Open challenges for microbial network construction and analysis

Ecological risk assessment and sources identification of heavy metals in surface sediments of a river-reservoir system

Heavy metal contamination of river water and sediments is a global issue affecting ecological health. To reveal heavy metals' ecological risks and biological toxicity in the middle and lower Han River (MLHR), sediment samples collected in this area were analyzed based on a modified ecological risk assessment method (NIRI) and a biological toxicity assessment method. Also, Spearman correlation analysis and Positive Matrix Factorization (PMF) methods were applied to identify the potential sources of heavy metals. The results indicated that the heavy metal content significantly exceeded the background concentrations in Hubei Province. The average potential risk of heavy metals at sampling sites was: Cd > Hg > As > Pb > Cu > Zn. Consequently, high biological toxicity occurred along the MLHR due to the heavy metal enrichment. River damming and water diversion significantly enhanced the hydrologic regime variations and ecological risk in the MLHR. Moreover, two possible pollution sources of the MLHR were identified: one is a combined source of traffic pollution, agricultural pollution, and partial industrial pollution consisting of five heavy metals, Pb, Hg, Zn, Cu, and As, the other is an industrial pollution source dominated by Cd and As. This study provides insights into sediment heavy metal pollution management and ecological risk control in the MLHR and similar rivers worldwide.

Effective data filtering is prerequisite for robust microbial association network construction

Microorganisms do not exist as individual population in the environment. Rather, they form complex assemblages that perform essential ecosystem functions and maintain ecosystem stability. Besides the diversity and composition of microbial communities, deciphering their potential interactions in the form of association networks has attracted many microbiologists and ecologists. Much effort has been made toward the methodological development for constructing microbial association networks. However, microbial profiles suffer dramatically from zero values, which hamper accurate association network construction. In this study, we investigated the effects of zero-value issues associated with microbial association network construction. Using the TARA Oceans microbial profile as an example, different zero-value-treatment approaches were comparatively investigated using different correlation methods. The results suggested dramatic variations of correlation coefficient values for differently treated microbial profiles. Most specifically, correlation coefficients among less frequent microbial taxa were more affected, whichever method was used. Negative correlation coefficients were more problematic and sensitive to network construction, as many of them were inferred from low-overlapped microbial taxa. Consequently, microbial association networks were greatly differed. Among various approaches, we recommend sequential calculation of correlation coefficients for microbial taxa pairs by excluding paired zero values. Filling missing values with pseudo-values is not recommended. As microbial association network analyses have become a widely used technique in the field of microbial ecology and environmental science, we urge cautions be made to critically consider the zero-value issues in microbial data.

Soil microbial community assembly and stability are associated with potato (Solanum tuberosum L.) fitness under continuous cropping regime

Continuous cropping obstacles caused by the over-cultivation of a single crop trigger soil degradation, yield reduction and the occurrence of plant disease. However, the relationships among stability, complexity and the assembly process of soil microbial community with continuous cropping obstacles remains unclear. In this study, molecular ecological networks analysis (MENs) and inter-domain ecological networks analysis (IDENs), and a new index named cohesion tools were used to calculate the stability and complexity of soil microbial communities from eight potato cultivars grown under a continuous cropping regime by using the high-throughput sequencing data. The results showed that the stability (i.e., robustness index) of the bacterial and fungal communities for cultivar ZS5 was significantly higher, and that the complexity (i.e., cohesion values) was also significantly higher in the bacterial, fungal and inter-domain communities (i.e., bacterial-fungal community) of cultivar ZS5 than other cultivars. Network analysis also revealed that Actinobacteria and Ascomycota were the dominant phyla within intra-domain networks of continuous cropping potato soil communities, while the phyla Proteobacteria and Ascomycota dominated the correlation of the bacterial-fungal network. Infer community assembly mechanism by phylogenetic-bin-based null model analysis (iCAMP) tools were used to calculate the soil bacterial and fungal communities' assembly processes of the eight potato cultivars under continuous cropping regime, and the results showed that the bacterial community was mainly dominated by deterministic processes (64.19% - 81.31%) while the fungal community was mainly dominated by stochastic processes (78.28% - 98.99%), indicating that the continuous-cropping regime mainly influenced the potato soil bacterial community assembly process. Moreover, cultivar ZS5 possessed a relatively lower homogeneous selection, and a higher TP, TN, AP and yield than other cultivars. Our results indicated that the soil microbial network stability and complexity, and community assemble might be associated with yield and soil properties, which would be helpful in the study for resistance to potato continuous cropping obstacles.

Phage-prokaryote coexistence strategy mediates microbial community diversity in the intestine and sediment microhabitats of shrimp culture pond ecosystem

Emerging evidence supports that the phage-prokaryote interaction drives ecological processes in various environments with different phage life strategies. However, the knowledge of phage-prokaryote interaction in the shrimp culture pond ecosystem (SCPE) is still limited. Here, the viral and prokaryotic community profiles at four culture stages in the intestine of Litopenaeus vannamei and cultural sediment microhabitats of SCPE were explored to elucidate the contribution of phage-prokaryote interaction in modulating microbial communities. The results demonstrated that the most abundant viral families in the shrimp intestine and sediment were Microviridae, Circoviridae, Inoviridae, Siphoviridae, Podoviridae, Myoviridae, Parvoviridae, Herelleviridae, Mimiviridae, and Genomoviridae, while phages dominated the viral community. The dominant prokaryotic genera were Vibrio, Formosa, Aurantisolimonas, and Shewanella in the shrimp intestine, and Formosa, Aurantisolimonas, Algoriphagus, and Flavobacterium in the sediment. The viral and prokaryotic composition of the shrimp intestine and sediment were significantly different at four culture stages, and the phage communities were closely related to the prokaryotic communities. Moreover, the phage-prokaryote interactions can directly or indirectly modulate the microbial community composition and function, including auxiliary metabolic genes and closed toxin genes. The interactional analysis revealed that phages and prokaryotes had diverse coexistence strategies in the shrimp intestine and sediment microhabitats of SCPE. Collectively, our findings characterized the composition of viral communities in the shrimp intestine and cultural sediment and revealed the distinct pattern of phage-prokaryote interaction in modulating microbial community diversity, which expanded our cognization of the phage-prokaryote coexistence strategy in aquatic ecosystems from the microecological perspective and provided theoretical support for microecological prevention and control of shrimp culture health management.

Estuarine microbial networks and relationships vary between environmentally distinct communities

Microbial interactions have profound impacts on biodiversity, biogeochemistry, and ecosystem functioning, and yet, they remain poorly understood in the ocean and with respect to changing environmental conditions. We applied hierarchical clustering of an annual 16S and 18S amplicon dataset in the Skidaway River Estuary, which revealed two similar clusters for prokaryotes (Bacteria and Archaea) and protists: Cluster 1 (March-May and November-February) and Cluster 2 (June-October). We constructed co-occurrence networks from each cluster to explore how microbial networks and relationships vary between environmentally distinct periods in the estuary. Cluster 1 communities were exposed to significantly lower temperature, sunlight, NO3, and SiO4; only NH4 was higher at this time. Several network properties (e.g., edge number, degree, and centrality) were elevated for networks constructed with Cluster 1 vs. 2 samples. There was also evidence that microbial nodes in Cluster 1 were more connected (e.g., higher edge density and lower path length) compared to Cluster 2, though opposite trends were observed when networks considered Prokaryote-Protist edges only. The number of Prokaryote-Prokaryote and Prokaryote-Protist edges increased by >100% in the Cluster 1 network, mainly involving Flavobacteriales, Rhodobacterales, Peridiniales, and Cryptomonadales associated with each other and other microbial groups (e.g., SAR11, Bacillariophyta, and Strombidiida). Several Protist-Protist associations, including Bacillariophyta correlated with Syndiniales (Dino-Groups I and II) and an Unassigned Dinophyceae group, were more prevalent in Cluster 2. Based on the type and sign of associations that increased in Cluster 1, our findings indicate that mutualistic, competitive, or predatory relationships may have been more representative among microbes when conditions were less favorable in the estuary; however, such relationships require further exploration and validation in the field and lab. Coastal networks may also be driven by shifts in the abundance of certain taxonomic or functional groups. Sustained monitoring of microbial communities over environmental gradients, both spatial and temporal, is critical to predict microbial dynamics and biogeochemistry in future marine ecosystems.

Cross-kingdom co-occurrence networks in the plant microbiome: Importance and ecological interpretations

Microbial co-occurrence network analysis is being widely used for data exploration in plant microbiome research. Still, challenges lie in how well these microbial networks represent natural microbial communities and how well we can interpret and extract eco-evolutionary insights from the networks. Although many technical solutions have been proposed, in this perspective, we touch on the grave problem of kingdom-level bias in network representation and interpretation. We underscore the eco-evolutionary significance of using cross-kingdom (bacterial-fungal) co-occurrence networks to increase the network's representability of natural communities. To do so, we demonstrate how ecosystem-level interpretation of plant microbiome evolution changes with and without multi-kingdom analysis. Then, to overcome oversimplified interpretation of the networks stemming from the stereotypical dichotomy between bacteria and fungi, we recommend three avenues for ecological interpretation: (1) understanding dynamics and mechanisms of co-occurrence networks through generalized Lotka-Volterra and consumer-resource models, (2) finding alternative ecological explanations for individual negative and positive fungal-bacterial edges, and (3) connecting cross-kingdom networks to abiotic and biotic (host) environments.

Statistical challenges in longitudinal microbiome data analysis

The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.

Animal Activities of the Key Herbivore Plateau Pika (Ochotona curzoniae) on the Qinghai-Tibetan Plateau Affect Grassland Microbial Networks and Ecosystem Functions

Plateau pikas (Ochotona curzoniae) are high-altitude model animals and famous "ecosystem engineers" on the Qinghai-Tibet Plateau. Pika activities may accelerate the degradation of alpine meadows. Nevertheless, little is known about the responses of bacterial, fungal, and archaeal communities, and ecosystem multifunctionality to pika perturbations. To address this question, we studied the impacts of only pika disturbance and combined disturbance (pika disturbance and grazing) on ecological networks of soil microbial communities and ecosystem multifunctionality. Our results demonstrated that Proteobacteria, Ascomycota, and Crenarchaeota were dominant in bacteria, fungi, and archaea, respectively. Bacteria, fungi, and archaea were all influenced by the combined disturbance of grazing and pika. Most fungal communities became convergent, while bacterial and archaeal communities became differentiated during the succession of surface types. In particular, the bacterial and fungal networks were less stable than archaeal networks. In response to the interference, cross-domain cooperation between bacterial and fungal communities increased, while competitive interactions between bacterial and archaeal communities increased. Pika disturbance at high intensity significantly reduced the ecosystem multifunctionality. However, the mixed effects of grazing and pika weakened such influences. This study revealed how pika activities affected microbial networks and ecosystem multifunctionality. These results provide insights to designing reasonable ecological management strategies for alpine grassland ecosystems.

Factors Influencing Bacterial and Fungal Skin Communities of Montane Salamanders of Central Mexico

Host microbial communities are increasingly seen as an important component of host health. In amphibians, the first land vertebrates that are threatened by a fungal skin disease globally, our understanding of the factors influencing the microbiome of amphibian skin remains incomplete because recent studies have focused almost exclusively on bacteria, and little information exists on fungal communities associated with wild amphibian species. In this study, we describe the effects of host phylogeny, climate, geographic distance, and infection with a fungal pathogen on the composition and structure of bacterial and fungal communities in seven tropical salamander species that occur in the Trans-Mexican Volcanic Belt of Central Mexico. We find that host phylogenetic relatedness is correlated with bacterial community composition while a composite climatic variable of temperature seasonality and precipitation is significantly associated with fungal community composition. We also estimated co-occurrence networks for bacterial and fungal taxa and found differences in the degree of connectivity and the distribution of negative associations between the two networks. Our results suggest that different factors may be responsible for structuring the bacterial and fungal communities of amphibian skin and that the inclusion of fungi in future studies could shed light on important functional interactions within the microbiome.

Complexity-stability trade-off in empirical microbial ecosystems

May's stability theory, which holds that large ecosystems can be stable up to a critical level of complexity, a product of the number of resident species and the intensity of their interactions, has been a central paradigm in theoretical ecology. So far, however, empirically demonstrating this theory in real ecological systems has been a long-standing challenge with inconsistent results. Especially, it is unknown whether this theory is pertinent in the rich and complex communities of natural microbiomes, mainly due to the challenge of reliably reconstructing such large ecological interaction networks. Here we introduce a computational framework for estimating an ecosystem's complexity without relying on a priori knowledge of its underlying interaction network. By applying this method to human-associated microbial communities from different body sites and sponge-associated microbial communities from different geographical locations, we found that in both cases the communities display a pronounced trade-off between the number of species and their effective connectance. These results suggest that natural microbiomes are shaped by stability constraints, which limit their complexity.

From diversity to complexity: Microbial networks in soils

Network analysis has been used for many years in ecological research to analyze organismal associations, for example in food webs, plant-plant or plant-animal interactions. Although network analysis is widely applied in microbial ecology, only recently has it entered the realms of soil microbial ecology, shown by a rapid rise in studies applying co-occurrence analysis to soil microbial communities. While this application offers great potential for deeper insights into the ecological structure of soil microbial ecosystems, it also brings new challenges related to the specific characteristics of soil datasets and the type of ecological questions that can be addressed. In this Perspectives Paper we assess the challenges of applying network analysis to soil microbial ecology due to the small-scale heterogeneity of the soil environment and the nature of soil microbial datasets. We review the different approaches of network construction that are commonly applied to soil microbial datasets and discuss their features and limitations. Using a test dataset of microbial communities from two depths of a forest soil, we demonstrate how different experimental designs and network constructing algorithms affect the structure of the resulting networks, and how this in turn may influence ecological conclusions. We will also reveal how assumptions of the construction method, methods of preparing the dataset, and definitions of thresholds affect the network structure. Finally, we discuss the particular questions in soil microbial ecology that can be approached by analyzing and interpreting specific network properties. Targeting these network properties in a meaningful way will allow applying this technique not in merely descriptive, but in hypothesis-driven research. Analysing microbial networks in soils opens a window to a better understanding of the complexity of microbial communities. However, this approach is unfortunately often used to draw conclusions which are far beyond the scientific evidence it can provide, which has damaged its reputation for soil microbial analysis. In this Perspectives Paper, we would like to sharpen the view for the real potential of microbial co-occurrence analysis in soils, and at the same time raise awareness regarding its limitations and the many ways how it can be misused or misinterpreted.

iNAP: An integrated network analysis pipeline for microbiome studies

Integrated network analysis pipeline (iNAP) is an online analysis pipeline for generating and analyzing comprehensive ecological networks in microbiome studies. It is implemented in two sections, that is, network construction and network analysis, and integrates many open-access tools. Network construction contains multiple feasible alternatives, including correlation-based approaches (Pearson's correlation and Spearman's rank correlation along with random matrix theory, and sparse correlations for compositional data) and conditional dependence-based methods (extended local similarity analysis and sparse inverse covariance estimation for ecological association inference), while network analysis provides topological structures at different levels and the potential effects of environmental factors on network structures. Considering the full workflow, from microbiome data set to network result, iNAP contains the molecular ecological network analysis pipeline and interdomain ecological network analysis pipeline (IDENAP), which correspond to the intradomain and interdomain associations of microbial species at multiple taxonomic levels. Here, we describe the detailed workflow by taking IDENAP as an example and show the comprehensive steps to assist researchers to conduct the relevant analyses using their own data sets. Afterwards, some auxiliary tools facilitating the pipeline are introduced to effectively aid in the switch from local analysis to online operations. Therefore, iNAP, as an easy-to-use platform that provides multiple network-associated tools and approaches, can enable researchers to better understand the organization of microbial communities. iNAP is available at http://mem.rcees.ac.cn:8081 with free registration.

Eutrophication in subtropical lakes reinforces the dominance of balanced-variation component in temporal bacterioplankton community heterogeneity by lessening stochastic processes

Unveiling the rules of bacterioplankton community assembly in anthropogenically disturbed lakes is a crucial issue in aquatic ecology. However, it is unclear how the ecological processes underlying the seasonally driven bacterioplankton community structure respond to varying degrees of lake eutrophication. We therefore collected water samples from three subtropical freshwater lakes with various trophic states (i.e. oligo-mesotrophic, mesotrophic and eutrophic states) on a quarterly basis between 2017 and 2018. To innovatively increase our understanding of bacterioplankton community assembly along the trophic state gradient, the total bacterioplankton community dissimilarity was subdivided into balanced variation in abundances and abundance gradients. The results indicated that balanced-variation component rather than abundance-gradient component dominated the total temporal β-diversity of bacterioplankton communities across all trophic categories. Ecological stochasticity contributed more to the overall bacterioplankton community assembly in the oligo-mesotrophic and mesotrophic lakes than in the eutrophic lake. The reduced bacterioplankton network complexity at the eutrophic level was closely associated with the enhancement of environmental filtering, showing that bacterioplankton communities in eutrophic lakes are likely to be less stable and more vulnerable to water quality degradation. Together, this study offers essential clues for biodiversity conservation in subtropical lakes under future intensified eutrophication.

A meta-analysis study of the robustness and universality of gut microbiota-shrimp diseases relationship

Intensive case study has shown dysbiosis in the gut microbiota-shrimp disease relationship, however, variability in experimental design and the diversity of diseases arise the question whether some gut indicators are robust and universal in response to shrimp health status, irrespective of causal agents. Through an unbiased subject-level meta-analysis framework, we re-analyzed 10 studies including 261 samples, 4 lifestages, 6 different diseases (the causal agents are virus, bacterial, eukaryotic pathogens, or unknown). Results showed that shrimp diseases reproducibly altered the structure of gut bacterial community, but not diversity. After ruling out the lifestage- and disease specific- discriminatory taxa (different diseases dependent indicators), we identify 18 common disease-discriminatory taxa (indicative of health status, irrespective of causal agents) that accurately diagnosed (90.0% accuracy) shrimp health status, regardless of different diseases. These optimizations substantially improved the performance (62.6% vs. 90.0%) diagnosing model. The robustness and universality of model was validated for effectiveness via leave-one-dataset-out validation and independent cohorts. Interspecies interaction and stability of the gut microbiotas were consistently compromised in diseased shrimp compared with corresponding healthy cohorts, while stochasticity and beta-dispersion exhibited the opposite trend. Collectively, our findings exemplify the utility of microbiome meta-analyses in identifying robust and reproducible features for quantitatively diagnosing disease incidence, and the downstream consequences for shrimp pathogenesis from an ecological prospective. This article is protected by copyright. All rights reserved.

Statistical Methods for Microbiome Compositional Data Network Inference: A Survey

Microbes can be found almost everywhere in the world. They are not isolated, but rather interact with each other and establish connections with their living environments. Studying these interactions is essential to an understanding of the organization and complex interplay of microbial communities, as well as the structure and dynamics of various ecosystems. A widely used approach toward this objective involves the inference of microbiome interaction networks. However, owing to the compositional, high-dimensional, sparse, and heterogeneous nature of observed microbial data, applying network inference methods to estimate their associations is challenging. In addition, external environmental interference and biological concerns also make it more difficult to deal with the network inference. In this article, we provide a comprehensive review of emerging microbiome interaction network inference methods. According to various research targets, estimated networks are divided into four main categories: correlation networks, conditional correlation networks, mixture networks, and differential networks. Their assumptions, high-level ideas, advantages, as well as limitations, are presented in this review. Since real microbial interactions can be complex and dynamic, no unifying method has, to date, captured all the aspects of interest. In addition, we discuss the challenges now confronting current microbial interaction study and future prospects. Finally, we point out several feasible directions of microbial network inference analysis and highlight that future research requires the joint promotion of statistical computation methods and experimental techniques.

Best practices in metabarcoding of fungi: From experimental design to results

The development of high-throughput sequencing (HTS) technologies has greatly improved our capacity to identify fungi and unveil their ecological roles across a variety of ecosystems. Here we provide an overview of current best practices in metabarcoding analysis of fungal communities, from experimental design through molecular and computational analyses. By reanalysing published data sets, we demonstrate that operational taxonomic units (OTUs) outperform amplified sequence variants (ASVs) in recovering fungal diversity, a finding that is particularly evident for long markers. Additionally, analysis of the full-length ITS region allows more accurate taxonomic placement of fungi and other eukaryotes compared to the ITS2 subregion. Finally, we show that specific methods for compositional data analyses provide more reliable estimates of shifts in community structure. We conclude that metabarcoding analyses of fungi are especially promising for integrating fungi into the full microbiome and broader ecosystem functioning context, recovery of novel fungal lineages and ancient organisms as well as barcoding of old specimens including type material.

Conceptual strategies for characterizing interactions in microbial communities

Understanding the sets of inter- and intraspecies interactions in microbial communities is a fundamental goal of microbial ecology. However, the study and quantification of microbial interactions pose several challenges owing to their complexity, dynamic nature, and the sheer number of unique interactions within a typical community. To overcome such challenges, microbial ecologists must rely on various approaches to distill the system of study to a functional and conceptualizable level, allowing for a practical understanding of microbial interactions in both simplified and complex systems. This review broadly addresses the role of several conceptual approaches available for the microbial ecologist’s arsenal, examines specific tools used to accomplish such approaches, and describes how the assumptions, expectations, and philosophies underlying these tools change across scales of complexity.

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

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

Strong reorganization of multi-domain microbial networks associated with primary producers sedimentation from oxic to anoxic conditions in an hypersaline lake

Understanding the role of microbial interactions in the functioning of natural systems is often impaired by the levels of complexity they encompass. In this study, we used the relative simplicity of an hypersaline crater lake hosting only microbial organisms (Dziani Dzaha) to provide a detailed analysis of the microbial networks including the three domains of life. We identified two main ecological zones, one euphotic and oxic zone in surface, where two phytoplanktonic organisms produce a very high biomass, and one aphotic and anoxic deeper zone, where this biomass slowly sinks and undergoes anaerobic degradation. We highlighted strong differences in the structure of microbial communities from the two zones and between the microbial consortia associated with the two primary producers. Primary producers sedimentation was associated with a major reorganization of the microbial network at several levels: global properties, modules composition, nodes and links characteristics. We evidenced the potential dependency of Woesearchaeota to the primary producers' exudates in the surface zone, and their disappearance in the deeper anoxic zone, along with the restructuration of the networks in the anoxic zone toward the decomposition of the organic matter. Altogether, we provided an in-depth analysis of microbial association network and highlighted putative changes in microbial interactions supporting the functioning of the two ecological zones in this unique ecosystem.

Mathematical-based microbiome analytics for clinical translation

Traditionally, human microbiology has been strongly built on the laboratory focused culture of microbes isolated from human specimens in patients with acute or chronic infection. These approaches primarily view human disease through the lens of a single species and its relevant clinical setting however such approaches fail to account for the surrounding environment and wide microbial diversity that exists in vivo. Given the emergence of next generation sequencing technologies and advancing bioinformatic pipelines, researchers now have unprecedented capabilities to characterise the human microbiome in terms of its taxonomy, function, antibiotic resistance and even bacteriophages. Despite this, an analysis of microbial communities has largely been restricted to ordination, ecological measures, and discriminant taxa analysis. This is predominantly due to a lack of suitable computational tools to facilitate microbiome analytics. In this review, we first evaluate the key concerns related to the inherent structure of microbiome datasets which include its compositionality and batch effects. We describe the available and emerging analytical techniques including integrative analysis, machine learning, microbial association networks, topological data analysis (TDA) and mathematical modelling. We also present how these methods may translate to clinical settings including tools for implementation. Mathematical based analytics for microbiome analysis represents a promising avenue for clinical translation across a range of acute and chronic disease states.

Autochthonous fungi are central components in microbial community structure in raw fermented sausages

Raw meat sausage represents a unique ecological niche rich in nutrients for microbial consumption, making it particularly vulnerable to microbial spoilage. Starter cultures are applied to improve product stability and safety as well as flavour characteristics. However, the influence of starter cultures on microbial community assembly and succession throughout the fermentation process is largely unknown. In particular the effect on the fungal community has not yet been explored. We evaluate the microbiological status of four different raw meat sausages using high‐throughput 16S rRNA gene and ITS2 gene sequencing. The objective was to study temporal changes of microbial composition during the fermentation process and to identify potential keystone species that play an important role within the microbial community. Our results suggest that fungi assigned to the species Debaryomyces hansenii and Alternaria alternata play a key role in microbial community dynamics during fermentation. In addition, bacteria related to the starter culture Lactobacillus sakei and the spoilage‐associated genera Acinetobacter, Pseudomonas and Psychrobacter are central components of the microbial ecosystem in raw fermented sausages. Elucidating the exact role and interactions of these microorganisms has the potential to have direct impacts on the quality and safety of fermented foods.

Modelling microbial communities: Harnessing consortia for biotechnological applications

Microbes propagate and thrive in complex communities, and there are many benefits to studying and engineering microbial communities instead of single strains. Microbial communities are being increasingly leveraged in biotechnological applications, as they present significant advantages such as the division of labour and improved substrate utilisation. Nevertheless, they also present some interesting challenges to surmount for the design of efficient biotechnological processes. In this review, we discuss key principles of microbial interactions, followed by a deep dive into genome-scale metabolic models, focussing on a vast repertoire of constraint-based modelling methods that enable us to characterise and understand the metabolic capabilities of microbial communities. Complementary approaches to model microbial communities, such as those based on graph theory, are also briefly discussed. Taken together, these methods provide rich insights into the interactions between microbes and how they influence microbial community productivity. We finally overview approaches that allow us to generate and test numerous synthetic community compositions, followed by tools and methodologies that can predict effective genetic interventions to further improve the productivity of communities. With impending advancements in high-throughput omics of microbial communities, the stage is set for the rapid expansion of microbial community engineering, with a significant impact on biotechnological processes.