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Domain-based prediction of the human isoform interactome provides insights into the functional impact of alternative splicing
Alternative splicing is known to remodel protein-protein interaction networks (“interactomes”), yet large-scale determination of isoform-specific interactions remains challenging. We present a domain-based method to predict the isoform interactome from the reference interactome. First, we construct the domain-resolved reference interactome by mapping known domain-domain interactions onto experimentally-determined interactions between reference proteins. Then, we construct the isoform interactome by predicting that an isoform loses an interaction if it loses the domain mediating the interaction. Our prediction framework is of high-quality when assessed by experimental data. The predicted human isoform interactome reveals extensive network remodeling by alternative splicing. Protein pairs interacting with different isoforms of the same gene tend to be more divergent in biological function, tissue expression, and disease phenotype than protein pairs interacting with the same isoforms. Our prediction method complements experimental efforts, and demonstrates that integrating structural domain information with interactomes provides insights into the functional impact of alternative splicing
From condition-specific interactions towards the differential complexome of proteins
While capturing the transcriptomic state of a cell is a comparably simple effort with modern sequencing techniques, mapping protein interactomes and complexomes in a sample-specific manner is currently not feasible on a large scale. To understand crucial biological processes, however, knowledge on the physical interplay between proteins can be more interesting than just their mere expression. In this thesis, we present and demonstrate four software tools that unlock the cellular wiring in a condition-specific manner and promise a deeper understanding of what happens upon cell fate transitions. PPIXpress allows to exploit the abundance of existing expression data to generate specific interactomes, which can even consider alternative splicing events when protein isoforms can be related to the presence of causative protein domain interactions of an underlying model. As an addition to this work, we developed the convenient differential analysis tool PPICompare to determine rewiring events and their causes within the inferred interaction networks between grouped samples. Furthermore, we present a new implementation of the combinatorial protein complex prediction algorithm DACO that features a significantly reduced runtime. This improvement facilitates an application of the method for a large number of samples and the resulting sample-specific complexes can ultimately be assessed quantitatively with our novel differential protein complex analysis tool CompleXChange.Das Transkriptom einer Zelle ist mit modernen Sequenzierungstechniken vergleichsweise einfach zu erfassen. Die Ermittlung von Proteininteraktionen und -komplexen wiederum ist in großem Maßstab derzeit nicht möglich. Um wichtige biologische Prozesse zu verstehen, kann das Zusammenspiel von Proteinen jedoch erheblich interessanter sein als deren reine Expression. In dieser Arbeit stellen wir vier Software-Tools vor, die es ermöglichen solche Interaktionen zustandsbezogen zu betrachten und damit ein tieferes Verständnis darüber versprechen, was in der Zelle bei Veränderungen passiert. PPIXpress ermöglicht es vorhandene Expressionsdaten zu nutzen, um die aktiven Interaktionen in einem biologischen Kontext zu ermitteln. Wenn Proteinvarianten mit Interaktionen von Proteindomänen in Verbindung gebracht werden können, kann hierbei sogar alternatives Spleißen berücksichtigen werden. Als Ergänzung dazu haben wir das komfortable Differenzialanalyse-Tool PPICompare entwickelt, welches Veränderungen des Interaktoms und deren Ursachen zwischen gruppierten Proben bestimmen kann. Darüber hinaus stellen wir eine neue Implementierung des Proteinkomplex-Vorhersagealgorithmus DACO vor, die eine deutlich reduzierte Laufzeit aufweist. Diese Verbesserung ermöglicht die Anwendung der Methode auf eine große Anzahl von Proben. Die damit bestimmten probenspezifischen Komplexe können schließlich mit unserem neuartigen Differenzialanalyse-Tool CompleXChange quantitativ bewertet werden
Characterization of NUMB as a Regulator of Anaplastic Lymphoma Kinase
Cellular events rely on protein-protein interactions that are often mediated by modular domains which recognize particular sequence motifs in binding partners. The NUMB protein is the first described cell fate determinant and multifaceted adaptor that is involved in a wide variety of cellular events. NUMB mainly mediates protein interactions via its modular PTB domain. Here we present a systematic investigation of the NUMB-PTB interactome by employing an integrative strategy combining both protein and peptide arrays. We profiled NUMB-PTB binding specificity and interacting proteins genome-wide. The receptor tyrosine kinases (RTKs) are found highly enriched in the interactome, raising the possibility that NUMB may act as a universal binding partner of RTKs. To further validate this hypothesis, we focused on the interaction between NUMB and Anaplastic Lymphoma Kinase (ALK), which promotes oncogenesis in a number of cancer types. Consistent with the prediction based on our proteomic study, NUMB-PTB directly binds to two motifs in ALK in vitro and in vivo. Intriguingly, functional analysis reveals that NUMB-ALK interaction regulates ALK activity antagonistically in an isoform dependent manner, by directing ALK to distinct post-endocytic trafficking destinations. Our study provides mechanistic insight into ALK regulation, explains the controversial behaviors of NUMB in tumorigenesis at the molecular level, and further reveals a biomarker of potential clinical value
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
Tripartite degrons confer diversity and specificity on regulated protein degradation in the ubiquitin-proteasome system
Specific signals (degrons) regulate protein turnover mediated by the ubiquitin-proteasome system. Here we systematically analyse known degrons and propose a tripartite model comprising the following: (1) a primary degron (peptide motif) that specifies substrate recognition by cognate E3 ubiquitin ligases, (2) secondary site(s) comprising a single or multiple neighbouring ubiquitinated lysine(s) and (3) a structurally disordered segment that initiates substrate unfolding at the 26S proteasome. Primary degron sequences are conserved among orthologues and occur in structurally disordered regions that undergo E3-induced folding-on-binding. Posttranslational modifications can switch primary degrons into E3-binding-competent states, thereby integrating degradation with signalling pathways. Degradation-linked lysines tend to be located within disordered segments that also initiate substrate degradation by effective proteasomal engagement. Many characterized mutations and alternative isoforms with abrogated degron components are implicated in disease. These effects result from increased protein stability and interactome rewiring. The distributed nature of degrons ensures regulation, specificity and combinatorial control of degradation. © 2016 Nature America, Inc
Global profiling of alternative RNA splicing events provides insights into molecular differences between various types of hepatocellular carcinoma
Protein families encoded by transcripts that are differentially spliced in various types of HCC. Table S2. Bioinformatical prediction of functional changes caused by some of ASEs identified. Table S3. List of tumor suppressors for which AS is dysregulated in various types of HCC. Table S4. List of oncogenes for which AS is dysregulated in various types of HCC. Table S5. List of kinases for which AS is dysregulated in various types of HCC. Table S6. List of transcription factors for which AS is dysregulated in various types of HCC. Table S7. List of genes for which AS is dysregulated in all types of HCC. Table S8. List of genes uniquely dysregulated in HBV-associated HCC. Table S9. List of genes uniquely dysregulated in HCV-associated HCC. Table S10. List of genes uniquely dysregulated in HBV&HCV-associated HCC. Table S11. List of genes uniquely dysregulated in virus-free HCC. Figure S1. Characterization of splicing mysregulation in HCC. Figure S2. Characterization of ASEs that are modified in HBV- and HCV-associated HCC. Figure S3. AS modifications in transcripts encoded by kinases and transcriptions factores in HBV- and HCV-associated HCC. Figure S4. Global profiling of ASE modifications in both HBV&HCV-associated HCC and virus-free-associated HCC. Figure S5. RNA splicing factors in HCC. Figure S6. Modifications to AS of 96 transcripts in response to knockdown of splicing factors with specific siRNAs (PDF 6675 kb
ISOGO: Functional annotation of protein-coding splice variants
The advent of RNA-seq technologies has switched the paradigm of genetic analysis from a genome
to a transcriptome-based perspective. Alternative splicing generates functional diversity in genes,
but the precise functions of many individual isoforms are yet to be elucidated. Gene Ontology was
developed to annotate gene products according to their biological processes, molecular functions and
cellular components. Despite a single gene may have several gene products, most annotations are not
isoform-specifc and do not distinguish the functions of the diferent proteins originated from a single
gene. Several approaches have tried to automatically annotate ontologies at the isoform level, but
this has shown to be a daunting task. We have developed ISOGO (ISOform+GO function imputation),
a novel algorithm to predict the function of coding isoforms based on their protein domains and their
correlation of expression along 11,373 cancer patients. Combining these two sources of information
outperforms previous approaches: it provides an area under precision-recall curve (AUPRC) fve times
larger than previous attempts and the median AUROC of assigned functions to genes is 0.82. We tested
ISOGO predictions on some genes with isoform-specifc functions (BRCA1, MADD,VAMP7 and ITSN1)
and they were coherent with the literature. Besides, we examined whether the main isoform of each
gene -as predicted by APPRIS- was the most likely to have the annotated gene functions and it occurs
in 99.4% of the genes. We also evaluated the predictions for isoform-specifc functions provided by
the CAFA3 challenge and results were also convincing. To make these results available to the scientifc
community, we have deployed a web application to consult ISOGO predictions (https://biotecnun.unav.
es/app/isogo). Initial data, website link, isoform-specifc GO function predictions and R code is available
at https://gitlab.com/icassol/isogo
Consequences of refining biological networks through detailed pathway information : From genes to proteoforms
Biologiske nettverk kan brukes til å modellere molekylære prosesser, forstå sykdomsprogresjon og finne nye behandlingsstrategier. Denne avhandlingen har undersøkt hvordan utformingen av slike nettverk påvirker deres struktur, og hvordan dette kan benyttes til å forbedre spesifisiteten for påfølgende analyser av slike modeller.
Det første som ble undersøkt var potensialet ved å bruke mer detaljerte molekylære data når man modellerer humane biokjemiske reaksjonsnettverk. Resultatene bekrefter at det er nok informasjon om proteoformer, det vil si proteiner i spesifikke post-translasjonelle tilstander, for systematiske analyser og viste også store forskjeller i strukturen mellom en gensentrisk og en proteoformsentrisk representasjon.
Deretter utviklet vi programmatisk tilgang og søk i slike nettverk basert på ulike typer av biomolekyler, samt en generisk algoritme som muliggjør fleksibel kartlegging av eksperimentelle data knyttet til den teoretiske representasjonen av proteoformer i referansedatabaser.
Til slutt ble det konstruert såkalte pathway-spesifikke nettverk ved bruk av ulike detaljnivåer ved representasjonen av biokjemiske reaksjoner. Her ble informasjon som vanligvis blir oversett i standard nettverksrepresentasjoner inkludert: små molekyler, isoformer og modifikasjoner. Strukturelle egenskaper, som nettverksstørrelse, graddistribusjon og tilkobling i både globale og lokale undernettverk, ble deretter analysert for å kvantifisere virkningene av endringene.Biological networks can be used to model molecular processes, understand disease progression, and find new treatment strategies. This thesis investigated how refining the design of biological networks influences their structure, and how this can be used to improve the specificity of pathway analyses.
First, we investigate the potential to use more detailed molecular data in current human biological pathways. We verified that there are enough proteoform annotations, i.e. information about proteins in specific post-translational states, for systematic analyses and characterized the structure of gene-centric versus proteoform-centric network representations of pathways.
Next, we enabled the programmatic search and mining of pathways using different models for biomolecules including proteoforms. We notably designed a generic proteoform matching algorithm enabling the flexible mapping of experimental data to the theoretic representation in reference databases.
Finally, we constructed pathway-based networks using different degrees of detail in the representation of biochemical reactions. We included information overlooked in most standard network representations: small molecules, isoforms, and post-translational modifications. Structural properties such as network size, degree distribution, and connectivity in both global and local subnetworks, were analysed to quantify the impact of the added molecular entities.Doktorgradsavhandlin
A novel combined scientific and artistic approach for the advanced characterization of interactomes: The akirin/subolesin model
The main objective of this study was to propose a novel methodology to approach challenges in molecular biology. Akirin/Subolesin (AKR/SUB) are vaccine protective antigens and are a model for the study of the interactome due to its conserved function in the regulation of different biological processes such as immunity and development throughout the metazoan. Herein, three visual artists and a music professor collaborated with scientists for the functional characterization of the AKR2 interactome in the regulation of the NF-¿B pathway in human placenta cells. The results served as a methodological proof-of-concept to advance this research area. The results showed new perspectives on unexplored characteristics of AKR2 with functional implications. These results included protein dimerization, the physical interactions with different proteins simultaneously to regulate various biological processes defined by cell type-specific AKR– protein interactions, and how these interactions positively or negatively regulate the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-¿B) signaling pathway in a biological context-dependent manner. These results suggested that AKR2-interacting proteins might constitute suitable secondary transcription factors for cell-and stimulus-specific regulation of NF-¿B. Musical perspective supported AKR/SUB evolutionary conservation in different species and provided new mechanistic insights into the AKR2 interactome. The combined scientific and artistic perspectives resulted in a multidisciplinary approach, advancing our knowledge on AKR/SUB interactome, and provided new insights into the function of AKR2–protein interactions in the regulation of the NF-¿B pathway. Additionally, herein we proposed an algorithm for quantum vaccinomics by focusing on the model proteins AKR/SUB. © 2020 by the authors. Licensee MDPI, Basel, Switzerland
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