Publications

@article{pmid36302651,

title = {Palmitate impairs circadian transcriptomics in muscle cells through histone modification of enhancers},

author = {Nicolas J Pillon and Laura Sardón Puig and Ali Altıntaş and Prasad G Kamble and Salvador Casaní-Galdón and Brendan M Gabriel and Romain Barrès and Ana Conesa and Alexander V Chibalin and Erik Näslund and Anna Krook and Juleen R Zierath},

doi = {10.26508/lsa.202201598},

issn = {2575-1077},

year  = {2023},

date = {2023-01-01},

journal = {Life Sci Alliance},

volume = {6},

number = {1},

abstract = {Obesity and elevated circulating lipids may impair metabolism by disrupting the molecular circadian clock. We tested the hypothesis that lipid overload may interact with the circadian clock and alter the rhythmicity of gene expression through epigenomic mechanisms in skeletal muscle. Palmitate reprogrammed the circadian transcriptome in myotubes without altering the rhythmic mRNA expression of core clock genes. Genes with enhanced cycling in response to palmitate were associated with post-translational modification of histones. The cycling of histone 3 lysine 27 acetylation (H3K27ac), a marker of active gene enhancers, was modified by palmitate treatment. Chromatin immunoprecipitation and sequencing confirmed that palmitate exposure altered the cycling of DNA regions associated with H3K27ac. The overlap between mRNA and DNA regions associated with H3K27ac and the pharmacological inhibition of histone acetyltransferases revealed novel cycling genes associated with lipid exposure of primary human myotubes. Palmitate exposure disrupts transcriptomic rhythmicity and modifies enhancers through changes in histone H3K27 acetylation in a circadian manner. Thus, histone acetylation is responsive to lipid overload and may redirect the circadian chromatin landscape, leading to the reprogramming of circadian genes and pathways involved in lipid biosynthesis in skeletal muscle.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid36494864,

title = {New roles for AP-1/JUNB in cell cycle control and tumorigenic cell invasion via regulation of cyclin E1 and TGF-β2},

author = {Beatriz Pérez-Benavente and Alihamze Fathinajafabadi and Lorena de la Fuente and Carolina Gandía and Arantxa Martínez-Férriz and José Miguel Pardo-Sánchez and Lara Milián and Ana Conesa and Octavio A Romero and Julián Carretero and Rune Matthiesen and Isabelle Jariel-Encontre and Marc Piechaczyk and Rosa Farràs},

doi = {10.1186/s13059-022-02800-0},

issn = {1474-760X},

year  = {2022},

date = {2022-12-01},

journal = {Genome Biol},

volume = {23},

number = {1},

pages = {252},

abstract = {BACKGROUND: JUNB transcription factor contributes to the formation of the ubiquitous transcriptional complex AP-1 involved in the control of many physiological and disease-associated functions. The roles of JUNB in the control of cell division and tumorigenic processes are acknowledged but still unclear.



RESULTS: Here, we report the results of combined transcriptomic, genomic, and functional studies showing that JUNB promotes cell cycle progression via induction of cyclin E1 and repression of transforming growth factor (TGF)-β2 genes. We also show that high levels of JUNB switch the response of TGF-β2 stimulation from an antiproliferative to a pro-invasive one, induce endogenous TGF-β2 production by promoting TGF-β2 mRNA translation, and enhance tumor growth and metastasis in mice. Moreover, tumor genomic data indicate that JUNB amplification associates with poor prognosis in breast and ovarian cancer patients.



CONCLUSIONS: Our results reveal novel functions for JUNB in cell proliferation and tumor aggressiveness through regulation of cyclin E1 and TGF-β2 expression, which might be exploited for cancer prognosis and therapy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid36097183,

title = {Independent phenotypic plasticity axes define distinct obesity sub-types},

author = {Chih-Hsiang Yang and Luca Fagnocchi and Stefanos Apostle and Vanessa Wegert and Salvador Casaní-Galdón and Kathrin Landgraf and Ilaria Panzeri and Erez Dror and Steffen Heyne and Till Wörpel and Darrell P Chandler and Di Lu and Tao Yang and Elizabeth Gibbons and Rita Guerreiro and Jose Bras and Martin Thomasen and Louise G Grunnet and Allan A Vaag and Linn Gillberg and Elin Grundberg and Ana Conesa and Antje Körner and  and J Andrew Pospisilik},

doi = {10.1038/s42255-022-00629-2},

issn = {2522-5812},

year  = {2022},

date = {2022-09-01},

journal = {Nat Metab},

volume = {4},

number = {9},

pages = {1150--1165},

abstract = {Studies in genetically 'identical' individuals indicate that as much as 50% of complex trait variation cannot be traced to genetics or to the environment. The mechanisms that generate this 'unexplained' phenotypic variation (UPV) remain largely unknown. Here, we identify neuronatin (NNAT) as a conserved factor that buffers against UPV. We find that Nnat deficiency in isogenic mice triggers the emergence of a bi-stable polyphenism, where littermates emerge into adulthood either 'normal' or 'overgrown'. Mechanistically, this is mediated by an insulin-dependent overgrowth that arises from histone deacetylase (HDAC)-dependent β-cell hyperproliferation. A multi-dimensional analysis of monozygotic twin discordance reveals the existence of two patterns of human UPV, one of which (Type B) phenocopies the NNAT-buffered polyphenism identified in mice. Specifically, Type-B monozygotic co-twins exhibit coordinated increases in fat and lean mass across the body; decreased NNAT expression; increased HDAC-responsive gene signatures; and clinical outcomes linked to insulinemia. Critically, the Type-B UPV signature stratifies both childhood and adult cohorts into four metabolic states, including two phenotypically and molecularly distinct types of obesity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid36066904,

title = {The absence of the queuosine tRNA modification leads to pleiotropic phenotypes revealing perturbations of metal and oxidative stress homeostasis in Escherichia coli K12},

author = {Leticia Pollo-Oliveira and Nick K Davis and Intekhab Hossain and Peiying Ho and Yifeng Yuan and Pedro Salguero García and Cécile Pereira and Shane R Byrne and Jiapeng Leng and Melody Sze and Crysten E Blaby-Haas and Agnieszka Sekowska and Alvaro Montoya and Thomas Begley and Antoine Danchin and Daniel P Aalberts and Alexander Angerhofer and John Hunt and Ana Conesa and Peter C Dedon and Valérie de Crécy-Lagard},

doi = {10.1093/mtomcs/mfac065},

issn = {1756-591X},

year  = {2022},

date = {2022-09-01},

journal = {Metallomics},

volume = {14},

number = {9},

abstract = {Queuosine (Q) is a conserved hypermodification of the wobble base of tRNA containing GUN anticodons but the physiological consequences of Q deficiency are poorly understood in bacteria. This work combines transcriptomic, proteomic and physiological studies to characterize a Q-deficient Escherichia coli K12 MG1655 mutant. The absence of Q led to an increased resistance to nickel and cobalt, and to an increased sensitivity to cadmium, compared to the wild-type (WT) strain. Transcriptomic analysis of the WT and Q-deficient strains, grown in the presence and absence of nickel, revealed that the nickel transporter genes (nikABCDE) are downregulated in the Q- mutant, even when nickel is not added. This mutant is therefore primed to resist to high nickel levels. Downstream analysis of the transcriptomic data suggested that the absence of Q triggers an atypical oxidative stress response, confirmed by the detection of slightly elevated reactive oxygen species (ROS) levels in the mutant, increased sensitivity to hydrogen peroxide and paraquat, and a subtle growth phenotype in a strain prone to accumulation of ROS.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid36046619,

title = {Reference-based comparison of adaptive immune receptor repertoires},

author = {Cédric R Weber and Teresa Rubio and Longlong Wang and Wei Zhang and Philippe A Robert and Rahmad Akbar and Igor Snapkov and Jinghua Wu and Marieke L Kuijjer and Sonia Tarazona and Ana Conesa and Geir K Sandve and Xiao Liu and Sai T Reddy and Victor Greiff},

doi = {10.1016/j.crmeth.2022.100269},

issn = {2667-2375},

year  = {2022},

date = {2022-08-01},

journal = {Cell Rep Methods},

volume = {2},

number = {8},

pages = {100269},

abstract = {B and T cell receptor (immune) repertoires can represent an individual's immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters. Here, we introduce immuneREF: a quantitative multidimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2,400 datasets from individuals with varying immune states (healthy, [autoimmune] disease, and infection). We discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF enables the population-wide study of adaptive immune response similarity across immune states.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35961013,

title = {A roadmap for the functional annotation of protein families: a community perspective},

author = {Valérie de Crécy-Lagard and Rocio Amorin de Hegedus and Cecilia Arighi and Jill Babor and Alex Bateman and Ian Blaby and Crysten Blaby-Haas and Alan J Bridge and Stephen K Burley and Stacey Cleveland and Lucy J Colwell and Ana Conesa and Christian Dallago and Antoine Danchin and Anita de Waard and Adam Deutschbauer and Raquel Dias and Yousong Ding and Gang Fang and Iddo Friedberg and John Gerlt and Joshua Goldford and Mark Gorelik and Benjamin M Gyori and Christopher Henry and Geoffrey Hutinet and Marshall Jaroch and Peter D Karp and Liudmyla Kondratova and Zhiyong Lu and Aron Marchler-Bauer and Maria-Jesus Martin and Claire McWhite and Gaurav D Moghe and Paul Monaghan and Anne Morgat and Christopher J Mungall and Darren A Natale and William C Nelson and Seán O'Donoghue and Christine Orengo and Katherine H O'Toole and Predrag Radivojac and Colbie Reed and Richard J Roberts and Dmitri Rodionov and Irina A Rodionova and Jeffrey D Rudolf and Lana Saleh and Gloria Sheynkman and Francoise Thibaud-Nissen and Paul D Thomas and Peter Uetz and David Vallenet and Erica Watson Carter and Peter R Weigele and Valerie Wood and Elisha M Wood-Charlson and Jin Xu},

doi = {10.1093/database/baac062},

issn = {1758-0463},

year  = {2022},

date = {2022-08-01},

journal = {Database (Oxford)},

volume = {2022},

abstract = {Over the last 25 years, biology has entered the genomic era and is becoming a science of 'big data'. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3-4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Rubio2022-sg,

title = {A Nextflow pipeline for T-cell receptor repertoire  reconstruction and analysis from RNA sequencing data},

author = {Teresa Rubio and Maria Chernigovskaya and Susanna Marquez and Cristina Marti and Paula Izquierdo-Altarejos and Amparo Urios and Carmina Montoliu and Vicente Felipo and Ana Conesa and Victor Greiff and Sonia Tarazona},

url = {https://www.immunoinformaticsjournal.com/action/showPdf?pii=S2667-1190%2822%2900004-0},

doi = {10.1016/j.immuno.2022.100012},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

journal = {ImmunoInformatics},

volume = {6},

pages = {100012},

publisher = {Elsevier},

abstract = {T-cell receptor (TCR) analysis is relevant for the study of immune system diseases. The expression of TCRs is usually measured with targeted sequencing approaches where TCR genes are selectively amplified. However, many non-targeted RNA-seq experiments also contain reads of TCR genes, which could be leveraged for TCR expression analysis while reducing sample requirements and costs. Moreover, a step-by-step pipeline for the processing of transcriptome RNA-seq reads to deliver immune repertoire data is missing, and these types of analyses are usually not included in RNA-seq studies of immunological conditions. This represents a missed opportunity for complementing them with the analysis of the immune repertoire.

We present a Nextflow pipeline for T-cell receptor repertoire reconstruction and analysis from RNA sequencing data. We used a case study where TCR repertoire profiles were recovered from bulk RNA-seq of isolated CD4 T cells from control patients, cirrhotic patients without and with Minimal Hepatic Encephalopathy (MHE). MHE is a neuropsychiatric syndrome, mediated by peripheral inflammation, that may affect cirrhotic patients. After the recovery of 498-1,114 distinct TCR beta chains per patient, repertoire analysis of patients resulted in few public clones, high diversity and elevated within-repertoire sequence similarity, independently of immune status. Additionally, TCRs associated with celiac disease and inflammatory bowel disease were significantly overrepresented in MHE patient repertoires. The provided computational pipeline functions as a resource to facilitate TCR profiling from RNA-seq data boosting immunophenotype analyses of immunological diseases.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35579358,

title = {Variation in leaf transcriptome responses to elevated ozone corresponds with physiological sensitivity to ozone across maize inbred lines},

author = {Adalena V Nanni and Alison M Morse and Jeremy R B Newman and Nicole E Choquette and Jessica M Wedow and Zihao Liu and Andrew D B Leakey and Ana Conesa and Elizabeth A Ainsworth and Lauren M McIntyre},

doi = {10.1093/genetics/iyac080},

issn = {1943-2631},

year  = {2022},

date = {2022-05-01},

journal = {Genetics},

abstract = {We examine the impact of sustained elevated ozone concentration on the leaf transcriptome of 5 diverse maize inbred genotypes, which vary in physiological sensitivity to ozone (B73, Mo17, Hp301, C123, NC338), using long reads to assemble transcripts and short reads to quantify expression of these transcripts. More than 99% of the long reads, 99% of the assembled transcripts, and 97% of the short reads map to both B73 and Mo17 reference genomes. Approximately 95% of the genes with assembled transcripts belong to known B73-Mo17 syntenic loci and 94% of genes with assembled transcripts are present in all temperate lines in the NAM pan-genome. While there is limited evidence for alternative splicing in response to ozone stress, there is a difference in the magnitude of differential expression among the 5 genotypes. The transcriptional response to sustained ozone stress in the ozone resistant B73 genotype (151 genes) was modest, while more than 3,300 genes were significantly differentially expressed in the more sensitive NC338 genotype. There is the potential for tandem duplication in 30% of genes with assembled transcripts, but there is no obvious association between potential tandem duplication and differential expression. Genes with a common response across the 5 genotypes (83 genes) were associated with photosynthesis, in particular photosystem I. The functional annotation of genes not differentially expressed in B73 but responsive in the other 4 genotypes (789) identifies reactive oxygen species. This suggests that B73 has a different response to long term ozone exposure than the other 4 genotypes. The relative magnitude of the genotypic response to ozone, and the enrichment analyses are consistent regardless of whether aligning short reads to: long read assembled transcripts; the B73 reference; the Mo17 reference. We find that prolonged ozone exposure directly impacts the photosynthetic machinery of the leaf.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35609982,

title = {PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases},

author = {Tianyuan Liu and Pedro Salguero and Marko Petek and Carlos Martinez-Mira and Leandro Balzano-Nogueira and Živa Ramšak and Lauren McIntyre and Kristina Gruden and Sonia Tarazona and Ana Conesa},

doi = {10.1093/nar/gkac352},

issn = {1362-4962},

year  = {2022},

date = {2022-05-01},

journal = {Nucleic Acids Res},

abstract = {PaintOmics is a web server for the integrative analysis and visualisation of multi-omics datasets using biological pathway maps. PaintOmics 4 has several notable updates that improve and extend analyses. Three pathway databases are now supported: KEGG, Reactome and MapMan, providing more comprehensive pathway knowledge for animals and plants. New metabolite analysis methods fill gaps in traditional pathway-based enrichment methods. The metabolite hub analysis selects compounds with a high number of significant genes in their neighbouring network, suggesting regulation by gene expression changes. The metabolite class activity analysis tests the hypothesis that a metabolic class has a higher-than-expected proportion of significant elements, indicating that these compounds are regulated in the experiment. Finally, PaintOmics 4 includes a regulatory omics module to analyse the contribution of trans-regulatory layers (microRNA and transcription factors, RNA-binding proteins) to regulate pathways. We show the performance of PaintOmics 4 on both mouse and plant data to highlight how these new analysis features provide novel insights into regulatory biology. PaintOmics 4 is available at https://paintomics.org/.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ugidos2022-sk,

title = {MultiBaC: An R package to remove batch effects in multi-omic  experiments},

author = {Manuel Ugidos and María José Nueda and José M Prats-Montalbán and Alberto Ferrer and Ana Conesa and Sonia Tarazona},

url = {https://watermark.silverchair.com/btac132.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAzEwggMtBgkqhkiG9w0BBwagggMeMIIDGgIBADCCAxMGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMeEcUTCRMnmsoFyy8AgEQgIIC5JWHJW_tG0ZfXPRWz1anVh55AhxVg26g2g9wdqQAgT7LBtdVIlHfR_v5GbzIud725UlWMpSwL-oFFwiYCDSoGweumU1IH6_XhfgfUfI-AFBEuQDXTRYjtTpSoH1VlIXMsXd1OtVdfE6aj8zG1iNSSirVljWzpRdJfZolvD1GoqXOySVndzFgV4FEC2eHxbvABFV8bVpqO9T6QWxgwy_5qY1faQ9XUsywBxq3SbGV1uKhqrcSEOePoLYUIXCRLnYaIXfsbZIKxHd4JWHYSUPzHfgH-tSUTkCZjqfv0PI1DNj9vXhsHL0eMSEdtSc2Kdh1h06r7Y0ShpDcDzv1d77wtlX00r6npnlnVwv8XKJURx8kY_vkVOJO5XFPqyl4JQm4Nd6r0kvuBPJ9p0JUTpz5kJOxrtfcS96Ql5YPC8p-5qccBtVbWzaApqdubHm1o7D7z9X3rKA3053pPoQ8HuEsPqe7Keuoqvo1gpYbJICPDm7-raQj7YIwFS9iZBCAphwsp4MXpOhEKA23vBdpv4b_pMANVBHHq33WVFJjBneTeJliYFv_Ynk6W1KrbgTqel8f0mF9knfrOhftEB-Eod3uj0nrCkmB31jwlGC0A6jaF_mJ06WY7hekKmrAki07356jI2cc5VGyGGfD8uRvuBV2wPxXTlkZfq9vwLZbX9qNuhSg14aTUXvW89nbF4uNI3h8dowM7bvUV6Qdf2eabAoBg7cYervp5twQLlMHfSmcwMnvB4Oit4w9UD3xaed-osA1o6jYVCofUN0Qcq9oeeutC9NxYpmziEcVXu4Uz5aIgpakIaHcUvYHtySGrk85Pu9-CmrY-Ek6M3OXJHV7drvq_8dfKKrlj-5xDoulwdRjCwOYkLr88o-r38-QfEsanwmjEqFjZH93e0Rm3wNZVn1j1NRGLrdzclEldx_DXTd3vuh6jxv-CrtRxQ9pK-n73Kq4msmTKiHV6Ubg-qcYREbMZ1e4NpKp},

doi = {10.1093/bioinformatics/btac132},

year  = {2022},

date = {2022-03-01},

urldate = {2022-03-01},

journal = {Bioinformatics},

publisher = {Öxford University Press (OUP)},

abstract = {MOTIVATION: Batch effects in omics datasets are usually a source 

 of technical noise that masks the biological signal and hampers 

 data analysis. Batch effect removal has been widely addressed 

 for individual omics technologies. However, multi-omic datasets 

 may combine data obtained in different batches where omics type 

 and batch are often confounded. Moreover, systematic biases may 

 be introduced without notice during data acquisition, which 

 creates a hidden batch effect. Current methods fail to address 

 batch effect correction in these cases. RESULTS: In this paper 

 we introduce the MultiBaC R package, a tool for batch effect 

 removal in multi-omics and hidden batch effect scenarios. The 

 package includes a diversity of graphical outputs for model 

 validation and assessment of the batch effect correction. 

 AVAILABILITY: MultiBaC package is available on Bioconductor 

 (https://www.bioconductor.org/packages/release/bioc/html/MultiBaC.html) 

 and GitHub (https://github.com/ConesaLab/MultiBaC.git). 

 SUPPLEMENTARY INFORMATION: Supplementary data are available at 

 Bioinformatics online.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Miller2022-sz,

title = {Enhanced protein isoform characterization through long-read  proteogenomics},

author = {Rachel M Miller and Ben T Jordan and Madison M Mehlferber and Erin D Jeffery and Christina Chatzipantsiou and Simi Kaur and Robert J Millikin and Yunxiang Dai and Simone Tiberi and Peter J Castaldi and Michael R Shortreed and Chance John Luckey and Ana Conesa and Lloyd M Smith and Anne Deslattes Mays and Gloria M Sheynkman},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8892804/pdf/13059_2022_Article_2624.pdf},

doi = {10.1186/s13059-022-02624-y},

year  = {2022},

date = {2022-03-01},

urldate = {2022-03-01},

journal = {Genome Biol.},

volume = {23},

number = {1},

pages = {69},

publisher = {Springer Science and Business Media LLC},

abstract = {BACKGROUND: The detection of physiologically relevant protein 

 isoforms encoded by the human genome is critical to biomedicine. 

 Mass spectrometry (MS)-based proteomics is the preeminent method 

 for protein detection, but isoform-resolved proteomic analysis 

 relies on accurate reference databases that match the sample; 

 neither a subset nor a superset database is ideal. Long-read RNA 

 sequencing (e.g., PacBio or Oxford Nanopore) provides 

 full-length transcripts which can be used to predict full-length 

 protein isoforms. RESULTS: We describe here a long-read 

 proteogenomics approach for integrating sample-matched long-read 

 RNA-seq and MS-based proteomics data to enhance isoform 

 characterization. We introduce a classification scheme for 

 protein isoforms, discover novel protein isoforms, and present 

 the first protein inference algorithm for the direct 

 incorporation of long-read transcriptome data to enable 

 detection of protein isoforms previously intractable to MS-based 

 detection. We have released an open-source Nextflow pipeline 

 that integrates long-read sequencing in a proteomic workflow for 

 isoform-resolved analysis. CONCLUSIONS: Our work suggests that 

 the incorporation of long-read sequencing and proteomic data can 

 facilitate improved characterization of human protein isoform 

 diversity. Our first-generation pipeline provides a strong 

 foundation for future development of long-read proteogenomics 

 and its adoption for both basic and translational research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{McIntyre2022-xu,

title = {GAIT-GM integrative cross-omics analyses reveal cholinergic  defects in a C. elegans model of Parkinson's disease},

author = {Lauren M McIntyre and Francisco Huertas and Alison M Morse and Rachel Kaletsky and Coleen T Murphy and Vrinda Kalia and Gary W Miller and Olexander Moskalenko and Ana Conesa and Danielle E Mor},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8885929/pdf/41598_2022_Article_7238.pdf},

doi = {10.1038/s41598-022-07238-9},

year  = {2022},

date = {2022-02-01},

urldate = {2022-02-01},

journal = {Sci. Rep.},

volume = {12},

number = {1},

pages = {3268},

publisher = {Springer Science and Business Media LLC},

abstract = {Parkinson's disease (PD) is a disabling neurodegenerative 

 disorder in which multiple cell types, including dopaminergic 

 and cholinergic neurons, are affected. The mechanisms of 

 neurodegeneration in PD are not fully understood, limiting the 

 development of therapies directed at disease-relevant molecular 

 targets. C. elegans is a genetically tractable model system that 

 can be used to disentangle disease mechanisms in complex 

 diseases such as PD. Such mechanisms can be studied combining 

 high-throughput molecular profiling technologies such as 

 transcriptomics and metabolomics. However, the integrative 

 analysis of multi-omics data in order to unravel disease 

 mechanisms is a challenging task without advanced bioinformatics 

 training. Galaxy, a widely-used resource for enabling 

 bioinformatics analysis by the broad scientific community, has 

 poor representation of multi-omics integration pipelines. We 

 present the integrative analysis of gene expression and 

 metabolite levels of a C. elegans PD model using GAIT-GM, a new 

 Galaxy tool for multi-omics data analysis. Using GAIT-GM, we 

 discovered an association between branched-chain amino acid 

 metabolism and cholinergic neurons in the C. elegans PD model. 

 An independent follow-up experiment uncovered cholinergic 

 neurodegeneration in the C. elegans model that is consistent 

 with cholinergic cell loss observed in PD. GAIT-GM is an easy to 

 use Galaxy-based tool for generating novel testable hypotheses 

 of disease mechanisms involving gene-metabolite relationships.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Beltrame2022-bt,

title = {Association between sex hormone levels and clinical outcomes in  patients with COVID-19 admitted to hospital: An observational,  retrospective, cohort study},

author = {Anna Beltrame and Pedro Salguero and Emanuela Rossi and Ana Conesa and Lucia Moro and Laura Rachele Bettini and Eleonora Rizzi and Mariella DÁngió and Michela Deiana and Chiara Piubelli and Paola Rebora and Silvia Duranti and Paolo Bonfanti and Ilaria Capua and Sonia Tarazona and Maria Grazia Valsecchi},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8829540/pdf/fimmu-13-834851.pdf},

doi = {10.3389/fimmu.2022.834851},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Front. Immunol.},

volume = {13},

pages = {834851},

abstract = {Understanding the cause of sex disparities in COVID-19 outcomes 

 is a major challenge. We investigate sex hormone levels and their 

 association with outcomes in COVID-19 patients, stratified by sex 

 and age. This observational, retrospective, cohort study included 

 138 patients aged 18 years or older with COVID-19, hospitalized 

 in Italy between February 1 and May 30, 2020. The association 

 between sex hormones (testosterone, estradiol, progesterone, 

 dehydroepiandrosterone) and outcomes (ARDS, severe COVID-19, 

 in-hospital mortality) was explored in 120 patients aged 50 years 

 and over. STROBE checklist was followed. The median age was 73.5 

 years [IQR 61, 82]; 55.8% were male. In older males, 

 testosterone was lower if ARDS and severe COVID-19 were reported than if not (3.6 vs. 5.3 nmol/L},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35799267,

title = {A high-resolution single-molecule sequencing-based Arabidopsis transcriptome using novel methods of Iso-seq analysis},

author = {Runxuan Zhang and Richard Kuo and Max Coulter and Cristiane P G Calixto and Juan Carlos Entizne and Wenbin Guo and Yamile Marquez and Linda Milne and Stefan Riegler and Akihiro Matsui and Maho Tanaka and Sarah Harvey and Yubang Gao and Theresa Wießner-Kroh and Alejandro Paniagua and Martin Crespi and Katherine Denby and Asa Ben Hur and Enamul Huq and Michael Jantsch and Artur Jarmolowski and Tino Koester and Sascha Laubinger and Qingshun Quinn Li and Lianfeng Gu and Motoaki Seki and Dorothee Staiger and Ramanjulu Sunkar and Zofia Szweykowska-Kulinska and Shih-Long Tu and Andreas Wachter and Robbie Waugh and Liming Xiong and Xiao-Ning Zhang and Ana Conesa and Anireddy S N Reddy and Andrea Barta and Maria Kalyna and John W S Brown},

doi = {10.1186/s13059-022-02711-0},

issn = {1474-760X},

year  = {2022},

date = {2022-01-01},

journal = {Genome Biol},

volume = {23},

number = {1},

pages = {149},

abstract = {BACKGROUND: Accurate and comprehensive annotation of transcript sequences is essential for transcript quantification and differential gene and transcript expression analysis. Single-molecule long-read sequencing technologies provide improved integrity of transcript structures including alternative splicing, and transcription start and polyadenylation sites. However, accuracy is significantly affected by sequencing errors, mRNA degradation, or incomplete cDNA synthesis.



RESULTS: We present a new and comprehensive Arabidopsis thaliana Reference Transcript Dataset 3 (AtRTD3). AtRTD3 contains over 169,000 transcripts-twice that of the best current Arabidopsis transcriptome and including over 1500 novel genes. Seventy-eight percent of transcripts are from Iso-seq with accurately defined splice junctions and transcription start and end sites. We develop novel methods to determine splice junctions and transcription start and end sites accurately. Mismatch profiles around splice junctions provide a powerful feature to distinguish correct splice junctions and remove false splice junctions. Stratified approaches identify high-confidence transcription start and end sites and remove fragmentary transcripts due to degradation. AtRTD3 is a major improvement over existing transcriptomes as demonstrated by analysis of an Arabidopsis cold response RNA-seq time-series. AtRTD3 provides higher resolution of transcript expression profiling and identifies cold-induced differential transcription start and polyadenylation site usage.



CONCLUSIONS: AtRTD3 is the most comprehensive Arabidopsis transcriptome currently. It improves the precision of differential gene and transcript expression, differential alternative splicing, and transcription start/end site usage analysis from RNA-seq data. The novel methods for identifying accurate splice junctions and transcription start/end sites are widely applicable and will improve single-molecule sequencing analysis from any species.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35460952,

title = {Gene-environment interaction analysis of redox-related metals and genetic variants with plasma metabolic patterns in a general population from Spain: The Hortega Study},

author = {Marta Galvez-Fernandez and Francisco Sanchez-Saez and Arce Domingo-Relloso and Zulema Rodriguez-Hernandez and Sonia Tarazona and Vannina Gonzalez-Marrachelli and Maria Grau-Perez and Jose M Morales-Tatay and Nuria Amigo and Tamara Garcia-Barrera and Jose L Gomez-Ariza and F Javier Chaves and Ana Barbara Garcia-Garcia and Rebeca Melero and Maria Tellez-Plaza and Juan C Martin-Escudero and Josep Redon and Daniel Monleon},

doi = {10.1016/j.redox.2022.102314},

issn = {2213-2317},

year  = {2022},

date = {2022-01-01},

journal = {Redox Biol},

volume = {52},

pages = {102314},

abstract = {BACKGROUND: Limited studies have evaluated the joint influence of redox-related metals and genetic variation on metabolic pathways. We analyzed the association of 11 metals with metabolic patterns, and the interacting role of candidate genetic variants, in 1145 participants from the Hortega Study, a population-based sample from Spain.



METHODS: Urine antimony (Sb), arsenic, barium (Ba), cadmium (Cd), chromium (Cr), cobalt (Co), molybdenum (Mo) and vanadium (V), and plasma copper (Cu), selenium (Se) and zinc (Zn) were measured by ICP-MS and AAS, respectively. We summarized 54 plasma metabolites, measured with targeted NMR, by estimating metabolic principal components (mPC). Redox-related SNPs (N = 291) were measured by oligo-ligation assay.



RESULTS: In our study, the association with metabolic principal component (mPC) 1 (reflecting non-essential and essential amino acids, including branched chain, and bacterial co-metabolism versus fatty acids and VLDL subclasses) was positive for Se and Zn, but inverse for Cu, arsenobetaine-corrected arsenic (As) and Sb. The association with mPC2 (reflecting essential amino acids, including aromatic, and bacterial co-metabolism) was inverse for Se, Zn and Cd. The association with mPC3 (reflecting LDL subclasses) was positive for Cu, Se and Zn, but inverse for Co. The association for mPC4 (reflecting HDL subclasses) was positive for Sb, but inverse for plasma Zn. These associations were mainly driven by Cu and Sb for mPC1; Se, Zn and Cd for mPC2; Co, Se and Zn for mPC3; and Zn for mPC4. The most SNP-metal interacting genes were NOX1, GSR, GCLC, AGT and REN. Co and Zn showed the highest number of interactions with genetic variants associated to enriched endocrine, cardiovascular and neurological pathways.



CONCLUSIONS: Exposures to Co, Cu, Se, Zn, As, Cd and Sb were associated with several metabolic patterns involved in chronic disease. Carriers of redox-related variants may have differential susceptibility to metabolic alterations associated to excessive exposure to metals.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35383181,

title = {acorde unravels functionally interpretable networks of isoform co-usage from single cell data},

author = {Angeles Arzalluz-Luque and Pedro Salguero and Sonia Tarazona and Ana Conesa},

doi = {10.1038/s41467-022-29497-w},

issn = {2041-1723},

year  = {2022},

date = {2022-01-01},

journal = {Nat Commun},

volume = {13},

number = {1},

pages = {1828},

abstract = {Alternative splicing (AS) is a highly-regulated post-transcriptional mechanism known to modulate isoform expression within genes and contribute to cell-type identity. However, the extent to which alternative isoforms establish co-expression networks that may be relevant in cellular function has not been explored yet. Here, we present acorde, a pipeline that successfully leverages bulk long reads and single-cell data to confidently detect alternative isoform co-expression relationships. To achieve this, we develop and validate percentile correlations, an innovative approach that overcomes data sparsity and yields accurate co-expression estimates from single-cell data. Next, acorde uses correlations to cluster co-expressed isoforms into a network, unraveling cell type-specific alternative isoform usage patterns. By selecting same-gene isoforms between these clusters, we subsequently detect and characterize genes with co-differential isoform usage (coDIU) across cell types. Finally, we predict functional elements from long read-defined isoforms and provide insight into biological processes, motifs, and domains potentially controlled by the coordination of post-transcriptional regulation. The code for acorde is available at https://github.com/ConesaLab/acorde .},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ugidos2022.10.09.511458,

title = {MAMBA: a model-driven, constraint-based multiomic integration method},

author = {Manuel Ugidos and Carme Nu no-Cabanes and Sonia Tarazona and Alberto Ferrer and Lars Keld Nielsen and Susana Rodr'iguez-Navarro and Igor Mar'in de Mas and Ana Conesa},

url = {https://www.biorxiv.org/content/early/2022/10/10/2022.10.09.511458},

doi = {10.1101/2022.10.09.511458},

year  = {2022},

date = {2022-01-01},

journal = {bioRxiv},

publisher = {Cold Spring Harbor Laboratory},

abstract = {The inclusion of omic data into constraint-based modeling (CBM) has improved metabolic network characterization of biological models. However, the integration of semi-quantitative metabolomic data into CBM remains challenging. Here, we present MAMBA (Metabolic Adjustment via Multiomic Blocks Aggregation), a CBM approach that enables the use of semi-quantitative metabolomic data together with a gene-centric omic data type (e.g. transcriptomics, ChIP-seq or chromatin accessibility data, among others), and the combination of different time points and conditions. MAMBA outperformed other methods in terms of metabolic network characterization and metabolite prediction accuracy. As a case study, we applied MAMBA to a yeast multiomic dataset with time series design where two different yeast strains where exposed to heat stress. MAMBA captured known biology of heat stress in yeast and identified novel affected metabolic pathways. MAMBA was implemented as an integer linear programming (ILP) problem to guarantee efficient computation, and coded for MATLAB (freely available at github.com/ConesaLab/MAMBA).Competing Interest StatementThe authors have declared no competing interest.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ugidos2022.10.09.511458b,

title = {MAMBA: a model-driven, constraint-based multiomic integration method},

author = {Manuel Ugidos and Carme Nu no-Cabanes and Sonia Tarazona and Alberto Ferrer and Lars Keld Nielsen and Susana Rodr'iguez-Navarro and Igor Mar'in de Mas and Ana Conesa},

url = {https://www.biorxiv.org/content/early/2022/10/10/2022.10.09.511458},

doi = {10.1101/2022.10.09.511458},

year  = {2022},

date = {2022-01-01},

journal = {bioRxiv},

publisher = {Cold Spring Harbor Laboratory},

abstract = {The inclusion of omic data into constraint-based modeling (CBM) has improved metabolic network characterization of biological models. However, the integration of semi-quantitative metabolomic data into CBM remains challenging. Here, we present MAMBA (Metabolic Adjustment via Multiomic Blocks Aggregation), a CBM approach that enables the use of semi-quantitative metabolomic data together with a gene-centric omic data type (e.g. transcriptomics, ChIP-seq or chromatin accessibility data, among others), and the combination of different time points and conditions. MAMBA outperformed other methods in terms of metabolic network characterization and metabolite prediction accuracy. As a case study, we applied MAMBA to a yeast multiomic dataset with time series design where two different yeast strains where exposed to heat stress. MAMBA captured known biology of heat stress in yeast and identified novel affected metabolic pathways. MAMBA was implemented as an integer linear programming (ILP) problem to guarantee efficient computation, and coded for MATLAB (freely available at github.com/ConesaLab/MAMBA).Competing Interest StatementThe authors have declared no competing interest.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Turpin-Sevilla2021-vy,

title = {Global methylome scores correlate with histological subtypes of  colorectal carcinoma and show different associations with common  clinical and molecular features},

author = {María Del Carmen Turpín-Sevilla and Fernando Pérez-Sanz and José García-Solano and Patricia Sebastián-León and Javier Trujillo-Santos and Pablo Carbonell and Eduardo Estrada and Anne Tuomisto and Irene Herruzo and Lochlan J Fennell and Markus J Mäkinen and Edith Rodríguez-Braun and Vicki L J Whitehall and Ana Conesa and Pablo Conesa-Zamora},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8533997/pdf/cancers-13-05165.pdf},

doi = {10.3390/cancers13205165},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

journal = {Cancers (Basel)},

volume = {13},

number = {20},

pages = {5165},

publisher = {MDPI AG},

abstract = {BACKGROUND: The typical methylation patterns associated with 

 cancer are hypermethylation at gene promoters and global genome 

 hypomethylation. Aberrant CpG island hypermethylation at 

 promoter regions and global genome hypomethylation have not been 

 associated with histological colorectal carcinomas (CRC) 

 subsets. Using Illumina's 450 k Infinium Human Methylation 

 beadchip, the methylome of 82 CRCs were analyzed, comprising 

 different histological subtypes: 40 serrated adenocarcinomas 

 (SAC), 32 conventional carcinomas (CC) and 10 CRCs showing 

 histological and molecular features of microsatellite 

 instability (hmMSI-H), and, additionally, 35 normal adjacent 

 mucosae. Scores reflecting the overall methylation at 250 bp, 1 

 kb and 2 kb from the transcription starting site (TSS) were 

 studied. RESULTS: SAC has an intermediate methylation pattern 

 between CC and hmMSI-H for the three genome locations. In 

 addition, the shift from promoter hypermethylation to genomic 

 hypomethylation occurs at a small sequence between 250 bp and 1 

 Kb from the gene TSS, and an asymmetric distribution of 

 methylation was observed between both sides of the CpG islands 

 (N vs. S shores). CONCLUSION: These findings show that different 

 histological subtypes of CRC have a particular global 

 methylation pattern depending on sequence distance to TSS and 

 highlight the so far underestimated importance of CpGs 

 aberrantly hypomethylated in the clinical phenotype of CRCs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tarazona2021,

title = {Undisclosed, unmet and neglected challenges in multi-omics studies},

author = {Sonia Tarazona and Angeles Arzalluz-Luque and Ana Conesa},

url = {http://conesalab.org/wp-content/uploads/2021/07/Undisclosed-unmet-and-neglected-challenges-in-multi-omics-studies-1.pdf},

doi = {10.1038/s43588-021-00086-z},

issn = {2662-8457},

year  = {2021},

date = {2021-06-01},

journal = {Nature Computational Science},

volume = {1},

number = {6},

pages = {395-402},

abstract = {Multi-omics approaches have become a reality in both large genomics projects and small laboratories. However, the multi-omics research community still faces a number of issues that have either not been sufficiently discussed or for which current solutions are still limited. In this Perspective, we elaborate on these limitations and suggest points of attention for future research. We finally discuss new opportunities and challenges brought to the field by the rapid development of single-cell high-throughput molecular technologies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}