Q20+ Nanopore sequencing data recover a high-quality Asticcacaulis sp. genome

We present here the complete genome of Asticcacaulis sp. ZE23SCel15. The strain was isolated from the surface water of Lake Zurich, Switzerland. The assembly of high-quality Q20+ Nanopore data yielded a circular genome with ~3.8 Mb (coverage: 34×) and a GC content of 56.81%

A high-quality genome of an undescribed Flavobacterium species uncovered using Q20+ Nanopore chemistry

Herein, we document the complete genome of the Flavobacterium strain ZE23DGlu08, isolated from Lake Zurich, Switzerland. The circular genome was assembled using long-read Nanopore data (coverage: 226×) with the Q20+ chemistry. The described strain displays a genome size of ~3.9 Mbp with a GC content of 34%

Validation of Anti-Oxidative Stress Genes from Genome-Wide Screening of Escherichia coli

The primary purpose of this project is to evaluate the genes that play a role in the oxidative stress response in Escherichia coli. In doing so, the entire genome of E. coli was subject to throughput in which individual genes were determined to have a role in the bacteria’s oxidative stress response. Moreover, this project focused on the validation of the genes that were able to pass the initial throughput stage. The genes were subject to two forms of validation. In the first validation technique, candidate genes were overexpressed and minimum inhibitory concentrations of hypochlorous acid were taken. Following, a second validation technique tested the minimum inhibitory concentrations of hypochlorous acid utilizing knock-out mutants. These experimental results will enable for better understanding of a common pathogen as well as ways to defend against it. Additionally, E. coli has often been dubbed as a model bacterium and is utilized and studied in many scientific investigations. Furthermore, this experiment may lead to a greater general understanding of the bacterium, which could assist in future studies

Identification of a gene encoding the last step of the L-rhamnose catabolic pathway in Aspergillus niger revealed the inducer of the pathway regulator

In fungi, L-rhamnose (Rha) is converted via four enzymatic steps into pyruvate and L-lactaldehyde, which enter central carbon metabolism. In Aspergillus niger, only the genes involved in the first three steps of the Rha catabolic pathway have been identified and characterized, and the inducer of the pathway regulator RhaR remained unknown. In this study, we identified the gene (lkaA) involved in the conversion of L-2-keto-3-deoxyrhamnonate (L-KDR) into pyruvate and L-lactaldehyde, which is the last step of the Rha pathway. Deletion of lkaA resulted in impaired growth on L-rhamnose, and potentially in accumulation of L-KDR. Contrary to Delta lraA, Delta lrlA and Delta lrdA, the expression of the Rha-responsive genes that are under control of RhaR, were at the same levels in Delta lkaA and the reference strain, indicating the role of L-KDR as the inducer of the Rha pathway regulator.Peer reviewe

Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species

Metabolic network reconstructions have become an important tool for probing cellular metabolism in the field of systems biology. They are used as tools for quantitative prediction but also as scaffolds for further knowledge contextualization. The yeast Saccharomyces cerevisiae was one of the first organisms for which a genome-scale metabolic model (GEM) was reconstructed, in 2003, and since then 45 metabolic models have been developed for a wide variety of relevant yeasts species. A systematic evaluation of these models revealed that-despite this long modeling history-the sequential process of tracing model files, setting them up for basic simulation purposes and comparing them across species and even different versions, is still not a generalizable task. These findings call the yeast modeling community to comply to standard practices on model development and sharing in order to make GEMs accessible and useful for a wider public

Metabolic engineering of Pseudomonas putida KT2440 for medium-chain-length fatty alcohol and ester production from fatty acids

publishedVersio

Genome-scale metabolic model of the rat liver predicts effects of diet restriction.

Mapping network analysis in cells and tissues can provide insights into metabolic adaptations to changes in external environment, pathological conditions, and nutrient deprivation. Here, we reconstructed a genome-scale metabolic network of the rat liver that will allow for exploration of systems-level physiology. The resulting in silico model (iRatLiver) contains 1,882 reactions, 1,448 metabolites, and 994 metabolic genes. We then used this model to characterize the response of the liver\u27s energy metabolism to a controlled perturbation in diet. Transcriptomics data were collected from the livers of Sprague Dawley rats at 4 or 14 days of being subjected to 15%, 30%, or 60% diet restriction. These data were integrated with the iRatLiver model to generate condition-specific metabolic models, allowing us to explore network differences under each condition. We observed different pathway usage between early and late time points. Network analysis identified several highly connected hub genes (Pklr, Hadha, Tkt, Pgm1, Tpi1, and Eno3) that showed differing trends between early and late time points. Taken together, our results suggest that the liver\u27s response varied with short- and long-term diet restriction. More broadly, we anticipate that the iRatLiver model can be exploited further to study metabolic changes in the liver under other conditions such as drug treatment, infection, and disease

Computational Systems Analysis on Polycystic Ovarian Syndrome (PCOS)

Complex diseases are caused by a combination of genetic and environmental factors. Unraveling the molecular pathways from the genetic factors that affect a phenotype is always difficult, but in the case of complex diseases, this is further complicated since genetic factors in affected individuals might be different. Polycystic ovarian syndrome (PCOS) is an example of a complex disease with limited molecular information. Recently, PCOS molecular omics data have increasingly appeared in many publications. We conduct extensive bioinformatics analyses on the data and perform strong integration of experimental and computational biology to understand its complex biological systems in examining multiple interacting genes and their products. PCOS involves networks of genes, and to understand them, those networks must be mapped. This approach has emerged as powerful tools for studying complex diseases and been coined as network biology. Network biology encompasses wide range of network types including those based on physical interactions between and among cellular components and those baised on similarity among patients or diseases. Each of these offers distinct biological clues that may help scientists transform their cellular parts list into insights about complex diseases. This chapter will discuss some computational analysis aspects on the omics studies that have been conducted in PCOS

Synthetic biology advanced natural product discovery

A wide variety of bacteria, fungi and plants can produce bioactive secondary metabolites, which are often referred to as natural products. With the rapid development of DNA sequencing technology and bioinformatics, a large number of putative biosynthetic gene clusters have been reported. However, only a limited number of natural products have been discovered, as most biosynthetic gene clusters are not expressed or are expressed at extremely low levels under conventional laboratory conditions. With the rapid development of synthetic biology, advanced genome mining and engineering strategies have been reported and they provide new opportunities for discovery of natural products. This review discusses advances in recent years that can accelerate the design, build, test, and learn (DBTL) cycle of natural product discovery, and prospects trends and key challenges for future research directions