Accommodation of powdery mildew fungi in intact plant cells

Parasitic powdery mildew fungi have to overcome basic resistance and manipulate host cells to establish a haustorium as a functional feeding organ in a host epidermal cell. Currently, it is of central interest how plant factors negatively regulate basal defense or whether they even support fungal development in compatible interactions. Additionally, creation of a metabolic sink in infected cells may involve host activity. Here, we review the current progress in understanding potential fungal targets for host reprogramming and nutrient acquisition.     

Engineering precursor supply for the high-level production of ergothioneine in Saccharomyces cerevisiae

Ergothioneine (ERG) is an unusual sulfur-containing amino acid. It is a potent antioxidant, which shows great potential for ameliorating neurodegenerative and cardiovascular diseases. L-ergothioneine is rare in nature, with mushrooms being the primary dietary source. The chemical synthesis process is complex and expensive. Alternatively, ERG can be produced by fermentation of recombinant microorganisms engineered for ERG overproduction. Here, we describe the engineering of S. cerevisiae for high-level ergothioneine production on minimal medium with glucose as the only carbon source. To this end, metabolic engineering targets in different layers of the amino acid metabolism were selected based on literature and tested. Out of 28 targets, nine were found to improve ERG production significantly by 10%–51%. These targets were then sequentially implemented to generate an ergothioneine-overproducing yeast strain capable of producing 106.2 ± 2.6 mg/L ERG in small-scale cultivations. Transporter engineering identified that the native Aqr1 transporter was capable of increasing the ERG production in a yeast strain with two copies of the ERG biosynthesis pathway, but not in the strain that was further engineered for improved precursor supply. Medium optimization indicated that additional supplementation of pantothenate improved the strain's productivity further and that no supplementation of amino acid precursors was necessary. Finally, the engineered strain produced 2.39 ± 0.08 g/L ERG in 160 h (productivity of 14.95 ± 0.49 mg/L/h) in a controlled fed-batch fermentation without supplementation of amino acids. This study paves the way for the low-cost fermentation-based production of ergothioneine.     

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.     

The mRNA–miRNA–lncRNA Regulatory Network and Factors Associated with Prognosis Prediction of Hepatocellular Carcinoma

The aim of this study was to identify novel prognostic mRNA and microRNA (miRNA) biomarkers for hepatocellular carcinoma (HCC) using methods in systems biology. Differentially expressed mRNAs, miRNAs, and long non-coding RNAs (lncRNAs) were compared between HCC tumor tissues and normal liver tissues in The Cancer Genome Atlas (TCGA) database. Subsequently, a prognosis-associated mRNA co-expression network, an mRNA–miRNA regulatory network, and an mRNA–miRNA–lncRNA regulatory network were constructed to identify prognostic biomarkers for HCC through Cox survival analysis. Seven prognosis-associated mRNA co-expression modules were obtained by analyzing these differentially expressed mRNAs. An expression module including 120 mRNAs was significantly correlated with HCC patient survival. Combined with patient survival data, several mRNAs and miRNAs, including CHST4, SLC22A8, STC2, hsa-miR-326, and hsa-miR-21 were identified from the network to predict HCC patient prognosis. Clinical significance was investigated using tissue microarray analysis of samples from 258 patients with HCC. Functional annotation of hsa-miR-326 and hsa-miR-21-5p indicated specific associations with several cancer-related pathways. The present study provides a bioinformatics method for biomarker screening, leading to the identification of an integrated mRNA–miRNA–lncRNA regulatory network and their co-expression patterns in relation to predicting HCC patient survival.     

The Kinetochore-Microtubule Coupling Machinery Is Repurposed in Sensory Nervous System Morphogenesis

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An Approach Based on Mutually Informed Neural Networks to Optimize the Generalization Capabilities of Decision Support Systems Developed for Heart Failure Prediction

Available clinical methods for heart failure (HF) diagnosis are expensive and require a high-level of experts intervention. Recently, various machine learning models have been developed for the prediction of HF where most of them have an issue of over-fitting. Over-fitting occurs when machine learning based predictive models show better performance on the training data yet demonstrate a poor performance on the testing data and the other way around. Developing a machine learning model which is able to produce generalization capabilities (such that the model exhibits better performance on both the training and the testing data sets) could overall minimize the prediction errors. Hence, such prediction models could potentially be helpful to cardiologists for the effective diagnose of HF. This paper proposes a two-stage decision support system to overcome the over-fitting issue and to optimize the generalization factor. The first stage uses a mutual information based statistical model while the second stage uses a neural network. We applied our approach to the HF subset of publicly available Cleveland heart disease database. Our experimental results show that the proposed decision support system has optimized the generalization capabilities and has reduced the mean percent error (MPE) to 8.8% which is significantly less than the recently published studies. In addition, our model exhibits a 93.33% accuracy rate which is higher than twenty eight recently developed HF risk prediction models that achieved accuracy in the range of 57.85% to 92.31%. We can hope that our decision support system will be helpful to cardiologists if deployed in clinical setup.     

Prioritization of SNPs for genome-wide association studies using an interaction model of genetic variation,gene expression,and trait variation

The identification of true causal loci to unravel the statistical evidence of genotype-phenotype correlations and the biological relevance of selected single-nucleotide polymorphisms (SNPs) is a challenging issue in genome-wide association studies (GWAS). Here, we introduced a novel method for the prioritization of SNPs based on p-values from GWAS. The method uses functional evidence from populations, including phenotype-associated gene expressions. Based on the concept of genetic interactions, such as perturbation of gene expression by genetic variation, phenotype and gene expression related SNPs were prioritized by adjusting the p-values of SNPs. We applied our method to GWAS data related to drug-induced cytotoxicity. Then, we prioritized loci that potentially play a role in druginduced cytotoxicity. By generating an interaction model, our approach allowed us not only to identify causal loci, but also to find intermediate nodes that regulate the flow of information among causal loci, perturbed gene expression, and resulting phenotypic variation.