Arthropods harbor complex microbiota that play a pivotal role in host fitness. While multiple factors, like host species and diet, shape microbiota in arthropods, their impact on community assembly in wild insects remains largely unknown. In this study, we surveyed bacterial and fungal community assembly in nine sympatric wild insect species that share a common citrus fruit diet. Source tracking analysis suggested that these insects acquire some bacteria and fungi from the citrus fruit with varying degrees. Although sharing a common diet led to microbiota convergence, the diversity, composition, and network of both bacterial and fungal communities varied significantly among surveyed insect groups. Null model analysis indicated that stochastic processes, particularly dispersal limitation and drift, are primary drivers of structuring insect bacterial and fungal communities. Importantly, the influence of each community assembly process varied strongly depending on the host species. Thus, we proposed a speculative view that the host specificity of the microbiome and mycobiome assembly is widespread in wild insects despite sharing the same regional species pool. Overall, this research solidifies the importance of host species in shaping microbiomes and mycobiomes, providing novel insights into their assembly mechanisms in wild insects.
OBJECTIVE: Since the microbiome has been shown to impact insect fitness, a mechanistic understanding of community assembly has potentially significant applications but remains largely unexplored. In this paper, we investigate bacterial and fungal community assembly in nine sympatric wild insect species that share a common diet. The main findings indicate that stochastic processes drive the divergence of microbiomes and mycobiomes in nine sympatric wild insect species. These findings offer novel insights into the assembly mechanisms of microbiomes and mycobiomes in wild insects.

University students predominantly spend their time indoors, where prolonged exposure raises the risk of contact with microorganisms of concern. However, our knowledge about the microbial community characteristics on university campus and their underpinnings is limited. To address it, we characterized bacterial communities from the surfaces of various built environments typical of a university campus, including cafeterias, classrooms, dormitories, offices, meeting rooms, and restrooms, in addition to human skin. The classrooms harbored the highest α-diversity, while the cafeterias had the lowest α-diversity. The bacterial community composition varied significantly across different building types. Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes, and Cyanobacteria were common phyla in university buildings, accounting for more than 90 % of total abundance. Staphylococcus aureus was the most abundant potential pathogen in classrooms, dormitories, offices, restrooms, and on human skin, indicating a potential risk for skin disease infections in these buildings. We further developed a new quantitative pathogenic risk assessment method according to the threat of pathogens to humans and found that classrooms exhibited the highest potential risk. The fast expectation-maximization algorithm identified 59 %-86 % of bacterial sources in buildings, with the human skin as the largest bacterial source for most buildings. As the sources of bacteria were highly traceable, we showed that homogeneous selection, dispersal limitation, and ecological drift were major ecological forces that drove community assembly. Our findings have important implications for predicting the distribution and sources of indoor dust bacterial communities on university campus.

Emergence and spread of antibiotic resistance genes (ARGs) in lakes have been considered as a global health threat. However, a thorough understanding of the distribution patterns and ecological processes that shape the ARGs profile in interconnected river-lake systems remains largely unexplored. In this study, we collected paired water and sediment samples from a typical interconnected river-lake system, Dongting Lake in China, during both wet and dry seasons. Using high-throughput quantitative PCR, we investigated the spatial and temporal distribution of ARGs and the factors that influence them. A total of 8 major antibiotic classes and 10 mobile genetic elements were detected across the Dongting Lake basin. The unique hydrological characteristics of this interconnected river-lake system result in a relatively stable abundance of ARGs across different seasons and interfaces. During the wet season, deterministic processes dominated the assembly of ARGs, allowing environmental factors, such as heavy metals, to serve as main driving forces of ARGs distribution. When the dry season arrived, variations in hydrological conditions and changes in ARGs sources caused stochastic processes to dominate the assembly of ARGs. Our findings provide valuable insights for understanding the ecological processes of ARGs in interconnected river-lake systems, emphasizing the necessity of upstream restoration and clarifying river-lake relationships to mitigate ARGs dissemination.

Objective.High-dose-rate (HDR) brachytherapy lacks routinely available treatment verification methods. Real-time tracking of the radiation source during HDR brachytherapy can enhance treatment verification capabilities. Recent developments in source tracking allow for measurement of dwell times and source positions with high accuracy. However, more clinically relevant information, such as dose discrepancies, is still needed. To address this, a real-time dose calculation implementation was developed to provide more relevant information from source tracking data. A proof-of-principle of the developed tool was shown using source tracking data obtained from a 3D-printed anthropomorphic phantom.Approach.Software was developed to calculate dose-volume-histograms (DVH) and clinical dose metrics from experimental HDR prostate treatment source tracking data, measured in a realistic pelvic phantom. Uncertainty estimation was performed using repeat measurements to assess the inherent dose measuring uncertainty of thein vivodosimetry (IVD) system. Using a novel approach, the measurement uncertainty can be incorporated in the dose calculation, and used for evaluation of cumulative dose and clinical dose-volume metrics after every dwell position, enabling real-time treatment verification.Main results.The dose calculated from source tracking measurements aligned with the generated uncertainty bands, validating the approach. Simulated shifts of 3 mm in 5/17 needles in a single plan caused DVH deviations beyond the uncertainty bands, indicating errors occurred during treatment. Clinical dose-volume metrics could be monitored in a time-resolved approach, enabling early detection of treatment plan deviations and prediction of their impact on the final dose that will be delivered in real-time.Significance.Integrating dose calculation with source tracking enhances the clinical relevance of IVD methods. Phantom measurements show that the developed tool aids in tracking treatment progress, detecting errors in real-time and post-treatment evaluation. In addition, it could be used to define patient-specific action limits and error thresholds, while taking the uncertainty of the measurement system into consideration.

Environmental-origin microbiota significantly influences Red Heart Qu (RH_Qu) stratification, but their microbial migration and metabolic mechanisms remain unclear. Using high-throughput sequencing and metabolomics, we divided the stratification of RH_Qu into three temperature-based stages. Phase I features rising temperatures, causing microbial proliferation and a two-layer division. Phase II, characterized by peak temperatures, sees the establishment of thermotolerant species like Bacillus, Thermoactinomyces, Rhodococcus, and Thermoascus, forming four distinct layers and markedly altering metabolite profiles. The Huo Quan (HQ), developing from the Pi Zhang (PZ), is driven by the tyrosine-melanin pathway and increased MRPs (Maillard reaction products). The Hong Xin evolves from the Rang, associated with the phenylalanine-coumarin pathway and QCs (Quinone Compounds) production. Phase III involves the stabilization of the microbial and metabolic profile as temperatures decline. These findings enhance our understanding of RH_Qu stratification and offer guidance for quality control in its fermentation process.

BACKGROUND: Artisanal cheeses usually contain a highly diverse microbial community which can significantly impact their quality and safety. Here, we describe a detailed longitudinal study assessing the impact of ripening in three natural caves on the microbiome and resistome succession across three different producers of Cabrales blue-veined cheese.
RESULTS: Both the producer and cave in which cheeses were ripened significantly influenced the cheese microbiome. Lactococcus and the former Lactobacillus genus, among other taxa, showed high abundance in cheeses at initial stages of ripening, either coming from the raw material, starter culture used, and/or the environment of processing plants. Along cheese ripening in caves, these taxa were displaced by other bacteria, such as Tetragenococcus, Corynebacterium, Brevibacterium, Yaniella, and Staphylococcus, predominantly originating from cave environments (mainly food contact surfaces), as demonstrated by source-tracking analysis, strain analysis at read level, and the characterization of 613 metagenome-assembled genomes. The high abundance of Tetragenococcus koreensis and Tetragenococcus halophilus detected in cheese has not been found previously in cheese metagenomes. Furthermore, Tetragenococcus showed a high level of horizontal gene transfer with other members of the cheese microbiome, mainly with Lactococcus and Staphylococcus, involving genes related to carbohydrate metabolism functions. The resistome analysis revealed that raw milk and the associated processing environments are a rich reservoir of antimicrobial resistance determinants, mainly associated with resistance to aminoglycosides, tetracyclines, and β-lactam antibiotics and harbored by aerobic gram-negative bacteria of high relevance from a safety point of view, such as Escherichia coli, Salmonella enterica, Acinetobacter, and Klebsiella pneumoniae, and that the displacement of most raw milk-associated taxa by cave-associated taxa during ripening gave rise to a significant decrease in the load of ARGs and, therefore, to a safer end product.
CONCLUSIONS: Overall, the cave environments represented an important source of non-starter microorganisms which may play a relevant role in the quality and safety of the end products. Among them, we have identified novel taxa and taxa not previously regarded as being dominant components of the cheese microbiome (Tetragenococcus spp.), providing very valuable information for the authentication of this protected designation of origin artisanal cheese. Video Abstract.

Antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) are prevalent in various environments on livestock farms, including livestock waste, soil, and groundwater. Contamination of groundwater by ARB and ARGs in livestock farms is a growing concern as it may have potentially huge risks to human health. However, the source of groundwater-borne ARB and ARGs in animal farms remains largely unknown. In this study, different types of samples including groundwater and its potential contamination sources from aboveground (pig feces, wastewater, and soil) from both working and abandoned swine feedlots in southern China were collected and subjected to metagenomic sequencing and ARB isolation. The source tracking based on metagenomic analysis revealed that 56-95 % of ARGs in groundwater was attributable to aboveground sources. Using metagenomic assembly, we found that 45 ARGs predominantly conferring resistance to aminoglycosides, sulfonamides, and tetracyclines could be transferred from the aboveground sources to groundwater, mostly through plasmid-mediated horizontal gene transfer. Furthermore, the full-length nucleotide sequences of sul1, tetA, and TEM-1 detected in ARB isolates exhibited the close evolutionary relationships between aboveground sources and groundwater. Some isolated strains of antibiotic-resistant Pseudomonas spp. from aboveground sources and groundwater had the high similarity (average nucleotide identity > 99 %). Notably, the groundwater-borne ARGs were identified as mainly carried by bacterial pathogens, potentially posing risks to human and animal health. Overall, this study underscores the dissemination of ARGs from aboveground sources to groundwater in animal farms and associated risks.

The occurrence and removal of 38 antibiotics from nine classes in two drinking water treatment plants (WTPs) were monitored monthly over one year to evaluate the efficiency of typical treatment processes, track the source of antibiotics in tap water and assess their potential risks to ecosystem and human health. In both source waters, 18 antibiotics were detected at least once, with average total antibiotic concentrations of 538.5 ng/L in WTP1 and 569.3 ng/L in WTP2. The coagulation/flocculation and sedimentation, sand filtration and granular activated carbon processes demonstrated limited removal efficiencies. Chlorination, on the other hand, effectively eliminated antibiotics by 48.7 ± 11.9%. Interestingly, negative removal was observed along the distribution system, resulting in a significant antibiotic presence in tap water, with average concentrations of 131.5 ng/L in WTP1 and 362.8 ng/L in WTP2. Source tracking analysis indicates that most antibiotics in tap water may originate from distribution system. The presence of antibiotics in raw water and tap water posed risks to the aquatic ecosystem. Untreated or partially treated raw water could pose a medium risk to infants under six months. Water parameters, for example, temperature, total nitrogen and total organic carbon, can serve as indicators to estimate antibiotic occurrence and associated risks. Furthermore, machine learning models were developed that successfully predicted risk levels using water quality parameters. Our study provides valuable insights into the occurrence, removal and risk of antibiotics in urban WTPs, contributing to the broader understanding of antibiotic pollution in water treatment systems.

Landfill leachate is a hotspot in antibiotic resistance development. However, little is known about antibiotic resistome and host pathogens in leachate and their effects on surrounding groundwater. Here, metagenomic sequencing was used to explore profiles, host bacteria, environmental risks and influencing factors of antibiotic resistome in raw and treated leachate and surrounding groundwater of three landfills. Results showed detection of a total of 324 antibiotic resistance genes (ARGs). The ARGs conferring resistance to multidrug (8.8 %-25.7 %), aminoglycoside (13.1 %-39.2 %), sulfonamide (10.0 %-20.9 %), tetracycline (5.7 %-34.4 %) and macrolide-lincosamide-streptogramin (MLS, 5.3 %-29.5 %) were dominant in raw leachate, while multidrug resistance genes were the major ARGs in treated leachate (64.1 %-83.0 %) and groundwater (28.7 %-76.6 %). Source tracking analysis suggests non-negligible influence of leachate on the ARGs in groundwater. The pathogens including Acinetobacter pittii, Pseudomonas stutzeri and P. alcaligenes were the major ARG-carrying hosts. Variance partitioning analysis indicates that the microbial community, abiotic variables and their interaction contributed most to the antibiotic resistance development. Our results shed light on the dissemination and driving mechanisms of ARGs from leachate to the groundwater, indicating that a comprehensive risk assessment and efficient treatment approaches are needed to deal with ARGs in landfill leachate and nearby groundwater. ENVIRONMENTAL IMPLICATIONS: Antibiotic resistance genes are found abundant in the landfill sites, and these genes could be disseminated into groundwater via leaching of wastewater and infiltration of leachate. This results in deterioration of groundwater quality and human health risks posed by these ARGs and related pathogens. Thus measures should be taken to minimize potential negative impacts of landfills on the surrounding environment.

We evaluated a unique model in which four full-scale wastewater treatment plants (WWTPs) with the same treatment schematic and fed with similar influent wastewater were tracked over an 8-month period to determine whether the community assembly would differ in the activated sludge (AS) and sand filtration (SF) stages. For each WWTP, AS and SF achieved an average of 1-log10 (90%) and <0.02-log10 (5%) reduction of total cells, respectively. Despite the removal of cells, both AS and SF had a higher alpha and beta diversity compared to the influent microbial community. Using the Sloan neutral model, it was observed that AS and SF were individually dominated by different assembly processes. Specifically, microorganisms from influent to AS were predominantly determined by the selective niche process for all WWTPs, while the microbial community in the SF was relatively favored by a stochastic, random migration process, except two WWTPs. AS also contributed more to the final effluent microbial community compared with the SF. Given that each WWTP operates the AS independently and that there is a niche selection process driven mainly by the chemical oxygen demand concentration, operational taxonomic units unique to each of the WWTPs were also identified. The findings from this study indicate that each WWTP has its distinct microbial signature and could be used for source-tracking purposes.IMPORTANCEThis study provided a novel concept that microorganisms follow a niche assembly in the activated sludge (AS) tank and that the AS contributed more than the sand filtration process toward the final microbial signature that is unique to each treatment plant. This observation highlights the importance of understanding the microbial community selected by the AS stage, which could contribute toward source-tracking the effluent from different wastewater treatment plants.