Navigating the microalgal maze: a comprehensive review of recent advances and future perspectives in biological networks

MAIN CONCLUSION

PPI analysis deepens our knowledge in critical processes like carbon fixation and nutrient sensing. Moreover, signaling networks, including pathways like MAPK/ERK and TOR, provide valuable information in how microalgae respond to environmental changes and stress. Additionally, species-species interaction networks for microalgae provide a comprehensive understanding of how different species interact within their environments. This review examines recent advancements in the study of biological networks within microalgae, with a focus on the intricate interactions that define these organisms. It emphasizes how network biology, an interdisciplinary field, offers valuable insights into microalgae functions through various methodologies. Crucial approaches, such as protein-protein interaction (PPI) mapping utilizing yeast two-hybrid screening and mass spectrometry, are essential for comprehending cellular processes and optimizing functions, such as photosynthesis and fatty acid biosynthesis. The application of advanced computational methods and information mining has significantly improved PPI analysis, revealing networks involved in critical processes like carbon fixation and nutrient sensing. The review also encompasses transcriptional networks, which play a role in gene regulation and stress responses, as well as metabolic networks represented by genome-scale metabolic models (GEMs), which aid in strain optimization and the prediction of metabolic outcomes. Furthermore, signaling networks, including pathways like MAPK/ERK and TOR, are crucial for understanding how microalgae respond to environmental changes and stress. Additionally, species-species interaction networks for microalgae provide a comprehensive understanding of how different species interact within their environments. The integration of these network biology approaches has deepened our understanding of microalgal interactions, paving the way for more efficient cultivation and new industrial applications.

Network-based restoration strategies maximize ecosystem recovery

Redressing global patterns of biodiversity loss requires quantitative frameworks that can predict ecosystem collapse and inform restoration strategies. By applying a network-based dynamical approach to synthetic and real-world mutualistic ecosystems, we show that biodiversity recovery following collapse is maximized when extirpated species are reintroduced based solely on their total number of connections in the original interaction network. More complex network-based strategies that prioritize the reintroduction of species that improve 'higher order' topological features such as compartmentalization do not provide meaningful performance improvements. These results suggest that it is possible to design nearly optimal restoration strategies that maximize biodiversity recovery for data-poor ecosystems in order to ensure the delivery of critical natural services that fuel economic development, food security, and human health around the globe.

Efficient keystone species identification strategy based on tabu search

As species extinction accelerates globally and biodiversity declines dramatically, identifying keystone species becomes an effective way to conserve biodiversity. In traditional approaches, it is considered that the extinction of species with high centrality poses the greatest threat to secondary extinction. However, the indirect effect, which is equally important as the local and direct effects, is not included. Here, we propose an optimized disintegration strategy model for quantitative food webs and introduced tabu search, a metaheuristic optimization algorithm, to identify keystone species. Topological simulations are used to record secondary extinctions during species removal and secondary extinction areas, as well as to evaluate food web robustness. The effectiveness of the proposed strategy is also validated by comparing it with traditional methods. Results of our experiments demonstrate that our strategy can optimize the effect of food web disintegration and identify the species whose extinction is most destructive to the food web through global search. The algorithm provides an innovative and efficient way for further development of keystone species identification in the ecosystem.

Inter-Kingdom Networks of Canola Microbiome Reveal Bradyrhizobium as Keystone Species and Underline the Importance of Bulk Soil in Microbial Studies to Enhance Canola Production

The subterranean microbiota of plants is of great importance for plant growth and health, as root-associated microbes can perform crucial ecological functions. As the microbial environment of roots is extremely diverse, identifying keystone microorganisms in plant roots, rhizosphere, and bulk soil is a necessary step towards understanding the network of influence within the microbial community associated with roots and enhancing its beneficial elements. To target these hot spots of microbial interaction, we used inter-kingdom network analysis on the canola growth phase of a long-term cropping system diversification experiment conducted at four locations in the Canadian Prairies. Our aims were to verify whether bacterial and fungal communities of canola roots, rhizosphere, and bulk soil are related and influenced by diversification of the crop rotation system; to determine whether there are common or specific core fungi and bacteria in the roots, rhizosphere, and bulk soil under canola grown in different environments and with different levels of cropping system diversification; and to identify hub taxa at the inter-kingdom level that could play an important ecological role in the microbiota of canola. Our results showed that fungi were influenced by crop diversification, which was not the case on bacteria. We found no core microbiota in canola roots but identified three core fungi in the rhizosphere, one core mycobiota in the bulk soil, and one core bacterium shared by the rhizosphere and bulk soil. We identified two bacterial and one fungal hub taxa in the inter-kingdom networks of the canola rhizosphere, and one bacterial and two fungal hub taxa in the bulk soil. Among these inter-kingdom hub taxa, Bradyrhizobium sp. and Mortierella sp. are particularly influential on the microbial community and the plant. To our knowledge, this is the first inter-kingdom network analysis utilized to identify hot spots of interaction in canola microbial communities.

Fungi in Permafrost-Affected Soils of the Canadian Arctic: Horizon- and Site-Specific Keystone Taxa Revealed by Co-Occurrence Network

Permafrost-affected soil stores a significant amount of organic carbon. Identifying the biological constraints of soil organic matter transformation, e.g., the interaction of major soil microbial soil organic matter decomposers, is crucial for predicting carbon vulnerability in permafrost-affected soil. Fungi are important players in the decomposition of soil organic matter and often interact in various mutualistic relationships during this process. We investigated four different soil horizon types (including specific horizons of cryoturbated soil organic matter (cryoOM)) across different types of permafrost-affected soil in the Western Canadian Arctic, determined the composition of fungal communities by sequencing (Illumina MPS) the fungal internal transcribed spacer region, assigned fungal lifestyles, and by determining the co-occurrence of fungal network properties, identified the topological role of keystone fungal taxa. Compositional analysis revealed a significantly higher relative proportion of the litter saprotroph Lachnum and root-associated saprotroph Phialocephala in the topsoil and the ectomycorrhizal close-contact exploring Russula in cryoOM, whereas Sites 1 and 2 had a significantly higher mean proportion of plant pathogens and lichenized trophic modes. Co-occurrence network analysis revealed the lowest modularity and average path length, and highest clustering coefficient in cryoOM, which suggested a lower network resistance to environmental perturbation. Zi-Pi plot analysis suggested that some keystone taxa changed their role from generalist to specialist, depending on the specific horizon concerned, Cladophialophora in topsoil, saprotrophic Mortierella in cryoOM, and Penicillium in subsoil were classified as generalists for the respective horizons but specialists elsewhere. The litter saprotrophic taxon Cadophora finlandica played a role as a generalist in Site 1 and specialist in the rest of the sites. Overall, these results suggested that fungal communities within cryoOM were more susceptible to environmental change and some taxa may shift their role, which may lead to changes in carbon storage in permafrost-affected soil.

Results of 2-Year Ring Testing of a Semifield Study Design to Investigate Potential Impacts of Plant Protection Products on the Solitary Bees Osmia Bicornis and Osmia Cornuta and a Proposal for a Suitable Test Design

There are various differences in size, behavior, and life history traits of non-Apis bee species compared with honey bees (Apis mellifera; Linnaeus, 1758). Currently, the risk assessment for bees in the international and national process of authorizing plant protection products has been based on honey bee data as a surrogate organism for non-Apis bees. To evaluate the feasibility of a semifield tunnel test for Osmia bicornis (Linnaeus, 1758) and Osmia cornuta (Latreille, 1805), a protocol was developed by the non-Apis working group of the International Commission for Plant-Pollinator Relationships, consisting of experts from authorities, academia, and industry. A total of 25 studies were performed over a 2-yr period testing a replicated control against a replicated positive control using either a dimethoate or diflubenzuron treatment. Studies were regarded to be valid, if ≥30% of released females were found to occupy the nesting units in the night/morning before the application (establishment). Thirteen studies were regarded to be valid and were analyzed further. Parameters analyzed were nest occupation, flight activity, cell production (total and per female), cocoon production (total and per female), emergence success, sex ratio, and mean weight of females and males. Dimethoate was a reliable positive control at the tested rate of 75 g a.i./ha, once >30% females had established, displaying acute effects such as reduction in flight activity, increase in adult mortality (shown by nest occupation), and reproduction ability of the females (total cell and cocoon production). On the other hand, no effects on larval and pupal development were observed. The growth regulator diflubenzuron had statistically significant effects on brood development, causing mortality of eggs and larvae at a rate of approximately 200 g a.i./ha, whereas fenoxycarb did not cause any significant effects at the tested rates of 300 and 600 g a.i./ha. In conclusion, the ring-test protocol proved to be adequate once the study comprised a well-established population of female Osmia bees, and the results improved in the second year as the laboratories increased their experience with the test organism. It is noted that the success of a study strongly depends on the experience of the experimenter, the crop quality, the quality of the cocoons, and the weather conditions. Based on these finding, recommendations for a semifield study design with Osmia spp. are proposed. Environ Toxicol Chem 2021;40:236-250. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

A network model for plant-pollinator community assembly

Community assembly models, usually constructed for food webs, are an important component of our understanding of how ecological communities are formed. However, models for mutualistic community assembly are still needed, especially because these communities are experiencing significant anthropogenic disturbances that affect their biodiversity. Here, we present a unique network model that simulates the colonization and extinction process of mutualistic community assembly. We generate regional source pools of species interaction networks on the basis of statistical properties reported in the literature. We develop a dynamic synchronous Boolean framework to simulate, with few free parameters, the dynamics of new mutualistic community formation from the regional source pool. This approach allows us to deterministically map out every possible trajectory of community formation. This level of detail is rarely observed in other analytic approaches and allows for thorough analysis of the dynamical properties of community formation. As for food web assembly, we find that the number of stable communities is quite low, and the composition of the source pool influences the abundance and nature of community outcomes. However, in contrast to food web assembly, stable mutualistic communities form rapidly. Small communities with minor fluctuations in species presence/absence (self-similar limit cycles) are the most common community outcome. The unique application of this Boolean network approach to the study of mutualistic community assembly offers a great opportunity to improve our understanding of these critical communities.