<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 Jan 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">450</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 1042 fecal metagenomic samples from seven publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allow extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes undergone by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Functional profiles have demonstrated superior accuracy in predicting CRC and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Méndez-Salazar, Eder Orlando</style></author><author><style face="normal" font="default" size="100%">Vázquez-Mellado, Janitzia</style></author><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Zamudio-Cuevas, Yessica</style></author><author><style face="normal" font="default" size="100%">Francisco-Balderas, Adriana</style></author><author><style face="normal" font="default" size="100%">Martínez-Flores, Karina</style></author><author><style face="normal" font="default" size="100%">Fernández-Torres, Javier</style></author><author><style face="normal" font="default" size="100%">Lozada-Pérez, Carlos</style></author><author><style face="normal" font="default" size="100%">Pineda, Carlos</style></author><author><style face="normal" font="default" size="100%">Sánchez-González, Austreberto</style></author><author><style face="normal" font="default" size="100%">Silveira, Luis H</style></author><author><style face="normal" font="default" size="100%">Burguete-García, Ana I</style></author><author><style face="normal" font="default" size="100%">Orbe-Orihuela, Citlalli</style></author><author><style face="normal" font="default" size="100%">Lagunas-Martínez, Alfredo</style></author><author><style face="normal" font="default" size="100%">Vazquez-Gomez, Alonso</style></author><author><style face="normal" font="default" size="100%">López-Reyes, Alberto</style></author><author><style face="normal" font="default" size="100%">Palacios-González, Berenice</style></author><author><style face="normal" font="default" size="100%">Martínez-Nava, Gabriela Angélica</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biodiversity</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Dysbiosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Gastrointestinal Microbiome</style></keyword><keyword><style  face="normal" font="default" size="100%">Gout</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Metagenome</style></keyword><keyword><style  face="normal" font="default" size="100%">metagenomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Maps</style></keyword><keyword><style  face="normal" font="default" size="100%">Uric Acid</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 05 24</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">50</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;OBJECTIVE: &lt;/b&gt;To evaluate the taxonomic composition of the gut microbiome in gout patients with and without tophi formation, and predict bacterial functions that might have an impact on urate metabolism.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Hypervariable V3-V4 regions of the bacterial 16S rRNA gene from fecal samples of gout patients with and without tophi (n = 33 and n = 25, respectively) were sequenced and compared to fecal samples from 53 healthy controls. We explored predictive functional profiles using bioinformatics in order to identify differences in taxonomy and metabolic pathways.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We identified a microbiome characterized by the lowest richness and a higher abundance of Phascolarctobacterium, Bacteroides, Akkermansia, and Ruminococcus_gnavus_group genera in patients with gout without tophi when compared to controls. The Proteobacteria phylum and the Escherichia-Shigella genus were more abundant in patients with tophaceous gout than in controls. Fold change analysis detected nine genera enriched in healthy controls compared to gout groups (Bifidobacterium, Butyricicoccus, Oscillobacter, Ruminococcaceae_UCG_010, Lachnospiraceae_ND2007_group, Haemophilus, Ruminococcus_1, Clostridium_sensu_stricto_1, and Ruminococcaceae_UGC_013). We found that the core microbiota of both gout groups shared Bacteroides caccae, Bacteroides stercoris ATCC 43183, and Bacteroides coprocola DSM 17136. These bacteria might perform functions linked to one-carbon metabolism, nucleotide binding, amino acid biosynthesis, and purine biosynthesis. Finally, we observed differences in key bacterial enzymes involved in urate synthesis, degradation, and elimination.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Our findings revealed that taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34030623?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">León, Carlos</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Llames, Sara</style></author><author><style face="normal" font="default" size="100%">García-Pérez, Eva</style></author><author><style face="normal" font="default" size="100%">Carretero, Marta</style></author><author><style face="normal" font="default" size="100%">Arriba, María Del Carmen</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Del Rio, Marcela</style></author><author><style face="normal" font="default" size="100%">Escamez, Maria José</style></author><author><style face="normal" font="default" size="100%">Martínez-Santamaría, Lucía</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transcriptomic Analysis of a Diabetic Skin-Humanized Mouse Model Dissects Molecular Pathways Underlying the Delayed Wound Healing Response.</style></title><secondary-title><style face="normal" font="default" size="100%">Genes (Basel)</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genes (Basel)</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Diabetes Mellitus, Experimental</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolic Networks and Pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice, Nude</style></keyword><keyword><style  face="normal" font="default" size="100%">Microarray Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Annotation</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal Component Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin Transplantation</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin Ulcer</style></keyword><keyword><style  face="normal" font="default" size="100%">Streptozocin</style></keyword><keyword><style  face="normal" font="default" size="100%">Tissue Engineering</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">Transplantation, Heterologous</style></keyword><keyword><style  face="normal" font="default" size="100%">Wound Healing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 12 31</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Defective healing leading to cutaneous ulcer formation is one of the most feared complications of diabetes due to its consequences on patients' quality of life and on the healthcare system. A more in-depth analysis of the underlying molecular pathophysiology is required to develop effective healing-promoting therapies for those patients. Major architectural and functional differences with human epidermis limit extrapolation of results coming from rodents and other small mammal-healing models. Therefore, the search for reliable humanized models has become mandatory. Previously, we developed a diabetes-induced delayed humanized wound healing model that faithfully recapitulated the major histological features of such skin repair-deficient condition. Herein, we present the results of a transcriptomic and functional enrichment analysis followed by a mechanistic analysis performed in such humanized wound healing model. The deregulation of genes implicated in functions such as angiogenesis, apoptosis, and inflammatory signaling processes were evidenced, confirming published data in diabetic patients that in fact might also underlie some of the histological features previously reported in the delayed skin-humanized healing model. Altogether, these molecular findings support the utility of such preclinical model as a valuable tool to gain insight into the molecular basis of the delayed diabetic healing with potential impact in the translational medicine field.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33396192?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hillung, Julia</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Cuevas, José M</style></author><author><style face="normal" font="default" size="100%">Elena, Santiago F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The transcriptomics of an experimentally evolved plant-virus interaction.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Ecotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Host-Pathogen Interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">Potyvirus</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 04 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">24901</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Models of plant-virus interaction assume that the ability of a virus to infect a host genotype depends on the matching between virulence and resistance genes. Recently, we evolved tobacco etch potyvirus (TEV) lineages on different ecotypes of Arabidopsis thaliana, and found that some ecotypes selected for specialist viruses whereas others selected for generalists. Here we sought to evaluate the transcriptomic basis of such relationships. We have characterized the transcriptomic responses of five ecotypes infected with the ancestral and evolved viruses. Genes and functional categories differentially expressed by plants infected with local TEV isolates were identified, showing heterogeneous responses among ecotypes, although significant parallelism existed among lineages evolved in the same ecotype. Although genes involved in immune responses were altered upon infection, other functional groups were also pervasively over-represented, suggesting that plant resistance genes were not the only drivers of viral adaptation. Finally, the transcriptomic consequences of infection with the generalist and specialist lineages were compared. Whilst the generalist induced very similar perturbations in the transcriptomes of the different ecotypes, the perturbations induced by the specialist were divergent. Plant defense mechanisms were activated when the infecting virus was specialist but they were down-regulated when infecting with generalist.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/27113435?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">García-Cazorla, Angels</style></author><author><style face="normal" font="default" size="100%">Oyarzabal, Alfonso</style></author><author><style face="normal" font="default" size="100%">Fort, Joana</style></author><author><style face="normal" font="default" size="100%">Robles, Concepción</style></author><author><style face="normal" font="default" size="100%">Castejón, Esperanza</style></author><author><style face="normal" font="default" size="100%">Ruiz-Sala, Pedro</style></author><author><style face="normal" font="default" size="100%">Bodoy, Susanna</style></author><author><style face="normal" font="default" size="100%">Merinero, Begoña</style></author><author><style face="normal" font="default" size="100%">Lopez-Sala, Anna</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Nunes, Virginia</style></author><author><style face="normal" font="default" size="100%">Ugarte, Magdalena</style></author><author><style face="normal" font="default" size="100%">Artuch, Rafael</style></author><author><style face="normal" font="default" size="100%">Palacín, Manuel</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Pombo, Pilar</style></author><author><style face="normal" font="default" size="100%">Alcaide, Patricia</style></author><author><style face="normal" font="default" size="100%">Navarrete, Rosa</style></author><author><style face="normal" font="default" size="100%">Sanz, Paloma</style></author><author><style face="normal" font="default" size="100%">Font-Llitjós, Mariona</style></author><author><style face="normal" font="default" size="100%">Vilaseca, Ma Antonia</style></author><author><style face="normal" font="default" size="100%">Ormaizabal, Aida</style></author><author><style face="normal" font="default" size="100%">Pristoupilova, Anna</style></author><author><style face="normal" font="default" size="100%">Agulló, Sergi Beltran</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Two novel mutations in the BCKDK (branched-chain keto-acid dehydrogenase kinase) gene are responsible for a neurobehavioral deficit in two pediatric unrelated patients.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Mutat</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Mutat</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acids, Branched-Chain</style></keyword><keyword><style  face="normal" font="default" size="100%">Developmental Disabilities</style></keyword><keyword><style  face="normal" font="default" size="100%">Fibroblasts</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mutation, Missense</style></keyword><keyword><style  face="normal" font="default" size="100%">Nervous System Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Pediatrics</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Kinases</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">470-7</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Inactivating mutations in the BCKDK gene, which codes for the kinase responsible for the negative regulation of the branched-chain α-keto acid dehydrogenase complex (BCKD), have recently been associated with a form of autism in three families. In this work, two novel exonic BCKDK mutations, c.520C&gt;G/p.R174G and c.1166T&gt;C/p.L389P, were identified at the homozygous state in two unrelated children with persistently reduced body fluid levels of branched-chain amino acids (BCAAs), developmental delay, microcephaly, and neurobehavioral abnormalities. Functional analysis of the mutations confirmed the missense character of the c.1166T&gt;C change and showed a splicing defect r.[520c&gt;g;521_543del]/p.R174Gfs1*, for c.520C&gt;G due to the presence of a new donor splice site. Mutation p.L389P showed total loss of kinase activity. Moreover, patient-derived fibroblasts showed undetectable (p.R174Gfs1*) or barely detectable (p.L389P) levels of BCKDK protein and its phosphorylated substrate (phospho-E1α), resulting in increased BCKD activity and the very rapid BCAA catabolism manifested by the patients' clinical phenotype. Based on these results, a protein-rich diet plus oral BCAA supplementation was implemented in the patient homozygous for p.R174Gfs1*. This treatment normalized plasma BCAA levels and improved growth, developmental and behavioral variables. Our results demonstrate that BCKDK mutations can result in neurobehavioral deficits in humans and support the rationale for dietary intervention. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/24449431?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carcel-Trullols, Jaime</style></author><author><style face="normal" font="default" size="100%">Aguilar-Gallardo, Cristóbal</style></author><author><style face="normal" font="default" size="100%">García-Alcalde, Fernando</style></author><author><style face="normal" font="default" size="100%">Pardo-Cea, Miguel Angel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Simon, Carlos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transdifferentiation of MALME-3M and MCF-7 Cells toward Adipocyte-like Cells is Dependent on Clathrin-mediated Endocytosis.</style></title><secondary-title><style face="normal" font="default" size="100%">SpringerPlus</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3725915/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">44</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">ABSTRACT: Enforced cell transdifferentiation of human cancer cells is a promising alternative to conventional chemotherapy. We previously identified albumin-associated lipid- and, more specifically, saturated fatty acid-induced transdifferentiation programs in human cancer cells (HCCLs). In this study, we further characterized the adipocyte-like cells, resulting from the transdifferentiation of human cancer cell lines MCF-7 and MALME-3M, and proposed a common mechanistic approach for these transdifferentiating programs. We showed the loss of pigmentation in MALME-3M cells treated with albumin-associated lipids, based on electron microscopic analysis, and the overexpression of perilipin 2 (PLIN2) by western blotting in MALME-3M and MCF-7 cells treated with unsaturated fatty acids. Comparing the gene expression profiles of naive melanoma MALME-3M cells and albumin-associated lipid-treated cells, based on RNA sequencing, we confirmed the transcriptional upregulation of some key adipogenic gene markers and also an alternative splicing of the adipogenic master regulator PPARG, that is probably related to the reported up regulated expression of the protein. Most importantly, these results also showed the upregulation of genes responsible for Clathrin (CLTC) and other adaptor-related proteins. An increase in CLTC expression in the transdifferentiated cells was confirmed by western blotting. Inactivation of CLTC by chlorpromazine (CHP), an inhibitor of CTLC mediated endocytosis (CME), and gene silencing by siRNAs, partially reversed the accumulation of neutral lipids observed in the transdifferentiated cells. These findings give a deeper insight into the phenotypic changes observed in HCCL to adipocyte-like transdifferentiation and point towards CME as a key pathway in distinct transdifferentiation programs. DISCLOSURES: Simon C and Aguilar-Gallardo C are co-inventors of the International Patent Application No. PCT/EP2011/004941 entitled &quot;Methods for tumor treatment and adipogenesis differentiation&quot;.</style></abstract></record></records></xml>