References of "Pires Pacheco, Maria Irene 50002864"
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See detailAmbra1 modulates the tumor immune microenvironment and response to PD-1 blockade in melanoma.
Frias, Alex; Di Leo, Luca; Antoranz, Asier et al

in Journal for immunotherapy of cancer (2023), 11(3),

BACKGROUND: Loss of Ambra1 (autophagy and beclin 1 regulator 1), a multifunctional scaffold protein, promotes the formation of nevi and contributes to several phases of melanoma development. The ... [more ▼]

BACKGROUND: Loss of Ambra1 (autophagy and beclin 1 regulator 1), a multifunctional scaffold protein, promotes the formation of nevi and contributes to several phases of melanoma development. The suppressive functions of Ambra1 in melanoma are mediated by negative regulation of cell proliferation and invasion; however, evidence suggests that loss of Ambra1 may also affect the melanoma microenvironment. Here, we investigate the possible impact of Ambra1 on antitumor immunity and response to immunotherapy. METHODS: This study was performed using an Ambra1-depleted Braf(V600E) /Pten(-/) (-) genetically engineered mouse (GEM) model of melanoma, as well as GEM-derived allografts of Braf(V600E) /Pten(-/) (-) and Braf(V600E) /Pten(-/) (-)/Cdkn2a(-/) (-) tumors with Ambra1 knockdown. The effects of Ambra1 loss on the tumor immune microenvironment (TIME) were analyzed using NanoString technology, multiplex immunohistochemistry, and flow cytometry. Transcriptome and CIBERSORT digital cytometry analyses of murine melanoma samples and human melanoma patients (The Cancer Genome Atlas) were applied to determine the immune cell populations in null or low-expressing AMBRA1 melanoma. The contribution of Ambra1 on T-cell migration was evaluated using a cytokine array and flow cytometry. Tumor growth kinetics and overall survival analysis in Braf(V600E) /Pten(-/) (-)/Cdkn2a(-/) (-) mice with Ambra1 knockdown were evaluated prior to and after administration of a programmed cell death protein-1 (PD-1) inhibitor. RESULTS: Loss of Ambra1 was associated with altered expression of a wide range of cytokines and chemokines as well as decreased infiltration of tumors by regulatory T cells, a subpopulation of T cells with potent immune-suppressive properties. These changes in TIME composition were associated with the autophagic function of Ambra1. In the Braf(V600E) /Pten(-/) (-)/Cdkn2a(-/) (-) model inherently resistant to immune checkpoint blockade, knockdown of Ambra1 led to accelerated tumor growth and reduced overall survival, but at the same time conferred sensitivity to anti-PD-1 treatment. CONCLUSIONS: This study shows that loss of Ambra1 affects the TIME and the antitumor immune response in melanoma, highlighting new functions of Ambra1 in the regulation of melanoma biology. [less ▲]

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See detailscFASTCORMICS: A Contextualization Algorithm to Reconstruct Metabolic Multi-Cell Population Models from Single-Cell RNAseq Data
Pires Pacheco, Maria Irene UL; Ji, Jimmy; Prohaska, Tessy et al

in Metabolites (2022)

Tumours are composed of various cancer cell populations with different mutation profiles, phenotypes and metabolism that cause them to react to drugs in diverse manners. Increasing the resolution of ... [more ▼]

Tumours are composed of various cancer cell populations with different mutation profiles, phenotypes and metabolism that cause them to react to drugs in diverse manners. Increasing the resolution of metabolic models based on single-cell expression data will provide deeper insight into such metabolic differences and improve the predictive power of the models. scFASTCORMICS is a network contextualization algorithm that builds multi-cell population genome-scale models from single-cell RNAseq data. The models contain a subnetwork for each cell population in a tumour, allowing to capture metabolic variations between these clusters. The subnetworks are connected by a union compartment that permits to simulate metabolite exchanges between cell populations in the microenvironment. scFASTCORMICS uses Pareto optimization to simultaneously maximise the compactness, completeness and specificity of the reconstructed metabolic models. scFASTCORMICS is implemented in MATLAB and requires the installation of the COBRA toolbox, rFASTCORMICS and the IBM CPLEX solver. [less ▲]

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See detailImproving Machine Learning-based Prediction of Frailty in Elderly People with Digital Wearables : Data from the Berlin Aging Study II (BASE-II)
Didier, Jeff UL; de Landtsheer, Sébastien UL; Pires Pacheco, Maria Irene UL et al

Poster (2022, October 26)

Frailty is a geriatric medical condition that is highly associated with age and age-related diseases. The multidimensional consequences of frailty are heavily impacting the quality of life, and will ... [more ▼]

Frailty is a geriatric medical condition that is highly associated with age and age-related diseases. The multidimensional consequences of frailty are heavily impacting the quality of life, and will inevitably increase the burden on healthcare systems in the future. Most importantly, the lack of a universal standard to describe, diagnose, or let alone treat frailty, is further complicating the situation in the long-term. Nowadays, more and more frailty assessment tools are being developed on a regional and institutional basis, which is continuing to drive the heterogeneity in the characterization of frailty further apart. Gaining better insights into the underlying causes and pathophysiology of frailty, and how it is developing in patients is, therefore, required to establish strong and accurately tailored response schemes for frail patients, where currently only symptoms are treated. Thus, in this study, we deployed machine learning-based classification and optimization techniques to predict frailty in elderly people aged 65 or above from the Berlin Aging Study II (BASE-II, n=1512, frail=484) and revealed some of the most informative biomedical information to characterize frailty, including new potential biomarkers. Frailty in BASE-II was measured by the Fried et al. 5-item frailty index, composed of the clinical variables grip strength, weight loss, exhaustion, physical activity, and gait. The level of frailty in BASE-II was adapted for binary classification purposes by merging the pre-frail and frail levels as frail. A configurable in-house pipeline was developed for pre-processing the clinical data and predicting the target disease by deploying Support Vector Machines Classification. The most informative and essential subgroup of clinical measurements with regards to frailty was investigated by re-optimizing an initially full data-driven model by sequentially leaving out one subgroup. The best prediction power was yielded with resampling and dimensionality reduction techniques using the F-beta-2 score, and was further improved by adding one item of the Fried et al. frailty index. Furthermore, differences between the gender in the data set led to the investigation of gender-specific model configurations, followed by re-optimizations. As a result, we were able to specifically increase the predictive power in gender-specific groups, and will simultaneously emphasize on the differences between the most informative clinical biomarkers as well as the most essential subgroups for mixed and gender-specific BASE-II. The results herein suggest that a combination of the detected easy-to-obtain biomedical information on frailty risk factors together with one Fried et al. phenotype information provided by i.e., smart wearable devices (gait, grip strength, …) could significantly improve the frailty prediction power in mixed and gender-specific clinical cohort data. [less ▲]

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See detailMachine learning-based prediction of frailty in elderly people : Data from the Berlin Aging Study II (BASE-II)
Didier, Jeff UL; de Landtsheer, Sébastien UL; Pires Pacheco, Maria Irene UL et al

Poster (2022, October 09)

Frailty is a geriatric medical condition that is highly associated with age and age-related diseases. The multidimensional consequences of frailty are heavily impacting the quality of life, and will ... [more ▼]

Frailty is a geriatric medical condition that is highly associated with age and age-related diseases. The multidimensional consequences of frailty are heavily impacting the quality of life, and will inevitably increase the burden on healthcare systems in the future. Most importantly, the lack of a universal standard to describe, diagnose, or let alone treat frailty, is further complicating the situation in the long-term. Nowadays, more and more frailty assessment tools are being developed on a regional and institutional basis, which is continuing to drive the heterogeneity in the characterization of frailty further apart. Gaining better insights into the underlying causes and pathophysiology of frailty, and how it is developing in patients is, therefore, required to establish strong and accurately tailored response schemes for frail patients, where currently only symptoms are treated. Thus, in this study, we deployed machine learning-based classification and optimization techniques to predict frailty in the Berlin Aging Study II (BASE-II, N=1512, frail=484) and revealed some of the most informative biomedical information to characterize frailty, including new potential biomarkers. Frailty in BASE-II was measured by the Fried et al. 5-item frailty index, composed of the clinical variables grip strength, weight loss, exhaustion, physical activity, and gait. The level of frailty in BASE-II was adapted for binary classification purposes by merging the pre-frail and frail levels as frail. A configurable in-house pipeline was developed for pre-processing the clinical data, predicting the target disease, and determining the most informative subgroup of clinical measurements with regards to frailty. The best prediction power was yielded with resampling and dimensionality reduction techniques using the F-beta-2 score, and was further increased by adding one item of the Fried et al. frailty index. We suggest that a combination of the easy-to-obtain biomedical information on frailty risk factors together with one Fried et al. phenotype information provided by i.e. smart wearable devices (gait, grip strength, . . . ) could significantly improve the frailty prediction power. [less ▲]

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See detailIntegration of external biomass reactions into existing metabolic models
Moscardo Garcia, Maria UL; Pires Pacheco, Maria Irene UL; Sauter, Thomas UL

E-print/Working paper (2022)

Currently, seven biomass objective functions have been defined in human metabolic reconstructions. The integration of published biomass reactions into alternative models can contribute to the prediction ... [more ▼]

Currently, seven biomass objective functions have been defined in human metabolic reconstructions. The integration of published biomass reactions into alternative models can contribute to the prediction power of the model. Thus, in this work, we present a workflow to integrate reactions and biomass functions originating from several genome-scale reconstructions into models other than their home models. Additionally, a benchmark to identify the biomass that confers the highest prediction accuracy in terms of gene essentiality and growth predictions is provided. [less ▲]

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See detailThe gut microbial metabolite formate exacerbates colorectal cancer progression
Ternes, Dominik UL; Tsenkova, Mina UL; Pozdeev, Vitaly UL et al

in Nature Metabolism (2022)

The gut microbiome is a key player in the immunomodulatory and protumorigenic microenvironment during colorectal cancer (CRC), as different gut-derived bacteria can induce tumour growth. However, the ... [more ▼]

The gut microbiome is a key player in the immunomodulatory and protumorigenic microenvironment during colorectal cancer (CRC), as different gut-derived bacteria can induce tumour growth. However, the crosstalk between the gut microbiome and the host in relation to tumour cell metabolism remains largely unexplored. Here we show that formate, a metabolite produced by the CRC-associated bacterium Fusobacterium nucleatum, promotes CRC development. We describe molecular signatures linking CRC phenotypes with Fusobacterium abundance. Cocultures of F. nucleatum with patient-derived CRC cells display protumorigenic effects, along with a metabolic shift towards increased formate secretion and cancer glutamine metabolism. We further show that microbiome-derived formate drives CRC tumour invasion by triggering AhR signalling, while increasing cancer stemness. Finally, F. nucleatum or formate treatment in mice leads to increased tumour incidence or size, and Th17 cell expansion, which can favour proinflammatory profiles. Moving beyond observational studies, we identify formate as a gut-derived oncometabolite that is relevant for CRC progression. [less ▲]

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See detailBruceine D Identified as a Drug Candidate against Breast Cancer by a Novel Drug Selection Pipeline and Cell Viability Assay.
Cipriani, Claudia; Pires Pacheco, Maria Irene UL; Kishk, Ali UL et al

in Pharmaceuticals (Basel, Switzerland) (2022), 15(2),

The multi-target effects of natural products allow us to fight complex diseases like cancer on multiple fronts. Unlike docking techniques, network-based approaches such as genome-scale metabolic modelling ... [more ▼]

The multi-target effects of natural products allow us to fight complex diseases like cancer on multiple fronts. Unlike docking techniques, network-based approaches such as genome-scale metabolic modelling can capture multi-target effects. However, the incompleteness of natural product target information reduces the prediction accuracy of in silico gene knockout strategies. Here, we present a drug selection workflow based on context-specific genome-scale metabolic models, built from the expression data of cancer cells treated with natural products, to predict cell viability. The workflow comprises four steps: first, in silico single-drug and drug combination predictions; second, the assessment of the effects of natural products on cancer metabolism via the computation of a dissimilarity score between the treated and control models; third, the identification of natural products with similar effects to the approved drugs; and fourth, the identification of drugs with the predicted effects in pathways of interest, such as the androgen and estrogen pathway. Out of the initial 101 natural products, nine candidates were tested in a 2D cell viability assay. Bruceine D, emodin, and scutellarein showed a dose-dependent inhibition of MCF-7 and Hs 578T cell proliferation with IC(50) values between 0.7 to 65 μM, depending on the drug and cell line. Bruceine D, extracted from Brucea javanica seeds, showed the highest potency. [less ▲]

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See detailReview of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases.
Kishk, Ali UL; Pires Pacheco, Maria Irene UL; Heurtaux, Tony UL et al

in Cells (2022), 11(16),

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for ... [more ▼]

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition. [less ▲]

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See detailDrug Target Prediction Using Context-Specific Metabolic Models Reconstructed from rFASTCORMICS.
Bintener, Tamara; Pires Pacheco, Maria Irene UL; Kishk, Ali UL et al

in Methods in Molecular Biology (2022)

Metabolic modeling is a powerful computational tool to analyze metabolism. It has not only been used to identify metabolic rewiring strategies in cancer but also to predict drug targets and candidate ... [more ▼]

Metabolic modeling is a powerful computational tool to analyze metabolism. It has not only been used to identify metabolic rewiring strategies in cancer but also to predict drug targets and candidate drugs for repurposing. Here, we will elaborate on the reconstruction of context-specific metabolic models of cancer using rFASTCORMICS and the subsequent prediction of drugs for repurposing using our drug prediction workflow. [less ▲]

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See detailProject-based learning course on metabolic network modelling in computational systems biology.
Sauter, Thomas UL; Bintener, Tamara; Kishk, Ali UL et al

in PLoS computational biology (2022), 18(1), 1009711

Project-based learning (PBL) is a dynamic student-centred teaching method that encourages students to solve real-life problems while fostering engagement and critical thinking. Here, we report on a PBL ... [more ▼]

Project-based learning (PBL) is a dynamic student-centred teaching method that encourages students to solve real-life problems while fostering engagement and critical thinking. Here, we report on a PBL course on metabolic network modelling that has been running for several years within the Master in Integrated Systems Biology (MISB) at the University of Luxembourg. This 2-week full-time block course comprises an introduction into the core concepts and methods of constraint-based modelling (CBM), applied to toy models and large-scale networks alongside the preparation of individual student projects in week 1 and, in week 2, the presentation and execution of these projects. We describe in detail the schedule and content of the course, exemplary student projects, and reflect on outcomes and lessons learned. PBL requires the full engagement of students and teachers and gives a rewarding teaching experience. The presented course can serve as a role model and inspiration for other similar courses. [less ▲]

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See detailDCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling.
Kishk, Ali UL; Pires Pacheco, Maria Irene UL; Sauter, Thomas UL

in iScience (2021), 24(11), 103331

The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks ... [more ▼]

The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression datasets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (FDA)-approved drugs on the putative disease-specific essential genes and gene pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov). [less ▲]

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See detailImportance of the biomass formulation for cancer metabolic modeling and drug prediction.
Moscardo Garcia, Maria UL; Pires Pacheco, Maria Irene UL; Bintener, Tamara Jean Rita UL et al

in iScience (2021), 24(10), 103110

Genome-scale metabolic reconstructions include all known biochemical reactions occurring in a cell. A typical application is the prediction of potential drug targets for cancer treatment. The precision of ... [more ▼]

Genome-scale metabolic reconstructions include all known biochemical reactions occurring in a cell. A typical application is the prediction of potential drug targets for cancer treatment. The precision of these predictions relies on the definition of the objective function. Generally, the biomass reaction is used to illustrate the growth capacity of a cancer cell. Today, seven human biomass reactions can be identified in published metabolic models. The impact of these differences on the metabolic model predictions has not been explored in detail. We explored this impact on cancer metabolic model predictions and showed that the metabolite composition and the associated coefficients had a large impact on the growth rate prediction accuracy, whereas gene essentiality predictions were mainly affected by the metabolite composition. Our results demonstrate the importance of defining a consensus biomass reaction compatible with most human models, which would contribute to ensuring the reproducibility and consistency of the results. [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 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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