ORCID Profile
0000-0003-2671-6763
Current Organisations
University of Potsdam
,
Max Planck Institute for Molecular Plant Physiology
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Publisher: Royal Society of Chemistry (RSC)
Date: 2012
DOI: 10.1039/C2MB25089F
Abstract: Inference of accurate gene annotations requires integration of existing biological knowledge, structured in a form of ontology, with data from transcriptomics high-throughput technologies. This undertaking requires developing algorithms that integrate genome-scale data, even for model organisms. Gene relevance networks have emerged as a powerful representative of the structure of the data. Such networks can be used for intra-species transfer of gene annotations following the guilt-by-association principle. An analogous principle can serve as a basis for inter-species transfer of gene annotations by comparing well-defined subnetworks. In this review, we compare and contrast the concepts of relevance and proximity networks and briefly review the concept of semantic similarity. We then provide a detailed account of quantitative guilt-by-association inference in the setting of genome-scale relevance networks. Moreover, we systematically survey the existing network-based approaches for automated gene function annotation and categorize them under one umbrella in terms of employed methodology. Furthermore, we discuss suitable data selection strategies required for deriving meaningful and unbiased genome-scale networks from large transcriptomics compendia. Lastly, by simulating gene function prediction with a classical network-based algorithm, we show how the number of genes of unknown function influences prediction within a species and pinpoint the need and the requirements for inter-species knowledge transfer.
Publisher: Springer Science and Business Media LLC
Date: 19-01-2021
DOI: 10.1038/S41467-020-20730-Y
Abstract: Cell shape is crucial for the function and development of organisms. Yet, versatile frameworks for cell shape quantification, comparison, and classification remain underdeveloped. Here, we introduce a visibility graph representation of shapes that facilitates network-driven characterization and analyses across shapes encountered in different domains. Using the ex le of complex shape of leaf pavement cells, we show that our framework accurately quantifies cell protrusions and invaginations and provides additional functionality in comparison to the contending approaches. We further show that structural properties of the visibility graphs can be used to quantify pavement cell shape complexity and allow for classification of plants into their respective phylogenetic clades. Therefore, the visibility graphs provide a robust and unique framework to accurately quantify and classify the shape of different objects.
Publisher: Oxford University Press (OUP)
Date: 26-08-2016
DOI: 10.1104/PP.16.01100
Abstract: Plant cell walls are essential for plant growth and development. The cell walls are traditionally ided into primary walls, which surround growing cells, and secondary walls, which provide structural support to certain cell types and promote their functions. While much information is available about the enzymes and components that contribute to the production of these two types of walls, much less is known about the transition from primary to secondary wall synthesis. To address this question, we made use of a transcription factor system in Arabidopsis (Arabidopsis thaliana) in which an overexpressed master secondary wall-inducing transcription factor, VASCULAR-RELATED NAC DOMAIN PROTEIN7, can be redirected into the nucleus by the addition of dexamethasone. We established the time frame during which primary wall synthesis changed into secondary wall production in dexamethasone-treated seedlings and measured transcript and metabolite abundance at eight time points after induction. Using cluster- and network-based analyses, we integrated the data sets to explore coordination between transcripts, metabolites, and the combination of the two across the time points. We provide the raw data as well as a range of network-based analyses. These data reveal links between hormone signaling and metabolic processes during the formation of secondary walls and provide a framework toward a deeper understanding of how primary walls transition into secondary walls.
Publisher: Oxford University Press (OUP)
Date: 04-2009
DOI: 10.1093/NAR/GKP206
Publisher: Wiley
Date: 20-06-2016
DOI: 10.1111/NPH.14030
Abstract: The mitochondrial alternative oxidase pathway (AOP) has been suggested to act as a sink for excess reducing power generated in the chloroplast under high-light (HL) stress and thus may reduce photoinhibition. The aim of this study was to compare different species to investigate the in vivo regulation and role of AOP under HL stress. The in vivo activities of AOP (νalt ) and the cytochrome oxidase pathway, chlorophyll fluorescence, metabolite profiles, alternative oxidase (AOX) capacity and protein amount were determined in leaves of five C3 species under growth light and after HL treatment. Differences in respiration and metabolite levels were observed among species under growth light conditions. The HL response of νalt was highly species dependent, correlated with the AOP capacity and independent of AOX protein content. Nevertheless, significant correlations were observed between νalt , levels of key metabolites and photosynthetic parameters. The results show that the species-specific response of νalt is caused by the differential post-translational regulation of AOX. Significant correlations between respiration, metabolites and photosynthetic performance across species suggest that AOP may permit stress-related amino acid synthesis, whilst maintaining photosynthetic activity under HL stress.
Publisher: Cold Spring Harbor Laboratory
Date: 17-09-2023
Publisher: Oxford University Press (OUP)
Date: 15-08-2019
DOI: 10.1104/PP.19.00497
Publisher: Oxford University Press (OUP)
Date: 06-07-2022
DOI: 10.1093/G3JOURNAL/JKAC170
Abstract: Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.
Publisher: Wiley
Date: 04-12-2017
DOI: 10.1111/NPH.14921
Abstract: Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co-function networks for Arabidopsis thaliana and demonstrate how the integration of these networks produces more accurate gene function predictions for a larger fraction of genes with unknown function. These predictions were used to identify genes involved in mitochondrial complex I formation, and for five of them, we confirmed the predictions experimentally. The ensemble predictions are provided as a user-friendly online database, EnsembleNet. The methods presented here demonstrate that ensemble gene function prediction is a powerful method to boost prediction performance, whereas the EnsembleNet database provides a cutting-edge community tool to guide experimentalists.
Publisher: Proceedings of the National Academy of Sciences
Date: 27-06-2017
Abstract: In the crowded interior of a cell, diffusion alone is insufficient to master varying transport requirements for cell sustenance and growth. The dynamic actin cytoskeleton is an essential cellular component that provides transport and cytoplasmic streaming in plant cells, but little is known about its system-level organization. Here, we resolve key challenges in understanding system-level actin-based transport. We present an automated image-based, network-driven framework that accurately incorporates both actin cytoskeleton and organelle trafficking. We demonstrate that actin cytoskeleton network properties support efficient transport in both growing and elongated hypocotyl cells. We show that organelle transport can be predicted from the system-wide cellular organization of the actin cytoskeleton. Our framework can be readily applied to investigate cytoskeleton-based transport in other organisms.
Publisher: The Company of Biologists
Date: 15-06-2022
DOI: 10.1242/DEV.200415
Abstract: Rice (Oryza sativa) is one of our main food crops, feeding ∼3.5 billion people worldwide. An increasing number of studies note the importance of the cytoskeleton, including actin filaments and microtubules, in rice development and environmental responses. Yet, reliable in vivo cytoskeleton markers are lacking in rice, which limits our knowledge of cytoskeletal functions in living cells. Therefore, we generated bright fluorescent marker lines of the actin and microtubule cytoskeletons in rice, suitable for live-cell imaging in a wide variety of rice tissues. Using these lines, we show that actin bundles and microtubules engage and co-function during pollen grain development, how the cytoskeletal components are coordinated during root cell development, and that the actin cytoskeleton is robust and facilitates microtubule responses during salt stress. Hence, we conclude that our cytoskeletal marker lines, highlighted by our findings of cytoskeletal associations and dynamics, will substantially further future investigations in rice biology.
Publisher: Oxford University Press (OUP)
Date: 30-01-2023
Publisher: The Royal Society
Date: 06-08-2014
Abstract: The actin and microtubule (MT) cytoskeletons are vital structures for cell growth and development across all species. While in idual molecular mechanisms underpinning actin and MT dynamics have been intensively studied, principles that govern the cytoskeleton organization remain largely unexplored. Here, we captured biologically relevant characteristics of the plant cytoskeleton through a network-driven imaging-based approach allowing us to quantitatively assess dynamic features of the cytoskeleton. By introducing suitable null models, we demonstrate that the plant cytoskeletal networks exhibit properties required for efficient transport, namely, short average path lengths and high robustness. We further show that these advantageous features are maintained during temporal cytoskeletal rearrangements. Interestingly, man-made transportation networks exhibit similar properties, suggesting general laws of network organization supporting erse transport processes. The proposed network-driven analysis can be readily used to identify organizational principles of cytoskeletons in other organisms.
Publisher: Oxford University Press (OUP)
Date: 11-01-2016
DOI: 10.1104/PP.15.01281
Location: Germany
No related grants have been discovered for Zoran Nikoloski.