Refine
Year of publication
- 2022 (228) (remove)
Document Type
- Article (157)
- Doctoral Thesis (43)
- Postprint (23)
- Review (2)
- Habilitation Thesis (1)
- Master's Thesis (1)
- Other (1)
Is part of the Bibliography
- yes (228)
Keywords
- functional traits (7)
- Arabidopsis thaliana (5)
- animal personality (5)
- light pollution (5)
- GPS (4)
- antibody (4)
- coexistence (4)
- fence ecology (4)
- giving-up density (4)
- ungulate (4)
Institute
- Institut für Biochemie und Biologie (228) (remove)
Climate change and human-driven eutrophication promote the spread of harmful cyanobacteria blooms in lakes worldwide, which affects water quality and impairs the aquatic food chain. In recent times, sedimentary ancient DNA-based (sedaDNA) studies were used to probe how centuries of climate and environmental changes have affected cyanobacterial assemblages in temperate lakes. However, there is a lack of information on the consistency between sediment-deposited cyanobacteria communities versus those of the water column, and on the individual role of natural climatic changes versus human pressure on cyanobacteria community dynamics over multi-millennia time scales.
Therefore, this thesis uses sedimentary ancient DNA of Lake Tiefer See in northeastern Germany to trace the deposition of cyanobacteria along the water column into the sediment, and to reconstruct cyanobacteria communities spanning the last 11,000 years using a set of molecular techniques including quantitative PCR, biomarkers, metabarcoding, and metagenome sequence analyses.
The results of this thesis proved that cyanobacterial composition and species richness did not significantly differ among different water depths, sediment traps, and surface sediments. This means that the cyanobacterial community composition from the sediments reflects the water column communities. However, there is a skewed sediment deposition of different cyanobacteria groups because of DNA alteration and/or deterioration during transport along the water column to the sediment. Specifically, single filament taxa, such as Planktothrix, are poorly represented in sediments despite being abundant in the water column as shown by an additional study of the thesis on cyanobacteria seasonality. In contrast, aggregate-forming taxa, like Aphanizomenon, are relatively overrepresented in sediment although they are not abundant in the water column. These different deposition patterns of cyanobacteria taxa should be considered in future DNA-based paleolimnological investigations. The thesis also reveals a substantial increase in total cyanobacteria abundance during the Bronze Age which is not apparent in prior phases of the early to middle Holocene and is suggested to be caused by human farming, deforestation, and excessive nutrient addition to the lake. Not only cyanobacterial abundance was influenced by human activity but also cyanobacteria community composition differed significantly between phases of no, moderate, and intense human impact.
The data presented in this thesis are the first on sedimentary cyanobacteria DNA since the early Holocene in a temperate lake. The results bring together archaeological, historical climatic, and limnological data with deep DNA-sequencing and paleoecology to reveal a legacy impact of human pressure on lake cyanobacteria populations dating back to approximately 4000 years.
Urokinase-type plasminogen activator is widely discussed as a marker for cancer prognosis and diagnosis and as a target for cancer therapies. Together with its receptor, uPA plays an important role in tumorigenesis, tumor progression and metastasis. In the present study, systematic evolution of ligands by exponential enrichment (SELEX) was used to select single-stranded DNA aptamers targeting different forms of human uPA. Selected aptamers allowed the distinction between HMW-uPA and LMW-uPA, and therefore, presumably, have different binding regions. Here, uPAapt-02-FR showed highly affine binding with a K-D of 0.7 nM for HMW-uPA and 21 nM for LMW-uPA and was also able to bind to pro-uPA with a K-D of 14 nM. Furthermore, no cross-reactivity to mouse uPA or tissue-type plasminogen activator (tPA) was measured, demonstrating high specificity. Suppression of the catalytic activity of uPA and inhibition of uPAR-binding could be demonstrated through binding with different aptamers and several of their truncated variants. Since RNA aptamers are already known to inhibit uPA-uPAR binding and other pathological functions of the uPA system, these aptamers represent a novel, promising tool not only for detection of uPA but also for interfering with the pathological functions of the uPA system by additionally inhibiting uPA activity.
Scope: Several studies show that excessive lipid intake can cause hepatic steatosis. To investigate lipotoxicity on cellular level, palmitate (PA) is often used to highly increase lipid droplets (LDs). One way to remove LDs is autophagy, while it is controversially discussed if autophagy is also affected by PA. It is aimed to investigate whether PA-induced LD accumulation can impair autophagy and punicalagin, a natural autophagy inducer from pomegranate, can improve it.
Methods and results: To verify the role of autophagy in LD degradation, HepG2 cells are treated with PA and analyzed for LD and perilipin 2 content in presence of autophagy inducer Torin 1 and inhibitor 3-Methyladenine. PA alone seems to initially induce autophagy-related proteins but impairs autophagic-flux in a time-dependent manner, considering 6 and 24 h PA. To examine whether punicalagin can prevent autophagy impairment, cells are cotreated for 24 h with PA and punicalagin. Results show that punicalagin preserves expression of autophagy-related proteins and autophagic flux, while simultaneously decreasing LDs and perilipin 2.
Conclusion: Data provide new insights into the role of PA-induced excessive LD content on autophagy and suggest autophagy-inducing properties of punicalagin, indicating that punicalagin can be a health-beneficial compound for future research on lipotoxicity in liver.
Methane (CH4) from aquatic ecosystems contributes to about half of total global CH4 emissions to the atmosphere. Until recently, aquatic biogenic CH4 production was exclusively attributed to methanogenic archaea living under anoxic or suboxic conditions in sediments, bottom waters, and wetlands. However, evidence for oxic CH4 production (OMP) in freshwater, brackish, and marine habitats is increasing. Possible sources were found to be driven by various planktonic organisms supporting different OMP mechanisms. Surprisingly, submerged macrophytes have been fully ignored in studies on OMP, yet they are key components of littoral zones of ponds, lakes, and coastal systems. High CH4 concentrations in these zones have been attributed to organic substrate production promoting classic methanogenesis in the absence of oxygen. Here, we review existing studies and argue that, similar to terrestrial plants and phytoplankton, macroalgae and submerged macrophytes may directly or indirectly contribute to CH4 formation in oxic waters. We propose several potential direct and indirect mechanisms: (1) direct production of CH4; (2) production of CH4 precursors and facilitation of their bacterial breakdown or chemical conversion; (3) facilitation of classic methanogenesis; and (4) facilitation of CH4 ebullition. As submerged macrophytes occur in many freshwater and marine habitats, they are important in global carbon budgets and can strongly vary in their abundance due to seasonal and boom-bust dynamics. Knowledge on their contribution to OMP is therefore essential to gain a better understanding of spatial and temporal dynamics of CH4 emissions and thus to substantially reduce current uncertainties when estimating global CH4 emissions from aquatic ecosystems.
COMMIT
(2022)
Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic reconstructions improves the quality of the draft reconstructions, measured by comparison to reference models. We then devise an approach for gap filling, termed COMMIT, that considers metabolites for secretion based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual reconstructions without affecting the genomic support. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile fully automated solution for large-scale modelling of microbial communities for diverse biotechnological applications. <br /> Author summaryMicrobial communities are important in ecology, human health, and crop productivity. However, detailed information on the interactions within natural microbial communities is hampered by the community size, lack of detailed information on the biochemistry of single organisms, and the complexity of interactions between community members. Metabolic models are comprised of biochemical reaction networks based on the genome annotation, and can provide mechanistic insights into community functions. Previous analyses of microbial community models have been performed with high-quality reference models or models generated using a single reconstruction pipeline. However, these models do not contain information on the composition of the community that determines the metabolites exchanged between the community members. In addition, the quality of metabolic models is affected by the reconstruction approach used, with direct consequences on the inferred interactions between community members. Here, we use fully automated consensus reconstructions from four approaches to arrive at functional models with improved genomic support while considering the community composition. We applied our pipeline to two soil communities from the Arabidopsis thaliana culture collection, providing only genome sequences. Finally, we show that the obtained models have 90% genomic support and demonstrate that the derived interactions are corroborated by independent computational predictions.
Understanding the complexity of metabolic networks has implications for manipulation of their functions. The complexity of metabolic networks can be characterized by identifying multireaction dependencies that are challenging to determine due to the sheer number of combinations to consider. Here, we propose the concept of concordant complexes that captures multireaction dependencies and can be efficiently determined from the algebraic structure and operational constraints of metabolic networks. The concordant complexes imply the existence of concordance modules based on which the apparent complexity of 12 metabolic networks of organisms from all kingdoms of life can be reduced by at least 78%. A comparative analysis against an ensemble of randomized metabolic networks shows that the metabolic network of Escherichia coli contains fewer concordance modules and is, therefore, more tightly coordinated than expected by chance. Together, our findings demonstrate that metabolic networks are considerably simpler than what can be perceived from their structure alone.
High-throughput proteomics approaches have resulted in large-scale protein–protein interaction (PPI) networks that have been employed for the prediction of protein complexes. However, PPI networks contain false-positive as well as false-negative PPIs that affect the protein complex prediction algorithms. To address this issue, here we propose an algorithm called CUBCO+ that: (1) employs GO semantic similarity to retain only biologically relevant interactions with a high similarity score, (2) based on link prediction approaches, scores the false-negative edges, and (3) incorporates the resulting scores to predict protein complexes. Through comprehensive analyses with PPIs from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we show that CUBCO+ performs as well as the approaches that predict protein complexes based on recently introduced graph partitions into biclique spanned subgraphs and outperforms the other state-of-the-art approaches. Moreover, we illustrate that in combination with GO semantic similarity, CUBCO+ enables us to predict more accurate protein complexes in 36% of the cases in comparison to CUBCO as its predecessor.
Selection of high-performance lines with respect to traits of interest is a key step in plant breeding. Genomic prediction allows to determine the genomic estimated breeding values of unseen lines for trait of interest using genetic markers, e.g. single-nucleotide polymorphisms (SNPs), and machine learning approaches, which can therefore shorten breeding cycles, referring to genomic selection (GS). Here, we applied GS approaches in two populations of Solanaceous crops, i.e. tomato and pepper, to predict morphometric and colorimetric traits. The traits were measured by using scoring-based conventional descriptors (CDs) as well as by Tomato Analyzer (TA) tool using the longitudinally and latitudinally cut fruit images. The GS performance was assessed in cross-validations of classification-based and regression-based machine learning models for CD and TA traits, respectively. The results showed the usage of TA traits and tag SNPs provide a powerful combination to predict morphology and color-related traits of Solanaceous fruits. The highest predictability of 0.89 was achieved for fruit width in pepper, with an average predictability of 0.69 over all traits. The multi-trait GS models are of slightly better predictability than single-trait models for some colorimetric traits in pepper. While model validation performs poorly on wild tomato accessions, the usage as many as one accession per wild species in the training set can increase the transferability of models to unseen populations for some traits (e.g. fruit shape for which predictability in unseen scenario increased from zero to 0.6). Overall, GS approaches can assist the selection of high-performance Solanaceous fruits in crop breeding.
Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determines cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA), which relies on the patterns of isotope labeling of metabolites in the network. The application of MFA also requires a stoichiometric model with atom mappings that are currently not available for the majority of large-scale metabolic network models, particularly of plants. While automated approaches such as the Reaction Decoder Toolkit (RDT) can produce atom mappings for individual reactions, tracing the flow of individual atoms of the entire reactions across a metabolic model remains challenging. Here we establish an automated workflow to obtain reliable atom mappings for large-scale metabolic models by refining the outcome of RDT, and apply the workflow to metabolic models of Arabidopsis thaliana. We demonstrate the accuracy of RDT through a comparative analysis with atom mappings from a large database of biochemical reactions, MetaCyc. We further show the utility of our automated workflow by simulating N-15 isotope enrichment and identifying nitrogen (N)-containing metabolites which show enrichment patterns that are informative for flux estimation in future N-15-MFA studies of A. thaliana. The automated workflow established in this study can be readily expanded to other species for which metabolic models have been established and the resulting atom mappings will facilitate MFA and graph-theoretic structural analyses with large-scale metabolic networks.
Nocardioides alcanivorans sp. nov., a novel hexadecane-degrading species isolated from plastic waste
(2022)
Strain NGK65(T), a novel hexadecane degrading, non-motile, Gram-positive, rod-to-coccus shaped, aerobic bacterium, was isolated from plastic polluted soil sampled at a landfill.
Strain NGK65(T) hydrolysed casein, gelatin, urea and was catalase-positive. It optimally grew at 28 degrees C. in 0-1% NaCl and at pH 7.5-8.0. Glycerol, D-glucose, arbutin, aesculin, salicin, potassium 5-ketogluconate. sucrose, acetate, pyruvate and hexadecane were used as sole carbon sources.
The predominant membrane fatty acids were iso-C-16:0 followed by iso-C(17:)0 and C-18:1 omega 9c. The major polar lipids were phosphatidylglycerol, phosphatidylethanolamine, phosphatidylinositol and hydroxyphosphatidylinositol.
The cell-wall peptidoglycan type was A3 gamma, with LL-diaminopimelic acid and glycine as the diagnostic amino acids. MK 8 (H-4) was the predominant menaquinone. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain NGK65(T) belongs to the genus Nocardioides (phylum Actinobacteria). appearing most closely related to Nocardioides daejeonensis MJ31(T) (98.6%) and Nocardioides dubius KSL-104(T) (98.3%).
The genomic DNA G+C content of strain NGK65(T) was 68.2%.
Strain NGK65(T) and the type strains of species involved in the analysis had average nucleotide identity values of 78.3-71.9% as well as digital DNA-DNA hybridization values between 22.5 and 19.7%, which clearly indicated that the isolate represents a novel species within the genus Nocardioides.
Based on phenotypic and molecular characterization, strain NGK65(T) can clearly be differentiated from its phylogenetic neighbours to establish a novel species, for which the name Nocardioides alcanivorans sp. nov. is proposed.
The type strain is NGK65(T) (=DSM 113112(T)=NCCB 100846(T)).
Species community structures of Afrotropical butterflies differ depending on the monitoring method
(2022)
Standardised biodiversity assessment is crucial to understand community structures and population dynamics of animals.
There exist various methods to monitor biodiversity. Approaches differ depending on the target species group and the aim of study, and show advantages and disadvantages.
The obtained data and results are influenced by local environmental conditions and seasonal variability.
In a comparative approach, we studied butterfly diversity and community structure in the dryland savannah biome of south-eastern Kenya with two different methods, transect counts and bait trapping.
We repeatedly collected data throughout the dry and rainy seasons, in both near natural and anthropogenically influenced landscapes. Significantly more species and individuals were recorded by transect counts than by bait trapping, though the larger and more mobile Nymphalid species (and in particular representatives of the genus Charaxes) were comparatively overrepresented in traps.
The transect data revealed much more pronounced effects of land-use and seasonality than the trap data. These results show that the choice of data collection methods must depend on the general research question, habitat conditions and season. To study the relative variation of species diversity and abundance, the collection of a fraction of the total species diversity might be sufficient.
However, if the focus is on a largely complete recording of species diversity, the use of various collection methods is essential. More specifically, our data clearly demonstrate that transect counts represent a reasonable method for assessing butterfly diversity for the African dryland savannah region, but fails to fully capture occurrences of all species. Bait trapping can be used only as a supplementary method for assessing some few highly mobile low-density species.
Incorporation of noncanonical amino acids (ncAAs) with bioorthogonal reactive groups by amber suppression allows the generation of synthetic proteins with desired novel properties. Such modified molecules are in high demand for basic research and therapeutic applications such as cancer treatment and in vivo imaging. The positioning of the ncAA-responsive codon within the protein's coding sequence is critical in order to maintain protein function, achieve high yields of ncAA-containing protein, and allow effective conjugation. Cell-free ncAA incorporation is of particular interest due to the open nature of cell-free systems and their concurrent ease of manipulation. In this study, we report a straightforward workflow to inquire ncAA positions in regard to incorporation efficiency and protein functionality in a Chinese hamster ovary (CHO) cell-free system. As a model, the well-established orthogonal translation components Escherichia coli tyrosyl-tRNA synthetase (TyrRS) and tRNATyr(CUA) were used to site-specifically incorporate the ncAA p-azido-l-phenylalanine (AzF) in response to UAG codons. A total of seven ncAA sites within an anti-epidermal growth factor receptor (EGFR) single-chain variable fragment (scFv) N-terminally fused to the red fluorescent protein mRFP1 and C-terminally fused to the green fluorescent protein sfGFP were investigated for ncAA incorporation efficiency and impact on antigen binding. The characterized cell-free dual fluorescence reporter system allows screening for ncAA incorporation sites with high incorporation efficiency that maintain protein activity. It is parallelizable, scalable, and easy to operate. We propose that the established CHO-based cell-free dual fluorescence reporter system can be of particular interest for the development of antibody-drug conjugates (ADCs).
The production of volatile organic compounds (VOCs) represents a promising strategy of plant-beneficial bacteria to control soil-borne phytopathogens.
Pseudomonas sp. PICF6 and Pseudomonas simiae PICF7 are two indigenous inhabitants of olive roots displaying effective biological control against Verticillium dahliae. Additionally, strain PICF7 is able to promote the growth of barley and Arabidopsis thaliana, VOCs being involved in the growth of the latter species.
In this study, the antagonistic capacity of these endophytic bacteria against relevant phytopathogens (Verticillium spp., Rhizoctonia solani, Sclerotinia sclerotiorum and Fusarium oxysporum f.sp. lycopersici) was assessed. Under in vitro conditions, PICF6 and PICF7 were only able to antagonize representative isolates of V. dahliae and V. longisporum. Remarkably, both strains produced an impressive portfolio of up to twenty VOCs, that included compounds with reported antifungal (e.g., 1-undecene, (methyldisulfanyl) methane and 1-decene) or plant growth promoting (e.g., tridecane, 1-decene) activities. Moreover, their volatilomes differed strongly in the absence and presence of V. dahliae.
For example, when co incubated with the defoliating pathotype of V. dahliae, the antifungal compound 4-methyl-2,6-bis(2-methyl-2-propanyl)phenol was produced. Results suggest that volatiles emitted by these endophytes may differ in their modes of action, and that potential benefits for the host needs further investigation in planta.
Water stable isotopes (delta O-18 and delta H-2) were analyzed in samples collected in lakes, associated with riverine systems in northeastern Germany, throughout 2020. The dataset (Aichner et al., 2021; https://doi.org/10.1594/PANGAEA.935633) is derived from water samples collected at (a) lake shores (sampled in March and July 2020), (b) buoys which were temporarily installed in deep parts of the lake (sampled monthly from March to October 2020), (c) multiple spatially distributed spots in four selected lakes (in September 2020), and (d) the outflow of Muggelsee (sampled biweekly from March 2020 to January 2021). At shores, water was sampled with a pipette from 40-60 cm below the water surface and directly transferred into a measurement vial, while at buoys a Limnos water sampler was used to obtain samples from 1 m below the surface. Isotope analysis was conducted at IGB Berlin, using a Picarro L2130-i cavity ring-down spectrometer, with a measurement uncertainty of < 0.15 parts per thousand (delta O-18) and < 0.0 parts per thousand (delta H-2). The data give information about the vegetation period and the full seasonal isotope amplitude in the sampled lakes and about spatial isotope variability in different branches of the associated riverine systems.
Pelagic bacteria can be classified into free-living and particle-attached life modes, which either dwell in the water column or attach to suspended particles. Bacteria with a generalist life style, however, can actively shift between these two habitats. Globally increasing densities of natural and artificial particles enhance habitat heterogeneity, with potential consequences for system stability and trophic transfer through aquatic food webs. To better decipher the dynamics of microbial communities, we investigated the influence of adaptive vs. fixed habitat choice on species coexistence for a simplified bacterial community by analyzing a corresponding food web model, consisting of two specialist bacterial prey species (free and attached), a generalist bacterial prey species with the ability to shift between both habitats, and two protist predators, specialized on either water or particle compartment. For simplicity we assume a shared resource pool, considering particles only for colonization but not as a source for nutrients or carbon, that is, inert particles like microplastics or inorganic sediments. The model predicts coexistence on a cyclic attractor between fixed and flexible bacteria, if the costs for adaptive habitat choice can be balanced by adaptation speed. The presence of adaptive prey dampens predator-prey cycle amplitudes, contributing to system stabilization resulting in higher mean predator biomass compared to specialist prey only. Thus, in pelagic microbial systems, flexible habitat choice at the prey level has important implications for system stability and magnitude of energy flow through the microbial loop.
Although aquatic and parasitic fungi have been well known for more than 100 years, they have only recently received increased awareness due to their key roles in microbial food webs and biogeochemical cycles. There is growing evidence indicating that fungi inhabit a wide range of marine habitats, from the deep sea all the way to surface waters, and recent advances in molecular tools, in particular metagenome approaches, reveal that their diversity is much greater and their ecological roles more important than previously considered. Parasitism constitutes one of the most widespread ecological interactions in nature, occurring in almost all environments. Despite that, the diversity of fungal parasites, their ecological functions, and, in particular their interactions with other microorganisms remain largely speculative, unexplored and are often missing from current theoretical concepts in marine ecology and biogeochemistry. In this review, we summarize and discuss recent research avenues on parasitic fungi and their ecological potential in marine ecosystems, e.g., the fungal shunt, and emphasize the need for further research.
The benefits of counting butterflies: recommendations for a successful citizen science project
(2022)
Citizen science (CS) projects, being popular across many fields of science, have recently also become a popular tool to collect biodiversity data. Although the benefits of such projects for science and policy making are well understood, relatively little is known about the benefits participants get from these projects as well as their personal backgrounds and motivations. Furthermore, very little is known about their expectations. We here examine these aspects, with the citizen science project "German Butterfly Monitoring" as an example. A questionnaire was sent to all participants of the project and the responses to the questionnaire indicated the following: center dot Most transect walkers do not have a professional background in this field, though they do have a high educational level, and are close to retirement, with a high number of females; center dot An important motivation to join the project is to preserve the natural environment and to contribute to scientific knowledge; center dot Participants benefit by enhancing their knowledge about butterflies and especially their ability to identify different species (taxonomic knowledge); center dot Participants do not have specific expectations regarding the project beyond proper management and coordination, but have an intrinsic sense of working for a greater good. The willingness to join a project is higher if the project contributes to the solution of a problem discussed in the media (here, insect decline). Based on our findings from the analysis of the questionnaire we can derive a set of recommendations for establishing a successful CS project. These include the importance of good communication, e.g., by explaining what the (scientific) purpose of the project is and what problems are to be solved with the help of the data collected in the project. The motivation to join a CS project is mostly intrinsic and CS is a good tool to engage people during difficult times such as the COVID-19 pandemic, giving participants the feeling of doing something useful.
This dissertation aimed to determine differential expressed miRNAs in the context of chronic pain in polyneuropathy. For this purpose, patients with chronic painful polyneuropathy were compared with age matched healthy patients. Taken together, all miRNA pre library preparation quality controls were successful and none of the samples was identified as an outlier or excluded for library preparation. Pre sequencing quality control showed that library preparation worked for all samples as well as that all samples were free of adapter dimers after BluePippin size selection and reached the minimum molarity for further processing. Thus, all samples were subjected to sequencing. The sequencing control parameters were in their optimal range and resulted in valid sequencing results with strong sample to sample correlation for all samples. The resulting FASTQ file of each miRNA library was analyzed and used to perform a differential expression analysis. The differentially expressed and filtered miRNAs were subjected to miRDB to perform a target prediction. Three of those four miRNAs were downregulated: hsa-miR-3135b, hsa-miR-584-5p and hsa-miR-12136, while one was upregulated: hsa-miR-550a-3p. miRNA target prediction showed that chronic pain in polyneuropathy might be the result of a combination of miRNA mediated high blood flow/pressure and neural activity dysregulations/disbalances. Thus, leading to the promising conclusion that these four miRNAs could serve as potential biomarkers for the diagnosis of chronic pain in polyneuropathy.
Since TRPV1 seems to be one of the major contributors of nociception and is associated with neuropathic pain, the influence of PKA phosphorylated ARMS on the sensitivity of TRPV1 as well as the part of AKAP79 during PKA phosphorylation of ARMS was characterized. Therefore, possible PKA-sites in the sequence of ARMS were identified. This revealed five canonical PKA-sites: S882, T903, S1251/52, S1439/40 and S1526/27. The single PKA-site mutants of ARMS revealed that PKA-mediated ARMS phosphorylation seems not to influence the interaction rate of TRPV1/ARMS. While phosphorylation of ARMST903 does not increase the interaction rate with TRPV1, ARMSS1526/27 is probably not phosphorylated and leads to an increased interaction rate. The calcium flux measurements indicated that the higher the interaction rate of TRPV1/ARMS, the lower the EC50 for capsaicin of TRPV1, independent of the PKA phosphorylation status of ARMS. In addition, the western blot analysis confirmed the previously observed TRPV1/ARMS interaction. More importantly, AKAP79 seems to be involved in the TRPV1/ARMS/PKA signaling complex. To overcome the problem of ARMS-mediated TRPV1 sensitization by interaction, ARMS was silenced by shRNA. ARMS silencing resulted in a restored TRPV1 desensitization without affecting the TRPV1 expression and therefore could be used as new topical therapeutic analgesic alternative to stop ARMS mediated TRPV1 sensitization.
High-throughput proteomics approaches have resulted in large-scale protein–protein interaction (PPI) networks that have been employed for the prediction of protein complexes. However, PPI networks contain false-positive as well as false-negative PPIs that affect the protein complex prediction algorithms. To address this issue, here we propose an algorithm called CUBCO+ that: (1) employs GO semantic similarity to retain only biologically relevant interactions with a high similarity score, (2) based on link prediction approaches, scores the false-negative edges, and (3) incorporates the resulting scores to predict protein complexes. Through comprehensive analyses with PPIs from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we show that CUBCO+ performs as well as the approaches that predict protein complexes based on recently introduced graph partitions into biclique spanned subgraphs and outperforms the other state-of-the-art approaches. Moreover, we illustrate that in combination with GO semantic similarity, CUBCO+ enables us to predict more accurate protein complexes in 36% of the cases in comparison to CUBCO as its predecessor.
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.