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.

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.

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.

Understanding the ADHD-Gut Axis by Metabolic Network Analysis.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder diagnosed with hyperactivity, impulsivity, and a lack of attention inconsistent with the patient's development level. The fact that people with ADHD frequently experience gastrointestinal (GI) dysfunction highlights the possibility that the gut microbiome may play a role in this condition. The proposed research aims to determine a biomarker for ADHD by reconstructing a model of the gut-microbial community. Genome-scale metabolic models (GEM) considering the relationship between gene-protein-reaction associations are used to simulate metabolic activities in organisms of gut. The production rates of dopamine and serotonin precursors and the key short chain fatty acids which affect the health status are determined under three diets (Western, Atkins', Vegan) and compared with those of healthy people. Elasticities are calculated to understand the sensitivity of exchange fluxes to changes in diet and bacterial abundance at the species level. The presence of Bacillota (genus Coprococcus and Subdoligranulum), Actinobacteria (genus Collinsella), Bacteroidetes (genus Bacteroides), and Bacteroidota (genus Alistipes) may be possible gut microbiota indicators of ADHD. This type of modeling approach taking microbial genome-environment interactions into account helps us understand the gastrointestinal mechanisms behind ADHD, and establish a path to improve the quality of life of ADHD patients.

Neuroinflammation, Energy and Sphingolipid Metabolism Biomarkers Are Revealed by Metabolic Modeling of Autistic Brains.

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.

Identification of Therapeutic Targets for Medulloblastoma by Tissue-Specific Genome-Scale Metabolic Model.

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.

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 adaptive laboratory evolution of multiple stress-resistant yeast strains by genome-scale modeling.

Stress responses triggered by external exposures in adaptive laboratory evolution studies alter the ordinary behavior of cells, and the identification of the differences between the starting and the evolved strains would provide ideal strategies to obtain the desired strains. Metabolic networks are one of the most useful tools to analyze data for this purpose. This study integrates differential expression profiles of multiple Saccharomyces cerevisiae strains that have evolved in eight different stress conditions (ethanol, caffeine, coniferyl aldehyde, iron, nickel, phenylethanol, and silver) and enzyme kinetics into a genome-scale metabolic model of yeast, following a new enhanced method. Flux balance analysis, flux variability analysis, robustness, phenotype phase plane, minimization of metabolic adjustment, survivability, sensitivity analyses, and random sampling are conducted to identify the most common and divergent points within strains. Results were examined both individually and comparatively, and the target reactions, metabolites, and enzymes were identified. Our results showed that the models reconstructed by our methodology were able to simulate experimental conditions where efficient protein allocation was the main goal for survival under stressful conditions, and most of the metabolic changes in the adaptation process mainly arose from the differences in the metabolic reactions of energy maintenance (through coenzyme-A and FAD utilization), cell division (folate requirement of DNA synthesis), and cell wall formation (through sterol and ergosterol biosynthesis).

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.

The effects of high intensity-interval training on vaspin, adiponectin and leptin levels in women with polycystic ovary syndrome.

AIM: We aimed to investigate the metabolic effects of HIIT exercise on PCOS patients and how it affects adiponectin, vaspin and leptin. MATERIAL AND METHODS: Twenty women with PCOS were included in the study and were divided into two groups. HIIT program was applied for 10 PCOS and Medium Intensity Continuous Training (MICT) program was applied for other 10 PCOS. At the beginning and at the end of the study, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), triglyceride(TG), insulin, Adiponectin, Leptin, Vaspin levels of both PCOS groups were evaluated. RESULTS: When PCOS patients by performed HIIT exercise for 12 weeks, we found that the levels of leptin and vaspin did not change while adiponectin levels increased. Moreover serum levels of insulin, TG, total cholesterol, LDL-C decreased but levels of HDL-C increased. CONCLUSION: HIIT increased in the adiponectin levels in women with PCOS and provided more weight loss.

Fabrication Protocol for Thermoplastic Microfluidic Devices: Nanoliter Volume Bioreactors for Cell Culturing.

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.

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.

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.

VASPIN, ADIPONECTIN AND LEPTIN LEVELS IN TYPE 1 DIABETIC RATS INDUCED BY STREPTOZOTOCIN.

BACKGROUND: Adiponectin, vaspin and leptin are only a few of these numerous adipocytokines. Little is known about the behavior of adipocytokines and how adipose tissue metabolism is affected in this Type 1 DM model. In this study we investigated the serum levels of adiponectin, leptin, vaspin in streptozotocin(STZ) induced diabetic rats. MATERIAL AND METHODS: Twelve Spraque Dawley albino rats were included in the study. The animals were divided into two groups. The first group was diabetic (D) (n: 6) and 60mg / kg STZ was administered intraperitoneally (i.p.) to these rats. The second group was the non-diabetic control (ND) group (n: 6). All the animals were euthanized by cervical dislocation. Quantification of vaspin, Adiponectin, leptin in serum was performed using the ELISA kit. RESULTS: Adiponectin, vaspin levels of diabetic group were found to be statistically lower than of control group (p<0.05). Leptin levels were significantly higher in the diabetic group (P<0.05). CONCLUSION: There is a need for new researches that can explain the relationship between Vaspin, Leptin and Adiponectin and Type 1 diabetes. New studies in this area will open new horizons for the identification of new biomarkers in the diagnosis and treatment of Type 1 diabetes.

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.

Cell trapping microfluidic chip made of Cyclo olefin polymer enabling two concurrent cell biology experiments with long term durability.

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.

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.