@article {762, title = {Assessing the Impact of SARS-CoV-2 Lineages and Mutations on Patient Survival.}, journal = {Viruses}, volume = {14}, year = {2022}, month = {2022 Aug 27}, abstract = {

OBJECTIVES: More than two years into the COVID-19 pandemic, SARS-CoV-2 still remains a global public health problem. Successive waves of infection have produced new SARS-CoV-2 variants with new mutations for which the impact on COVID-19 severity and patient survival is uncertain.

METHODS: A total of 764 SARS-CoV-2 genomes, sequenced from COVID-19 patients, hospitalized from 19th February 2020 to 30 April 2021, along with their clinical data, were used for survival analysis.

RESULTS: A significant association of B.1.1.7, the alpha lineage, with patient mortality (log hazard ratio (LHR) = 0.51, C.I. = [0.14,0.88]) was found upon adjustment by all the covariates known to affect COVID-19 prognosis. Moreover, survival analysis of mutations in the SARS-CoV-2 genome revealed 27 of them were significantly associated with higher mortality of patients. Most of these mutations were located in the genes coding for the S, ORF8, and N proteins.

CONCLUSIONS: This study illustrates how a combination of genomic and clinical data can provide solid evidence for the impact of viral lineage on patient survival.

}, keywords = {COVID-19, Genome, Viral, Humans, mutation, Pandemics, Phylogeny, SARS-CoV-2}, issn = {1999-4915}, doi = {10.3390/v14091893}, author = {Loucera, Carlos and Perez-Florido, Javier and Casimiro-Soriguer, Carlos S and Ortuno, Francisco M and Carmona, Rosario and Bostelmann, Gerrit and Mart{\'\i}nez-Gonz{\'a}lez, L Javier and Mu{\~n}oyerro-Mu{\~n}iz, Dolores and Villegas, Rom{\'a}n and Rodr{\'\i}guez-Ba{\~n}o, Jes{\'u}s and Romero-G{\'o}mez, Manuel and Lorusso, Nicola and Garcia-Le{\'o}n, Javier and Navarro-Mar{\'\i}, Jose M and Camacho-Martinez, Pedro and Merino-Diaz, Laura and Salazar, Adolfo de and Vi{\~n}uela, Laura and Lepe, Jose A and Garc{\'\i}a, Federico and Dopazo, Joaquin} } @article {672, title = {Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples.}, journal = {Biol Direct}, volume = {14}, year = {2019}, month = {2019 08 20}, pages = {15}, abstract = {

BACKGROUND: The availability of hundreds of city microbiome profiles allows the development of increasingly accurate predictors of the origin of a sample based on its microbiota composition. Typical microbiome studies involve the analysis of bacterial abundance profiles.

RESULTS: Here we use a transformation of the conventional bacterial strain or gene abundance profiles to functional profiles that account for bacterial metabolism and other cell functionalities. These profiles are used as features for city classification in a machine learning algorithm that allows the extraction of the most relevant features for the classification.

CONCLUSIONS: We demonstrate here that the use of functional profiles not only predict accurately the most likely origin of a sample but also to provide an interesting functional point of view of the biogeography of the microbiota. Interestingly, we show how cities can be classified based on the observed profile of antibiotic resistances.

REVIEWERS: Open peer review: Reviewed by Jin Zhuang Dou, Jing Zhou, Torsten Semmler and Eran Elhaik.

}, keywords = {biomarkers, Cities, Drug Resistance, Microbial, Machine Learning, Metabolome, Metagenome, metagenomics, Microbiota}, issn = {1745-6150}, doi = {10.1186/s13062-019-0246-9}, author = {Casimiro-Soriguer, Carlos S and Loucera, Carlos and Perez Florido, Javier and L{\'o}pez-L{\'o}pez, Daniel and Dopazo, Joaquin} } @article {432, title = {ATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data.}, journal = {BMC Bioinformatics}, volume = {18}, year = {2017}, month = {2017 Feb 22}, pages = {121}, abstract = {

BACKGROUND: In the last years, applications based on massively parallelized RNA sequencing (RNA-seq) have become valuable approaches for studying non-model species, e.g., without a fully sequenced genome. RNA-seq is a useful tool for detecting novel transcripts and genetic variations and for evaluating differential gene expression by digital measurements. The large and complex datasets resulting from functional genomic experiments represent a challenge in data processing, management, and analysis. This problem is especially significant for small research groups working with non-model species.

RESULTS: We developed a web-based application, called ATGC transcriptomics, with a flexible and adaptable interface that allows users to work with new generation sequencing (NGS) transcriptomic analysis results using an ontology-driven database. This new application simplifies data exploration, visualization, and integration for a better comprehension of the results.

CONCLUSIONS: ATGC transcriptomics provides access to non-expert computer users and small research groups to a scalable storage option and simple data integration, including database administration and management. The software is freely available under the terms of GNU public license at http://atgcinta.sourceforge.net .

}, keywords = {Animals, Databases, Genetic, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Internet, Sequence Analysis, RNA, Transcriptome, User-Computer Interface}, issn = {1471-2105}, doi = {10.1186/s12859-017-1494-2}, author = {Gonzalez, Sergio and Clavijo, Bernardo and Rivarola, M{\'a}ximo and Moreno, Patricio and Fernandez, Paula and Dopazo, Joaquin and Paniego, Norma} } @article {493, title = {The Activation of the Sox2 RR2 Pluripotency Transcriptional Reporter in Human Breast Cancer Cell Lines is Dynamic and Labels Cells with Higher Tumorigenic Potential.}, journal = {Front Oncol}, volume = {4}, year = {2014}, month = {2014}, pages = {308}, abstract = {

The striking similarity displayed at the mechanistic level between tumorigenesis and the generation of induced pluripotent stem cells and the fact that genes and pathways relevant for embryonic development are reactivated during tumor progression highlights the link between pluripotency and cancer. Based on these observations, we tested whether it is possible to use a pluripotency-associated transcriptional reporter, whose activation is driven by the SRR2 enhancer from the Sox2 gene promoter (named S4+ reporter), to isolate cancer stem cells (CSCs) from breast cancer cell lines. The S4+ pluripotency transcriptional reporter allows the isolation of cells with enhanced tumorigenic potential and its activation was switched on and off in the cell lines studied, reflecting a plastic cellular process. Microarray analysis comparing the populations in which the reporter construct is active versus inactive showed that positive cells expressed higher mRNA levels of cytokines (IL-8, IL-6, TNF) and genes (such as ATF3, SNAI2, and KLF6) previously related with the CSC phenotype in breast cancer.

}, issn = {2234-943X}, doi = {10.3389/fonc.2014.00308}, author = {Iglesias, Juan Manuel and Leis, Olatz and P{\'e}rez Ruiz, Est{\'\i}baliz and Gumuzio Barrie, Juan and Garcia-Garcia, Francisco and Aduriz, Ariane and Beloqui, Izaskun and Hernandez-Garcia, Susana and Lopez-Mato, Maria Paz and Dopazo, Joaquin and Pandiella, Atanasio and Menendez, Javier A and Martin, Angel Garcia} } @article {1087, title = {Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.}, journal = {Nature communications}, volume = {5}, year = {2014}, month = {2014}, pages = {5125}, abstract = {There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard {\textquoteright}dashboard{\textquoteright} of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.}, keywords = {RNA-seq}, issn = {2041-1723}, doi = {10.1038/ncomms6125}, url = {http://www.nature.com/ncomms/2014/140925/ncomms6125/full/ncomms6125.html}, author = {Munro, Sarah A and Lund, Steven P and Pine, P Scott and Binder, Hans and Clevert, Djork-Arn{\'e} and Ana Conesa and Dopazo, Joaquin and Fasold, Mario and Hochreiter, Sepp and Hong, Huixiao and Jafari, Nadereh and Kreil, David P and Labaj, Pawe{\l} P and Li, Sheng and Liao, Yang and Lin, Simon M and Meehan, Joseph and Mason, Christopher E and Santoyo-L{\'o}pez, Javier and Setterquist, Robert A and Shi, Leming and Shi, Wei and Smyth, Gordon K and Stralis-Pavese, Nancy and Su, Zhenqiang and Tong, Weida and Wang, Charles and Wang, Jian and Xu, Joshua and Ye, Zhan and Yang, Yong and Yu, Ying and Salit, Marc} } @article {21479216, title = {Analysis of normal-tumour tissue interaction in tumours: prediction of prostate cancer features from the molecular profile of adjacent normal cells.}, journal = {PloS one}, volume = {6}, year = {2011}, month = {2011}, pages = {e16492}, abstract = {

Statistical modelling, in combination with genome-wide expression profiling techniques, has demonstrated that the molecular state of the tumour is sufficient to infer its pathological state. These studies have been extremely important in diagnostics and have contributed to improving our understanding of tumour biology. However, their importance in in-depth understanding of cancer patho-physiology may be limited since they do not explicitly take into consideration the fundamental role of the tissue microenvironment in specifying tumour physiology. Because of the importance of normal cells in shaping the tissue microenvironment we formulate the hypothesis that molecular components of the profile of normal epithelial cells adjacent the tumour are predictive of tumour physiology. We addressed this hypothesis by developing statistical models that link gene expression profiles representing the molecular state of adjacent normal epithelial cells to tumour features in prostate cancer. Furthermore, network analysis showed that predictive genes are linked to the activity of important secreted factors, which have the potential to influence tumor biology, such as IL1, IGF1, PDGF BB, AGT, and TGF\β.

}, author = {Trevino, Victor and Tadesse, Mahlet G and Vannucci, Marina and Fatima Al-Shahrour and Antczak, Philipp and Durant, Sarah and Bikfalvi, Andreas and Dopazo, Joaquin and Campbell, Moray J and Falciani, Francesco} } @article {21408226, title = {Assessing the biological significance of gene expression signatures and co-expression modules by studying their network properties.}, journal = {PloS one}, volume = {6}, year = {2011}, month = {2011}, pages = {e17474}, abstract = {

Microarray experiments have been extensively used to define signatures, which are sets of genes that can be considered markers of experimental conditions (typically diseases). Paradoxically, in spite of the apparent functional role that might be attributed to such gene sets, signatures do not seem to be reproducible across experiments. Given the close relationship between function and protein interaction, network properties can be used to study to what extent signatures are composed of genes whose resulting proteins show a considerable level of interaction (and consequently a putative common functional role).We have analysed 618 signatures and 507 modules of co-expression in cancer looking for significant values of four main protein-protein interaction (PPI) network parameters: connection degree, cluster coefficient, betweenness and number of components. A total of 3904 gene ontology (GO) modules, 146 KEGG pathways, and 263 Biocarta pathways have been used as functional modules of reference.Co-expression modules found in microarray experiments display a high level of connectivity, similar to the one shown by conventional modules based on functional definitions (GO, KEGG and Biocarta). A general observation for all the classes studied is that the networks formed by the modules improve their topological parameters when an external protein is allowed to be introduced within the paths (up to the 70\% of GO modules show network parameters beyond the random expectation). This fact suggests that functional definitions are incomplete and some genes might still be missing. Conversely, signatures are clearly not capturing the altered functions in the corresponding studies. This is probably because the way in which the genes have been selected in the signatures is too conservative. These results suggest that gene selection methods which take into account relationships among genes should be superior to methods that assume independence among genes outside their functional contexts.

}, doi = {doi:10.1371/journal.pone.0017474}, url = {http://www.plosone.org/article/info\%3Adoi\%2F10.1371\%2Fjournal.pone.0017474}, author = {Minguez, Pablo and Dopazo, Joaquin} }