulated genes by CR include nicotinamide phosphoribosyltransferase and heat shock protein 90, which are involved in declining the aging process and thus potential therapeutic targets for aging-related diseases. The data obtained here provide information on early response markers and key mediators of the CR-induced delay in aging as well as on age-associated pathological changes in mammals.

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Section: Introductionmentioning

confidence: 99%

A Comparison of Gene Expression Profiles of Rat Tissues after Mild and Short-Term Calorie Restrictions

et al. 2021

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“…On-demand access and analysis of integrated and individual patient clinical and transcriptomics data can lead to the identification of diagnostic signatures for early dissemination of oncology and non-oncology disorders [ 51 , 52 ]. It can support better aligning of known disease biomarkers with established treatments necessary for real-time personalized care.…”

Section: Discussionmentioning

confidence: 99%

Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

et al. 2021

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Background Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. Results In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. Conclusions We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

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“…However, these data can still be used to improve the GEM by finding reactions directionality as well as finding missing functions. Additionally, analysis of metabolomics data alone or in combination with proteomics data, can promise accurate assessment of pathway flux, cofactor imbalance as well as carbon balance diversion which will further aid in finding pathway bottlenecks in strain design (Amer & Baidoo, 2021).…”

Section: Discussionmentioning

confidence: 99%

Systems‐level approaches for understanding and engineering of the oleaginous cell factory Yarrowia lipolytica

Poorinmohammad

Kerkhoven

2021

Biotech & Bioengineering

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Concerns about climate change and the search for renewable energy sources together with the goal of attaining sustainable product manufacturing have boosted the use of microbial platforms to produce fuels and high‐value chemicals. In this regard, Yarrowia lipolytica has been known as a promising yeast with potentials in diverse array of biotechnological applications such as being a host for different oleochemicals, organic acid, and recombinant protein production. Having a rapidly increasing number of molecular and genetic tools available, Y. lipolytica has been well studied amongst oleaginous yeasts and metabolic engineering has been used to explore its potentials. More recently, with the advancement in systems biotechnology and the implementation of mathematical modeling and high throughput omics data‐driven approaches, in‐depth understanding of cellular mechanisms of cell factories have been made possible resulting in enhanced rational strain design. In case of Y. lipolytica, these systems‐level studies and the related cutting‐edge technologies have recently been initiated which is expected to result in enabling the biotechnology sector to rationally engineer Y. lipolytica‐based cell factories with favorable production metrics. In this regard, here, we highlight the current status of systems metabolic engineering research and assess the potential of this yeast for future cell factory design development.

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Omics-Driven Biotechnology for Industrial Applications

Cited by 65 publications

References 208 publications

A Comparison of Gene Expression Profiles of Rat Tissues after Mild and Short-Term Calorie Restrictions

A Comparison of Gene Expression Profiles of Rat Tissues after Mild and Short-Term Calorie Restrictions

Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

Systems‐level approaches for understanding and engineering of the oleaginous cell factory Yarrowia lipolytica

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