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Scientific BACKGROUND: Neurofibromatosis is an autosomal dominant genetic disease which affects skin, nervous system and eyes. Neurofibromatosis has two frequently seen subtypes which are called Type 1 (Von Recklinghausen disease: periferal neurofibromatosis) and Type 2. Type 1 is characterized with cafe au lait spots, periferal neurofibroms, Lisch nodules and axillar freckles. Type 2 is central neurofibromatosis which appears with bilateral acoustic neurinoms with slightly cutaneous changes around twenty years old. Convulsive disorders are seen in about 5% of patients. OBJECTIVE: We emphasized the importance of early diagnosis and genetic counseiling on epilepsy. CASE: We examined two cases that belong to the same family with definitively clinical diagnosis on neurofibromatosis type 1. The mother had amnesia, dermal invasion, forgetfulness, Lisch nodules and no family history. Her daughter had learning disability, dermal invasion, and refractory epilepsy. CONCLUSION: We considered genetic transmission which was possible mutational transmission for mother and autosomal dominant transmission for daughter. We emphasized that there may be different clinical features in the same family

Scientific BACKGROUND: Syphilis is an systemic infection that can effect the central nervous system in all stages. Without treatment, it reaches the primary, secondary and tertiary stages, it also affects heart, central nervous system and other organs. OBJECTIVE: With the introduction of penicillin, effects of syphilis on central nervous system are decreased. Spinal cord involvement is very rare manifestation of syphilis. Syphilis should be kept in mind in transverse myelitis cases that presented with acute paraparesis. CASE: A 61 years old man presented with acute paraparesis, hyperestesia below level of T9 dermatom, fecal and uriner retantion. CONCLUSION: Our case is presented due to rare manifestation of syphilis like spinal myelitis with acute paraparesia and concomitant cardiac, cerebral, renal, aortic and retinal involvement by syphilis

Scientific BACKGROUND: It has been known that a lot of drugs negatively influence the neuromuscular junction. These drugs cause either manifesting the underlying neuromuscular transport disorder of taking form a myasthenic syndrome due to the drug itself. Lithium from them, is a drug that has been used in the treatment of bipolar affective disorder and may cause temporary fatigue. CASE: Fatigue and bilateral ptosis complaints that developed in our 41 years old, female case attributed to lithium use. After discontiniung, recovering her symptoms have been found related to lithium chronic use. CONCLUSION: Lithium’s chronic use may cause serious neurologic and systemic complications and it is important to follow up the patient regarding developing complications

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.

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., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

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., (© 2024 The Authors. Molecular Informatics published by Wiley‐VCH GmbH.)

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., (© 2023 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders generally characterized by repetitive behaviors and difficulties in communication and social behavior. Despite its heterogeneous nature, several metabolic dysregulations are prevalent in individuals with ASD. This work aims to understand ASD brain metabolism by constructing an ASD-specific prefrontal cortex genome-scale metabolic model (GEM) using transcriptomics data to decipher novel neuroinflammatory biomarkers. The healthy and ASD-specific models are compared via uniform sampling to identify ASD-exclusive metabolic features. Noticeably, the results of our simulations and those found in the literature are comparable, supporting the accuracy of our reconstructed ASD model. We identified that several oxidative stress, mitochondrial dysfunction, and inflammatory markers are elevated in ASD. While oxidative phosphorylation fluxes were similar for healthy and ASD-specific models, and the fluxes through the pathway were nearly undisturbed, the tricarboxylic acid (TCA) fluxes indicated disruptions in the pathway. Similarly, the secretions of mitochondrial dysfunction markers such as pyruvate are found to be higher, as well as the activities of oxidative stress marker enzymes like alanine and aspartate aminotransferases (ALT and AST) and glutathione-disulfide reductase (GSR). We also detected abnormalities in the sphingolipid metabolism, which has been implicated in many inflammatory and immune processes, but its relationship with ASD has not been thoroughly explored in the existing literature. We suggest that important sphingolipid metabolites, such as sphingosine-1-phosphate (S1P), ceramide, and glucosylceramide, may be promising biomarkers for the diagnosis of ASD and provide an opportunity for the adoption of early intervention for young children.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Tezcan, Demirtas, Cakar and Ulgen.)

Medulloblastoma (MB), occurring in the cerebellum, is the most common childhood brain tumor. Because conventional methods decline life quality and endanger children with detrimental side effects, computer models are needed to imitate the characteristics of cancer cells and uncover effective therapeutic targets with minimum toxic effects on healthy cells. In this study, metabolic changes specific to MB were captured by the genome-scale metabolic brain model integrated with transcriptome data. To determine the roles of sphingolipid metabolism in proliferation and metastasis in the cancer cell, 79 reactions were incorporated into the MB model. The pathways employed by MB without a carbon source and the link between metastasis and the Warburg effect were examined in detail. To reveal therapeutic targets for MB, biomass-coupled reactions, the essential genes/gene products, and the antimetabolites, which might deplete the use of metabolites in cells by triggering competitive inhibition, were determined. As a result, interfering with the enzymes associated with fatty acid synthesis (FAs) and the mevalonate pathway in cholesterol synthesis, suppressing cardiolipin production, and tumor-supporting sphingolipid metabolites might be effective therapeutic approaches for MB. Moreover, decreasing the activity of succinate synthesis and GABA-catalyzing enzymes concurrently might be a promising strategy for metastatic MB.

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., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Microfluidic devices consist of microchannels etched or embossed into substrates made of polymer, glass or silicon. Intricate connections of the microchannels to reactors with some smart mechanical structures such as traps or curvatures fulfil the desired functionalities such as mixing, separation, flow control or setting the environment for biochemical reactions. Here, we describe the fabrication methods of a thermoplastic microbioreactor step by step. First, material selection is made, then, production methods are determined with the equipment that can be easily procured in a laboratory. COP with its outstanding characteristics among many polymers was chosen. Two types of microbioreactors, with and without electrodes, are designed with AutoCAD and L-edit softwares. Photolithography and electrochemical wet etching are used for master mold preparation. Thermal evaporator is employed for pure chromium and gold deposition on COP substrate and etchants are used to form the interdigitated electrodes. Once the master mold produced, hot embossing is used to obtain the designed shape on drilled and planarized COP. Cover COP, with or without electrodes, is bonded to the hot embossed COP via thermo-compression and thermoplastic microfluidic device is realized. Tubings are connected to the device and a bridge between the macro and micro world is established. Yeast or mammalian cells labeled or tagged with GFP/RFP on specific gene products are loaded into the microfluidic device, and real time data on cell dimensions and fluorescence intensity are collected using inverted fluorescence microscope, and finally image processing is used to analyze the acquired data., (© 2021. Springer Science+Business Media, LLC.)

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.

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.

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., (Copyright © 2020 Sertbas and Ulgen.)

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  μ m) 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., (Copyright © 2020 Author(s).)

Cyclo Olefin Polymer (COP) based microbioreactors on a microfluidic chip were produced in house by hot-embossing and thermo-compression bonding methods. The chip allows two different experiments to be performed on trapped cells at the same time. On one side of the chip, red fluorescent protein (RFP) tagged nucleolar Nop56 protein was used to track changes in cell cycle as well as protein synthesis within the yeast cells under the application of the anti-tumor agent hydroxyurea (HU). Simultaneously, on the other side of the chip, the response of yeast cells to the drug metformin, mTOR inhibitor, was investigated to reveal the role of TOR signaling in ribosome biogenesis and cell proliferation. The results of 20 h long experiments are captured by taking brightfield and fluorescent microscopy images of the trapped cells every 9 min. The expression of Nop56 protein of ribosome assembly and synthesis was densely observed during G1 phase of cell cycle, and later towards the end of cell cycle the ribosomal protein expression slowed down. Under HU treatment, the morphology of yeast cells changed, but after cessation of HU, the biomass synthesis rate was sustained as monitored by the cell perimeter. Under metformin treatment, the perimeters of single cells were observed to decrease, implying a decrease in biomass growth; however these cells continued their proliferation during and after the drug application. The relation between ribosome biogenesis and cell cycle was successfully investigated on single cell basis, capturing cell-to-cell variations, which cannot be tracked by regular macroscale bioreactors.

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.

Lysosome is the organelle responsible for breaking down macromolecules to maintain homeostasis and to fight infection. The disruption of normal lysosomal function due to mutations in the sphingolipid metabolism proteins leads to a class of lysosomal storage diseases (LSDs). Defective autophagy and activation of inflammation are observed in most LSDs. The crosstalk between these key metabolic pathways suggests that therapeutic approaches used in the treatment of LSDs may provide anti-inflammatory therapies against chronic inflammatory diseases such as multiple sclerosis, rheumatoid arthritis, inflammatory bowel disease. Here, we review the role of sphingolipids in the inflammatory response and build a protein-protein interaction network for proteins related with sphingolipid metabolism and inflammation to identify key interaction partners for the crosstalk between sphingolipids and inflammation. In addition, we present an overview of LSDs in relation with sphingolipids and inflammation, and review the pharmacological chaperones identified for these diseases., (© 2019 Elsevier Inc. All rights reserved.)

Inhibition of DNA damage response pathway in combination with DNA alkylating agents may enhance the selective killing of cancer cells leading to better therapeutic effects. MDM2 binding protein (MTBP) in human has a role in G1 phase (interphase of cell cycle) and its overexpression leads to breast and ovarian cancers. Sld7 is an uncharacterized protein in budding yeast and a potential functional homologue of MTBP. To investigate the role of Sld7 as a therapeutic target, the behavior of the wild-type cells and sld7∆ mutants were monitored in 0.5 nL microbioreactors. The brightfield microscopy images were used to analyze the change in the cell size and to determine the durations of G1 and S/G2/M phases of wild type cells and mutants. With the administration of the alkylating agent, the cell size decreased and the duration of cell cycle increased. The replacement of the medium with the fresh one enabled the cells to repair their DNA. The application of calorie restriction together with DNA alkylating agent to mutant cells resulted in smaller cell size and longer G1 phase compared to those in control environment. For therapeutic purposes, the potential of MTBP in humans or Sld7 in yeast as a drug target deserves further exploration. The fabrication simplicity, robustness and low-cost of this microfluidic bioreactor made of polystyrene allowed us to perform yeast culturing experiments and show a potential for further cell culturing studies. The device can successfully be used for therapeutic applications including the discovery of new anti-microbial, anti-inflammatory, anti-cancer drugs.

Neurology research and clinical practice are transforming toward postgenomics integrative biology. One such example is the study of human brain metabolism that is highly sophisticated due to reactions occurring in and between the astrocytes and neurons. Because of the inherent difficulty of performing experimental studies in human brain, metabolic network modeling has grown in importance to decipher the contribution of brain metabolite kinetics to human health and disease. Multiomics system science-driven metabolic models, using genome-scale and transcriptomics Big Data, offer the promise of new insights on metabolic networks in human brain. Added to this, the availability of omics technologies in both developed and developing world, neurology research, and clinical practice ought to be repositioned with a view to systems medicine. In this expert analysis, we present a critical and in-depth overview of the basic tenets of human brain metabolism, together with the most recent metabolic modeling strategies and computational studies of brain in health and neurological diseases. Human genome-scale metabolic models developed in a both global and brain-specific manner and multiomics synthesis of knowledge are highlighted in particular. We conclude by underscoring the value of multiomics modeling for metabolic diseases and computational investigations of the brain networks, with a view to unlocking the pathophysiology of Alzheimer's disease, Parkinson's disease, migraine, stroke, epilepsy, and multiple sclerosis, among other neurological disorders of importance for global health.

Background: Sphingosine kinase 1 (SK1) overexpression and elevated sphingosine-1-phosphate (S1P) levels have been correlated with many disease states from cancer to inflammatory diseases to diabetes. Even though SK1 inhibitors are of consideberable interest as effective chemotherapeutic agents, poor potency, lack of selectivity and poor pharmacokinetic properties have been major problems in the first generation SK1 inhibitors., Objective: There is an urgent need for the discovery of novel in vivo, stable selective SK1 inhibitors with improved potency. The primary object of this study was to identify potential novel leads for orthosteric inhibition of SK1., Methods: We propose a series of compounds from different chemotypes as potential selective SK1 inhibitors via virtual screening of the ZINC database using ligand-based and structure-based pharmacophore models, molecular docking, substructure search, selectivity calculations. Molecular dynamics (MD) simulations revealed key insights into the binding mode and the stability of the SK1-ligand complex., Results: Ten ligands were proposed as potential SK1 inhibitors based on the high induced fit docking scores, BEI, LLE and %HOA. Ligands 2, 3, 5 and 9 were found to be selective toward SK1 with favorable binding free energy of - 95 ± 5 kcal/mol. MD simulation of ligand 5 showed that the ligand-SK1 complex reached equilibrium with favorable hydrogen bonding and hydrophobic interactions. The four selective compounds have less than 0.24 similarity with previously discovered potent inhibitors., Conclusion: The proposed compounds may serve as potential novel leads for orthosteric inhibition of SK1., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

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.

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.

Genome instability has long been implicated as a salient causal factor in aging and age-related diseases such as cancer and neurodegeneration. However, the molecular mechanisms associated with genome instability remain unclear. Recent studies suggest growth signaling in the organism Saccharomyces cerevisiae and in higher eukaryotes might affect oxidative stress and aging/age-related diseases by activating DNA replication stress that causes DNA damage. In this broader integrative biology and clinical context, MDM two binding protein (MTBP) is a binding protein that has a role in activation of the tumor suppressor protein p53. Clinical studies suggest that the increase in expression of MTBP leads to reduction in survival of breast cancer patients. The functional homolog of MTBP in yeast, Sld7, is a hitherto uncharacterized protein that decreases the affinity of Sld3 (Treslin/Ticrr in humans) toward Cdc45 in the cell cycle process of S. cerevisiae. To investigate the putative function of Sld7 in chronological aging and replicative life span, we applied a network biology approach, integrating interactome and transcriptome data of budding yeast. The cell cycle and chronological aging networks of proteins in budding yeast were reconstructed. Furthermore, through meta-analysis of cell cycle and chronological aging-associated transcriptome datasets, we constructed coexpression networks and identified coexpressed gene clusters. In this study, we propose a dual role for Sld7: it participates in macromolecular complex binding in the cell cycle and has oxidoreductase activity in chronological aging in budding yeast. These potential roles of Sld7 in yeast can offer new insights on the role of MTBP in humans and consequently might lead to novel strategies for treatment of cancers and aging-related diseases.

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.

Decrease in sphingosine 1-phosphate (S1P) concentration induces migration of pathogenic T cells to the blood stream, disrupts the CNS and it is implicated in multiple sclerosis (MS), a progressive inflammatory disorder of the central nervous system (CNS), and Alzheimer's disease (AD). A promising treatment alternative for MS and AD is inhibition of the activity of the microsomal enzyme sphingosine 1-phosphate lyase (S1PL), which degrades intracellular S1P. This report describes an integrated systematic approach comprising virtual screening, molecular docking, substructure search and molecular dynamics simulation to discover novel S1PL inhibitors. Virtual screening of the ZINC database via ligand-based and structure-based pharmacophore models yielded 10000 hits. After molecular docking, common substructures of the top ranking hits were identified. The ligand binding poses were optimized by induced fit docking. MD simulations were performed on the complex structures to determine the stability of the S1PL-ligand complex and to calculate the binding free energy. Selectivity of the selected molecules was examined by docking them to hERG and cytochrome P450 receptors. As a final outcome, 15 compounds from different chemotypes were proposed as potential S1PL inhibitors. These molecules may guide future medicinal chemistry efforts in the discovery of new compounds against the destructive action of pathogenic T cells., (Copyright © 2015 Elsevier Inc. All rights reserved.)

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., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

One of the factors affecting chronological life span (CLS) in budding yeast is nutrient, especially carbon limitation. Aside from metabolites in the growth medium such as glucose, amino acids, and acetic acid, many pharmaceuticals have also been proven to alter CLS. Besides their impact on life span, these drugs are also prospective chemicals to treat the age-associated diseases, so the identification of their action mechanism and their potential side effects is of crucial importance. In this study, the effects of caloric restriction and metformin, a dietary mimetic pharmaceutical, on yeast CLS are compared. Saccharomyces cerevisiae cells grown in synthetic dextrose complete (SDC) up to mid-exponential phase were either treated with metformin or were subjected to glucose limitation. The impacts of these perturbations were analyzed via transcriptomics, and the common (stimulation of glucose uptake, induction of mitochondrial maintenance, and reduction of protein translation) and divergent (stimulation of aerobic respiration and reprogramming of respiratory electron transport chain (ETC)) cellular responses specific to each treatment were determined. These results revealed that both glucose limitation and metformin treatment stimulate CLS extension and involve the mitochondrial function, probably by creating an efficient mitochondria-to-nucleus signaling of either aerobic respiration or ETC signaling stimulation, respectively.

Abstract Salmonella outer protein E, SopE, is a virulence factor that is secreted from gram-negative Salmonella enterica, which is pathogenic to humans and is responsible for gastroenteritis and typhoid fever. SopE targets the Rho GTPase family proteins of the host cell and manipulates them by mimicking GTPase regulatory protein guanine nucleotide exchange factor. The aim of this work is the investigation of novel inhibitors against SopE. Structure-based pharmacophore modeling was used to identify structural features that would be important for SopE recognition. Glide fragment library was used for four-point pharmacophore hypothesis development. Small-molecule database screening was performed based on a 3D similarity to the best pharmacophore hypothesis. Binding affinity of filtered database molecules to SopE was predicted quantitatively by molecular docking and scoring using Glide software. Top scoring hits were further analyzed and five molecules were proposed as potent and selective SopE inhibitors. Four out of five proposed molecules were found to be aminopurine derivatives.

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., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Because of its multifactorial nature, aging is one of the most complicated cell phenomena known. A systems biology approach, which aims to understand the organism as a whole rather than concentrate on the behaviors of individual genes, thus comprises a seamless tool for investigating the aging machinery, which arises mainly as a result of degeneration of the collaboration between signaling and regulatory pathways. In the present study, the effects of medium buffering on the chronological life span are investigated via transcriptome analyses and subsequent integration of the data obtained with the chronological aging network of yeast. The comparative inquiry of transcriptome data of young and old cells grown in buffered and unbuffered media reveals new roles for pH control (e.g. the re-organization of lipid metabolism and intracellular signaling cascades) that have beneficial consequences on chronological longevity. Integration of the transcriptome data onto the aging network, as well as validation experiments, suggest that Snf1p is a possible intermediate player in the interjunction of sphingolipid and ergosterol metabolisms with extracellular pH control with respect to regulation of the chronological life span. Consequently, a more detailed insight of the chronological aging mechanism of yeast is obtained. The results of the present study provide a solid basis for further research focusing on uncovering the agents that affect aging and age-related diseases in humans., (© 2014 FEBS.)

Sphingolipids are essential building blocks of the plasma membranes and are highly bioactive in the regulation of diverse cellular functions and pathological processes, a fact which renders the sphingolipid metabolism an important research area. In this study, a computational framework was recruited for the reconstruction of a functional interaction network for sphingolipid metabolism in Baker's yeast, SSN. Gene Ontology (GO) annotations were integrated with functional interaction data of the BIOGRID database and the reconstructed protein interaction network was subjected to topological and descriptive analyses. SSN was of a scale-free nature, following a power law model with γ=1.41. Prominent processes of SSN revealed that the reconstructed network encapsulated the involvement of sphingolipid metabolism in vital cellular processes such as energy homeostasis, cell growth and/or death and synthesis of building blocks. To investigate the potential of SSN for predicting signal transduction pathways regulating and/or being regulated by sphingolipid biosynthesis in yeast, a case study involving the S. cerevisiae counterpart of AMP-activated protein kinase, the Snf1 kinase complex, was conducted. The mutant strain lacking the catalytic α subunit, snf1Δ/snf1Δ, had elevated inositol phosphorylceramide and mannosyl-inositol phosphorylceramide levels, and decreased mannosyl-diinositol phosphorylceramide levels compared to the wild type strain, revealing that Snf1p has a regulatory role in the sphingolipid metabolism. Transcriptome data belonging to that strain available in the literature were mapped onto SSN and the correlated SSN was further investigated to evaluate the possible crosstalk machineries where sphingolipids and Snf1p function in coordination, in other words the crosstalk points between sphingolipid-mediated and Snf1 kinase signalling. The subsequent investigation of the discovered candidate crosstalk processes by performing sensitivity experiments imply a tight interconnection between sphingolipids and Snf1p in the regulation of calcineurin activity, cellular metal ion homeostasis and response to cell wall and endoplasmic reticulum stresses in yeast.

Gram-negative bacteria Yersinia secrete virulence factors that invade eukaryotic cells via type III secretion system. One particular virulence member, Yersinia outer protein E (YopE), targets Rho family of small GTPases by mimicking regulator GAP protein activity, and its secretion mainly induces cytoskeletal disruption and depolymerization of actin stress fibers within the host cell. In this work, potent drug-like inhibitors of YopE are investigated with virtual screening approaches. More than 500,000 unique small molecules from ZINC database were screened with a five-point pharmacophore, comprising three hydrogen acceptors, one hydrogen donor, and one ring, and derived from different salicylidene acylhydrazides. Binding modes and features of these molecules were investigated with a multistep molecular docking approach using Glide software. Virtual screening hits were further analyzed based on their docking score, chemical similarity, pharmacokinetic properties, and the key Arg144 interaction along with other active site residue interactions with the receptor. As a final outcome, a diverse set of ligands with inhibitory potential were proposed.

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., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

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., (© 2011 Borklu Yucel, Ulgen.)

Sphingolipids regulate cellular processes that are critically important in cell's fate and function in cancer development and progression. This fact underlies the basics of the novel cancer therapy approach. The pharmacological manipulation of the sphingolipid metabolism in cancer therapeutics necessitates the detailed understanding of the pathway. Two computational systems biology tools are used to identify potential drug target enzymes among sphingolipid pathway that can be further utilized in drug design studies for cancer therapy. The enzymes in sphingolipid pathway were ranked according to their roles in controlling the metabolic network by metabolic control analysis. The physiologically connected reactions, i.e. biologically significant and functional modules of network, were identified by metabolic pathway analysis. The final set of candidate drug target enzymes are selected such that their manipulation leads to ceramide accumulation and long chain base phosphates depletion. The mathematical tools' efficiency for drug target identification performed in this study is validated by clinically available drugs., (Copyright 2010 Elsevier Inc. All rights reserved.)

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.

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.

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.

Flux balance analysis and phenotypic data were used to provide clues to the relationships between the activities of gene products and the phenotypes resulting from the deletion of genes involved in respiratory function in Saccharomyces cerevisiae. The effect of partial or complete respiratory deficiency on the ethanol production and growth characteristics of hap4Delta/hap4Delta, mig1Delta/mig1Delta, qdr3Delta/qdr3Delta, pdr3Delta/pdr3Delta, qcr7Delta/qcr7Delta, cyt1Delta/cyt1Delta, and rip1Delta/rip1Delta mutants grown in microaerated chemostats was investigated. The study provided additional evidence for the importance of the selection of a physiologically relevant objective function, and it may improve quantitative predictions of exchange fluxes, as well as qualitative estimations of changes in intracellular fluxes. Ethanol production was successfully predicted by flux balance analysis in the case of the qdr3Delta/qdr3Delta mutant, with maximization of ethanol production as the objective function, suggesting an additional role for Qdr3p in respiration. The absence of similar changes in estimated intracellular fluxes in the qcr7Delta/qcr7Delta mutant compared to the rip1Delta/rip1Delta and cyt1Delta/cyt1Delta mutants indicated that the effect of the deletion of this subunit of complex III was somehow compensated for. Analysis of predicted flux distributions indicated self-organization of intracellular fluxes to avoid NAD(+)/NADH imbalance in rip1Delta/rip1Delta and cyt1Delta/cyt1Delta mutants, but not the qcr7Delta/qcr7Delta mutant. The flux through the glycerol efflux channel, Fps1p, was estimated to be zero in all strains under the investigated conditions. This indicates that previous strategies for improving ethanol production, such as the overexpression of the glutamate synthase gene GLT1 in a GDH1 deletion background or deletion of the glycerol efflux channel gene FPS1 and overexpression of GLT1, are unnecessary in a respiration-deficient background.

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.

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., ((c) 2007 Wiley Periodicals, Inc.)

Background: Control effective flux (CEF) of a reaction is the weighted sum of all fluxes through that reaction, derived from elementary flux modes (EFM) of a metabolic network. Change in CEFs under different environmental conditions has earlier been proven to be correlated with the corresponding changes in the transcriptome. Here we use this to investigate the degree of transcriptional regulation of fluxes in the metabolism of Saccharomyces cerevisiae. We do this by quantifying correlations between changes in CEFs and changes in transcript levels for shifts in carbon source, i.e. between the fermentative carbon source glucose and nonfermentative carbon sources like ethanol, acetate, and lactate. The CEF analysis is based on a simple stoichiometric model that includes reactions of the central carbon metabolism and the amino acid metabolism., Results: The effect of the carbon shift on the metabolic fluxes was investigated for both batch and chemostat cultures. For growth on glucose in batch (respiro-fermentative) cultures, EFMs with no by-product formation were removed from the analysis of the CEFs, whereas those including any by-products (ethanol, glycerol, acetate, succinate) were omitted in the analysis of growth on glucose in chemostat (respiratory) cultures. This resulted in improved correlations between CEF changes and transcript levels. A regression correlation coefficient of 0.60 was obtained between CEF changes and gene expression changes in the central carbon metabolism for the analysis of 5 different perturbations. Out of 45 data points there were no more than 6 data points deviating from the correlation. Additionally, up- or down-regulation of at least 75% of the genes were in qualitative agreement with the CEF changes for all perturbations studied., Conclusion: The analysis indicates that changes in carbon source are associated with a high degree of hierarchical regulation of metabolic fluxes in the central carbon metabolism as the change in fluxes are correlating directly with the change in transcript levels of genes encoding their corresponding enzymes. For amino acid biosynthesis there was, however, not found to exist a similar correlation, and this may point to either post-transcriptional and/or metabolic regulation, or be due to the absence of a direct perturbation on the amino acid pathways in these experiments.

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.

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.

Background: New analysis methods are being developed to integrate data from transcriptome, proteome, interactome, metabolome, and other investigative approaches. At the same time, existing methods are being modified to serve the objectives of systems biology and permit the interpretation of the huge datasets currently being generated by high-throughput methods., Results: Transcriptomic and metabolic data from chemostat fermentors were collected with the aim of investigating the relationship between these two data sets. The variation in transcriptome data in response to three physiological or genetic perturbations (medium composition, growth rate, and specific gene deletions) was investigated using linear modelling, and open reading-frames (ORFs) whose expression changed significantly in response to these perturbations were identified. Assuming that the metabolic profile is a function of the transcriptome profile, expression levels of the different ORFs were used to model the metabolic variables via Partial Least Squares (Projection to Latent Structures--PLS) using PLS toolbox in Matlab., Conclusion: The experimental design allowed the analyses to discriminate between the effects which the growth medium, dilution rate, and the deletion of specific genes had on the transcriptome and metabolite profiles. Metabolite data were modelled as a function of the transcriptome to determine their congruence. The genes that are involved in central carbon metabolism of yeast cells were found to be the ORFs with the most significant contribution to the model.

Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from Saccharomyces cerevisiae. The algorithm includes preprocessing of a genome-scale yeast model such that the fraction of measured metabolites within the model is enhanced, and thus it is possible to map significant alterations associated with a perturbation even though a small fraction of the complete metabolome is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through combination of metabolome and transcriptome data.