Impacts of NO2 Impurities on the Indigenous Microbial Community Structure and Diversity in CO2-Saline-Sandstone Interaction System

In recent years, the frequency and intensity of anthropogenic wildfires have drastically increased, significantly altering terrestrial ecosystems worldwide. These fires not only devastate vegetative cover but also impact soil environments and microbial communities, affecting ecosystem structure and function. The extent to which fire severity, soil depth, and their interaction influence these effects remains unclear, particularly in Pinus tabulaeformis forests. This study investigated the impact of wildfire intensity and soil stratification on soil physicochemical properties and microbial diversity within P. tabulaeformis forests in North China. Soil samples were collected from different fire severity zones (Control, Light, Moderate, High) and depths (topsoil: 0-10 cm; subsoil: 10-20 cm). Analyses included measurements of soil pH, organic carbon (SOC), total nitrogen (TN), and other nutrients. Microbial diversity was assessed using 16S rRNA gene sequencing. Our findings revealed significant variations in soil pH, SOC, TN, and other nutrients with fire severity and soil depth, profoundly affecting microbial community composition and diversity. Soil pH emerged as a critical determinant, closely linked to microbial α-diversity and community structure. We found that fire severity significantly altered soil pH (p = 0.001), pointing to noteworthy changes in acidity linked to varying severity levels. Topsoil microbial communities primarily differentiated between burned and unburned conditions, whereas subsoil layers showed more pronounced effects of fire severity on microbial structures. Analysis of bacterial phyla across different fire severity levels and soil depths revealed significant shifts in microbial communities. Proteobacteria consistently dominated across all conditions, indicating strong resilience, while Acidobacteriota and Actinobacteriota showed increased abundances in high-severity and light/moderate-severity areas, respectively. Verrucomicrobiota were more prevalent in control samples and decreased significantly in fire-impacted soils. Chloroflexi and Bacteroidota displayed increased abundance in moderate and high-severity areas, respectively. Correlation analyses illustrated significant relationships between soil environmental factors and dominant bacterial phyla. Soil organic carbon (SOC) showed positive correlations with total nitrogen (TN) and alkaline hydrolysable nitrogen (AN). Soil pH exhibited a negative correlation with multiple soil environmental factors. Soil pH and available phosphorus (AP) significantly influenced the abundance of the phylum Myxococcota. Soil water content (WC) significantly affected the abundances of Acidobacteriota and Actinobacteriota. Additionally, ammonium nitrogen (NH4 +-N) and nitrate nitrogen (NO3 --N) jointly and significantly impacted the abundance of the phylum Chloroflexi. This study highlights the significant long-term effects of anthropogenic wildfires on soil microenvironment heterogeneity and bacterial community structure in P. tabulaeformis forests in North China, 6 years post-fire. Our findings demonstrate that fire severity significantly influences soil pH, which in turn affects soil nutrient dynamics and enhances microbial diversity. We observed notable shifts in the abundance of dominant bacterial phyla, emphasizing the critical role of soil pH and nutrient availability in shaping microbial communities. The results underscore the importance of soil stratification, as different soil layers showed varying responses to fire severity, highlighting the need for tailored management strategies. Future research should focus on long-term monitoring to further elucidate the temporal dynamics of soil microbial recovery and nutrient cycling following wildfires. Studies investigating the roles of specific microbial taxa in ecosystem resilience and their functional contributions under varying fire regimes will provide deeper insights. Additionally, exploring soil amendments and management practices aimed at optimizing pH and nutrient availability could enhance post-fire recovery processes, supporting sustainable ecosystem recovery and resilience.

The microbial community structure and function under forest in tropical peatlands are poorly understood. In this study, we investigated the microbial community structure and diversity in natural peat swamp forest soil, disturbed peat soil and mineral soil in Central Kalimantan, Indonesia, using 454 pyrosequencing. The results showed that the natural peat soil had the greatest fungal species richness (Chao1), which was significantly ( p < .05) larger than that in the other two soils. Community structure of both fungi and bacteria in natural peat soil differed significantly from that in the disturbed peat soil ( p = .039 and p = .045, respectively). Ascomycota (40.5%) was the most abundant phylum across the three soils followed by Basidiomycota (18.8%), Zygomycota (<0.1%) and Glomeromycota (<0.1%). The linear discriminant analysis with effect size ( LE fSe) showed that Ascomycota ( p < .05) and genus Gliocephalotrichum ( p < .05) dominated in natural peat soil. Functionally, pathotrophs were more abundant in disturbed peat soil ( p < .05). Proteobacteria (43.8%) were the most abundant phylum followed by Acidobacteria (32.6%), Actinobacteria (9.8%), Planctomycetes (1.7%). Methylocystis , Telmatospirillum , Syntrophobacter , Sorangium and Opitutus were the more abundant genera in disturbed peat soil, whereas Nevskia and Schlesneria were more abundant in mineral soil and natural peat soil, respectively. The natural peat forest soil supported a more diverse microbiology; however, the land use of such a soil can change its microbial community structure. The results provide evidence that the disturbance of tropical peat land could lead to the introduction and spread of a large number of fungal diseases

Soil of tropical forest ecosystem plays very crucial part in controlling the universal carbon cycle. The isolation of microorganisms and their identification are important for understanding their vital role on transformation of organic matter of this ecosystem. Soil storage maximum microbial genetic diversity because of it’s a multilayered environment. No complete methods were discovered yet to cultivate majority of soil microorganisms. A little is known about microbial structure and their essentiality in tropical peat lands compared to most other terrestrial and oceanic habitats. In providing insight to the impacts of land-use of peat land on microbes in Central Kalimantan, Borneo Indonesia, we examined the community structure and diversity of bacteria and fungi in different peat forest soil including: i) natural peat swamp forest (well mixed swamp forest type); ii) disturbed peat soil and iii) mineral soils by using 454 pyrosequencing technology. The results showed that no significant difference was found for diversity and evenness among the sites of fungal community. However, natural peat swamp forest had the highest species richness (Chao1), which was significantly higher than the other two sites (P<0.05 and P<0.05). According to the OTUs analysis four fungi phyla were obtained of which 45 species were classified. The Ascomycota was the most abundant phylum, followed by Basidiomycota, Zygomycota and Glomeromycota. The natural peat swamp forest and disturbed peat soil harbored the maximum number Ascomycota. On the other hand, mineral soil and natural peat soil contained the highest number of Basidiomycota. The top species in natural peat swamp forest included Sugiyamaella paludigena, Polyancora globosa and Ganoderma gibbosum. The mineral soil enriched the abundance of Penicillium herquei, Sugiyamaella paludigena and the disturbed peat soil contained the highest frequency of Polyancora globosa, Gymnopilus lepidotus. According to the PCoA analysis, the community structure of fungus in natural peat soil differed significantly from mineral soil (P=0.04) and disturbed peat soil (P=0.039). No significant difference was found for bacterial species richness (Chao1) among the sites. The diversity of bacteria in disturbed peat soil significantly differed from the other sites (P<0.05 and P<0.05). eleven bacterial phyla and 53 genera were examined. All of the three sites contained the similar abundance of Proteobacteria. The natural peat swamp forest and disturbed peat soil harbored the most abundant Acidobactria. Genera Mycobacterium, Gp1, Gp13, Gp2, Burkholderia, Actinospica, Aciditerrimonas were found in all the sites. Genera Granulibacter, Gp4, Acidisoma, Clostridium_sensu, Clostridium_ XI were only observed in natural peat swamp forest. Genera Rudaea, Rhodopila, Streptomycetes were found only mineral soil. The PCoA analysis showed that the structure of bacteria in natural peat swamp forest significantly differed from the disturbed peat soil (P=0.045). Overall, the bacterial species richness and diversity are more among the sites than of the fungi.
 J. Biodivers. Conserv. Bioresour. Manag. 2019, 5(1): 133-144

The match between microbial community structure and soil properties is modulated by land use types and sample origin within an integrated agroecosystem

Sewage sludges generation and their disposal have become one of the greatest challenges of the 21st century. They have great microbial diversity that may impact wastewater treatment plant (WWTP) efficiency and soil quality whether used as fertilizers. Therefore, this research aimed to characterize microbial community diversity and structure of 19 sewage sludges from São Paulo, Brazil, as well as to draw their relations to sludge sources [domestic and mixed (domestic+industrial)], biological treatments (redox conditions and liming), and chemical attributes, using molecular biology as a tool. All sludges revealed high bacterial diversity, but their sources and redox operating conditions as well as liming did not consistently affect bacterial community structures. Proteobacteria was the dominant phylum followed by Bacteroidetes and Firmicutes; whereas Clostridium was the dominant genus followed by Treponema, Propionibacterium, Syntrophus, and Desulfobulbus. The sludge samples could be clustered into six groups (C1 to C6) according their microbial structure similarities. Very high pH (≥11.9) was the main sludge attribute segregating C6, that presented very distinct microbial structure from the others. Its most dominant genera were Propionibacterium > > Comamonas > Brevundimonas > Methylobacterium ∼Stenotrophomonas ∼Cloacibacterium. The other clusters’ dominant genera were Clostridium > > Treponema > Desulfobulbus ∼Syntrophus. Moreover, high Fe and S were important modulators of microbial structure in certain sludges undertaking anaerobic treatment and having relatively low N-Kj, B, and P contents (C5). However, high N-Kj, B, P, and low Fe and Al contents were typical of domestic, unlimed, and aerobically treated sludges (C1). In general, heavy metals had little impact on microbial community structure of the sludges. However, our sludges shared a common core of 77 bacteria, mostly Clostridium, Treponema, Syntrophus, and Comamonas. They should dictate microbial functioning within WWTPs, except by SS12 and SS13.

Soil microbial metabolic potential and ecosystem function have received little attention owing to difficulties in methodology. In this study, we selected natural mature forest and natural secondary forest and analyzed the soil microbial community and metabolic potential combing the high-throughput sequencing and GeoChip technologies. Phylogenetic analysis based on 16S rRNA sequencing showed that one known archaeal phylum and 15 known bacterial phyla as well as unclassified phylotypes were presented in these forest soils, and Acidobacteria, Protecobacteria, and Actinobacteria were three of most abundant phyla. The detected microbial functional gene groups were related to different biogeochemical processes, including carbon degradation, carbon fixation, methane metabolism, nitrogen cycling, phosphorus utilization, sulfur cycling, etc. The Shannon index for detected functional gene probes was significantly higher (P<0.05) at natural secondary forest site. The regression analysis showed that a strong positive (P<0.05) correlation was existed between the soil microbial functional gene diversity and phylogenetic diversity. Mantel test showed that soil oxidizable organic carbon, soil total nitrogen and cellulose, glucanase, and amylase activities were significantly linked (P<0.05) to the relative abundance of corresponded functional gene groups. Variance partitioning analysis showed that a total of 81.58% of the variation in community structure was explained by soil chemical factors, soil temperature, and plant diversity. Therefore, the positive link of soil microbial structure and composition to functional activity related to ecosystem functioning was existed, and the natural secondary forest soil may occur the high microbial metabolic potential. Although the results can't directly reflect the actual microbial populations and functional activities, this study provides insight into the potential activity of the microbial community and associated feedback responses of the terrestrial ecosystem to environmental changes.

When, where and how does microbial community composition matter?

This study assessed the bacterial populations in a non-sanitary landfill around Guarani Aquifer recharge zone in Brazil. Samples from two different positions (sites 1 and 2) at three different depths were evaluated, totaling six solid waste samples; two samples from an impacted stream were also collected. 16S rRNA sequencing was performed using the Ion S5TM XL platform; 3113 operational taxonomic units (OTUs) and 52 phyla were identified. Proteobacteria (37%) and Firmicutes (28%) were the most abundant phyla in the landfill, whereas Proteobacteria (~ 50%) and Bacteroidetes (~ 10%) were more profuse in surface water samples. Canonical correlation analysis (CCA) enabled us to clearly separate the samples according to their spatial location (site 1 or 2) or environmental matrix (surface water or solid waste samples), showing that microbiological populations are strongly associated with site-specific conditions and the kind of environmental matrix they come from. Environmental factors that mostly influenced the microbial communities were organic matter, oxidation-reduction potential, moisture, alkalinity, nitrogen (TKN), sodium, potassium, and zinc. Exiguobacterium (phylum Firmicutes) was overwhelmingly dominant at site 1 and was associated with higher concentrations of organic matter and potassium. Differently, site 2 did not present such dominant genera and was more diverse having lower concentrations of organic matter and nutrients. Distinct environments co-exist inside the same waste deposit, including zones which are representative of active and closed landfills and the occurrence of considerable physicochemical and microbiological shifts within short distances. Those shifts indicate that microbial populations are well adapted to the heterogeneity typical of urban solid waste, which is possibly beneficial to contaminant degradation. Graphical abstract.

Microbial ecology is a science that studies the relationships between microorganisms and their biotic and abiotic environments. The ultimate goal of microbial ecology is to predict who is where with whom doing what, why and when. To achieve this predictive goal, computational modelling and simulation of microbial community dynamics and behaviour at both the structural and functional levels is essential. However, in contrast to the intensive modelling studies in plant and animal community ecology, the modelling of microbial community behaviour within different environmental contexts has not been initiated. The vast majority of the community studies in microbial ecology are still at a descriptive level rather than at a quantitative and predictive level. One of the major problems is the lack of reliable experimental information on community-wide spatial and temporal dynamics of microbial community structure, activities and functions. Because microorganisms are difficult to directly count, obtaining enough data required for modelling is extremely difficult and even impossible with conventional molecular techniques such as PCR-based cloning, in situ hybridization and quantitative PCR. Although applications of such conventional molecular techniques over the last two decades have provided new insights into microbial diversity and structure, they have failed to provide community-wide quantitative information in a rapid fashion for modelling and predicting microbial community dynamics. Lack of sufficient reliable data prevents microbial ecologists from addressing quantitatively important ecological and environmental questions such as competition, stability, succession, and adaptation in microbial communities. Such questions are extremely important for the applied manipulation of microbial communities for desired functions related to human health, food, energy production, environmental cleanup, and industrial and agricultural practices. With the recent development and application of large-scale high-throughput sequencing (see Sogin et al., 2006; Huber et al., 2007; Hamady et al. 2008) and associated metagenomics technologies such as GeoChip related functional gene arrays (He et al., 2007; Zhou et al., 2008; Handelsman et al., 2007), community-wide spatial and temporal information on microbial community functional structure and activities can be rapidly obtained. This will be the first time that microbial ecologists will be not frustrated by the paucity of experimental data to address ecological questions. In turn, microbial ecologists may very well be overwhelmed by massive amounts of metagenomics data and will be perplexed as to how to utilize and interpret the data within ecological and environmental contexts. Here, I will argue that we are in the new era of transforming microbial ecology from descriptive studies to a quantitative and predictive science. Using high-throughput metagenomics technologies and computational modelling, it is possible to tackle some fundamental ecological questions (Zhou et al., 2004), which have been difficult to address before such as: (i) How do the phylogenetic and functional structure of microbial communities change across various spatial (from micrometers, kilometers to tens of thousands kilometers) and temporal scales and what are the forces shaping their diversity and structure? (ii) How can the complex ecological networks in microbial communities be identified and whether are they important to ecosystem functioning? (iii) What is the molecular basis for functional stability and adaptation of microbial communities? (iv) How is the functional stability of a microbial community related to its genetic and metabolic diversity as well as environmental disturbance? (v) Can the functional stability and future status of a microbial community be predicted based on the metabolic functional conservation and differentiation of individual microbial populations? (vi) Can a microbial community be manipulated to achieve a desired stable function by manipulating the metabolic traits of the community? (vii) How can the information be scaled from molecules to populations, to communities, and to ecosystems for understanding ecosystem behaviours and dynamics? (viii) Can the molecular-level understanding of microbial community structure improve our predictive power of the ecological and evolutionary responses of microbial communities to environmental changes, especially global climate changes? Addressing the above questions in a quantitative and predictive way requires rigorous experimental designing and systematic intensive sampling of the microbial systems studied. Selection of experimental systems with appropriate complexity and replications could be very important. I believe that two general strategies can be employed. One is to focus on surveying complex natural microbial systems by using high-throughput metagenomics technologies to systematically compare the commonality and differences of microbial community diversity patterns, metabolic capacities, and functional activities across various spatial and temporal scales. While such survey-based approach provides rich information on microbial community diversity patterns and dynamics, it could be difficult to establish detailed definitive mechanistic linkages between microbial diversity and ecosystem functioning because the microbial systems in natural settings are generally very complex. Another complementary strategy is to establish well-controlled laboratory systems such as bioreactors with simplified communities to systematically examine the responses of microbial communities to environmental changes and the impacts of their responses on ecosystem functioning. Such laboratory systems are important to establish cause-and-effect relationships, because they have great advantages in terms of system controls, monitoring, data collection, replications and modelling. Determination of cause-and-effect relationships is much easier with simpler, engineered, laboratory-based bioreactor systems than with complex natural communities, as input and output parameters can be controlled, along with environmental conditions. Although the community in a controlled system is not a natural community, such systems would offer the best opportunity to acquire mechanistic understanding of the fundamental principles of interactions among various microorganisms and the molecular level ecological and evolutionary responses of microbial communities to environmental changes. Therefore, well-controlled laboratory engineered systems will be critical to predictive microbial ecology studies. Predictive microbial ecology requires not only high-throughput experimental tools but also high performance computational capabilities. System-level understanding of the dynamic behaviour of microbial community structure, functions and their relationships to ecosystem functioning faces several grand computational challenges. First, microbial diversity is extremely high. The number of genes in a genome or populations in a community far exceeds the number of sample measurements due to high cost of measurements. It is difficult to apply classical mathematical tools such as differential equations to simulate high-throughput metagenomics data because no sole solution can be obtained for the constructed models. New mathematical theories and approaches are needed to deal with such dimensionality problems. Second, metagenomic data from analyses of transcriptomes, proteomes and metabolomes, as well as physiological and geochemical data, are heterogeneous. Synthesizing various types of large-scale data together to make biological sense is also difficult. Rapid high performance parallel computational tools are needed for data processing, computation and visualization. In addition, because the dynamic behaviours of biological systems at various levels (cells, individuals, populations, communities, and ecosystems) are measured on different temporal and spatial scales, linking cellular-level genomic information to ecosystem-level functional information for predicting ecosystem dynamics is even more challenging. Novel mathematical framework and computational tools are needed for achieving systems-level understanding and prediction of microbial community dynamics, behaviour and functional stability. With the rapid continuing advances of metagenomics-based high-throughput experimental technologies and associated high performance computational tools, microbiologists should be able to perform more quantitative modelling studies of microbial systems as macroecologists have done since last half century. There is no doubt that the era of quantitative predictive microbial ecology is coming.

Xenobiotics and the Human Gut Microbiome: Metatranscriptomics Reveal the Active Players

Organic management pattern improves microbial community diversity and alters microbial network structure in karst tea plantation

Pollution characteristics and microbial community succession of a rural informal landfill in an arid climate

Indigenous Microbial Community Structure in Rhizosphere of Chinese Kale as Affected by Plant Growth-Promoting Rhizobacteria Inoculation

In situ remediation of Cr(VI) contaminated groundwater by ZVI-PRB and the corresponding indigenous microbial community responses: a field-scale study

Responses of microbial community to pH stress in bioleaching of low grade copper sulfide