Correlation network analysis applied to complex biofilm communities

Salt stress alters the selectivity of mature pecan for the rhizosphere community and its associated functional traits

Introduction

Salt stress is a major global environmental factor limiting plant growth. Rhizosphere bacteria, recruited from bulk soil, play a pivotal role in enhancing salt stress resistance in herbaceous and crop species. However, whether the rhizosphere bacterial community of a mature tree can respond to salt stress, particularly in saline-alkalitolerant trees, remains unexplored. Pecan (Carya illinoinensis), an important commercially cultivated nut tree, is considered saline-alkali tolerant.

Methods

Pecan trees (12 years) were subjected to different NaCl concentrations for 12 weeks. Collected samples included bulk soil, rhizosphere soil, roots, leaves, and fruit. Amplicon sequencing data and shotgun metagenomic sequencing data obtained from the samples were investigated: 1) microbial communities in various ecological niches of mature pecan trees; 2) the characteristic of the rhizosphere bacteria community and the associated functional traits when pecan suffered from salt stress.

Results and discussion

We characterized the mature pecan-associated microbiome (i.e., fruit, leaf, root, and rhizosphere soil) for the first time. These findings suggest that niche-based processes, such as habitat selection, drive bacterial and fungal community assembly in pecan tissues. Salt stress reduced bacterial diversity, altered community composition, and shifted pecan's selective pressure on Proteobacteria and Actinobacteria. Shotgun metagenomic sequencing further revealed functional traits of the rhizosphere microbiome in response to salt stress. This study enhances our understanding of mature tree-associated microbiomes and supports the theory that shaping the rhizosphere microbiome may be a strategy for saline-alkali-tolerant mature trees to resist salt stress. These findings provide insights into salt tolerance in mature trees and suggest potential applications, such as the development of bio-inoculants, for managing saline environments in agricultural and ecological contexts.

Heavy metal contamination impacts the structure and co-occurrence patterns of bacterial communities in agricultural soils

Heavy metal (HM) contamination caused by mining and smelting activities can be harmful to soil microbiota, which are highly sensitive to HM stress. Here, we explore the effects of HM contamination on the taxonomic composition, predicted function, and co-occurrence patterns of soil bacterial communities in two agricultural fields with contrasting levels of soil HMs (i.e., contaminated and uncontaminated natural areas). Our results indicate that HM contamination does not significantly influence soil bacterial α diversity but changes the bacterial community composition by enriching the phyla Gemmatimonadetes, Planctomycetes, and Parcubacteria and reducing the relative abundance of Actinobacteria. Our results further demonstrate that HM contamination can strengthen the complexity and modularity of the bacterial co-occurrence network but weaken positive interactions between keystone taxa, leading to the gradual disappearance of some taxa that originally played an important role in healthy soil, thereby possibly reducing the resistance of bacterial communities to HM toxicity. The predicted functions of bacterial communities are related to membrane transport, amino acid metabolism, energy metabolism, and carbohydrate metabolism. Among these, functions related to HM detoxification and antioxidation are enriched in uncontaminated soils, while HM contamination enriches functions related to metal resistance. This study demonstrated that microorganisms adapt to the stress of HM pollution by adjusting their composition and enhancing their network complexity and potential ecological functions.

Disturbance frequency directs microbial community succession in marine biofilms exposed to shear

IMPORTANCE

Disturbances are major drivers of community succession in many microbial systems; however, relatively little is known about marine biofilm community succession, especially under antifouling disturbance. Antifouling technologies exert strong local disturbances on marine biofilms, and resulting biomass losses can be accompanied by shifts in biofilm community composition and succession. We address this gap in knowledge by bridging microbial ecology with antifouling technology development. We show that disturbance by shear can strongly alter marine biofilm community succession, acting as a selective filter influenced by frequency of exposure. Examining marine biofilm succession patterns with and without shear revealed stable associations between key prokaryotic and eukaryotic taxa, highlighting the importance of cross-domain assessment in future marine biofilm research. Describing how compounded top-down and bottom-up disturbances shape the succession of marine biofilms is valuable for understanding the assembly and stability of these complex microbial communities and predicting species invasiveness.

Social networking at the microbiome-host interface

Microbial species colonizing host ecosystems in health or disease rarely do so alone. Organisms conglomerate into dynamic heterotypic communities or biofilms in which interspecies and interkingdom interactions drive functional specialization of constituent species and shape community properties, including nososymbiocity or pathogenic potential. Cell-to-cell binding, exchange of signaling molecules, and nutritional codependencies can all contribute to the emergent properties of these communities. Spatial constraints defined by community architecture also determine overall community function. Multilayered interactions thus occur between individual pairs of organisms, and the relative impact can be determined by contextual cues. Host responses to heterotypic communities and impact on host surfaces are also driven by the collective action of the community. Additionally, the range of interspecies interactions can be extended by bacteria utilizing host cells or host diet to indirectly or directly influence the properties of other organisms and the community microenvironment. In contexts where communities transition to a dysbiotic state, their quasi-organismal nature imparts adaptability to nutritional availability and facilitates resistance to immune effectors and, moreover, exploits inflammatory and acidic microenvironments for their persistence.

Community response of soil microorganisms to combined contamination of polycyclic aromatic hydrocarbons and potentially toxic elements in a typical coking plant

Both polycyclic aromatic hydrocarbons (PAHs) and potentially toxic elements (PTEs) of coking industries impose negative effects on the stability of soil ecosystem. Soil microbes are regarded as an essential moderator of biochemical processes and soil remediation, while their responses to PAHs-PTEs combined contamination are largely unknown. In the present study, soil microbial diversity and community composition in the typical coking plant under the chronic co-exposure of PAHs and PTEs were investigated and microbial interaction networks were built to reveal microbial co-occurrence patterns. The results indicated that the concentrations of PAHs in the soil inside the coking plant were significantly higher than those outside the plant. The mean concentration of ∑16PAHs was 2894.4 ng·g-1, which is 5.58 times higher than that outside the plant. The average Hg concentration inside the coking plant was 22 times higher than the background value of Hebei province. The soil fungal community inside the coking plant showed lower richness compared with that of outside community, and there are significant difference in the bacterial and fungal community composition between inside and outside of coking plant (p < 0.01). Predicted contribution of different environmental factors to each dominant species based on random forest identified 20 and 25 biomarkers in bacteria and fungi, respectively, that were highly sensitive to coking plant soil in operation, such as Betaproteobacteria，Sordariomycetes and Dothideomycetes. Bacterial and fungal communities were shaped by the soil chemical properties (pH), PTEs (Hg), and PAHs together in the coking plant soils. Furthermore, the bacterial and fungal interaction patterns were investigated separately or jointly by intradomain and interdomain networks. Competition is the main strategy based on the co-exclusion pattern in fungal community, and the competitive relationship inside the coking plant is more complex than that outside the plant. In contrast, cooperation is the dominant strategy in bacterial networks based on the co-occurrence pattern. The present study provided insights into microbial response strategies and the interactions between bacteria and fungi under long-term combined contamination.

Revisiting fecal metatranscriptomics analyses of macaques with idiopathic chronic diarrhoea with a focus on trichomonad parasites

Trichomonads, anaerobic microbial eukaryotes members of the phylum Parabasalia, are common obligate extracellular symbionts that can lead to pathological or asymptomatic colonization of various mucosal surfaces in a wide range of animal hosts. Results from previous in vitro studies have suggested a number of intriguing mucosal colonization strategies by Trichomonads, notably highlighting the importance of interactions with bacteria. However, in vivo validation is currently lacking. A previous metatranscriptomics study into the cause of idiopathic chronic diarrhoea in macaques reported the presence of an unidentified protozoan parasite related to Trichomonas vaginalis. In this work, we performed a reanalysis of the published data in order to identify the parasite species present in the macaque gut. We also leveraged the information-rich metatranscriptomics data to investigate the parasite behaviour in vivo. Our results indicated the presence of at least 3 genera of Trichomonad parasite; Tetratrichomonas, Pentatrichomonas and Trichomitus, 2 of which had not been previously reported in the macaque gut. In addition, we identified common in vivo expression profiles shared amongst the Trichomonads. In agreement with previous findings for other Trichomonads, our results highlighted a relationship between Trichomonads and mucosal bacterial diversity which could be influential in health and disease.

AtbFinder Diagnostic Test System Improves Optimal Selection of Antibiotic Therapy in Persons with Cystic Fibrosis

Cystic fibrosis (CF) is characterized by mutations of CFTR that lead to increased viscous secretions, bacterial colonization, and recurrent infections. Chronic Pseudomonas aeruginosa infection in persons with CF is associated with progressive and accelerated lung function decline despite aggressive antibiotic treatment. We report the management of respiratory infections in persons with CF with antibiotic therapy that was based on the recommendations of AtbFinder, a novel, rapid, culture-based diagnostic test system that employs a novel paradigm of antibiotic selection. AtbFinder mimics bacterial interactions with antibiotics at concentrations that can be achieved in affected tissues or organs and models conditions of interbacterial interactions within polymicrobial biofilms. This open-label, single-arm, investigator-initiated clinical study was designed to identify the efficacy of antibiotics selected using AtbFinder in persons with CF. Microbiological and clinical parameters were assessed following the change of antibiotic therapy to antibiotics selected with AtbFinder between January 2016 and December 2018 and retrospectively compared with clinical data collected between January 2013 and December 2015. We enrolled 35 persons with CF (33 with chronic P. aeruginosa colonization). Antibiotics selected using AtbFinder resulted in clearance of P. aeruginosa in 81.8% of subsequent cultures, decreased pulmonary exacerbations from 1.21 per patient per annum to 0, and an increase in predicted percent predicted forced expiratory volume in 1 s up to 28.4% from baseline. The number of systemic antibiotic courses used in patients after switching to the AtbFinder-selected therapy was reduced from 355 to 178. These findings describe the superiority of antibiotic regimens selected with AtbFinder compared with routine antimicrobial susceptibility testing.

Structure of co-expression networks of Bifidobacterium species in response to human milk oligosaccharides

Biological systems respond to environmental perturbations and a large diversity of compounds through gene interactions, and these genetic factors comprise complex networks. Experimental information from transcriptomic studies has allowed the identification of gene networks that contribute to our understanding of microbial adaptations. In this study, we analyzed the gene co-expression networks of three Bifidobacterium species in response to different types of human milk oligosaccharides (HMO) using weighted gene co-expression analysis (WGCNA). RNA-seq data obtained from Geo Datasets were obtained for Bifidobacterium longum subsp. Infantis, Bifidobacterium bifidum and Bifidobacterium longum subsp. Longum. Between 10 and 20 co-expressing modules were obtained for each dataset. HMO-associated genes appeared in the modules with more genes for B. infantis and B. bifidum, in contrast with B. longum. Hub genes were identified in each module, and in general they participated in conserved essential processes. Certain modules were differentially enriched with LacI-like transcription factors, and others with certain metabolic pathways such as the biosynthesis of secondary metabolites. The three Bifidobacterium transcriptomes showed distinct regulation patterns for HMO utilization. HMO-associated genes in B. infantis co-expressed in two modules according to their participation in galactose or N-Acetylglucosamine utilization. Instead, B. bifidum showed a less structured co-expression of genes participating in HMO utilization. Finally, this category of genes in B. longum clustered in a small module, indicating a lack of co-expression with main cell processes and suggesting a recent acquisition. This study highlights distinct co-expression architectures in these bifidobacterial genomes during HMO consumption, and contributes to understanding gene regulation and co-expression in these species of the gut microbiome.

Fe-Based Nanomaterial-Induced Root Nodulation Is Modulated by Flavonoids to Improve Soybean (Glycine max) Growth and Quality

Innovative technology to increase efficient nitrogen (N) use while avoiding environmental damages is needed because of the increasing food demand of the rapidly growing global population. Soybean (Glycine max) has evolved a complex symbiosis with N-fixing bacteria that forms nodules to fix N. Herein, foliar application of 10 mg L^-1 Fe₇(PO₄)₆ and Fe₃O₄ nanomaterials (NMs) (Fe-based NMs) promoted soybean growth and root nodulation, thus improving the yield and quality over that of the unexposed control, EDTA-control, and 1 and 5 mg L^-1 NMs. Mechanistically, flavonoids, key signaling molecules at the initial signaling steps in nodulation, were increased by more than 20% upon exposure to 10 mg L^-1 Fe-based NMs, due to enhanced key enzyme (phenylalanine ammonia-lyase, PAL) activity and up-regulation of flavonoid biosynthetic genes (GmPAL, GmC4H, Gm4CL, and GmCHS). Accumulated flavonoids were secreted to the rhizosphere, recruiting rhizobia for colonization. Fe₇(PO₄)₆ NMs increased Allorhizobium by 87.3%, and Fe₃O₄ NMs increased Allorhizobium and Mesorhizobium by 142.2% and 34.9%, leading to increased root nodules by 50.0% and 35.4% over the unexposed control, respectively. Leghemoglobin content was also noticeably improved by 8.2-46.5% upon Fe-based NMs. The higher levels of nodule number and leghemoglobin content resulted in enhanced N content by 15.5-181.2% during the whole growth period. Finally, the yield (pod number and grain biomass) and quality (flavonoids, soluble protein, and elemental nutrients) were significantly increased more than 14% by Fe-based NMs. Our study provides an effective nanoenabled strategy for inducing root nodules to increase N use efficiency, and then both yield and quality of soybean.

Long-term nickel contamination increased soil fungal diversity and altered fungal community structure and co-occurrence patterns in agricultural soils

Nickel (Ni) contamination imposes deleterious effects on the stability of soil ecosystem. Soil fungal community as a crucial moderator of soil remediation and biochemical processes has attracted more and more research interests. In the present study, soil fungal community composition and diversity under long-term Ni contamination were investigated and fungal interaction networks were built to reveal fungal co-occurrence patterns. The results showed that moderate Ni contamination significantly increased fungal diversity and altered fungal community structure. Functional predictions based on FUNGuild suggested that the relative abundance of arbuscular mycorrhizal fungi (AMF) significantly increased at moderate Ni contamination level. Ni contamination strengthened fungal interactions. Keystone taxa at different Ni contamination levels, such as Penicillium at light contamination, were identified, which might have ecological significance in maintaining the stability of fungal community to Ni stress. The present study provided a deeper insight into the effect of long-term Ni contamination on fungal community composition and co-occurrence patterns, and was helpful to further explore ecological risk of Ni contamination in cultivated field.

Biochar-induced variations in crop yield are closely associated with the abundance and diversity of keystone species

Biochar application has widely been used to improve crop yield, but its effectiveness is uncertain. Soil microbial communities may play critical roles, but we lack experimental evidences on the relationships between these communities and crop yield following biochar application. Here, we used cooccurrence networks to demonstrate the importance of ecological clusters (cooccurring taxa of soil microbes including bacteria and fungi) and to identify specific keystone species that were closely connected with the variations in crop yield in a pot experiment. The experiment included two soils (i.e., red soil and yellow-cinnamon soil) for wheat growth, with each soil receiving three biochar application rate. The grain yield in the red soil significantly increased while that in the yellow-cinnamon soil significantly decreased with the biochar application rate. Generally, the grain yield from the two soils showed close correlations with the relative abundance as well as with the diversity of keystone species within major clusters rather than with the soil properties and enzyme activities. This contrasting effectiveness was mainly associated with the enrichment of beneficial and suppression of detrimental keystone species in the red soil and the suppression of beneficial and enrichment of detrimental keystone species in the yellow-cinnamon soil. These species together mainly accounted for the variation in the relative abundance of keystone species within major clusters of each soil, suggesting their potential to affect crop yield following biochar application.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Fungal-Bacterial Cooccurrence Patterns Differ between Arbuscular Mycorrhizal Fungi and Nonmycorrhizal Fungi across Soil Niches

Soil bacteria and fungi are known to form niche-specific communities that differ between actively growing and decaying roots. Yet almost nothing is known about the cross-kingdom interactions that frame these communities and the environmental filtering that defines these potentially friendly or competing neighbors. We explored the temporal and spatial patterns of soil fungal (mycorrhizal and nonmycorrhizal) and bacterial cooccurrence near roots of wild oat grass, Avena fatua, growing in its naturalized soil in a greenhouse experiment. Amplicon sequences of the fungal internal transcribed spacer (ITS) and bacterial 16S rRNA genes from rhizosphere and bulk soils collected at multiple plant growth stages were used to construct covariation-based networks as a step toward identifying fungal-bacterial associations. Corresponding stable-isotope-enabled metagenome-assembled genomes (MAGs) of bacteria identified in cooccurrence networks were used to inform potential mechanisms underlying the observed links. Bacterial-fungal networks were significantly different in rhizosphere versus bulk soils and between arbuscular mycorrhizal fungi (AMF) and nonmycorrhizal fungi. Over 12 weeks of plant growth, nonmycorrhizal fungi formed increasingly complex networks with bacteria in rhizosphere soils, while AMF more frequently formed networks with bacteria in bulk soils. Analysis of network-associated bacterial MAGs suggests that some of the fungal-bacterial links that we identified are potential indicators of bacterial breakdown and consumption of fungal biomass, while others intimate shared ecological niches.IMPORTANCE Soils near living and decomposing roots form distinct niches that promote microorganisms with distinctive environmental preferences and interactions. Yet few studies have assessed the community-level cooccurrence of bacteria and fungi in these soil niches as plant roots grow and senesce. With plant growth, we observed increasingly complex cooccurrence networks between nonmycorrhizal fungi and bacteria in the rhizosphere, while mycorrhizal fungal (AMF) and bacterial cooccurrence was more pronounced in soil further from roots, in the presence of decaying root litter. This rarely documented phenomenon suggests niche sharing of nonmycorrhizal fungi and bacteria, versus niche partitioning between AMF and bacteria; both patterns are likely driven by C substrate availability and quality. Although the implications of species cooccurrence are fiercely debated, MAGs matching the bacterial nodes in our networks possess the functional potential to interact with the fungi that they are linked to, suggesting an ecological significance of fungal-bacterial cooccurrence patterns.

Polymicrobial communities in periodontal disease: Their quasi-organismal nature and dialogue with the host

In health, indigenous polymicrobial communities at mucosal surfaces maintain an ecological balance via both inter-microbial and host-microbial interactions that promote their own and the host's fitness, while preventing invasion by exogenous pathogens. However, genetic and acquired destabilizing factors (including immune deficiencies, immunoregulatory defects, smoking, diet, obesity, diabetes and other systemic diseases, and aging) may disrupt this homeostatic balance, leading to selective outgrowth of species with the potential for destructive inflammation. This process, known as dysbiosis, underlies the development of periodontitis in susceptible hosts. The pathogenic process is not linear but involves a positive-feedback loop between dysbiosis and the host inflammatory response. The dysbiotic community is essentially a quasi-organismal entity, where constituent organisms communicate via sophisticated physical and chemical signals and display functional specialization (eg, accessory pathogens, keystone pathogens, pathobionts), which enables polymicrobial synergy and dictates the community's pathogenic potential or nososymbiocity. In this review, we discuss early and recent studies in support of the polymicrobial synergy and dysbiosis model of periodontal disease pathogenesis. According to this concept, disease is not caused by individual "causative pathogens" but rather by reciprocally reinforced interactions between physically and metabolically integrated polymicrobial communities and a dysregulated host inflammatory response.

Network analysis of nitrate-sensitive oral microbiome reveals interactions with cognitive function and cardiovascular health across dietary interventions

Many oral bacteria reduce inorganic nitrate, a natural part of a vegetable-rich diet, into nitrite that acts as a precursor to nitric oxide, a regulator of vascular tone and neurotransmission. Aging is hallmarked by reduced nitric oxide production with associated detriments to cardiovascular and cognitive function. This study applied a systems-level bacterial co-occurrence network analysis across 10-day dietary nitrate and placebo interventions to test the stability of relationships between physiological and cognitive traits and clusters of co-occurring oral bacteria in older people. Relative abundances of Proteobacteria increased, while Bacteroidetes, Firmicutes and Fusobacteria decreased after nitrate supplementation. Two distinct microbiome modules of co-occurring bacteria, that were sensitive to nitrate supplementation, showed stable relationships with cardiovascular (Rothia-Streptococcus) and cognitive (Neisseria-Haemophilus) indices of health across both dietary conditions. A microbiome module (Prevotella-Veillonella) that has been associated with pro-inflammatory metabolism was diminished after nitrate supplementation, including a decrease in relative abundance of pathogenic Clostridium difficile. These nitrate-sensitive oral microbiome modules are proposed as potential pre- and probiotic targets to ameliorate age-induced impairments in cardiovascular and cognitive health.

Pantoea stewartii WceF is a glycan biofilm-modifying enzyme with a bacteriophage tailspike-like fold

Pathogenic microorganisms often reside in glycan-based biofilms. Concentration and chain length distribution of these mostly anionic exopolysaccharides (EPS) determine the overall biophysical properties of a biofilm and result in a highly viscous environment. Bacterial communities regulate this biofilm state via intracellular small-molecule signaling to initiate EPS synthesis. Reorganization or degradation of this glycan matrix, however, requires the action of extracellular glycosidases. So far, these were mainly described for bacteriophages that must degrade biofilms for gaining access to host bacteria. The plant pathogen Pantoea stewartii (P. stewartii) encodes the protein WceF within its EPS synthesis cluster. WceF has homologs in various biofilm forming plant pathogens of the Erwinia family. In this work, we show that WceF is a glycosidase active on stewartan, the main P. stewartii EPS biofilm component. WceF has remarkable structural similarity with bacteriophage tailspike proteins (TSPs). Crystal structure analysis showed a native trimer of right-handed parallel β-helices. Despite its similar fold, WceF lacks the high stability found in bacteriophage TSPs. WceF is a stewartan hydrolase and produces oligosaccharides, corresponding to single stewartan repeat units. However, compared with a stewartan-specific glycan hydrolase of bacteriophage origin, WceF showed lectin-like autoagglutination with stewartan, resulting in notably slower EPS cleavage velocities. This emphasizes that the bacterial enzyme WceF has a role in P. stewartii biofilm glycan matrix reorganization clearly different from that of a bacteriophage exopolysaccharide depolymerase.

A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?

In order to understand and represent the importance of nodes within networks better, most of the studies that investigate graphs compute the nodes' centrality within their network(s) of interest. In the literature, the most frequent measures used are degree, closeness and/or betweenness centrality, even if other measures might be valid candidates for representing the importance of nodes within networks. The main contribution of this paper is the development of a methodology that allows one to understand, compare and validate centrality indices when studying a particular network of interest. The proposed methodology integrates the following steps: choosing the centrality measures for the network of interest; developing a theoretical taxonomy of these measures; identifying, by means of Principal Component Analysis (PCA), latent dimensions of centrality within the network of interest; verifying the proposed taxonomy of centrality measures; and identifying the centrality measures that best represent the network of interest. Also, we applied the proposed methodology to an existing graph of interest, in our case a real friendship student network. We chose eighteen centrality measures that were developed in SNA and are available and computed in a specific library (CINNA), defined them thoroughly, and proposed a theoretical taxonomy of these eighteen measures. PCA showed the emergence of six latent dimensions of centrality within the student network and saturation of most of the centrality indices on the same categories as those proposed by the theoretical taxonomy. Additionally, the results suggest that indices other than the ones most frequently applied might be more relevant for research on friendship student networks. Finally, the integrated methodology that we propose can be applied to other centrality indices and/or other network types than student graphs.

Environmental heterogeneity determines the ecological processes that govern bacterial metacommunity assembly in a floodplain river system

How diversity is structured has been a central goal of microbial ecology. In freshwater ecosystems, selection has been found to be the main driver shaping bacterial communities. However, its relative importance compared with other processes (dispersal, drift, diversification) may depend on spatial heterogeneity and the dispersal rates within a metacommunity. Still, a decrease in the role of selection is expected with increasing dispersal homogenization. Here, we investigate the main ecological processes modulating bacterial assembly in contrasting scenarios of environmental heterogeneity. We carried out a spatiotemporal survey in the floodplain system of the Paraná River. The bacterioplankton metacommunity was studied using both statistical inferences based on phylogenetic and taxa turnover as well as co-occurrence networks. We found that selection was the main process determining community assembly even at both extremes of environmental heterogeneity and homogeneity, challenging the general view that the strength of selection is weakened due to dispersal homogenization. The ecological processes acting on the community also determined the connectedness of bacterial networks associations. Heterogeneous selection promoted more interconnected networks increasing β-diversity. Finally, spatiotemporal heterogeneity was an important factor determining the number and identity of the most highly connected taxa in the system. Integrating all these empirical evidences, we propose a new conceptual model that elucidates how the environmental heterogeneity determines the action of the ecological processes shaping the bacterial metacommunity.

Bacteria associated with periodontal disease are also increased in health

Background

The objective of this cross-sectional clinical study was to analyze the differences in the microbiome in gingival sulci of adult patients in the presence or absence of chronic periodontitis.

Material and Methods

Patients with or without periodontal disease were included in this cross-sectional study. Subgingival biofilm samples were collected and analyzed by 16S massive pyrosequencing. Functional analyses were also performed.

Results

A total of 15 phyla, 154 genera and 351 species were detected globally. Differences between disease and non-disease samples were observed in all taxonomical levels which suggest functional profile changes in the community. It was found that the main species associated with non-disease samples were reduced in disease but not completely suppressed. Analysis of the functional potential of the biofilms revealed a significantly higher activity related to endocytosis and phosphatidylinositol signaling in the disease group but lower cell adhesion molecules.

Conclusions

Specific differences between health and disease suggest functional profile changes in the community, although bacteria associated with periodontal disease are also increased in health. Transcriptome studies should be conducted to confirm and deepen metabolic dysfunctions.

Key words:Pyrosequencing, 16S rRNA, oral microbiome, periodontitis, functional potential.

A Comprehensive Coexpression Network Analysis in Vibrio cholerae

Cholera is a devastating illness that kills tens of thousands of people annually. Vibrio cholerae, the causative agent of cholera, is an important model organism to investigate both bacterial pathogenesis and the impact of horizontal gene transfer on the emergence and dissemination of new virulent strains. Despite the importance of this pathogen, roughly one-third of V. cholerae genes are functionally unannotated, leaving large gaps in our understanding of this microbe. Through coexpression network analysis of existing RNA sequencing data, this work develops an approach to uncover novel gene-gene relationships and contextualize genes with no known function, which will advance our understanding of V. cholerae virulence and evolution.

Research into the evolution and pathogenesis of Vibrio cholerae has benefited greatly from the generation of high-throughput sequencing data to drive molecular analyses. The steady accumulation of these data sets now provides a unique opportunity for in silico hypothesis generation via coexpression analysis. Here, we leverage all published V. cholerae RNA sequencing data, in combination with select data from other platforms, to generate a gene coexpression network that validates known gene interactions and identifies novel genetic partners across the entire V. cholerae genome. This network provides direct insights into genes influencing pathogenicity, metabolism, and transcriptional regulation, further clarifies results from previous sequencing experiments in V. cholerae (e.g., transposon insertion sequencing [Tn-seq] and chromatin immunoprecipitation sequencing [ChIP-seq]), and expands upon microarray-based findings in related Gram-negative bacteria.

IMPORTANCE Cholera is a devastating illness that kills tens of thousands of people annually. Vibrio cholerae, the causative agent of cholera, is an important model organism to investigate both bacterial pathogenesis and the impact of horizontal gene transfer on the emergence and dissemination of new virulent strains. Despite the importance of this pathogen, roughly one-third of V. cholerae genes are functionally unannotated, leaving large gaps in our understanding of this microbe. Through coexpression network analysis of existing RNA sequencing data, this work develops an approach to uncover novel gene-gene relationships and contextualize genes with no known function, which will advance our understanding of V. cholerae virulence and evolution.

Sediment Microbial Communities and Their Potential Role as Environmental Pollution Indicators in Xuande Atoll, South China Sea

In this study, 39 sediment samples were collected from Qilian Island, Iltis Bank, and Yongxing Island in Xuande Atoll in the South China Sea (SCS), and the microbial community structures and distribution were analyzed. The microbial community was influenced by both natural environmental factors and human activities. The abundance of genera Vibrio and Pseudoalteromonas, which are associated with pathogenicity and pollutant degradation, were significantly higher in Qilian Island than in Yongxing Island and Iltis Bank, suggesting possible contamination of Qilian Island area through human activities. Pathogenic or typical pollutants-degrading bacteria were found to be negatively correlated with most of the commonly occurring bacterial populations in marine sediment, and these bacteria were more likely to appear in the sediment of deep water layer. This co-occurrence pattern may be due to bacterial adaptation to environmental changes such as depth and contaminations from human activities, including garbage disposal, farming, and oil spills from ships. The findings of this study could help in understanding the potential influences of human activities on the ecosystem at the microbial level.

Electromagnetic fields for biofouling mitigation in reclaimed water distribution systems

Biofouling is ubiquitous in reclaimed water distribution systems and causes various industrial, economic, and health issues. This paper investigated the anti-biofouling efficacy of electromagnetic fields (EMFs) for agricultural emitters used for two types of reclaimed water. 16S rRNA gene sequencing and X-ray diffraction were applied to determine the microbial communities and mineral compositions in biofilms. The obtained results revealed that EMF treatment significantly changed the bacterial communities and reduced their diversities in biofilm by affecting water quality parameters. Network analysis results indicated that EMFs were detrimental to the co-occurrence patterns of mutualistic relationships among bacterial species, destroyed the connectivity and complexity of the networks, and inhibited biofilm formation [decreased total biomass and extracellular polymeric substance (EPS) content]. EMF treatment could also decrease the deposition of mineral precipitates, reducing the carbonate and silicate content in biofilm. The decrease of EPS content appeared to reduce biofilm-induced mineral crystallization, while the ion precipitations accelerated by EMFs caused an erosive effect on biofilm. The results demonstrated that EMF treatment is an effective, chemical-free, and anti-biofouling treatment method with great potential for biofouling control in reclaimed water distribution systems.

Alteration of the gut microbiome in first-episode drug-naïve and chronic medicated schizophrenia correlate with regional brain volumes

The human gut microbiome plays an important role in the basic neurodevelopmental processes of the central nervous system and has been implicated in several neuropsychiatric disorders. However, the connection between the gut microbiome and the underlying pathogenesis of schizophrenia (SCZ) is poorly defined. Here we analyzed the faecal samples from 40 first-episode drug-naïve SCZ (FSCZ) patients, 85 chronically antipsychotic-treated SCZ (TSCZ) patients and 69 healthy controls (HCs) using 16S rRNA gene sequence to determine whether the alterations of the gut microbiome were associated with SCZ or antipsychotic treatment. In addition, we acquired the T1-weighted brain imaging data by using structural magnetic resonance imaging to test whether microbial composition correlated with structural brain signatures. Our analyses revealed low microbiome alpha-diversity indexes in TSCZ patients but not in FSCZ patients as compared to HCs. Importantly, both FSCZ and TSCZ patients had distinct changes in gut microbial composition at certain taxa including Christensenellaceae, Enterobacteriaceae, Pasteurellaceae, Turicibacteraceae at the family level and Escherichia at genus level as compared to HCs. We also found significant disturbances of gut microbial composition in TSCZ versus FSCZ patients (eg. Enterococcaceae and Lactobacillaceae). Most interestingly, our exploratory analyses found specific SCZ-associated microbiota to be correlated with the right middle frontal gyrus (rMFG) volume which was aberrant in SCZ patients. Our findings extend prior work and suggest a possible link between the gut microbiome and brain structure which may be implicated in the pathology of SCZ.

Odor emission and microbial community succession during biogas residue composting covered with a molecular membrane

A membrane-covered composting system was used to investigate the odor emission and microbial community succession during biogas residue composting. Results showed that in comparison with the control (CK) group, the NH₃ and H₂S emissions outside the membrane of the membrane-covered (CT) group decreased by 58.64% and 38.13%, respectively. The nitrogen preservation rate of the CT group was increased by 17.27% in comparison with the CK group. Moreover, the ammonium nitrogen and nitrate nitrogen contents of the CT group were 37.68% and 11.77% higher than those of the CK group, respectively. Microbial analysis showed that the average abundance and co-occurrence rate of ammonification bacteria dominated by Pseudomonas and Bacillus in the CT group were lower than those in the CK group, and the abundance of anaerobic sulfate-reducing bacteria (SRB) dominated by Desulfovibrio in the CT group was higher than that in the CK group.

Rhizoplane Bacteria and Plant Species Co-determine Phosphorus-Mediated Microbial Legacy Effect

Much effort has been directed toward increasing the availability of soil residual phosphorus (P). However, little information is available for the P fertilization-induced biotic P legacy and its mediation of plant P uptake. We collected microbial inocula from a monoculture maize field site with a 10-year P-fertilization history. A greenhouse experiment was conducted to investigate whether bacterial communities, as a result of different P-fertilization history (nil P, 33 and/or 131 kg P kg ha-1 yr-1), affected the growth of a conspecific (maize) or heterospecific (clover) plant, at two levels of current P application (5 and 30 mg P kg-1 soil; P5 and P30). Deep amplicon sequencing (16S rRNA) was used to determine the maize and clover root-associated bacterial microbiome in different rhizocompartments (rhizoplane, rhizosphere, bulk soil). For both maize and clover, rhizocompartment and host identity were the dominant factors shaping bacterial assemblages, followed by P supply level and the inoculum effect was smallest. Bacterial operational taxonomic unit (OTU) numbers decreased from bulk soil to rhizoplane, whilst specific OTUs were enriched from bulk soil to rhizoplane. A clear hierarchical habitat filtering of bacterial communities was observed in the rhizoplane of the two plant species. The functional prediction of dominant bacterial taxa in the rhizoplane differed between clover and maize, and clover microbiota were more closely associated with P metabolism and maize with carbon cycling. More connected and complex interactions were observed in the clover rhizoplane compared to maize. The microbial legacy effect caused by long-term P fertilization is overridden by host identity and rhizocompartment. Our results highlight the importance of crop diversification in improving P efficiency. The fine-tuning of rhizosphere microbiome in host metabolism indicates that the functions of microbial communities should be integrated into P management to increase P use efficiency and sustainable food production.

Aggregatibacter actinomycetemcomitans colonization and persistence in a primate model

Aggregatibacter actinomycetemcomitans is associated with aggressive periodontitis resulting in premature tooth loss in adolescents. Tooth adherence and biofilm persistence are prerequisites for survival in the oral domain. Here, using a rhesus monkey model, 16S rRNA sequencing, and weighted network analysis, we assessed colonization of A. actinomycetemcomitans variants and ascertained microbial interactions in biofilm communities. Variants in A. actinomycetemcomitans leukotoxin (ltx) were created, labeled, inoculated, and compared with their progenitor strain for in vivo colonization. Samples of tooth-related plaque were assessed for colonization at baseline and after debridement and inoculation of labeled strains. Null, minimal, and hyper-Ltx-producing strains were created and assessed for hydroxyapatite binding and biofilm formation in vitro. Ltx-hyperproducing strains colonized with greater prevalence and at higher levels than wild type or ltx mutants (P = 0.05). Indigenous and inoculated A. actinomycetemcomitans strains that attached were associated with lactate-producing species (i.e., Leptotrichia, Abiotrophia, and Streptoccocci). A. actinomycetemcomitans was found at 0.13% of the total flora at baseline and at 0.05% 4 wk after inoculation. In vivo data were supported by in vitro results. We conclude that hyper-Ltx production affords these strains with an attachment advantage providing a foothold for competition with members of the indigenous microbiota. Increased attachment can be linked to ltx gene expression and up-regulation of adherence-associated genes. Growth of attached A. actinomycetemcomitans in vivo was enhanced by lactate availability due to consorting species. These associations provide A. actinomycetemcomitans with the constituents required for its colonization and survival in the complex and competitive oral environment.

Japanese subgingival microbiota in health vs disease and their roles in predicted functions associated with periodontitis

The present study aimed to identify and compare the microbial signatures between periodontally healthy and periodontitis subjects using 454 sequences of 16S rRNA genes. Subgingival plaque samples were collected from ten periodontally healthy subjects and ten matched chronic periodontitis patients. Bacterial DNA was extracted and next-generation sequencing of 16S rRNA genes was performed. The microbial composition differed between healthy subjects and periodontitis patients at all phylogenetic levels. Particularly, 16 species, including Lautropia mirabilis and Neisseria subflava predominated in healthy subjects, whereas nine species, including Porphyromonas gingivalis and Filifactor alocis predominated in periodontitis. UniFrac, a principal coordinate and network analysis, confirmed distinct community profiles in healthy subjects and periodontitis patients. Using predicted function profiling, pathways involved in phenylpropanoid, GPI-anchor biosynthesis, and metabolism of alanine, arginine, aspartate, butanoate, cyanoamino acid, fatty acid, glutamate, methane, proline, and vitamin B6 were significantly over-represented in periodontitis patients. These results highlight the oral microbiota alterations in microbial composition in periodontitis and suggest the genes and metabolic pathways associated with health and periodontitis. Our findings help to further elucidate microbial composition and interactions in health and periodontitis.

Detecting interaction networks in the human microbiome with conditional Granger causality

Human microbiome research is rife with studies attempting to deduce microbial correlation networks from sequencing data. Standard correlation and/or network analyses may be misleading when taken as an indication of taxon interactions because "correlation is neither necessary nor sufficient to establish causation"; environmental filtering can lead to correlation between non-interacting taxa. Unfortunately, microbial ecologists have generally used correlation as a proxy for causality although there is a general consensus about what constitutes a causal relationship: causes both precede and predict effects. We apply one of the first causal models for detecting interactions in human microbiome samples. Specifically, we analyze a long duration, high resolution time series of the human microbiome to decipher the networks of correlation and causation of human-associated microbial genera. We show that correlation is not a good proxy for biological interaction; we observed a weak negative relationship between correlation and causality. Strong interspecific interactions are disproportionately positive, whereas almost all strong intraspecific interactions are negative. Interestingly, intraspecific interactions also appear to act at a short timescale causing vast majority of the effects within 1-3 days. We report how different taxa are involved in causal relationships with others, and show that strong interspecific interactions are rarely conserved across two body sites whereas strong intraspecific interactions are much more conserved, ranging from 33% between the gut and right-hand to 70% between the two hands. Therefore, in the absence of guiding assumptions about ecological interactions, Granger causality and related techniques may be particularly helpful for understanding the driving factors governing microbiome composition and structure.

Karst rocky desertification progress: Soil calcium as a possible driving force

Karst rocky desertification is a severe irreversible ecosystem failure. The karst ecosystem is so fragile that it is vulnerable to environmental changes, degrading into rocky desertification. Prior studies revealed the potential connections between the soil bacterial community, the edaphic properties and the aboveground vegetation cover in the karst ecosystem. However, how these three elements affect each other and work together in propelling in the karst rocky desertification progress largely remains unexplored. To answer this question, we monitored the bacterial community variations in soils sampled from multiple sites at a successional karst rocky desertification region by sequencing the 16S rRNA V3-V4 regions. Overall, we detected 34 bacterial phyla in the karst soils, of which Proteobacteria, Actinobacteria, and Acidobacteria are the most abundant. Network analysis of the bacterial community- vegetation-edaphic property-vegetation interactions identified 6 bacterial herds that had significant correlation with soil Ca²⁺ and available phosphorus change during vegetation degradation. Further functional simulation of these bacterial herds unveiled the change of Ca²⁺ and available phosphorus might disturb the soil carbon and nitrogen metabolism, and thus weakened soil quality. In summary, we hypothesized a calcium-driven bacterial response mechanism in the karst rocky desertification progress.

Increased virulence of the oral microbiome in oral squamous cell carcinoma revealed by metatranscriptome analyses

Oral squamous cell carcinoma (OSCC) is the most prevalent and most commonly studied oral cancer. However, there is a void regarding the role that the oral microbiome may play in OSCC. Although the relationship between microbial community composition and OSCC has been thoroughly investigated, microbial profiles of the human microbiome in cancer are understudied. Here we performed a small pilot study of community-wide metatranscriptome analysis to profile mRNA expression in the entire oral microbiome in OSCC to reveal molecular functions associated with this disease. Fusobacteria showed a statistically significantly higher number of transcripts at tumour sites and tumour-adjacent sites of cancer patients compared to the healthy controls analysed. Regardless of the community composition, specific metabolic signatures were consistently found in disease. Activities such as iron ion transport, tryptophanase activity, peptidase activities and superoxide dismutase were over-represented in tumour and tumour-adjacent samples when compared to the healthy controls. The expression of putative virulence factors in the oral communities associated with OSCC showed that activities related to capsule biosynthesis, flagellum synthesis and assembly, chemotaxis, iron transport, haemolysins and adhesins were upregulated at tumour sites. Moreover, activities associated with protection against reactive nitrogen intermediates, chemotaxis, flagellar and capsule biosynthesis were also upregulated in non-tumour sites of cancer patients. Although they are preliminary, our results further suggest that Fusobacteria may be the leading phylogenetic group responsible for the increase in expression of virulence factors in the oral microbiome of OSCC patients.

Untargeted metabolic profiling reveals geography as the strongest predictor of metabolic phenotypes of a cosmopolitan weed

Plants produce a multitude of metabolites that contribute to their fitness and survival and play a role in local adaptation to environmental conditions. The effects of environmental variation are particularly well studied within the genus Plantago; however, previous studies have largely focused on targeting specific metabolites. Studies exploring metabolome-wide changes are lacking, and the effects of natural environmental variation and herbivory on the metabolomes of plants growing in situ remain unknown. An untargeted metabolomic approach using ultra-high-performance liquid chromatography-mass spectrometry, coupled with variation partitioning, general linear mixed modeling, and network analysis was used to detect differences in metabolic phenotypes of Plantago major in fifteen natural populations across Denmark. Geographic region, distance, habitat type, phenological stage, soil parameters, light levels, and leaf area were investigated for their relative contributions to explaining differences in foliar metabolomes. Herbivory effects were further investigated by comparing metabolomes from damaged and undamaged leaves from each plant. Geographic region explained the greatest number of significant metabolic differences. Soil pH had the second largest effect, followed by habitat and leaf area, while phenological stage had no effect. No evidence of the induction of metabolic features was found between leaves damaged by herbivores compared to undamaged leaves on the same plant. Differences in metabolic phenotypes explained by geographic factors are attributed to genotypic variation and/or unmeasured environmental factors that differ at the regional level in Denmark. A small number of specialized features in the metabolome may be involved in facilitating the success of a widespread species such as Plantago major into such wide range of environmental conditions, although overall resilience in the metabolome was found in response to environmental parameters tested. Untargeted metabolomic approaches have great potential to improve our understanding of how specialized plant metabolites respond to environmental change and assist in adaptation to local conditions.

Microbial community networks associated with variations in community respiration rates during upwelling in nearshore Monterey Bay, California

Respiration of organic material is a central process in the global carbon (C) cycle catalysed by diverse microbial communities. In the coastal ocean, upwelling can drive variation in both community respiration (CR) and the microbial community, but linkages between the two are not well-understood. We measured CR rates and analysed microbial dynamics via 16S rRNA gene sequencing, to assess whether CR correlated with upwelling irrespective of changes in the microbial community, or if the particular microbial community present was a factor in explaining variations in CR. CR varied significantly over time as a function of temperature, dissolved oxygen (DO) and chlorophyll-all of which are altered by upwelling-but also varied with a 'subnetwork' (i.e., a group of microbial taxa that covaried with one another) of the whole community. One subnetwork was associated with higher CR and warmer temperatures, while another was associated with lower CR and DO. Our results suggest that CR in the coastal ocean varies with both environmental variables, and a portion of the microbial community that is not directly correlated with upwelling intensity.

The P/N (Positive-to-Negative Links) Ratio in Complex Networks-A Promising In Silico Biomarker for Detecting Changes Occurring in the Human Microbiome

Relatively little progress in the methodology for differentiating between the healthy and diseased microbiomes, beyond comparing microbial community diversities with traditional species richness or Shannon index, has been made. Network analysis has increasingly been called for the task, but most currently available microbiome datasets only allows for the construction of simple species correlation networks (SCNs). The main results from SCN analysis are a series of network properties such as network degree and modularity, but the metrics for these network properties often produce inconsistent evidence. We propose a simple new network property, the P/N ratio, defined as the ratio of positive links to the number of negative links in the microbial SCN. We postulate that the P/N ratio should reflect the balance between facilitative and inhibitive interactions among microbial species, possibly one of the most important changes occurring in diseased microbiome. We tested our hypothesis with five datasets representing five major human microbiome sites and discovered that the P/N ratio exhibits contrasting differences between healthy and diseased microbiomes and may be harnessed as an in silico biomarker for detecting disease-associated changes in the human microbiome, and may play an important role in personalized diagnosis of the human microbiome-associated diseases.

Detection of stable community structures within gut microbiota co-occurrence networks from different human populations

Microbes in the gut microbiome form sub-communities based on shared niche specialisations and specific interactions between individual taxa. The inter-microbial relationships that define these communities can be inferred from the co-occurrence of taxa across multiple samples. Here, we present an approach to identify comparable communities within different gut microbiota co-occurrence networks, and demonstrate its use by comparing the gut microbiota community structures of three geographically diverse populations. We combine gut microbiota profiles from 2,764 British, 1,023 Dutch, and 639 Israeli individuals, derive co-occurrence networks between their operational taxonomic units, and detect comparable communities within them. Comparing populations we find that community structure is significantly more similar between datasets than expected by chance. Mapping communities across the datasets, we also show that communities can have similar associations to host phenotypes in different populations. This study shows that the community structure within the gut microbiota is stable across populations, and describes a novel approach that facilitates comparative community-centric microbiome analyses.

The subgingival periodontal microbiota of the aging mouth

Different mechanisms have been hypothesized to explain the increase in prevalence and severity of periodontitis in older adults, including shifts in the periodontal microbiota. However, the actual impact of aging on the composition of subgingival biofilms remains unclear. In the present article, we provide an overview of the composition of the subgingival biofilm in older adults and the potential effects of age on the oral microbiome. In particular, this review covers the following topics: (i) the oral microbiota of an aging mouth; (ii) the effects of age and time on the human oral microbiome; (iii) the potential impact of inflammaging and immunosenescence in the host-oral microbiota interactions; and (iv) the relationship of the aging oral microbiota and Alzheimer's disease. Finally, we present analyses of data compiled from large clinical studies that evaluated the subgingival microbiota of periodontally healthy subjects and patients with periodontitis from a wide age spectrum (20-83 years of age).

Structure of association networks in food bacterial communities

The structure of microbial association networks was investigated for seventeen studies on food bacterial communities using the CoNet app. The results were compared with those for host and environmental microbiomes. Microbial association networks of food bacterial communities shared several properties with those of host microbiomes, although they were less complex and lacked a scale-free, small world structure that is characteristic of environmental microbial communities. This may depend on both the initial contamination pattern, whose main source is the raw material microbiome, and on the copiotrophic nature of food environments, with lack of well defined, specific niches. The selective factors which are characteristic of fermentation and spoilage drastically simplified microbial association networks and showed the emergence of negative hubs. Co-presence and mutual exclusion networks had a radically different structure, with high clustering coefficient in the first and high heterogeneity in the latter. Node properties (degree, positive degree, betweenness centrality, abundance) can be combined in plots, which allow a rapid identification of hub species. The combined use of three network inference tools (CoNet, SparCC, and SPIEC-EASI) confirmed that microbial association network detection is method specific, but several coherent copresence or mutual exclusion relationships were detected by at least two different methods.

Response of soil microbial communities and microbial interactions to long-term heavy metal contamination

Due to the persistence of metals in the ecosystem and their threat to all living organisms, effects of heavy metal on soil microbial communities were widely studied. However, little was known about the interactions among microorganisms in heavy metal-contaminated soils. In the present study, microbial communities in Non (CON), moderately (CL) and severely (CH) contaminated soils were investigated through high-throughput Illumina sequencing of 16s rRNA gene amplicons, and networks were constructed to show the interactions among microbes. Results showed that the microbial community composition was significantly, while the microbial diversity was not significantly affected by heavy metal contamination. Bacteria showed various response to heavy metals. Bacteria that positively correlated with Cd, e.g. Acidobacteria_Gp and Proteobacteria_thiobacillus, had more links between nodes and more positive interactions among microbes in CL- and CH-networks, while bacteria that negatively correlated with Cd, e.g. Longilinea, Gp2 and Gp4 had fewer network links and more negative interactions in CL and CH-networks. Unlike bacteria, members of the archaeal domain, i.e. phyla Crenarchaeota and Euryarchaeota, class Thermoprotei and order Thermoplasmatales showed only positive correlation with Cd and had more network interactions in CH-networks. The present study indicated that (i) the microbial community composition, as well as network interactions was shift to strengthen adaptability of microorganisms to heavy metal contamination, (ii) archaea were resistant to heavy metal contamination and may contribute to the adaption to heavy metals. It was proposed that the contribution might be achieved either by improving environment conditions or by cooperative interactions.

Trios-promising in silico biomarkers for differentiating the effect of disease on the human microbiome network

Recent advances in the HMP (human microbiome project) research have revealed profound implications of the human microbiome to our health and diseases. We postulated that there should be distinctive features associated with healthy and/or diseased microbiome networks. Following Occam's razor principle, we further hypothesized that triangle motifs or trios, arguably the simplest motif in a complex network of the human microbiome, should be sufficient to detect changes that occurred in the diseased microbiome. Here we test our hypothesis with six HMP datasets that cover five major human microbiome sites (gut, lung, oral, skin, and vaginal). The tests confirm our hypothesis and demonstrate that the trios involving the special nodes (e.g., most abundant OTU or MAO, and most dominant OTU or MDO, etc.) and interactions types (positive vs. negative) can be a powerful tool to differentiate between healthy and diseased microbiome samples. Our findings suggest that 12 kinds of trios (especially, dominantly inhibitive trio with mixed strategy, dominantly inhibitive trio with pure strategy, and fully facilitative strategy) may be utilized as in silico biomarkers for detecting disease-associated changes in the human microbiome, and may play an important role in personalized precision diagnosis of the human microbiome associated diseases.

Inferring microbial interactions in thermophilic and mesophilic anaerobic digestion of hog waste

Anaerobic digestion (AnD) is a microbiological process that converts organic waste materials into biogas. Because of its high methane content, biogas is a combustible energy source and serves as an important environmental technology commonly used in the management of animal waste generated on large animal farms. Much work has been done on hardware design and process engineering for the generation of biogas. However, little is known about the complexity of the microbiology in this process. In particular, how microbes interact in the digester and eventually breakdown and convert organic matter into biogas is still regarded as a "black box." We used 16S rRNA sequencing as a tool to study the microbial community in laboratory hog waste digesters under tightly controlled conditions, and systematically unraveled the distinct interaction networks of two microbial communities from mesophilic (MAnD) and thermophilic anaerobic digestion (TAnD). Under thermophilic conditions, the well-known association between hydrogen-producing bacteria, e.g., Ruminococcaceae and Prevotellaceae, and hydrotrophic methanogens, Methanomicrobiaceae, was reverse engineered by their interactive topological niches. The inferred interaction network provides a sketch enabling the determination of microbial interactive relationships that conventional strategy of finding differential taxa was hard to achieve. This research is still in its infancy, but it can help to depict the dynamics of microbial ecosystems and to lay the groundwork for understanding how microorganisms cohabit in the anaerobic digester.

The Underlying Ecological Processes of Gut Microbiota Among Cohabitating Retarded, Overgrown and Normal Shrimp

Increasing evidence of tight links among the gut microbiota, obesity, and host health has emerged, but knowledge of the ecological processes that shape the variation in microbial assemblages across growth rates remains elusive. Moreover, inadequately control for differences in factors that profoundly affect the gut microbial community, hampers evaluation of the gut microbiota roles in regulating growth rates. To address this gap, we evaluated the composition and ecological processes of the gut bacterial community in cohabitating retarded, overgrown, and normal shrimps from identically managed ponds. Gut bacterial community structures were distinct (P = 0.0006) among the shrimp categories. Using a structural equation modeling (SEM), we found that changes in the gut bacterial community were positively related to digestive activities, which subsequently affected shrimp growth rate. This association was further supported by intensified interspecies interaction and enriched lineages with high nutrient intake efficiencies in overgrown shrimps. However, the less phylogenetic clustering of gut microbiota in overgrown and retarded subjects may offer empty niches for pathogens invasion, as evidenced by higher abundances of predicted functional pathways involved in disease infection. Given no differences in biotic and abiotic factors among the cohabitating shrimps, we speculated that the distinct gut community assembly could be attributed to random colonization in larval shrimp (e.g., priority effects) and that an altered microbiota could be a causative factor in overgrowth or retardation in shrimp. To our knowledge, this is the first study to provide an integrated overview of the direct roles of gut microbiota in shaping shrimp growth rate and the underlying ecological mechanisms.

Competitive Adsorption of Polyelectrolytes onto and into Pellicle-Coated Hydroxyapatite Investigated by QCM-D and Force Spectroscopy

A current effort in preventive dentistry is to inhibit surface attachment of bacteria using antibacterial polymer coatings on the tooth surface. For the antibacterial coatings, the physisorption of anionic and cationic polymers directly onto hydroxyapatite (HA) and saliva-treated HA surfaces was studied using quartz crystal microbalance, force spectroscopy, and atomic force microscopy. First, single species adsorption is shown to be stronger on HA surfaces than on silicon oxide surfaces for all polymers (i.e., Gantrez, sodium hyaluronate (NaHa), and poly(allylamine-co-allylguanidinium) (PAA-G75)). It is observed through pH dependence of Gantrez, NaHa, and PAA-G75 adsorption on HA surfaces that anionic polymers swell at high pH and collapse at low pH, whereas cationic polymers behave in the opposite fashion. Thicknesses of Gantrez, NaHa, and PAA-G75 are 52 nm (46 nm), 35 nm (11 nm), and 6 nm (54 nm) at pH 7 (3.5), respectively. Second, absorption of charged polymer is followed by absorption of the oppositely charged polymer. Upon exposure of the anionic polymer layers, Gantrez and NaHa, to the cationic polymer, PAA-G75, films collapse from 52 to 8 nm and 35 to 11 nm, respectively. This decrease in film thickness is attributed to the electrostatic cross-linking between anionic and cationic polymers. Third, for HA surfaces pretreated with artificial saliva (AS), the total thickness decreases from 25 to 16 nm upon exposure to PAA-G75. Force spectroscopy is used to further investigate the PAA-G75/AS coating. The results show that the interaction between a negatively charged colloidal bead and the AS surface is strongly repulsive, whereas PAA-G75/AS is attractive but varies across the surface. Additionally, AFM studies show that AS/HA is smooth with a RMS roughness of 1.7 nm, and PAA-G75-treated AS/HA is rough (RMS roughness of 5.4 nm) with patches of polymer distributed across the surface with an underlying coating. The high roughness of PAA-G75 treated AS/HA is attributed to the strong adsorption of the relatively small PAA-G75 onto the heterogeneously distributed negatively charged AS surface. In addition, uptake of PAA-G75 by pellicle layer (saliva-treated HA surface) is observed, and the adsorbed amount of PAA-G75 on/into pellicle layer is ∼2 times more than that on/into AS layer. These studies show that polymer adsorption onto HA and saliva-coated HA depends strongly on the polymer type and size and that there is an electrostatic interaction between polymer and saliva and/or oppositely charged polymers that stabilizes the coatings on HA. Lastly, assessing the viability of the adherent bacteria collected from the PAA-G75-coated surfaces showed a significant reduction (∼93%) in bacterial viability when compared to bacteria collected from untreated and Gantrez-coated HA. These results suggest the potential antimicrobial activity of PAA-G75.

Genome composition and phylogeny of microbes predict their co-occurrence in the environment

The genomic information of microbes is a major determinant of their phenotypic properties, yet it is largely unknown to what extent ecological associations between different species can be explained by their genome composition. To bridge this gap, this study introduces two new genome-wide pairwise measures of microbe-microbe interaction. The first (genome content similarity index) quantifies similarity in genome composition between two microbes, while the second (microbe-microbe functional association index) summarizes the topology of a protein functional association network built for a given pair of microbes and quantifies the fraction of network edges crossing organismal boundaries. These new indices are then used to predict co-occurrence between reference genomes from two 16S-based ecological datasets, accounting for phylogenetic relatedness of the taxa. Phylogenetic relatedness was found to be a strong predictor of ecological associations between microbes which explains about 10% of variance in co-occurrence data, but genome composition was found to be a strong predictor as well, it explains up to 4% the variance in co-occurrence when all genomic-based indices are used in combination, even after accounting for evolutionary relationships between the species. On their own, the metrics proposed here explain a larger proportion of variance than previously reported more complex methods that rely on metabolic network comparisons. In summary, results of this study indicate that microbial genomes do indeed contain detectable signal of organismal ecology, and the methods described in the paper can be used to improve mechanistic understanding of microbe-microbe interactions.

It is still unknown to what extent ecological associations between microbes, as measured by co-occurrence of different taxa in 16S rRNA surveys, can be explained, or predicted, using composition and structure of microbial genomes alone. Here I introduce two new genome-wide, pairwise indices for quantifying the propensity of microbial species to interact with each other. The first measure quantifies similarity in genome composition between two microbes. The second measure summarizes the topology of a protein functional association network built for a given pair of microbes and quantifies the fraction of network edges crossing organismal boundaries. I then study the ability of two newly proposed and two previously reported indices to explain variation in microbial co-occurrence. All four measures are significantly correlated with co-occurrence of microbes even when accounting for evolutionary relationships between the species. One of the newly developed indices outperforms previously proposed ones and explains up to 3.5% of the variance in co-occurrence. In summary, the indices described here are able to detect ecological associations between species using only their genomic information; however, additional methods are needed to provide more reliable genomic tools for microbial ecology.

Network analysis reveals seasonal variation of co-occurrence correlations between Cyanobacteria and other bacterioplankton

Association network approaches have recently been proposed as a means for exploring the associations between bacterial communities. In the present study, high-throughput sequencing was employed to investigate the seasonal variations in the composition of bacterioplankton communities in six eutrophic urban lakes of Nanjing City, China. Over 150,000 16S rRNA sequences were derived from 52 water samples, and correlation-based network analyses were conducted. Our results demonstrated that the architecture of the co-occurrence networks varied in different seasons. Cyanobacteria played various roles in the ecological networks during different seasons. Co-occurrence patterns revealed that members of Cyanobacteria shared a very similar niche and they had weak positive correlations with other phyla in summer. To explore the effect of environmental factors on species-species co-occurrence networks and to determine the most influential environmental factors, the original positive network was simplified by module partitioning and by calculating module eigengenes. Module eigengene analysis indicated that temperature only affected some Cyanobacteria; the rest were mainly affected by nitrogen associated factors throughout the year. Cyanobacteria were dominant in summer which may result from strong co-occurrence patterns and suitable living conditions. Overall, this study has improved our understanding of the roles of Cyanobacteria and other bacterioplankton in ecological networks.

Derivation and Validation of the Periodontal and Tooth Profile Classification System for Patient Stratification

BACKGROUND

The goal of this study is to use bioinformatics tools to explore identification and definition of distinct periodontal and tooth profile classes (PPCs/TPCs) among a cohort of individuals by using detailed clinical measures at the tooth level, including both periodontal measurements and tooth loss.

METHODS

Full-mouth clinical periodontal measurements (seven clinical parameters) from 6,793 individuals from the Dental Atherosclerosis Risk in Communities Study (DARIC) were used to identify PPC. A custom latent class analysis (LCA) procedure was developed to identify clinically distinct PPCs and TPCs. Three validation cohorts were used: NHANES (2009 to 2010 and 2011 to 2012) and the Piedmont Study population (7,785 individuals).

RESULTS

The LCA method identified seven distinct periodontal profile classes (PPCs A to G) and seven distinct tooth profile classes (TPCs A to G) ranging from health to severe periodontal disease status. The method enabled identification of classes with common clinical manifestations that are hidden under the current periodontal classification schemas. Class assignment was robust with small misclassification error in the presence of missing data. The PPC algorithm was applied and confirmed in three distinct cohorts.

CONCLUSIONS

The findings suggest PPC and TPC using LCA can provide robust periodontal clinical definitions that reflect disease patterns in the population at an individual and tooth level. These classifications can potentially be used for patient stratification and thus provide tools for integrating multiple datasets to assess risk for periodontitis progression and tooth loss in dental patients.

Distinct interacting core taxa in co-occurrence networks enable discrimination of polymicrobial oral diseases with similar symptoms

Polymicrobial diseases, which can be life threatening, are caused by the presence and interactions of multiple microbes. Peri-implantitis and periodontitis are representative polymicrobial diseases that show similar clinical symptoms. To establish a means of differentiating between them, we compared microbial species and functional genes in situ by performing metatranscriptomic analyses of peri-implantitis and periodontitis samples obtained from the same subjects (n = 12 each). Although the two diseases differed in terms of 16S rRNA-based taxonomic profiles, they showed similarities with respect to functional genes and taxonomic and virulence factor mRNA profiles. The latter—defined as microbial virulence types—differed from those of healthy periodontal sites. We also showed that networks based on co-occurrence relationships of taxonomic mRNA abundance (co-occurrence networks) were dissimilar between the two diseases. Remarkably, these networks consisted mainly of taxa with a high relative mRNA-to-rRNA ratio, with some showing significant co-occurrence defined as interacting core taxa, highlighting differences between the two groups. Thus, peri-implantitis and periodontitis have shared as well as distinct microbiological characteristics. Our findings provide insight into microbial interactions in polymicrobial diseases with unknown etiologies.