Interactions of E. coli with algae and aquatic vegetation in natural waters

Using machine learning models to estimate Escherichia coli concentration in an irrigation pond from water quality and drone-based RGB imagery data

The rapid and efficient quantification of Escherichia coli concentrations is crucial for monitoring water quality. Remote sensing techniques and machine learning algorithms have been used to detect E. coli in water and estimate its concentrations. The application of these approaches, however, is challenged by limited sample availability and unbalanced water quality datasets. In this study, we estimated the E. coli concentration in an irrigation pond in Maryland, USA, during the summer season using demosaiced natural color (red, green, and blue: RGB) imagery in the visible and infrared spectral ranges, and a set of 14 water quality parameters. We did this by deploying four machine learning models - Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), and K-nearest Neighbor (KNN) - under three data utilization scenarios: water quality parameters only, combined water quality and small unmanned aircraft system (sUAS)-based RGB data, and RGB data only. To select the training and test datasets, we applied two data-splitting methods: ordinary and quantile data splitting. These methods provided a constant splitting ratio in each decile of the E. coli concentration distribution. Quantile data splitting resulted in better model performance metrics and smaller differences between the metrics for both the training and testing datasets. When trained with quantile data splitting after hyperparameter optimization, models RF, GBM, and XGB had R2 values above 0.847 for the training dataset and above 0.689 for the test dataset. The combination of water quality and RGB imagery data resulted in a higher R2 value (>0.896) for the test dataset. Shapley additive explanations (SHAP) of the relative importance of variables revealed that the visible blue spectrum intensity and water temperature were the most influential parameters in the RF model. Demosaiced RGB imagery served as a useful predictor of E. coli concentration in the studied irrigation pond.

Impact of Biotic and Abiotic Factors on Listeria monocytogenes, Salmonella enterica, and Enterohemorrhagic Escherichia coli in Agricultural Soil Extracts

Outbreaks of Enterohemorrhagic Escherichia coli (EHEC), Salmonella enterica, and Listeria monocytogenes linked to fresh produce consumption pose significant food safety concerns. These pathogens can contaminate pre-harvest produce through various routes, including contaminated water. Soil physicochemical properties and flooding can influence pathogen survival in soils. We investigated survival of EHEC, S. enterica, and L. monocytogenes in soil extracts designed to represent soils with stagnant water. We hypothesized pathogen survival would be influenced by soil extract nutrient levels and the presence of native microbes. A chemical analysis revealed higher levels of total nitrogen, phosphorus, and carbon in high-nutrient soil extracts compared to low-nutrient extracts. Pathogen survival was enhanced in high-nutrient, sterile soil extracts, while the presence of native microbes reduced pathogen numbers. A microbiome analysis showed greater diversity in low-nutrient soil extracts, with distinct microbial compositions between extract types. Our findings highlight the importance of soil nutrient composition and microbial dynamics in influencing pathogen behavior. Given key soil parameters, a long short-term memory model (LSTM) effectively predicted pathogen survival. Integrating these factors can aid in developing predictive models for pathogen persistence in agricultural systems. Overall, our study contributes to understanding the complex interplay in agricultural ecosystems, facilitating informed decision-making for crop production and food safety enhancement.

A typical acidic extracellular polysaccharide alludes to algae-bacteria-collaboration in microalgal-bacterial symbiosis

Microalgal-bacterial symbioses are prevalent in aquatic ecosystems and play a pivotal role in carbon sequestration, significantly contributing to global carbon cycling. The understanding of the contribution of exopolysaccharides (EPSs), a crucial carbon-based component, to the structural integrity of microalgal-bacterial symbioses remains insufficiently elucidated. To address this gap, our study aims to enhance our comprehension of the composition and primary structure of EPSs within a specific type of granular microalgal-bacterial symbiosis named microalgal-bacterial granular sludge (MBGS). Our investigation reveals that the acidic EPSs characteristic of this symbiosis have molecular weights ranging from several hundred thousand to over one million Daltons, including components like glucopyranose, galactopyranose, mannose, and rhamnose. Our elucidation of the backbone linkage of a representative exopolysaccharide revealed a →3)-β-D-Galp-(1→4)-β-D-Glcp-(1→ glycosidic linkage. This linear structure closely resembles bacterial xanthan, while the branched chain structure bears similarities to algal EPSs. Our findings highlight the collaborative synthesis of acidic EPSs by both microalgae and bacteria, emphasizing their joint contribution in the production of macromolecules within microalgal-bacterial symbiosis. This collaborative synthesis underscores the intricate molecular interactions contributing to the stability and function of these symbiotic relationships.

Prevalence of STEC virulence markers and Salmonella as a function of abiotic factors in agricultural water in the southeastern United States

Fresh produce can be contaminated by enteric pathogens throughout crop production, including through contact with contaminated agricultural water. The most common outbreaks and recalls in fresh produce are due to contamination by Salmonella enterica and Shiga toxin-producing E. coli (STEC). Thus, the objectives of this study were to investigate the prevalence of markers for STEC (wzy, hly, fliC, eaeA, rfbE, stx-I, stx-II) and Salmonella (invA) in surface water sources (n = 8) from produce farms in Southwest Georgia and to determine correlations among the prevalence of virulence markers for STEC, water nutrient profile, and environmental factors. Water samples (500 mL) from eight irrigation ponds were collected from February to December 2021 (n = 88). Polymerase chain reaction (PCR) was used to screen for Salmonella and STEC genes, and Salmonella samples were confirmed by culture-based methods. Positive samples for Salmonella were further serotyped. Particularly, Salmonella was detected in 6/88 (6.81%) water samples from all ponds, and the following 4 serotypes were detected: Saintpaul 3/6 (50%), Montevideo 1/6 (16.66%), Mississippi 1/6 (16.66%), and Bareilly 1/6 (16.66%). Salmonella isolates were only found in the summer months (May-Aug.). The most prevalent STEC genes were hly 77/88 (87.50%) and stx-I 75/88 (85.22%), followed by fliC 54/88 (61.63%), stx-II 41/88 (46.59%), rfbE 31/88 (35.22%), and eaeA 28/88 (31.81%). The wzy gene was not detected in any of the samples. Based on a logistic regression analysis, the odds of codetection for STEC virulence markers (stx-I, stx-II, and eaeA) were negatively correlated with calcium and relative humidity (p < 0.05). A conditional forest analysis was performed to assess predictive performance (AUC = 0.921), and the top predictors included humidity, nitrate, calcium, and solar radiation. Overall, information from this research adds to a growing body of knowledge regarding the risk that surface water sources pose to produce grown in subtropical environmental conditions and emphasizes the importance of understanding the use of abiotic factors as a holistic approach to understanding the microbial quality of water.

Fine-scale spatiotemporal variations in bacterial community diversity in agricultural pond water

Microbial communities in surface waters are affected by environmental conditions and can influence changes in water quality. To explore the hypothesis that the microbiome in agricultural waters associates with spatiotemporal variations in overall water quality and, in turn, has implications for resource monitoring and management, we characterized the relationships between the microbiota and physicochemical properties in a model irrigation pond as a factor of sampling time (i.e., 9:00, 12:00, 15:00) and location within the pond (i.e., bank vs. interior sites and cross-sectional depths at 0, 1, and 2 m). The microbial communities, which were defined by 16S rRNA gene sequencing analysis, significantly varied based on all sampling factors (PERMANOVA P < 0.05 for each). While the relative abundances of dominant phyla (e.g., Proteobacteria and Bacteroidetes) were relatively stable throughout the pond, subtle yet significant increases in α-diversity were observed as the day progressed (ANOVA P < 0.001). Key water quality properties that also increased between the morning and afternoon (i.e., pH, dissolved oxygen, and temperature) positively associated with relative abundances of Cyanobacteria, though were inversely proportional to Verrucomicrobia. These properties, among additional parameters such as bioavailable nutrients (e.g., NH3, NO3, PO4), chlorophyll, phycocyanin, conductivity, and colored dissolved organic matter, exhibited significant relationships with relative abundances of various bacterial genera as well. Further investigation of the microbiota in underlying sediments revealed significant differences between the bank and interior sites of the pond (P < 0.05 for α- and β-diversity). Overall, our findings emphasize the importance of accounting for time of day and water sampling location and depth when surveying the microbiomes of irrigation ponds and other small freshwater sources.

Exploitation of environmental DNA (eDNA) for ecotoxicological research: A critical review on eDNA metabarcoding in assessing marine pollution

The rise in worldwide population has led to a noticeable spike in the production, consumption, and transportation of energy and food, contributing to elevated environmental pollution. Marine pollution is a significant global environmental issue with ongoing challenges, including plastic waste, oil spills, chemical pollutants, and nutrient runoff, threatening marine ecosystems, biodiversity, and human health. Pollution detection and assessment are crucial to understanding the state of marine ecosystems. Conventional approaches to pollution evaluation usually represent laborious and prolonged physical and chemical assessments, constraining their efficacy and expansion. The latest advances in environmental DNA (eDNA) are valuable methods for the detection and surveillance of pollution in the environment, offering enhanced sensibility, efficacy, and involvement. Molecular approaches allow genetic information extraction from natural resources like water, soil, or air. The application of eDNA enables an expanded evaluation of the environmental condition by detecting both identified and unidentified organisms and contaminants. eDNA methods are valuable for assessing community compositions, providing indirect insights into the intensity and quality of marine pollution through their effects on ecological communities. While eDNA itself is not direct evidence of pollution, its analysis offers a sensitive tool for monitoring changes in biodiversity, serving as an indicator of environmental health and allowing for the indirect estimation of the impact and extent of marine pollution on ecosystems. This review explores the potential of eDNA metabarcoding techniques for detecting and identifying marine pollutants. This review also provides evidence for the efficacy of eDNA assessment in identifying a diverse array of marine pollution caused by oil spills, harmful algal blooms, heavy metals, ballast water, and microplastics. In this report, scientists can expand their knowledge and incorporate eDNA methodologies into ecotoxicological research.

Microfluidic investigation of the impacts of flow fluctuations on the development of Pseudomonas putida biofilms

Biofilms play critical roles in wastewater treatment, bioremediation, and medical-device-related infections. Understanding the dynamics of biofilm formation and growth is essential for controlling and exploiting their properties. However, the majority of current studies have focused on the impact of steady flows on biofilm growth, while flow fluctuations are common in natural and engineered systems such as water pipes and blood vessels. Here, we reveal the effects of flow fluctuations on the development of Pseudomonas putida biofilms through systematic microfluidic experiments and the development of a theoretical model. Our experimental results showed that biofilm growth under fluctuating flow conditions followed three phases: lag, exponential, and fluctuation phases. In contrast, biofilm growth under steady-flow conditions followed four phases: lag, exponential, stationary, and decline phases. Furthermore, we demonstrated that low-frequency flow fluctuations promoted biofilm growth, while high-frequency fluctuations inhibited its development. We attributed the contradictory impacts of flow fluctuations on biofilm growth to the adjustment time (T0) needed for biofilm to grow after the shear stress changed from high to low. Furthermore, we developed a theoretical model that explains the observed biofilm growth under fluctuating flow conditions. Our insights into the mechanisms underlying biofilm development under fluctuating flows can inform the design of strategies to control biofilm formation in diverse natural and engineered systems.

Antibiotic-Induced Recruitment of Specific Algae-Associated Microbiome Enhances the Adaptability of Chlorella vulgaris to Antibiotic Stress and Incidence of Antibiotic Resistance

Insights into the symbiotic relation between eukaryotic hosts and their microbiome lift the curtain on the crucial roles of microbes in host fitness, behavior, and ecology. However, it remains unclear whether and how abiotic stress shapes the microbiome and further affects host adaptability. This study first investigated the effect of antibiotic exposure on behavior across varying algae taxa at the community level. Chlorophyta, in particular Chlorella vulgaris, exhibited remarkable adaptability to antibiotic stress, leading to their dominance in phytoplankton communities. Accordingly, we isolated C. vulgaris strains and compared the growth of axenic and nonaxenic ones under antibiotic conditions. The positive roles of antibiotics in algal growth were apparent only in the presence of bacteria. Results of 16S rRNA sequencing further revealed that antibiotic challenges resulted in the recruitment of specific bacterial consortia in the phycosphere, whose functions were tightly linked to the host growth promotion and adaptability enhancement. In addition, the algal phycosphere was characterized with 47-fold higher enrichment capability of antibiotic resistance genes (ARGs) than the surrounding water. Under antibiotic stress, specific ARG profiles were recruited in C. vulgaris phycosphere, presumably driven by the specific assembly of bacterial consortia and mobile genetic elements induced by antibiotics. Moreover, the antibiotics even enhanced the dissemination potential of the bacteria carrying ARGs from the algal phycosphere to broader environmental niches. Overall, this study provides an in-depth understanding into the potential functional significance of antibiotic-mediated recruitment of specific algae-associated bacteria for algae adaptability and ARG proliferation in antibiotic-polluted waters.

Intensive poultry farming: A review of the impact on the environment and human health

Poultry farming is one of the most efficient animal husbandry methods and it provides nutritional security to a significant number of the world population. Using modern intensive farming techniques, global production has reached 133.4 mil. t in 2020, with a steady growth each year. Such intensive growth methods however lead to a significant environmental footprint. Waste materials such as poultry litter and manure can pose a serious threat to environmental and human health, and need to be managed properly. Poultry production and waste by-products are linked to NH₃, N₂O and CH₄ emissions, and have an impact on global greenhouse gas emissions, as well as animal and human health. Litter and manure can contain pesticide residues, microorganisms, pathogens, pharmaceuticals (antibiotics), hormones, metals, macronutrients (at improper ratios) and other pollutants which can lead to air, soil and water contamination as well as formation of antimicrobial/multidrug resistant strains of pathogens. Dust emitted from intensive poultry production operations contains feather and skin fragments, faeces, feed particles, microorganisms and other pollutants, which can adversely impact poultry health as well as the health of farm workers and nearby inhabitants. Fastidious odours are another problem that can have an adverse impact on health and quality of life of workers and surrounding population. This study discusses the current knowledge on the impact of intensive poultry farming on environmental and human health, as well as taking a look at solutions for a sustainable future.

New concept of biodiesel production using food waste digestate powder: Co-culturing algae-activated sludge symbiotic system in low N and P paper mill wastewater

This paper aims to demonstrate an innovative process for the conversion of food waste digestate (FWD) powder into biofuel. The effects of different doses of FWD are investigated on microalgae-activated sludge (MAS) in treating pulp and paper mill wastewater (PPW) which generally contains insufficient nitrogen and phosphorus. FWD was added to adjust the initial N:P molar ratio in MAS at various levels (8:1 to 15:1). The highest Auxenochlorella protothecoides biomass achieved was 1.67 gL^-1 at a 13.45:1 N/P molar ratio of PPW. After 10 days of cultivation, Auxenochlorella protothecoides-activated sludge system removed 91.7 %, 74.6 %, and 91.5 % of total nitrogen, phosphorus, and sCOD respectively at D_0.836 gL^-1 DD. The highest lipid productivity was reported as 41.27 ± 2.43 mg L^-1 day^-1. Fatty acid methyl ester (FAME) analysis showed the presence of an appreciable percentage of balanced saturated and unsaturated fatty acids i.e. palmitic, oleic, and linoleic acid, rendering its potential as a feedstock for biodiesel production. Activated sludge induced flocculation of Auxenochlorella protothecoides was measured. The whole process establishes an effective means of circular economy, where the secondary source of recyclable nutrients i.e. FWD will be used as a source of N and P in PPW to obtain algal biodiesel from a negative value industrial wastewater.