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IntroductionTo date, most studies of natural variation and metabolite quantitative trait loci (mQTL) in tomato have focused on fruit metabolism, leaving aside the identification of genomic regions involved in the regulation of leaf metabolism.ObjectiveThis study was conducted to identify leaf mQTL in tomato and to assess the association of leaf metabolites and physiological traits with the metabolite levels from other tissues.MethodsThe analysis of components of leaf metabolism was performed by phenotypying 76 tomato ILs with chromosome segments of the wild species Solanum pennellii in the genetic background of a cultivated tomato (S. lycopersicum) variety M82. The plants were cultivated in two different environments in independent years and samples were harvested from mature leaves of non-flowering plants at the middle of the light period. The non-targeted metabolite profiling was obtained by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). With the data set obtained in this study and already published metabolomics data from seed and fruit, we performed QTL mapping, heritability and correlation analyses.ResultsChanges in metabolite contents were evident in the ILs that are potentially important with respect to stress responses and plant physiology. By analyzing the obtained data, we identified 42 positive and 76 negative mQTL involved in carbon and nitrogen metabolism.ConclusionsOverall, these findings allowed the identification of S. lycopersicum genome regions involved in the regulation of leaf primary carbon and nitrogen metabolism, as well as the association of leaf metabolites with metabolites from seeds and fruits.
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.
In soil, Acidobacteria constitute on average 20% of all bacteria, are highly diverse, and are physiologically active in situ. However, their individual functions and interactions with higher taxa in soil are still unknown. Here, potential effects of land use, soil properties, plant diversity, and soil nanofauna on acidobacterial community composition were studied by cultivation-independent methods in grassland and forest soils from three different regions in Germany. The analysis of 16S rRNA gene clone libraries representing all studied soils revealed that grassland soils were dominated by subgroup Gp6 and forest soils by subgroup Gp1 Acidobacteria. The analysis of a large number of sites (n = 57) by 16S rRNA gene fingerprinting methods (terminal restriction fragment length polymorphism [T-RFLP] and denaturing gradient gel electrophoresis [DGGE]) showed that Acidobacteria diversities differed between grassland and forest soils but also among the three different regions. Edaphic properties, such as pH, organic carbon, total nitrogen, C/N ratio, phosphorus, nitrate, ammonium, soil moisture, soil temperature, and soil respiration, had an impact on community composition as assessed by fingerprinting. However, interrelations with environmental parameters among subgroup terminal restriction fragments (T-RFs) differed significantly, e.g., different Gp1 T-RFs correlated positively or negatively with nitrogen content. Novel significant correlations of Acidobacteria subpopulations (i.e., individual populations within subgroups) with soil nanofauna and vascular plant diversity were revealed only by analysis of clone sequences. Thus, for detecting novel interrelations of environmental parameters with Acidobacteria, individual populations within subgroups have to be considered.
Metagenomic sequencing has revolutionised our knowledge of virus diversity, with new virus sequences being reported faster than ever before. However, virus discovery from metagenomic sequencing usually depends on detectable homology: without a sufficiently close relative, so-called ‘dark’ virus sequences remain unrecognisable. An alternative approach is to use virus-identification methods that do not depend on detecting homology, such as virus recognition by host antiviral immunity. For example, virus-derived small RNAs have previously been used to propose ‘dark’ virus sequences associated with the Drosophilidae (Diptera). Here, we combine published Drosophila data with a comprehensive search of transcriptomic sequences and selected meta-transcriptomic datasets to identify a completely new lineage of segmented positive-sense single-stranded RNA viruses that we provisionally refer to as the Quenyaviruses. Each of the five segments contains a single open reading frame, with most encoding proteins showing no detectable similarity to characterised viruses, and one sharing a small number of residues with the RNA-dependent RNA polymerases of single- and double-stranded RNA viruses. Using these sequences, we identify close relatives in approximately 20 arthropods, including insects, crustaceans, spiders, and a myriapod. Using a more conserved sequence from the putative polymerase, we further identify relatives in meta-transcriptomic datasets from gut, gill, and lung tissues of vertebrates, reflecting infections of vertebrates or of their associated parasites. Our data illustrate the utility of small RNAs to detect viruses with limited sequence conservation, and provide robust evidence for a new deeply divergent and phylogenetically distinct RNA virus lineage.
Metagenomic sequencing has revolutionised our knowledge of virus diversity, with new virus sequences being reported faster than ever before. However, virus discovery from metagenomic sequencing usually depends on detectable homology: without a sufficiently close relative, so-called ‘dark’ virus sequences remain unrecognisable. An alternative approach is to use virus-identification methods that do not depend on detecting homology, such as virus recognition by host antiviral immunity. For example, virus-derived small RNAs have previously been used to propose ‘dark’ virus sequences associated with the Drosophilidae (Diptera). Here, we combine published Drosophila data with a comprehensive search of transcriptomic sequences and selected meta-transcriptomic datasets to identify a completely new lineage of segmented positive-sense single-stranded RNA viruses that we provisionally refer to as the Quenyaviruses. Each of the five segments contains a single open reading frame, with most encoding proteins showing no detectable similarity to characterised viruses, and one sharing a small number of residues with the RNA-dependent RNA polymerases of single- and double-stranded RNA viruses. Using these sequences, we identify close relatives in approximately 20 arthropods, including insects, crustaceans, spiders, and a myriapod. Using a more conserved sequence from the putative polymerase, we further identify relatives in meta-transcriptomic datasets from gut, gill, and lung tissues of vertebrates, reflecting infections of vertebrates or of their associated parasites. Our data illustrate the utility of small RNAs to detect viruses with limited sequence conservation, and provide robust evidence for a new deeply divergent and phylogenetically distinct RNA virus lineage.
Plants can be primed to survive the exposure to a severe heat stress (HS) by prior exposure to a mild HS. The information about the priming stimulus is maintained by the plant for several days. This maintenance of acquired thermotolerance, or HS memory, is genetically separable from the acquisition of thermotolerance itself and several specific regulatory factors have been identified in recent years.
On the molecular level, HS memory correlates with two types of transcriptional memory, type I and type II, that characterize a partially overlapping subset of HS-inducible genes. Type I transcriptional memory or sustained induction refers to the sustained transcriptional induction above non-stressed expression levels of a gene for a prolonged time period after the end of the stress exposure. Type II transcriptional memory refers to an altered transcriptional response of a gene after repeated exposure to a stress of similar duration and intensity. In particular, enhanced re-induction refers to a transcriptional pattern in which a gene is induced to a significantly higher degree after the second stress exposure than after the first.
This thesis describes the functional characterization of a novel positive transcriptional regulator of type I transcriptional memory, the heat shock transcription factor HSFA3, and compares it to HSFA2, a known positive regulator of type I and type II transcriptional memory. It investigates type I transcriptional memory and its dependence on HSFA2 and HSFA3 for the first time on a genome-wide level, and gives insight on the formation of heteromeric HSF complexes in response to HS. This thesis confirms the tight correlation between transcriptional memory and H3K4 hyper-methylation, reported here in a case study that aimed to reduce H3K4 hyper-methylation of the type II transcriptional memory gene APX2 by CRISPR/dCas9-mediated epigenome editing. Finally, this thesis gives insight into the requirements for a heat shock transcription factor to function as a positive regulator of transcriptional memory, both in terms of its expression profile and protein abundance after HS and the contribution of individual functional domains.
In summary, this thesis contributes to a more detailed understanding of the molecular processes underlying transcriptional memory and therefore HS memory, in Arabidopsis thaliana.
A temperature-inducible epigenome editing system to knock down histone methylation can be used to study the role of histone H3K4 methylation during heat stress memory in Arabidopsis. <br /> Histone modifications play a crucial role in the integration of environmental signals to mediate gene expression outcomes. However, genetic and pharmacological interference often causes pleiotropic effects, creating the urgent need for methods that allow locus-specific manipulation of histone modifications, preferably in an inducible manner. Here, we report an inducible system for epigenome editing in Arabidopsis (Arabidopsis thaliana) using a heat-inducible dCas9 to target a JUMONJI (JMJ) histone H3 lysine 4 (H3K4) demethylase domain to a locus of interest. As a model locus, we target the ASCORBATE PEROXIDASE2 (APX2) gene that shows transcriptional memory after heat stress (HS), correlating with H3K4 hyper-methylation. We show that dCas9-JMJ is targeted in a HS-dependent manner to APX2 and that the HS-induced overaccumulation of H3K4 trimethylation (H3K4me3) decreases when dCas9-JMJ binds to the locus. This results in reduced HS-mediated transcriptional memory at the APX2 locus. Targeting an enzymatically inactive JMJ protein in an analogous manner affected transcriptional memory less than the active JMJ protein; however, we still observed a decrease in H3K4 methylation levels. Thus, the inducible targeting of dCas9-JMJ to APX2 was effective in reducing H3K4 methylation levels. As the effect was not fully dependent on enzyme activity of the eraser domain, the dCas9-JMJ fusion protein may act in part independently of its demethylase activity. This underlines the need for caution in the design and interpretation of epigenome editing studies. We expect our versatile inducible epigenome editing system to be especially useful for studying temporal dynamics of chromatin modifications.
As sessile organisms, plants have evolved sophisticated ways to constantly gauge and adapt to changing environmental conditions including extremes that may be harmful to their growth and development and are thus perceived as stress. In nature, stressful events are often chronic or recurring and thus an initial stress may prime a plant to respond more efficiently to a subsequent stress event. An epigenetic basis of such stress memory was long postulated and in recent years it has been shown that this is indeed the case. High temperature stress has proven an excellent system to unpick the molecular basis of somatic stress memory, which includes histone modifications and nucleosome occupancy. This review discusses recent findings and pinpoints open questions in the field.
Swimming is of vital importance for aquatic organisms because it determines several aspects of fitness, such as encounter rates with food, predators, and mates. Generally, rotifer swimming speed is measured by manual tracking of the swimming paths filmed in videos. Recently, an open-source package has been developed that integrates different open-source software and allows direct processing and analysis of the swimming paths of moving organisms. Here, we filmed groups of females and males of Keratella cochlearis separately and in a mixed experimental setup. We extracted movement trajectories and swimming speeds and applied the classification method random forest to assign sex to individuals of the mixed setup. Finally, we used advanced statistical methods of movement ecology, namely a hidden Markov model, to investigate swimming states of females and males. When not discriminating swimming states, females swam faster than males, while when discriminating states males swam faster. Specifically, females and males showed two main states of movement with many individuals switching between states resulting in four modes of swimming. We suggest that switching between states is related to predator avoidance. Males of K. cochlearis especially exhibited switching between turning in a restricted area and swimming over longer distances. No mating or other male-female interactions were observed. Our study elucidates the steps necessary for automatic analysis of rotifer trajectories with open-source software. Application of sophisticated software and analytical models will broaden our understanding of zooplankton ecology from the individual to the population level.
Extremophilic organisms are gaining increasing interest because of their unique metabolic capacities and great biotechnological potential. The unicellular acidophilic and mesothermophilic red alga Galdieria sulphuraria (074G) can grow autotrophically in light as well as heterotrophically in the dark. In this paper, the effects of externally added glucose on primary and secondary photosynthetic reactions are assessed to elucidate mixotrophic capacities of the alga. Photosynthetic O-2 evolution was quantified in an open system with a constant Supply Of CO2 to avoid rapid volatilization of dissolved inorganic carbon at low pH levels. In the presence of glucose, O-2 evolution was repressed even in illuminated cells. Ratios of variable to maximum chlorophyll fluorescence (F-v/F-m) and 77 Kfluorescence spectra indicated a reduced photochemical efficiency of photosystem II. The results were corroborated by strongly reduced levels of the photosystem 11 reaction centre protein D1. The downregulation of primary photosynthetic reactions was accompanied by reduced levels of the Calvin Cycle enzyme ribu lose-1,5-bisphosphate carboxylaselfoxygenase (Rubisco). Both effects depended on functional sugar uptake and are thus initiated by intracellular rather than extracellular glucose. Following glucose depletion, photosynthetic O-2 evolution of illuminated cells commenced after 15 h and Rubisco levels again reached the levels of autotrophic cells. It is concluded that true mixotrophy, involving electron transport across both photosystems, does not occur in G. sulphuraria 074G, and that heterotrophic growth is favoured over autotrophic growth if sufficient organic carbon is available.
Chloroplasts as bioreactors : high-yield production of active bacteriolytic protein antibiotics
(2008)
Plants, more precisely their chloroplasts with their bacterial-like expression machinery inherited from their cyanobacterial ancestors, can potentially offer a cheap expression system for proteinaceous pharmaceuticals. This system would be easily scalable and provides appropriate safety due to chloroplasts maternal inheritance. In this work, it was shown that three phage lytic enzymes (Pal, Cpl-1 and PlyGBS) could be successfully expressed at very high levels and with high stability in tobacco chloroplasts. PlyGBS expression reached an amount of foreign protein accumulation (> 70% TSP) that has never been obtained before. Although the high expression levels of PlyGBS caused a pale green phenotype with retarded growth, presumably due to exhaustion of plastid protein synthesis capacity, development and seed production were not impaired under greenhouse conditions. Since Pal and Cpl-1 showed toxic effects when expressed in E. coli, a special plastid transformation vector (pTox) was constructed to allow DNA amplification in bacteria. The construction of the pTox transformation vector allowing a recombinase-mediated deletion of an E. coli transcription block in the chloroplast, leading to an increase of foreign protein accumulation to up to 40% of TSP for Pal and 20% of TSP for Cpl-1. High dose-dependent bactericidal efficiency was shown for all three plant-derived lytic enzymes using their pathogenic target bacteria S. pyogenes and S. pneumoniae. Confirmation of specificity was obtained for the endotoxic proteins Pal and Cpl-1 by application to E. coli cultures. These results establish tobacco chloroplasts as a new cost-efficient and convenient production platform for phage lytic enzymes and address the greatest obstacle for clinical application. The present study is the first report of lysin production in a non-bacterial system. The properties of chloroplast-produced lysins described in this work, their stability, high accumulation rate and biological activity make them highly attractive candidates for future antibiotics.
Uncovering the interplay between nutrient availability and cellulose biosynthesis inhibitor activity
(2022)
All plant cells are surrounded by a dynamic, carbohydrate-rich extracellular matrix known as the cell wall. Nutrient availability affects cell wall composition via uncharacterized regulatory mechanisms, and cellulose deficient mutants develop a hypersensitive root response to growth on high concentrations of nitrate. Since cell walls account for the bulk of plant biomass, it is important to understand how nutrients regulate cell walls. This could provide important knowledge for directing fertilizer treatments and engineering plants with higher nutrient use efficiency. The direct effect of nitrate on cell wall synthesis was investigated through growth assays on varying concentrations of nitrate, measuring cellulose content of roots and shoots, and assessing cellulose synthase activity (CESA) using live cell imaging with spinning disk confocal microscopy. A forward genetic screen was developed to isolate mutants impaired in nutrient-mediated cell wall regulation, revealing that cellulose biosynthesis inhibitor (CBI) activity is modulated by nutrient availability. Various non-CESA mutants were isolated that displayed CBI resistance, with the majority of mutations causing perturbation of mitochondria-localized proteins. To investigate mitochondrial involvement, the CBI mechanism of action was investigated using a reverse genetic screen, a targeted pharmacological screen, and -omics approaches. The results generated suggest that CBI-induced cellulose inhibition is due to off-target effects. This provides the groundwork to investigate uncharacterized processes of CESA regulation and adds valuable knowledge to the understanding of CBI activity, which could be harnessed to develop new and improved herbicides.
Sulfur is essential for the functionality of some important biomolecules in humans. Biomolecules like the Iron-sulfur clusters, tRNAs, Molybdenum cofactor, and some vitamins. The trafficking of sulfur involves proteins collectively called sulfurtransferase. Among these are TUM1, MOCS3, and NFS1.
This research investigated the role of TUM1 for molybdenum cofactor biosynthesis and cytosolic tRNA thiolation in humans. The rhodanese-like protein MOCS3 and the L-cysteine desulfurase (NFS1) have been previously demonstrated to interact with TUM1. These interactions suggested a dual function of TUM1 in sulfur transfer for Moco biosynthesis and cytosolic tRNA thiolation. TUM1 deficiency has been implicated to be responsible for a rare inheritable disorder known as mercaptolactate cysteine disulfiduria (MCDU), which is associated with a mental disorder. This mental disorder is similar to the symptoms of sulfite oxidase deficiency which is characterised by neurological disorders. Therefore, the role of TUM1 as a sulfurtransferase in humans was investigated, in CRISPR/Cas9 generated TUM1 knockout HEK 293T cell lines.
For the first time, TUM1 was implicated in Moco biosynthesis in humans by quantifying the intermediate product cPMP and Moco using HPLC. Comparing the TUM1 knockout cell lines to the wild-type, accumulation and reduction of cPMP and Moco were observed respectively. The effect of TUM1 knockout on the activity of a Moco-dependent enzyme, Sulfite oxidase, was also investigated. Sulfite oxidase is essential for the detoxification of sulfite to sulfate. Sulfite oxidase activity and protein abundance were reduced due to less availability of Moco. This shows that TUM1 is essential for efficient sulfur transfer for Moco biosynthesis. Reduction in cystathionin -lyase in TUM1 knockout cells was quantified, a possible coping mechanism of the cell against sulfite production through cysteine catabolism.
Secondly, the involvement of TUM1 in tRNA thio-modification at the wobble Uridine-34 was reported by quantifying the amount of mcm5s2U and mcm5U via HPLC. The reduction and accumulation of mcm5s2U and mcm5U in TUM1 knockout cells were observed in the nucleoside analysis. Herein, exogenous treatment with NaHS, a hydrogen sulfide donor, rescued the Moco biosynthesis, cytosolic tRNA thiolation, and cell proliferation deficits in TUM1 knockout cells.
Further, TUM1 was shown to impact mitochondria bioenergetics through the measurement of the oxygen consumption rate and extracellular acidification rate (ECAR) via the seahorse cell Mito stress analyzer. Reduction in total ATP production was also measured. This reveals how important TUM1 is for H2S biosynthesis in the mitochondria of HEK 293T.
Finally, the inhibition of NFS1 in HEK 293T and purified NFS1 protein by 2-methylene 3-quinuclidinone was demonstrated via spectrophotometric and radioactivity quantification. Inhibition of NFS1 by MQ further affected the iron-sulfur cluster-dependent enzyme aconitase activity.
Sulfur is an important element that is incorporated into many biomolecules in humans. The incorporation and transfer of sulfur into biomolecules is, however, facilitated by a series of different sulfurtransferases. Among these sulfurtransferases is the human mercaptopyruvate sulfurtransferase (MPST) also designated as tRNA thiouridine modification protein (TUM1). The role of the human TUM1 protein has been suggested in a wide range of physiological processes in the cell among which are but not limited to involvement in Molybdenum cofactor (Moco) biosynthesis, cytosolic tRNA thiolation and generation of H2S as signaling molecule both in mitochondria and the cytosol. Previous interaction studies showed that TUM1 interacts with the L-cysteine desulfurase NFS1 and the Molybdenum cofactor biosynthesis protein 3 (MOCS3). Here, we show the roles of TUM1 in human cells using CRISPR/Cas9 genetically modified Human Embryonic Kidney cells. Here, we show that TUM1 is involved in the sulfur transfer for Molybdenum cofactor synthesis and tRNA thiomodification by spectrophotometric measurement of the activity of sulfite oxidase and liquid chromatography quantification of the level of sulfur-modified tRNA. Further, we show that TUM1 has a role in hydrogen sulfide production and cellular bioenergetics.
Sulfur is an important element that is incorporated into many biomolecules in humans. The incorporation and transfer of sulfur into biomolecules is, however, facilitated by a series of different sulfurtransferases. Among these sulfurtransferases is the human mercaptopyruvate sulfurtransferase (MPST) also designated as tRNA thiouridine modification protein (TUM1). The role of the human TUM1 protein has been suggested in a wide range of physiological processes in the cell among which are but not limited to involvement in Molybdenum cofactor (Moco) biosynthesis, cytosolic tRNA thiolation and generation of H2S as signaling molecule both in mitochondria and the cytosol. Previous interaction studies showed that TUM1 interacts with the L-cysteine desulfurase NFS1 and the Molybdenum cofactor biosynthesis protein 3 (MOCS3). Here, we show the roles of TUM1 in human cells using CRISPR/Cas9 genetically modified Human Embryonic Kidney cells. Here, we show that TUM1 is involved in the sulfur transfer for Molybdenum cofactor synthesis and tRNA thiomodification by spectrophotometric measurement of the activity of sulfite oxidase and liquid chromatography quantification of the level of sulfur-modified tRNA. Further, we show that TUM1 has a role in hydrogen sulfide production and cellular bioenergetics.
Metabolites influence flowering time, and thus are among the major determinants of yield. Despite the reported role of trehalose 6-phosphate and nitrate signaling on the transition from the vegetative to the reproductive phase, little is known about other metabolites contributing and responding to developmental phase changes. To increase our understanding which metabolic traits change throughout development in Arabidopsis thaliana and to identify metabolic markers for the vegetative and reproductive phases, especially among individual amino acids (AA), we profiled metabolites of plants grown in optimal (ON) and limited nitrogen (N) (LN) conditions, the latter providing a mild but consistent limitation of N. We found that although LN plants adapt their growth to a decreased level of N, their metabolite profiles are strongly distinct from ON plant profiles, with N as the driving factor for the observed differences. We demonstrate that the vegetative and the reproductive phase are not only marked by growth parameters such as biomass and rosette area, but also by specific metabolite signatures including specific single AA. In summary, we identified N-dependent and -independent indicators manifesting developmental stages, indicating that the plant's metabolic status also reports on the developmental phases.
All roads lead to growth
(2020)
Plant growth is a highly complex biological process that involves innumerable interconnected biochemical and signalling pathways. Many different techniques have been developed to measure growth, unravel the various processes that contribute to plant growth, and understand how a complex interaction between genotype and environment determines the growth phenotype. Despite this complexity, the term 'growth' is often simplified by researchers; depending on the method used for quantification, growth is viewed as an increase in plant or organ size, a change in cell architecture, or an increase in structural biomass. In this review, we summarise the cellular and molecular mechanisms underlying plant growth, highlight state-of-the-art imaging and non-imaging-based techniques to quantitatively measure growth, including a discussion of their advantages and drawbacks, and suggest a terminology for growth rates depending on the type of technique used.
Nitrogen (N) is an essential macronutrient for optimal plant growth and ultimately for crop productivity Nitrate serves as the main N source for most plants. Although it seems a well-established fact that nitrate concentration affects flowering, its molecular mode of action in flowering time regulation was poorly understood. We recently found how nitrate, present at the shoot apical meristem (SAM), controls flowering time In this short communication, we present data on the tissue-specific expression patterns of NITRATE REDUCTASE 1 (NIA1) and NIA2 in planta. We show that transcripts of both genes are present throughout the life cycle of Arabidopsis thaliana plants with NIA1 being predominantly active in leaves and NIA2 in meristematic tissues.
Background: Microbiome assembly was identified as an important factor for plant growth and health, but this process is largely unknown, especially for the fruit microbiome. Therefore, we analyzed strawberry plants of two cultivars by focusing on microbiome tracking during the different growth stages and storage using amplicon sequencing, qPCR, and microscopic approaches. <br /> Results: Strawberry plants carried a highly diverse microbiome, therein the bacterial families Sphingomonadaceae (25%), Pseudomonadaceae (17%), and Burkholderiaceae (11%); and the fungal family Mycosphaerella (45%) were most abundant. All compartments were colonized by high number of bacteria and fungi (10(7)-10(10) marker gene copies per g fresh weight), and were characterized by high microbial diversity (6049 and 1501 ASVs); both were higher for the belowground samples than in the phyllosphere. Compartment type was the main driver of microbial diversity, structure, and abundance (bacterial: 45%; fungal: 61%) when compared to the cultivar (1.6%; 2.2%). Microbiome assembly was strongly divided for belowground habitats and the phyllosphere; only a low proportion of the microbiome was transferred from soil via the rhizosphere to the phyllosphere. During fruit development, we observed the highest rates of microbial transfer from leaves and flowers to ripe fruits, where most of the bacteria occured inside the pulp. In postharvest fruits, microbial diversity decreased while the overall abundance increased. Developing postharvest decay caused by Botrytis cinerea decreased the diversity as well, and induced a reduction of potentially beneficial taxa. <br /> Conclusion: Our findings provide insights into microbiome assembly in strawberry plants and highlight the importance of microbe transfer during fruit development and storage with potential implications for food health and safety.
Endothelial cells (ECs) are involved in a variety of cellular responses. As multifunctional components of vascular structures, endothelial (progenitor) cells have been utilized in cellular therapies and are required as an important cellular component of engineered tissue constructs and in vitro disease models. Although primary ECs from different sources are readily isolated and expanded, cell quantity and quality in terms of functionality and karyotype stability is limited. ECs derived from human induced pluripotent stem cells (hiPSCs) represent an alternative and potentially superior cell source, but traditional culture approaches and 2D differentiation protocols hardly allow for production of large cell numbers. Aiming at the production of ECs, we have developed a robust approach for efficient endothelial differentiation of hiPSCs in scalable suspension culture. The established protocol results in relevant numbers of ECs for regenerative approaches and industrial applications that show in vitro proliferation capacity and a high degree of chromosomal stability.
Endothelial cells (ECs) are involved in a variety of cellular responses. As multifunctional components of vascular structures, endothelial (progenitor) cells have been utilized in cellular therapies and are required as an important cellular component of engineered tissue constructs and in vitro disease models. Although primary ECs from different sources are readily isolated and expanded, cell quantity and quality in terms of functionality and karyotype stability is limited. ECs derived from human induced pluripotent stem cells (hiPSCs) represent an alternative and potentially superior cell source, but traditional culture approaches and 2D differentiation protocols hardly allow for production of large cell numbers. Aiming at the production of ECs, we have developed a robust approach for efficient endothelial differentiation of hiPSCs in scalable suspension culture. The established protocol results in relevant numbers of ECs for regenerative approaches and industrial applications that show in vitro proliferation capacity and a high degree of chromosomal stability.
Magnetite nanoparticles and their assembly comprise a new area of development for new technologies. The magnetic particles can interact and assemble in chains or networks. Magnetotactic bacteria are one of the most interesting microorganisms, in which the assembly of nanoparticles occurs. These microorganisms are a heterogeneous group of gram negative prokaryotes, which all show the production of special magnetic organelles called magnetosomes, consisting of a magnetic nanoparticle, either magnetite (Fe3O4) or greigite (Fe3S4), embedded in a membrane. The chain is assembled along an actin-like scaffold made of MamK protein, which makes the magnetosomes to arrange in mechanically stable chains. The chains work as a compass needle in order to allow cells to orient and swim along the magnetic field of the Earth.
The formation of magnetosomes is known to be controlled at the molecular level. The physico–chemical conditions of the surrounding environment also influence biomineralization. The work presented in this manuscript aims to understand how such external conditions, in particular the extracellular oxidation reduction potential (ORP) influence magnetite formation in the strain Magnetospirillum magneticum AMB-1. A controlled cultivation of the microorganism was developed in a bioreactor and the formation of magnetosomes was characterized.
Different techniques have been applied in order to characterize the amount of iron taken up by the bacteria and in consequence the size of magnetosomes produced at different ORP conditions. By comparison of iron uptake, morphology of bacteria, size and amount of magnetosomes per cell at different ORP, the formation of magnetosomes was inhibited at ORP 0 mV, whereas reduced conditions, ORP – 500 mV facilitate biomineralization process.
Self-assembly of magnetosomes occurring in magnetotactic bacteria became an inspiration to learn from nature and to construct nanoparticles assemblies by using the bacteriophage M13 as a template. The M13 bacteriophage is an 800 nm long filament with encapsulated single-stranded DNA that has been recently used as a scaffold for nanoparticle assembly. I constructed two types of assemblies based on bacteriophages and magnetic nanoparticles. A chain – like assembly was first formed where magnetite nanoparticles are attached along the phage filament. A sperm – like construct was also built with a magnetic head and a tail formed by phage filament.
The controlled assembly of magnetite nanoparticles on the phage template was possible due to two different mechanism of nanoparticle assembly. The first one was based on the electrostatic interactions between positively charged polyethylenimine coated magnetite nanoparticles and negatively charged phages. The second phage –nanoparticle assembly was achieved by bioengineered recognition sites. A mCherry protein is displayed on the phage and is was used as a linker to a red binding nanobody (RBP) that is fused to the one of the proteins surrounding the magnetite crystal of a magnetosome.
Both assemblies were actuated in water by an external magnetic field showing their swimming behavior and potentially enabling further usage of such structures for medical applications. The speed of the phage - nanoparticles assemblies are relatively slow when compared to those of microswimmers previously published. However, only the largest phage-magnetite assemblies could be imaged and it is therefore still unclear how fast these structures can be in their smaller version.
Leaf growth is a complex process that involves the action of diverse transcription factors (TFs) and their downstream gene regulatory networks. In this study, we focus on the functional characterization of the Arabidopsis thaliana TF GROWTH-REGULATING FACTOR9 (GRF9) and demonstrate that it exerts its negative effect on leaf growth by activating expression of the bZIP TF OBP3-RESPONSIVE GENE 3 (ORG3). While grf9 knockout mutants produce bigger incipient leaf primordia at the shoot apex, rosette leaves and petals than the wild type, the sizes of those organs are reduced in plants overexpressing GRF9 (GRF9ox). Cell measurements demonstrate that changes in leaf size result from alterations in cell numbers rather than cell sizes. Kinematic analysis and 5-ethynyl-2'-deoxyuridine (EdU) incorporation assay revealed that GRF9 restricts cell proliferation in the early developing leaf. Performing in vitro binding site selection, we identified the 6-base motif 5'-CTGACA-3' as the core binding site of GRF9. By global transcriptome profiling, electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (ChIP) we identified ORG3 as a direct downstream, and positively regulated target of GRF9. Genetic analysis of grf9 org3 and GRF9ox org3 double mutants reveals that both transcription factors act in a regulatory cascade to control the final leaf dimensions by restricting cell number in the developing leaf.
Growth-regulating factors (GRFs) are plant-specific transcription factors that were originally identified for their roles in stem and leaf development, but recent studies highlight them to be similarly important for other central developmental processes including flower and seed formation, root development, and the coordination of growth processes under adverse environmental conditions. The expression of several GRFs is controlled by microRNA miR396, and the GRF-miRNA396 regulatory module appears to be central to several of these processes. In addition, transcription factors upstream of GRFs and miR396 have been discovered, and gradually downstream target genes of GRFs are being unraveled. Here, we review the current knowledge of the biological functions performed by GRFs and survey available molecular data to illustrate how they exert their roles at the cellular level.
The control of gene expression by transcriptional regulators and other types of functionally relevant DNA transactions such as chromatin remodeling and replication underlie a vast spectrum of biological processes in all organisms. DNA transactions require the controlled interaction of proteins with DNA sequence motifs which are often located in nucleosome-depleted regions (NDRs) of the chromatin. Formaldehyde-assisted isolation of regulatory elements (FAIRE) has been established as an easy-to-implement method for the isolation of NDRs from a number of eukaryotic organisms, and it has been successfully employed for the discovery of new regulatory segments in genomic DNA from, for example, yeast, Drosophila, and humans. Until today, however, FAIRE has only rarely been employed in plant research and currently no detailed FAIRE protocol for plants has been published. Here, we provide a step-by-step FAIRE protocol for NDR discovery in Arabidopsis thaliana. We demonstrate that NDRs isolated from plant chromatin are readily amenable to quantitative polymerase chain reaction and next-generation sequencing. Only minor modification of the FAIRE protocol will be needed to adapt it to other plants, thus facilitating the global inventory of regulatory regions across species.
Inferring gene regulatory networks and cellular phases from time-resolved transcriptomics data
(2014)
Devising computational methods to accurately reconstruct gene regulatory networks given gene expression data is key to systems biology applications. Here we propose a method for reconstructing gene regulatory networks by simultaneous consideration of data sets from different perturbation experiments and corresponding controls. The method imposes three biologically meaningful constraints: (1) expression levels of each gene should be explained by the expression levels of a small number of transcription factor coding genes, (2) networks inferred from different data sets should be similar with respect to the type and number of regulatory interactions, and (3) relationships between genes which exhibit similar differential behavior over the considered perturbations should be favored. We demonstrate that these constraints can be transformed in a fused LASSO formulation for the proposed method. The comparative analysis on transcriptomics time-series data from prokaryotic species, Escherichia coli and Mycobacterium tuberculosis, as well as a eukaryotic species, mouse, demonstrated that the proposed method has the advantages of the most recent approaches for regulatory network inference, while obtaining better performance and assigning higher scores to the true regulatory links. The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions.
Recent analyses have demonstrated that plant metabolic networks do not differ in their structural properties and that genes involved in basic metabolic processes show smaller coexpression than genes involved in specialized metabolism. By contrast, our analysis reveals differences in the structure of plant metabolic networks and patterns of coexpression for genes in (non)specialized metabolism. Here we caution that conclusions concerning the organization of plant metabolism based on network-driven analyses strongly depend on the computational approaches used.
Molecular phenotyping technologies (e.g., transcriptomics, proteomics, and metabolomics) offer the possibility to simultaneously obtain multivariate time series (MTS) data from different levels of information processing and metabolic conversions in biological systems. As a result, MTS data capture the dynamics of biochemical processes and components whose couplings may involve different scales and exhibit temporal changes. Therefore, it is important to develop methods for determining the time segments in MTS data, which may correspond to critical biochemical events reflected in the coupling of the system's components. Here we provide a novel network-based formalization of the MTS segmentation problem based on temporal dependencies and the covariance structure of the data. We demonstrate that the problem of partitioning MTS data into k segments to maximize a distance function, operating on polynomially computable network properties, often used in analysis of biological network, can be efficiently solved. To enable biological interpretation, we also propose a breakpoint-penalty (BP-penalty) formulation for determining MTS segmentation which combines a distance function with the number/length of segments. Our empirical analyses of synthetic benchmark data as well as time-resolved transcriptomics data from the metabolic and cell cycles of Saccharomyces cerevisiae demonstrate that the proposed method accurately infers the phases in the temporal compartmentalization of biological processes. In addition, through comparison on the same data sets, we show that the results from the proposed formalization of the MTS segmentation problem match biological knowledge and provide more rigorous statistical support in comparison to the contending state-of-the-art methods.
The levels of cellular organization, from gene transcription to translation to protein-protein interaction and metabolism, operate via tightly regulated mutual interactions, facilitating organismal adaptability and various stress responses. Characterizing the mutual interactions between genes, transcription factors, and proteins involved in signaling, termed crosstalk, is therefore crucial for understanding and controlling cells' functionality. We aim at using high-throughput transcriptomics data to discover previously unknown links between signaling networks. We propose and analyze a novel method for crosstalk identification which relies on transcriptomics data and overcomes the lack of complete information for signaling pathways in Arabidopsis thaliana. Our method first employs a network-based transformation of the results from the statistical analysis of differential gene expression in given groups of experiments under different signal-inducing conditions. The stationary distribution of a random walk (similar to the PageRank algorithm) on the constructed network is then used to determine the putative transcripts interrelating different signaling pathways. With the help of the proposed method, we analyze a transcriptomics data set including experiments from four different stresses/signals: nitrate, sulfur, iron, and hormones. We identified promising gene candidates, downstream of the transcription factors (TFs), associated to signaling crosstalk, which were validated through literature mining. In addition, we conduct a comparative analysis with the only other available method in this field which used a biclustering-based approach. Surprisingly, the biclustering-based approach fails to robustly identify any candidate genes involved in the crosstalk of the analyzed signals. We demonstrate that our proposed method is more robust in identifying gene candidates involved downstream of the signaling crosstalk for species for which large transcriptomics data sets, normalized with the same techniques, are available. Moreover, unlike approaches based on biclustering, our approach does not rely on any hidden parameters.
Novel algorithms for prediction of protein complexes from protein-protein interacton networks
(2022)
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.
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.
Physically interacting proteins form macromolecule complexes that drive diverse cellular processes. Advances in experimental techniques that capture interactions between proteins provide us with protein-protein interaction (PPI) networks from several model organisms. These datasets have enabled the prediction and other computational analyses of protein complexes. Here we provide a systematic review of the state-of-the-art algorithms for protein complex prediction from PPI networks proposed in the past two decades. The existing approaches that solve this problem are categorized into three groups, including: cluster-quality-based, node affinity-based, and network embedding-based approaches, and we compare and contrast the advantages and disadvantages. We further include a comparative analysis by computing the performance of eighteen methods based on twelve well-established performance measures on four widely used benchmark protein-protein interaction networks. Finally, the limitations and drawbacks of both, current data and approaches, along with the potential solutions in this field are discussed, with emphasis on the points that pave the way for future research efforts in this field. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).
In nature, the cellular environment of DNA includes not only water and ions, but also other components and co-solutes, which can exert both stabilizing and destabilizing effects on particular oligonucleotide conformations. Among them, ectoine, known as an important osmoprotectant organic co-solute in a broad range of pharmaceutical products, turns out to be of particular relevance. In this article, we study the influence of ectoine on a short single-stranded DNA fragment and on double-stranded helical B-DNA in aqueous solution by means of atomistic molecular dynamics (MD) simulations in combination with molecular theories of solution. Our results demonstrate a conformation-dependent binding behavior of ectoine, which favors the unfolded state of DNA by a combination of electrostatic and dispersion interactions. In conjunction with the Kirkwood-Buff theory, we introduce a simple framework to compute the influence of ectoine on the DNA melting temperature. Our findings reveal a significant linear decrease of the melting temperature with increasing ectoine concentration, which is found to be in qualitative agreement with results from denaturation experiments. The outcomes of our computer simulations provide a detailed mechanistic rationale for the surprising destabilizing influence of ectoine on distinct DNA structures.
Tubers of potato (Solanum tuberosum L.), one of the most important crops, are a prominent example for an efficient production of storage starch. Nevertheless, the synthesis of this storage starch is not completely understood. The plastidial phosphorylase (Phol; EC 2.4.11) catalyzes the reversible transfer of glucosyl residues from glucose-1-phosphate to the non-reducing end of alpha-glucans with the release of orthophosphate. Thus, the enzyme is in principle able to act during starch synthesis. However, so far under normal growth conditions no alterations in tuber starch metabolism were observed. Based on analyses of other species and also from in vitro experiments with potato tuber slices it was supposed, that Phol has a stronger impact on starch metabolism, when plants grow under low temperature conditions. Therefore, we analyzed the starch content, granule size, as well as the internal structure of starch granules isolated from potato plants grown under low temperatures. Besides wild type, transgenic potato plants with a strong reduction in the Phol activity were analyzed. No significant alterations in starch content and granule size were detected. In contrast, when plants were cultivated at low temperatures the chain length distributions of the starch granules were altered. Thus, the granules contained more short glucan chains. That was not observed in the transgenic plants, revealing that Pho1 in wild type is involved in the formation of the short glucan chains, at least at low temperatures. (C) 2016 Elsevier Masson SAS. All rights reserved.
Plants are often challenged by an array of unfavorable environmental conditions. During cold exposure, many changes occur that include, for example, the stabilization of cell membranes, alterations in gene expression and enzyme activities, as well as the accumulation of metabolites. In the presented study, the carbohydrate metabolism was analyzed in the very early response of plants to a low temperature (2 degrees C) in the leaves of 5-week-old potato plants of the Russet Burbank cultivar during the first 12 h of cold treatment (2 h dark and 10 h light). First, some plant stress indicators were examined and it was shown that short-term cold exposure did not significantly affect the relative water content and chlorophyll content (only after 12 h), but caused an increase in malondialdehyde concentration and a decrease in the expression of NDA1, a homolog of the NADH dehydrogenase gene. In addition, it was shown that the content of transitory starch increased transiently in the very early phase of the plant response (3-6 h) to cold treatment, and then its decrease was observed after 12 h. In contrast, soluble sugars such as glucose and fructose were significantly increased only at the end of the light period, where a decrease in sucrose content was observed. The availability of the monosaccharides at constitutively high levels, regardless of the temperature, may delay the response to cold, involving amylolytic starch degradation in chloroplasts. The decrease in starch content, observed in leaves after 12 h of cold exposure, was preceded by a dramatic increase in the transcript levels of the key enzymes of starch degradation initiation, the alpha-glucan, water dikinase (GWD-EC 2.7.9.4) and the phosphoglucan, water dikinase (PWD-EC 2.7.9.5). The gene expression of both dikinases peaked at 9 h of cold exposure, as analyzed by real-time PCR. Moreover, enhanced activities of the acid invertase as well as of both glucan phosphorylases during exposure to a chilling temperature were observed. However, it was also noticed that during the light phase, there was a general increase in glucan phosphorylase activities for both control and cold-stressed plants irrespective of the temperature. In conclusion, a short-term cold treatment alters the carbohydrate metabolism in the leaves of potato, which leads to an increase in the content of soluble sugars.
A biosensor for phenolic compounds based on a chemically modified laccase from Coriolus hirsula immobilized on functionalized screen-printed carbon electrodes (SPCEs) was achieved. Different enzyme modifications and immobilization strategies were analyzed. The electrochemical response of the immobilized laccase on SPCEs modified with carboxyl functionalized multi-walled carbon nanotubes (COOH-MWCNT) was the highest when laccase was aminated prior to the adsorption onto the working electrode. The developed lactase biosensor sensitivity toward different phenolic compounds was assessed to determine the biosensor response with several phenolic compounds. The highest response was obtained for ABTS with a saturation value of I-max = 27.94 mu A. The electrocatalytic efficiency (I-max/K-m(app)) was the highest for ABTS (5588 mu A mu M-1) followed by syringaldazine (3014 mu A.mu M-1). The sensors were considerably stable, whereby 99.5, 82 and 77% of the catalytic response using catechol as substrate was retained after 4, 8 and 10 successive cycles of reuse respectively, with response time average of 5 s for 12 cycles. No loss of activity was observed after 20 days of storage.
The periplasmic aldehyde oxidoreductase PaoABC from Escherichia coli is a molybdenum enzyme involved in detoxification of aldehydes in the cell. It is an example of an heterotrimeric enzyme of the xanthine oxidase family of enzymes which does not dimerize via its molybdenum cofactor binding domain. In order to structurally characterize PaoABC, X-ray crystallography and small angle X-ray scattering (SAXS) have been carried out. The protein crystallizes in the presence of 20% (w/v) polyethylene glycol 3350 using the hanging-drop vapour diffusion method. Although crystals were initially twinned, several experiments were done to overcome twinning and lowering the crystallization temperature (293 K to 277 K) was the solution to the problem. The non-twinned crystals used to solve the structure diffract X-rays to beyond 1.80 angstrom and belong to the C2 space group, with cell parameters a = 109.42 angstrom, b = 78.08 angstrom, c = 151.77 angstrom, = 99.77 degrees, and one molecule in the asymmetric unit. A molecular replacement solution was found for each subunit separately, using several proteins as search models. SAXS data of PaoABC were also collected showing that, in solution, the protein is also an heterotrimer.