Beyond the black box: promoting mathematical collaborations for elucidating interactions in soil ecology

Actinobacteria Community and Their Antibacterial and Cytotoxic Activity on the Weizhou and Xieyang Volcanic Islands in the Beibu Gulf of China

Weizhou Island and Xieyang Island are two large and young volcanic sea islands in the northern part of the South China Sea. In this study, high-throughput sequencing (HTS) of 16S rRNA genes was used to explore the diversity of Actinobacteria in the Weizhou and Xieyang Islands. Moreover, a traditional culture-dependent method was utilized to isolate Actinobacteria, and their antibacterial and cytotoxic activities were detected. The alpha diversity indices (ACE metric) of the overall bacterial communities for the larger island (Weizhou) were higher than those for the smaller island (Xieyang). A beta diversity analysis showed a more dispersive pattern of overall bacterial and actinobacterial communities on a larger island (Weizhou). At the order level, Frankiales, Propionibacteriales, Streptomycetales, Micrococcales, Pseudonocardiales, Micromonosporales, Glycomycetales, Corynebacteriales, and Streptosporangiales were the predominant Actinobacteria. A total of 22.7% of the OTUs shared 88%-95% similarity with some known groups. More interestingly, 15 OTUs formed a distinct and most predominant clade, and shared identities of less than 95% with any known families. This is the first report about this unknown group and their 16S rRNA sequences obtained from volcanic soils. A total of 268 actinobacterial strains were isolated by the culture-dependent method. Among them, 55 Streptomyces species were isolated, representing that 76.6% of the total. S. variabilis and S. flavogriseus were the most abundant. Moreover, some rare Actinobacteria were isolated. These included Micromonospora spp., Nocardia spp., Amycolatopsis spp., Tsukamurella spp., Mycobacterium spp., and Nonomuraea spp. Among them, eight Streptomyces spp. exhibited antibacterial activity against Bacillus cereus. Only three strains inhibited the growth of Escherichia coli. Four strains showed good activity against aquatic pathogenic bacterial strains of Streptococcus iniae. The cytotoxicity assay results showed that 27 strains (10.07%) exhibited cytotoxic activity against HeLa and A549 cell lines. Many actinobacterial strains with cytotoxic activity were identified as rare Actinobacteria, which illustrated that volcanic islands are vast reservoirs for Actinobacteria with promising antibacterial and cytotoxic activity. This study may significantly improve our understanding of actinobacterial communities on volcanic islands. The isolated Actinobacteria showed promising prospects for future use.

The Costs and Benefits of Plant-Arbuscular Mycorrhizal Fungal Interactions

The symbiotic interaction between plants and arbuscular mycorrhizal (AM) fungi is often perceived as beneficial for both partners, though a large ecological literature highlights the context dependency of this interaction. Changes in abiotic variables, such as nutrient availability, can drive the interaction along the mutualism-parasitism continuum with variable outcomes for plant growth and fitness. However, AM fungi can benefit plants in more ways than improved phosphorus nutrition and plant growth. For example, AM fungi can promote abiotic and biotic stress tolerance even when considered parasitic from a nutrient provision perspective. Other than being obligate biotrophs, very little is known about the benefits AM fungi gain from plants. In this review, we utilize both molecular biology and ecological approaches to expand our understanding of the plant-AM fungal interaction across disciplines.

Differential Responses of the Catalytic Efficiency of Ammonia and Nitrite Oxidation to Changes in Temperature

Microbially mediated nitrification plays an important role in the nitrogen (N) cycle, and rates of activity have been shown to change significantly with temperature. Despite this, the substrate affinities of nitrifying bacteria and archaea have not been comprehensively measured and are often assumed to be static in mathematical models of environmental systems. In this study, we measured the oxidation kinetics of ammonia- (NH3) oxidizing archaea (AOA), NH3-oxidizing bacteria (AOB), and two distinct groups of nitrite (NO2 -)-oxidizing bacteria (NOB), of the genera Nitrobacter and Nitrospira, by measuring the maximum rates of apparent activity (V max(app)), the apparent half-saturation constant (K m(app)), and the overall catalytic efficiency (V max(app) /K m(app)) over a range of temperatures. Changes in V max(app) and K m(app) with temperature were different between groups, with V max(app) and catalytic efficiency increasing with temperature in AOA, while V max(app) , K m(app), and catalytic efficiency increased in AOB. In Nitrobacter NOB, V max(app) and K m(app) increased, but catalytic efficiency decreased significantly with temperature. Nitrospira NOB were variable, but V max(app) increased while catalytic efficiency and K m(app) remained relatively unchanged. Michaelis-Menten (MM) and Haldane (H) kinetic models of NH3 oxidation and NO2 - oxidation based on the collected data correctly predict nitrification potential in some soil incubation experiments, but not others. Despite previous observations of coupled nitrification in many natural systems, our results demonstrate significant differences in response to temperature strategies between the different groups of nitrifiers; and indicate the need to further investigate the response of nitrifiers to environmental changes.

Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.