Hasil untuk "Microbial ecology"

A. Zehnder

Aaron Marc Saunders, M. Albertsen, J. Vollertsen et al.

Understanding the microbial ecology of a system requires that the observed population dynamics can be linked to their metabolic functions. However, functional characterization is laborious and the choice of organisms should be prioritized to those that are frequently abundant (core) or transiently abundant, which are therefore putatively make the greatest contribution to carbon turnover in the system. We analyzed the microbial communities in 13 Danish wastewater treatment plants with nutrient removal in consecutive years and a single plant periodically over 6 years, using Illumina sequencing of 16S ribosomal RNA amplicons of the V4 region. The plants contained a core community of 63 abundant genus-level operational taxonomic units (OTUs) that made up 68% of the total reads. A core community consisting of abundant OTUs was also observed within the incoming wastewater to three plants. The net growth rate for individual OTUs was quantified using mass balance, and it was found that 10% of the total reads in the activated sludge were from slow or non-growing OTUs, and that their measured abundance was primarily because of immigration with the wastewater. Transiently abundant organisms were also identified. Among them the genus Nitrotoga (class Betaproteobacteria) was the most abundant putative nitrite oxidizer in a number of activated sludge plants, which challenges previous assumptions that Nitrospira (phylum Nitrospirae) are the primary nitrite-oxidizers in activated sludge systems with nutrient removal.

T. Lawley, A. Walker

Jennifer M. Brulc, D. Antonopoulos, Margret E. Berg Miller et al.

E. Franzosa, Katherine H. Huang, James F. Meadow et al.

A. Shade

Local diversity (within-sample or alpha diversity) is often implicated as a cause of success or failure of a microbial community. However, the relationships between diversity and emergent properties of a community, such as its stability, productivity or invasibility, are much more nuanced. I argue that diversity without context provides limited insights into the mechanisms underpinning community patterns. I provide examples from traditional and microbial ecology to discuss common complications and assumptions about within-sample diversity that may prevent us from digging deeper into the more specific mechanisms underpinning community outcomes. I suggest that measurement of diversity should serve as a starting point for further inquiry of ecological mechanisms rather than an 'answer' to community outcomes.

I. Martínez, J. Lattimer, Kelcie Hubach et al.

A. Cydzik‐Kwiatkowska, M. Zielińska

Bacterial metabolism determines the effectiveness of biological treatment of wastewater. Therefore, it is important to define the relations between the species structure and the performance of full-scale installations. Although there is much laboratory data on microbial consortia, our understanding of dependencies between the microbial structure and operational parameters of full-scale wastewater treatment plants (WWTP) is limited. This mini-review presents the types of microbial consortia in WWTP. Information is given on extracellular polymeric substances production as factor that is key for formation of spatial structures of microorganisms. Additionally, we discuss data on microbial groups including nitrifiers, denitrifiers, Anammox bacteria, and phosphate- and glycogen-accumulating bacteria in full-scale aerobic systems that was obtained with the use of molecular techniques, including high-throughput sequencing, to shed light on dependencies between the microbial ecology of biomass and the overall efficiency and functional stability of wastewater treatment systems. Sludge bulking in WWTPs is addressed, as well as the microbial composition of consortia involved in antibiotic and micropollutant removal.

Judit Kosztik, Erzsébet Baka, András Táncsics et al.

ABSTRACT Rhodococcus erythropolis NI86/21, isolated from maize rhizosphere in Hungary, possesses one of the largest genomes (8.046 Mb) within the species. The genome comprises a 6.83 Mb chromosome and 1.22 Mb of extrachromosomal elements, including three circular and two fragmented linear plasmids. Comparative analysis identified five horizontally acquired genomic islands (HGTi), totaling 0.64 Mb with mosaic-like architecture derived from plasmids, phages, and chromosomal segments of other Nocardiaceae. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomic analysis revealed a lower expression of genes located in HGT elements (53%) compared to core chromosomal genes (73%), indicating regulatory silencing of foreign DNA. Nevertheless, an inducible cytochrome P450 monooxygenase (CYP116) responsible for thiocarbamate and atrazine degradation is encoded on HGTi_V and actively expressed upon herbicide exposure. Strikingly, an identical CYP450 locus is present on a conjugative plasmid in Rhodococcus sp. TE1 isolated from thiocarbamate-treated soil in Canada, demonstrating independent acquisition of the same catabolic module from a high GC% content Rhodococcus, under similar selective pressure. Frequent recombination between chromosomal and mobile elements generates the observed mosaic-like HGT structures, which we found common for R. erythropolis strains. These results highlight extraordinary genomic plasticity and rapid adaptive evolution in Rhodococci, enabling efficient colonization of herbicide-contaminated agro-ecosystems.IMPORTANCERhodococcus erythropolis NI86/21 exemplifies how bacterial genomes evolve through horizontal gene transfer and mobile elements. Its unusually large, plastic genome contains extensive HGT islands and a high load of active transposons, which shape mosaic genomic architectures and hinder complete genome assembly. These horizontally acquired regions, although partially silenced, encode key adaptive functions such as an inducible CYP116 monooxygenase enabling thiocarbamate and atrazine degradation. Remarkably, an identical CYP116 module is present in Rhodococcus sp. TE1 from thiocarbamate-treated Canadian soil, demonstrating that similar environmental pressures can drive independent acquisition of the same biodegradation trait. Together, the dynamic transposon activity, mosaic HGT structure, and geographically convergent gene recruitment highlight the extraordinary genomic plasticity of R. erythropolis and underscore its rapid adaptive potential in agro-ecosystems, with implications for microbial evolution and bioremediation strategies.

Valerie Zermatten, Chiara Vanalli, Gencer Sumbul et al.

While integrating multiple modalities has the potential to improve environmental monitoring, current approaches struggle to combine data sources with heterogeneous formats or contents. A central difficulty arises when combining continuous gridded data (e.g., remote sensing) with sparse and irregular point observations such as species records. Existing geostatistical and deep-learning-based approaches typically operate on a single modality or focus on spatially aligned inputs, and thus cannot seamlessly overcome this difficulty. We propose a Geolocation-Aware MultiModal Approach (GAMMA), a transformer-based fusion approach designed to integrate heterogeneous ecological data using explicit spatial context. Instead of interpolating observations into a common grid, GAMMA first represents all inputs as location-aware embeddings that preserve spatial relationships between samples. GAMMA dynamically selects relevant neighbours across modalities and spatial scales, enabling the model to jointly exploit continuous remote sensing imagery and sparse geolocated observations. We evaluate GAMMA on the task of predicting 103 environmental variables from the SWECO25 data cube across Switzerland. Inputs combine aerial imagery with biodiversity observations from GBIF and textual habitat descriptions from Wikipedia, provided by the EcoWikiRS dataset. Experiments show that multimodal fusion consistently improves prediction performance over single-modality baselines and that explicit spatial context further enhances model accuracy. The flexible architecture of GAMMA also allows to analyse the contribution of each modality through controlled ablation experiments. These results demonstrate the potential of location-aware multimodal learning for integrating heterogeneous ecological data and for supporting large-scale environmental mapping tasks and biodiversity monitoring.

J. Andrews, Robin F. Harris

Xinyi Han, Haichao Li, Saibo Xu et al.

Abstract RNAi technology, which can induce mortality by disrupting the transcription of essential growth and development-related genes in insects, has emerged as a groundbreaking pest control method. However, insects have developed defense mechanisms to counteract the efficiency of RNAi. The specific role of symbiotic microorganisms in this process remains poorly understood and requires further exploration. This study examines the reduced RNAi efficiency in Lepidopteran pest Helicoverpa armigera. Through screening, six Bacillus strains exhibiting dsRNA-degrading activity were identified through in vitro assays. Further investigation into one representative strain Ba 6 revealed that it significantly decreased RNAi efficiency by secreting ribonuclease into the insect gut fluid, directly degrading dsRNA, thus reducing its accumulation and blocking RNAi effects. These findings clarify the mechanism by which symbiotic bacteria influence the host’s RNAi efficiency and provides a valuable reference for the development and large-scale implementation of RNA biopesticides targeting H. armigera and other lepidopteran pests.

Fernando Rodrigues-Silva, Daniel A.S. Rodrigues, Pâmela B. Vilela et al.

As global water demand rises – driven by climate change, population growth, and agricultural expansion – treated wastewater irrigation (WWI) offers a promising strategy for water conservation and nutrient recycling. Agriculture consumes nearly 70 % of global freshwater, while only 50.8 % of wastewater is treated in Brazil, where WWI represents less than 0.1 % of total irrigation. This review critically assesses the potential and challenges of WWI in Brazilian agriculture by comparing global practices, regulatory frameworks, and treatment technologies. WWI can significantly reduce freshwater withdrawals and dependence on chemical fertilizers, enhancing soil fertility through the recycling of nitrogen, phosphorus, and potassium. However, persistent contaminants of emerging concern (CECs) – including antibiotic-resistant bacteria (ARB), resistance genes (ARGs), microplastics, and heavy metals – pose environmental and health risks, as conventional systems such as UASB reactors and stabilization ponds, which are vastly implemented in Brazil, were not designed to remove them efficiently. Despite successful examples in high-income countries, regulatory gaps persist in low- and middle-income countries like Brazil, where only 9 of 27 states have local guidelines for wastewater reuse. The adoption of advanced technologies (e.g., membrane filtration, ozonation, UV disinfection) and the development of risk-based regulatory approaches are essential to ensure safety and public acceptance. Educational initiatives and participatory governance can further promote informed decision-making. By investing in technological innovation, harmonized regulation, and interdisciplinary research, WWI could evolve from a niche practice to a mainstream solution for sustainable agriculture, food security, and water resource management in Brazil and globally.

Adelina-Gabriela Niculescu, Mihaela Magdalena Mitache, Alexandru Mihai Grumezescu et al.

Antibiotic resistance represents a growing public health threat, with airborne drug-resistant strains being especially alarming due to their ease of transmission and association with severe respiratory infections. The respiratory microbiome plays a pivotal role in maintaining respiratory health, influencing the dynamics of antibiotic resistance among airborne pathogenic microorganisms. In this context, this review proposes the exploration of the complex interplay between the respiratory microbiota and antimicrobial resistance, highlighting the implications of microbiome diversity in health and disease. Moreover, strategies to mitigate antibiotic resistance, including stewardship programs, alternatives to traditional antibiotics, probiotics, microbiota restoration techniques, and nanotechnology-based therapeutic interventions, are critically presented, setting an updated framework of current management options. Therefore, through a better understanding of respiratory microbiome roles in antibiotic resistance, alongside emerging therapeutic strategies, this paper aims to shed light on how the global health challenges posed by multi-drug-resistant pathogens can be addressed.

Elia Moretti, Michel Loreau, Michael Benzaquen

The intensification of European agriculture, characterized by increasing farm sizes, landscape simplification and reliance on synthetic pesticides, remains a key driver of biodiversity decline. While many studies have investigated this phenomenon, they often focus on isolated elements, resulting in a lack of holistic understanding and leaving policymakers and farmers with unclear priorities. This study addresses this gap by developing a spatially explicit ecological economic model designed to dissect the complex interplay between landscape structure and pesticide application, and their combined effects on natural enemy populations and farmers' economic returns. In particular, the model investigates how these relationships are modulated by farm size (a crucial aspect frequently overlooked in prior research). By calibrating on the European agricultural sector, we explore the ecological and economic consequences of various policy scenarios. We show that the effectiveness of ecological restoration strategies is strongly contingent upon farm size. Small to medium-sized farms can experience economic benefits from reduced pesticide use when coupled with hedgerow restoration, owing to enhanced natural pest control. In contrast, large farms encounter challenges in achieving comparable economic gains due to inherent landscape characteristics. This highlights the need to account for farm size in agri-environmental policies in order to promote biodiversity conservation and agricultural sustainability.

Santiago Andrés Villamil Chacón, Mauricio Santos-Vega

Dengue, a mosquito-borne viral disease common in tropical areas, is spread by Aedes aegypti and Aedes albopictus. Temperature changes driven by climate affect vector ecology and expand regions of species coexistence. The combined effect of temperature and larval competition on mosquito dynamics and dengue transmission is unclear. We built a deterministic model with temperature-dependent parameters to study larval-stage interactions, linked with a SEIR framework for human infection. We assessed invasion potential, coexistence, and infection peaks. The basic reproduction number (R0) was calculated using the Next Generation Matrix, and the effective reproduction number (Rt) came from simulations with larval competition. Aedes albopictus invades aegypti-dominated systems when the aegypti competition coefficient is below 0.47, with neutral equilibrium from 0.47 to 0.60 and exclusion above 0.60 in stable conditions. In temperature-dependent settings, invasion extends to a coefficient of 0.75. Coexistence analysis showed aegypti dominance (~87% abundance) in stable settings, while temperature-dependent conditions led to ~50% abundance for both species. Dengue cases peaked at 156-168 in stable conditions and 195-220 in temperature-dependent ones. Stronger albopictus competition lowered peaks in both cases. Temperature boosts albopictus invasion and coexistence, while aegypti drives higher infection peaks. Balanced species abundances raise transmission risks, emphasizing the need to factor temperature and competition into vector control.

Kwangwook Kim, Cynthia Jinno, Xunde Li et al.

Abstract Background Our previous study has reported that supplementation of oligosaccharide-based polymer enhances gut health and disease resistance of pigs infected with enterotoxigenic E. coli (ETEC) F18 in a manner similar to carbadox. The objective of this study was to investigate the impacts of oligosaccharide-based polymer or antibiotic on the host metabolic profiles and colon microbiota of weaned pigs experimentally infected with ETEC F18. Results Multivariate analysis highlighted the differences in the metabolic profiles of serum and colon digesta which were predominantly found between pigs supplemented with oligosaccharide-based polymer and antibiotic. The relative abundance of metabolic markers of immune responses and nutrient metabolisms, such as amino acids and carbohydrates, were significantly differentiated between the oligosaccharide-based polymer and antibiotic groups (q < 0.2 and fold change > 2.0). In addition, pigs in antibiotic had a reduced (P < 0.05) relative abundance of Lachnospiraceae and Lactobacillaceae, whereas had greater (P < 0.05) Clostridiaceae and Streptococcaceae in the colon digesta on d 11 post-inoculation (PI) compared with d 5 PI. Conclusions The impact of oligosaccharide-based polymer on the metabolic and microbial profiles of pigs is not fully understood, and further exploration is needed. However, current research suggest that various mechanisms are involved in the enhanced disease resistance and performance in ETEC-challenged pigs by supplementing this polymer.

Jack H. Buckner, Zechariah D. Meunier, Jorge Arroyo-Esquivel et al.

Ecological systems often exhibit complex nonlinear dynamics like oscillations, chaos, and regime shifts. Universal dynamic equations have shown promise in modeling complex dynamics by combining known functional forms with neural networks that represent unknown relationships. However, these methods do not yet accommodate the forms of uncertainty common to ecological datasets. To address this limitation, we developed state-space universal dynamic equations by combining universal differential equations with a state-space modeling framework, accounting for uncertainty. We tested this framework on two simulated and two empirical case studies and found that this method can recover nonlinear biological interactions that produce complex behaviors, including chaos and regime shifts. Their forecasting performance is context-dependent, with the best performance being achieved on chaotic and oscillating time series. This new approach leveraging both ecological theory and data-driven machine learning offers a promising new way to make accurate and useful predictions of ecosystem change.

Dan Bohus, Sean Andrist, Yuwei Bao et al.

We report initial work towards constructing ecologically valid benchmarks to assess the capabilities of large multimodal models for engaging in situated collaboration. In contrast to existing benchmarks, in which question-answer pairs are generated post hoc over preexisting or synthetic datasets via templates, human annotators, or large language models (LLMs), we propose and investigate an interactive system-driven approach, where the questions are generated by users in context, during their interactions with an end-to-end situated AI system. We illustrate how the questions that arise are different in form and content from questions typically found in existing embodied question answering (EQA) benchmarks and discuss new real-world challenge problems brought to the fore.

D. M. Ward, M. Ferris, S. Nold et al.