Applications of 3D Bioprinting Technology to Brain Cells and Brain Tumor Models: Special Emphasis to Glioblastoma

Primary brain tumor is one of the most fatal diseases. The most malignant type among them, glioblastoma (GBM), has low survival rates. Standard treatments reduce the life quality of patients due to serious side effects. Tumor aggressiveness and the unique structure of the brain render the removal of tumors and the development of new therapies challenging. To elucidate the characteristics of brain tumors and examine their response to drugs, realistic systems that mimic the tumor environment and cellular crosstalk are desperately needed. In the past decade, 3D GBM models have been presented as excellent platforms as they allowed the investigation of the phenotypes of GBM and testing innovative therapeutic strategies. In that scope, 3D bioprinting technology offers utilities such as fabricating realistic 3D bioprinted structures in a layer-by-layer manner and precisely controlled deposition of materials and cells, and they can be integrated with other technologies like the microfluidics approach. This Review covers studies that investigated 3D bioprinted brain tumor models, especially GBM using 3D bioprinting techniques and essential parameters that affect the result and quality of the study like frequently used cells, the type and physical characteristics of hydrogel, bioprinting conditions, cross-linking methods, and characterization techniques.

Uncovering the Effect of SARS-CoV-2 on Liver Metabolism via Genome-Scale Metabolic Modeling for Reprogramming and Therapeutic Strategies

Genome-scale metabolic models (GEMs) are promising computational tools that contribute to elucidating host-virus interactions at the system level and developing therapeutic strategies against viral infection. In this study, the effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on liver metabolism was investigated using integrated GEMs of human hepatocytes and SARS-CoV-2. They were generated for uninfected and infected hepatocytes using transcriptome data. Reporter metabolite analysis resulted in significant transcriptional changes around several metabolites involved in xenobiotics, drugs, arachidonic acid, and leukotriene metabolisms due to SARS-CoV-2 infection. Flux balance analysis and minimization of metabolic adjustment approaches unraveled possible virus-induced hepatocellular reprogramming in fatty acid, glycerophospholipid, sphingolipid cholesterol, and folate metabolisms, bile acid biosynthesis, and carnitine shuttle among others. Reaction knockout analysis provided critical reactions in glycolysis, oxidative phosphorylation, purine metabolism, and reactive oxygen species detoxification subsystems. Computational analysis also showed that administration of dopamine, glucosamine, D-xylose, cysteine, and (R)-3-hydroxybutanoate contributes to alleviating viral infection. In essence, the reconstructed host-virus GEM helps us understand metabolic programming and develop therapeutic strategies to battle SARS-CoV-2.

Exploring data-driven chemical SMILES tokenization approaches to identify key protein-ligand binding moieties

Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular sequences are easily available, simple, and informative. The sequence-based models often segment molecular sequences into pieces called chemical words, analogous to the words that make up sentences in human languages, and then apply advanced natural language processing techniques for tasks such as de novo drug design, property prediction, and binding affinity prediction. However, the chemical characteristics and significance of these building blocks, chemical words, remain unexplored. To address this gap, we employ data-driven SMILES tokenization techniques such as Byte Pair Encoding, WordPiece, and Unigram to identify chemical words and compare the resulting vocabularies. To understand the chemical significance of these words, we build a language-inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf-idf weighting. The experiments on multiple protein-ligand affinity datasets show that despite differences in words, lengths, and validity among the vocabularies generated by different subword tokenization algorithms, the identified key chemical words exhibit similarity. Further, we conduct case studies on a number of target to analyze the impact of key chemical words on binding. We find that these key chemical words are specific to protein targets and correspond to known pharmacophores and functional groups. Our approach elucidates chemical properties of the words identified by machine learning models and can be used in drug discovery studies to determine significant chemical moieties.

Circadian clock crosstalks with autism

BACKGROUND

The mechanism underlying autism spectrum disorder (ASD) remains incompletely understood, but researchers have identified over a thousand genes involved in complex interactions within the brain, nervous, and immune systems, particularly during the mechanism of brain development. Various contributory environmental effects including circadian rhythm have also been studied in ASD. Thus, capturing the global picture of the ASD-clock network in combined form is critical.

METHODS

We reconstructed the protein-protein interaction network of ASD and circadian rhythm to understand the connection between autism and the circadian clock. A graph theoretical study is undertaken to evaluate whether the network attributes are biologically realistic. The gene ontology enrichment analyses provide information about the most important biological processes.

RESULTS

This study takes a fresh look at metabolic mechanisms and the identification of potential key proteins/pathways (ribosome biogenesis, oxidative stress, insulin/IGF pathway, Wnt pathway, and mTOR pathway), as well as the effects of specific conditions (such as maternal stress or disruption of circadian rhythm) on the development of ASD due to environmental factors.

CONCLUSION

Understanding the relationship between circadian rhythm and ASD provides insight into the involvement of these essential pathways in the pathogenesis/etiology of ASD, as well as potential early intervention options and chronotherapeutic strategies for treating or preventing the neurodevelopmental disorder.

Comprehensive genome-scale metabolic model of the human pathogen Cryptococcus neoformans: A platform for understanding pathogen metabolism and identifying new drug targets

Introduction: The fungal priority pathogen Cryptococcus neoformans causes cryptococcal meningoencephalitis in immunocompromised individuals and leads to hundreds of thousands of deaths per year. The undesirable side effects of existing treatments, the need for long application times to prevent the disease from recurring, the lack of resources for these treatment methods to spread over all continents necessitate the search for new treatment methods. Methods: Genome-scale models have been shown to be valuable in studying the metabolism of many organisms. Here we present the first genome-scale metabolic model for C. neoformans, iCryptococcus. This comprehensive model consists of 1,270 reactions, 1,143 metabolites, 649 genes, and eight compartments. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources and growth rate in comparison to experimental data. Results and Discussion: The compatibility of the in silico Cryptococcus metabolism under infection conditions was assessed. The steroid and amino acid metabolisms found in the essentiality analyses have the potential to be drug targets for the therapeutic strategies to be developed against Cryptococcus species. iCryptococcus model can be applied to explore new targets for antifungal drugs along with essential gene, metabolite and reaction analyses and provides a promising platform for elucidation of pathogen metabolism.

Exploiting pretrained biochemical language models for targeted drug design

MOTIVATION

The development of novel compounds targeting proteins of interest is one of the most important tasks in the pharmaceutical industry. Deep generative models have been applied to targeted molecular design and have shown promising results. Recently, target-specific molecule generation has been viewed as a translation between the protein language and the chemical language. However, such a model is limited by the availability of interacting protein-ligand pairs. On the other hand, large amounts of unlabelled protein sequences and chemical compounds are available and have been used to train language models that learn useful representations. In this study, we propose exploiting pretrained biochemical language models to initialize (i.e. warm start) targeted molecule generation models. We investigate two warm start strategies: (i) a one-stage strategy where the initialized model is trained on targeted molecule generation and (ii) a two-stage strategy containing a pre-finetuning on molecular generation followed by target-specific training. We also compare two decoding strategies to generate compounds: beam search and sampling.

RESULTS

The results show that the warm-started models perform better than a baseline model trained from scratch. The two proposed warm-start strategies achieve similar results to each other with respect to widely used metrics from benchmarks. However, docking evaluation of the generated compounds for a number of novel proteins suggests that the one-stage strategy generalizes better than the two-stage strategy. Additionally, we observe that beam search outperforms sampling in both docking evaluation and benchmark metrics for assessing compound quality.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/boun-tabi/biochemical-lms-for-drug-design and the materials (i.e., data, models, and outputs) are archived in Zenodo at https://doi.org/10.5281/zenodo.6832145.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Understanding the Link between Inflammasome and Apoptosis through the Response of THP-1 Cells against Drugs Using Droplet-Based Microfluidics

Droplet-based microfluidic devices are used to investigate monocytic THP-1 cells in response to drug administration. Consistent and reproducible droplets are created, each of which acts as a bioreactor to carry out single cell experiments with minimized contamination and live cell tracking under an inverted fluorescence microscope for more than 2 days. Here, the effects of three different drugs (temsirolimus, rifabutin, and BAY 11-7082) on THP-1 are examined and the results are analyzed in the context of the inflammasome and apoptosis relationship. The ASC adaptor gene tagged with GFP is monitored as the inflammasome reporter. Thus, a systematic way is presented for deciphering cell-to-cell heterogeneity, which is an important issue in cancer treatment. The drug temsirolimus, which has effects of disrupting the mTOR pathway and triggering apoptosis in tumor cells, causes THP-1 cells to express ASC and to be involved in apoptosis. Treatment with rifabutin, which inhibits proliferation and initiates apoptosis in cells, affects ASC expression by first increasing and then decreasing it. CASP-3, which has a role in apoptosis and is directly related to ASC, has an increasing level in inflammasome conditioning. Thus, the cell under the effect of rifabutin might be faced with programmed cell death faster. The drug BAY 11-7082, which is responsible for NFκB inhibition, shows similar results to temsirolimus with more than 60% of cells having high fluorescence intensity (ASC expression). The microfluidic platform presented here offers strong potential for studying newly developed small-molecule inhibitors for personalized/precision medicine.

Real-Time Single-Cell Monitoring of Drug Effects Using Droplet-Based Microfluidic Technology: A Proof-of-Concept Study

Drugs that act on ribosome biogenesis and cell proliferation play important roles in treatment of human diseases. Moreover, measurement of drug effects at a single-cell level would create vast opportunities for pharmaceutical innovation. We present in this study an original proof-of-concept study of single-cell measurement of drug effects with a focus on inhibition of ribosome biogenesis and cell proliferation, and using yeast (Saccharomyces cerevisiae) as a model eukaryotic organism. We employed a droplet-based microfluidic technology and nucleolar protein-tagged strain of the yeast for real-time monitoring of the cells. We report a comprehensive account of the ways in which interrelated pathways are impacted by drug treatment in a single-cell level. Self-organizing maps, transcription factor, and Gene Ontology enrichment analyses were utilized to these ends. This article makes a contribution to advance single-cell measurement of drug effects. We anticipate the microfluidic technology platform presented herein is well poised for future applications in personalized/precision medicine research as well as in industrial settings for drug discovery and clinical development. In addition, the study offers new insights on ribosome biogenesis and cell proliferation that should prove useful in cancer research and other complex human diseases impacted by these key cellular processes.

Advances in Genome-Scale Metabolic Modeling toward Microbial Community Analysis of the Human Microbiome

A genome-scale metabolic model (GEM) represents metabolic pathways of an organism in a mathematical form and can be built using biochemistry and genome annotation data. GEMs are invaluable for understanding organisms since they analyze the metabolic capabilities and behaviors quantitatively and can predict phenotypes. The development of high-throughput data collection techniques led to an immense increase in omics data such as metagenomics, which expand our knowledge on the human microbiome, but this also created a need for systematic analysis of these data. In recent years, GEMs have also been reconstructed for microbial species, including human gut microbiota, and methods for the analysis of microbial communities have been developed to examine the interaction between the organisms or the host. The purpose of this review is to provide a comprehensive guide for the applications of GEMs in microbial community analysis. Starting with GEM repositories, automatic GEM reconstruction tools, and quality control of models, this review will give insights into microbe-microbe and microbe-host interaction predictions and optimization of microbial community models. Recent studies that utilize microbial GEMs and personalized models to infer the influence of microbiota on human diseases such as inflammatory bowel diseases (IBD) or Parkinson's disease are exemplified. Being powerful system biology tools for both species-level and community-level analysis of microbes, GEMs integrated with omics data and machine learning techniques will be indispensable for studying the microbiome and their effects on human physiology as well as for deciphering the mechanisms behind human diseases.

The Toxicity of Polystyrene-Based Nanoparticles in Saccharomyces cerevisiae Is Associated with Nanoparticle Charge and Uptake Mechanism

Polystyrene latex (PSL) nanoparticles (NPs), 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) liposomes, and hybrid NPs that have different concentrations, sizes, surface charges, and functional groups were used to determine their toxicity to Saccharomyces cerevisiae cells. The size, charge, and morphology of the nanoparticles were characterized by dynamic light scattering, electrophoretic light scattering, scanning transmission electron microscopy, and transmission electron microscopy analysis. The cell viabilities were determined by colony forming unit analysis and confocal laser scanning microscopy imaging. Uptake inhibition studies were performed to determine the internalization mechanism of PSL NPs. At 50 mg/L, both positively and negatively charged NPs were slightly toxic. With increasing concentration, however, full toxicities were observed with positively charged PSL NPs, while a marginal increase in toxicity was obtained with negatively charged PSL NPs. For negatively charged and carboxyl-functionalized NPs, an increase in size induced toxicity, whereas for positively charged and amine-functionalized NPs, smaller-sized NPs were more toxic to yeast cells. Negatively charged NPs were internalized by the yeast cells, but they showed toxicity when they entered the cell vacuole. Positively charged NPs, however, accumulated on the cell surface and caused toxicity. When coated with DOPC liposomes, positively charged NPs became significantly less toxic. We attribute this reduction to the larger-diameter and/or more-agglomerated NPs in the extracellular environment, which resulted in lower interactions with the cell. In addition to endocytosis, it is possible that the negatively charged NPs (30-C-n) were internalized by the cells, partly via direct permeation, which is preferred for high drug delivery efficiency. Negatively charged PSL NP exposure to the yeast cells at low-to-moderate concentrations resulted in low toxicities in the long term. Our results indicate that negatively charged PSL NPs provide safer alternatives as cargo carriers in drug delivery applications. Moreover, the variations in NP size, concentration, and exposure time, along with the use of hybrid systems, have significant roles in nanoparticle-based drug delivery applications in terms of their effects on living organisms.

Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens

Pathogens give rise to a wide range of diseases threatening global health and hence drawing public health agencies' attention to establish preventative and curative solutions. Genome-scale metabolic modeling is ever increasingly used tool for biomedical applications including the elucidation of antibiotic resistance, virulence, single pathogen mechanisms and pathogen-host interaction systems. With this approach, the sophisticated cellular system of metabolic reactions inside the pathogens as well as between pathogen and host cells are represented in conjunction with their corresponding genes and enzymes. Along with essential metabolic reactions, alternate pathways and fluxes are predicted by performing computational flux analyses for the growth of pathogens in a very short time. The genes or enzymes responsible for the essential metabolic reactions in pathogen growth are regarded as potential drug targets, as a priori guide to researchers in the pharmaceutical field. Pathogens alter the key metabolic processes in infected host, ultimately the objective of these integrative constraint-based context-specific metabolic models is to provide novel insights toward understanding the metabolic basis of the acute and chronic processes of infection, revealing cellular mechanisms of pathogenesis, identifying strain-specific biomarkers and developing new therapeutic approaches including the combination drugs. The reaction rates predicted during different time points of pathogen development enable us to predict active pathways and those that only occur during certain stages of infection, and thus point out the putative drug targets. Among others, fatty acid and lipid syntheses reactions are recent targets of new antimicrobial drugs. Genome-scale metabolic models provide an improved understanding of how intracellular pathogens utilize the existing microenvironment of the host. Here, we reviewed the current knowledge of genome-scale metabolic modeling in pathogen cells as well as pathogen host interaction systems and the promising applications in the extension of curative strategies against pathogens for global preventative healthcare.

Thermoplastic microfluidic bioreactors with integrated electrodes to study tumor treating fields on yeast cells

Tumor-treating fields (TTFields) are alternating electrical fields of intermediate frequency and low intensity that can slow or inhibit tumor growth by disrupting mitosis division of cancerous cells through cell cycle proteins. In this work, for the first time, an in-house fabricated cyclo-olefin polymer made microfluidic bioreactors are integrated with Cr/Au interdigitated electrodes to test TTFields on yeast cells with fluorescent protein:Nop56 gene. A small gap between electrodes (50 μm) allows small voltages (<150 mV) to be applied on the cells; hence, uninsulated gold electrodes are used in the non-faradaic region without causing any electrochemical reaction at the electrode-medium interface. Electrochemical modeling as well as impedance characterization and analysis of the electrodes are done using four different cell nutrient media. The experiments with yeast cells are done with 150 mV, 150 kHz and 30 mV, 200 kHz sinusoidal signals to generate electrical field magnitudes of 6.58 V/cm and 1.33 V/cm, respectively. In the high electrical field experiment, the cells go through electroporation. In the experiment with the low electrical field magnitude for TTFields, the cells have prolonged mitosis from typical 80-90 min to 200-300 min. Our results confirm the validity of the electrochemical model and the importance of applying a correct magnitude of the electrical field. Compared to the so far reported alternatives with insulated electrodes, the here developed thermoplastic microfluidic bioreactors with uninsulated electrodes provide a new, versatile, and durable platform for in vitro cell studies toward the improvement of anti-cancer therapies including personalized treatment.

A Drug Repurposing and Protein-Protein Interaction Network Study of Ribosomopathies Using Yeast as a Model System

Ribosomopathies result in various cancers, neurodegenerative and viral diseases, and other pathologies such as Diamond-Blackfan anemia and Shwachman-Diamond syndrome. Their pathophysiology at a proteome and functional level remains to be determined. Protein networks and highly connected hub proteins for ribosome biogenesis in Saccharomyces cerevisiae offer a potential as a model system to inform future therapeutic innovation in ribosomopathies. In this context, we report a ribosome biogenesis protein-protein interaction network in S. cerevisiae, created with 1772 proteins and 22,185 physical interactions connecting them. Moreover, by network decomposition analysis, we determined the linear pathways between the transcription factors and target proteins with a view to drug repurposing. While considering only the paths containing the three C/D box proteins (Nop56, Nop58, and Nop1), the most frequently encountered proteins were Aft1, Htz1, Ssa1, Ssb1, Ssb2, Gcn5, Cka1, Tef1, Nop1, Cdc28, Act1, Krr1, Rpl8B, and Tor1, which were then identified as potential drug targets. For drug repurposing, these candidate proteins were further searched in the DrugBank to find other diseases associated with them, as well as the drugs used to treat these diseases. To support the computational results, an experimental study was conducted using in-house manufactured microfluidic bioreactor platform, while the effect of the drug temsirolimus, Tor1 inhibitor, on yeast cells was investigated by following Nop56 protein expression. In conclusion, these results inform the ways in which ribosomopathies and associated common complex human diseases materialize and how drug repurposing might accelerate therapeutic innovation through bioinformatic studies of yeast.

Fabrication of cyclo olefin polymer microfluidic devices for trapping and culturing of yeast cells

A microfluidic platform is designed and fabricated to investigate the role of uncharacterized YOR060C (Sld7) protein in aging in yeast cells for the first time. Saccharomyces cerevisiae yeast cells are trapped in the series of C-shaped regions (0.5 nL) of COP (cyclo olefin polymer), PMMA (poly methylmethacrylate), or PS (polystyrene) microbioreactors. The devices are fabricated using hot embossing and thermo-compression bonding methods. Photolithography and electrochemical etching are used to form the steel mold needed for hot embossing. The cell cycle processes are investigated by monitoring green fluorescent protein (GFP) tagged Sld7 expressions under normal as well as calorie restricted conditions. The cells are loaded at 1 μL/min flowrate and trapped successfully within each chamber. The medium is continuously fed at 0.1 μL/min throughout the experiments. Fluorescent signals of the low abundant Sld7 proteins could be distinguished only on COP devices. The background fluorescence of COP is found 1.22 and 7.24 times lower than that of PMMA, and PS, respectively. Hence, experiments are continued with COP, and lasted for more than 40 h without any contamination. The doubling time of the yeast cells are found as 72 min and 150 min, and the growth rates as 9.63 × 10^-3 min^-1 and 4.62 × 10^-3 min^-1, in 2% glucose containing YPD and YNB medium, respectively. The product concentration (Sld7p:GFP) increased in accordance with cell growth. The dual role of Sld7 protein in both cell cycle and chronological aging needs to be further investigated following the preliminary experimental results.

Advances in microfluidic devices made from thermoplastics used in cell biology and analyses

Silicon and glass were the main fabrication materials of microfluidic devices, however, plastics are on the rise in the past few years. Thermoplastic materials have recently been used to fabricate microfluidic platforms to perform experiments on cellular studies or environmental monitoring, with low cost disposable devices. This review describes the present state of the development and applications of microfluidic systems used in cell biology and analyses since the year 2000. Cultivation, separation/isolation, detection and analysis, and reaction studies are extensively discussed, considering only microorganisms (bacteria, yeast, fungi, zebra fish, etc.) and mammalian cell related studies in the microfluidic platforms. The advantages/disadvantages, fabrication methods, dimensions, and the purpose of creating the desired system are explained in detail. An important conclusion of this review is that these microfluidic platforms are still open for research and development, and solutions need to be found for each case separately.

Screening applications in drug discovery based on microfluidic technology

Microfluidics has been the focus of interest for the last two decades for all the advantages such as low chemical consumption, reduced analysis time, high throughput, better control of mass and heat transfer, downsizing a bench-top laboratory to a chip, i.e., lab-on-a-chip, and many others it has offered. Microfluidic technology quickly found applications in the pharmaceutical industry, which demands working with leading edge scientific and technological breakthroughs, as drug screening and commercialization are very long and expensive processes and require many tests due to unpredictable results. This review paper is on drug candidate screening methods with microfluidic technology and focuses specifically on fabrication techniques and materials for the microchip, types of flow such as continuous or discrete and their advantages, determination of kinetic parameters and their comparison with conventional systems, assessment of toxicities and cytotoxicities, concentration generations for high throughput, and the computational methods that were employed. An important conclusion of this review is that even though microfluidic technology has been in this field for around 20 years there is still room for research and development, as this cutting edge technology requires ingenuity to design and find solutions for each individual case. Recent extensions of these microsystems are microengineered organs-on-chips and organ arrays.

Identification of potential Tpx inhibitors against pathogen-host interactions

Yersinia organisms cause many infectious diseases by invading human cells and delivering their virulence factors via the type three secretion system (T3SS). One alternative strategy in the fight against these pathogenic organisms is to interfere with their T3SS. Previous studies demonstrated that thiol peroxidase, Tpx is functional in the assembly of T3SS and its inhibition by salicylidene acylhydrazides prevents the secretion of pathogenic effectors. In this study, the aim was to identify potential inhibitors of Tpx using an integrated approach starting with high throughput virtual screening and ending with molecular dynamics simulations of selected ligands. Virtual screening of ZINC database of 500,000 compounds via ligand-based and structure-based pharmacophore models retrieved 10,000 hits. The structure-based pharmacophore model was validated using high-throughput virtual screening (HTVS). After multistep docking (SP and XP), common scaffolds were used to find common substructures and the ligand binding poses were optimized using induced fit docking. The stability of the protein-ligand complex was examined with molecular dynamics simulations and the binding free energy of the complex was calculated. As a final outcome eight compounds with different chemotypes were proposed as potential inhibitors for Tpx. The eight ligands identified by a detailed virtual screening protocol can serve as leads in future drug design efforts against the destructive actions of pathogenic bacteria.

Targeting the Akt1 allosteric site to identify novel scaffolds through virtual screening

Preclinical data and tumor specimen studies report that AKT kinases are related to many human cancers. Therefore, identification and development of small molecule inhibitors targeting AKT and its signaling pathway can be therapeutic in treatment of cancer. Numerous studies report inhibitors that target the ATP-binding pocket in the kinase domains, but the similarity of this site, within the kinase family makes selectivity a major problem. The sequence identity amongst PH domains is significantly lower than that in kinase domains and developing more selective inhibitors is possible if PH domain is targeted. This in silico screening study is the first time report toward the identification of potential allosteric inhibitors expected to bind the cavity between kinase and PH domains of Akt1. Structural information of Akt1 was used to develop structure-based pharmacophore models comprising hydrophobic, acceptor, donor and ring features. The 3D structural information of previously identified allosteric Akt inhibitors obtained from literature was employed to develop a ligand-based pharmacophore model. Database was generated with drug like subset of ZINC and screening was performed based on 3D similarity to the selected pharmacophore hypotheses. Binding modes and affinities of the ligands were predicted by Glide software. Top scoring hits were further analyzed considering 2D similarity between the compounds, interactions with Akt1, fitness to pharmacophore models, ADME, druglikeness criteria and Induced-Fit docking. Using virtual screening methodologies, derivatives of 3-methyl-xanthine, quinoline-4-carboxamide and 2-[4-(cyclohexa-1,3-dien-1-yl)-1H-pyrazol-3-yl]phenol were proposed as potential leads for allosteric inhibition of Akt1.

A network-based approach on elucidating the multi-faceted nature of chronological aging in S. cerevisiae

BACKGROUND

Cellular mechanisms leading to aging and therefore increasing susceptibility to age-related diseases are a central topic of research since aging is the ultimate, yet not understood mechanism of the fate of a cell. Studies with model organisms have been conducted to ellucidate these mechanisms, and chronological aging of yeast has been extensively used as a model for oxidative stress and aging of postmitotic tissues in higher eukaryotes.

METHODOLOGY/PRINCIPAL FINDINGS

The chronological aging network of yeast was reconstructed by integrating protein-protein interaction data with gene ontology terms. The reconstructed network was then statistically "tuned" based on the betweenness centrality values of the nodes to compensate for the computer automated method. Both the originally reconstructed and tuned networks were subjected to topological and modular analyses. Finally, an ultimate "heart" network was obtained via pooling the step specific key proteins, which resulted from the decomposition of the linear paths depicting several signaling routes in the tuned network.

CONCLUSIONS/SIGNIFICANCE

The reconstructed networks are of scale-free and hierarchical nature, following a power law model with γ  =  1.49. The results of modular and topological analyses verified that the tuning method was successful. The significantly enriched gene ontology terms of the modular analysis confirmed also that the multifactorial nature of chronological aging was captured by the tuned network. The interplay between various signaling pathways such as TOR, Akt/PKB and cAMP/Protein kinase A was summarized in the "heart" network originated from linear path analysis. The deletion of four genes, TCB3, SNA3, PST2 and YGR130C, was found to increase the chronological life span of yeast. The reconstructed networks can also give insight about the effect of other cellular machineries on chronological aging by targeting different signaling pathways in the linear path analysis, along with unraveling of novel proteins playing part in these pathways.

Stoichiometric network reconstruction and analysis of yeast sphingolipid metabolism incorporating different states of hydroxylation

The first elaborate metabolic model of Saccharomyces cerevisiae sphingolipid metabolism was reconstructed in silico. The model considers five different states of sphingolipid hydroxylation, rendering it unique among other models. It is aimed to clarify the significance of hydroxylation on sphingolipids and hence to interpret the preferences of the cell between different metabolic pathway branches under different stress conditions. The newly constructed model was validated by single, double and triple gene deletions with experimentally verified phenotypes. Calcium sensitivity and deletion mutations that may suppress calcium sensitivity were examined by CSG1 and CSG2 related deletions. The model enabled the analysis of complex sphingolipid content of the plasma membrane coupled with diacylglycerol and phosphatidic acid biosynthesis and ATP consumption in in silico cell. The flux data belonging to these critically important key metabolites are integrated with the fact of phytoceramide induced cell death to propose novel potential drug targets for cancer therapeutics. In conclusion, we propose that IPT1, GDA1, CSG and AUR1 gene deletions may be novel candidates of drug targets for cancer therapy according to the results of flux balance and variability analyses coupled with robustness analysis.

Molecular facets of sphingolipids: mediators of diseases

Sphingolipids constitute a biologically active lipid class that is significantly important from both structural and regulatory aspects. The manipulation of sphingolipid metabolism is currently being studied as a novel strategy for cancer therapy. The basics of this therapeutic approach lie in the regulation property of sphingolipids on cellular processes, which are important in a cell's fate, such as cell proliferation, apoptosis, cell cycle arrest, senescence, and inflammation. Furthermore, the mutations in the enzymes catalyzing some specific reactions in the sphingolipid metabolism cause mortal lysosomal storage diseases like Fabry, Gaucher, Niemann-Pick, Farber, Krabbe, and Metachromatic Leukodystrophy. Therefore, the alteration of the sphingolipid metabolic pathway determines the choice between life and death. Understanding the sphingolipid metabolism and regulation is significant for the development of new therapeutic approaches for all sphingolipid-related diseases, as well as for cancer. An important feature of the sphingolipid metabolic pathway is the compartmentalization into endoplasmic reticulum, the Golgi apparatus, lysosome and plasma membrane, and this compartmentalization makes the transport of sphingolipids critical for proper functioning. This paper focuses on the structures, metabolic pathways, localization, transport mechanisms, and diseases of sphingolipids in Saccharomyces cerevisiae and humans, and provides the latest comprehensive information on sphingolipid research.

Exometabolic and transcriptional response in relation to phenotype and gene copy number in respiration-related deletion mutants of S. cerevisiae

The transcriptional and metabolic impact of deleting one or both copies of a respiration-related gene has been studied in glucose-limited chemostats. Integration of literature information on phenotype with our exometabolome and transcriptome data enabled the identification of novel relationships between gene copy number, transcriptional regulation and phenotype. We found that the effect of complete respiratory deficiency on transcription was limited to downregulation of genes involved in oxidoreductase activity and iron assimilation. Partial respiratory deficiency had no significant impact on gene transcription. Changes in the copy number of two transcription-factor genes, HAP4 and MIG1, had a major impact on genes involved in mitochondrial function. Regulation of respiratory chain components encoded in the nucleus and mitochondria appears to be divided between Hap4p and Oxa1p, respectively. Similarly, repression of respiration may be imposed by the action of Mig1p and Mba1p on nuclear and mitochondrial gene expression, respectively. However, it is not clear whether Oxa1p and Mba1p regulate mitochondrial gene expression via their interaction with mitochondrial ribosomes or by some indirect means. The phenotype of nuclear petite mutants may not simply be due to the absence of respiration; e.g. Oxa1p or Bcs1p may play a role in the regulation of ribosome assembly in the nucleolus. Integration between respiration and cell growth may also result from the action of a single transcription factor. Thus, Hap4p targets genes that are required for respiration and for fitness in nutrient-limited conditions. This suggests that Hap4p may enable cells to adapt to nutrient limitation as well as diauxy.

Drug targets for tumorigenesis: insights from structural analysis of EGFR signaling network

Deciphering the complex network structure is crucial in drug target identification. This study presents a framework incorporating graph theoretic and network decomposition methods to analyze system-level properties of the comprehensive map of the epidermal growth factor receptor (EGFR) signaling, which is a good candidate model system to study the general mechanisms of signal transduction. The graph theoretic analysis of the EGFR network indicates that it has small-world characteristics with scale-free topology. The employment of network decomposition analysis enlightened the system-level properties, such as network cross-talk, specific molecules in each pathway and participation of molecules in the network. Participating in a significant fraction of the fundamental paths connecting the ligands to the phenotypes, cofactor GTP and complex Gbeta/Ggamma were identified as "housekeeping" molecules, through which all pathways of EGFR network are cross-talking. c-Src-Shc complex is identified as important due to its role in all fundamental paths through tumorigenesis and being specific to this phenotype. Inhibitors of this complex may be good anti-cancer agents having very little or no effect on other phenotypes.

Reconstruction and flux analysis of coupling between metabolic pathways of astrocytes and neurons: application to cerebral hypoxia

BACKGROUND

It is a daunting task to identify all the metabolic pathways of brain energy metabolism and develop a dynamic simulation environment that will cover a time scale ranging from seconds to hours. To simplify this task and make it more practicable, we undertook stoichiometric modeling of brain energy metabolism with the major aim of including the main interacting pathways in and between astrocytes and neurons.

MODEL

The constructed model includes central metabolism (glycolysis, pentose phosphate pathway, TCA cycle), lipid metabolism, reactive oxygen species (ROS) detoxification, amino acid metabolism (synthesis and catabolism), the well-known glutamate-glutamine cycle, other coupling reactions between astrocytes and neurons, and neurotransmitter metabolism. This is, to our knowledge, the most comprehensive attempt at stoichiometric modeling of brain metabolism to date in terms of its coverage of a wide range of metabolic pathways. We then attempted to model the basal physiological behaviour and hypoxic behaviour of the brain cells where astrocytes and neurons are tightly coupled.

RESULTS

The reconstructed stoichiometric reaction model included 217 reactions (184 internal, 33 exchange) and 216 metabolites (183 internal, 33 external) distributed in and between astrocytes and neurons. Flux balance analysis (FBA) techniques were applied to the reconstructed model to elucidate the underlying cellular principles of neuron-astrocyte coupling. Simulation of resting conditions under the constraints of maximization of glutamate/glutamine/GABA cycle fluxes between the two cell types with subsequent minimization of Euclidean norm of fluxes resulted in a flux distribution in accordance with literature-based findings. As a further validation of our model, the effect of oxygen deprivation (hypoxia) on fluxes was simulated using an FBA-derivative approach, known as minimization of metabolic adjustment (MOMA). The results show the power of the constructed model to simulate disease behaviour on the flux level, and its potential to analyze cellular metabolic behaviour in silico.

CONCLUSION

The predictive power of the constructed model for the key flux distributions, especially central carbon metabolism and glutamate-glutamine cycle fluxes, and its application to hypoxia is promising. The resultant acceptable predictions strengthen the power of such stoichiometric models in the analysis of mammalian cell metabolism.

Understanding signaling in yeast: Insights from network analysis

Reconstruction of protein interaction networks that represent groups of proteins contributing to the same cellular function is a key step towards quantitative studies of signal transduction pathways. Here we present a novel approach to reconstruct a highly correlated protein interaction network and to identify previously unknown components of a signaling pathway through integration of protein-protein interaction data, gene expression data, and Gene Ontology annotations. A novel algorithm is designed to reconstruct a highly correlated protein interaction network which is composed of the candidate proteins for signal transduction mechanisms in yeast Saccharomyces cerevisiae. The high efficiency of the reconstruction process is proved by a Receiver Operating Characteristic curve analysis. Identification and scoring of the possible linear pathways enables reconstruction of specific sub-networks for glucose-induction signaling and high osmolarity MAPK signaling in S. cerevisiae. All of the known components of these pathways are identified together with several new "candidate" proteins, indicating the successful reconstructions of two model pathways involved in S. cerevisiae. The integrated approach is hence shown useful for (i) prediction of new signaling pathways, (ii) identification of unknown members of documented pathways, and (iii) identification of network modules consisting of a group of related components that often incorporate the same functional mechanism.

Analysis of enzymopathies in the human red blood cells by constraint-based stoichiometric modeling approaches

The human red blood cell (RBC) metabolism is investigated by calculating steady state fluxes using constraint-based stoichiometric modeling approaches. For the normal RBC metabolism, flux balance analysis (FBA) is performed via optimization of various alternative objective functions, and the maximization of production of ATP and NADPH is found to be the primary objective of the RBC metabolism. FBA and two novel approaches, minimization of metabolic adjustment (MOMA) and regulatory on-off minimization (ROOM), which can describe the behavior of the metabolic networks in case of enzymopathies, are applied to observe the relative changes in the flux distribution of the deficient network. The deficiencies in several enzymes in RBC metabolism are investigated and the flux distributions are compared with the non-deficient FBA distribution to elucidate the metabolic changes in response to enzymopathies. It is found that the metabolism is mostly affected by the glucose-6-phosphate dehydrogenase (G6PDH) and phosphoglycerate kinase (PGK) enzymopathies, whereas the effects of the deficiency in DPGM on the metabolism are negligible. These stoichiometric modeling results are found to be in accordance with the experimental findings in the literature related to metabolic behavior of the human red blood cells, showing that human RBC metabolism can be modeled stoichiometrically.

Annotation of unknown yeast ORFs by correlation analysis of microarray data and extensive literature searches

Changes in the expression of genes were used to elucidate the metabolic pathways and regulatory mechanisms that respond to environmental or genetic modifications. Results from previously published chemostat datasets were merged with novel data generated in the present study. ORFs displaying significant changes in expression that correlated with those of other ORFs were analysed using GO mapping tools and supplemented by literature information. The strategy developed was used to propose annotations for ORFs of unknown function. The following ORFs were assigned functions as a result of this study: YMR090w, YGL157w, YGR243w, YLR327c, YER121w, YFR017c, YGR067c, YKL187c, YGR236c (SPG1), YMR107w (SPG4), YMR206w, YER067w, YJL103c, YNL175C (NOP13) YJL200C, YDL070C (FMP16) and YGR173W.

Metabolic pathway analysis of yeast strengthens the bridge between transcriptomics and metabolic networks

Central carbon metabolism of the yeast Saccharomyces cerevisiae was analyzed using metabolic pathway analysis tools. Elementary flux modes for three substrates (glucose, galactose, and ethanol) were determined using the catabolic reactions occurring in yeast. Resultant elementary modes were used for gene deletion phenotype analysis and for the analysis of robustness of the central metabolism and network functionality. Control-effective fluxes, determined by calculating the efficiency of each mode, were used for the prediction of transcript ratios of metabolic genes in different growth media (glucose-ethanol and galactose-ethanol). A high correlation was obtained between the theoretical and experimental expression levels of 38 genes when ethanol and glucose media were considered. Such analysis was shown to be a bridge between transcriptomics and fluxomics. Control-effective flux distribution was found to be promising in in silico predictions by incorporating functionality and regulation into the metabolic network structure.

Recovery of antithrombin III from milk by expanded bed chromatography

In the present work, a new method of purification for antithrombin was developed using an expanded bed chromatography technique. Milk fat was removed by centrifugation and caseins were precipitated selectively by addition of zinc chloride. Crude skim milk was then directly fed to an expanded bed column containing the ion-exchange matrix. The use of a cation-exchanger (P-11) resulted in 100% adsorption and 13% recovery whereas the use of an anion-exchanger (DE-52) resulted in 100% adsorption and 84% recovery and up to five-fold purification of antithrombin. The buffer, 25 mM Tris-HCl pH 8.0; the eluting agent, 2 M (NH4)2SO4; and 100% expansion of settled bed were determined to be the optimum conditions for the purification of antithrombin by ion-exchange expanded bed chromatography. A comparison of column diameters revealed that the elution yields increase by two-fold while the column diameter increases from 1 to 2.5 cm. However, antithrombin III was concentrated to a higher degree by using the column with an internal diameter of 1 cm.

Recovery of actinorhodin from fermentation broth

In the present work, a new method of purification for actinorhodin was developed using an expanded bed chromatography technique in which antibiotic capture, feedstock clarification, centrifugation, dialysis and concentration are done in one step. The cation-exchanger (P-11) resulted in 26% adsorption and 2% recovery whereas the anion-exchanger (DE-52) resulted in 99% adsorption and 56% recovery of adsorbed antibiotic using methanol buffer and 2 M NH4Cl as eluting agent. Streamline DEAE anion-exchanger, which is especially designed for EBA applications, yields 82% adsorption and 50% elution of actinorhodin fed into the chromatography column directly from the fermentation broth. Isocratic elution resulted in extremely efficient yield compared to linear gradient elution, i.e. 13.5-fold more recovery in the column with an aspect ratio (L:D) of 4. Expansion by 150% of settled bed resulted in the best recovery of actinorhodin among 100 and 200% expansions. A comparison of breakthrough profiles in packed and expanded bed adsorption showed that the performance of the expanded bed is better (by 33%) at allowing more volume of the fermentation broth to pass through the chromatography column.

Purification of TaqI endonuclease from Thermus aquaticus

A purification procedure for the thermostable restriction enzyme TaqI was developed using high-performance ion-exchange liquid chromatography. The effects of various operating conditions on the separation behaviour of TaqI endonuclease from the cell extracts were investigated for optimisation and scaling up. The separation of the enzyme by HPLC was found to be strongly dependent on the sample volume, slope of linear gradient and order of the ion-exchange columns. The final yield of the enzyme is also dependent to a great extent upon the number of fractionation steps employed to purify the enzyme. In the present study, 4000 U TaqI endonuclease per mg protein was recovered from 2 g Thermus aquaticus cells with a two-step purification protocol in one day. The purification factor was 24. Compared to other classical methods of purification reported in literature with 4000 or 32,000 U enzyme from 200 g of Thermus aquaticus cells, HPLC yielded 190,000 U enzyme from 200 g cells using cation and anion HPLC columns sequentially and thus resulted in a higher efficiency.