58,834 research outputs found
Cross-species network and transcript transfer
Metabolic processes, signal transduction, gene regulation, as well as gene and protein expression are largely controlled by biological networks. High-throughput experiments allow the measurement of a wide range of cellular states and interactions. However, networks are often not known in detail for specific biological systems and conditions. Gene and protein annotations are often transferred from model organisms to the species of interest. Therefore, the question arises whether biological networks can be transferred between species or whether they are specific for individual contexts. In this thesis, the following aspects are investigated: (i) the conservation and (ii) the cross-species transfer of eukaryotic protein-interaction and gene regulatory (transcription factor- target) networks, as well as (iii) the conservation of alternatively spliced variants.
In the simplest case, interactions can be transferred between species, based solely on the sequence similarity of the orthologous genes. However, such a transfer often results either in the transfer of only a few interactions (medium/high sequence similarity threshold) or in the transfer of many speculative interactions (low sequence similarity threshold). Thus, advanced network transfer approaches also consider the annotations of orthologous genes involved in the interaction transfer, as well as features derived from the network structure, in order to enable a reliable interaction transfer, even between phylogenetically very distant species. In this work, such an approach for the transfer of protein interactions is presented (COIN). COIN uses a sophisticated machine-learning model in order to label transferred interactions as either correctly transferred (conserved) or as incorrectly transferred (not conserved).
The comparison and the cross-species transfer of regulatory networks is more difficult than the transfer of protein interaction networks, as a huge fraction of the known regulations is only described in the (not machine-readable) scientific literature. In addition, compared to protein interactions, only a few conserved regulations are known, and regulatory elements appear to be strongly context-specific. In this work, the cross-species analysis of regulatory interaction networks is enabled with software tools and databases for global (ConReg) and thousands of context-specific (CroCo) regulatory interactions that are derived and integrated from the scientific literature, binding site predictions and experimental data.
Genes and their protein products are the main players in biological networks. However, to date, the aspect is neglected that a gene can encode different proteins. These alternative proteins can differ strongly from each other with respect to their molecular structure, function and their role in networks. The identification of conserved and species-specific splice variants and the integration of variants in network models will allow a more complete cross-species transfer and comparison of biological networks. With ISAR we support the cross-species transfer and comparison of alternative variants by introducing a gene-structure aware (i.e. exon-intron structure aware) multiple sequence alignment approach for variants from orthologous and paralogous genes.
The methods presented here and the appropriate databases allow the cross-species transfer of biological networks, the comparison of thousands of context-specific networks, and the cross-species comparison of alternatively spliced variants. Thus, they can be used as a starting point for the understanding of regulatory and signaling mechanisms in many biological systems.In biologischen Systemen werden Stoffwechselprozesse, Signalübertragungen sowie die Regulation von Gen- und Proteinexpression maßgeblich durch biologische Netzwerke gesteuert. Hochdurchsatz-Experimente ermöglichen die Messung einer Vielzahl von zellulären Zuständen und Wechselwirkungen. Allerdings sind für die meisten Systeme und Kontexte biologische Netzwerke nach wie vor unbekannt. Gen- und Proteinannotationen werden häufig von Modellorganismen übernommen. Demnach stellt sich die Frage, ob auch biologische Netzwerke und damit die systemischen Eigenschaften ähnlich sind und übertragen werden können. In dieser Arbeit wird: (i) Die Konservierung und (ii) die artenübergreifende Übertragung von eukaryotischen Protein-Interaktions- und regulatorischen (Transkriptionsfaktor-Zielgen) Netzwerken, sowie (iii) die Konservierung von Spleißvarianten untersucht.
Interaktionen können im einfachsten Fall nur auf Basis der Sequenzähnlichkeit zwischen orthologen Genen übertragen werden. Allerdings führt eine solche Übertragung oft dazu, dass nur sehr wenige Interaktionen übertragen werden können (hoher bis mittlerer Sequenzschwellwert) oder dass ein Großteil der übertragenden Interaktionen sehr spekulativ ist (niedriger Sequenzschwellwert). Verbesserte Methoden berücksichtigen deswegen zusätzlich noch die Annotationen der Orthologen, Eigenschaften der Interaktionspartner sowie die Netzwerkstruktur und können somit auch Interaktionen auf phylogenetisch weit entfernte Arten (zuverlässig) übertragen. In dieser Arbeit wird ein solcher Ansatz für die Übertragung von Protein-Interaktionen vorgestellt (COIN). COIN verwendet Verfahren des maschinellen Lernens, um Interaktionen als richtig (konserviert) oder als falsch übertragend (nicht konserviert) zu klassifizieren.
Der Vergleich und die artenübergreifende Übertragung von regulatorischen Interaktionen ist im Vergleich zu Protein-Interaktionen schwieriger, da ein Großteil der bekannten Regulationen nur in der (nicht maschinenlesbaren) wissenschaftlichen Literatur beschrieben ist. Zudem sind im Vergleich zu Protein-Interaktionen nur wenige konservierte Regulationen bekannt und regulatorische Elemente scheinen stark kontextabhängig zu sein. In dieser Arbeit wird die artenübergreifende Analyse von regulatorischen Netzwerken mit Softwarewerkzeugen und Datenbanken für globale (ConReg) und kontextspezifische (CroCo) regulatorische Interaktionen ermöglicht. Regulationen wurden dafür aus Vorhersagen, experimentellen Daten und aus der wissenschaftlichen Literatur abgeleitet und integriert.
Grundbaustein für viele biologische Netzwerke sind Gene und deren Proteinprodukte. Bisherige Netzwerkmodelle vernachlässigen allerdings meist den Aspekt, dass ein Gen verschiedene Proteine kodieren kann, die sich von der Funktion, der Proteinstruktur und der Rolle in Netzwerken stark voneinander unterscheiden können. Die Identifizierung von konservierten und artspezifischen Proteinprodukten und deren Integration in Netzwerkmodelle würde einen vollständigeren Übertrag und Vergleich von Netzwerken ermöglichen. In dieser Arbeit wird der artenübergreifende Vergleich von Proteinprodukten mit einem multiplen Sequenzalignmentverfahren für alternative Varianten von paralogen und orthologen Genen unterstützt, unter Berücksichtigung der bekannten Exon-Intron-Grenzen (ISAR).
Die in dieser Arbeit vorgestellten Verfahren, Datenbanken und Softwarewerkzeuge ermöglichen die Übertragung von biologischen Netzwerken, den Vergleich von tausenden kontextspezifischen Netzwerken und den artenübergreifenden Vergleich von alternativen Varianten. Sie können damit die Ausgangsbasis für ein Verständnis von Kommunikations- und Regulationsmechanismen in vielen biologischen Systemen bilden
Cross-species network and transcript transfer
Metabolic processes, signal transduction, gene regulation, as well as gene and protein expression are largely controlled by biological networks. High-throughput experiments allow the measurement of a wide range of cellular states and interactions. However, networks are often not known in detail for specific biological systems and conditions. Gene and protein annotations are often transferred from model organisms to the species of interest. Therefore, the question arises whether biological networks can be transferred between species or whether they are specific for individual contexts. In this thesis, the following aspects are investigated: (i) the conservation and (ii) the cross-species transfer of eukaryotic protein-interaction and gene regulatory (transcription factor- target) networks, as well as (iii) the conservation of alternatively spliced variants.
In the simplest case, interactions can be transferred between species, based solely on the sequence similarity of the orthologous genes. However, such a transfer often results either in the transfer of only a few interactions (medium/high sequence similarity threshold) or in the transfer of many speculative interactions (low sequence similarity threshold). Thus, advanced network transfer approaches also consider the annotations of orthologous genes involved in the interaction transfer, as well as features derived from the network structure, in order to enable a reliable interaction transfer, even between phylogenetically very distant species. In this work, such an approach for the transfer of protein interactions is presented (COIN). COIN uses a sophisticated machine-learning model in order to label transferred interactions as either correctly transferred (conserved) or as incorrectly transferred (not conserved).
The comparison and the cross-species transfer of regulatory networks is more difficult than the transfer of protein interaction networks, as a huge fraction of the known regulations is only described in the (not machine-readable) scientific literature. In addition, compared to protein interactions, only a few conserved regulations are known, and regulatory elements appear to be strongly context-specific. In this work, the cross-species analysis of regulatory interaction networks is enabled with software tools and databases for global (ConReg) and thousands of context-specific (CroCo) regulatory interactions that are derived and integrated from the scientific literature, binding site predictions and experimental data.
Genes and their protein products are the main players in biological networks. However, to date, the aspect is neglected that a gene can encode different proteins. These alternative proteins can differ strongly from each other with respect to their molecular structure, function and their role in networks. The identification of conserved and species-specific splice variants and the integration of variants in network models will allow a more complete cross-species transfer and comparison of biological networks. With ISAR we support the cross-species transfer and comparison of alternative variants by introducing a gene-structure aware (i.e. exon-intron structure aware) multiple sequence alignment approach for variants from orthologous and paralogous genes.
The methods presented here and the appropriate databases allow the cross-species transfer of biological networks, the comparison of thousands of context-specific networks, and the cross-species comparison of alternatively spliced variants. Thus, they can be used as a starting point for the understanding of regulatory and signaling mechanisms in many biological systems.In biologischen Systemen werden Stoffwechselprozesse, Signalübertragungen sowie die Regulation von Gen- und Proteinexpression maßgeblich durch biologische Netzwerke gesteuert. Hochdurchsatz-Experimente ermöglichen die Messung einer Vielzahl von zellulären Zuständen und Wechselwirkungen. Allerdings sind für die meisten Systeme und Kontexte biologische Netzwerke nach wie vor unbekannt. Gen- und Proteinannotationen werden häufig von Modellorganismen übernommen. Demnach stellt sich die Frage, ob auch biologische Netzwerke und damit die systemischen Eigenschaften ähnlich sind und übertragen werden können. In dieser Arbeit wird: (i) Die Konservierung und (ii) die artenübergreifende Übertragung von eukaryotischen Protein-Interaktions- und regulatorischen (Transkriptionsfaktor-Zielgen) Netzwerken, sowie (iii) die Konservierung von Spleißvarianten untersucht.
Interaktionen können im einfachsten Fall nur auf Basis der Sequenzähnlichkeit zwischen orthologen Genen übertragen werden. Allerdings führt eine solche Übertragung oft dazu, dass nur sehr wenige Interaktionen übertragen werden können (hoher bis mittlerer Sequenzschwellwert) oder dass ein Großteil der übertragenden Interaktionen sehr spekulativ ist (niedriger Sequenzschwellwert). Verbesserte Methoden berücksichtigen deswegen zusätzlich noch die Annotationen der Orthologen, Eigenschaften der Interaktionspartner sowie die Netzwerkstruktur und können somit auch Interaktionen auf phylogenetisch weit entfernte Arten (zuverlässig) übertragen. In dieser Arbeit wird ein solcher Ansatz für die Übertragung von Protein-Interaktionen vorgestellt (COIN). COIN verwendet Verfahren des maschinellen Lernens, um Interaktionen als richtig (konserviert) oder als falsch übertragend (nicht konserviert) zu klassifizieren.
Der Vergleich und die artenübergreifende Übertragung von regulatorischen Interaktionen ist im Vergleich zu Protein-Interaktionen schwieriger, da ein Großteil der bekannten Regulationen nur in der (nicht maschinenlesbaren) wissenschaftlichen Literatur beschrieben ist. Zudem sind im Vergleich zu Protein-Interaktionen nur wenige konservierte Regulationen bekannt und regulatorische Elemente scheinen stark kontextabhängig zu sein. In dieser Arbeit wird die artenübergreifende Analyse von regulatorischen Netzwerken mit Softwarewerkzeugen und Datenbanken für globale (ConReg) und kontextspezifische (CroCo) regulatorische Interaktionen ermöglicht. Regulationen wurden dafür aus Vorhersagen, experimentellen Daten und aus der wissenschaftlichen Literatur abgeleitet und integriert.
Grundbaustein für viele biologische Netzwerke sind Gene und deren Proteinprodukte. Bisherige Netzwerkmodelle vernachlässigen allerdings meist den Aspekt, dass ein Gen verschiedene Proteine kodieren kann, die sich von der Funktion, der Proteinstruktur und der Rolle in Netzwerken stark voneinander unterscheiden können. Die Identifizierung von konservierten und artspezifischen Proteinprodukten und deren Integration in Netzwerkmodelle würde einen vollständigeren Übertrag und Vergleich von Netzwerken ermöglichen. In dieser Arbeit wird der artenübergreifende Vergleich von Proteinprodukten mit einem multiplen Sequenzalignmentverfahren für alternative Varianten von paralogen und orthologen Genen unterstützt, unter Berücksichtigung der bekannten Exon-Intron-Grenzen (ISAR).
Die in dieser Arbeit vorgestellten Verfahren, Datenbanken und Softwarewerkzeuge ermöglichen die Übertragung von biologischen Netzwerken, den Vergleich von tausenden kontextspezifischen Netzwerken und den artenübergreifenden Vergleich von alternativen Varianten. Sie können damit die Ausgangsbasis für ein Verständnis von Kommunikations- und Regulationsmechanismen in vielen biologischen Systemen bilden
Metallochaperones regulate intracellular copper levels.
Copper (Cu) is an important enzyme co-factor that is also extremely toxic at high intracellular concentrations, making active efflux mechanisms essential for preventing Cu accumulation. Here, we have investigated the mechanistic role of metallochaperones in regulating Cu efflux. We have constructed a computational model of Cu trafficking and efflux based on systems analysis of the Cu stress response of Halobacterium salinarum. We have validated several model predictions via assays of transcriptional dynamics and intracellular Cu levels, discovering a completely novel function for metallochaperones. We demonstrate that in addition to trafficking Cu ions, metallochaperones also function as buffers to modulate the transcriptional responsiveness and efficacy of Cu efflux. This buffering function of metallochaperones ultimately sets the upper limit for intracellular Cu levels and provides a mechanistic explanation for previously observed Cu metallochaperone mutation phenotypes
Genome-Wide Association Study for Maize Leaf Cuticular Conductance Identifies Candidate Genes Involved in the Regulation of Cuticle Development.
The cuticle, a hydrophobic layer of cutin and waxes synthesized by plant epidermal cells, is the major barrier to water loss when stomata are closed at night and under water-limited conditions. Elucidating the genetic architecture of natural variation for leaf cuticular conductance (g c) is important for identifying genes relevant to improving crop productivity in drought-prone environments. To this end, we conducted a genome-wide association study of g c of adult leaves in a maize inbred association panel that was evaluated in four environments (Maricopa, AZ, and San Diego, CA, in 2016 and 2017). Five genomic regions significantly associated with g c were resolved to seven plausible candidate genes (ISTL1, two SEC14 homologs, cyclase-associated protein, a CER7 homolog, GDSL lipase, and β-D-XYLOSIDASE 4). These candidates are potentially involved in cuticle biosynthesis, trafficking and deposition of cuticle lipids, cutin polymerization, and cell wall modification. Laser microdissection RNA sequencing revealed that all these candidate genes, with the exception of the CER7 homolog, were expressed in the zone of the expanding adult maize leaf where cuticle maturation occurs. With direct application to genetic improvement, moderately high average predictive abilities were observed for whole-genome prediction of g c in locations (0.46 and 0.45) and across all environments (0.52). The findings of this study provide novel insights into the genetic control of g c and have the potential to help breeders more effectively develop drought-tolerant maize for target environments
Emerging connections between small RNAs and phytohormones
Small RNAs (sRNAs), mainly including miRNAs and siRNAs, are ubiquitous in eukaryotes. sRNAs mostly negatively regulate gene expression via (post-)transcriptional gene silencing through DNA methylation, mRNA cleavage, or translation inhibition. The mechanisms of sRNA biogenesis and function in diverse biological processes, as well as the interactions between sRNAs and environmental factors, like (a)biotic stress, have been deeply explored. Phytohormones are central in the plant’s response to stress, and multiple recent studies highlight an emerging role for sRNAs in the direct response to, or the regulation of, plant hormonal pathways. In this review, we discuss recent progress on the unraveling of crossregulation between sRNAs and nine plant hormones
Centralized Modularity of N-Linked Glycosylation Pathways in Mammalian Cells
Glycosylation is a highly complex process to produce a diverse repertoire of
cellular glycans that are attached to proteins and lipids. Glycans are involved
in fundamental biological processes, including protein folding and clearance,
cell proliferation and apoptosis, development, immune responses, and
pathogenesis. One of the major types of glycans, N-linked glycans, is formed by
sequential attachments of monosaccharides to proteins by a limited number of
enzymes. Many of these enzymes can accept multiple N-linked glycans as
substrates, thereby generating a large number of glycan intermediates and their
intermingled pathways. Motivated by the quantitative methods developed in
complex network research, we investigated the large-scale organization of such
N-linked glycosylation pathways in mammalian cells. The N-linked glycosylation
pathways are extremely modular, and are composed of cohesive topological
modules that directly branch from a common upstream pathway of glycan
synthesis. This unique structural property allows the glycan production between
modules to be controlled by the upstream region. Although the enzymes act on
multiple glycan substrates, indicating cross-talk between modules, the impact
of the cross-talk on the module-specific enhancement of glycan synthesis may be
confined within a moderate range by transcription-level control. The findings
of the present study provide experimentally-testable predictions for
glycosylation processes, and may be applicable to therapeutic glycoprotein
engineering
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Succession of physiological stages hallmarks the transcriptomic response of the fungus Aspergillus niger to lignocellulose.
BackgroundUnderstanding how fungi degrade lignocellulose is a cornerstone of improving renewables-based biotechnology, in particular for the production of hydrolytic enzymes. Considerable progress has been made in investigating fungal degradation during time-points where CAZyme expression peaks. However, a robust understanding of the fungal survival strategies over its life time on lignocellulose is thereby missed. Here we aimed to uncover the physiological responses of the biotechnological workhorse and enzyme producer Aspergillus niger over its life time to six substrates important for biofuel production.ResultsWe analysed the response of A. niger to the feedstock Miscanthus and compared it with our previous study on wheat straw, alone or in combination with hydrothermal or ionic liquid feedstock pretreatments. Conserved (substrate-independent) metabolic responses as well as those affected by pretreatment and feedstock were identified via multivariate analysis of genome-wide transcriptomics combined with targeted transcript and protein analyses and mapping to a metabolic model. Initial exposure to all substrates increased fatty acid beta-oxidation and lipid metabolism transcripts. In a strain carrying a deletion of the ortholog of the Aspergillus nidulans fatty acid beta-oxidation transcriptional regulator farA, there was a reduction in expression of selected lignocellulose degradative CAZyme-encoding genes suggesting that beta-oxidation contributes to adaptation to lignocellulose. Mannan degradation expression was wheat straw feedstock-dependent and pectin degradation was higher on the untreated substrates. In the later life stages, known and novel secondary metabolite gene clusters were activated, which are of high interest due to their potential to synthesize bioactive compounds.ConclusionIn this study, which includes the first transcriptional response of Aspergilli to Miscanthus, we highlighted that life time as well as substrate composition and structure (via variations in pretreatment and feedstock) influence the fungal responses to lignocellulose. We also demonstrated that the fungal response contains physiological stages that are conserved across substrates and are typically found outside of the conditions with high CAZyme expression, as exemplified by the stages that are dominated by lipid and secondary metabolism
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