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See detailL-plastin Ser5 phosphorylation is modulated by the PI3K/SGK pathway and promotes breast cancer cell invasiveness
Machado, Raquel A.C.; Stojevski, Dunja; de Landtsheer, Sébastien UL et al

in Cell Communication and Signaling (2021), 19(22), 1-22

Background: Metastasis is the predominant cause for cancer morbidity and mortality accounting for approxima‑ tively 90% of cancer deaths. The actin‑bundling protein L‑plastin has been proposed as a ... [more ▼]

Background: Metastasis is the predominant cause for cancer morbidity and mortality accounting for approxima‑ tively 90% of cancer deaths. The actin‑bundling protein L‑plastin has been proposed as a metastatic marker and phos‑ phorylation on its residue Ser5 is known to increase its actin‑bundling activity. We recently showed that activation of the ERK/MAPK signalling pathway leads to L‑plastin Ser5 phosphorylation and that the downstream kinases RSK1 and RSK2 are able to directly phosphorylate Ser5. Here we investigate the involvement of the PI3K pathway in L‑plastin Ser5 phosphorylation and the functional effect of this phosphorylation event in breast cancer cells. Methods: To unravel the signal transduction network upstream of L‑plastin Ser5 phosphorylation, we performed computational modelling based on immunoblot analysis data, followed by experimental validation through inhi‑ bition/overexpression studies and in vitro kinase assays. To assess the functional impact of L‑plastin expression/ Ser5 phosphorylation in breast cancer cells, we either silenced L‑plastin in cell lines initially expressing endogenous L‑plastin or neoexpressed L‑plastin wild type and phosphovariants in cell lines devoid of endogenous L‑plastin. The established cell lines were used for cell biology experiments and confocal microscopy analysis. Results: Our modelling approach revealed that, in addition to the ERK/MAPK pathway and depending on the cellular context, the PI3K pathway contributes to L‑plastin Ser5 phosphorylation through its downstream kinase SGK3. The results of the transwell invasion/migration assays showed that shRNA‑mediated knockdown of L‑plastin in BT‑20 or HCC38 cells significantly reduced cell invasion, whereas stable expression of the phosphomimetic L‑plastin Ser5Glu variant led to increased migration and invasion of BT‑549 and MDA‑MB‑231 cells. Finally, confocal image analysis combined with zymography experiments and gelatin degradation assays provided evidence that L‑plastin Ser5 phosphorylation promotes L‑plastin recruitment to invadopodia, MMP‑9 activity and concomitant extracellular matrix degradation. Conclusion: Altogether, our results demonstrate that L‑plastin Ser5 phosphorylation increases breast cancer cell invasiveness. Being a downstream molecule of both ERK/MAPK and PI3K/SGK pathways, L‑plastin is proposed here as a potential target for therapeutic approaches that are aimed at blocking dysregulated signalling outcome of both pathways and, thus, at impairing cancer cell invasion and metastasis formation. [less ▲]

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See detailLoss of Ambra1 promotes melanoma growth and invasion.
Di Leo, Luca; Bodemeyer, Valérie; Bosisio, Francesca M. et al

in Nature communications (2021), 12(1), 2550

Melanoma is the deadliest skin cancer. Despite improvements in the understanding of the molecular mechanisms underlying melanoma biology and in defining new curative strategies, the therapeutic needs for ... [more ▼]

Melanoma is the deadliest skin cancer. Despite improvements in the understanding of the molecular mechanisms underlying melanoma biology and in defining new curative strategies, the therapeutic needs for this disease have not yet been fulfilled. Herein, we provide evidence that the Activating Molecule in Beclin-1-Regulated Autophagy (Ambra1) contributes to melanoma development. Indeed, we show that Ambra1 deficiency confers accelerated tumor growth and decreased overall survival in Braf/Pten-mutated mouse models of melanoma. Also, we demonstrate that Ambra1 deletion promotes melanoma aggressiveness and metastasis by increasing cell motility/invasion and activating an EMT-like process. Moreover, we show that Ambra1 deficiency in melanoma impacts extracellular matrix remodeling and induces hyperactivation of the focal adhesion kinase 1 (FAK1) signaling, whose inhibition is able to reduce cell invasion and melanoma growth. Overall, our findings identify a function for AMBRA1 as tumor suppressor in melanoma, proposing FAK1 inhibition as a therapeutic strategy for AMBRA1 low-expressing melanoma. [less ▲]

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See detailA dynamic multi-tissue model to study human metabolism.
Martins Conde, Patricia UL; Pfau, Thomas; Pires Pacheco, Maria Irene UL et al

in NPJ systems biology and applications (2021), 7(1), 5

Metabolic modeling enables the study of human metabolism in healthy and in diseased conditions, e.g., the prediction of new drug targets and biomarkers for metabolic diseases. To accurately describe blood ... [more ▼]

Metabolic modeling enables the study of human metabolism in healthy and in diseased conditions, e.g., the prediction of new drug targets and biomarkers for metabolic diseases. To accurately describe blood and urine metabolite dynamics, the integration of multiple metabolically active tissues is necessary. We developed a dynamic multi-tissue model, which recapitulates key properties of human metabolism at the molecular and physiological level based on the integration of transcriptomics data. It enables the simulation of the dynamics of intra-cellular and extra-cellular metabolites at the genome scale. The predictive capacity of the model is shown through the accurate simulation of different healthy conditions (i.e., during fasting, while consuming meals or during exercise), and the prediction of biomarkers for a set of Inborn Errors of Metabolism with a precision of 83%. This novel approach is useful to prioritize new biomarkers for many metabolic diseases, as well as for the integration of various types of personal omics data, towards the personalized analysis of blood and urine metabolites. [less ▲]

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See detailIDARE2-Simultaneous Visualisation of Multiomics Data in Cytoscape.
Pfau, Thomas; Galhardo, Mafalda; Lin, Jake et al

in Metabolites (2021), 11(5),

Visual integration of experimental data in metabolic networks is an important step to understanding their meaning. As genome-scale metabolic networks reach several thousand reactions, the task becomes ... [more ▼]

Visual integration of experimental data in metabolic networks is an important step to understanding their meaning. As genome-scale metabolic networks reach several thousand reactions, the task becomes more difficult and less revealing. While databases like KEGG and BioCyc provide curated pathways that allow a navigation of the metabolic landscape of an organism, it is rather laborious to map data directly onto those pathways. There are programs available using these kind of databases as a source for visualization; however, these programs are then restricted to the pathways available in the database. Here, we present IDARE2 a cytoscape plugin that allows the visualization of multiomics data in cytoscape in a user-friendly way. It further provides tools to disentangle highly connected network structures based on common properties of nodes and retains structural links between the generated subnetworks, offering a straightforward way to traverse the splitted network. The tool is extensible, allowing the implementation of specialised representations and data format parsers. We present the automated reproduction of the original IDARE nodes using our tool and show examples of other data being mapped on a network of E. coli. The extensibility is demonstrated with two plugins that are available on github. IDARE2 provides an intuitive way to visualise data from multiple sources and allows one to disentangle the often complex network structure in large networks using predefined properties of the network nodes. [less ▲]

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See detailDegree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes.
Badkas, Apurva UL; Nguyen, Thanh-Phuong UL; Caberlotto, Laura et al

in Biology (2021), 10(2),

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic ... [more ▼]

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities. [less ▲]

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See detailPituitary Tumor Transforming Gene 1 Orchestrates Gene Regulatory Variation in Mouse Ventral Midbrain During Aging
Gui, Yujuan UL; Thomas, Mélanie H.; Garcia, Pierre et al

in Frontiers in Genetics (2020)

Dopaminergic neurons in the midbrain are of particular interest due to their role in diseases such as Parkinson’s disease and schizophrenia. Genetic variation between individuals can affect the integrity ... [more ▼]

Dopaminergic neurons in the midbrain are of particular interest due to their role in diseases such as Parkinson’s disease and schizophrenia. Genetic variation between individuals can affect the integrity and function of dopaminergic neurons but the DNA variants and molecular cascades modulating dopaminergic neurons and other cells types of ventral midbrain remain poorly defined. Three genetically diverse inbred mouse strains – C57BL/6J, A/J, and DBA/2J – differ significantly in their genomes (∼7 million variants), motor and cognitive behavior, and susceptibility to neurotoxins. To further dissect the underlying molecular networks responsible for these variable phenotypes, we generated RNA-seq and ChIP-seq data from ventral midbrains of the 3 mouse strains. We defined 1000–1200 transcripts that are differentially expressed among them. These widespread differences may be due to altered activity or expression of upstream transcription factors. Interestingly, transcription factors were significantly underrepresented among the differentially expressed genes, and only one transcription factor, Pttg1, showed significant differences between all three strains. The changes in Pttg1 expression were accompanied by consistent alterations in histone H3 lysine 4 trimethylation at Pttg1 transcription start site. The ventral midbrain transcriptome of 3-month-old C57BL/6J congenic Pttg1–/– mutants was only modestly altered, but shifted toward that of A/J and DBA/2J in 9-month-old mice. Principle component analysis (PCA) identified the genes underlying the transcriptome shift and deconvolution of these bulk RNA-seq changes using midbrain single cell RNA-seq data suggested that the changes were occurring in several different cell types, including neurons, oligodendrocytes, and astrocytes. Taken together, our results show that Pttg1 contributes to gene regulatory variation between mouse strains and influences mouse midbrain transcriptome during aging. [less ▲]

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See detailTesting informed SIR based epidemiological model for COVID-19 in Luxembourg
Sauter, Thomas UL; Pires Pacheco, Maria Irene UL

E-print/Working paper (2020)

The interpretation of the number of COVID-19 cases and deaths in a country or region is strongly dependent on the number of performed tests. We developed a novel SIR based epidemiological model (SIVRT ... [more ▼]

The interpretation of the number of COVID-19 cases and deaths in a country or region is strongly dependent on the number of performed tests. We developed a novel SIR based epidemiological model (SIVRT) which allows the country-specific integration of testing information and other available data. The model thereby enables a dynamic inspection of the pandemic and allows estimating key figures, like the number of overall detected and undetected COVID-19 cases and the infection fatality rate. As proof of concept, the novel SIVRT model was used to simulate the first phase of the pandemic in Luxembourg. An overall number of infections of 13.000 and an infection fatality rate of 1,3 was estimated, which is in concordance with data from population-wide testing. Furthermore based on the data as of end of May 2020 and assuming a partial deconfinement, an increase of cases is predicted from mid of July 2020 on. This is consistent with the current observed rise and shows the predictive potential of the novel SIVRT model. [less ▲]

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See detailTowards the routine use of in silico screenings for drug discovery using metabolic modelling
Bintener, Tamara Jean Rita UL; Pires Pacheco, Maria Irene UL; Sauter, Thomas UL

in Biochemical Society Transactions (2020)

Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer ... [more ▼]

Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects. [less ▲]

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See detailComputational models of melanoma.
Albrecht, Marco; Lucarelli, Philippe; Kulms, Dagmar et al

in Theoretical biology & medical modelling (2020), 17(1), 8

Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics ... [more ▼]

Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics and bioinformatics sharpen and structure our perception. However, additionally considering the interconnectivity between biological elements promises a deeper and more coherent understanding of melanoma. For instance, integrative network-based tools and well-grounded inductive in silico research reveal disease mechanisms, stratify patients, and support treatment individualization. This review gives an overview of different modeling techniques beyond statistics, shows how different strategies align with the respective medical biology, and identifies possible areas of new computational melanoma research. [less ▲]

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See detailReduced sialylation triggers homeostatic synapse and neuronal loss in middle-aged mice
Klaus, Christine; Hansen, Jan N.; Ginolhac, Aurélien UL et al

in Neurobiology of Aging (2020)

Sialic acid-binding receptors (Siglecs) are linked to neurodegenerative processes, but the role of sialic acids in physiological aging is still not fully understood. We investigated the impact of reduced ... [more ▼]

Sialic acid-binding receptors (Siglecs) are linked to neurodegenerative processes, but the role of sialic acids in physiological aging is still not fully understood. We investigated the impact of reduced sialylation in the brain of mice heterozygous for the enzyme glucosamine-2-epimerase/N-acetylmannosamine kinase (GNE+/-) that is essential for sialic acid biosynthesis. We demonstrate that GNE+/- mice have hyposialylation in different brain regions, less synapses in the hippocampus and reduced microglial arborization already at 6 months followed by increased loss of neurons at 12 months. A transcriptomic analysis revealed no pro-inflammatory changes indicating an innate homeostatic immune process leading to the removal of synapses and neurons in GNE+/- mice during aging. Crossbreeding with complement C3-deficient mice rescued the earlier onset of neuronal and synaptic loss as well as the changes in microglial arborization. Thus, sialic acids of the glycocalyx contribute to brain homeostasis and act as a recognition system for the innate immune system in the brain. [less ▲]

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See detailAn efficient machine learning-based approach for screening individuals at risk of hereditary haemochromatosis.
Martins Conde, Patricia UL; Sauter, Thomas UL; Nguyen, Thanh-Phuong UL

in Scientific reports (2020), 10(1), 20613

Hereditary haemochromatosis (HH) is an autosomal recessive disease, where HFE C282Y homozygosity accounts for 80-85% of clinical cases among the Caucasian population. HH is characterised by the ... [more ▼]

Hereditary haemochromatosis (HH) is an autosomal recessive disease, where HFE C282Y homozygosity accounts for 80-85% of clinical cases among the Caucasian population. HH is characterised by the accumulation of iron, which, if untreated, can lead to the development of liver cirrhosis and liver cancer. Since iron overload is preventable and treatable if diagnosed early, high-risk individuals can be identified through effective screening employing artificial intelligence-based approaches. However, such tools expose novel challenges associated with the handling and integration of large heterogeneous datasets. We have developed an efficient computational model to screen individuals for HH using the family study data of the Hemochromatosis and Iron Overload Screening (HEIRS) cohort. This dataset, consisting of 254 cases and 701 controls, contains variables extracted from questionnaires and laboratory blood tests. The final model was trained on an extreme gradient boosting classifier using the most relevant risk factors: HFE C282Y homozygosity, age, mean corpuscular volume, iron level, serum ferritin level, transferrin saturation, and unsaturated iron-binding capacity. Hyperparameter optimisation was carried out with multiple runs, resulting in 0.94 ± 0.02 area under the receiving operating characteristic curve (AUCROC) for tenfold stratified cross-validation, demonstrating its outperformance when compared to the iron overload screening (IRON) tool. [less ▲]

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See detailCancer Cells Employ Nuclear Caspase-8 to Overcome the p53-Dependent G2/M Checkpoint through Cleavage of USP28.
Muller, Ines; Strozyk, Elwira; Schindler, Sebastian et al

in Molecular cell (2020)

Cytosolic caspase-8 is a mediator of death receptor signaling. While caspase-8 expression is lost in some tumors, it is increased in others, indicating a conditional pro-survival function of caspase-8 in ... [more ▼]

Cytosolic caspase-8 is a mediator of death receptor signaling. While caspase-8 expression is lost in some tumors, it is increased in others, indicating a conditional pro-survival function of caspase-8 in cancer. Here, we show that tumor cells employ DNA-damage-induced nuclear caspase-8 to override the p53-dependent G2/M cell-cycle checkpoint. Caspase-8 is upregulated and localized to the nucleus in multiple human cancers, correlating with treatment resistance and poor clinical outcome. Depletion of caspase-8 causes G2/M arrest, stabilization of p53, and induction of p53-dependent intrinsic apoptosis in tumor cells. In the nucleus, caspase-8 cleaves and inactivates the ubiquitin-specific peptidase 28 (USP28), preventing USP28 from de-ubiquitinating and stabilizing wild-type p53. This results in de facto p53 protein loss, switching cell fate from apoptosis toward mitosis. In summary, our work identifies a non-canonical role of caspase-8 exploited by cancer cells to override the p53-dependent G2/M cell-cycle checkpoint. [less ▲]

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See detailTopological network measures for drug repositioning.
Badkas, Apurva UL; De Landtsheer, Sébastien; Sauter, Thomas UL

in Briefings in bioinformatics (2020)

Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these ... [more ▼]

Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a key role. Various structural (topological) network measures have thereby contributed to uncovering unintuitive functional relationships and repositioning candidates in drug-disease and other networks. This review gives a broad overview of the topic, and offers perspectives on the application of topological measures for network analysis. It also discusses unexplored measures, and draws attention to a wider scope of application efforts, especially in drug repositioning. [less ▲]

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See detailTowards the Integration of Metabolic Network Modelling and Machine Learning for the Routine Analysis of High-Throughput Patient Data
Pacheco, Maria UL; Bintener, Tamara Jean Rita UL; Sauter, Thomas UL

in Automated Reasoning for Systems Biology and Medicine (2019)

The decreasing cost of high-throughput technologies allows to consider their use in healthcare and medicine. To prepare for this upcoming revolution, the community is assembling large disease-dedicated ... [more ▼]

The decreasing cost of high-throughput technologies allows to consider their use in healthcare and medicine. To prepare for this upcoming revolution, the community is assembling large disease-dedicated datasets such as TCGA or METABRIC. These datasets will serve as references to compare new patient samples to in order to assign them to a predefined category (i.e. ‘patients associated with poor prognosis’). Some problems affecting the downstream analysis remain to be solved, the bottleneck is no longer data generation itself but the integration of the existing datasets with the present knowledge. Constraint-based modelling, that only requires the setting of a few parameters, became popular for the integration of high-throughput data in a metabolic context. Notably, context-specific building algorithms that extract a subnetwork from a reference network are largely used to study metabolic changes in various diseases. Reference networks are composed of canonical pathways while extracted subnetworks include only active pathways in the context of interest based on high-throughput data. Even though these algorithms can be part of automated pipelines, to be applied by clinicians, the model-building pipelines must be coupled to a standardized semi-automated analysis workflow based on machine learning approaches to avoid bias and reduce the cost of diagnostics. [less ▲]

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See detailIdentification of genes under dynamic post-transcriptional regulation from time-series epigenomic data
Becker, Julia Christina UL; Gerard, Déborah UL; Ginolhac, Aurélien UL et al

in Epigenomics (2019)

Aim: Prediction of genes under dynamic post-transcriptional regulation from epigenomic data. Materials & methods: We used time-series profiles of chromatin immunoprecipitation-seq data of histone ... [more ▼]

Aim: Prediction of genes under dynamic post-transcriptional regulation from epigenomic data. Materials & methods: We used time-series profiles of chromatin immunoprecipitation-seq data of histone modifications from differentiation of mesenchymal progenitor cells toward adipocytes and osteoblasts to predict gene expression levels at five time points in both lineages and estimated the deviation of those predictions from the RNA-seq measured expression levels using linear regression. Results & conclusion: The genes with biggest changes in their estimated stability across the time series are enriched for noncoding RNAs and lineage-specific biological processes. Clustering mRNAs according to their stability dynamics allows identification of post-transcriptionally coregulated mRNAs and their shared regulators through sequence enrichment analysis. We identify miR-204 as an early induced adipogenic microRNA targeting Akr1c14 and Il1rl1. [less ▲]

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See detailIdentifying and targeting cancer-specific metabolism with network-based drug target prediction
Pacheco, Maria UL; Bintener, Tamara Jean Rita UL; Ternes, Dominik UL et al

in EBioMedicine (2019), 43(May 2019), 98-106

Background Metabolic rewiring allows cancer cells to sustain high proliferation rates. Thus, targeting only the cancer-specific cellular metabolism will safeguard healthy tissues. Methods We developed the ... [more ▼]

Background Metabolic rewiring allows cancer cells to sustain high proliferation rates. Thus, targeting only the cancer-specific cellular metabolism will safeguard healthy tissues. Methods We developed the very efficient FASTCORMICS RNA-seq workflow (rFASTCORMICS) to build 10,005 high-resolution metabolic models from the TCGA dataset to capture metabolic rewiring strategies in cancer cells. Colorectal cancer (CRC) was used as a test case for a repurposing workflow based on rFASTCORMICS. Findings Alternative pathways that are not required for proliferation or survival tend to be shut down and, therefore, tumours display cancer-specific essential genes that are significantly enriched for known drug targets. We identified naftifine, ketoconazole, and mimosine as new potential CRC drugs, which were experimentally validated. Interpretation The here presented rFASTCORMICS workflow successfully reconstructs a metabolic model based on RNA-seq data and successfully predicted drug targets and drugs not yet indicted for colorectal cancer. [less ▲]

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See detailIntegrated In Vitro and In Silico Modeling Delineates the Molecular Effects of a Synbiotic Regimen on Colorectal-Cancer-Derived Cells
Greenhalgh, Kacy UL; Ramiro Garcia, Javier UL; Heinken et al

in Cell Reports (2019), 27

By modulating the human gut microbiome, prebiotics and probiotics (combinations of which are called synbiotics) may be used to treat diseases such as colorectal cancer (CRC). Methodological limitations ... [more ▼]

By modulating the human gut microbiome, prebiotics and probiotics (combinations of which are called synbiotics) may be used to treat diseases such as colorectal cancer (CRC). Methodological limitations have prevented determining the potential combina- torial mechanisms of action of such regimens. We expanded our HuMiX gut-on-a-chip model to co-culture CRC-derived epithelial cells with a model probiotic under a simulated prebiotic regimen, and we integrated the multi-omic results with in silico metabolic modeling. In contrast to individual prebi- otic or probiotic treatments, the synbiotic regimen caused downregulation of genes involved in procarci- nogenic pathways and drug resistance, and reduced levels of the oncometabolite lactate. Distinct ratios of organic and short-chain fatty acids were produced during the simulated regimens. Treatment of primary CRC-derived cells with a molecular cocktail reflecting the synbiotic regimen attenuated self-renewal ca- pacity. Our integrated approach demonstrates the potential of modeling for rationally formulating synbi- otics-based treatments in the future. [less ▲]

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See detailCreation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.
Heirendt, Laurent UL; Arreckx, Sylvain; Pfau, Thomas UL et al

in Nature protocols (2019), 14(3), 639-702

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of ... [more ▼]

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods. [less ▲]

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See detailAn Efficient Machine Learning Method to Solve Imbalanced Data in Metabolic Disease Prediction
Cecchini, Vania Filipa UL; Nguyen, Thanh-Phuong UL; Pfau, Thomas UL et al

in Cecchini, Vania Filipa (Ed.) An Efficient Machine Learning Method to Solve Imbalanced Data in Metabolic Disease Prediction (2019)

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See detailImpaired serine metabolism complements LRRK2-G2019S pathogenicity in PD patients
Nickels, Sarah UL; Walter, Jonas; Bolognin, Silvia UL et al

in Parkinsonism and Related Disorders (2019)

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