scholarly journals Metabolic and Immunological Subtypes of Esophageal Cancer Reveal Potential Therapeutic Opportunities

2021 ◽  
Vol 9 ◽  
Author(s):  
Ryan J. King ◽  
Fang Qiu ◽  
Fang Yu ◽  
Pankaj K. Singh
Keyword(s):  
Esophageal Cancer ◽  
Chemotherapy Resistance ◽  
Tca Cycle ◽  
Machine Learning Techniques ◽  
Histological Subtypes ◽  
K Nearest Neighbors ◽  
Transport Chain ◽  
Novel Biomarkers ◽  
Supervised Classification Methods ◽  
Gene Ontologies

BackgroundEsophageal cancer has the sixth highest rate of cancer-associated deaths worldwide, with many patients displaying metastases and chemotherapy resistance. We sought to find subtypes to see if precision medicine could play a role in finding new potential targets and predicting responses to therapy. Since metabolism not only drives cancers but also serves as a readout, metabolism was examined as a key reporter for differences.MethodsUnsupervised and supervised classification methods, including hierarchical clustering, partial least squares discriminant analysis, k-nearest neighbors, and machine learning techniques, were used to discover and display two major subgroups. Genes, pathways, gene ontologies, survival, and immune differences between the groups were further examined, along with biomarkers between the groups and against normal tissue.ResultsEsophageal cancer had two major unique metabolic profiles observed between the histological subtypes esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). The metabolic differences suggest that ESCC depends on glycolysis, whereas EAC relies more on oxidative metabolism, catabolism of glycolipids, the tricarboxylic acid (TCA) cycle, and the electron transport chain. We also noted a robust prognostic risk associated with COQ3 expression. In addition to the metabolic alterations, we noted significant alterations in key pathways regulating immunity, including alterations in cytokines and predicted immune infiltration. ESCC appears to have increased signature associated with dendritic cells, Th17, and CD8 T cells, the latter of which correlate with survival in ESCC. We bioinformatically observed that ESCC may be more responsive to checkpoint inhibitor therapy than EAC and postulate targets to enhance therapy further. Lastly, we highlight correlations between differentially expressed enzymes and the potential immune status.ConclusionOverall, these results highlight the extreme differences observed between the histological subtypes and may lead to novel biomarkers, therapeutic strategies, and differences in therapeutic response for targeting each esophageal cancer subtype.

scholarly journals Comprehensive Plasma Metabolomic Profile of Patients with Advanced Neuroendocrine Tumors (NETs). Diagnostic and Biological Relevance

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2634
Author(s):  
Beatriz Soldevilla ◽  
Angeles López-López ◽  
Alberto Lens-Pardo ◽  
Carlos Carretero-Puche ◽  
Angeles Lopez-Gonzalvez ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Metabolic Networks ◽  
Tca Cycle ◽  
Metabolomic Profile ◽  
Diagnostic Potential ◽  
The Tca Cycle ◽  
Novel Biomarkers ◽  
Potential Methods ◽  
Clinical Potential ◽  
First Time

Purpose: High-throughput “-omic” technologies have enabled the detailed analysis of metabolic networks in several cancers, but NETs have not been explored to date. We aim to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. Methods: Plasma samples from 77 NETs and 68 controls were profiled by GC−MS, CE−MS and LC−MS untargeted metabolomics. OPLS-DA was performed to evaluate metabolomic differences. Related pathways were explored using Metaboanalyst 4.0. Finally, ROC and OPLS-DA analyses were performed to select metabolites with biomarker potential. Results: We identified 155 differential compounds between NETs and controls. We have detected an increase of bile acids, sugars, oxidized lipids and oxidized products from arachidonic acid and a decrease of carnitine levels in NETs. MPA/MSEA identified 32 enriched metabolic pathways in NETs related with the TCA cycle and amino acid metabolism. Finally, OPLS-DA and ROC analysis revealed 48 metabolites with diagnostic potential. Conclusions: This study provides, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use. A reduced set of metabolites of high diagnostic accuracy has been identified. Additionally, new enriched metabolic pathways annotated may open innovative avenues of clinical research.

scholarly journals Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 183
Author(s):  
Paul Muñoz ◽  
Johanna Orellana-Alvear ◽  
Jörg Bendix ◽  
Jan Feyen ◽  
Rolando Célleri
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Early Warning Systems ◽  
Data Representation ◽  
Machine Learning Techniques ◽  
Lead Times ◽  
Warning Systems ◽  
Tropical Andes ◽  
K Nearest Neighbors ◽  
Learning Techniques

Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWSs with three river states, No-alert, Pre-alert and Alert for flooding, for lead times between 1 to 12 h using the most common ML techniques, such as multi-layer perceptron (MLP), logistic regression (LR), K-nearest neighbors (KNN), naive Bayes (NB), and random forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as a case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1 h and 12 h cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. The proposed methodology for selecting the optimal ML technique for a FEWS can be extrapolated to other case studies. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of society of floods.

scholarly journals Human Activity Recognition using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3553-3556
Author(s):  
Chaudhari Shraddha
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Activity Recognition ◽  
Human Activity ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Machine Learning Techniques ◽  
K Nearest Neighbors ◽  
Random Forest Classification ◽  
Medical Health Care

Activity recognition in humans is one of the active challenges that find its application in numerous fields such as, medical health care, military, manufacturing, assistive techniques and gaming. Due to the advancements in technologies the usage of smartphones in human lives has become inevitable. The sensors in the smartphones help us to measure the essential vital parameters. These measured parameters enable us to monitor the activities of humans, which we call as human activity recognition. We have applied machine learning techniques on a publicly available dataset. K-Nearest Neighbors and Random Forest classification algorithms are applied. In this paper, we have designed and implemented an automatic human activity recognition system that independently recognizes the actions of the humans. This system is able to recognize the activities such as Laying, Sitting, Standing, Walking, Walking downstairs and Walking upstairs. The results obtained show that, the KNN and Random Forest Algorithms gives 90.22% and 92.70% respectively of overall accuracy in detecting the activities.

Abstract 241: Acetylomic and Metabolomic Alterations in Aged Hearts: Role for Silent Information Regulator (SIRT) 3

2015 ◽  
Vol 117 (suppl_1) ◽  
Author(s):  
Jimmy Zhang ◽  
William R Urciuoli ◽  
Paul S Brookes ◽  
George A Porter ◽  
Sergiy M Nadtochiy
Keyword(s):  
Metabolic Pathways ◽  
Carbon Flux ◽  
Tca Cycle ◽  
Adult Group ◽  
Protein Levels ◽  
Mitochondrial Alterations ◽  
Age Related ◽  
Transport Chain ◽  
Tca Cycle Intermediates ◽  
Substantial Decline

Introduction: SIRT3 is a mitochondrial metabolic regulator, and a decline in function of SIRT3 may play a role in age-related mitochondrial alterations. The aim of this study was to investigate the possible down-regulation of SIRT3 activity in aged hearts, and to identify which metabolic pathways in aged hearts may be impaired due to SIRT3 dysfunction. Methods: Mitochondria were isolated from WT adult (7 mo.), SIRT3 -/- adult (7 mo.) and WT aged (18 mo.) hearts. Acetylated proteins in mitochondrial samples were identified using 2D gels and mass spectrometry. Metabolite concentrations and carbon fluxes through core metabolic pathways were determined using 13 C-labeled substrates and LC-MS/MS. Results: Mitochondrial acetylation patterns in the SIRT3 -/- adult group matched those found in the WT aged group; the level of acetylation was significantly higher than in WT adult. While the SIRT3 -/- samples exhibited zero SIRT3 protein content, no difference in SIRT3 protein level was seen between adult and aged WT hearts. Mechanistically, this suggests that alterations in mitochondrial acetylation during aging were not caused by lower SIRT3 protein levels, but rather by a lower SIRT3 enzymatic activity. Furthermore, aged myocardium exhibited 40% lower NAD + levels, which may underlie compromised SIRT3 activity. ATP levels were decreased in both SIRT3 -/- and WT aged hearts, suggesting possible defects in energy metabolism. Using metabolomics, we demonstrated that alterations of TCA cycle intermediates were similar in SIRT3 -/- and WT aged hearts (relative to WT adult), and included a substantial decline of carbon flux through α-ketoglutarate and malate. Furthermore, regulation of energy production might also be impaired at the level of the electron transport chain, where Complex I was significantly inhibited in both SIRT3 deficient and aged hearts. Conclusions: Collectively these data suggested that acetylomic and metabolomic fingerprints observed in SIRT3 -/- hearts were recapitulated in aged hearts.

Low glucose and high pyruvate reduce the production of 2-oxoaldehydes, improving mitochondrial efficiency, redox regulation and stallion sperm function

2021 ◽  
Author(s):  
J M Ortiz-Rodríguez ◽  
F E Martín-Cano ◽  
G Gaitskell-Phillips ◽  
A Silva ◽  
C Ortega-Ferrusola ◽  
...  
Keyword(s):  
Redox Regulation ◽  
Krebs Cycle ◽  
Tca Cycle ◽  
Mitochondrial Activity ◽  
Atp Production ◽  
Transport Chain ◽  
Low Glucose ◽  
Mitochondrial Efficiency ◽  
Metabolic Strategies ◽  
Go Terms

Abstract Energy metabolism in spermatozoa is complex and involves the metabolism of carbohydrate fatty acids and amino acids. The ATP produced in the electron transport chain (ETC) in the mitochondria appears to be crucial for both sperm motility and maintaining viability, while glycolytic enzymes in the flagella may contribute to ATP production to sustain motility and velocity. Stallion spermatozoa seemingly use diverse metabolic strategies, and in this regard, a study of the metabolic proteome showed that gene ontology (GO) terms and Reactome pathways related to pyruvate metabolism and the Krebs cycle were predominant. Following this, the hypothesis that low glucose concentrations can provide sufficient support for motility and velocity, and thus glucose concentration can be significantly reduced in the medium, was tested. Aliquots of stallion semen in four different media were stored for 48 h at 18°C; a commercial extender containing 67 mM glucose was used as a control. Stallion spermatozoa stored in media with low glucose (1 mM) and high pyruvate (10 mM) (LG-HP) sustained better motility and velocities than those stored in the commercial extender formulated with very high glucose (61.7 ± 1.2% in INRA 96 vs 76.2 ± 1.0% in LG-HP media after 48 h of incubation at 18°C P < 0.0001). Moreover, mitochondrial activity was superior in LG-HP extenders (24.1 ± 1.8% in INRA 96 vs 51.1 ± 0.7% in LG-HP of spermatozoa with active mitochondria after 48 h of storage at 18°C P < 0.0001). Low glucose concentrations may permit more efficient sperm metabolism and redox regulation when substrates for an efficient TCA cycle are provided. The improvement seen using low glucose extenders is due to reductions in the levels of glyoxal and methylglyoxal, 2-oxoaldehydes formed during glycolysis; these compounds are potent electrophiles able to react with proteins, lipids and DNA, causing sperm damage.

scholarly journals SUN-219 Electron Transport Chain Complex 2 in Mitochondrial Pregnenolone Synthesis

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Himangshu S Bose ◽  
Brendan Marshall ◽  
Dilip Debnath ◽  
Elizabeth W Perry ◽  
Randy M Whittal
Keyword(s):  
Electron Transport ◽  
Intermediate State ◽  
Electron Transport Chain ◽  
Molten Globule ◽  
Tca Cycle ◽  
Steroid Synthesis ◽  
Aberrant Expression ◽  
Complex Ii ◽  
The Tca Cycle ◽  
Transport Chain

Abstract The mitochondrial P450 family of enzymes (SCC), which require the electron transport chain (ETC) complexes III, IV and V, initiate steroidogenesis by cleaving the sidechain of cholesterol to synthesize steroid hormones, an essential component for mammalian survival. SCC is required for full-term gestation, and aberrant expression may cause pseudohermaphroditism, breast cancer or polycystic ovary syndrome. Complex II or succinate dehydrogenase (quinone) is shared with the TCA cycle and has no proton pumping capacity and no known role in steroid synthesis. We now show that succinate is an intermediate metabolite in the TCA cycle and plays a central role physiologically. Specifically, complex II is required for SCC activation, where the proton pump facilitates an active intermediate state conformation at the matrix, so that in the presence of succinate, ATP can add phosphate. A longer intermediate equilibrium state generates a transient stabilization to enhance the binding of phosphate anions in the presence of succinate anions, resulting in higher enthalpy and activity. An inhibition of the processing at the intermediate state stops phosphate addition and activity. We further describe that phosphate circulation brings the molten globule, an intermediate, to an active folded state. This is the first report showing that an intermediate state activated by succinate facilitates ETC complex II interaction with complexes III and IV for metabolism.

Protein hyperacylation links mitochondrial dysfunction with nuclear organization

2020 ◽  
Author(s):  
John Smestad ◽  
Micah McCauley ◽  
Matthew Amato ◽  
Yuning Xiong ◽  
Juan Liu ◽  
...  
Keyword(s):  
Chromatin Structure ◽  
Metabolic Regulation ◽  
Tca Cycle ◽  
Interphase Chromatin ◽  
Histone Octamer ◽  
The Tca Cycle ◽  
Transport Chain ◽  
Topologically Associated Domains ◽  
Biophysical Effects ◽  
Liquid Liquid Phase Separation

SummaryCellular metabolism is linked to epigenetics, but the biophysical effects of metabolism on chromatin structure and implications for gene regulation remain largely unknown. Here, using a broken tricarboxylic acid (TCA) cycle and disrupted electron transport chain (ETC) exemplified by succinate dehydrogenase subunit C (SDHC) deficiency, we investigated the effects of metabolism on chromatin architecture over multiple distance scales [nucleosomes (∼102 bp), topologically-associated domains (TADs; ∼105 – 106 bp), and chromatin compartments (106 – 108 bp)]. Metabolically-driven hyperacylation of histones led to weakened nucleosome positioning in multiple types of chromatin, and we further demonstrate that lysine acylation directly destabilizes histone octamer-DNA interactions. Hyperacylation of cohesin subunits correlated with decreased mobility on interphase chromatin and increased TAD boundary strength, suggesting that cohesin is metabolically regulated. Erosion of chromatin compartment distinctions reveals metabolic regulation of chromatin liquid-liquid phase separation. The TCA cycle and ETC thus modulate chromatin structure over multiple distance scales.

A Hybrid Domain Adaptation Approach for Identifying Crisis-Relevant Tweets

2019 ◽  
Vol 11 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Reza Mazloom ◽  
Hongmin Li ◽  
Doina Caragea ◽  
Cornelia Caragea ◽  
Muhammad Imran
Keyword(s):  
Domain Adaptation ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
K Nearest Neighbors ◽  
Parameter Adaptation ◽  
Emergency Situations ◽  
Hybrid Domain ◽  
Learning Techniques ◽  
Source Data ◽  
Supervised Classifiers

Huge amounts of data generated on social media during emergency situations is regarded as a trove of critical information. The use of supervised machine learning techniques in the early stages of a crisis is challenged by the lack of labeled data for that event. Furthermore, supervised models trained on labeled data from a prior crisis may not produce accurate results, due to inherent crisis variations. To address these challenges, the authors propose a hybrid feature-instance-parameter adaptation approach based on matrix factorization, k-nearest neighbors, and self-training. The proposed feature-instance adaptation selects a subset of the source crisis data that is representative for the target crisis data. The selected labeled source data, together with unlabeled target data, are used to learn self-training domain adaptation classifiers for the target crisis. Experimental results have shown that overall the hybrid domain adaptation classifiers perform better than the supervised classifiers learned from the original source data.

scholarly journals Metabolic Perturbation and Potential Markers in Patients with Esophageal Cancer

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Xianlan Zhu ◽  
Kun Wang ◽  
Gaoshuang Liu ◽  
Yuqing Wang ◽  
Jin Xu ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Early Stage ◽  
Principal Component ◽  
Roc Curves ◽  
Tca Cycle ◽  
Metabolic Reprogramming ◽  
Serum Samples ◽  
Diagnosis And Prognosis ◽  
Flight Mass Spectrometry ◽  
Amino Acid Turnover

Clinical diagnosis of esophageal cancer (EC) at early stage is rather difficult. This study aimed to profile the molecules in serum and tissue and identify potential biomarkers in patients with EC. A total of 64 volunteers were recruited, and 83 samples (24 EC serum samples, 21 serum controls, 19 paired EC tissues, and corresponding tumor-adjacent tissues) were analyzed. The gas chromatography time-of-flight mass spectrometry (GC/TOF-MS) was employed, and principal component analysis was used to reveal the discriminatory metabolites and identify the candidate markers of EC. A total of 41 in serum and 36 identified compounds in tissues were relevant to the malignant prognosis. A marked metabolic reprogramming of EC was observed, including enhanced anaerobic glycolysis and glutaminolysis, inhibited tricarboxylic acid (TCA) cycle, and altered lipid metabolism and amino acid turnover. Based on the potential markers of glucose, glutamic acid, lactic acid, and cholesterol, the receiver operating characteristic (ROC) curves indicated good diagnosis and prognosis of EC. EC patients showed distinct reprogrammed metabolism involved in glycolysis, TCA cycle, glutaminolysis, and fatty acid metabolism. The pivotal molecules in the metabolic pathways were suggested as the potential markers to facilitate the early diagnosis of human EC.

scholarly journals Machine learning in diachronic corpus phonology: mining verse data to infer trajectories in English phonotactics

2018 ◽  
Vol 3 ◽  
Author(s):  
Andreas Baumann
Keyword(s):  
Machine Learning ◽  
Middle English ◽  
Large Data ◽  
Large Data Sets ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Powerful Method ◽  
K Nearest Neighbors ◽  
Learning Techniques ◽  
Standard Techniques

Machine learning is a powerful method when working with large data sets such as diachronic corpora. However, as opposed to standard techniques from inferential statistics like regression modeling, machine learning is less commonly used among phonological corpus linguists. This paper discusses three different machine learning techniques (K nearest neighbors classifiers; Naïve Bayes classifiers; artificial neural networks) and how they can be applied to diachronic corpus data to address specific phonological questions. To illustrate the methodology, I investigate Middle English schwa deletion and when and how it potentially triggered reduction of final /mb/ clusters in English.

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