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Results for “microbial interaction network”

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At least 19 recordsLinked to original sources

PubMed2026

Decoding microbial metabolic complementarity from individual traits to community structuring.

A fundamental challenge in microbiome research lies in elucidating the functional capacity of microbial communities through community membership and genomic data. As community structuring and emergent functional traits are determined by bacterial community metabolic networks, it is important to gain insights into the principles that govern bacteria-bacteria interactions. Here, we applied an integrative framework linking individual strain-level traits to community structuring in a simplified synthetic bacterial community (SSC8) that promotes the growth of ungrafted watermelon. By combining mono- and coculture assays with genome-scale metabolic modeling and metabolomic profiling of spent media, we characterized directional interactions and resource dependencies among community members. Our findings show that positive interactions dominated the community network, accounting for 55% of all pairwise combinations, indicating a high prevalence of growth-promoting effects among strains. Genome-scale metabolic modeling showed that functional divergence among strains enhanced the potential for metabolic complementarity as phylogenetic distance increased. Integrating metabolic modeling with metabolomics further suggested that Pseudomonas azotifigens Q6 not only benefited from all other community members, but also exhibited mutualistic interactions with the other three strains, with metabolite exchange involving compounds such as L-lysine and L-cysteine. Pseudomonas azotifigens Q6 acted as an important driver of community composition by affecting the abundance of several other consortium members in vitro. These findings highlight the role of metabolic complementarity in driving community structuring by promoting selective persistence of specific strains. Our work provides mechanistic insights into microbial interaction networks in vitro and offers a conceptual foundation for the rational design of functionally robust and plant-beneficial microbiomes.

Bacteria↗

PubMed2005

Interaction network containing conserved and essential protein complexes in Escherichia coli.

Proteins often function as components of multi-subunit complexes. Despite its long history as a model organism, no large-scale analysis of protein complexes in Escherichia coli has yet been reported. To this end, we have targeted DNA cassettes into the E. coli chromosome to create carboxy-terminal, affinity-tagged alleles of 1,000 open reading frames (approximately 23% of the genome). A total of 857 proteins, including 198 of the most highly conserved, soluble non-ribosomal proteins essential in at least one bacterial species, were tagged successfully, whereas 648 could be purified to homogeneity and their interacting protein partners identified by mass spectrometry. An interaction network of protein complexes involved in diverse biological processes was uncovered and validated by sequential rounds of tagging and purification. This network includes many new interactions as well as interactions predicted based solely on genomic inference or limited phenotypic data. This study provides insight into the function of previously uncharacterized bacterial proteins and the overall topology of a microbial interaction network, the core components of which are broadly conserved across Prokaryota.

Alleles↗

PubMed2026

Amplicon and metagenomic sequencing reveal thifluzamide drive rhizosphere microbial structural shifts and functional adaption.

Thifluzamide (TF) is a widely used phenyl urea fungicide in rice production; however, its impacts on the structural composition and functional dynamics of the rhizosphere microbiome remain poorly understood. Here, we systematically investigated the effects of TF on the structure, interactions, and functional potential of the rice (Oryza sativa L.) rhizosphere microbiome using integrated amplicon sequencing and metagenomic approaches. TF application significantly altered both bacterial and fungal community composition, bacterial diversity was markedly reduced, whereas fungal diversity increased. With bacterial diversity markedly reduced while fungal diversity increased. Beta-diversity analyses revealed strong treatment-driven community separation, indicating pronounced TF-induced microbial restructuring. Co-occurrence network analysis demonstrated reduced complexity and connectivity in bacterial networks but increased negative co-occurrence patterns within fungal communities, suggesting contrasting stability responses between microbial kingdoms. Metagenomic profiling further revealed substantial functional shifts, including the differential enrichment of KEGG and COG pathways associated with xenobiotic metabolism. Notably, while total ARG abundance remained stable, TF exposure altered the resistome profile by selectively enriching specific classes of antibiotic resistance genes (ARGs), biocide resistance genes (BRGs), and mobile genetic elements (MGEs). Strong positive correlations between MGEs and ARGs highlighted an elevated potential for horizontal gene transfer. Metagenome-assembled genome (MAG) analysis identified specific TF-enriched bacterial taxa, including Methylophilus, Sulfurospirillum, and Azospirillum, which harbored genes involved in pesticide degradation and xenobiotic transformation. Collectively, these findings demonstrate that TF profoundly reshapes the rice rhizosphere microbiome by altering microbial diversity, interaction networks, resistance gene profiles, and functional capacities. This study provides genomic insights into fungicide-microbiome interactions, underscoring the potential ecological implications associated with TF application, while identifying candidate microbial taxa that may contribute to pesticide degradation and rhizosphere microecology resilience.

Rhizosphere↗

PubMed2006

Identification of bacterial micropredators distinctively active in a soil microbial food web.

The understanding of microbial interactions and trophic networks is a prerequisite for the elucidation of the turnover and transformation of organic materials in soils. To elucidate the incorporation of biomass carbon into a soil microbial food web, we added 13C-labeled Escherichia coli biomass to an agricultural soil and identified those indigenous microbes that were specifically active in its mineralization and carbon sequestration. rRNA stable isotope probing (SIP) revealed that uncultivated relatives of distinct groups of gliding bacterial micropredators (Lysobacter spp., Myxococcales, and the Bacteroidetes) lead carbon sequestration and mineralization from the added biomass. In addition, fungal populations within the Microascaceae were shown to respond to the added biomass after only 1 h of incubation and were thus surprisingly reactive to degradable labile carbon. This RNA-SIP study identifies indigenous microbes specifically active in the transformation of a nondefined complex carbon source, bacterial biomass, directly in a soil ecosystem.

Bacteria↗

PubMed2006

Microbioreactor arrays with integrated mixers and fluid injectors for high-throughput experimentation with pH and dissolved oxygen control.

We have developed an integrated array of microbioreactors, with 100 microL working volume, comprising a peristaltic oxygenating mixer and microfluidic injectors. These integrated devices were fabricated in a single chip and can provide a high oxygen transfer rate (k(L)a approximately 0.1 s(-1)) without introducing bubbles, and closed loop control over dissolved oxygen and pH (+/-0.1). The system was capable of supporting eight simultaneous Escherichia coli fermentations to cell densities greater than 13 g-dcw L(-1) (1 cm OD(650 nm) > 40). This cell density was comparable to that achieved in a 4 litre reference fermentation, conducted with the same strain, in a bench scale stirred tank bioreactor and is more than four times higher than cell densities previously achieved in microbioreactors. Bubble free oxygenation permitted near real time optical density measurements which could be used to observe subtle changes in the growth rate and infer changes in the state of microbial genetic networks. Our system provides a platform for the study of the interaction of microbial populations with different environmental conditions, which has applications in basic science and industrial bioprocess development. We leverage the advantages of microfluidic integration to deliver a disposable, parallel bioreactor in a single chip, rather than robotically multiplexing independent bioreactors, which opens a new avenue for scaling small scale bioreactor arrays with the capabilities of bench scale stirred tank reactors.

Ammonium Hydroxide↗

PubMed2026

Oral bacteriome in pediatric patients with malignancies prior to chemotherapy: a pilot study using full-length 16S rRNA sequencing.

OBJECTIVE: To characterize the composition, diversity, and ecological features of the oral bacteriome in pediatric patients with malignancies prior to chemotherapy initiation. METHODS: In this prospective pilot study,supragingival plaque samples were collected from 10 pediatric cancer patients prior to the initiation of chemotherapy. Bacterial genomic DNA was extracted from each sample, and the full-length 16S rRNA gene was amplified and sequenced on the PacBio Sequel II platform using circular consensus sequencing (CCS). Raw CCS reads were quality-filtered and denoised into amplicon sequence variants (ASVs) using DADA2, and taxonomic assignment was performed against the SILVA 138 reference database. Alpha diversity was assessed using the Chao1, Shannon, Simpson, and Faith's phylogenetic diversity (PD whole tree) indices, while beta diversity was evaluated through principal coordinate analysis (PCoA), and non-metric multidimensional scaling (NMDS). Microbial co-occurrence networks were constructed to characterize bacterial interactions, and functional potential was predicted using PICRUSt2, and BugBase. RESULTS: A total of 614,473 high-quality CCS reads were generated, yielding 1,697 ASVs. Alpha diversity analysis revealed substantial inter-individual variation in microbial richness and diversity among the pediatric cancer patients. The bacterial community was dominated by the phyla Firmicutes, Proteobacteria, Bacteroidota, Actinobacteriota. At the genus level, Streptococcus, Prevotella, Neisseria, and Haemophilus were the most abundant taxa. Beta diversity analysis revealed distinct clustering patterns, indicating highly individualized microbial profiles. Co-occurrence network analysis identified several keystone taxa and potential pathogenic associations within the supragingival plaque community. Functional prediction indicated that the dominant metabolic pathways were related to amino acid metabolism, carbohydrate metabolism, and membrane transport. CONCLUSION: These preliminary findings reveal a taxonomically diverse, highly individualized pre-chemotherapy oral bacteriome, providing foundational baseline profiles to guide future longitudinal investigations of chemotherapy-induced dysbiosis and personalized interventions.

Humans↗

PubMed2002

Genomes to Life "Center for Molecular and Cellular Systems": a research program for identification and characterization of protein complexes.

Goal 1 of Department of Energy's Genomes to Life (GTL) program seeks to identify and characterize the complete set of protein complexes within a cell. Goal 1 forms the foundation necessary to accomplish the other objectives of the GTL program, which focus on gene regulatory networks and molecular level characterization of interactions in microbial communities. Together this information would allow cells and their components to be understood in sufficient detail to predict, test and understand the responses of a biological system to its environment. The Center for Molecular and Cellular Systems has been established to identify and characterize protein complexes using high through-put analytical technologies.A dynamic research program is being developed that supports the goals of the Center by focusing on the development new capabilities for sample preparation and complex separations, molecular level identification of the protein complexes by mass spectrometry, characterization of the complexes in living cells by imaging techniques, and bioinformatics and computational tools for the collection and interpretation of data and formation of databases and tools to allow the data to be shared by the biological community.

Animals↗

PubMed2005

How C-type lectins detect pathogens.

Glycosylation of proteins has proven extremely important in a variety of cellular processes, including enzyme trafficking, tissue homing and immune functions. In the past decade, increasing interest in carbohydrate-mediated mechanisms has led to the identification of novel carbohydrate-recognizing receptors expressed on cells of the immune system. These non-enzymatic lectins contain one or more carbohydrate recognition domains (CRDs) that determine their specificity. In addition to their cell adhesion functions, lectins now also appear to play a major role in pathogen recognition. Depending on their structure and mode of action, lectins are subdivided in several groups. In this review, we focus on the calcium (Ca(2+))-dependent lectin group, known as C-type lectins, with the dendritic cell-specific ICAM-3 grabbing non-integrin (DC-SIGN) as a prototype type II C-type lectin organized in microdomains, and their role as pathogen recognition receptors in sensing microbes. Moreover, the cross-talk of C-type lectins with other receptors, such as Toll-like receptors, will be discussed, highlighting the emerging model that microbial recognition is based on a complex network of interacting receptors.

Animals↗

PubMed2026

Integrative machine learning models to unravel gut microbial dysbiosis and functional disruption in polycystic ovary syndrome.

OBJECTIVE: To study gut microbial diversity and metabolic pathway disruptions in women with PolyCystic Ovary Syndrome (PCOS) compared with healthy controls, and to evaluate the diagnostic potential of microbiome-driven machine learning models. DESIGN: Case-controlled metagenomic data analysis SUBJECTS: Gut metagenomic data from women diagnosed with PCOS and age-matched healthy female controls EXPOSURE: Presence of PCOS MAIN OUTCOME MEASURES: The primary outcome measures will include gut microbial alpha and beta diversity indices, microbial taxon abundance, functional pathway profiles, predicted metabolite levels, microbe-functional pathway-metabolite interaction networks, and the diagnostic accuracy of microbiome-based machine learning models. RESULTS: Alpha and beta diversity analyses revealed marked gut microbial dysbiosis in women with PCOS, despite comparable species richness to healthy controls. Differential abundance analysis identified 41 significantly altered microbial species, including enrichment of proinflammatory taxa, such as Bacteroides vulgatus and Ruminococcus gnavus, and depletion of beneficial commensals, including Roseburia hominis and Prevotella copri. These compositional shifts indicate a proinflammatory microbial community structure in PCOS. Functional profiling demonstrated the upregulation of pathways involved in nucleotide turnover, lipid and carbohydrate metabolism, and neurotransmitter synthesis, potentially contributing to metabolic and neuroendocrine disruption. Network analysis revealed fragmented and unstable microbial-metabolite associations in PCOS compared with cohesive networks in controls. Microbiome-based machine learning models achieved a diagnostic accuracy of 84.25% (area under the curve 0.93), underscoring their predictive potential. CONCLUSION: The gut microbiome in PCOS is characterized by a proinflammatory community structure and disrupted metabolic pathways. These findings demonstrate the diagnostic potential of microbiome-based models and underscore the gut microbiome as a promising target for therapeutic interventions in the management of PCOS.

Polycystic Ovary Syndrome↗

PubMed2026

Metagenomic Insights into Microbial Assembly and Key Metabolic Genes Driving Flavor Formation in Spontaneously Fermented Zhejiang Rosy Vinegar.

The spontaneous fermentation of Zhejiang rosy vinegar (ZRV) is driven by environmental microbiota, but the processes underlying its flavor formation remain poorly understood. Using metagenomic sequencing, we investigated microbial community assembly, environmental drivers, and metabolic networks during industrial-scale ZRV fermentation. Acetic acid dominated the final organic acids. Community assembly shifted toward deterministic selection with rising acidity, with a slight rebound of stochastic processes in the late stage (R2 values of 0.442 and 0.346 for bacteria and fungi, respectively). Mantel tests confirmed that environmental factors significantly regulated microbial assembly. Co-occurrence networks grew more complex, with positive interactions accounting for 85.24% (bacteria) and 90.10% (fungi) in the late stage. Key genes (ldh, gapA, pgk) from Acetobacter pasteurianus and Lactobacillus acetotolerans dominated late-stage fermentation, while genes (adhP, SDH) from Aspergillus oryzae and Saccharomyces cerevisiae supported early- and mid-stage fermentation. These findings elucidate microbiota-driven metabolic pathways in ZRV, supporting the fermentation window optimization and industrial vinegar quality standardization.

Acetic Acid↗

PubMed2026

Analysis of the dual role of amyloid-beta in Alzheimer's disease through multi-omics integration.

Accumulation of amyloid-beta is highly important in the development of Alzheimer's disease. Given the limitations of the amyloid cascade hypothesis and the repeated clinical failures of anti-amyloid-beta therapies, researchers are increasingly exploring the infection hypothesis. This review explores the dual behaviors of amyloid-beta in Alzheimer's disease, with a particular focus on its protective role against infection by microorganisms and its complicated connections with innate immune system. This new opinion holds that amyloid-beta can play an antimicrobial peptide role. During microbial invasion, its original role is to protect neural tissue, but prolonged accumulation leads to chronic deposition and involvement in pathological processes. Evidence from in vitro experiments, animal models, and clinical studies indicates that amyloid-beta may possess antiviral and antibacterial properties, particularly against infections such as herpes simplex virus, human immunodeficiency virus, and Porphyromonas gingivalis . However, excessive accumulation of amyloid beta triggers a neuroinflammatory cascade that impairs neuronal regeneration and cognitive function. Despite substantial research into Alzheimer's disease, current treatments have not yielded significant clinical benefits. Although monoclonal antibodies such as Aducanumab , Lecanemab , and Donanemab have been approved for marketing, their strict indications and high costs pose challenges for widespread promotion. The infection hypothesis of amyloid-beta has spurred clinical trials investigating vaccines targeting specific pathogens to assess their potential in preventing or treating Alzheimer's disease. This highlights the need for further exploring the multifaceted role of amyloid-beta in Alzheimer's disease. In addition, microbial infections can also trigger or regulate genetic and epigenetic factors, accelerating amyloid beta deposition. Among them, the apolipoprotein E epsilon 4 allele is the strongest genetic risk factor for Alzheimer's disease, as it exacerbates the accumulation of amyloid beta and promotes neuroinflammation. Strategies targeting epigenetic regulation may provide novel approaches to inhibit Alzheimer's disease pathology. This review also integrates various technologies such as genomics, proteomics, and metabolomics. This provides a broader system-level understanding of the risk gene loci, protein interaction networks, and metabolic changes associated with amyloid beta under the influence of microbial infections. Such techniques may lead to the identification of new molecular targets, the development of individualized treatment strategies, and the creation of early biomarkers for use in clinical research. In conclusion, this review suggests that amyloid-beta is not merely a pathological by-product but an environmentally responsive molecule with dual functions. A deeper understanding of the dynamic regulation of amyloid-beta, considering infection status and disease stage, can provide new directions for treatment strategies aimed at the prevention and treatment of Alzheimer's disease.

Herpesvirus 1↗

PubMed2024

Effects of initial corncob particle size on the short-term composting for preparation of cultivation substrates for Pleurotus ostreatus.

The short-term composting based on corncob for preparing Pleurotus ostreatus cultivation medium originated from agricultural production practices and so lacked systematic investigation. In this study, the influences of a Dafen (15 mm, DFT) and Xiaofen (5 mm, XFT) initial particle size (IPS) of corncob on the microbial succession and compost quality were examined. Results demonstrated that XFT compost was better suited for mushroom cultivation due to its high biological efficiency of 70 % and the absence of contamination. The composting microbes differed significantly between the DFT and XFT composts. During composting, the genera of Bacillus, Acinetobacter, Lactobacillus, Streptomyces, and Paenibacillus were majorly found in the DFT compost, while Acinetobacter, Lactobacillus, Puccinia, Bacteroides, and Bacillus genera dominated the XFT compost. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that throughout the thermophilic phase, XFT compost had much greater relative abundances of sequences relevant to energy, carbohydrate, and amino acid metabolism than DFT compost. Analysis of network correlations and Mantel tests indicated that IPS reduction could increase microbial interactions. Overall, adjusting the IPS of corncob to 5 mm increased microbial interactions, improved compost quality, and thereby boosted the P. ostreatus yield. These findings will be pertinent in optimizing the composting process of cultivation medium for P. ostreatus.

Composting↗

PubMed2003

Subversion of the chemokine world by microbial pathogens.

It is well known that microbial pathogens are able to subvert the host immune system in order to increase microbial replication and propagation. Recent research indicates that another arm of the immune response, that of the chemokine system, is also subject to this sabotage, and is undermined by a range of microbial pathogens, including viruses, bacteria, and parasites. Currently, it is known that the chemokine system is being challenged by a number of mechanisms, and still more are likely to be discovered with further research. Here we first review the general mechanisms by which microbial pathogens bypass mammalian chemokine defences. Broadly, these can be grouped as viral chemokine interacting proteins, microbial manipulation of host chemokine and chemokine receptor expression, microbial blockade of host chemokine receptor signalling, and the largely hypothetical mechanisms of microbial enhancement of host anti-chemokine networks (including digestion, antagonism, and neutralisation of host chemokines and chemokine receptors). We then discuss the potential results of these interactions in terms of outcome of infection.

Animals↗

PubMed1998

Application of artificial neural networks as a non-linear modular modeling technique to describe bacterial growth in chilled food products.

In many chilled, prepared food products, the effects of temperature, pH and %NaCl on microbial activity interact and this should be taken into account. A grey box model for prediction of microbial growth is developed. The time dependence is modeled by a Gompertz model-based, non-linear differential equation. The influence of temperature, pH and %NaCl reflected in the model parameters is described by using low-complexity, black box artificial neural networks (ANN's). The use of this non-linear modeling technique makes it possible to describe more accurately interacting effects of environmental factors when compared with classical predictive microbiology models. When experimental results on the influence of other environmental factors become available, the ANN models can be extended simply by adding more neurons and/or layers.

Bacteria↗

PubMed2026

Unraveling critical role of photosynthetic bacteria in sustaining aquatic microbial community stability and function through large-scale genomic data analyses.

The application of photosynthetic bacteria (PB) in water remediation has demonstrated exceptional advantages in terms of high efficiency and low-carbon benefits. However, the limited understanding of PB across natural aquatic environments has constrained the rational development of this strategy. Here, we analyzed 3198 genomic sequencing samples from seven types of natural aquatic ecosystems to investigate the distribution and functions of 42 PB genera commonly used in water remediation. The results showed that the average abundance of the targeted PB reached 9.83 %, with the highest value of 14.93 % observed in River, while Lake harbored the greatest PB genus diversity. PB genera exhibited high sensitivity to salinity, with Rhodoferax dominating freshwater habitats, whereas Rhodovulum was predominant in marine environments. Notably, co-occurrence network analysis revealed that PB were closely associated with microbial community stability and optimized interspecific interactions. Aquatic microbial communities with high PB abundance were characterized by efficient division-of-labor modules, accompanied by enhanced PB-associated functional potential for carbon fixation, denitrification, and sulfur oxidation. In summary, this study systematically elucidates the regional biogeographical patterns and ecological roles of PB in natural aquatic environments, providing a comprehensive scientific basis and theoretical guidance for the development and practical application of PB-based water remediation technologies.

Bacteria↗

PubMed2004

Expression of HLA-G in inflammatory bowel disease provides a potential way to distinguish between ulcerative colitis and Crohn's disease.

In addition to being involved in nutrient uptake, the epithelial mucosa constitute the first line of defense against microbial pathogens. A direct consequence of this physiological function is a very complex network of immunological interactions that lead to a strong control of the mucosal immune balance. The dysfunction of immunological tolerance is likely to be a cause of inflammatory bowel disease (IBD), ulcerative colitis (UC) and Crohn's disease (CD). HLA-G is a non-classical major histocompatibility complex (HLA) class I molecule, which is highly expressed by human cytotrophoblast cells. These cells play a role in immune tolerance by protecting trophoblasts from being killed by uterine NK cells. Because of the deregulation of immune system activity in IBD, as well as the immunoregulatory role of HLA-G, we have analyzed the expression of HLA-G in intestinal biopsies of patients with UC and CD. Our study shows that the differential expression of HLA-G provides a potential way to distinguish between UC and CD. Although the reason for this differential expression is unclear, it might involve a different mechanism of immune regulation. In addition, we demonstrate that in the lamina propria of the colon of patients with UC, IL-10 is strongly expressed. In conclusion, the presence of HLA-G on the surface of intestinal epithelial cell in patients with UC lends support to the notion that this molecule may serve as a regulator of mucosal immune responses to antigens of undefined origin. Thus, this different pattern of HLA-G expression may help to differentiate between the immunopathogenesis of CD and UC.

Biopsy↗

PubMed2026

In silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity.

BACKGROUND: Understanding how metabolic capabilities diverge across microbial species is essential for deciphering community function, ecological interactions, and the design of synthetic microbiomes. Despite shared core pathways, microbial phenotypes can differ markedly due to evolutionary adaptations and metabolic specialization. Genome-scale metabolic models (GEMs) provide a systems-level framework to explore these differences; however, their complexity hinders direct comparison. RESULTS: We introduce NIS (Neidhardt-Ingraham-Schaechter), a computational workflow that integrates the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce genome-scale models into biologically interpretable modules. This approach enables direct, quantitative comparison of fueling pathways, biomass biosynthetic routes, and environmental exchange processes while retaining essential metabolic information. We first demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, which revealed both conserved and divergent strategies in central metabolism, biosynthetic cost, and substrate utilization. We then applied NIS to the core honeybee gut microbiome, uncovering distinct metabolic traits, functional redundancy, and complementarity that help explain auxotrophy, cross-feeding interactions, and microbial coexistence. CONCLUSIONS: NIS provides an automated, scalable, and reproducible framework for dissecting microbial metabolic networks beyond gene content or taxonomy. By linking metabolism to ecological function, NIS offers new opportunities to interpret microbial community dynamics and to support the rational design of microbiomes in health, agriculture, and environmental applications. Video Abstract.

Metabolic Networks and Pathways↗

PubMed2004

Microbial-gut interactions in health and disease. Mucosal immune responses.

The host gastrointestinal tract is exposed to countless numbers of foreign antigens and has embedded a unique and complex network of immunological and non-immunological mechanisms, often termed the gastrointestinal 'mucosal barrier', to protect the host from potentially harmful pathogens while at the same time 'tolerating' other resident microbes to allow absorption and utilization of nutrients. Of the many important roles of this barrier, it is the distinct responsibility of the mucosal immune system to sample and discriminate between harmful and beneficial antigens and to prevent entry of food-borne pathogens through the gastrointestinal (GI) tract. This system comprises an immunological network termed the gut-associated lymphoid tissue (GALT) that consists of unique arrangements of B cells, T cells and phagocytes which sample luminal antigens through specialized epithelia termed the follicle associated epithelia (FAE) and orchestrate co-ordinated molecular responses between immune cells and other components of the mucosal barrier. Certain pathogens have developed ways to bypass and/or withstand defence by the mucosal immune system to establish disease in the host. Some 'opportunistic' pathogens (such as Clostridium difficile) take advantage of host or other factors (diet, stress, antibiotic use) which may alter or weaken the response of the immune system. Other pathogens have developed mechanisms for invading gastrointestinal epithelium and evading phagocytosis/destruction by immune system defences. Once cellular invasion occurs, host responses are activated to limit local mucosal damage and repel the foreign influence. Some pathogens (Shigella spp, parasites and viruses) primarily establish localized disease while others (Salmonella, Yersinia, Listeria) use the lymphatic system to enter organs or the bloodstream and cause more systemic illness. In some cases, pathogens (Helicobacter pylori and Salmonella typhi) colonize the GI tract or associated lymphoid structures for extended periods of time and these persistent pathogens may also be potential triggers for other chronic or inflammatory diseases, including inflammatory bowel disease and malignancies. The ability of certain pathogens to avoid or withstand the host's immune assault and/or utilize these host responses to their own advantage (i.e. enhance further colonization) will dictate the pathogen's success in promoting illness and furthering its own survival.

Bacteria↗