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Plastic pollution is ubiquitous on the planet since several millions of tons of plastic waste enter aquatic ecosystems each year. Furthermore, the amount of plastic produced is expected to increase exponentially shortly. The heterogeneity of materials, additives and physical characteristics of plastics are typical of these emerging contaminants and affect their environmental fate in marine and freshwaters. Consequently, plastics can be found in the water column, sediments or littoral habitats of all aquatic ecosystems. Most of this plastic debris will fragment as a product of physical, chemical and biological forces, producing particles of small size. These particles (< 5mm) are known as “microplastics” (MP). Given their high surface-to-volume ratio, MP stimulate biofouling and the formation of biofilms in aquatic systems.
As a result of their unique structure and composition, the microbial communities in MP biofilms are referred to as the “Plastisphere.” While there is increasing data regarding the distinctive composition and structure of the microbial communities that form part of the plastisphere, scarce information exists regarding the activity of microorganisms in MP biofilms. This surface-attached lifestyle is often associated with the increase in horizontal gene transfer (HGT) among bacteria. Therefore, this type of microbial activity represents a relevant function worth to be analyzed in MP biofilms. The horizontal exchange of mobile genetic elements (MGEs) is an essential feature of bacteria. It accounts for the rapid evolution of these prokaryotes and their adaptation to a wide variety of environments. The process of HGT is also crucial for spreading antibiotic resistance and for the evolution of pathogens, as many MGEs are known to contain antibiotic resistance genes (ARGs) and genetic determinants of pathogenicity.
In general, the research presented in this Ph.D. thesis focuses on the analysis of HGT and heterotrophic activity in MP biofilms in aquatic ecosystems. The primary objective was to analyze the potential of gene exchange between MP bacterial communities vs. that of the surrounding water, including bacteria from natural aggregates. Moreover, the thesis addressed the potential of MP biofilms for the proliferation of biohazardous bacteria and MGEs from wastewater treatment plants (WWTPs) and associated with antibiotic resistance. Finally, it seeks to prove if the physiological profile of MP biofilms under different limnological conditions is divergent from that of the water communities. Accordingly, the thesis is composed of three independent studies published in peer-reviewed journals. The two laboratory studies were performed using both model and environmental microbial communities. In the field experiment, natural communities from freshwater ecosystems were examined.
In Chapter I, the inflow of treated wastewater into a temperate lake was simulated with a concentration gradient of MP particles. The effects of MP on the microbial community structure and the occurrence of integrase 1 (int 1) were followed. The int 1 is a marker associated with mobile genetic elements and known as a proxy for anthropogenic effects on the spread of antimicrobial resistance genes. During the experiment, the abundance of int1 increased in the plastisphere with increasing MP particle concentration, but not in the surrounding water. In addition, the microbial community on MP was more similar to the original wastewater community with increasing microplastic concentrations. Our results show that microplastic particles indeed promote persistence of standard indicators of microbial anthropogenic pollution in natural waters.
In Chapter II, the experiments aimed to compare the permissiveness of aquatic bacteria towards model antibiotic resistance plasmid pKJK5, between communities that form biofilms on MP vs. those that are free-living. The frequency of plasmid transfer in bacteria associated with MP was higher when compared to bacteria that are free-living or in natural aggregates. Moreover, comparison increased gene exchange occurred in a broad range of phylogenetically-diverse bacteria. The results indicate a different activity of HGT in MP biofilms, which could affect the ecology of aquatic microbial communities on a global scale and the spread of antibiotic resistance.
Finally, in Chapter III, physiological measurements were performed to assess whether microorganisms on MP had a different functional diversity from those in water. General heterotrophic activity such as oxygen consumption was compared in microcosm assays with and without MP, while diversity and richness of heterotrophic activities were calculated by using Biolog® EcoPlates. Three lakes with different nutrient statuses presented differences in MP-associated biomass build up. Functional diversity profiles of MP biofilms in all lakes differed from those of the communities in the surrounding water, but only in the oligo-mesotrophic lake MP biofilms had a higher functional richness compared to the ambient water. The results support that MP surfaces act as new niches for aquatic microorganisms and can affect global carbon dynamics of pelagic environments.
Overall, the experimental works presented in Chapters I and II support a scenario where MP pollution affects HGT dynamics among aquatic bacteria. Among the consequences of this alteration is an increase in the mobilization and transfer efficiency of ARGs. Moreover, it supposes that changes in HGT can affect the evolution of bacteria and the processing of organic matter, leading to different catabolic profiles such as demonstrated in Chapter III. The results are discussed in the context of the fate and magnitude of plastic pollution and the importance of HGT for bacterial evolution and the microbial loop, i.e., at the base of aquatic food webs. The thesis supports a relevant role of MP biofilm communities for the changes observed in the aquatic microbiome as a product of intense human intervention.
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.
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.
Cells and organelles are not homogeneous but include microcompartments that alter the spatiotemporal characteristics of cellular processes. The effects of microcompartmentation on metabolic pathways are however difficult to study experimentally. The pyrenoid is a microcompartment that is essential for a carbon concentrating mechanism (CCM) that improves the photosynthetic performance of eukaryotic algae. Using Chlamydomonas reinhardtii, we obtained experimental data on photosynthesis, metabolites, and proteins in CCM-induced and CCM-suppressed cells. We then employed a computational strategy to estimate how fluxes through the Calvin-Benson cycle are compartmented between the pyrenoid and the stroma. Our model predicts that ribulose-1,5-bisphosphate (RuBP), the substrate of Rubisco, and 3-phosphoglycerate (3PGA), its product, diffuse in and out of the pyrenoid, respectively, with higher fluxes in CCM-induced cells. It also indicates that there is no major diffusional barrier to metabolic flux between the pyrenoid and stroma. Our computational approach represents a stepping stone to understanding microcompartmentalized CCM in other organisms.