Improvement of Free Fatty Acid Secretory Productivity in Aspergillus oryzae by Comprehensive Analysis on Time-Series Gene Expression

Aspergillus oryzae is a filamentous fungus that has historically been utilized in the fermentation of food products. In recent times, it has also been introduced as a component in the industrial biosynthesis of consumable compounds, including free fatty acids (FFAs), which are valuable and versatile products that can be utilized as feedstocks in the production of other commodities, such as pharmaceuticals and dietary supplements. To improve the FFA secretory productivity of A. oryzae in the presence of Triton X-100, we analyzed the gene expression of a wild-type control strain and a disruptant strain of an acyl-CoA synthetase gene, faaA, in a time-series experiment. We employed a comprehensive analysis strategy using the baySeq, DESeq2, and edgeR algorithms to clarify the vital pathways for FFA secretory productivity and select genes for gene modification. We found that the transport and metabolism of inorganic ions are crucial in the initial stages of FFA production and revealed 16 candidate genes to be modified in conjunction with the faaA disruption. These genes were verified through the construction of overexpression strains, and showed that the manipulation of reactions closer to the FFA biosynthesis step led to a higher increase in FFA secretory productivity. This resulted in the most successful overexpression strains to have an FFA secretory productivity more than two folds higher than that of the original faaA disruptant. Our study provides guidance for further gene modification for FFA biosynthesis in A. oryzae and for enhancing the productivity of other metabolites in other microorganisms through metabolic engineering.

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Inference of Gene Regulatory Networks Using Time Sliding Comparison and Transcriptional Lagging Time from Time Series Gene Expression Profiles

2007 IEEE 7th International Symposium on BioInformatics and BioEngineering ◽

10.1109/bibe.2007.4375684 ◽

2007 ◽

Author(s):

Sheehyun Kim ◽

Dongsup Kim

Keyword(s):

Gene Expression ◽

Time Series ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Time Series Gene Expression ◽

Gene Regulatory

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Phase-Wise Clustering of Time Series Gene Expression Data

2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications ◽

10.1109/trustcom.2011.231 ◽

2011 ◽

Author(s):

Poonam Goyal ◽

Navneet Goyal ◽

Rohan Sunil Karwa ◽

Matthew John

Keyword(s):

Gene Expression ◽

Time Series ◽

Gene Expression Data ◽

Expression Data ◽

Time Series Gene Expression

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Profiling Cellular Processes in Adipose Tissue during Weight Loss Using Time Series Gene Expression

Genes ◽

10.3390/genes9110525 ◽

2018 ◽

Vol 9 (11) ◽

pp. 525 ◽

Cited By ~ 2

Author(s):

Samar Tareen ◽

Michiel Adriaens ◽

Ilja Arts ◽

Theo de Kok ◽

Roel Vink ◽

...

Keyword(s):

Gene Expression ◽

Time Series ◽

Weight Loss ◽

Adipose Tissue ◽

Molecular Mechanisms ◽

Dietary Intervention ◽

Network Biology ◽

Analysis Tool ◽

Cellular Processes ◽

Time Series Gene Expression

Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans.

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Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

10.1101/170027 ◽

2017 ◽

Author(s):

Anthony Szedlak ◽

Spencer Sims ◽

Nicholas Smith ◽

Giovanni Paternostro ◽

Carlo Piermarocchi

Keyword(s):

Neural Network ◽

Gene Expression ◽

Cell Cycle ◽

Time Series ◽

Time Series Data ◽

Series Data ◽

Data Sets ◽

Expression Data ◽

Time Series Gene Expression ◽

Human Cervical Cancer

AbstractModern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa cells causes simulated cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.Author SummaryCell cycle – the process in which a parent cell replicates its DNA and divides into two daughter cells – is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the “edge of chaos” is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

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