Mussels and Local Conditions Interact to Influence Microbial Communities in Mussel Beds

Microbiomes are increasingly recognized as widespread regulators of function from individual organism to ecosystem scales. However, the manner in which animals influence the structure and function of environmental microbiomes has received considerably less attention. Using a comparative field study, we investigated the relationship between freshwater mussel microbiomes and environmental microbiomes. We used two focal species of unionid mussels, Amblema plicata and Actinonaias ligamentina, with distinct behavioral and physiological characteristics. Mussel microbiomes, those of the shell and biodeposits, were less diverse than both surface and subsurface sediment microbiomes. Mussel abundance was a significant predictor of sediment microbial community composition, but mussel species richness was not. Our data suggest that local habitat conditions which change dynamically along streams, such as discharge, water turnover, and canopy cover, work in tandem to influence environmental microbial community assemblages at discreet rather than landscape scales. Further, mussel burrowing activity and mussel shells may provide habitat for microbial communities critical to nutrient cycling in these systems.

Differential microbial communities in paddy soils between Guiyang plateaus and Chengdu basins drive the incidence of rice bacterial diseases

Southwest China has the most complex rice-growing regions in China. With great differences in topography, mainly consisting of basins and plateaus, ecological factors in above region differ greatly. In this study, bulk paddy soils collected from a long-term rice field in Chengdu (basins) and in Guiyang (plateaus) were used to study the correlation between microbial diversity and the incidence of rice bacterial diseases. Results showed that the microbial community composition in paddy soils and the microbial functional categories differed significantly between basins and plateaus. They shared more than 70% of the dominant genera (abundance > 1%), but the abundance of the dominant genera differed significantly. Functional analysis found that bulk paddy soils from Chengdu were significantly enriched in virulence factor-related genes; soils from Guiyang were enriched in biosynthesis of secondary metabolites especially antibiotics. Correspondingly, Chengdu was significantly enriched in leaf bacterial pathogens Acidovorax, Xanthomonas, and Pseudomonas. Greenhouse experiments and correlation analysis showed that soil chemical properties had a greater effect on microbial community composition and positively related with the higher incidence of rice bacterial foot rot in Guiyang, while temperature had a greater effect on soil microbial functions and positively related with the higher severity index of leaf bacterial diseases in Chengdu. Our results provide a new perspective on how differences in microbial communities in paddy soils can influence the incidence of rice bacterial diseases in areas with different topographies.

Fresh Rumen Liquid Inoculant Enhances the Rumen Microbial Community Establishment in Pre-weaned Dairy Calves

The development of the functional rumen in calves involves a complex interplay between the host and host-related microbiome. Attempts to modulate rumen microbial community establishment may therefore have an impact on weaning success, calf health, and animal performance later in life. In this experiment, we aimed to elucidate how rumen liquid inoculum from an adult cow, provided to calves during the pre-weaning period, influences the establishment of rumen bacterial, archaeal, fungal, and ciliate protozoan communities in monozygotic twin calves (n = 6 pairs). The calves were divided into treatment (T-group) and control (C-group) groups, where the T-group received fresh rumen liquid as an oral inoculum during a 2–8-week period. The C-group was not inoculated. The rumen microbial community composition was determined using bacterial and archaeal 16S ribosomal RNA (rRNA) gene, protozoal 18S rRNA gene, and fungal ITS1 region amplicon sequencing. Animal weight gain and feed intake were monitored throughout the experiment. The T-group tended to have a higher concentrate intake (Treatment: p < 0.08) and had a significantly higher weekly weight gain (Treatment: p < 0.05), but no significant difference in volatile fatty acid concentrations between the groups was observed. In the T-group, the inoculum stimulated the earlier establishment of mature rumen-related bacterial taxa, affecting significant differences between the groups until 6 weeks of age. The inoculum also increased the archaeal operational taxonomic unit (OTU) diversity (Treatment: p < 0.05) but did not affect the archaeal quantity. Archaeal communities differed significantly between groups until week 4 (p = 0.02). Due to the inoculum, ciliate protozoa were detected in the T-group in week 2, while the C-group remained defaunated until 6 weeks of age. In week 8, Eremoplastron dilobum was the dominant ciliate protozoa in the C-group and Isotricha sp. in the T-group, respectively. The Shannon diversity of rumen anaerobic fungi reduced with age (Week: p < 0.01), and community establishment was influenced by a change of diet and potential interaction with other rumen microorganisms. Our results indicate that an adult cow rumen liquid inoculum enhanced the maturation of bacterial and archaeal communities in pre-weaning calves’ rumen, whereas its effect on eukaryotic communities was less clear and requires further investigation.

Compared the Microbiota Profiles between Samples from Bronchoalveolar Lavage and Endotracheal Aspirates in Severe Pneumonia: A Real-World Experience

Lower respiratory tract sampling from endotracheal aspirate (EA) and bronchoalveolar lavage (BAL) are both common methods to identify pathogens in severe pneumonia. However, the difference between these two methods in microbiota profiles remains unclear. We compared the microbiota profiles of pairwise EA and BAL samples in ICU patients with respiratory failure due to severe pneumonia. We prospectively enrolled 50 ICU patients with new onset of pneumonia requiring mechanical ventilation. EA and BAL were performed on the first ICU day, and samples were analyzed for microbial community composition via 16S rRNA metagenomic sequencing. Pathogens were identified in culture medium from BAL samples in 21 (42%) out of 50 patients. No difference was observed in the antibiotic prescription pattern, ICU mortality, or hospital mortality between BAL-positive and BAL-negative patients. The microbiota profiles in the EA and BAL samples are similar with respect to diversity, microbial composition, and microbial community correlations. The antibiotic treatment regimen was rarely changed based on the BAL findings. The samples from BAL did not provide more information than EA in the microbiota profiles. We suggest that EA is more useful than BAL for microbiome identification in mechanically ventilated patients.

Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications

Boosted by the exponential growth of microbiome-based studies, analyzing microbiome patterns is now a hot-topic, finding different fields of application. In particular, the use of machine learning techniques is increasing in microbiome studies, providing deep insights into microbial community composition. In this context, in order to investigate microbial patterns from 16S rRNA metabarcoding data, we explored the effectiveness of Association Rule Mining (ARM) technique, a supervised-machine learning procedure, to extract patterns (in this work, intended as groups of species or taxa) from microbiome data. ARM can generate huge amounts of data, making spurious information removal and visualizing results challenging. Our work sheds light on the strengths and weaknesses of pattern mining strategy into the study of microbial patterns, in particular from 16S rRNA microbiome datasets, applying ARM on real case studies and providing guidelines for future usage. Our results highlighted issues related to the type of input and the use of metadata in microbial pattern extraction, identifying the key steps that must be considered to apply ARM consciously on 16S rRNA microbiome data. To promote the use of ARM and the visualization of microbiome patterns, specifically, we developed microFIM (microbial Frequent Itemset Mining), a versatile Python tool that facilitates the use of ARM integrating common microbiome outputs, such as taxa tables. microFIM implements interest measures to remove spurious information and merges the results of ARM analysis with the common microbiome outputs, providing similar microbiome strategies that help scientists to integrate ARM in microbiome applications. With this work, we aimed at creating a bridge between microbial ecology researchers and ARM technique, making researchers aware about the strength and weaknesses of association rule mining approach.

The Influence of Residue Mixing on the Decomposition of Pepper Root Residues

(1) Background: Residue degradation plays a very important role in terrestrial ecosystems and residue mixing is the main factor affecting the degradation rates. However, in the agricultural systems, the effect of residue mixing on the degradation of pepper residues and the microbial community in pepper root residues is not clear. (2) Methods: In this study, we added different residues into soil by using double-layered nylon litterbags in culture bottles. The treatments including pepper root (P: Capsicum annuum L.), soybean [S: Glycine max (L.) Merr.] and maize (M: Zea mays L.) residue, as well as mixtures of soybean + pepper (SP), maize + pepper (MP), maize + soybean + pepper (MSP) mixtures. Litterbags were harvested after 7, 14, 28, and 56 days, respectively. Mass loss and nitrogen and phosphorus contents in pepper residue were quantified and bacterial and fungal community levels in pepper residues were analyzed using quantitative PCR and high throughput amplicon sequencing; (3) Results: The study showed that the mass loss and fungal community abundance of pepper root residue in mixtures were higher than P, except day 7. The phosphorus contents in MSP-P and MP-P were significantly lower than that for P at day 28 and day 56. Illumina MiSeq sequencing showed that the presence of maize residue significantly altered the microbial community composition of pepper root pepper. Day 56. (4) Conclusions: Our results suggest that residue mixing changed the microbial community abundance in pepper residue and promoted the degradation of pepper residues compared to pepper residue decomposition alone, especially for mixtures with soybean.

Particulate Matter in Swine Barns: A Comprehensive Review

Particulate matter (PM) represents an air quality management challenge for confined swine production systems. Because of the limited space and ventilation rate, PM can reach relatively high concentrations in swine barns. PM in swine barns possesses different physical, chemical, and biological characteristics than that in the atmosphere and other indoor environments. As a result, it exerts different environmental and health effects and creates some unique challenges regarding PM measurement and mitigation. Numerous research efforts have been made, generating massive data and information. However, relevant review reports are sporadic. This study aims to provide an updated comprehensive review of swine barn PM, focusing on publications since 1990. It covers various topics, including PM characteristics, sources, measurement methods, and in-barn mitigation technologies. Since PM in swine barns is of primarily biological origins, bioaerosols are reviewed in great detail. Relevant topics include bacterial/fungal counts, viruses, microbial community composition, antibiotic-resistant bacteria, antibiotic resistance genes, endotoxins, and (1→3)-β-D-glucans. For each topic, existing knowledge is summarized and discussed and knowledge gaps are identified. Overall, PM in swine barns is complicated in chemical and biological composition and highly variable in mass concentrations, size, and microbial abundance. Feed, feces, and skins constitute the major PM sources. Regarding in-barn PM mitigation, four technologies (oil/water sprinkling, ionization, alternation of feed and feeders, and recirculating air filtration) are dominant. However, none of them have been widely used in commercial barns. A collective discussion of major knowledge gaps and future research needs is offered at the end of the report.

Comparison and Analysis of Gut Microbiota in Children With IgA Vasculitis With Different Clinical Symptoms

Background: Henoch-Schönlein purpura, now called immunoglobulin A (IgA) vasculitis, is a common autoimmune disease in children, its association with gut microbiota composition remains unknown.Methods: The collected cases were divided into three groups: G1 group of simple skin type, G2 group with no digestive tract expression, G3 group of mixed digestive tract, and C group of healthy children. The fecal samples of each group of children were collected and the sequencing data was processed and analyzed. The dilution curve reflected the reasonableness of the amount of sequencing data.Results: The number of species composition sequences in the G1, G2 and G3 groups was lower than that in the C group, especially for the G2 and G3 groups. The four most abundant bacteria were Bacteroidetes, Firmicutes, Proteobacteria and Actinobacteria. The relative abundance of Proteobacteria in the G2 and G3 groups was significantly higher than that in the G1 and C groups, while the relative abundance of Actinobacteria was significantly reduced, and the relative abundance of Actinobacteria in the G1 group was lower than that in the C group. Principal component analysis of the UPGMA clustering tree and each group of samples showed that the microbial community composition of the same group of samples was similar.Conclusions: The abundance of intestinal microbes in children with IgA vasculitis is lower than in normal children. Bacteroidetes, Firmicutes, Proteobacteria and Actinobacteria are the four most abundant bacteria in the intestinal flora of children. Proteobacteria and Actinobacteria are associated with organ involvement in IgA vasculitis.