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Perturbation-Based Modeling Unveils the Autophagic Modulation of Chemosensitivity and Immunogenicity in Breast Cancer Cells (2021)

Quiros-Fernandez, Isaac ; Figueroa-Protti, Lucía ; Arias-Arias, Jorge L. ; [et al.]

Molecular Diversity Preservation International

In: Metabolites. 2021; 11(9): 637. Published 2021 Sep 18. doi: 10.3390/metabo11090637.

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Publication Date: 2021-09-18

Description: In the absence of new therapeutic strategies, chemotherapeutic drugs are the most widely used strategy against metastatic breast cancer, in spite of eliciting multiple adverse effects and having low responses with an average 5-year patient survival rate. Among the new therapeutic targets that are currently in clinical trials, here, we addressed the association between the regulation of the metabolic process of autophagy and the exposure of damage-associated molecular patterns associated (DAMPs) to immunogenic cell death (ICD), which has not been previously studied. After validating an mCHR-GFP tandem LC3 sensor capacity to report dynamic changes of the autophagic metabolic flux in response to external stimuli and demonstrating that both basal autophagy levels and response to diverse autophagy regulators fluctuate among different cell lines, we explored the interaction between autophagy modulators and chemotherapeutic agents in regards of cytotoxicity and ICD using three different breast cancer cell lines. Since these interactions are very complex and variable throughout different cell lines, we designed a perturbation-based model in which we propose specific modes of action of chemotherapeutic agents on the autophagic flux and the corresponding strategies of modulation to enhance the response to chemotherapy. Our results point towards a promising therapeutic potential of the metabolic regulation of autophagy to overcome chemotherapy resistance by eliciting ICD.

Electronic ISSN: 2218-1989

Topics: Biology , Medicine

Published by Molecular Diversity Preservation International

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A Unified Methodology for Heartbeats Detection in Seismocardiogram and Ballistocardiogram Signals (2020)

Mora, Niccolò ; Cocconcelli, Federico ; Matrella, Guido ; [et al.]

Molecular Diversity Preservation International

In: Computers. 2020; 9(2): 41. Published 2020 May 22. doi: 10.3390/computers9020041.

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Publication Date: 2020-05-22

Description: This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets ( p 〉 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found ( p 〈 0.01 ) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors.

Electronic ISSN: 2073-431X

Topics: Computer Science

Published by Molecular Diversity Preservation International

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Statistical Workflow for Feature Selection in Human Metabolomics Data (2019)

Antonelli, ; Claggett, ; Henglin, ; [et al.]

Molecular Diversity Preservation International

In: Metabolites. 2019; 9(7): 143. Published 2019 Jul 12. doi: 10.3390/metabo9070143.

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Publication Date: 2019-07-12

Description: High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.

Electronic ISSN: 2218-1989

Topics: Biology , Medicine

Published by Molecular Diversity Preservation International

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One-Year Effects of Omega-3 Treatment on Fatty Acids, Oxylipins, and Related Bioactive Lipids and Their Associations with Clinical Lipid and Inflammatory Biomarkers: Findings from a Substudy of the Vitamin D and Omega-3 Trial (VITAL) (2020)

Demler, Olga V. ; Liu, Yanyan ; Luttmann-Gibson, Heike ; [et al.]

Molecular Diversity Preservation International

In: Metabolites. 2020; 10(11): 431. Published 2020 Oct 27. doi: 10.3390/metabo10110431.

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Publication Date: 2020-10-27

Description: Omega-3 (n-3) treatment may lower cardiovascular risk, yet its effects on the circulating lipidome and relation to cardiovascular risk biomarkers are unclear. We hypothesized that n-3 treatment is associated with favorable changes in downstream fatty acids (FAs), oxylipins, bioactive lipids, clinical lipid and inflammatory biomarkers. We examined these VITAL200, a nested substudy of 200 subjects balanced on demographics and treatment and randomly selected from the Vitamin D and Omega-3 Trial (VITAL). VITAL is a randomized double-blind trial of 840 mg/d eicosapentaenoic acid (EPA) + docosahexaenoic acid (DHA) vs. placebo among 25,871 individuals. Small polar bioactive lipid features, oxylipins and FAs from plasma and red blood cells were measured using three independent assaying techniques at baseline and one year. The Women’s Health Study (WHS) was used for replication with dietary n-3 intake. Randomized n-3 treatment led to changes in 143 FAs, oxylipins and bioactive lipids (False Discovery Rate (FDR) 〈 0.05 in VITAL200, validated (p-values 〈 0.05)) in WHS with increases in 95 including EPA, DHA, n-3 docosapentaenoic acid (DPA-n3), and decreases in 48 including DPA-n6, dihomo gamma linolenic (DGLA), adrenic and arachidonic acids. N-3 related changes in the bioactive lipidome were heterogeneously associated with changes in clinical lipid and inflammatory biomarkers. N-3 treatment significantly modulates the bioactive lipidome, which may contribute to its clinical benefits.

Electronic ISSN: 2218-1989

Topics: Biology , Medicine

Published by Molecular Diversity Preservation International

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