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Plant-derived Transcription Factors for Orthologous Regulation of Gene Expression in the Yeast Saccharomyces cerevisiae
Control of gene expression by transcription factors (TFs) is central in many synthetic biology projects where tailored expression of one or multiple genes is often needed. As TFs from evolutionary distant organisms are unlikely to affect gene expression in a host of choice, they represent excellent candidates for establishing orthogonal control systems. To establish orthogonal regulators for use in yeast (Saccharomyces cerevisiae), we chose TFs from the plant Arabidopsis thaliana. We established a library of 106 different combinations of chromosomally integrated TFs, activation domains (yeast GAL4 AD, herpes simplex virus VP64, and plant EDLL) and synthetic promoters harbouring cognate cis-regulatory motifs driving a yEGFP reporter. Transcriptional output of the different driver / reporter combinations varied over a wide spectrum, with EDLL being a considerably stronger transcription activation domain in yeast, than the GAL4 activation domain, in particular when fused to Arabidopsis NAC TFs. Notably, the strength of several NAC - EDLL fusions exceeded that of the strong yeast TDH3 promoter by 6- to 10-fold. We furthermore show that plant TFs can be used to build regulatory systems encoded by centromeric or episomal plasmids. Our library of TF – DNA-binding site combinations offers an excellent tool for diverse synthetic biology applications in yeast.
COMPASS: Rapid combinatorial optimization of biochemical pathways based on artificial transcription factors
We established a high-throughput cloning method, called COMPASS for COMbinatorial Pathway ASSembly, for the balanced expression of multiple genes in Saccharomyces cerevisiae. COMPASS employs orthogonal, plant-derived artificial transcription factors (ATFs) for controlling the expression of pathway genes, and homologous recombination-based cloning for the generation of thousands of individual DNA constructs in parallel. The method relies on a positive selection of correctly assembled pathway variants from both, in vivo and in vitro cloning procedures. To decrease the turnaround time in genomic engineering, we equipped COMPASS with multi-locus CRISPR/Cas9-mediated modification capacity. In its current realization, COMPASS allows combinatorial optimization of up to ten pathway genes, each transcriptionally controlled by nine different ATFs spanning a 10-fold difference in expression strength. The application of COMPASS was demonstrated by generating cell libraries producing beta-carotene and co-producing beta-ionone and biosensor-responsive naringenin. COMPASS will have many applications in other synthetic biology projects that require gene expression balancing.
CaPRedit: Genome editing using CRISPR-Cas9 and plant-derived transcriptional regulators for the redirection of flux through the FPP branch-point in yeast. Technologies developed over the past decade have made Saccharomyces cerevisiae a promising platform for production of different natural products. We developed CRISPR/Ca9- and plant derived regulator-mediated genome editing approach (CaPRedit) to greatly accelerate strain modification and to facilitate very low to very high expression of key enzymes using inducible regulators. CaPRedit can be implemented to enhance the production of yeast endogenous or heterologous metabolites in the yeast S. cerevisiae. The CaPRedit system aims to faciltiate modification of multiple targets within a complex metabolic pathway through providing new tools for increased expression of genes encoding rate-limiting enzymes, decreased expression of essential genes, and removed expression of competing pathways. This approach is based on CRISPR/Cas9-mediated one-step double-strand breaks to integrate modules containing IPTG-inducible plant-derived artificial transcription factor and promoter pair(s) in a desired locus or loci. Here, we used CaPRedit to redirect the yeast endogenous metabolic flux toward production of farnesyl diphosphate (FPP), a central precursor of nearly all yeast isoprenoid products, by overexpression of the enzymes lead to produce FPP from glutamate. We found significantly higher beta-carotene accumulation in the CaPRedit-mediated modified strain than in the wild type (WT) strain. More specifically, CaPRedit_FPP 1.0 strain was generated, in which three genes involved in FPP synthesis, tHMG1, ERG20, and GDH2, were inducibly overexpressed under the control of strong plant-derived ATFPs. The beta–carotene accumulated in CaPRedit_FPP 1.0 strain to a level 1.3-fold higher than the previously reported optimized strain that carries the same overexpressed genes (as well as additional genetic modifications to redirect yeast endogenous metabolism toward FPP production). Furthermore, the genetic modifications implemented in CaPRedit_FPP 1.0 strain resulted in only a very small growth defect (growth rate relative to the WT is ~ -0.03).
East Africa is a natural laboratory: Studying its unique geological and biological history can help us better inform our theories and models. Studying its present and future can help us protect its globally important biodiversity and ecosystem services. East African vegetation plays a central role in all these aspects, and this dissertation aims to quantify its dynamics through computer simulations.
Computer models help us recreate past settings, forecast into the future or conduct simulation experiments that we cannot otherwise perform in the field. But before all that, one needs to test their performance. The outputs that the model produced using the present day-inputs, agreed well with present-day observations of East African vegetation. Next, I simulated past vegetation for which we have fossil pollen data to compare. With computer models, we can fill the gaps of knowledge between sites where we have fossil pollen data from, and create a more complete picture of the past. Good level of agreement between model and pollen data where they overlapped in space further validated our model performance.
Once the model was tested and validated for the region, it became possible to probe one of the long standing questions regarding East African vegetation: How did East Africa lose its tropical forests? The present-day vegetation in the tropics is mainly characterized by continuous forests worldwide except in tropical East Africa, where forests only occur as patches. In a series of simulation experiments, I was able to show under which conditions these forest patches could have been connected and fragmented in the past. This study showed the sensitivity of East African vegetation to climate change and variability such as those expected under future climate change.
El Niño Southern Oscillation (ENSO) events that result from the fluctuations in temperature between the ocean and atmosphere, bring further variability to East African climate and are predicted to increase in intensity in the future. But climate models are still not good at capturing the pattens of these events. In a study where I quantified the influence of ENSO events on East African vegetation, I showed how different the future vegetation could be from what we currently predict with these climate models that lack accurate ENSO contribution. Consideration of these discrepancies is important for our future global carbon budget calculations and management decisions.
The movement of organisms has formed our planet like few other processes. Movements shape populations, communities, entire ecosystems, and guarantee fundamental ecosystem functions and services, like seed dispersal and pollination. Global, regional and local anthropogenic impacts influence animal movements across ecosystems all around the world. In particular, land-use modification, like habitat loss and fragmentation disrupt movements between habitats with profound consequences, from increased disease transmissions to reduced species richness and abundance. However, neither the influence of anthropogenic change on animal movement processes nor the resulting effects on ecosystems are well understood. Therefore, we need a coherent understanding of organismal movement processes and their underlying mechanisms to predict and prevent altered animal movements and their consequences for ecosystem functions.
In this thesis I aim at understanding the influence of anthropogenically caused land-use change on animal movement processes and their underlying mechanisms. In particular, I am interested in the synergistic influence of large-scale landscape structure and fine-scale habitat features on basic-level movement behaviours (e.g. the daily amount of time spend running, foraging, and resting) and their emerging higher-level movements (home range formation). Based on my findings, I identify the likely consequences of altered animal movements that lead to the loss of species richness and abundances.
The study system of my thesis are hares in agricultural landscapes. European brown hares (Lepus europaeus) are perfectly suited to study animal movements in agricultural landscapes, as hares are hermerophiles and prefer open habitats. They have historically thrived in agricultural landscapes, but their numbers are in decline. Agricultural areas are undergoing strong land-use changes due to increasing food demand and fast developing agricultural technologies. They are already the largest land-use class, covering 38% of the world’s terrestrial surface. To consider the relevance of a given landscape structure for animal movement behaviour I selected two differently structured agricultural landscapes – a simple landscape in Northern Germany with large fields and few landscape elements (e.g. hedges and tree stands), and a complex landscape in Southern Germany with small fields and many landscape elements.
I applied GPS devices (hourly fixes) with internal high-resolution accelerometers (4 min samples) to track hares, receiving an almost continuous observation of the animals’ behaviours via acceleration analyses. I used the spatial and behavioural information in combination with remote sensing data (normalized difference vegetation index, or NDVI, a proxy for resource availability), generating an almost complete idea of what the animal was doing when, why and where. Apart from landscape structure (represented by the two differently structured study areas), I specifically tested whether the following fine-scale habitat features influence animal movements: resource, agricultural management events, habitat diversity, and habitat structure.
My results show that, irrespective of the movement process or mechanism and the type of fine-scale habitat features, landscape structure was the overarching variable influencing hare movement behaviour. High resource variability forces hares to enlarge their home ranges, but only in the simple and not in the complex landscape. Agricultural management events result in home range shifts in both landscapes, but force hares to increase their home ranges only in the simple landscape. Also the preference of habitat patches with low vegetation and the avoidance of high vegetation, was stronger in the simple landscape. High and dense crop fields restricted hare movements temporarily to very local and small habitat patch remnants. Such insuperable barriers can separate habitat patches that were previously connected by mobile links. Hence, the transport of nutrients and genetic material is temporarily disrupted. This mechanism is also working on a global scale, as human induced changes from habitat loss and fragmentation to expanding monocultures cause a reduction in animal movements worldwide.
The mechanisms behind those findings show that higher-level movements, like increasing home ranges, emerge from underlying basic-level movements, like the behavioural modes. An increasing landscape simplicity first acts on the behavioural modes, i.e. hares run and forage more, but have less time to rest. Hence, the emergence of increased home range sizes in simple landscapes is based on an increased proportion of time running and foraging, largely due to longer travelling times between distant habitats and scarce resource items in the landscape. This relationship was especially strong during the reproductive phase, demonstrating the importance of high-quality habitat for reproduction and the need to keep up self-maintenance first, in low quality areas. These changes in movement behaviour may release a cascade of processes that start with more time being allocated to running and foraging, resulting into an increased energy expenditure and may lead to a decline in individual fitness. A decrease in individual fitness and reproductive output will ultimately affect population viability leading to local extinctions.
In conclusion, I show that landscape structure has one of the most important effects on hare movement behaviour. Synergistic effects of landscape structure, and fine-scale habitat features, first affect and modify basic-level movement behaviours, that can scales up to altered higher-level movements and may even lead to the decline of species richness and abundances, and the disruption of ecosystem functions. Understanding the connection between movement mechanisms and processes can help to predict and prevent anthropogenically induced changes in movement behaviour. With regard to the paramount importance of landscape structure, I strongly recommend to decrease the size of agricultural fields and increase crop diversity. On the small-scale, conservation policies should assure the year round provision of areas with low vegetation height and high quality forage. This could be done by generating wildflower strips and additional (semi-) natural habitat patches. This will not only help to increase the populations of European brown hares and other farmland species, but also ensure and protects the continuity of mobile links and their intrinsic value for sustaining important ecosystem functions and services.
For more than two centuries, plant ecologists have aimed to understand how environmental gradients and biotic interactions shape the distribution and co-occurrence of plant species. In recent years, functional trait–based approaches have been increasingly used to predict patterns of species co-occurrence and species distributions along environmental gradients (trait–environment relationships). Functional traits are measurable properties at the individual level that correlate well with important processes. Thus, they allow us to identify general patterns by synthesizing studies across specific taxonomic compositions, thereby fostering our understanding of the underlying processes of species assembly. However, the importance of specific processes have been shown to be highly dependent on the spatial scale under consideration. In particular, it remains uncertain which mechanisms drive species assembly and allow for plant species coexistence at smaller, more local spatial scales. Furthermore, there is still no consensus on how particular environmental gradients affect the trait composition of plant communities. For example, increasing drought because of climate change is predicted to be a main threat to plant diversity, although it remains unclear which traits of species respond to increasing aridity. Similarly, there is conflicting evidence of how soil fertilization affects the traits related to establishment ability (e.g., seed mass). In this cumulative dissertation, I present three empirical trait-based studies that investigate specific research questions in order to improve our understanding of species distributions along environmental gradients.
In the first case study, I analyze how annual species assemble at the local scale and how environmental heterogeneity affects different facets of biodiversity—i.e. taxonomic, functional, and phylogenetic diversity—at different spatial scales. The study was conducted in a semi-arid environment at the transition zone between desert and Mediterranean ecosystems that features a sharp precipitation gradient (Israel). Different null model analyses revealed strong support for environmentally driven species assembly at the local scale, since species with similar traits tended to co-occur and shared high abundances within microsites (trait convergence). A phylogenetic approach, which assumes that closely related species are functionally more similar to each other than distantly related ones, partly supported these results. However, I observed that species abundances within microsites were, surprisingly, more evenly distributed across the phylogenetic tree than expected (phylogenetic overdispersion). Furthermore, I showed that environmental heterogeneity has a positive effect on diversity, which was higher on functional than on taxonomic diversity and increased with spatial scale. The results of this case study indicate that environmental heterogeneity may act as a stabilizing factor to maintain species diversity at local scales, since it influenced species distribution according to their traits and positively influenced diversity. All results were constant along the precipitation gradient.
In the second case study (same study system as case study one), I explore the trait responses of two Mediterranean annuals (Geropogon hybridus and Crupina crupinastrum) along a precipitation gradient that is comparable to the maximum changes in precipitation predicted to occur by the end of this century (i.e., −30%). The heterocarpic G. hybridus showed strong trends in seed traits, suggesting that dispersal ability increased with aridity. By contrast, the homocarpic C. crupinastrum showed only a decrease in plant height as aridity increased, while leaf traits of both species showed no consistent pattern along the precipitation gradient. Furthermore, variance decomposition of traits revealed that most of the trait variation observed in the study system was actually found within populations. I conclude that trait responses towards aridity are highly species-specific and that the amount of precipitation is not the most striking environmental factor at this particular scale.
In the third case study, I assess how soil fertilization mediates—directly by increased nutrient addition and indirectly by increased competition—the effect of seed mass on establishment ability. For this experiment, I used 22 species differing in seed mass from dry grasslands in northeastern Germany and analyzed the interacting effects of seed mass with nutrient availability and competition on four key components of seedling establishment: seedling emergence, time of seedling emergence, seedling survival, and seedling growth. (Time of) seedling emergence was not affected by seed mass. However, I observed that the positive effect of seed mass on seedling survival is lowered under conditions of high nutrient availability, whereas the positive effect of seed mass on seedling growth was only reduced by competition. Based on these findings, I developed a conceptual model of how seed mass should change along a soil fertility gradient in order to reconcile conflicting findings from the literature. In this model, seed mass shows a U-shaped pattern along the soil fertility gradient as a result of changing nutrient availability and competition.
Overall, the three case studies highlight the role of environmental factors on species distribution and co-occurrence. Moreover, the findings of this thesis indicate that spatial heterogeneity at local scales may act as a stabilizing factor that allows species with different traits to coexist. In the concluding discussion, I critically debate intraspecific trait variability in plant community ecology, the use of phylogenetic relationships and easily measured key functional traits as a proxy for species’ niches. Finally, I offer my outlook for the future of functional plant community research.
Systems biology aims at investigating biological systems in its entirety by gathering and analyzing large-scale data sets about the underlying components. Computational systems biology approaches use these large-scale data sets to create models at different scales and cellular levels. In addition, it is concerned with generating and testing hypotheses about biological processes. However, such approaches are inevitably leading to computational challenges due to the high dimensionality of the data and the differences in the dimension of data from different cellular layers.
This thesis focuses on the investigation and development of computational approaches to analyze metabolite profiles in the context of cellular networks. This leads to determining what aspects of the network functionality are reflected in the metabolite levels. With these methods at hand, this thesis aims to answer three questions: (1) how observability of biological systems is manifested in metabolite profiles and if it can be used for phenotypical comparisons; (2) how to identify couplings of reaction rates from metabolic profiles alone; and (3) which regulatory mechanism that affect metabolite levels can be distinguished by integrating transcriptomics and metabolomics read-outs.
I showed that sensor metabolites, identified by an approach from observability theory, are more correlated to each other than non-sensors. The greater correlations between sensor metabolites were detected both with publicly available metabolite profiles and synthetic data simulated from a medium-scale kinetic model. I demonstrated through robustness analysis that correlation was due to the position of the sensor metabolites in the network and persisted irrespectively of the experimental conditions. Sensor metabolites are therefore potential candidates for phenotypical comparisons between conditions through targeted metabolic analysis.
Furthermore, I demonstrated that the coupling of metabolic reaction rates can be investigated from a purely data-driven perspective, assuming that metabolic reactions can be described by mass action kinetics. Employing metabolite profiles from domesticated and wild wheat and tomato species, I showed that the process of domestication is associated with a loss of regulatory control on the level of reaction rate coupling. I also found that the same metabolic pathways in Arabidopsis thaliana and Escherichia coli exhibit differences in the number of reaction rate couplings.
I designed a novel method for the identification and categorization of transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approach determines the partial correlation of metabolites with control by the principal components of the transcript levels. The principle components contain the majority of the transcriptomic information allowing to partial out the effect of the transcriptional layer from the metabolite profiles. Depending whether the correlation between metabolites persists upon controlling for the effect of the transcriptional layer, the approach allows us to group metabolite pairs into being associated due to post-transcriptional or transcriptional regulation, respectively. I showed that the classification of metabolite pairs into those that are associated due to transcriptional or post-transcriptional regulation are in agreement with existing literature and findings from a Bayesian inference approach.
The approaches developed, implemented, and investigated in this thesis open novel ways to jointly study metabolomics and transcriptomics data as well as to place metabolic profiles in the network context. The results from these approaches have the potential to provide further insights into the regulatory machinery in a biological system.
Microbial processing of organic matter (OM) in the freshwater biosphere is a key component of global biogeochemical cycles. Freshwaters receive and process valuable amounts of leaf OM from their terrestrial landscape. These terrestrial subsidies provide an essential source of energy and nutrients to the aquatic environment as a function of heterotrophic processing by fungi and bacteria. Particularly in freshwaters with low in-situ primary production from algae (microalgae, cyanobacteria), microbial turnover of leaf OM significantly contributes to the productivity and functioning of freshwater ecosystems and not least their contribution to global carbon cycling.
Based on differences in their chemical composition, it is believed that leaf OM is less bioavailable to microbial heterotrophs than OM photosynthetically produced by algae. Especially particulate leaf OM, consisting predominantly of structurally complex and aromatic polymers, is assumed highly resistant to enzymatic breakdown by microbial heterotrophs. However, recent research has demonstrated that OM produced by algae promotes the heterotrophic breakdown of leaf OM in aquatic ecosystems, with profound consequences for the metabolism of leaf carbon (C) within microbial food webs. In my thesis, I aimed at investigating the underlying mechanisms of this so called priming effect of algal OM on the use of leaf C in natural microbial communities, focusing on fungi and bacteria.
The works of my thesis underline that algal OM provides highly bioavailable compounds to the microbial community that are quickly assimilated by bacteria (Paper II). The substrate composition of OM pools determines the proportion of fungi and bacteria within the microbial community (Paper I). Thereby, the fraction of algae OM in the aquatic OM pool stimulates the activity and hence contribution of bacterial communities to leaf C turnover by providing an essential energy and nutrient source for the assimilation of the structural complex leaf OM substrate. On the contrary, the assimilation of algal OM remains limited for fungal communities as a function of nutrient competition between fungi and bacteria (Paper I, II). In addition, results provide evidence that environmental conditions determine the strength of interactions between microalgae and heterotrophic bacteria during leaf OM decomposition (Paper I, III). However, the stimulatory effect of algal photoautotrophic activities on leaf C turnover remained significant even under highly dynamic environmental conditions, highlighting their functional role for ecosystem processes (Paper III).
The results of my thesis provide insights into the mechanisms by which algae affect the microbial turnover of leaf C in freshwaters. This in turn contributes to a better understanding of the function of algae in freshwater biogeochemical cycles, especially with regard to their interaction with the heterotrophic community.
Characterization of altered inflorescence architecture in Arabidopsis thaliana BG-5 x Kro-0 hybrid
(2018)
A reciprocal cross between two A. thaliana accessions, Kro-0 (Krotzenburg, Germany) and BG-5 (Seattle, USA), displays purple rosette leaves and dwarf bushy phenotype in F1 hybrids when grown at 17 °C and a parental-like phenotype when grown at 21 °C. This F1 temperature-dependent-dwarf-bushy phenotype is characterized by reduced growth of the primary stem together with an increased number of branches. The reduced stem growth was the strongest at the first internode. In addition, we found that a temperature switch from 21 °C to 17 °C induced the phenotype only before the formation of the first internode of the stem. Similarly, the F1 dwarf-bushy phenotype could not be reversed when plants were shifted from 17 °C to 21 °C after the first internode was formed. Metabolic analysis showed that the F1 phenotype was associated with a significant upregulation of anthocyanin(s), kaempferol(s), salicylic acid, jasmonic acid and abscisic acid. As it has been previously shown that the dwarf-bushy phenotype is linked to two loci, one on chromosome 2 from Kro-0 and one on chromosome 3 from BG-5, an artificial micro-RNA approach was used to investigate the necessary genes on these intervals. From the results obtained, it was found that two genes, AT2G14120 that encodes for a DYNAMIN RELATED PROTEIN3B and AT2G14100 that encodes a member of the Cytochrome P450 family protein CYP705A13, were necessary for the appearance of the F1 phenotype on chromosome 2. It was also discovered that AT3G61035 that encodes for another cytochrome P450 family protein CYP705A13 and AT3G60840 that encodes for a MICROTUBULE-ASSOCIATED PROTEIN65-4 on chromosome 3 were both necessary for the induction of the F1 phenotype. To prove the causality of these genes, genomic constructs of the Kro-0 candidate genes on chromosome 2 were transferred to BG-5 and genomic constructs of the chromosome 3 candidate genes from BG-5 were transferred to Kro-0. The T1 lines showed that these genes are not sufficient alone to induce the phenotype. In addition to the F1 phenotype, more severe phenotypes were observed in the F2 generations that were grouped into five different phenotypic classes. Whilst seed yield was comparable between F1 hybrids and parental lines, three phenotypic classes in the F2 generation exhibited hybrid breakdown in the form of reproductive failure. This F2 hybrid breakdown was less sensitive to temperature and showed a dose-dependent effect of the loci involved in F1 phenotype. The severest class of hybrid breakdown phenotypes was observed only in the population of backcross with the parent Kro-0, which indicates a stronger contribution of the BG-5 allele when compared to the Kro-0 allele on the hybrid breakdown phenotypes. Overall, the findings of my thesis provide a further understanding of the genetic and metabolic factors underlying altered shoot architecture in hybrid dysfunction.
Neuroinflammatory and neurodegenerative diseases such as Parkinson's (PD) and multiple sclerosis (MS) often result in a severe impairment of the patient´s quality of life. Effective therapies for the treatment are currently not available, which results in a high socio-economic burden. Due to the heterogeneity of the disease subtypes, stratification is particularly difficult in the early phase of the disease and is mainly based on clinical parameters such as neurophysiological tests and central nervous imaging. Due to good accessibility and stability, blood and cerebrospinal fluid metabolite markers could serve as surrogates for neurodegenerative processes. This can lead to an improved mechanistic understanding of these diseases and further be used as "treatment response" biomarkers in preclinical and clinical development programs. Therefore, plasma and CSF metabolite profiles will be identified that allow differentiation of PD from healthy controls, association of PD with dementia (PDD) and differentiation of PD subtypes such as akinetic rigid and tremor dominant PD patients. In addition, plasma metabolites for the diagnosis of primary progressive MS (PPMS) should be investigated and tested for their specificity to relapsing-remitting MS (RRMS) and their development during PPMS progression.
By applying untargeted high-resolution metabolomics of PD patient samples and in using random forest and partial least square machine learning algorithms, this study identified 20 plasma metabolites and 14 CSF metabolite biomarkers. These differentiate against healthy individuals with an AUC of 0.8 and 0.9 in PD, respectively. We also identify ten PDD specific serum metabolites, which differentiate against healthy individuals and PD patients without dementia with an AUC of 1.0, respectively. Furthermore, 23 akinetic-rigid specific plasma markers were identified, which differentiate against tremor-dominant PD patients with an AUC of 0.94 and against healthy individuals with an AUC of 0.98. These findings also suggest more severe disease pathology in the akinetic-rigid PD than in tremor dominant PD. In the analysis of MS patient samples a partial least square analysis yielded predictive models for the classification of PPMS and resulted in 20 PPMS specific metabolites. In another MS study unknown changes in human metabolism were identified after administration of the multiple sclerosis drug dimethylfumarate, which is used for the treatment of RRMS. These results allow to describe and understand the hitherto completely unknown mechanism of action of this new drug and to use these findings for the further development of new drugs and targets against RRMS.
In conclusion, these results have the potential for improved diagnosis of these diseases and improvement of mechanistic understandings, as multiple deregulated pathways were identified. Moreover, novel Dimethylfumarate targets can be used to aid drug development and treatment efficiency. Overall, metabolite profiling in combination with machine learning identified as a promising approach for biomarker discovery and mode of action elucidation.