CRISPR-Cas9 mediated genome editing of Kluyveromyces marxianus for iterative, multiplexed gene disruption and pathway integration.

Kluyveromyces marxianus, a thermotolerant, fast-growing, Crabtree-negative yeast, is a promising chassis for the manufacture of various bioproducts. Although several genome editing tools are available for this yeast, these tools still require refinement to enable more convenient and efficient genetic modification. In this study, we engineered the K. marxianus NBRC 104275 strain by impairing the nonhomologous end joining and enhancing the homologous recombination machinery, which resulted in improved homology-directed repair effective on homology arms of up to 40 bp in length. Additionally, we simplified the CRISPR-Cas9 editing system by constructing a strain for integrative expression of Cas9 nuclease and plasmids bearing different selection markers for gRNA expression, thereby facilitating iterative genome editing without the need for plasmid curing. We demonstrated that tRNA was more effective than the hammerhead ribozyme for processing gRNA primary transcripts, and readily assembled tRNA-gRNA arrays were used for multiplexed editing of at least four targets. This editing tool was further employed for simultaneous scarless in vivo assembly of a 12-kb cassette from three fragments and marker-free integration for expressing a fusion variant of fatty acid synthase, as well as the integration of genes for starch hydrolysis. Together, the genome editing tool developed in this study makes K. marxianus more amenable to genetic modification and will facilitate more extensive engineering of this nonconventional yeast for chemical production.

A study protocol for improving the delivery of acute kidney replacement therapy (KRT) to critically ill patients in Alberta - DIALYZING WISELY.

BACKGROUND: Acute kidney replacement therapy (KRT) is delivered to acutely ill patients to support organ function and life in the Intensive Care Unit (ICU). Implementing standardized acute KRT pathways can ensure its safe and effective management. At present, there is no standardized approach to the management of acute KRT in Alberta ICUs. METHODS: Dialyzing Wisely is a registry embedded, stepped-wedge, interrupted time-series evaluation of the implementation of a standardized, stakeholder-informed, and evidence-based acute KRT pathway into Alberta ICUs. The acute KRT pathway will consist of two distinct phases. First, we will implement routine monitoring of evidence-informed key performance indicators (KPIs) of acute KRT. Second, we will provide prescriber and program reports for acute KRT initiation patterns. After the implementation of both phases of the pathway, we will evaluate acute KRT performance quarterly and implement a customized suite of interventions aimed at improving performance. We will compare this with baseline and evaluate iterative post implementation effects of the care pathway. DISCUSSION: Dialyzing Wisely will implement, monitor, and report a suite of KPIs of acute KRT, coupled with a care pathway that will transform the quality of acute KRT across ICUs in Alberta. This program will provide a framework for scaling evidence-informed approaches to monitoring and management of acute KRT in other jurisdictions. We anticipate improvements in acute KRT performance, decreased healthcare system costs and improved patient quality of life by decreasing patient dependence on maintenance dialysis. TRIAL REGISTRATION: Clinicaltrials.gov , NCT05186636. Registered 11, January, 2022.

A control theoretic three timescale model for analyzing energy management in mammalian cancer cells.

Interaction among different pathways, such as metabolic, signaling and gene regulatory networks, of cellular system is responsible to maintain homeostasis in a mammalian cell. Malfunctioning of this cooperation may lead to many complex diseases, such as cancer and type 2 diabetes. Timescale differences among these pathways make their integration a daunting task. Metabolic, signaling and gene regulatory networks have three different timescales, such as, ultrafast, fast and slow respectively. The article deals with this problem by developing a support vector regression (SVR) based three timescale model with the application of genetic algorithm based nonlinear controller. The proposed model can successfully capture the nonlinear transient dynamics and regulations of such integrated biochemical pathway under consideration. Besides, the model is quite capable of predicting the effects of certain drug targets for many types of complex diseases. Here, energy and cell proliferation management of mammalian cancer cells have been explored and analyzed with the help of the proposed novel approach. Previous investigations including in silico/in vivo/in vitro experiments have validated the results (the regulations of glucose transporter 1 (glut1), hexokinase (HK), and hypoxia-inducible factor-1 α (HIF-1 α ) among others, and the switching of pyruvate kinase (M2 isoform) between dimer and tetramer) generated by this model proving its effectiveness. Subsequently, the model predicts the effects of six selected drug targets, such as, the deactivation of transketolase and glucose-6-phosphate isomerase among others, in the case of mammalian malignant cells in terms of growth, proliferation, fermentation, and energy supply in the form of adenosine triphosphate (ATP).

Development of an efficient pathway construction strategy for rapid evolution of the biodegradation capacity of Pseudomonas putida KT2440 and its application in bioremediation.

In this work, we developed an efficient pathway construction strategy, consisting of DNA assembler-assisted pathway assembly and counterselection system-based chromosomal integration, for the rapid and efficient integration of synthetic biodegradation pathways into the chromosome of Pseudomonas putida KT2440. Using this strategy, we created a novel degrader capable of complete mineralization of γ-hexachlorocyclohexane (γ-HCH) and 1,2,3-trichloropropane (TCP) by integrating γ-HCH and TCP biodegradation pathways into the chromosome of P. putida KT2440. Furthermore, the chromosomal integration efficiencies of γ-HCH and TCP biodegradation pathways were improved to 50% and 41.6% in P. putida KT2440, respectively, by the inactivation of a type I DNA restriction-modification system. The currently developed pathway construction strategy coupled with the mutant KTUΔhsdRMS will facilitate implantation of heterologous catabolic pathways into the chromosome for rapid evolution of the biodegradation capacity of P. putida. More importantly, the successful removal of γ-HCH (10 mg/kg soil) and TCP (0.2 mM) from soil and wastewater within 14 days, respectively, highlighted the potential of the novel degrader for in situ bioremediation of γ-HCH- and TCP-contaminated sites. Moreover, chromosomal integration of gfp made the degrader to be monitored easily during bioremediation. In the future, this strategy can be expanded to a broad range of bacterial species for widespread applications in bioremediation.

Detecting Prognosis Risk Biomarkers for Colon Cancer Through Multi-Omics-Based Prognostic Analysis and Target Regulation Simulation Modeling.

BACKGROUND: Colon cancer is one of the most common health threats for humans since its high morbidity and mortality. Detecting potential prognosis risk biomarkers (PRBs) is essential for the improvement of therapeutic strategies and drug development. Currently, although an integrated prognostic analysis of multi-omics for colon cancer is insufficient, it has been reported to be valuable for improving PRBs' detection in other cancer types. AIM: This study aims to detect potential PRBs for colon adenocarcinoma (COAD) samples through the cancer genome atlas (TCGA) by integrating muti-omics. MATERIALS AND METHODS: The multi-omics-based prognostic analysis (MPA) model was first constructed to systemically analyze the prognosis of colon cancer based on four-omics data of gene expression, exon expression, DNA methylation and somatic mutations on COAD samples. Then, the essential features related to prognosis were functionally annotated through protein-protein interaction (PPI) network and cancer-related pathways. Moreover, the significance of those essential prognostic features were further confirmed by the target regulation simulation (TRS) model. Finally, an independent testing dataset, as well as the single cell-based expression dataset were utilized to validate the generality and repeatability of PRBs detected in this study. RESULTS: By integrating the result of MPA modeling, as well the PPI network, integrated pathway and TRS modeling, essential features with gene symbols such as EPB41, PSMA1, FGFR3, MRAS, LEP, C7orf46, LOC285000, LBP, ZNF35, SLC30A3, LECT2, RNF7, and DYNC1I1 were identified as PRBs which provide high potential as drug targets for COAD treatment. Validation on the independent testing dataset demonstrated that these PRBs could be applied to distinguish the prognosis of COAD patients. Moreover, the prognosis of patients with different clinical conditions could also be distinguished by the above PRBs. CONCLUSIONS: The MPA and TRS models constructed in this paper, as well as the PPI network and integrated pathway analysis, could not only help detect PRBs as potential therapeutic targets for COAD patients but also make it a paradigm for the prognostic analysis of other cancers.

redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast.

Over the last decades, yeast has become a key model organism for the study of lipid biochemistry. Because the regulation of lipids has been closely linked to various physiopathologies, the study of these biomolecules could lead to new diagnostics and treatments. Before the field can reach this point, however, sufficient tools for integrating and analyzing the ever-growing availability of lipidomics data will need to be developed. To this end, genome-scale models (GEMs) of metabolic networks are useful tools, though their large size and complexity introduces too much uncertainty in the accuracy of predicted outcomes. Ideally, therefore, a model for studying lipids would contain only the pathways required for the proper analysis of these biomolecules, but would not be an ad hoc reduction. We hereby present a metabolic model that focuses on lipid metabolism constructed through the integration of detailed lipid pathways into an already existing GEM of Saccharomyces cerevisiae. Our model was then systematically reduced around the subsystems defined by these pathways to provide a more manageable model size for complex studies. We show that this model is as consistent and inclusive as other yeast GEMs regarding the focus and detail on the lipid metabolism, and can be used as a scaffold for integrating lipidomics data to improve predictions in studies of lipid-related biological functions.

Mechanistic insights into skeletal development gained from genetic disorders.

A complex cascade of highly regulated processes of cell fate determination, differentiation, proliferation and transdifferentiation dictate the patterning, morphogenesis and growth of the vertebrate skeleton, perturbation of which results in malformation. In humans over 450 different dysplasias involving the skeletal system constitute a significant fraction of documented Mendelian disorders. The combination of clinical, phenotypic characterization of rare human skeletal dysmorphologies, the discovery of causative mutations and functional validation in animal models has contributed enormously to the understanding of molecular control of skeletal development. These studies revealed a myriad of genes and pathways, such as WNT, Hedgehog (HH), planar cell polarity and primary cilia, as key regulators for skeletal patterning, growth and homeostasis. The generation of mouse models recapitulating human congenital skeletal dysplasia has provided mechanistic insights into the diverse pathologies caused by single gene mutations, integrated action of developmental pathways such as WNT and HH and the role of stress responses. Technological developments in whole genome and exome sequencing have accelerated the discovery of disease-causing mutations and are changing approaches for diagnosis. The discovery that non-coding variants and disorganization of the 3D genome are associated with limb patterning disorders has revealed an additional level of complexity in the regulatory framework of skeletal development and disease mechanisms. This chapter focuses on a selection of human skeletal pathologies which illustrate how new findings about the coding and noncoding genome, combined with functional modeling, are contributing to deeper understanding of skeletal development, mechanisms of disease, with therapeutic potential for chondrodysplasias.