181.
Monzó, Carolina; Liu, Tianyuan; Conesa, Ana
Transcriptomics in the era of long-read sequencing Journal Article
In: Nature Reviews Genetics, pp. 1–21, 2025.
@article{monzo2025transcriptomics,
title = {Transcriptomics in the era of long-read sequencing},
author = {Carolina Monzó and Tianyuan Liu and Ana Conesa},
url = {https://www.nature.com/articles/s41576-025-00828-z},
doi = {doi:10.1038/s41576-025-00828-z},
year = {2025},
date = {2025-03-28},
urldate = {2025-01-01},
journal = {Nature Reviews Genetics},
pages = {1–21},
publisher = {Nature Publishing Group UK London},
abstract = {Transcriptome sequencing revolutionized the analysis of gene expression, providing an unbiased approach to gene detection and quantification that enabled the discovery of novel isoforms, alternative splicing events and fusion transcripts. However, although short-read sequencing technologies have surpassed the limited dynamic range of previous technologies such as microarrays, they have limitations, for example, in resolving full-length transcripts and complex isoforms. Over the past 5 years, long-read sequencing technologies have matured considerably, with improvements in instrumentation and analytical methods, enabling their application to RNA sequencing (RNA-seq). Benchmarking studies are beginning to identify the strengths and limitations of long-read RNA-seq, although there remains a need for comprehensive resources to guide newcomers through the intricacies of this approach. In this Review, we provide a comprehensive overview of the long-read RNA-seq workflow, from library preparation and sequencing challenges to core data processing, downstream analyses and emerging developments. We present an extensive inventory of experimental and analytical methods and discuss current challenges and prospects.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Transcriptome sequencing revolutionized the analysis of gene expression, providing an unbiased approach to gene detection and quantification that enabled the discovery of novel isoforms, alternative splicing events and fusion transcripts. However, although short-read sequencing technologies have surpassed the limited dynamic range of previous technologies such as microarrays, they have limitations, for example, in resolving full-length transcripts and complex isoforms. Over the past 5 years, long-read sequencing technologies have matured considerably, with improvements in instrumentation and analytical methods, enabling their application to RNA sequencing (RNA-seq). Benchmarking studies are beginning to identify the strengths and limitations of long-read RNA-seq, although there remains a need for comprehensive resources to guide newcomers through the intricacies of this approach. In this Review, we provide a comprehensive overview of the long-read RNA-seq workflow, from library preparation and sequencing challenges to core data processing, downstream analyses and emerging developments. We present an extensive inventory of experimental and analytical methods and discuss current challenges and prospects.
180.
Monzó, Carolina; Aguerralde-Martin, Maider; Martínez-Mira, Carlos; Arzalluz-Luque, Ángeles; Conesa, Ana; Tarazona, Sonia
MOSim: bulk and single-cell multilayer regulatory network simulator Journal Article
In: Briefings in Bioinformatics, vol. 26, no. 2, pp. bbaf110, 2025.
@article{monzo2025mosim,
title = {MOSim: bulk and single-cell multilayer regulatory network simulator},
author = {Carolina Monzó and Maider Aguerralde-Martin and Carlos Martínez-Mira and Ángeles Arzalluz-Luque and Ana Conesa and Sonia Tarazona},
url = {https://academic.oup.com/bib/article/26/2/bbaf110/8089935},
doi = {https://doi.org/10.1093/bib/bbaf110},
year = {2025},
date = {2025-03-21},
urldate = {2025-01-01},
journal = {Briefings in Bioinformatics},
volume = {26},
number = {2},
pages = {bbaf110},
publisher = {Oxford University Press},
abstract = {As multi-omics sequencing technologies advance, the need for simulation tools capable of generating realistic and diverse (bulk and single-cell) multi-omics datasets for method testing and benchmarking becomes increasingly important. We present MOSim, an R package that simulates both bulk (via mosim function) and single-cell (via sc_mosim function) multi-omics data. The mosim function generates bulk transcriptomics data (RNA-seq) and additional regulatory omics layers (ATAC-seq, miRNA-seq, ChIP-seq, Methyl-seq, and transcription factors), while sc_mosim simulates single-cell transcriptomics data (scRNA-seq) with scATAC-seq and transcription factors as regulatory layers. The tool supports various experimental designs, including simulation of gene co-expression patterns, biological replicates, and differential expression between conditions. MOSim enables users to generate quantification matrices for each simulated omics data type, capturing the heterogeneity and complexity of bulk and single-cell multi-omics datasets. Furthermore, MOSim provides differentially abundant features within each omics layer and elucidates the active regulatory relationships between regulatory omics and gene expression data at both bulk and single-cell levels. By leveraging MOSim, researchers will be able to generate realistic and customizable bulk and single-cell multi-omics datasets to benchmark and validate analytical methods specifically designed for the integrative analysis of diverse regulatory omics data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
As multi-omics sequencing technologies advance, the need for simulation tools capable of generating realistic and diverse (bulk and single-cell) multi-omics datasets for method testing and benchmarking becomes increasingly important. We present MOSim, an R package that simulates both bulk (via mosim function) and single-cell (via sc_mosim function) multi-omics data. The mosim function generates bulk transcriptomics data (RNA-seq) and additional regulatory omics layers (ATAC-seq, miRNA-seq, ChIP-seq, Methyl-seq, and transcription factors), while sc_mosim simulates single-cell transcriptomics data (scRNA-seq) with scATAC-seq and transcription factors as regulatory layers. The tool supports various experimental designs, including simulation of gene co-expression patterns, biological replicates, and differential expression between conditions. MOSim enables users to generate quantification matrices for each simulated omics data type, capturing the heterogeneity and complexity of bulk and single-cell multi-omics datasets. Furthermore, MOSim provides differentially abundant features within each omics layer and elucidates the active regulatory relationships between regulatory omics and gene expression data at both bulk and single-cell levels. By leveraging MOSim, researchers will be able to generate realistic and customizable bulk and single-cell multi-omics datasets to benchmark and validate analytical methods specifically designed for the integrative analysis of diverse regulatory omics data.
179.
Keil, Netanya; Monzó, Carolina; McIntyre, Lauren; Conesa, Ana
Quality assessment of long read data in multisample lrRNA-seq experiments using SQANTI-reads Journal Article
In: Genome Research, 2025.
@article{keil2025quality,
title = {Quality assessment of long read data in multisample lrRNA-seq experiments using SQANTI-reads},
author = {Netanya Keil and Carolina Monzó and Lauren McIntyre and Ana Conesa},
url = {https://genome.cshlp.org/content/early/2025/03/14/gr.280021.124},
doi = {doi: 10.1101/gr.280021.124},
year = {2025},
date = {2025-03-14},
urldate = {2025-03-14},
journal = {Genome Research},
publisher = {Cold Spring Harbor Lab},
abstract = {SQANTI-reads leverages SQANTI3, a tool for the analysis of the quality of transcript models, to develop a read-level quality control framework for replicated long-read RNA-seq experiments. The number and distribution of reads, as well as the number and distribution of unique junction chains (transcript splicing patterns), in SQANTI3 structural categories are informative of raw data quality. Multi-sample visualizations of QC metrics are presented by experimental design factors to identify outliers. We introduce new metrics for 1) the identification of potentially under-annotated genes and putative novel transcripts and for 2) quantifying variation in junction donors and acceptors. We applied SQANTI-reads to two different datasets, a Drosophila developmental experiment and a multi-platform dataset from the LRGASP project and demonstrate that the tool effectively reveals the impact of read coverage on data quality, and readily identifies strong and weak splicing sites. SQANTI-reads is open source and available for download at GitHub.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
SQANTI-reads leverages SQANTI3, a tool for the analysis of the quality of transcript models, to develop a read-level quality control framework for replicated long-read RNA-seq experiments. The number and distribution of reads, as well as the number and distribution of unique junction chains (transcript splicing patterns), in SQANTI3 structural categories are informative of raw data quality. Multi-sample visualizations of QC metrics are presented by experimental design factors to identify outliers. We introduce new metrics for 1) the identification of potentially under-annotated genes and putative novel transcripts and for 2) quantifying variation in junction donors and acceptors. We applied SQANTI-reads to two different datasets, a Drosophila developmental experiment and a multi-platform dataset from the LRGASP project and demonstrate that the tool effectively reveals the impact of read coverage on data quality, and readily identifies strong and weak splicing sites. SQANTI-reads is open source and available for download at GitHub.
178.
Monzó, Carolina; Frankish, Adam; Conesa, Ana
Notable challenges posed by long-read sequencing for the study of transcriptional diversity and genome annotation Journal Article
In: Genome Research, pp. gr–279865, 2025.
@article{monzo2025notable,
title = {Notable challenges posed by long-read sequencing for the study of transcriptional diversity and genome annotation},
author = {Carolina Monzó and Adam Frankish and Ana Conesa},
url = {https://genome.cshlp.org/content/early/2025/03/19/gr.279865.124.abstract},
doi = {doi:10.1101/gr.279865.124},
year = {2025},
date = {2025-03-01},
urldate = {2025-01-01},
journal = {Genome Research},
pages = {gr–279865},
publisher = {Cold Spring Harbor Lab},
abstract = {Long-read sequencing (LRS) technologies have revolutionized transcriptomic research by enabling the comprehensive sequencing of full-length transcripts. Using these technologies, researchers have reported tens of thousands of novel transcripts, even in well-annotated genomes, while developing new algorithms and experimental approaches to handle the noisy data. The Long-read RNA-seq Genome Annotation Assessment Project community effort benchmarked LRS methods in transcriptomics and validated many novel, lowly expressed, often times sample-specific transcripts identified by long reads. These molecules represent deviations of the major transcriptional program that were overlooked by short-read sequencing methods but are now captured by the full-length, single-molecule approach. This Perspective discusses the challenges and opportunities associated with LRS’ capacity to unravel this fraction of the transcriptome, in terms of both transcriptome biology and genome annotation. For transcriptome biology, we need to develop novel experimental and computational methods to effectively differentiate technology errors from rare but real molecules. For genome annotation, we must agree on the strategy to capture molecular variability while still defining reference annotations that are useful for the genomics community.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Long-read sequencing (LRS) technologies have revolutionized transcriptomic research by enabling the comprehensive sequencing of full-length transcripts. Using these technologies, researchers have reported tens of thousands of novel transcripts, even in well-annotated genomes, while developing new algorithms and experimental approaches to handle the noisy data. The Long-read RNA-seq Genome Annotation Assessment Project community effort benchmarked LRS methods in transcriptomics and validated many novel, lowly expressed, often times sample-specific transcripts identified by long reads. These molecules represent deviations of the major transcriptional program that were overlooked by short-read sequencing methods but are now captured by the full-length, single-molecule approach. This Perspective discusses the challenges and opportunities associated with LRS’ capacity to unravel this fraction of the transcriptome, in terms of both transcriptome biology and genome annotation. For transcriptome biology, we need to develop novel experimental and computational methods to effectively differentiate technology errors from rare but real molecules. For genome annotation, we must agree on the strategy to capture molecular variability while still defining reference annotations that are useful for the genomics community.
177.
Liu, Tianyuan; Conesa, Ana
Profiling the epigenome using long-read sequencing Journal Article
In: Nat Genet, 2025, ISSN: 1546-1718.
@article{Liu2025,
title = {Profiling the epigenome using long-read sequencing},
author = {Tianyuan Liu and Ana Conesa},
doi = {10.1038/s41588-024-02038-5},
issn = {1546-1718},
year = {2025},
date = {2025-01-08},
urldate = {2025-01-08},
journal = {Nat Genet},
publisher = {Springer Science and Business Media LLC},
abstract = {The advent of single-molecule, long-read sequencing (LRS) technologies by Oxford Nanopore Technologies and Pacific Biosciences has revolutionized genomics, transcriptomics and, more recently, epigenomics research. These technologies offer distinct advantages, including the direct detection of methylated DNA and simultaneous assessment of DNA sequences spanning multiple kilobases along with their modifications at the single-molecule level. This has enabled the development of new assays for analyzing chromatin states and made it possible to integrate data for DNA methylation, chromatin accessibility, transcription factor binding and histone modifications, thereby facilitating comprehensive epigenomic profiling. Owing to recent advancements, alternative, nascent and translating transcripts can be detected using LRS approaches. This Review discusses LRS-based experimental and computational strategies for characterizing chromatin states and highlights their advantages over short-read sequencing methods. Furthermore, we demonstrate how various long-read methods can be integrated to design multi-omics studies to investigate the relationship between chromatin states and transcriptional dynamics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The advent of single-molecule, long-read sequencing (LRS) technologies by Oxford Nanopore Technologies and Pacific Biosciences has revolutionized genomics, transcriptomics and, more recently, epigenomics research. These technologies offer distinct advantages, including the direct detection of methylated DNA and simultaneous assessment of DNA sequences spanning multiple kilobases along with their modifications at the single-molecule level. This has enabled the development of new assays for analyzing chromatin states and made it possible to integrate data for DNA methylation, chromatin accessibility, transcription factor binding and histone modifications, thereby facilitating comprehensive epigenomic profiling. Owing to recent advancements, alternative, nascent and translating transcripts can be detected using LRS approaches. This Review discusses LRS-based experimental and computational strategies for characterizing chromatin states and highlights their advantages over short-read sequencing methods. Furthermore, we demonstrate how various long-read methods can be integrated to design multi-omics studies to investigate the relationship between chromatin states and transcriptional dynamics.
176.
Pop, Mihai; Attwood, Teresa K; Blake, Judith A; Bourne, Philip E; Conesa, Ana; Gaasterland, Terry; Hunter, Lawrence; Kingsford, Carl; Kohlbacher, Oliver; Lengauer, Thomas; Markel, Scott; Moreau, Yves; Noble, William S; Orengo, Christine; Ouellette, B F Francis; Parida, Laxmi; Przulj, Natasa; Przytycka, Teresa M; Ranganathan, Shoba; Schwartz, Russell; Valencia, Alfonso; Warnow, Tandy
Biological databases in the age of generative artificial intelligence Journal Article
In: Bioinformatics Advances, vol. 5, no. 1, pp. vbaf044, 2025, ISSN: 2635-0041.
@article{10.1093/bioadv/vbaf044,
title = {Biological databases in the age of generative artificial intelligence},
author = {Mihai Pop and Teresa K Attwood and Judith A Blake and Philip E Bourne and Ana Conesa and Terry Gaasterland and Lawrence Hunter and Carl Kingsford and Oliver Kohlbacher and Thomas Lengauer and Scott Markel and Yves Moreau and William S Noble and Christine Orengo and B F Francis Ouellette and Laxmi Parida and Natasa Przulj and Teresa M Przytycka and Shoba Ranganathan and Russell Schwartz and Alfonso Valencia and Tandy Warnow},
url = {https://doi.org/10.1093/bioadv/vbaf044},
doi = {10.1093/bioadv/vbaf044},
issn = {2635-0041},
year = {2025},
date = {2025-01-01},
journal = {Bioinformatics Advances},
volume = {5},
number = {1},
pages = {vbaf044},
abstract = {Modern biological research critically depends on public databases. The introduction and propagation of errors within and across databases can lead to wasted resources as scientists are led astray by bad data or have to conduct expensive validation experiments. The emergence of generative artificial intelligence systems threatens to compound this problem owing to the ease with which massive volumes of synthetic data can be generated. We provide an overview of several key issues that occur within the biological data ecosystem and make several recommendations aimed at reducing data errors and their propagation. We specifically highlight the critical importance of improved educational programs aimed at biologists and life scientists that emphasize best practices in data engineering. We also argue for increased theoretical and empirical research on data provenance, error propagation, and on understanding the impact of errors on analytic pipelines. Furthermore, we recommend enhanced funding for the stewardship and maintenance of public biological databases.Not applicable.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Modern biological research critically depends on public databases. The introduction and propagation of errors within and across databases can lead to wasted resources as scientists are led astray by bad data or have to conduct expensive validation experiments. The emergence of generative artificial intelligence systems threatens to compound this problem owing to the ease with which massive volumes of synthetic data can be generated. We provide an overview of several key issues that occur within the biological data ecosystem and make several recommendations aimed at reducing data errors and their propagation. We specifically highlight the critical importance of improved educational programs aimed at biologists and life scientists that emphasize best practices in data engineering. We also argue for increased theoretical and empirical research on data provenance, error propagation, and on understanding the impact of errors on analytic pipelines. Furthermore, we recommend enhanced funding for the stewardship and maintenance of public biological databases.Not applicable.
175.
Paniagua, Alejandro; Agustin-García, Cristina; Pardo-Palacios, Francisco J.; Brown, Thomas; de Maria, Maite; Denslow, Nancy D.; Mazzoni, Camila J.; Conesa, Ana
Evaluation of strategies for evidence-driven genome annotation using long-read RNA-seq Journal Article
In: Genome Research, iss. March, 35, 2024.
@article{Paniagua2024b,
title = {Evaluation of strategies for evidence-driven genome annotation using long-read RNA-seq},
author = {Alejandro Paniagua and Cristina Agustin-García and Francisco J. Pardo-Palacios and Thomas Brown and Maite de Maria and Nancy D. Denslow and Camila J. Mazzoni and Ana Conesa },
doi = {doi:1101/gr.279864.124},
year = {2024},
date = {2024-12-23},
journal = {Genome Research},
issue = {March, 35},
abstract = {While the production of a draft genome has become more accessible due to long-read sequencing, the annotation of these new genomes has not been developed at the same pace. Long-read RNA sequencing offers a promising solution for enhancing gene annotation. In this study, we explore how sequencing platforms, Oxford Nanopore R9.4.1 chemistry or Pacific Biosciences (PacBio) Sequel II CCS, and data processing methods influence evidence-driven genome annotation using long reads. Incorporating PacBio transcripts into our annotation pipeline significantly outperformed traditional methods, such as ab initio predictions and short-read-based annotations. We applied this strategy to a nonmodel species, the Florida manatee, and compared our results to existing short-read-based annotation. At the loci level, both annotations were highly concordant, with 90% agreement. However, at the transcript level, the agreement was only 35%. We identified 4906 novel loci, represented by 5707 isoforms, with 64% of these isoforms matching known sequences in other mammalian species. Overall, our findings underscore the importance of using high-quality curated transcript models in combination with ab initio methods for effective genome annotation.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
While the production of a draft genome has become more accessible due to long-read sequencing, the annotation of these new genomes has not been developed at the same pace. Long-read RNA sequencing offers a promising solution for enhancing gene annotation. In this study, we explore how sequencing platforms, Oxford Nanopore R9.4.1 chemistry or Pacific Biosciences (PacBio) Sequel II CCS, and data processing methods influence evidence-driven genome annotation using long reads. Incorporating PacBio transcripts into our annotation pipeline significantly outperformed traditional methods, such as ab initio predictions and short-read-based annotations. We applied this strategy to a nonmodel species, the Florida manatee, and compared our results to existing short-read-based annotation. At the loci level, both annotations were highly concordant, with 90% agreement. However, at the transcript level, the agreement was only 35%. We identified 4906 novel loci, represented by 5707 isoforms, with 64% of these isoforms matching known sequences in other mammalian species. Overall, our findings underscore the importance of using high-quality curated transcript models in combination with ab initio methods for effective genome annotation.
174.
Paniagua, Alejandro; Agustin-García, Cristina; Pardo-Palacios, Francisco J; Brown, Thomas; Maria, Maite De; Denslow, Nancy D; Mazzoni, Camila; Conesa, Ana
Evaluation of strategies for evidence-driven genome annotation using long-read RNA-seq Journal Article
In: Genome Res., 2024, ISSN: 1088-9051.
@article{Paniagua2024,
title = {Evaluation of strategies for evidence-driven genome annotation using long-read RNA-seq},
author = {Alejandro Paniagua and Cristina Agustin-García and Francisco J Pardo-Palacios and Thomas Brown and Maite De Maria and Nancy D Denslow and Camila Mazzoni and Ana Conesa},
doi = {0.1101/gr.279864.124},
issn = {1088-9051},
year = {2024},
date = {2024-12-23},
urldate = {2024-12-23},
journal = {Genome Res.},
publisher = {Cold Spring Harbor Laboratory},
abstract = {<jats:p>While the production of a draft genome has become more accessible due to long-read sequencing, the annotation of these new genomes has not been developed at the same pace. Long-read RNA sequencing (lrRNA-seq) offers a promising solution for enhancing gene annotation. In this study, we explore how sequencing platforms, Oxford Nanopore R9.4.1 chemistry or PacBio Sequel II CCS, and data processing methods influence evidence-driven genome annotation using long reads. Incorporating PacBio transcripts into our annotation pipeline significantly outperformed traditional methods, such as ab initio predictions and short-read-based annotations. We applied this strategy to a nonmodel species, the Florida manatee, and compared our results to existing short-read-based annotation. At the loci level, both annotations were highly concordant, with 90% agreement. However, at the transcript level, the agreement was only 35%. We identified 4,906 novel loci, represented by 5,707 isoforms, with 64% of these isoforms matching known sequences in other mammalian species. Overall, our findings underscore the importance of using high-quality curated transcript models in combination with ab initio methods for effective genome annotation.</jats:p>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
<jats:p>While the production of a draft genome has become more accessible due to long-read sequencing, the annotation of these new genomes has not been developed at the same pace. Long-read RNA sequencing (lrRNA-seq) offers a promising solution for enhancing gene annotation. In this study, we explore how sequencing platforms, Oxford Nanopore R9.4.1 chemistry or PacBio Sequel II CCS, and data processing methods influence evidence-driven genome annotation using long reads. Incorporating PacBio transcripts into our annotation pipeline significantly outperformed traditional methods, such as ab initio predictions and short-read-based annotations. We applied this strategy to a nonmodel species, the Florida manatee, and compared our results to existing short-read-based annotation. At the loci level, both annotations were highly concordant, with 90% agreement. However, at the transcript level, the agreement was only 35%. We identified 4,906 novel loci, represented by 5,707 isoforms, with 64% of these isoforms matching known sequences in other mammalian species. Overall, our findings underscore the importance of using high-quality curated transcript models in combination with ab initio methods for effective genome annotation.</jats:p>
173.
Keil, Netanya; Monzó, Carolina; McIntyre, Lauren; Conesa, Ana
SQANTI-reads: a tool for the quality assessment of long read data in multi-sample lrRNA-seq experiments Unpublished
bioRxiv, 2024.
@unpublished{Keil2024,
title = {SQANTI-reads: a tool for the quality assessment of long read data in multi-sample lrRNA-seq experiments},
author = {Netanya Keil and Carolina Monzó and Lauren McIntyre and Ana Conesa},
url = {http://biorxiv.org/lookup/doi/10.1101/2024.08.23.609463},
doi = {10.1101/2024.08.23.609463},
year = {2024},
date = {2024-08-25},
publisher = {Cold Spring Harbor Laboratory},
abstract = {ABSTRACT SQANTI-reads leverages SQANTI3, a tool for the analysis of the quality of transcript models, to develop a read-level quality control framework for replicated long-read RNA-seq experiments. The number and distribution of reads, as well as the number and distribution of unique junction chains (transcript splicing patterns), in SQANTI3 structural categories are informative of raw data quality. Multi-sample visualizations of QC metrics are presented by experimental design factors to identify outliers. We introduce new metrics for 1) the identification of potentially under-annotated genes and putative novel transcripts and for 2) quantifying variation in junction donors and acceptors. We applied SQANTI-reads to two different datasets, aDrosophila developmental experiment and a multi-platform dataset from the LRGASP project and demonstrate that the tool effectively reveals the impact of read coverage on data quality, and readily identifies strong and weak splicing sites. SQANTI-reads is open source and available for download at GitHub. },
howpublished = {bioRxiv},
keywords = {},
pubstate = {published},
tppubtype = {unpublished}
}
172.
Marta Benegas Coll Priyansh Srivastava, Stefan Götz
scMaSigPro: differential expression analysis along single-cell trajectories Journal Article
In: Bioinformatics, vol. 40, iss. 7, 2024.
@article{nokey,
title = {scMaSigPro: differential expression analysis along single-cell trajectories },
author = {Priyansh Srivastava, Marta Benegas Coll, Stefan Götz, María José Nueda, Ana Conesa},
doi = {https://doi.org/10.1093/bioinformatics/btae443},
year = {2024},
date = {2024-07-08},
journal = {Bioinformatics},
volume = {40},
issue = {7},
abstract = {Motivation
Understanding the dynamics of gene expression across different cellular states is crucial for discerning the mechanisms underneath cellular differentiation. Genes that exhibit variation in mean expression as a function of Pseudotime and between branching trajectories are expected to govern cell fate decisions. We introduce scMaSigPro, a method for the identification of differential gene expression patterns along Pseudotime and branching paths simultaneously.
Results
We assessed the performance of scMaSigPro using synthetic and public datasets. Our evaluation shows that scMaSigPro outperforms existing methods in controlling the False Positive Rate and is computationally efficient.
Availability and implementation
scMaSigPro is available as a free R package (version 4.0 or higher) under the GPL(>2) license on GitHub at ‘github.com/BioBam/scMaSigPro’ and archived with version 0.03 on Zenodo at ‘zenodo.org/records/12568922’.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Motivation
Understanding the dynamics of gene expression across different cellular states is crucial for discerning the mechanisms underneath cellular differentiation. Genes that exhibit variation in mean expression as a function of Pseudotime and between branching trajectories are expected to govern cell fate decisions. We introduce scMaSigPro, a method for the identification of differential gene expression patterns along Pseudotime and branching paths simultaneously.
Results
We assessed the performance of scMaSigPro using synthetic and public datasets. Our evaluation shows that scMaSigPro outperforms existing methods in controlling the False Positive Rate and is computationally efficient.
Availability and implementation
scMaSigPro is available as a free R package (version 4.0 or higher) under the GPL(>2) license on GitHub at ‘github.com/BioBam/scMaSigPro’ and archived with version 0.03 on Zenodo at ‘zenodo.org/records/12568922’.
Understanding the dynamics of gene expression across different cellular states is crucial for discerning the mechanisms underneath cellular differentiation. Genes that exhibit variation in mean expression as a function of Pseudotime and between branching trajectories are expected to govern cell fate decisions. We introduce scMaSigPro, a method for the identification of differential gene expression patterns along Pseudotime and branching paths simultaneously.
Results
We assessed the performance of scMaSigPro using synthetic and public datasets. Our evaluation shows that scMaSigPro outperforms existing methods in controlling the False Positive Rate and is computationally efficient.
Availability and implementation
scMaSigPro is available as a free R package (version 4.0 or higher) under the GPL(>2) license on GitHub at ‘github.com/BioBam/scMaSigPro’ and archived with version 0.03 on Zenodo at ‘zenodo.org/records/12568922’.
171.
Pardo-Palacios, Francisco J.; Wang, Dingjie; Reese, Fairlie; Diekhans, Mark; Carbonell-Sala, Sílvia; Williams, Brian; Loveland, Jane E.; María, Maite De; Adams, Matthew S.; Balderrama-Gutierrez, Gabriela; Behera, Amit K.; Martinez, Jose M. Gonzalez; Hunt, Toby; Lagarde, Julien; Liang, Cindy E.; Li, Haoran; Meade, Marcus Jerryd; Amador, David A. Moraga; Prjibelski, Andrey D.; Birol, Inanc; Bostan, Hamed; Brooks, Ashley M.; Çelik, Muhammed Hasan; Chen, Ying; Du, Mei R. M.; Felton, Colette; Göke, Jonathan; Hafezqorani, Saber; Herwig, Ralf; Kawaji, Hideya; Lee, Joseph; Li, Jian-Liang; Lienhard, Matthias; Mikheenko, Alla; Mulligan, Dennis; Nip, Ka Ming; Pertea, Mihaela; Ritchie, Matthew E.; Sim, Andre D.; Tang, Alison D.; Wan, Yuk Kei; Wang, Changqing; Wong, Brandon Y.; Yang, Chen; Barnes, If; Berry, Andrew E.; Capella-Gutierrez, Salvador; Cousineau, Alyssa; Dhillon, Namrita; Fernandez-Gonzalez, Jose M.; Ferrández-Peral, Luis; Garcia-Reyero, Natàlia; Götz, Stefan; Hernández-Ferrer, Carles; Kondratova, Liudmyla; Liu, Tianyuan; Martinez-Martin, Alessandra; Menor, Carlos; Mestre-Tomás, Jorge; Mudge, Jonathan M.; Panayotova, Nedka G.; Paniagua, Alejandro; Repchevsky, Dmitry; Ren, Xingjie; Rouchka, Eric; Saint-John, Brandon; Sapena, Enrique; Sheynkman, Leon; Smith, Melissa Laird; Suner, Marie-Marthe; Takahashi, Hazuki; Youngworth, Ingrid A.; Carninci, Piero; Denslow, Nancy D.; Guigó, Roderic; Hunter, Margaret E.; Maehr, Rene; Shen, Yin; Tilgner, Hagen U.; Wold, Barbara J.; Vollmers, Christopher; Frankish, Adam; Au, Kin Fai; Sheynkman, Gloria M.; Mortazavi, Ali; Conesa, Ana; Brooks, Angela N.
Systematic assessment of long-read RNA-seq methods for transcript identification and quantification Journal Article
In: Nat Methods, 2024, ISSN: 1548-7105.
@article{Pardo-Palacios2024,
title = {Systematic assessment of long-read RNA-seq methods for transcript identification and quantification},
author = {Francisco J. Pardo-Palacios and Dingjie Wang and Fairlie Reese and Mark Diekhans and Sílvia Carbonell-Sala and Brian Williams and Jane E. Loveland and Maite De María and Matthew S. Adams and Gabriela Balderrama-Gutierrez and Amit K. Behera and Jose M. Gonzalez Martinez and Toby Hunt and Julien Lagarde and Cindy E. Liang and Haoran Li and Marcus Jerryd Meade and David A. Moraga Amador and Andrey D. Prjibelski and Inanc Birol and Hamed Bostan and Ashley M. Brooks and Muhammed Hasan Çelik and Ying Chen and Mei R. M. Du and Colette Felton and Jonathan Göke and Saber Hafezqorani and Ralf Herwig and Hideya Kawaji and Joseph Lee and Jian-Liang Li and Matthias Lienhard and Alla Mikheenko and Dennis Mulligan and Ka Ming Nip and Mihaela Pertea and Matthew E. Ritchie and Andre D. Sim and Alison D. Tang and Yuk Kei Wan and Changqing Wang and Brandon Y. Wong and Chen Yang and If Barnes and Andrew E. Berry and Salvador Capella-Gutierrez and Alyssa Cousineau and Namrita Dhillon and Jose M. Fernandez-Gonzalez and Luis Ferrández-Peral and Natàlia Garcia-Reyero and Stefan Götz and Carles Hernández-Ferrer and Liudmyla Kondratova and Tianyuan Liu and Alessandra Martinez-Martin and Carlos Menor and Jorge Mestre-Tomás and Jonathan M. Mudge and Nedka G. Panayotova and Alejandro Paniagua and Dmitry Repchevsky and Xingjie Ren and Eric Rouchka and Brandon Saint-John and Enrique Sapena and Leon Sheynkman and Melissa Laird Smith and Marie-Marthe Suner and Hazuki Takahashi and Ingrid A. Youngworth and Piero Carninci and Nancy D. Denslow and Roderic Guigó and Margaret E. Hunter and Rene Maehr and Yin Shen and Hagen U. Tilgner and Barbara J. Wold and Christopher Vollmers and Adam Frankish and Kin Fai Au and Gloria M. Sheynkman and Ali Mortazavi and Ana Conesa and Angela N. Brooks},
doi = {10.1038/s41592-024-02298-3},
issn = {1548-7105},
year = {2024},
date = {2024-06-07},
urldate = {2024-06-07},
journal = {Nat Methods},
publisher = {Springer Science and Business Media LLC},
abstract = {<jats:title>Abstract</jats:title><jats:p>The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.</jats:p>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
<jats:title>Abstract</jats:title><jats:p>The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.</jats:p>
170.
Nanni, Adalena; Titus-McQuillan, James; Bankole, Kinfeosioluwa S; Pardo-Palacios, Francisco; Signor, Sarah; Vlaho, Srna; Moskalenko, Oleksandr; Morse, Alison M; Rogers, Rebekah L; Conesa, Ana; McIntyre, Lauren M
Nucleotide-level distance metrics to quantify alternative splicing implemented in TranD Journal Article
In: vol. 52, no. 5, pp. e28–e28, 2024, ISSN: 1362-4962.
@article{Nanni2024,
title = {Nucleotide-level distance metrics to quantify alternative splicing implemented in \textit{TranD}},
author = {Adalena Nanni and James Titus-McQuillan and Kinfeosioluwa S Bankole and Francisco Pardo-Palacios and Sarah Signor and Srna Vlaho and Oleksandr Moskalenko and Alison M Morse and Rebekah L Rogers and Ana Conesa and Lauren M McIntyre},
doi = {10.1093/nar/gkae056},
issn = {1362-4962},
year = {2024},
date = {2024-03-21},
volume = {52},
number = {5},
pages = {e28--e28},
publisher = {Oxford University Press (OUP)},
abstract = {Abstract
Advances in affordable transcriptome sequencing combined with better exon and gene prediction has motivated many to compare transcription across the tree of life. We develop a mathematical framework to calculate complexity and compare transcript models. Structural features, i.e. intron retention (IR), donor/acceptor site variation, alternative exon cassettes, alternative 5′/3′ UTRs, are compared and the distance between transcript models is calculated with nucleotide level precision. All metrics are implemented in a PyPi package, TranD and output can be used to summarize splicing patterns for a transcriptome (1GTF) and between transcriptomes (2GTF). TranD output enables quantitative comparisons between: annotations augmented by empirical RNA-seq data and the original transcript models; transcript model prediction tools for longread RNA-seq (e.g. FLAIR versus Isoseq3); alternate annotations for a species (e.g. RefSeq vs Ensembl); and between closely related species. In C. elegans, Z. mays, D. melanogaster, D. simulans and H. sapiens, alternative exons were observed more frequently in combination with an alternative donor/acceptor than alone. Transcript models in RefSeq and Ensembl are linked and both have unique transcript models with empirical support. D. melanogaster and D. simulans, share many transcript models and long-read RNAseq data suggests that both species are under-annotated. We recommend combined references. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
169.
Tobias Nespital Maarouf Baghdadi, Carolina Monzó
Intermittent rapamycin feeding recapitulates some effects of continuous treatment while maintaining lifespan extension Journal Article
In: Molecular Metabolism, vol. 81, iss. March 2024, no. 101902, 2024.
@article{Baghdadi2024,
title = {Intermittent rapamycin feeding recapitulates some effects of continuous treatment while maintaining lifespan extension},
author = {Maarouf Baghdadi, Tobias Nespital, Carolina Monzó, Joris Deelen, Sebastian Grönke, Linda Partridge },
doi = {https://doi.org/10.1016/j.molmet.2024.101902},
year = {2024},
date = {2024-03-01},
urldate = {2024-03-01},
journal = {Molecular Metabolism},
volume = {81},
number = {101902},
issue = {March 2024},
abstract = {Objective
Rapamycin, a powerful geroprotective drug, can have detrimental effects when administered chronically. We determined whether intermittent treatment of mice can reduce negative effects while maintaining benefits of chronic treatment.
Methods
From 6 months of age, male and female C3B6F1 hybrid mice were either continuously fed with 42 mg/kg rapamycin, or intermittently fed by alternating weekly feeding of 42 mg/kg rapamycin food with weekly control feeding. Survival of these mice compared to control animals was measured. Furthermore, longitudinal phenotyping including metabolic (body composition, GTT, ITT, indirect calorimetry) and fitness phenotypes (treadmil, rotarod, electrocardiography and open field) was performed. Organ specific pathology was assessed at 24 months of age.
Results
Chronic rapamycin treatment induced glucose intolerance, which was partially ameliorated by intermittent treatment. Chronic and intermittent rapamycin treatments increased lifespan equally in males, while in females chronic treatment resulted in slightly higher survival. The two treatments had equivalent effects on testicular degeneration, heart fibrosis and liver lipidosis. In males, the two treatment regimes led to a similar increase in motor coordination, heart rate and Q-T interval, and reduction in spleen weight, while in females, they equally reduced BAT inflammation and spleen weight and maintained heart rate and Q-T interval. However, other health parameters, including age related pathologies, were better prevented by continuous treatment.
Conclusions
Intermittent rapamycin treatment is effective in prolonging lifespan and reduces some side-effects of chronic treatment, but chronic treatment is more beneficial to healthspan.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective
Rapamycin, a powerful geroprotective drug, can have detrimental effects when administered chronically. We determined whether intermittent treatment of mice can reduce negative effects while maintaining benefits of chronic treatment.
Methods
From 6 months of age, male and female C3B6F1 hybrid mice were either continuously fed with 42 mg/kg rapamycin, or intermittently fed by alternating weekly feeding of 42 mg/kg rapamycin food with weekly control feeding. Survival of these mice compared to control animals was measured. Furthermore, longitudinal phenotyping including metabolic (body composition, GTT, ITT, indirect calorimetry) and fitness phenotypes (treadmil, rotarod, electrocardiography and open field) was performed. Organ specific pathology was assessed at 24 months of age.
Results
Chronic rapamycin treatment induced glucose intolerance, which was partially ameliorated by intermittent treatment. Chronic and intermittent rapamycin treatments increased lifespan equally in males, while in females chronic treatment resulted in slightly higher survival. The two treatments had equivalent effects on testicular degeneration, heart fibrosis and liver lipidosis. In males, the two treatment regimes led to a similar increase in motor coordination, heart rate and Q-T interval, and reduction in spleen weight, while in females, they equally reduced BAT inflammation and spleen weight and maintained heart rate and Q-T interval. However, other health parameters, including age related pathologies, were better prevented by continuous treatment.
Conclusions
Intermittent rapamycin treatment is effective in prolonging lifespan and reduces some side-effects of chronic treatment, but chronic treatment is more beneficial to healthspan.
Rapamycin, a powerful geroprotective drug, can have detrimental effects when administered chronically. We determined whether intermittent treatment of mice can reduce negative effects while maintaining benefits of chronic treatment.
Methods
From 6 months of age, male and female C3B6F1 hybrid mice were either continuously fed with 42 mg/kg rapamycin, or intermittently fed by alternating weekly feeding of 42 mg/kg rapamycin food with weekly control feeding. Survival of these mice compared to control animals was measured. Furthermore, longitudinal phenotyping including metabolic (body composition, GTT, ITT, indirect calorimetry) and fitness phenotypes (treadmil, rotarod, electrocardiography and open field) was performed. Organ specific pathology was assessed at 24 months of age.
Results
Chronic rapamycin treatment induced glucose intolerance, which was partially ameliorated by intermittent treatment. Chronic and intermittent rapamycin treatments increased lifespan equally in males, while in females chronic treatment resulted in slightly higher survival. The two treatments had equivalent effects on testicular degeneration, heart fibrosis and liver lipidosis. In males, the two treatment regimes led to a similar increase in motor coordination, heart rate and Q-T interval, and reduction in spleen weight, while in females, they equally reduced BAT inflammation and spleen weight and maintained heart rate and Q-T interval. However, other health parameters, including age related pathologies, were better prevented by continuous treatment.
Conclusions
Intermittent rapamycin treatment is effective in prolonging lifespan and reduces some side-effects of chronic treatment, but chronic treatment is more beneficial to healthspan.
168.
Conesa, Ana; Hoischen, Alexander; Sedlazeck, Fritz J.
Revolutionizing genomics and medicine—one long molecule at a time Journal Article
In: Genome Research, 2024.
@article{Conesa2024RevolutionizingGA,
title = {Revolutionizing genomics and medicine—one long molecule at a time},
author = {Ana Conesa and Alexander Hoischen and Fritz J. Sedlazeck},
url = {https://api.semanticscholar.org/CorpusID:274261201},
year = {2024},
date = {2024-01-01},
journal = {Genome Research},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
167.
de Hegedüs, Rocío Amorín; Conesa, Ana; Foster, Jamie S.
Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems Journal Article
In: Front. Microbiol., vol. 14, 2023, ISSN: 1664-302X.
@article{AmoríndeHegedüs2023,
title = {Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems},
author = {Rocío Amorín de Hegedüs and Ana Conesa and Jamie S. Foster},
doi = {10.3389/fmicb.2023.1174685},
issn = {1664-302X},
year = {2023},
date = {2023-07-28},
urldate = {2023-07-28},
journal = {Front. Microbiol.},
volume = {14},
publisher = {Frontiers Media SA},
abstract = {<jats:p>Microbes continually shape Earth’s biochemical and physical landscapes by inhabiting diverse metabolic niches. Despite the important role microbes play in ecosystem functioning, most microbial species remain unknown highlighting a gap in our understanding of structured complex ecosystems. To elucidate the relevance of these unknown taxa, often referred to as “microbial dark matter,” the integration of multiple high throughput sequencing technologies was used to evaluate the co-occurrence and connectivity of all microbes within the community. Since there are no standard methodologies for multi-omics integration of microbiome data, we evaluated the abundance of “microbial dark matter” in microbialite-forming communities using different types meta-omic datasets: amplicon, metagenomic, and metatranscriptomic sequencing previously generated for this ecosystem. Our goal was to compare the community structure and abundances of unknown taxa within the different data types rather than to perform a functional characterization of the data. Metagenomic and metatranscriptomic data were input into SortMeRNA to extract 16S rRNA gene reads. The output, as well as amplicon sequences, were processed through QIIME2 for taxonomy analysis. The R package mdmnets was utilized to build co-occurrence networks. Most hubs presented unknown classifications, even at the phyla level. Comparisons of the highest scoring hubs of each data type using sequence similarity networks allowed the identification of the most relevant hubs within the microbialite-forming communities. This work highlights the importance of unknown taxa in community structure and proposes that ecosystem network construction can be used on several types of data to identify keystone taxa and their potential function within microbial ecosystems.</jats:p>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
<jats:p>Microbes continually shape Earth’s biochemical and physical landscapes by inhabiting diverse metabolic niches. Despite the important role microbes play in ecosystem functioning, most microbial species remain unknown highlighting a gap in our understanding of structured complex ecosystems. To elucidate the relevance of these unknown taxa, often referred to as “microbial dark matter,” the integration of multiple high throughput sequencing technologies was used to evaluate the co-occurrence and connectivity of all microbes within the community. Since there are no standard methodologies for multi-omics integration of microbiome data, we evaluated the abundance of “microbial dark matter” in microbialite-forming communities using different types meta-omic datasets: amplicon, metagenomic, and metatranscriptomic sequencing previously generated for this ecosystem. Our goal was to compare the community structure and abundances of unknown taxa within the different data types rather than to perform a functional characterization of the data. Metagenomic and metatranscriptomic data were input into SortMeRNA to extract 16S rRNA gene reads. The output, as well as amplicon sequences, were processed through QIIME2 for taxonomy analysis. The R package mdmnets was utilized to build co-occurrence networks. Most hubs presented unknown classifications, even at the phyla level. Comparisons of the highest scoring hubs of each data type using sequence similarity networks allowed the identification of the most relevant hubs within the microbialite-forming communities. This work highlights the importance of unknown taxa in community structure and proposes that ecosystem network construction can be used on several types of data to identify keystone taxa and their potential function within microbial ecosystems.</jats:p>
166.
Figueiredo, C C; Balzano-Nogueira, L; Bisinotto, D Z; Ruiz, A Revilla; Duarte, G A; Conesa, A; Galvão, K N; Bisinotto, R S
Differences in uterine and serum metabolome associated with metritis in dairy cows Journal Article
In: J Dairy Sci, vol. 106, no. 5, pp. 3525–3536, 2023, ISSN: 1525-3198.
@article{pmid36894419,
title = {Differences in uterine and serum metabolome associated with metritis in dairy cows},
author = {C C Figueiredo and L Balzano-Nogueira and D Z Bisinotto and A Revilla Ruiz and G A Duarte and A Conesa and K N Galvão and R S Bisinotto},
doi = {10.3168/jds.2022-22552},
issn = {1525-3198},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
journal = {J Dairy Sci},
volume = {106},
number = {5},
pages = {3525--3536},
abstract = {Objectives were to evaluate differences in the uterine and serum metabolomes associated with metritis in dairy cows. Vaginal discharge was evaluated using a Metricheck device (Simcro) at 5, 7, and 11 d in milk (DIM; herd 1) or 4, 6, 8, 10, and 12 DIM (herd 2). Cows with reddish or brownish, watery, and fetid discharge were diagnosed with metritis (n = 24). Cows with metritis were paired with herdmates without metritis (i.e., clear mucous vaginal discharge or clear lochia with ≤50% of pus) based on DIM and parity (n = 24). Day of metritis diagnosis was considered study d 0. All cows diagnosed with metritis received antimicrobial therapy. The metabolome of uterine lavage collected on d 0 and 5, and serum samples collected on d 0 were evaluated using untargeted gas chromatography time-of-flight mass spectrometry. Normalized data were subjected to multivariate canonical analysis of population using the MultBiplotR and MixOmics packages in R Studio. Univariate analyses including t-test, principal component analyses, partial least squares discriminant analyses, and pathway analyses were conducted using Metaboanalyst. The uterine metabolome differed between cows with and without metritis on d 0. Differences in the uterine metabolome associated with metritis on d 0 were related to the metabolism of butanoate, amino acids (i.e., glycine, serine, threonine, alanine, aspartate, and glutamate), glycolysis and gluconeogenesis, and the tricarboxylic acid cycle. No differences in the serum metabolome were observed between cows diagnosed with metritis and counterparts without metritis on d 0. Similarly, no differences in uterine metabolome were observed between cows with metritis and counterparts not diagnosed with metritis on d 5. These results indicate that the establishment of metritis in dairy cows is associated with local disturbances in amino acid, lipid, and carbohydrate metabolism in the uterus. The lack of differences in the uterine metabolome on d 5 indicates that processes implicated with the disease are reestablished by d 5 after diagnosis and treatment.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objectives were to evaluate differences in the uterine and serum metabolomes associated with metritis in dairy cows. Vaginal discharge was evaluated using a Metricheck device (Simcro) at 5, 7, and 11 d in milk (DIM; herd 1) or 4, 6, 8, 10, and 12 DIM (herd 2). Cows with reddish or brownish, watery, and fetid discharge were diagnosed with metritis (n = 24). Cows with metritis were paired with herdmates without metritis (i.e., clear mucous vaginal discharge or clear lochia with ≤50% of pus) based on DIM and parity (n = 24). Day of metritis diagnosis was considered study d 0. All cows diagnosed with metritis received antimicrobial therapy. The metabolome of uterine lavage collected on d 0 and 5, and serum samples collected on d 0 were evaluated using untargeted gas chromatography time-of-flight mass spectrometry. Normalized data were subjected to multivariate canonical analysis of population using the MultBiplotR and MixOmics packages in R Studio. Univariate analyses including t-test, principal component analyses, partial least squares discriminant analyses, and pathway analyses were conducted using Metaboanalyst. The uterine metabolome differed between cows with and without metritis on d 0. Differences in the uterine metabolome associated with metritis on d 0 were related to the metabolism of butanoate, amino acids (i.e., glycine, serine, threonine, alanine, aspartate, and glutamate), glycolysis and gluconeogenesis, and the tricarboxylic acid cycle. No differences in the serum metabolome were observed between cows diagnosed with metritis and counterparts without metritis on d 0. Similarly, no differences in uterine metabolome were observed between cows with metritis and counterparts not diagnosed with metritis on d 5. These results indicate that the establishment of metritis in dairy cows is associated with local disturbances in amino acid, lipid, and carbohydrate metabolism in the uterus. The lack of differences in the uterine metabolome on d 5 indicates that processes implicated with the disease are reestablished by d 5 after diagnosis and treatment.
165.
Thompson, Sharon C.; Ford, Amanda L.; Moothedan, Elijah J.; Stafford, Lauren S.; Garrett, Timothy J.; Dahl, Wendy J.; Conesa, Ana; Gonzalez, Claudio F.; Lorca, Graciela L.
Identification of food and nutrient components as predictors of Lactobacillus colonization Journal Article
In: Front. Nutr., vol. 10, 2023, ISSN: 2296-861X.
@article{Thompson2023,
title = {Identification of food and nutrient components as predictors of Lactobacillus colonization},
author = {Sharon C. Thompson and Amanda L. Ford and Elijah J. Moothedan and Lauren S. Stafford and Timothy J. Garrett and Wendy J. Dahl and Ana Conesa and Claudio F. Gonzalez and Graciela L. Lorca},
doi = {10.3389/fnut.2023.1118679},
issn = {2296-861X},
year = {2023},
date = {2023-04-21},
journal = {Front. Nutr.},
volume = {10},
publisher = {Frontiers Media SA},
abstract = {A previous double-blind, randomized clinical trial of 42 healthy individuals conducted with Lactobacillus johnsonii N6.2 found that the probiotic’s mechanistic tryptophan pathway was significantly modified when the data was stratified based on the individuals’ lactic acid bacteria (LAB) stool content. These results suggest that confounding factors such as dietary intake which impact stool LAB content may affect the response to the probiotic treatment. Using dietary intake, serum metabolite, and stool LAB colony forming unit (CFU) data from a previous clinical trial, the relationships between diet, metabolic response, and fecal LAB were assessed. The diets of subject groups with high vs. low CFUs of LAB/g of wet stool differed in their intakes of monounsaturated fatty acids, vegetables, proteins, and dairy. Individuals with high LAB consumed greater amounts of cheese, fermented meats, soy, nuts and seeds, alcoholic beverages, and oils whereas individuals with low LAB consumed higher amounts of tomatoes, starchy vegetables, and poultry. Several dietary variables correlated with LAB counts; positive correlations were determined for nuts and seeds, fish high in N-3 fatty acids, soy, and processed meats, and negative correlations to consumption of vegetables including tomatoes. Using machine learning, predictors of LAB count included cheese, nuts and seeds, fish high in N-3 fatty acids, and erucic acid. Erucic acid alone accurately predicted LAB categorization, and was shown to be utilized as a sole fatty acid source by several Lactobacillus species regardless of their mode of fermentation. Several metabolites were significantly upregulated in each group based on LAB titers, notably polypropylene glycol, caproic acid, pyrazine, and chondroitin sulfate; however, none were correlated with the dietary intake variables. These findings suggest that dietary variables may drive the presence of LAB in the human gastrointestinal tract and potentially impact response to probiotic interventions. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
164.
de Hegedüs, Rocío Amorín; Conesa, Ana; Foster, Jamie S
Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems Journal Article
In: Front Microbiol, vol. 14, pp. 1174685, 2023, ISSN: 1664-302X.
@article{pmid37577445,
title = {Integration of multi-omics data to elucidate keystone unknown taxa within microbialite-forming ecosystems},
author = {Rocío Amorín de Hegedüs and Ana Conesa and Jamie S Foster},
doi = {10.3389/fmicb.2023.1174685},
issn = {1664-302X},
year = {2023},
date = {2023-01-01},
journal = {Front Microbiol},
volume = {14},
pages = {1174685},
abstract = {Microbes continually shape Earth's biochemical and physical landscapes by inhabiting diverse metabolic niches. Despite the important role microbes play in ecosystem functioning, most microbial species remain unknown highlighting a gap in our understanding of structured complex ecosystems. To elucidate the relevance of these unknown taxa, often referred to as "microbial dark matter," the integration of multiple high throughput sequencing technologies was used to evaluate the co-occurrence and connectivity of all microbes within the community. Since there are no standard methodologies for multi-omics integration of microbiome data, we evaluated the abundance of "microbial dark matter" in microbialite-forming communities using different types meta-omic datasets: amplicon, metagenomic, and metatranscriptomic sequencing previously generated for this ecosystem. Our goal was to compare the community structure and abundances of unknown taxa within the different data types rather than to perform a functional characterization of the data. Metagenomic and metatranscriptomic data were input into SortMeRNA to extract 16S rRNA gene reads. The output, as well as amplicon sequences, were processed through QIIME2 for taxonomy analysis. The R package mdmnets was utilized to build co-occurrence networks. Most hubs presented unknown classifications, even at the phyla level. Comparisons of the highest scoring hubs of each data type using sequence similarity networks allowed the identification of the most relevant hubs within the microbialite-forming communities. This work highlights the importance of unknown taxa in community structure and proposes that ecosystem network construction can be used on several types of data to identify keystone taxa and their potential function within microbial ecosystems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Microbes continually shape Earth's biochemical and physical landscapes by inhabiting diverse metabolic niches. Despite the important role microbes play in ecosystem functioning, most microbial species remain unknown highlighting a gap in our understanding of structured complex ecosystems. To elucidate the relevance of these unknown taxa, often referred to as "microbial dark matter," the integration of multiple high throughput sequencing technologies was used to evaluate the co-occurrence and connectivity of all microbes within the community. Since there are no standard methodologies for multi-omics integration of microbiome data, we evaluated the abundance of "microbial dark matter" in microbialite-forming communities using different types meta-omic datasets: amplicon, metagenomic, and metatranscriptomic sequencing previously generated for this ecosystem. Our goal was to compare the community structure and abundances of unknown taxa within the different data types rather than to perform a functional characterization of the data. Metagenomic and metatranscriptomic data were input into SortMeRNA to extract 16S rRNA gene reads. The output, as well as amplicon sequences, were processed through QIIME2 for taxonomy analysis. The R package mdmnets was utilized to build co-occurrence networks. Most hubs presented unknown classifications, even at the phyla level. Comparisons of the highest scoring hubs of each data type using sequence similarity networks allowed the identification of the most relevant hubs within the microbialite-forming communities. This work highlights the importance of unknown taxa in community structure and proposes that ecosystem network construction can be used on several types of data to identify keystone taxa and their potential function within microbial ecosystems.
163.
Pillon, Nicolas J; Puig, Laura Sardón; Altıntaş, Ali; Kamble, Prasad G; Casaní-Galdón, Salvador; Gabriel, Brendan M; Barrès, Romain; Conesa, Ana; Chibalin, Alexander V; Näslund, Erik; Krook, Anna; Zierath, Juleen R
Palmitate impairs circadian transcriptomics in muscle cells through histone modification of enhancers Journal Article
In: Life Sci Alliance, vol. 6, no. 1, 2023, ISSN: 2575-1077.
@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}
}
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.
162.
Pérez-Benavente, Beatriz; Fathinajafabadi, Alihamze; de la Fuente, Lorena; Gandía, Carolina; Martínez-Férriz, Arantxa; Pardo-Sánchez, José Miguel; Milián, Lara; Conesa, Ana; Romero, Octavio A; Carretero, Julián; Matthiesen, Rune; Jariel-Encontre, Isabelle; Piechaczyk, Marc; Farràs, Rosa
New roles for AP-1/JUNB in cell cycle control and tumorigenic cell invasion via regulation of cyclin E1 and TGF-β2 Journal Article
In: Genome Biol, vol. 23, no. 1, pp. 252, 2022, ISSN: 1474-760X.
@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}
}
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.
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.