2024
Krautwurst, Sarah; Lamkiewicz, Kevin
RNA-protein interaction prediction without high-throughput data: An overview and benchmark of in silico tools Journal Article
In: Computational and Structural Biotechnology Journal, vol. 23, pp. 4036-4046, 2024, ISSN: 2001-0370.
Abstract | Links | BibTeX | Tags: ncRNAs, proteins, RNA / transcriptomics, RNA structure
@article{nokey_66,
title = {RNA-protein interaction prediction without high-throughput data: An overview and benchmark of \textit{in silico} tools},
author = {Sarah Krautwurst and Kevin Lamkiewicz},
doi = {10.1016/j.csbj.2024.11.015},
issn = {2001-0370},
year = {2024},
date = {2024-11-08},
journal = {Computational and Structural Biotechnology Journal},
volume = {23},
pages = {4036-4046},
abstract = {RNA-protein interactions (RPIs) are crucial for accurately operating various processes in and between organisms across kingdoms of life. Mutual detection of RPI partner molecules depends on distinct sequential, structural, or thermodynamic features, which can be determined via experimental and bioinformatic methods. Still, the underlying molecular mechanisms of many RPIs are poorly understood. It is further hypothesized that many RPIs are not even described yet. Computational RPI prediction is continuously challenged by the lack of data and detailed research of very specific examples. With the discovery of novel RPI complexes in all kingdoms of life, adaptations of existing RPI prediction methods are necessary. Continuously improving computational RPI prediction is key in advancing the understanding of RPIs in detail and supplementing experimental RPI determination. The growing amount of data covering more species and detailed mechanisms support the accuracy of prediction tools, which in turn support specific experimental research on RPIs. Here, we give an overview of RPI prediction tools that do not use high-throughput data as the user's input. We review the tools according to their input, usability, and output. We then apply the tools to known RPI examples across different kingdoms of life. Our comparison shows that the investigated prediction tools do not favor a certain species and equip the user with results varying in degree of information, from an overall RPI score to detailed interacting residues. Furthermore, we provide a guide tree to assist users which RPI prediction tool is appropriate for their available input data and desired output.},
keywords = {ncRNAs, proteins, RNA / transcriptomics, RNA structure},
pubstate = {published},
tppubtype = {article}
}
RNA-protein interactions (RPIs) are crucial for accurately operating various processes in and between organisms across kingdoms of life. Mutual detection of RPI partner molecules depends on distinct sequential, structural, or thermodynamic features, which can be determined via experimental and bioinformatic methods. Still, the underlying molecular mechanisms of many RPIs are poorly understood. It is further hypothesized that many RPIs are not even described yet. Computational RPI prediction is continuously challenged by the lack of data and detailed research of very specific examples. With the discovery of novel RPI complexes in all kingdoms of life, adaptations of existing RPI prediction methods are necessary. Continuously improving computational RPI prediction is key in advancing the understanding of RPIs in detail and supplementing experimental RPI determination. The growing amount of data covering more species and detailed mechanisms support the accuracy of prediction tools, which in turn support specific experimental research on RPIs. Here, we give an overview of RPI prediction tools that do not use high-throughput data as the user's input. We review the tools according to their input, usability, and output. We then apply the tools to known RPI examples across different kingdoms of life. Our comparison shows that the investigated prediction tools do not favor a certain species and equip the user with results varying in degree of information, from an overall RPI score to detailed interacting residues. Furthermore, we provide a guide tree to assist users which RPI prediction tool is appropriate for their available input data and desired output.
2021
Heinze, Karolin; Hölzer, Martin; Ungelenk, Martin; Gerth, Melanie; Thomale, Jürgen; Heller, Regine; Morden, Claire R.; McManus, Kirk J.; Mosig, Alexander S.; Dürst, Matthias; Runnebaum, Ingo B.; Häfner, Norman
RUNX3 Transcript Variants Have Distinct Roles in Ovarian Carcinoma and Differently Influence Platinum Sensitivity and Angiogenesis Journal Article
In: Cancers, vol. 13, no. 3, pp. 476, 2021.
Abstract | Links | BibTeX | Tags: cancer, differential expression analysis, proteins, RNA / transcriptomics
@article{Heinze:21,
title = {RUNX3 Transcript Variants Have Distinct Roles in Ovarian Carcinoma and Differently Influence Platinum Sensitivity and Angiogenesis},
author = {Karolin Heinze and Martin Hölzer and Martin Ungelenk and Melanie Gerth and Jürgen Thomale and Regine Heller and Claire R. Morden and Kirk J. McManus and Alexander S. Mosig and Matthias Dürst and Ingo B. Runnebaum and Norman Häfner},
doi = {10.3390/cancers13030476},
year = {2021},
date = {2021-01-26},
urldate = {2021-01-26},
journal = {Cancers},
volume = {13},
number = {3},
pages = {476},
publisher = {MDPI AG},
abstract = {The prognosis of late-stage epithelial ovarian cancer (EOC) patients is affected by chemotherapy response and the malignant potential of the tumor cells. In earlier work, we identified hypermethylation of the runt-related transcription factor 3 gene (RUNX3) as a prognostic biomarker and contrary functions of transcript variants (TV1 and TV2) in A2780 and SKOV3 cells. The aim of the study was to further validate these results and to increase the knowledge about RUNX3 function in EOC. New RUNX3 overexpression models of high-grade serous ovarian cancer (HGSOC) were established and analyzed for phenotypic (IC50 determination, migration, proliferation and angiogenesis assay, DNA damage analysis) and transcriptomic consequences (NGS) of RUNX3 TV1 and TV2 overexpression. Platinum sensitivity was affected by a specific transcript variant depending on BRCA background. RUNX3 TV2 induced an increased sensitivity in BRCA1wt cells (OVCAR3), whereas TV1 increased the sensitivity and induced a G2/M arrest under treatment in BRCA1mut cells (A13-2-12). These different phenotypes relate to differences in DNA repair: homologous recombination deficient A13-2-12 cells show less γH2AX foci despite higher levels of Pt-DNA adducts. RNA-Seq analyses prove transcript variant and cell-line-specific RUNX3 effects. Pathway analyses revealed another clinically important function of RUNX3—regulation of angiogenesis. This was confirmed by thrombospondin1 analyses, HUVEC spheroid sprouting assays and proteomic profiling. Importantly, conditioned media (CM) from RUNX3 TV1 overexpressing A13-2-12 cells induced an increased HUVEC sprouting. Altogether, the presented data support the hypothesis of different functions of RUNX3 transcript variants related to the clinically relevant processes—platinum resistance and angiogenesis. },
keywords = {cancer, differential expression analysis, proteins, RNA / transcriptomics},
pubstate = {published},
tppubtype = {article}
}
The prognosis of late-stage epithelial ovarian cancer (EOC) patients is affected by chemotherapy response and the malignant potential of the tumor cells. In earlier work, we identified hypermethylation of the runt-related transcription factor 3 gene (RUNX3) as a prognostic biomarker and contrary functions of transcript variants (TV1 and TV2) in A2780 and SKOV3 cells. The aim of the study was to further validate these results and to increase the knowledge about RUNX3 function in EOC. New RUNX3 overexpression models of high-grade serous ovarian cancer (HGSOC) were established and analyzed for phenotypic (IC50 determination, migration, proliferation and angiogenesis assay, DNA damage analysis) and transcriptomic consequences (NGS) of RUNX3 TV1 and TV2 overexpression. Platinum sensitivity was affected by a specific transcript variant depending on BRCA background. RUNX3 TV2 induced an increased sensitivity in BRCA1wt cells (OVCAR3), whereas TV1 increased the sensitivity and induced a G2/M arrest under treatment in BRCA1mut cells (A13-2-12). These different phenotypes relate to differences in DNA repair: homologous recombination deficient A13-2-12 cells show less γH2AX foci despite higher levels of Pt-DNA adducts. RNA-Seq analyses prove transcript variant and cell-line-specific RUNX3 effects. Pathway analyses revealed another clinically important function of RUNX3—regulation of angiogenesis. This was confirmed by thrombospondin1 analyses, HUVEC spheroid sprouting assays and proteomic profiling. Importantly, conditioned media (CM) from RUNX3 TV1 overexpressing A13-2-12 cells induced an increased HUVEC sprouting. Altogether, the presented data support the hypothesis of different functions of RUNX3 transcript variants related to the clinically relevant processes—platinum resistance and angiogenesis.
2020
Jordan-Paiz, Ana; Nevot, Maria; Lamkiewicz, Kevin; Lataretu, Marie; Franco, Sandra; Marz, Manja; Martinez, Miguel Angel
HIV-1 lethality and loss of Env protein expression induced by single synonymous substitutions in the virus genome intronic splicing silencer Journal Article
In: J Virol, vol. 94, no. 21, 2020.
Abstract | Links | BibTeX | Tags: proteins, RNA structure, splicing, viruses
@article{Jordan-Paiz:20,
title = {HIV-1 lethality and loss of Env protein expression induced by single synonymous substitutions in the virus genome intronic splicing silencer},
author = {Ana Jordan-Paiz and Maria Nevot and Kevin Lamkiewicz and Marie Lataretu and Sandra Franco and Manja Marz and Miguel Angel Martinez},
doi = {10.1128/jvi.01108-20},
year = {2020},
date = {2020-10-14},
urldate = {2020-01-01},
journal = {J Virol},
volume = {94},
number = {21},
publisher = {American Society for Microbiology},
abstract = {Synonymous genome recoding has been widely used to study different aspects of virus biology. Codon usage affects the temporal regulation of viral gene expression. In this study, we performed synonymous codon mutagenesis to investigate whether codon usage affected HIV-1 Env protein expression and virus viability. We replaced the codons AGG, GAG, CCU, ACU, CUC, and GGG of the HIV-1 env gene with the synonymous codons CGU, GAA, CCG, ACG, UUA, and GGA, respectively. We found that recoding the Env protein gp120 coding region (excluding the Rev response element [RRE]) did not significantly affect virus replication capacity, even though we introduced 15 new CpG dinucleotides. In contrast, changing a single codon (AGG to CGU) located in the gp41 coding region (HXB2 env position 2125 to 2127), which was included in the intronic splicing silencer (ISS), completely abolished virus replication and Env expression. Computational analyses of this mutant revealed a severe disruption in the ISS RNA secondary structure. A variant that restored ISS secondary RNA structure also reestablished Env production and virus viability. Interestingly, this codon variant prevented both virus replication and Env translation in a eukaryotic expression system. These findings suggested that disrupting mRNA splicing was not the only means of inhibiting translation. Our findings indicated that synonymous gp120 recoding was not always deleterious to HIV-1 replication. Importantly¸ we found that disrupting an external ISS loop strongly affected HIV-1 replication and Env translation.
},
keywords = {proteins, RNA structure, splicing, viruses},
pubstate = {published},
tppubtype = {article}
}
Synonymous genome recoding has been widely used to study different aspects of virus biology. Codon usage affects the temporal regulation of viral gene expression. In this study, we performed synonymous codon mutagenesis to investigate whether codon usage affected HIV-1 Env protein expression and virus viability. We replaced the codons AGG, GAG, CCU, ACU, CUC, and GGG of the HIV-1 env gene with the synonymous codons CGU, GAA, CCG, ACG, UUA, and GGA, respectively. We found that recoding the Env protein gp120 coding region (excluding the Rev response element [RRE]) did not significantly affect virus replication capacity, even though we introduced 15 new CpG dinucleotides. In contrast, changing a single codon (AGG to CGU) located in the gp41 coding region (HXB2 env position 2125 to 2127), which was included in the intronic splicing silencer (ISS), completely abolished virus replication and Env expression. Computational analyses of this mutant revealed a severe disruption in the ISS RNA secondary structure. A variant that restored ISS secondary RNA structure also reestablished Env production and virus viability. Interestingly, this codon variant prevented both virus replication and Env translation in a eukaryotic expression system. These findings suggested that disrupting mRNA splicing was not the only means of inhibiting translation. Our findings indicated that synonymous gp120 recoding was not always deleterious to HIV-1 replication. Importantly¸ we found that disrupting an external ISS loop strongly affected HIV-1 replication and Env translation.
2011
Lechner, Marcus; Findeiss, Sven; Steiner, Lydia; Marz, Manja; Stadler, Peter F; Prohaska, Sonja J
Proteinortho: detection of (co-)orthologs in large-scale analysis Journal Article
In: BMC Bioinf, vol. 12, pp. 124, 2011.
Abstract | Links | BibTeX | Tags: alignment, bacteria, phylogenetics, proteins, software
@article{Lechner:11,
title = {Proteinortho: detection of (co-)orthologs in large-scale analysis},
author = {Marcus Lechner and Sven Findeiss and Lydia Steiner and Manja Marz and Peter F Stadler and Sonja J Prohaska},
url = {http://bioinf.pharmazie.uni-marburg.de/supplements/proteinortho/},
doi = {10.1186/1471-2105-12-124},
year = {2011},
date = {2011-04-28},
urldate = {2011-04-28},
journal = {BMC Bioinf},
volume = {12},
pages = {124},
abstract = {Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases. The program Proteinortho described here is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. We apply Proteinortho to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. We identified thirty proteins present in 99% of all bacterial proteomes. Proteinortho significantly reduces the required amount of memory for orthology analysis compared to existing tools, allowing such computations to be performed on off-the-shelf hardware.},
keywords = {alignment, bacteria, phylogenetics, proteins, software},
pubstate = {published},
tppubtype = {article}
}
Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases. The program Proteinortho described here is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. We apply Proteinortho to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. We identified thirty proteins present in 99% of all bacterial proteomes. Proteinortho significantly reduces the required amount of memory for orthology analysis compared to existing tools, allowing such computations to be performed on off-the-shelf hardware.
2009
Marz, Manja; Donath, Alexander; Verstraete, Nina; Nguyen, Van Trung; Stadler, Peter F; Bensaude, Olivier
Evolution of 7SK RNA and its protein partners in metazoa Journal Article
In: Mol Biol Evol, vol. 26, no. 12, pp. 2821–2830, 2009.
Abstract | Links | BibTeX | Tags: evolution, ncRNAs, proteins, RNA structure
@article{Marz:09,
title = {Evolution of 7SK RNA and its protein partners in metazoa},
author = {Manja Marz and Alexander Donath and Nina Verstraete and Van Trung Nguyen and Peter F Stadler and Olivier Bensaude},
doi = {10.1093/molbev/msp198},
year = {2009},
date = {2009-09-04},
urldate = {2009-09-04},
journal = {Mol Biol Evol},
volume = {26},
number = {12},
pages = {2821--2830},
abstract = {7SK RNA is a key player in the regulation of polymerase II transcription. 7SK RNA was considered as a highly conserved vertebrate innovation. The discovery of poorly conserved homologs in several insects and lophotrochozoans, however, implies a much earlier evolutionary origin. The mechanism of 7SK function requires interaction with the proteins HEXIM and La-related protein 7. Here, we present a comprehensive computational analysis of these two proteins in metazoa, and we extend the collection of 7SK RNAs by several additional candidates. In particular, we describe 7SK homologs in Caenorhabditis species. Furthermore, we derive an improved secondary structure model of 7SK RNA, which shows that the structure is quite well-conserved across animal phyla despite the extreme divergence at sequence level.},
keywords = {evolution, ncRNAs, proteins, RNA structure},
pubstate = {published},
tppubtype = {article}
}
7SK RNA is a key player in the regulation of polymerase II transcription. 7SK RNA was considered as a highly conserved vertebrate innovation. The discovery of poorly conserved homologs in several insects and lophotrochozoans, however, implies a much earlier evolutionary origin. The mechanism of 7SK function requires interaction with the proteins HEXIM and La-related protein 7. Here, we present a comprehensive computational analysis of these two proteins in metazoa, and we extend the collection of 7SK RNAs by several additional candidates. In particular, we describe 7SK homologs in Caenorhabditis species. Furthermore, we derive an improved secondary structure model of 7SK RNA, which shows that the structure is quite well-conserved across animal phyla despite the extreme divergence at sequence level.