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Genome-scale metabolic models are mathematical representations of all known reactions occurring in a cell. Combined with constraints based on physiological measurements, these models have been used to accurately predict metabolic fluxes and effects of perturbations (e.g. knock-outs) and to inform metabolic engineering strategies. Recently, protein-constrained models have been shown to increase predictive potential (especially in overflow metabolism), while alleviating the need for measurement of nutrient uptake rates. The resulting modelling frameworks quantify the upkeep cost of a certain metabolic flux as the minimum amount of enzyme required for catalysis. These improvements are based on the use of in vitro turnover numbers or in vivo apparent catalytic rates of enzymes for model parameterization. In this thesis several tools for the estimation and refinement of these parameters based on in vivo proteomics data of Escherichia coli, Saccharomyces cerevisiae, and Chlamydomonas reinhardtii have been developed and applied. The difference between in vitro and in vivo catalytic rate measures for the three microorganisms was systematically analyzed. The results for the facultatively heterotrophic microalga C. reinhardtii considerably expanded the apparent catalytic rate estimates for photosynthetic organisms. Our general finding pointed at a global reduction of enzyme efficiency in heterotrophy compared to other growth scenarios. Independent of the modelled organism, in vivo estimates were shown to improve accuracy of predictions of protein abundances compared to in vitro values for turnover numbers. To further improve the protein abundance predictions, machine learning models were trained that integrate features derived from protein-constrained modelling and codon usage. Combining the two types of features outperformed single feature models and yielded good prediction results without relying on experimental transcriptomic data. The presented work reports valuable advances in the prediction of enzyme allocation in unseen scenarios using protein constrained metabolic models. It marks the first successful application of this modelling framework in the biotechnological important taxon of green microalgae, substantially increasing our knowledge of the enzyme catalytic landscape of phototrophic microorganisms.
Nature conservation is currently shaping many terrestrial ecosystems in Africa.
This is particularly evident in Sub-Saharan Africa (SSA), where conservation is intended to recover wildlife populations, with special focus on elephants.
Rising numbers of elephants induce woody biomass losses but increase soil organic carbon (SOC) stocks from decaying wood and dung.
We hypothesized that these increases under wildlife conservation in SSA go along with rising contents of plant residues in SOC, traceable by the molecular markers lignin and n-alkanes.
In contrast, agricultural intensification would reduce them due to lower C input and faster SOC turnover through tillage.
To test this, we analyzed lignin by the CuO oxidation method and n-alkanes by fast pressurized solvent extraction in topsoils (0-10 cm) of Arenosols and corresponding plant samples (trees, grasses and crops).
Sampling sites followed conservation gradients with low, medium and high elephant densities and intensification gradients with rangeland and cropland in the woodland savanna of the Namibian Zambezi Region.
Patterns of lignin-derived phenols were retained in the soil, whereas n-alkanes showed shifts in chain lengths. n-Alkanes also showed no clear increase or decrease under conservation or intensification, respectively.
Differently, lignin-derived phenols showed lower values under intensification than under conservation. Confirming our hypothesis, rising SOC contents with rising elephant densities (from 4.4 at low to 5.7 g kg(-1) SOC at high elephant densities) went along with an increasing accumulation of lignin-derived phenols (24.4-34.8 g kg(-1) VSCOC).
This increase is associated with the input of woody debris to the soil, as indicated by V-units and carbon isotopes, modulated by clay and woody biomass.
We conclude, that increasing input of woody residues into soil by browsing behaviour of elephants is an important mechanism for controlling SOC supply in the context of wildlife conservation and is traceable with lignin-derived phenols, but not with n-alkanes.
Rhizophagus irregularis is one of the most extensively studied arbuscular mycorrhizal fungi (AMF) that forms symbioses with and improves the performance of many crops. Lack of transformation protocol for R. irregularis renders it challenging to investigate molecular mechanisms that shape the physiology and interactions of this AMF with plants.
Here, we used all published genomics, transcriptomics, and metabolomics resources to gain insights into the metabolic functionalities of R. irregularis by reconstructing its high-quality genome-scale metabolic network that considers enzyme constraints. Extensive validation tests with the enzyme-constrained metabolic model demonstrated that it can be used to (i) accurately predict increased growth of R. irregularis on myristate with minimal medium; (ii) integrate enzyme abundances and carbon source concentrations that yield growth predictions with high and significant Spearman correlation (rS = 0.74) to measured hyphal dry weight; and (iii) simulate growth rate increases with tighter association of this AMF with the host plant across three fungal structures.
Based on the validated model and system-level analyses that integrate data from transcriptomics studies, we predicted that differences in flux distributions between intraradical mycelium and arbuscles are linked to changes in amino acid and cofactor biosynthesis.
Therefore, our results demonstrated that the enzyme-constrained metabolic model can be employed to pinpoint mechanisms driving developmental and physiological responses of R. irregularis to different environmental cues.
In conclusion, this model can serve as a template for other AMF and paves the way to identify metabolic engineering strategies to modulate fungal metabolic traits that directly affect plant performance.
IMPORTANCE Mounting evidence points to the benefits of the symbiotic interactions between the arbuscular mycorrhiza fungus Rhizophagus irregularis and crops; however, the molecular mechanisms underlying the physiological responses of this fungus to different host plants and environments remain largely unknown.
We present a manually curated, enzyme-constrained, genome-scale metabolic model of R. irregularis that can accurately predict experimentally observed phenotypes.
We show that this high-quality model provides an entry point into better understanding the metabolic and physiological responses of this fungus to changing environments due to the availability of different nutrients.
The model can be used to design metabolic engineering strategies to tailor R. irregularis metabolism toward improving the performance of host plants.
Mantodea, commonly known as mantids, have captivated researchers owing to their enigmatic behavior and ecological significance. This order comprises a diverse array of predatory insects, boasting over 2,400 species globally and inhabiting a wide spectrum of ecosystems. In Iran, the mantid fauna displays remarkable diversity, yet numerous facets of this fauna remain poorly understood, with a significant dearth of systematic and ecological research. This substantial knowledge gap underscores the pressing need for a comprehensive study to advance our understanding of Mantodea in Iran and its neighboring regions.
The principal objective of this investigation was to delve into the ecology and phylogeny of Mantodea within these areas. To accomplish this, our research efforts concentrated on three distinct genera within Iranian Mantodea. These genera were selected due to their limited existing knowledge base and feasibility for in-depth study. Our comprehensive methodology encompassed a multifaceted approach, integrating morphological analysis, molecular techniques, and ecological observations.
Our research encompassed a comprehensive revision of the genus Holaptilon, resulting in the description of four previously unknown species. This extensive effort substantially advanced our understanding of the ecological roles played by Holaptilon and refined its systematic classification. Furthermore, our investigation into Nilomantis floweri expanded its known distribution range to include Iran. By conducting thorough biological assessments, genetic analyses, and ecological niche modeling, we obtained invaluable insights into distribution patterns and genetic diversity within this species. Additionally, our research provided a thorough comprehension of the life cycle, behaviors, and ecological niche modeling of Blepharopsis mendica, shedding new light on the distinctive characteristics of this mantid species. Moreover, we contributed essential knowledge about parasitoids that infect mantid ootheca, laying the foundation for future studies aimed at uncovering the intricate mechanisms governing ecological and evolutionary interactions between parasitoids and Mantodea.
The inclusion of exotic germplasm serves as a crucial means to enhance allelic and
consequently phenotypic diversity in inbred crop species. Such species have experienced a reduction in diversity due to artificial selection focused on a limited set of traits. The natural biodiversity within ecosystems presents an opportunity to explore various traits influencing plant survival, reproductive fitness and yield potential. In agricultural research, the study of wild species closely related to cultivated plants serves as a means to comprehend the genetic foundations of past domestication events and the polymorphisms essential for future breeding efforts to develop superior varieties. In order to examine the metabolic composition, pinpoint quantitative trait loci (QTL) and facilitate their resolution an extensive large-scale analysis of metabolic QTL (mQTL) was conducted on tomato backcross inbred lines (BILs) derived from a cross between the wild species S. pennellii (5240) incorporated into the background of S. lycopersicum cv. LEA determinate inbred which can be grown in open fields and cv. TOP indeterminate which can be grown in greenhouse conditions. A large number of mQTL associated with primary secondary and lipid metabolism in fruit were identified across the two BIL populations. Epistasis, the interactions between genes at different loci, has been an interest in molecular and quantitative genetics for many decades. The study of epistasis requires the analysis of very large populations with multiple independent genotypes that carry specific genomic regions. In order to understand the genetic basis of tomato fruit metabolism, I extended the work to investigate epistatic interactions of the genomic regions. In addition, two candidate genes were identified through quantitative trait loci underlying fruit-specific sucrose and jasmonic acid derivatives. Finally, in this study, I assessed the genetic framework of fruit metabolic traits with a high level of detail, utilizing the newly created Solanum pennellii (5240) backcrossed introgression lines (n=3000). This investigation resulted in the discovery of promising candidate loci associated with significant fruit quality traits, including those to the abundance of glutamic acid and aspartic acid crucial elements contributing to the development of acidity and flavors.
Cell division and the resulting changes to the cell organization affect the shape and functionality of all tissues.
Thus, understanding the determinants of the tissue-wide changes imposed by cell division is a key question in developmental biology.
Here, we use a network representation of live cell imaging data from shoot apical meristems (SAMs) in Arabidopsis thaliana to predict cell division events and their consequences at the tissue level.
We show that a support vector machine classifier based on the SAM network properties is predictive of cell division events, with test accuracy of 76%, which matches that based on cell size alone.
Furthermore, we demonstrate that the combination of topological and biological properties, including cell size, perimeter, distance and shared cell wall between cells, can further boost the prediction accuracy of resulting changes in topology triggered by cell division.
Using our classifiers, we demonstrate the importance of microtubule-mediated cell-to-cell growth coordination in influencing tissue-level topology.
Together, the results from our network-based analysis demonstrate a feedback mechanism between tissue topology and cell division in A. thaliana SAMs.
In plants, the trehalose biosynthetic pathway plays key roles in the regulation of carbon allocation and stress adaptation.
Engineering of the pathway holds great promise to increase the stress resilience of crop plants.
The synthesis of trehalose proceeds by a two-step pathway in which a trehalose-phosphate synthase (TPS) uses UDP-glucose and glucose-6-phosphate to produce trehalose-6 phosphate (T6P) that is subsequently dephosphorylated by trehalose-6 phosphate phosphatase (TPP).
While plants usually do not accumulate high amounts of trehalose, their genome encodes large families of putative trehalose biosynthesis genes, with many members lacking obvious enzymatic activity.
Thus, the function of putative trehalose biosynthetic proteins in plants is only vaguely understood. To gain a deeper insight into the role of trehalose biosynthetic proteins in crops, we assessed the enzymatic activity of the TPS/TPP family from tomato (Solanum lycopersicum L.) and investigated their expression pattern in different tissues as well as in response to temperature shifts. From the 10 TPS isoforms tested, only the 2 proteins belonging to class I showed enzymatic activity, while all 5 TPP isoforms investigated were catalytically active.
Most of the TPS/TPP family members showed the highest expression in mature leaves, and promoter-reporter gene studies suggest that the two class I TPS genes have largely overlapping expression patterns within the vasculature, with only subtle differences in expression in fruits and flowers.
The majority of tomato TPS/TPP genes were induced by heat stress, and individual family members also responded to cold.
This suggests that trehalose biosynthetic pathway genes could play an important role during temperature stress adaptation.
In summary, our study represents a further step toward the exploitation of the TPS and TPP gene families for the improvement of tomato stress resistance.
Pichia pastoris (syn. Komagataella phaffii) represents a commonly used expression system in the biotech industry. High clonal variation of transformants, however, typically results in a broad range of specific productivities for secreted proteins. To isolate rare clones with exceedingly high product titers, an extensive number of clones need to be screened.
In contrast to high-throughput screenings of P. pastoris clones in microtiter plates, secrete-and -capture methodologies have the potential to efficiently isolate high-producer clones among millions of cells through fluorescence-activated cell sorting (FACS).Here, we describe a novel approach for the non-covalent binding of fragment antigen-binding (Fab) proteins to the cell surface for the isolation of high-producing clones.
Eight different single-chain variable fragment (scFv)-based capture matrices specific for the constant part of the Fabs were fused to the Saccharomyces cerevisiae alpha -agglutinin (SAG1) anchor protein for surface display in P. pastoris. By encoding the capture matrix on an episomal plasmid harboring inherently unstable autonomously replicating sequences (ARS), this secrete-and -capture system offers a switchable scFv display.
Efficient plasmid clearance upon removal of selective pres-sure enabled the direct use of isolated clones for subsequent Fab production. Flow-sorted clones (n = 276) displaying high amounts of Fabs showed a significant increase in median Fab titers detected in the cell-free supernatant (CFS) compared to unsorted clones (n = 276) when cells were cultivated in microtiter plates (fac-tor in the range of-21-49). Fab titers of clones exhibiting the highest product titer observed for each of the two approaches were increased by up to 8-fold for the sorted clone.
Improved Fab yields of sorted cells vs. unsorted cells were confirmed in an upscaled shake flask cultivation of selected candidates (factor in the range of-2-3). Hence, the developed display-based selection method proved to be a valuable tool for efficient clone screening in the early stages of our bioprocess development.