The identification of optimal pathways in Synechocystis sp. PCC 6803 by flux balance analysis

Cyanobacteria are microorganisms considered advantageous for producing valuable compounds because of their high growth rates compared to plants. They also can be grown at large scale in photobioreactors. This research aims to use metabolic engineering strategies to maximize the phenylalanine yield in Synechocystis sp. PCC 6803. Our hypothesis is flux balance analysis will give different flux distributions with different objective functions. The scope of the project is modeling photoautotrophic metabolism of cyanobacteria with a genome scale stoichiometric model, testing several alternative objective functions. We also examined the tradeoff between growth and L-phenylalanine production with flux balance analysis. A linear programming problem is constructed to solve for the fluxes. Using an available genome-scale model and the COnstraint Based Reconstruction and Analysis (COBRA) toolbox available in MATLAB, we solved for the flux value for each reaction in the wild type strain with different objective functions such as maximizing biomass, maximizing carbon dioxide uptake and minimizing total flux. Of particular interest to metabolic engineers is the production of L-phenylalanine, an essential amino acid. In plants, Phe is the precursor to phenylpropanoids, a family of thousands of compounds with wide ranging applications from pharmaceuticals to cosmetics. Using FBA, we quantitatively defined the tradeoff between directing the carbon flux towards phenylalanine instead of biomass. Future work will involve validating the model’s predictions and making improvements to it, as well as exploring the tradeoff in the production of other molecules in cyanobacteria

Multifaceted plant responses to circumvent Phe hyperaccumulation by downregulation of flux through the shikimate pathway and by vacuolar Phe sequestration

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139951/1/tpj13730_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139951/2/tpj13730.pd

Whole exome sequencing identifies frequent somatic mutations in cell-cell adhesion genes in chinese patients with lung squamous cell carcinoma

Lung squamous cell carcinoma (SQCC) accounts for about 30% of all lung cancer cases. Understanding of mutational landscape for this subtype of lung cancer in Chinese patients is currently limited. We performed whole exome sequencing in samples from 100 patients with lung SQCCs to search for somatic mutations and the subsequent target capture sequencing in another 98 samples for validation. We identified 20 significantly mutated genes, including TP53, CDH10, NFE2L2 and PTEN. Pathways with frequently mutated genes included those of cell-cell adhesion/Wnt/Hippo in 76%, oxidative stress response in 21%, and phosphatidylinositol-3-OH kinase in 36% of the tested tumor samples. Mutations of Chromatin regulatory factor genes were identified at a lower frequency. In functional assays, we observed that knockdown of CDH10 promoted cell proliferation, soft-agar colony formation, cell migration and cell invasion, and overexpression of CDH10 inhibited cell proliferation. This mutational landscape of lung SQCC in Chinese patients improves our current understanding of lung carcinogenesis, early diagnosis and personalized therapy

Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets

Mathematical Modeling of Phenylalanine and Lignin Biosynthetic Networks in Plants

L-phenylalanine (Phe) is an important amino acid which is the precursor of various plant secondary metabolisms. Its biosynthesis and consumption are governed by different levels of regulatory mechanisms, yet our understanding to them are still far from complete. The plant has evolved a complex regulation over Phe, likely due to the fact that a significant portion of carbon assimilated by photosynthesis is diverted to its downstream products. In particular, lignin as one of them, is among the most abundant polymers in plant secondary cell wall. Studies have unraveled the interconnected metabolism involved in lignin biosynthesis, and a hierarchical gene regulatory network on top of it is also being uncovered by different research groups. These biological processes function together for sufficient lignification to ensure cell wall hydrophobicity and rigidity for plant normal growth. Yet on the other hand, the presence of lignin hinders the efficient saccharification process for biofuel production. Therefore, it is fundamental to understand lignin biosynthesis and its upstream Phe biosynthesis in a systematic way, to guide rational metabolic engineering to either reduce lignin content or manipulate its composition in planta. he biosynthesis was predominantly existed in plastids according to previous studies, and there exists a cytosolic synthetic route as well. Yet how two pathways are metabolically coordinated are largely under-explored. Here I describe a flux analysis using time course datasets from 15N L-tyrosine (Tyr) isotopic labeling studies to show the contributions from two alternative Phe biosynthetic routes in Petunia flower. The flux split between cytosolic and plastidial routes were sensitive to genetic perturbations to either upstream chorismate mutase within shikimate pathway, or downstream plastidial cationic amino-acid transporter. These results indicate the biological significance of having an alternative biosynthetic route to this important amino acid, so that defects of the plastidial route can be partially compensated to maintain Phe homeostasis. To understand the metabolic dynamics of the upstream part of lignin biosynthesis, we developed a multicompartmental kinetic model of the general phenylpropanoid metabolism in Arabidopsis basal lignifying stems. The model was parameterized by Markov Chain Monte Carlo sampling, with data from feeding plants with ring labeled [13C6]-Phe. The existence of vacuole storage for both Phe and p-coumarate was supported by an information theoretic approach. Metabolic control analysis with the model suggested the plastidial cationic amino-acid transporter to be the step with the highest flux controlling coefficient for lignin deposition rate. This model provides a deeper understanding of the metabolic connections between Phe biosynthesis and phenylpropanoid metabolism, suggesting the transporter step to be the promising target if one aims to manipulate lignin pathway flux Hundreds of gene regulatory interactions between transcription factors and structural genes involved in lignin biosynthesis has been reported with different experimental evidence in model plant Arabidopsis, however, a public database is missing to summarize and present all these findings. In this work, we documented all reported gene regulatory interactions in Arabidopsis lignin biosynthesis, and ended up with a gene regulatory network consisting of 438 interactions between 72 genes. A network is then constructed with linear differential equations, and its parameters were estimated and evaluated with RNA-seq datasets from 13 genetic backgrounds in Arabidopsis basal stems. We combined this network with a kinetic model of lignin biosynthesis starting from Phe and ending with all monolignols participated in lignin polymerization. This hierarchical kinetic model is the first model integrating dynamic information between transcriptional machinery and metabolic network for lignin biosynthesis

Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current

Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC) balancing. To solve these problems, multi-objective predictive balancing control (MOPBC) based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process

Novel strategy for oncogenic alteration-induced lipid metabolism reprogramming in pancreatic cancer

The pathogenesis of pancreatic cancer involves substantial metabolic reprogramming, resulting in abnormal proliferation of tumor cells. This tumorigenic reprogramming is often driven by genetic mutations, such as activating mutations of the KRAS oncogene and inactivating or deletions of the tumor suppressor genes SMAD4, CDKN2A, and TP53, which play a critical role in the initiation and development of pancreatic cancer. As a normal cell gradually develops into a cancer cell, a series of signature characteristics are acquired: activation of signaling pathways that sustain proliferation; an ability to resist growth inhibitory signals and evade apoptosis; and an ability to generate new blood vessels and invade and metastasize. In addition to these features, recent research has revealed that metabolic reprogramming and immune escape are two other novel characteristics of tumor cells. The effect of the interactions between tumor and immune cells on metabolic reprogramming is a key factor determining the antitumor immunotherapy response. Lipid metabolism reprogramming, a feature of many malignancies, not only plays a role in maintaining tumor cell proliferation but also alters the tumor microenvironment by inducing the release of metabolites that in turn affect the metabolism of normal immune cells, ultimately leading to the attenuation of the antitumor immune response and resistance to immunotherapy. Pancreatic cancer has been found to have substantial lipid metabolism reprogramming, but the mechanisms remain elusive. Therefore, this review focuses on the mechanisms regulating lipid metabolism reprogramming in pancreatic cancer cells to provide new therapeutic targets and aid the development of new therapeutic strategies for pancreatic cancer

Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

An estimation of the power battery state of charge (SOC) is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium-ion power battery is used in an electric vehicle, the SOC displays a very strong time-dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second-order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS) with a forgetting factor and the adaptive unscented Kalman filter (AUKF) algorithm are used jointly in the estimation of the power battery SOC. Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm

Research on a Novel Power Inductor-Based Bidirectional Lossless Equalization Circuit for Series-Connected Battery Packs

Cell balancing plays an important role in preserving the life of series-connected battery packs; without a suitable balancing system, the individual cell voltages will differ over time, and the battery pack capacity will decrease quickly. This paper presents a novel power inductor-based bidirectional lossless equalization circuit. This circuit consists of several balancing sub-circuits, which allow the dynamic adjustment of the equalization path and equalization threshold. The simulation and experiment results demonstrate that the proposed circuit, which features a simple control method, fast balancing, and a large equalization current, exhibits outstanding equalization performance