Articles de revista
http://hdl.handle.net/2117/3759
2024-03-28T18:00:27Z
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Time-frequency features for impedance cardiography signals during anesthesia using different distribution kernels
http://hdl.handle.net/2117/115165
Time-frequency features for impedance cardiography signals during anesthesia using different distribution kernels
Escrivá, Jesús; Gambus, Pedro L.; Jensen, Erik Weber; Vallverdú Ferrer, Montserrat
Objective: This works investigates the time-frequency content of impedance cardiography signals during a propofol-remifentanil anesthesia.
Materials and Methods: In the last years, impedance cardiography (ICG) is a technique which has gained much attention. However, ICG signals need further investigation. Time-Frequency Distributions (TFDs) with 5 different kernels are used in order to analyze impedance cardiography signals (ICG) before the start of the anesthesia and after the loss of consciousness. In total, ICG signals from one hundred and thirty-one consecutive patients undergoing major surgery under general anesthesia were analyzed. Several features were extracted from the calculated TFDs in order to characterize the time-frequency content of the ICG signals. Differences between those features before and after the loss of consciousness were studied.
Results: The Extended Modified Beta Distribution (EMBD) was the kernel for which most features shows statistically significant changes between before and after the loss of consciousness. Among all analyzed features, those based on entropy showed a sensibility, specificity and area under the curve of the receiver operating characteristic above 60%.
Conclusion: The anesthetic state of the patient is reflected on linear and non-linear features extracted from the TFDs of the ICG signals. Especially, the EMBD is a suitable kernel for the analysis of ICG signals and offers a great range of features which change according to the patient’s anesthesia state in a statistically significant way.
2018-03-14T11:39:27Z
Escrivá, Jesús
Gambus, Pedro L.
Jensen, Erik Weber
Vallverdú Ferrer, Montserrat
Objective: This works investigates the time-frequency content of impedance cardiography signals during a propofol-remifentanil anesthesia.
Materials and Methods: In the last years, impedance cardiography (ICG) is a technique which has gained much attention. However, ICG signals need further investigation. Time-Frequency Distributions (TFDs) with 5 different kernels are used in order to analyze impedance cardiography signals (ICG) before the start of the anesthesia and after the loss of consciousness. In total, ICG signals from one hundred and thirty-one consecutive patients undergoing major surgery under general anesthesia were analyzed. Several features were extracted from the calculated TFDs in order to characterize the time-frequency content of the ICG signals. Differences between those features before and after the loss of consciousness were studied.
Results: The Extended Modified Beta Distribution (EMBD) was the kernel for which most features shows statistically significant changes between before and after the loss of consciousness. Among all analyzed features, those based on entropy showed a sensibility, specificity and area under the curve of the receiver operating characteristic above 60%.
Conclusion: The anesthetic state of the patient is reflected on linear and non-linear features extracted from the TFDs of the ICG signals. Especially, the EMBD is a suitable kernel for the analysis of ICG signals and offers a great range of features which change according to the patient’s anesthesia state in a statistically significant way.
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Assessment of heart rate variability during an ultra-endurance mountain trail race by multi-scale entropy analysis
http://hdl.handle.net/2117/115160
Assessment of heart rate variability during an ultra-endurance mountain trail race by multi-scale entropy analysis
Vallverdú Ferrer, Montserrat; Ruiz Muñoz, Aroa; Roca Rodríguez, Emma; Caminal Magrans, Pere; Rodríguez Guisado, Ferran Agustin; Irurtia Amigo, Alfredo; Perera Lluna, Alexandre
The aim of the study was to analyze heart rate variability (HRV) response to high-intensity exercise during a 35-km mountain trail race and to ascertain whether fitness level could influence autonomic nervous system (ANS) modulation. Time-domain, frequency-domain, and multi-scale entropy (MSE) indexes were calculated for eleven mountain-trail runners who completed the race. Many changes were observed, mostly related to exercise load and fatigue. These changes were characterized by increased mean values and standard deviations of the normal-to-normal intervals associated with sympathetic activity, and by decreased differences between successive intervals related to parasympathetic activity. Normalized low frequency (LF) power suggested that ANS modulation varied greatly during the race and between individuals. Normalized high frequency (HF) power, associated with parasympathetic activity, varied considerably over the race, and tended to decrease at the final stages, whereas changes in the LF/HF ratio corresponded to intervals with varying exercise load. MSE indexes, related to system complexity, indicated the existence of many interactions between the heart and its neurological control mechanism. The time-domain, frequency-domain, and MSE indexes were also able to discriminate faster from slower runners, mainly in the more difficult and in the final stages of the race. These findings suggest the use of HRV analysis to study cardiac function mechanisms in endurance sports
2018-03-14T11:12:44Z
Vallverdú Ferrer, Montserrat
Ruiz Muñoz, Aroa
Roca Rodríguez, Emma
Caminal Magrans, Pere
Rodríguez Guisado, Ferran Agustin
Irurtia Amigo, Alfredo
Perera Lluna, Alexandre
The aim of the study was to analyze heart rate variability (HRV) response to high-intensity exercise during a 35-km mountain trail race and to ascertain whether fitness level could influence autonomic nervous system (ANS) modulation. Time-domain, frequency-domain, and multi-scale entropy (MSE) indexes were calculated for eleven mountain-trail runners who completed the race. Many changes were observed, mostly related to exercise load and fatigue. These changes were characterized by increased mean values and standard deviations of the normal-to-normal intervals associated with sympathetic activity, and by decreased differences between successive intervals related to parasympathetic activity. Normalized low frequency (LF) power suggested that ANS modulation varied greatly during the race and between individuals. Normalized high frequency (HF) power, associated with parasympathetic activity, varied considerably over the race, and tended to decrease at the final stages, whereas changes in the LF/HF ratio corresponded to intervals with varying exercise load. MSE indexes, related to system complexity, indicated the existence of many interactions between the heart and its neurological control mechanism. The time-domain, frequency-domain, and MSE indexes were also able to discriminate faster from slower runners, mainly in the more difficult and in the final stages of the race. These findings suggest the use of HRV analysis to study cardiac function mechanisms in endurance sports
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diffuStats: an R package to compute diffusion-based scores on biological networks
http://hdl.handle.net/2117/114063
diffuStats: an R package to compute diffusion-based scores on biological networks
Picart Armada, Sergio; Thompson, Wesley; Buil, Alfonso; Perera Lluna, Alexandre
Label propagation and diffusion over biological networks are a common mathematical formalism in computational biology for giving context to molecular entities and prioritising novel candidates in the area of study.
There are several choices in conceiving the diffusion process -involving the graph kernel, the score definitions and the presence of a posterior statistical normalisation- which have an impact on the results.
This manuscript describes diffuStats, an R package that provides a collection of graph kernels and diffusion scores, as well as a parallel permutation analysis for the normalised scores, that eases the computation of the scores and their benchmarking for an optimal choice.
This is a pre-copyedited, author-produced version of an article accepted for publication in Bioinformatics following peer review. The version of record Sergio Picart-Armada, Wesley K Thompson, Alfonso Buil, Alexandre Perera-Lluna; diffuStats: an R package to compute diffusion-based scores on biological networks, Bioinformatics, Volume 34, Issue 3, 1 February 2018, Pages 533–534 is available online at: https://doi.org/10.1093/bioinformatics/btx632.
2018-02-12T15:59:16Z
Picart Armada, Sergio
Thompson, Wesley
Buil, Alfonso
Perera Lluna, Alexandre
Label propagation and diffusion over biological networks are a common mathematical formalism in computational biology for giving context to molecular entities and prioritising novel candidates in the area of study.
There are several choices in conceiving the diffusion process -involving the graph kernel, the score definitions and the presence of a posterior statistical normalisation- which have an impact on the results.
This manuscript describes diffuStats, an R package that provides a collection of graph kernels and diffusion scores, as well as a parallel permutation analysis for the normalised scores, that eases the computation of the scores and their benchmarking for an optimal choice.
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Assessment of heart rate variability during an endurance mountain trail race by multi-scale entropy analysis
http://hdl.handle.net/2117/113838
Assessment of heart rate variability during an endurance mountain trail race by multi-scale entropy analysis
Vallverdú Ferrer, Montserrat; Ruiz Muñoz, Aroa; Roca Rodríguez, Emma; Caminal Magrans, Pere; Rodríguez Guisado, Ferran Agustin; Irurtia Amigo, Alfredo; Perera Lluna, Alexandre
© 2017 by the authors. The aim of the study was to analyze heart rate variability (HRV) response to high-intensity exercise during a 35-km mountain trail race and to ascertain whether fitness level could influence autonomic nervous system (ANS) modulation. Time-domain, frequency-domain, and multi-scale entropy (MSE) indexes were calculated for eleven mountain-trail runners who completed the race. Many changes were observed, mostly related to exercise load and fatigue. These changes were characterized by increased mean values and standard deviations of the normal-to-normal intervals associated with sympathetic activity, and by decreased differences between successive intervals related to parasympathetic activity. Normalized low frequency (LF) power suggested that ANS modulation varied greatly during the race and between individuals. Normalized high frequency (HF) power, associated with parasympathetic activity, varied considerably over the race, and tended to decrease at the final stages, whereas changes in the LF/HF ratio corresponded to intervals with varying exercise load. MSE indexes, related to system complexity, indicated the existence of many interactions between the heart and its neurological control mechanism. The time-domain, frequency-domain, and MSE indexes were also able to discriminate faster from slower runners, mainly in the more difficult and in the final stages of the race. These findings suggest the use of HRV analysis to study cardiac function mechanisms in endurance sports.
2018-02-07T09:02:43Z
Vallverdú Ferrer, Montserrat
Ruiz Muñoz, Aroa
Roca Rodríguez, Emma
Caminal Magrans, Pere
Rodríguez Guisado, Ferran Agustin
Irurtia Amigo, Alfredo
Perera Lluna, Alexandre
© 2017 by the authors. The aim of the study was to analyze heart rate variability (HRV) response to high-intensity exercise during a 35-km mountain trail race and to ascertain whether fitness level could influence autonomic nervous system (ANS) modulation. Time-domain, frequency-domain, and multi-scale entropy (MSE) indexes were calculated for eleven mountain-trail runners who completed the race. Many changes were observed, mostly related to exercise load and fatigue. These changes were characterized by increased mean values and standard deviations of the normal-to-normal intervals associated with sympathetic activity, and by decreased differences between successive intervals related to parasympathetic activity. Normalized low frequency (LF) power suggested that ANS modulation varied greatly during the race and between individuals. Normalized high frequency (HF) power, associated with parasympathetic activity, varied considerably over the race, and tended to decrease at the final stages, whereas changes in the LF/HF ratio corresponded to intervals with varying exercise load. MSE indexes, related to system complexity, indicated the existence of many interactions between the heart and its neurological control mechanism. The time-domain, frequency-domain, and MSE indexes were also able to discriminate faster from slower runners, mainly in the more difficult and in the final stages of the race. These findings suggest the use of HRV analysis to study cardiac function mechanisms in endurance sports.
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Multiview and multifeature spectral clustering using common eigenvectors
http://hdl.handle.net/2117/113182
Multiview and multifeature spectral clustering using common eigenvectors
Kanaan Izquierdo, Samir; Ziyatdinov, Andrey; Perera Lluna, Alexandre
An ever-increasing number of data analysis problems include more than one view of the data, i.e. differ-
ent measurement approaches to the population under study. In consequence, pattern analysis methods
that deal appropriately with multiview data are becoming increasingly useful. In this paper, a novel mul-
tiview spectral clustering algorithm is presented (multiview spectral clustering by common eigenvectors,
or MVSC-CEV), based on computing the common eigenvectors of the Laplacian matrices derived from
the similarity matrices of the input data. This algorithm maintains the features of spectral clustering,
while allowing the use of an arbitrary number of input views, possibly of a different nature (feature or
graph space) and with different dimensions. The method has been tested on four standard multiview
data sets (UCI’s Handwritten, BBC segmented news, Max Planck Institute’s Animal With Attributes and
Reuters multilingual), and compared with seven methods in the state of the art. Seven standard clus-
tering evaluation metrics have been used in the experiments. The quality of the clustering produced by
MVSC-CEV is above those obtained by other state-of-the-art methods in the majority of evaluation met-
rics and dataset combinations. The computation times of this method are approximately twice those of
the baseline spectral clustering of the concatenated data views.
2018-01-25T09:50:04Z
Kanaan Izquierdo, Samir
Ziyatdinov, Andrey
Perera Lluna, Alexandre
An ever-increasing number of data analysis problems include more than one view of the data, i.e. differ-
ent measurement approaches to the population under study. In consequence, pattern analysis methods
that deal appropriately with multiview data are becoming increasingly useful. In this paper, a novel mul-
tiview spectral clustering algorithm is presented (multiview spectral clustering by common eigenvectors,
or MVSC-CEV), based on computing the common eigenvectors of the Laplacian matrices derived from
the similarity matrices of the input data. This algorithm maintains the features of spectral clustering,
while allowing the use of an arbitrary number of input views, possibly of a different nature (feature or
graph space) and with different dimensions. The method has been tested on four standard multiview
data sets (UCI’s Handwritten, BBC segmented news, Max Planck Institute’s Animal With Attributes and
Reuters multilingual), and compared with seven methods in the state of the art. Seven standard clus-
tering evaluation metrics have been used in the experiments. The quality of the clustering produced by
MVSC-CEV is above those obtained by other state-of-the-art methods in the majority of evaluation met-
rics and dataset combinations. The computation times of this method are approximately twice those of
the baseline spectral clustering of the concatenated data views.
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Null diffusion-based enrichment for metabolomics data
http://hdl.handle.net/2117/113111
Null diffusion-based enrichment for metabolomics data
Picart Armada, Sergio; Fernández Albert, Francesc; Vinaixa, Maria; Rodríguez Hernandez, Miguel A.; Aivio, Suvi; Stracker, Travis H.; Yanes Torrado, Óscar; Perera Lluna, Alexandre
Metabolomics experiments identify metabolites whose abundance varies as the conditions under study change. Pathway enrichment tools help in the identification of key metabolic processes and in building a plausible biological explanation for these variations. Although several methods are available for pathway enrichment using experimental evidence, metabolomics does not yet have a comprehensive overview in a network layout at multiple molecular levels. We propose a novel pathway enrichment procedure for analysing summary metabolomics data based on sub-network analysis in a graph representation of a reference database. Relevant entries are extracted from the database according to statistical measures over a null diffusive process that accounts for network topology and pathway crosstalk. Entries are reported as a sub-pathway network, including not only pathways, but also modules, enzymes, reactions and possibly other compound candidates for further analyses. This provides a richer biological context, suitable for generating new study hypotheses and potential enzymatic targets. Using this method, we report results from cells depleted for an uncharacterised mitochondrial gene using GC and LC-MS data and employing KEGG as a knowledge base. Partial validation is provided with NMR-based tracking of 13C glucose labelling of these cells.
2018-01-23T16:02:00Z
Picart Armada, Sergio
Fernández Albert, Francesc
Vinaixa, Maria
Rodríguez Hernandez, Miguel A.
Aivio, Suvi
Stracker, Travis H.
Yanes Torrado, Óscar
Perera Lluna, Alexandre
Metabolomics experiments identify metabolites whose abundance varies as the conditions under study change. Pathway enrichment tools help in the identification of key metabolic processes and in building a plausible biological explanation for these variations. Although several methods are available for pathway enrichment using experimental evidence, metabolomics does not yet have a comprehensive overview in a network layout at multiple molecular levels. We propose a novel pathway enrichment procedure for analysing summary metabolomics data based on sub-network analysis in a graph representation of a reference database. Relevant entries are extracted from the database according to statistical measures over a null diffusive process that accounts for network topology and pathway crosstalk. Entries are reported as a sub-pathway network, including not only pathways, but also modules, enzymes, reactions and possibly other compound candidates for further analyses. This provides a richer biological context, suitable for generating new study hypotheses and potential enzymatic targets. Using this method, we report results from cells depleted for an uncharacterised mitochondrial gene using GC and LC-MS data and employing KEGG as a knowledge base. Partial validation is provided with NMR-based tracking of 13C glucose labelling of these cells.
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Affected pathways and transcriptional regulators in gene expression response to an ultra-marathon trail: Global and independent activity approaches
http://hdl.handle.net/2117/112994
Affected pathways and transcriptional regulators in gene expression response to an ultra-marathon trail: Global and independent activity approaches
Maqueda González, María de los Ángeles; Roca, Emma; Brotons Cuixart, Daniel; Soria, Jose Manuel; Perera Lluna, Alexandre
Gene expression (GE) analyses on blood samples from marathon and half-marathon runners have reported significant impacts on the immune and inflammatory systems. An ultra-marathon trail (UMT) represents a greater effort due to its more testing conditions. For the first time, we report the genome-wide GE profiling in a group of 16 runners participating in an 82 km UMT competition. We quantified their differential GE profile before and after the race using HuGene2.0st microarrays (Affymetrix Inc., California, US). The results obtained were decomposed by means of an independent component analysis (ICA) targeting independent expression modes. We observed significant differences in the expression levels of 5,084 protein coding genes resulting in an overrepresentation of 14% of the human biological pathways from the Kyoto Encyclopedia of Genes and Genomes database. These were mainly clustered on terms related with protein synthesis repression, altered immune system and infectious diseases related mechanisms. In a second analysis, 27 out of the 196 transcriptional regulators (TRs) included in the Open Regulatory Annotation database were overrepresented. Among these TRs, we identified transcription factors from the hypoxia-inducible factors (HIF) family EPAS1 (p< 0.01) and HIF1A (p<0.001), and others jointly described in the gluconeogenesis program such as HNF4 (p< 0.001), EGR1 (p<0.001), CEBPA (p< 0.001) and a highly specific TR, YY1 (p<0.01). The five independent components, obtained from ICA, further revealed a down-regulation of 10 genes distributed in the complex I, III and V from the electron transport chain. This mitochondrial activity reduction is compatible with HIF-1 system activation. The vascular endothelial growth factor (VEGF) pathway, known to be regulated by HIF, also emerged (p<0.05). Additionally, and related to the brain rewarding circuit, the endocannabinoid signalling pathway was overrepresented (p<0.05).
2018-01-19T13:28:29Z
Maqueda González, María de los Ángeles
Roca, Emma
Brotons Cuixart, Daniel
Soria, Jose Manuel
Perera Lluna, Alexandre
Gene expression (GE) analyses on blood samples from marathon and half-marathon runners have reported significant impacts on the immune and inflammatory systems. An ultra-marathon trail (UMT) represents a greater effort due to its more testing conditions. For the first time, we report the genome-wide GE profiling in a group of 16 runners participating in an 82 km UMT competition. We quantified their differential GE profile before and after the race using HuGene2.0st microarrays (Affymetrix Inc., California, US). The results obtained were decomposed by means of an independent component analysis (ICA) targeting independent expression modes. We observed significant differences in the expression levels of 5,084 protein coding genes resulting in an overrepresentation of 14% of the human biological pathways from the Kyoto Encyclopedia of Genes and Genomes database. These were mainly clustered on terms related with protein synthesis repression, altered immune system and infectious diseases related mechanisms. In a second analysis, 27 out of the 196 transcriptional regulators (TRs) included in the Open Regulatory Annotation database were overrepresented. Among these TRs, we identified transcription factors from the hypoxia-inducible factors (HIF) family EPAS1 (p< 0.01) and HIF1A (p<0.001), and others jointly described in the gluconeogenesis program such as HNF4 (p< 0.001), EGR1 (p<0.001), CEBPA (p< 0.001) and a highly specific TR, YY1 (p<0.01). The five independent components, obtained from ICA, further revealed a down-regulation of 10 genes distributed in the complex I, III and V from the electron transport chain. This mitochondrial activity reduction is compatible with HIF-1 system activation. The vascular endothelial growth factor (VEGF) pathway, known to be regulated by HIF, also emerged (p<0.05). Additionally, and related to the brain rewarding circuit, the endocannabinoid signalling pathway was overrepresented (p<0.05).
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How to merge observational and physiological data? A case study of motor skills patterns and heart rate in exercise programs for adult women
http://hdl.handle.net/2117/109696
How to merge observational and physiological data? A case study of motor skills patterns and heart rate in exercise programs for adult women
Castañer Balcells, Marta; Puigarnau, Sílvia; Benítez Iglesias, Raúl; Zurloni, Valentino; Camerino, Oleguer
The present study analyzes individual and group heart rate responses in exercising adult women. The specific aim was to compare responses during routine workout sessions within a community exercise program with responses during a purpose-designed workout session targeting diverse motor skills. Sixty-seven adult women with a mean ± SD age of 65.1 ± 11.7 years participated in the study. Two representative sessions were analyzed: a standard workout session the women took at the local community center and an ad hoc session designed to build on a variety of motor skills and capabilities. Observational methodology was used to collect categorical data on motor skill performance during each session using the OSMOS-in context observation instrument. Continuous heart rate data were recorded for all participants during each session. A combined analysis of categorical and continuous data was undertaken using Hidden Markov Models. The results show that the session targeting a greater diversity of motor skills not only met the cardiovascular fitness recommendations of the American College of Sports Medicine, but resulted in greater individual variability and greater synchrony between participants than the routine session.
2017-11-03T09:35:19Z
Castañer Balcells, Marta
Puigarnau, Sílvia
Benítez Iglesias, Raúl
Zurloni, Valentino
Camerino, Oleguer
The present study analyzes individual and group heart rate responses in exercising adult women. The specific aim was to compare responses during routine workout sessions within a community exercise program with responses during a purpose-designed workout session targeting diverse motor skills. Sixty-seven adult women with a mean ± SD age of 65.1 ± 11.7 years participated in the study. Two representative sessions were analyzed: a standard workout session the women took at the local community center and an ad hoc session designed to build on a variety of motor skills and capabilities. Observational methodology was used to collect categorical data on motor skill performance during each session using the OSMOS-in context observation instrument. Continuous heart rate data were recorded for all participants during each session. A combined analysis of categorical and continuous data was undertaken using Hidden Markov Models. The results show that the session targeting a greater diversity of motor skills not only met the cardiovascular fitness recommendations of the American College of Sports Medicine, but resulted in greater individual variability and greater synchrony between participants than the routine session.
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Baitmet, a computational approach for GC–MS library-driven metabolite profiling
http://hdl.handle.net/2117/105910
Baitmet, a computational approach for GC–MS library-driven metabolite profiling
Domingo, Xavier; Brezmes, Jesus; Venturini, G; Vivó-Truyols, Gabriel; Perera Lluna, Alexandre; Vinaixa, Maria
Current computational tools for gas chromatography – mass spectrometry (GC – MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual d ata reviewing. Metabolomics ad vent has fostered the development of public metabolite repositories containing mass spectra and retentio n indices, two orthogonal prop erties needed for metabol ite identification. Such libraries can be used for library - driven compound profiling of large datasets produced in metabolomics, a complementary approach to current GC – MS non - targeted data analysis solutions that can eventually help to assess metabolite i dentities more efficiently. Results: This paper introduces Baitmet, an integrated open - source computational tool written in R enclosing a complete workflow to perform high - throughput library - driven GC – MS profiling in complex samples. Baitmet capabilities w ere assa yed in a metabolomics study in volving 182 human serum samples where a set of 61 metabolites were profiled given a reference library. Conclusions: Baitmet allows high - thr oughput and wide scope interro gation on the metabolic composition of complex sa mples analyzed using GC – MS via freely available spectral data
2017-06-28T06:31:17Z
Domingo, Xavier
Brezmes, Jesus
Venturini, G
Vivó-Truyols, Gabriel
Perera Lluna, Alexandre
Vinaixa, Maria
Current computational tools for gas chromatography – mass spectrometry (GC – MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual d ata reviewing. Metabolomics ad vent has fostered the development of public metabolite repositories containing mass spectra and retentio n indices, two orthogonal prop erties needed for metabol ite identification. Such libraries can be used for library - driven compound profiling of large datasets produced in metabolomics, a complementary approach to current GC – MS non - targeted data analysis solutions that can eventually help to assess metabolite i dentities more efficiently. Results: This paper introduces Baitmet, an integrated open - source computational tool written in R enclosing a complete workflow to perform high - throughput library - driven GC – MS profiling in complex samples. Baitmet capabilities w ere assa yed in a metabolomics study in volving 182 human serum samples where a set of 61 metabolites were profiled given a reference library. Conclusions: Baitmet allows high - thr oughput and wide scope interro gation on the metabolic composition of complex sa mples analyzed using GC – MS via freely available spectral data
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DNA methylation profiling unveils TGF-ß hyperresponse in tumor associated fibroblasts from lung cancer patients
http://hdl.handle.net/2117/102534
DNA methylation profiling unveils TGF-ß hyperresponse in tumor associated fibroblasts from lung cancer patients
Vizioso, Miguel; Puig, Marta; Carmona, F. Javier; Maqueda González, María de los Ángeles; Gomez, Antonio; Labernardie, nna; Gabasa, Marta; Mendizuri, Saloa; Ikemori, Rafael; Trepat Guixer, Xavier; Moran, Sebastian; Vidal, Enrique; Reguart, Noemí; Perera Lluna, Alexandre; Esteller, Manel; Alcaraz, Jordi
There is growing interest in defining the aberrant molecular differences between normal and tumor-associated fibroblasts (TAFs) that support tumor progression. For this purpose, we recently conducted a genome-wide DNA methylation profiling of TAFs and paired control fibroblasts (CFs) from non-small cell lung cancer (NSCLC) patients, and reported a widespread hypomethylation concomitantly with focal gain of DNA methylation; in addition, we found evidence that a fraction of lung TAFs are fibrocytes in origin. Of note, the aberrant epigenome of lung TAFs had a global impact in gene expression and a selective impact on the TGF-ß pathway. To get insights on the functional implications of the latter impact, we analyzed the response of lung TAFs to exogenous TGF-ß1 in terms of activation and contractility. We found a larger expression of a panel of activation markers including a-SMA and collagen-I in TAFs compared to control fibroblasts. Likewise, TGF-ß1 elicited a larger contractility in TAFs than in CFs as assessed by traction force microscopy. These findings reveal that lung TAFs are hyperresponsive to TGF-ß1, which may underlie the expansion and/or maintenance of the tumor-promoting desmoplastic stroma in lung cancer.
2017-03-15T16:34:23Z
Vizioso, Miguel
Puig, Marta
Carmona, F. Javier
Maqueda González, María de los Ángeles
Gomez, Antonio
Labernardie, nna
Gabasa, Marta
Mendizuri, Saloa
Ikemori, Rafael
Trepat Guixer, Xavier
Moran, Sebastian
Vidal, Enrique
Reguart, Noemí
Perera Lluna, Alexandre
Esteller, Manel
Alcaraz, Jordi
There is growing interest in defining the aberrant molecular differences between normal and tumor-associated fibroblasts (TAFs) that support tumor progression. For this purpose, we recently conducted a genome-wide DNA methylation profiling of TAFs and paired control fibroblasts (CFs) from non-small cell lung cancer (NSCLC) patients, and reported a widespread hypomethylation concomitantly with focal gain of DNA methylation; in addition, we found evidence that a fraction of lung TAFs are fibrocytes in origin. Of note, the aberrant epigenome of lung TAFs had a global impact in gene expression and a selective impact on the TGF-ß pathway. To get insights on the functional implications of the latter impact, we analyzed the response of lung TAFs to exogenous TGF-ß1 in terms of activation and contractility. We found a larger expression of a panel of activation markers including a-SMA and collagen-I in TAFs compared to control fibroblasts. Likewise, TGF-ß1 elicited a larger contractility in TAFs than in CFs as assessed by traction force microscopy. These findings reveal that lung TAFs are hyperresponsive to TGF-ß1, which may underlie the expansion and/or maintenance of the tumor-promoting desmoplastic stroma in lung cancer.