scholarly journals Microbial association networks in cheese: a meta-analysis

2021 ◽  
Author(s):  
Eugenio Parente ◽  
Teresa Zotta ◽  
Annamaria Ricciardi
Keyword(s):  
Network Inference ◽  
Network Science ◽  
Meta Analysis ◽  
Raw Milk ◽  
Microbial Interactions ◽  
Taxonomic Resolution ◽  
Microbial Association ◽  
Complex Dynamic Systems ◽  
Metabolic Interactions ◽  
Science Concepts

Interactions among starter and non-starter microorganisms (starter bacteria, naturally occurring or intentionally added non-starter bacteria, yeasts and filamentous fungi, spoilage and pathogenic microorganisms and, finally bacteriophages and even arthropods) deeply affect the dynamics of cheese microbial communities and, as a consequence, multiple aspects of cheese quality, from metabolites affecting the taste, aroma and flavor, to body, texture and color. Understanding and exploiting microbial interactions is therefore key to managing cheese quality. This is true for the simplest systems (fresh cheeses produced from pasteurized milk using defined starters composed solely of Lactic Acid Bacteria) and the more so for complex, dynamic systems, like surface ripened cheese produced from raw milk, in which a dynamic succession of diverse microorganisms is essential for obtained the desired combination of sensory properties while guaranteeing safety. Positive (commensalism, protocooperation) and negative (competition, amensalism, predation and parasitism) among members of the cheese biota have been reviewed multiple times. Although the complex, multidimensional datasets generated by multi-omic approaches to cheese microbiology and biochemistry are ideally suited for the representation of biotic and metabolic interactions as networks, network science concepts and approaches are rarely applied to cheese microbiology. In this review we first illustrate concepts relevant to the description of microbial interaction networks using network science concepts. Then, we briefly review methods used for the inference and analysis of microbial association networks and their potential use in the interpretation of the cheese interactome. Since these methods can only be used for mining microbial associations, a review of the experimental methods used to confirm the nature of microbial interactions among cheese microbes. Finally, we demonstrate the potential of microbial association network inference by mining metataxonomic data stored in the public database DairyFMBN, a specialized version of FoodMicrobionet which collates data on 74 metataxonomic studies on dairy products. Microbial association networks were inferred from 34 studies on cheese with up to 4 different methods and the results discussed to evaluate several aspects (choice of method, level of taxonomic resolution for the analysis, network, node and edge properties) which provide insight on the usefulness of this approach as explorative tool in the detection of microbial interactions in cheese.

scholarly journals The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

2018 ◽  
Vol 35 (13) ◽  
pp. 2332-2334 ◽  
Author(s):  
Federico Baldini ◽  
Almut Heinken ◽  
Laurent Heirendt ◽  
Stefania Magnusdottir ◽  
Ronan M T Fleming ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Microbial Interactions ◽  
Microbial Genome ◽  
Metagenomic Data ◽  
Metabolic Interactions ◽  
Constraint Based Modeling ◽  
Genome Scale

Abstract Motivation The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. Results To address this gap, we created a comprehensive toolbox to model (i) microbe–microbe and host–microbe metabolic interactions, and (ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the constraint-based reconstruction and analysis toolbox. Availability and implementation The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

scholarly journals Disentangling environmental effects in microbial association networks

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Ina Maria Deutschmann ◽  
Gipsi Lima-Mendez ◽  
Anders K. Krabberød ◽  
Jeroen Raes ◽  
Sergio M. Vallina ◽  
...  
Keyword(s):  
Data Processing ◽  
Simulated Data ◽  
Potential Interaction ◽  
Microbial Interactions ◽  
Ecological Interactions ◽  
Sign Pattern ◽  
Interaction Information ◽  
Microbial Association ◽  
Environmental Selection ◽  
Data Processing Inequality

Abstract Background Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not. Results We present EnDED (environmentally driven edge detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally driven. The four approaches are sign pattern, overlap, interaction information, and data processing inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e., environmentally driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally driven edges—87% sign pattern and overlap, 67% interaction information, and 44% data processing inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. Conclusions To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses.

scholarly journals Microbial Association with Genus Actinomyces in Primary and Secondary Endodontic Lesions, Review

Antibiotics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 433
Author(s):  
Mario Dioguardi ◽  
Cristian Quarta ◽  
Mario Alovisi ◽  
Vito Crincoli ◽  
Riccardo Aiuto ◽  
...  
Keyword(s):  
Root Canal ◽  
Primary Outcome ◽  
Meta Analysis ◽  
Salivary Flow ◽  
Secondary Outcome ◽  
Microbial Association ◽  
Oral Tissue ◽  
Root Canal System ◽  
Endodontic Infections ◽  
Prevalent Species

The main reason for root canal treatment failure is the persistence of microorganisms after therapy, or the recontamination of the root canal system due to an inadequate seal. In the mouth, Actinomyces spp. constitute a significant part of the normal flora, which is indicative of their ability to adhere to oral tissue and resist cleansing mechanisms, such as salivary flow. This review, performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), aims to clarify the prevalence of microbial genera that are associated with the genus Actinomyces in primary and secondary endodontic infections (primary outcome), and to identify the most prevalent species of the Actinomyces genus in endodontic lesions (secondary outcome). A total of 11 studies were included in the qualitative and quantitative analysis, and a total of 331 samples were analyzed. Bacteria of the genus Actinomyces were found in 58 samples, and 46 bacterial genera were detected in association with bacteria of the genus Actinomyces. Bacteria of the genus Streptococcus and Propionibacterium were those most frequently associated with Actinomyces in the endodontic lesions considered, and Actinomyces israelii was the most frequently involved species.

scholarly journals Temporal profiling of cytokine-induced genes in pancreatic β-cells by meta-analysis and network inference

Genomics ◽  
2014 ◽  
Vol 103 (4) ◽  
pp. 264-275 ◽  
Author(s):  
Miguel Lopes ◽  
Burak Kutlu ◽  
Michela Miani ◽  
Claus H. Bang-Berthelsen ◽  
Joachim Størling ◽  
...  
Keyword(s):  
Network Inference ◽  
Meta Analysis ◽  
Β Cells ◽  
Pancreatic Β Cells ◽  
Induced Genes

scholarly journals Root renovation: how an improved understanding of basic root biology could inform the development of elite crops that foster sustainable soil health

2019 ◽  
Vol 46 (7) ◽  
pp. 597 ◽  
Author(s):  
Johanna W.-H. Wong ◽  
Jonathan M. Plett
Keyword(s):  
Management Practices ◽  
Agricultural Research ◽  
Soil Health ◽  
Value Added ◽  
Soil Management ◽  
Breeding Strategy ◽  
Microbial Interactions ◽  
Crop Species ◽  
Soil Microbial ◽  
Microbial Association

A major goal in agricultural research is to develop ‘elite’ crops with stronger, resilient root systems. Within this context, breeding practices have focussed on developing plant varieties that are, primarily, able to withstand pathogen attack and, secondarily, able to maximise plant productivity. Although great strides towards breeding disease-tolerant or -resistant root stocks have been made, this has come at a cost. Emerging studies in certain crop species suggest that domestication of crops, together with soil management practices aimed at improving plant yield, may hinder beneficial soil microbial association or reduce microbial diversity in soil. To achieve more sustainable management of agricultural lands, we must not only shift our soil management practices but also our breeding strategy to include contributions from beneficial microbes. For this latter point, we need to advance our understanding of how plants communicate with, and are able to differentiate between, microbes of different lifestyles. Here, we present a review of the key findings on belowground plant–microbial interactions that have been made over the past decade, with a specific focus on how plants and microbes communicate. We also discuss the currently unresolved questions in this area, and propose plausible ways to use currently available research and integrate fast-emerging ‘-omics’ technologies to tackle these questions. Combining past and developing research will enable the development of new crop varieties that will have new, value-added phenotypes belowground.

scholarly journals Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

2021 ◽  
Author(s):  
Rachita K Kumar ◽  
Nitin K Singh ◽  
Sanjaay Balakrishnan ◽  
Ceth W Parker ◽  
Karthik Raman ◽  
...  
Keyword(s):  
International Space Station ◽  
Space Station ◽  
Keystone Species ◽  
Microbial Interactions ◽  
Integrated Modelling ◽  
Klebsiella Pneumonia ◽  
Potential Contribution ◽  
International Space ◽  
Metabolic Interactions ◽  
Modelling Approach

Background: Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumonia. However, the interactions between the various microbes aboard the ISS, and how it shapes the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization. Results: Through a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modelling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and its biofilm biofilm-forming structures. Conclusions: Our study underscores the importance of K. pneumoniae in the ISS, and its potential contribution to the survival (mutualism) and eradication (parasitism) of other microbes, including potential pathogens. This integrated modelling approach, combined with experiments, demonstrates immense potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies.

scholarly journals Shedding Light on the Formation and Structure of Kombucha Biofilm Using Two-Photon Fluorescence Microscopy

2021 ◽  
Vol 12 ◽  
Author(s):  
Thierry Tran ◽  
Cosette Grandvalet ◽  
Pascale Winckler ◽  
François Verdier ◽  
Antoine Martin ◽  
...  
Keyword(s):  
De Novo ◽  
Acetic Acid Bacteria ◽  
Spatial Optimization ◽  
Microbial Interactions ◽  
Yeast Cells ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Metabolic Interactions ◽  
Two Photon Fluorescence ◽  
The Impact

Kombucha pellicles are often used as inoculum to produce this beverage and have become a signature feature. This cellulosic biofilm produced by acetic acid bacteria (AAB) involves yeasts, which are also part of the kombucha consortia. The role of microbial interactions in the de novo formation and structure of kombucha pellicles was investigated during the 3 days following inoculation, using two-photon microscopy coupled with fluorescent staining. Aggregated yeast cells appear to serve as scaffolding to which bacterial cellulose accumulates. This initial foundation leads to a layered structure characterized by a top cellulose-rich layer and a biomass-rich sublayer. This sublayer is expected to be the microbiologically active site for cellulose production and spatial optimization of yeast–AAB metabolic interactions. The pellicles then grow in thickness while expanding their layered organization. A comparison with pellicles grown from pure AAB cultures shows differences in consistency and structure that highlight the impact of yeasts on the structure and properties of kombucha pellicles.

Aflatoxin M1 contamination level in Iranian milk and dairy products: a systematic review and meta-analysis

2020 ◽  
Vol 13 (1) ◽  
pp. 67-82 ◽  
Author(s):  
S. Hamzeh Pour ◽  
S. Mahmoudi ◽  
S. Masoumi ◽  
S. Rezaie ◽  
A. Barac ◽  
...  
Keyword(s):  
Systematic Review ◽  
Dairy Products ◽  
Meta Analysis ◽  
Raw Milk ◽  
Aflatoxin M1 ◽  
Contamination Level ◽  
Livestock Feeds ◽  
Aflatoxin B ◽  
Mean Concentration ◽  
Milk And Dairy Products

Aflatoxin M1 is a derivate of aflatoxin B1 and an important contaminant of milk and dairy products. This systematic review and meta-analysis was conducted on relevant Persian and English original articles in national and international databases with no time limits until 1 January 2018. In total 605 articles were found among which 70 articles met the inclusion criteria for meta-analysis. The prevalence (95% confidence interval (CI)) and mean concentration (95% CI) of aflatoxin M1 was found to be 64% (53-75%) and 39.7 ng/l (31.9-47.4 ng/l) in raw milk, 95% (89-98%) and 62.3 ng/l (40.6-84 ng/l) in pasteurised milk, 71% (56-84%) and 60.1 ng/l (30.9-89.3 ng/l) in sterilised milk, 59% (20-93%) and 5.5 ng/l (3.3-7.7 ng/l) in breast milk and 72% (61-81%) and 82.3 ng/kg (63.7-100.9 ng/kg) in dairy products. In general, 9% (4-16%) of milks and 10% (4-17%) of dairy products had aflatoxin M1 in concentrations exceeding the permitted level of Iranian standards (500 ng/l). Based on the maximum permitted aflatoxin M1 concentration in standards of Europe (50 ng/l), these percentages increase to 25% (18-32%) for milks and 18% (9-29%) for dairy products. According to the results, further control and preventive measures should be applied on livestock feeds because decreased aflatoxin B1 contamination at this level results in decreased aflatoxin M1 in milk and dairy products.

Bacterial Dynamics in Model Cheese Systems, Aiming at Safety and Quality of Portuguese-Style Traditional Ewe's Cheeses

2009 ◽  
Vol 72 (11) ◽  
pp. 2243-2251 ◽  
Author(s):  
CLÁUDIA I. PEREIRA ◽  
JOÃO A. GRAÇA ◽  
NATACHA S. OGANDO ◽  
ANA M. P. GOMES ◽  
F. XAVIER MALCATA
Keyword(s):  
Lactobacillus Brevis ◽  
Raw Milk ◽  
Microbial Interactions ◽  
Initial Inoculum ◽  
Bacterial Dynamics ◽  
Spoilage Bacteria ◽  
Ewe's Milk ◽  
Raw Milk Cheese ◽  
D Values

An experiment using model ewe's milk cheeses was designed to characterize microbial interactions that arise in actual raw milk cheese environments. These model cheeses were manufactured according to Portuguese artisanal practices, except that the microbial load and biodiversity were fully controlled: single potential pathogens and spoilage bacteria, or a combination thereof, were combined at various initial inoculum levels in sterilized raw ewe's milk with several lactic acid bacteria (LAB) normally found in traditional cheeses. Viable microbial counts were monitored throughout a 60-day ripening period. Two alternative mathematical approaches were used to fit the experimental data generated in terms of population dynamics: percent of inhibition and D-values. These were able to explain the complex competitive interactions between the contaminant microorganisms and the LAB adventitious populations. In general, the tested LAB were less able to inhibit contaminants present in combination and in higher concentrations. Lactococcus lactis, with its strong acidifying potential, was the most effective factor in controlling the unwanted bacterial population, especially single Staphylococcus aureus. The two lactobacilli studied, especially Lactobacillus brevis, were shown to be less effective; Escherichia coli and Listeria innocua were the contaminants least inhibited by the LAB.

scholarly journals High Level of Interaction between Phages and Bacteria in an Artisanal Raw Milk Cheese Microbial Community

2021 ◽  
Author(s):  
Luciano Lopes Queiroz ◽  
Gustavo Augusto Lacorte ◽  
William Ricardo Isidorio ◽  
Mariza Landgraf ◽  
Bernadette Dora Gombossy de Melo Franco ◽  
...  
Keyword(s):  
Viral Infections ◽  
Production Systems ◽  
Meta Analysis ◽  
Starter Culture ◽  
Growth Dynamics ◽  
Raw Milk ◽  
Starter Cultures ◽  
Bacteria Growth ◽  
The World ◽  
High Level

Endogenous starter cultures are used in the production of several cheeses around the world, such as Parmigiano Reggiano, in Italy, Epoisses, in France, and Canastra, in Brazil. These microbial communities are responsible for many of the intrinsic characteristics of each of these cheeses. Bacteriophages are ubiquitous around the world, well known to be involved in the modulation of complex microbiological processes. However, little is known about phage bacteria growth dynamics in cheese production systems, where phages are normally treated as problems, as the viral infections can negatively affect or even eliminate the starter culture during production. Furthermore, a recent metagenomic based meta-analysis has reported that cheeses contain a high abundance of phage-associated sequences. Here, we analyse the viral and bacterial metagenomes of Canastra cheese, a tradition artisanal cheese produced using an endogenous starter culture. We observe a very high phage diversity level, mostly composed of novel sequences. We detect several metagenomic assembled bacterial genomes at strain level resolution, and several putative phage-bacteria interactions, evidenced by the recovered viral and bacterial genomic signatures. We postulate that at least one bacterial strain detected could be endogenous to the Canastra region, in Brazil, and that its growth seems to be modulated by native phages present in this artisanal production system. This relationship is likely to influence the fermentation dynamics and ultimately the sensorial profile of these cheeses, with implications for all cheeses that employ similar production processes around the world.

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