Metagenomics study in aquatic resource management: Recent trends, applied methodologies and future needs

Plasmonic nanoparticle sensors: current progress, challenges, and future prospects

Plasmonic nanoparticles (NPs) have played a significant role in the evolution of modern nanoscience and nanotechnology in terms of colloidal synthesis, general understanding of nanocrystal growth mechanisms, and their impact in a wide range of applications. They exhibit strong visible colors due to localized surface plasmon resonance (LSPR) that depends on their size, shape, composition, and the surrounding dielectric environment. Under resonant excitation, the LSPR of plasmonic NPs leads to a strong field enhancement near their surfaces and thus enhances various light-matter interactions. These unique optical properties of plasmonic NPs have been used to design chemical and biological sensors. Over the last few decades, colloidal plasmonic NPs have been greatly exploited in sensing applications through LSPR shifts (colorimetry), surface-enhanced Raman scattering, surface-enhanced fluorescence, and chiroptical activity. Although colloidal plasmonic NPs have emerged at the forefront of nanobiosensors, there are still several important challenges to be addressed for the realization of plasmonic NP-based sensor kits for routine use in daily life. In this comprehensive review, researchers of different disciplines (colloidal and analytical chemistry, biology, physics, and medicine) have joined together to summarize the past, present, and future of plasmonic NP-based sensors in terms of different sensing platforms, understanding of the sensing mechanisms, different chemical and biological analytes, and the expected future technologies. This review is expected to guide the researchers currently working in this field and inspire future generations of scientists to join this compelling research field and its branches.

Considerable declines in odor in a drinking water reservoir: Variations of odorous community, precursor enzymes abundance, distribution, and environmental dominant factors

The presence of 2-methylisoborneol (2-MIB) is acknowledged as a prevalent source of odor-related challenges in drinking water reservoirs. Among the three in situ experiments conducted in drinking water reservoir, the water-lifting aerator with bio-filling system exhibited the most pronounced overall effects. It achieved a remarkable 98.70 % removal of 2-MIB and a 99.30 % reduction in the abundance of the mic gene. Metagenomic sequencing identified key genes including methyl transferase gene (mtf), 2-MIB cyclase gene (mic), cyclic nucleotide-binding protein gene (cnb), underscoring the potential role of Actinobacteria, Cyanobacteria, and Proteobacteria communities in contributing to odor occurrences in the reservoir. Furthermore, the abundance of odorous precursor enzymes in the MVA pathway and MEP/DOXP pathway were inhibited in the systems with bio-filling. Total nitrogen (TN) and nitrate (NO3--N) were identified as pivotal factors influencing the presence of 2-MIB odor-producing microorganisms. Effective management of odor-producing species in reservoir water was closely related to the efficient removal of pollutants. These findings will provide valuable insights for the development of odor removal techniques in reservoirs and offer researchers deeper understanding into the mechanisms underlying odor processes.

Discordant patterns between nitrogen-cycling functional traits and taxa in distant coastal sediments reveal important community assembly mechanisms

A central question in microbial ecology is how immense microbes are assembled in changing natural environments while executing critical ecosystem functions. Over the past decade, effort has been made to unravel the contribution of stochasticity and determinism to the compositional of microbial communities. However, most studies focus on microbial taxa, ignoring the importance of functional traits. By employing shotgun metagenomic sequencing and state-of-the-art bioinformatics approaches, this study comprehensively investigated the microbially mediated nitrogen (N) cycling processes in two geographically distant coastal locations. Both shotgun and 16S rRNA gene amplicon sequencing demonstrated significantly differed taxonomic compositions between the two sites. The relative abundance of major microbial phyla, such as Pseudomonadota, Thaumarchaeota, and Bacteroidota, significantly differed. In contrast, high homogeneity was observed for N-cycling functional traits. Statistical analyses suggested that N-cycling taxonomic groups were more related to geographic distance, whereas microbial functional traits were more influenced by environmental factors. Multiple community assembly models demonstrated that determinism strongly governed the microbial N-cycling functional traits, whereas their carrying taxonomic groups were highly stochastic. Such discordant patterns between N-cycling functional traits and taxa demonstrated an important mechanism in microbial ecology in which essential ecosystem functions are stably maintained despite geographic distance and stochastic community assembly.

Lead induced structural and functional damage and microbiota dysbiosis in the intestine of crucian carp (Carassius auratus)

Lead (Pb) is a hazardous pollutant in water environments that can cause significant damage to aquatic animals and humans. In this study, crucian carp (Carassius auratus) were exposed to waterborne Pb for 96 h; then, histopathological analysis, quantitative qPCR analysis, and 16S high-throughput sequencing were performed to explore the effects of Pb on intestinal bioaccumulation, structural damage, oxidative stress, immune response, and microbiota imbalance of C. auratus. After Pb exposure, the intestinal morphology was obviously damaged, including significantly increasing the thickness of the intestinal wall and the number of goblet cells and reducing the depth of intestinal crypts. Pb exposure reduced the mRNA expressions of Claudin-7 and villin-1 while significantly elevated the level of GST, GSH, CAT, IL-8, IL-10, IL-1, and TNF-α. Furthermore, 16S rRNA analysis showed that the Shannon and Simpson indices decreased at 48 h after Pb exposure, and the abundance of pathogenic bacteria (Erysipelotrichaceae, Weeksellaceae, and Vibrionaceae) increased after Pb exposure. In addition, the correlation network analysis found that Proteobacteria were negatively correlated with Firmicutes and positively correlated with Bacteroidetes. Functional prediction analysis of bacteria speculated that the change in intestinal microbiota led to the PPAR signaling pathway and peroxisome function of the intestine of crucian carp was increased, while the immune system and membrane transport function were decreased. Finally, canonical correlation analysis (CCA) found that there were correlations between the intestinal microbiota, morphology, antioxidant factors, and immune factors of crucian carp after Pb exposure. Taken together, our results demonstrated that intestinal flora dysbiosis, morphological disruption, oxidative stress, and immune injury are involved in the toxic damage of Pb exposure to the intestinal structure and function of crucian carp. Meanwhile, Pb exposure rapidly increased the abundance of pathogenic bacteria, leading to intestinal disorders, further aggravating the damage of Pb to intestinal structure and function. These findings provide us a basis for the link between gut microbiome changes and heavy metal toxicity, and gut microbiota can be used as biomarkers for the evaluation of heavy metal pollution in future.

Bacteriophages diversity in India's major river Ganga: a repository to regulate pathogenic bacteria in the aquatic environment

Bacteriophages are key viruses that can kill thousands of harmful microbes generally present at polluted sites. Such bacteriophages are abundantly present in the river Ganga, where millions of people in India and abroad drink its water and take baths every day for spiritual reasons. Besides bacteriophages, several pathogenic and zoonotic microbes are present in the river Ganga. It is interesting to study the diversity and abundance of bacteria and their respective phages present in polluted or non-polluted sites. Thus, the metagenomics study was carried out at the most polluted sites of river Ganga near Kanpur and non-polluted sites at Farakka, which harbors several harmful bacteria and their phages. The results revealed a significantly higher percentage of Microviridae phage family, ssDNA viruses, and Mimiviridae virus family near Kanpur than Farakka. In addition, compared to Kanpur, Farakka has a more significant percentage of Myoviridae, an unidentified phage family, and Retroviridae viral families. Despite heavy drainage of untreated and contaminated effluents from the leather industry, pesticide industry, paper mills, metropolitan cities, and other sources, the vast number of said phages kills several harmful pathogenic microbes in polluted sites to maintain the Ganga water's healing power or natural sterility. In a polluted aquatic environment, the varieties of bacteriophages were identified in the Ganga and their interaction with the microbial host. The taxonomic diversity of several bacteriophages found in pathogenic host systems was investigated to get exceptional knowledge of these small viruses in the aquatic environment.

Taxonomic profiling and functional gene annotation of microbial communities in sediment of river Ganga at Kanpur, India: insights from whole-genome metagenomics study

The perennial river Ganga is recognized as one of India's largest rivers of India, but due to continuous anthropogenic activities, the river's ecosystem is under threat. Next-generation sequencing technology has transformed metagenomics in the exploration of microbiome and their imperative function in diverse aquatic ecosystems. In this study, we have uncovered the structure of community microbiome and their functions in sediments of river Ganga at Kanpur, India, at three polluted stretches through a high-resolution metagenomics approach using Illumina HiSeq 2500. Among the microbes, bacteria dominate more than 82% in the three polluted sediment samples of river Ganga. Pseudomonadota (alpha, beta, and gamma) is the major phylum of bacteria that dominates in three sediment samples. Genes involved in degradation of xenobiotic compounds involving nitrotoluene, benzoate, aminobenzoate, chlorocyclohexane, and chlorobenzene were significantly enriched in the microbiome of polluted stretches. Pathway analysis using KEGG database revealed a higher abundance of genes involved in energy metabolism such as oxidative phosphorylation, nitrogen, methane, sulfur, and carbon fixation pathways in the sediment metagenome data from the river Ganga. A higher abundance of pollutant degrading enzymes like 4-hydroxybenzoate 3-monooxygenase, catalase-peroxidase, and altronate hydrolase in the polluted microbiome indicates their role in degradation of plastics and dyes. Overall, our study has provided bacterial diversity and their dynamics in community structure and function from polluted river microbiome, which is expected to open up better avenues for exploration of novel functional genes/enzymes with potential application in health and bioremediation.

Clean and Safe Drinking Water Systems via Metagenomics Data and Artificial Intelligence: State-of-the-Art and Future Perspective

The use of next-generation sequencing technologies in drinking water distribution systems (DWDS) has shed insight into the microbial communities' composition, and interaction in the drinking water microbiome. For the past two decades, various studies have been conducted in which metagenomics data have been collected over extended periods and analyzed spatially and temporally to understand the dynamics of microbial communities in DWDS. In this literature review, we outline the findings which were reported in the literature on what kind of occupancy-abundance patterns are exhibited in the drinking water microbiome, how the drinking water microbiome dynamically evolves spatially and temporally in the distribution networks, how different microbial communities co-exist, and what kind of clusters exist in the drinking water ecosystem. While data analysis in the current literature concerns mainly with confirmatory and exploratory questions pertaining to the use of metagenomics data for the analysis of DWDS microbiome, we present also future perspectives and the potential role of artificial intelligence (AI) and mechanistic models to address the predictive and mechanistic questions. The integration of meta-omics, AI, and mechanistic models transcends metagenomics into functional metagenomics, enabling deterministic understanding and control of DWDS for clean and safe drinking water systems of the future.