https://orcid.org/0000-0002-8330-4071
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
ONE SENTENCE SUMMARY
Computer-assisted biosynthetic capacities elucidation accelerates our ability to interpret microbial interactions, allowing us to understand better and establish a balance within ecosystems.
1. Introduction
A microorganism, a microbe, is a living organism that can only be seen under a microscope. Microbes are social organisms, living in intra- or inter-species communities, and this umbrella term includes a wide range of organisms, such as bacteria, fungi, algae, protozoa, or viruses. Microbial communities are found in almost every environment on Earth, including soil, water, and the human body, and govern many ecological and biological processes (Vincent et al. Citation2021). To thrive in such a variety of ecosystems, microbes have evolved to adapt and reshape their environment in multiple ways, particularly through metabolite and peptide signaling, predation, cross-feeding, or co-metabolism among diverse microbial populations (Phelan et al. Citation2012).
Being omnipresent, the impact of microbial interaction can sometimes be easily observed by the naked eye in nature, for example, in the case of bioluminescence, the ability of a diverse range of organisms to produce and emit light for communication or defense. It is particularly stunning in bacterial communities in the marine ecosystem (Timsit et al. Citation2021). Microbes also affect terrestrial and marine biogeochemical cycling and adapt to climate changes (Zhang and Straight Citation2019; Abdel-Razek et al. Citation2020). Research in the biomedical domain also showed that the effects of metabolic exchange within microbial communities can boost host immunity (Wang et al. Citation2019b). The metabolites released from such microbe-microbe, microbe-host, or microbe-environment interactions exhibit various biological and biochemical activities like an antibiotic, anticancer, immunosuppressant, chemotherapeutic, and anti-inflammatory, which can be explored for drug discovery (Zhang and Straight Citation2019; Abdel-Razek et al. Citation2020). Consequently, gaining an understanding of the complex nature of microbial interactions is essential for interpreting the environment and harnessing the resulting knowledge to advance various scientific disciplines, including drug discovery, agriculture, and bioenergy.
For the study of microbial communities and their communication, a traditional approach is first to establish the composition of the given community, qualitatively, e.g. its biodiversity, and quantitatively, e.g. relative proportions of each species within this community. Microbiologists have, for centuries, relied on culturing techniques and phenotypic trait characterization to identify and study microbes. To this date, the most common laboratory techniques in microbiology are culturing, staining, and isolating (Essam Citation2022). These techniques are used to isolate a specific microbial strain which is then grown on culture media to study the growth of the strain, or to differentiate between different microbes present in a sample via staining techniques using single or multiple dyes. However, these approaches disregard the presence of uncultivable microbes (Staley and Konopka Citation1985), overlooking the metabolic potential of 99% of specimens in the sample (Riesenfeld et al. Citation2004).
The advent of molecular biology techniques, such as ribosomal sequencing, and more recently, cost-effective whole genome sequencing, has revolutionized the study of microbial communities. These advancements have made it possible to study these communities without the need for traditional culturing techniques. The last two decades saw the rise of a multitude of culture-independent methods, such as metagenomics, metatranscriptomics, and metaproteomics (all together gathered under the “meta-omics” term), providing new and powerful ways to analyze the genetic and functional diversity of microbial communities. These methods allow for the direct analysis of environmental samples, bypassing the need for cultivation and providing a more comprehensive picture of the complex interactions and behaviors within microbial communities (Su et al. Citation2012).
The large-scale data generated from meta-omics approaches require the use of computational tools, to operate the large data volume, data diversity, and data complexity and to ensure data accuracy and analysis reproducibility. Understanding the complex interactions within microbial communities requires the analysis of multiple key players participating in metabolic exchanges. These interactions can also be affected by spatiotemporal factors. Here, standard omics computational tools are not sufficient to analyze such complicated systems, so specialized algorithms and computational tools have been developed for this purpose. Such tools rely on the data acquired from meta-omics samples which are used as input for the prediction of the biodiversity and chemo-diversity of the community. Most tools are developed on simpler and highly controlled systems, such as two organisms’ co-culture metabolic interactions or paired omics techniques. These can be baseline approaches to study larger complex microbial communities and multi-omics integration (Franzosa et al. Citation2015; Thuan et al. Citation2022). Various secondary metabolites are generated in nature by metabolic interactions between microorganisms (Schroeckh et al. Citation2009), therefore it is important to analyze these interactions as they shape the chemo- and bio-diversity and the functional aspect of the communities, which expands the available knowledge on microbial ecology (Lv et al. Citation2020).
In this review, we explore the computational tools that unravel the ability of the multi-species consortia to produce and exchange metabolites via chemical pathways and refer to the latter as “biosynthetic capacities elucidation of microbial communities” (Chiu et al. Citation2014). Biosynthetic capacities not only consist in the elucidation of the molecular structure and function but also the structure-activity relationship, types of microbial interactions, and the physical environment of the community. Here we will address the set of computational methods to assess the metabolic interactions among microbial communities as Computer-Assisted Biosynthetic capacities ELucidation (CABEL). We also review the various computer-aided tools, algorithms, and approaches in the field of microbial ecology focusing on the computer-assisted biosynthetic capacity elucidation (CABEL) of the microorganisms in a community. While specific aspects of microbial communities have been extensively covered in existing literature (Faust and Raes Citation2012; Segata et al. Citation2013; Song et al. Citation2014; Franzosa et al. Citation2015; Braga et al. Citation2016; Ang et al. Citation2018; Pena et al. Citation2019; Liu et al. Citation2021; Bhosle et al. Citation2022; Guo et al. Citation2022), this review offers a comprehensive and holistic perspective on researching microbial communities, ranging from the observable traits to genetic makeup, chemical interactions, and the dynamic changes taking place within the community. CABEL is focused on deciphering the biosynthetic capacities of individual components within microbial communities which leads the way to understanding the microbial interactions using multi-omics and metabolic network modeling. CABEL also incorporates analysis of metabolome, secretome, and species composition, which aids in addressing the challenges of masked components in community settings. We discuss (1) how to assess the metabolome/secretome of the communities and (2) profile the microbes that take part in the metabolic exchange, (3) integrate multi-meta-omics data to generate complex metabolic networks which are used to (4) study the chemical interactions and the possible perturbations in the microbial communities.
2. Microbial communication is mainly happening in the extracellular space
One of the historically first, most direct, and simplest ways of observing microbial communities is through their phenotypic traits. Algal blooms are examples of microbial communities exhibiting strikingly visible phenotypic characteristics (Schleyer and Vardi Citation2020). These blooms occur in fresh and seawater due to aggregation of phytoplankton which causes coloration of water or colored scum on the surface. To study how these aggregates or colonies are changing their environment from the biochemical perspective, the chemical constituents are structurally and functionally annotated.
2.1. Analytical methods to analyze chemical constituents
Various analytical techniques are used to elucidate the chemical structures of the metabolites and proteins involved in the phenotypic expression from microbial communities. After sample collection from such community sites or aggregates, cells are separated from their natural growth media, and the latter is analyzed for molecular composition. The identification and quantification of these compounds are typically addressed by metabolomics, a field that has constantly evolved since the 1940s. The most widely used analytical approach to identify metabolites from a community sample is the cost-effective untargeted Liquid Chromatography Mass Spectrometry (LC-MS) which measures a broad range of signals from the sample. The significant signals from LC-MS are further fragmented for higher structural resolution via the tandem MS (MS2) approach. The data acquired from these analytical techniques are further analyzed quantitatively (signal intensity) and qualitatively (structure annotation). Significantly intense signals from LC-MS that could not be structurally annotated, can be reexamined with a more costly approach, Nuclear Magnetic Resonance (NMR). NMR typically takes a larger volume of an isolated compound as it is less sensitive than MS, but performs de novo elucidation of chemical structures at the atomic level. MS and NMR techniques complement each other. Advances in these analytical technologies produce a plethora of spectral data, and many workflows and pipelines have been developed to analyze it (Putri et al. Citation2013). The data acquired from the metabolome of the microbes is highly diverse and the identification of such chemo-diverse data remains the bottleneck in metabolomics analysis.
2.2. Factors influencing interaction
The interactions between the key players in a microbial community are shaped by three main factors: environmental conditions (such as light, temperature, pH, water, or stress), biological factors (such as the introduction of novel organisms into the community), and chemical factors (such as the exometabolome produced by community members, naturally occurring chemicals in the environment, and chemicals introduced by human intervention) (Weiland-Bräuer Citation2021). These factors can influence the mode of action for interactions, such as through quorum sensing or cell-to-cell contact, as depicted in . Bacterial cells have evolved complex secretion systems that transport signaling peptides and molecules to the extracellular environment or neighboring cells (Pena et al. Citation2019). Microbes, including both prokaryotes and eukaryotes, release secretory vesicles that carry the signaling compounds to the extracellular environment, thereby enabling communication and regulation of the microbial community (Gill et al. Citation2019).
Figure 1. Pathways activated/inhibited by the exchange of metabolites via extracellular space. This figure illustrates four participant species exchanging metabolites. Species B and species C have a mutualistic interaction. Species C facilitates the growth of species A. Species A has a parasitic relation with species B, where species A is facilitated by metabolites released from species B but the metabolites released from species A have adverse effects on species B. While species B and D are in contact through cell-to-cell contact via gap junctions. The small nodes here represent metabolites, the connections between nodes represent reaction, and the Mono-colored nodes and connections represent a biochemical pathway. This is a simple representation of metabolic pathways and the effect of metabolites from neighboring organisms.
2.2.1. Microbial interaction modes of action
Quorum sensing (QS) is a mode of communication within mainly bacterial communities. It governs different biological processes (Bronesky et al. Citation2016), such as stress adaptation (Joelsson et al. Citation2007), formation of biofilms (Solano et al. Citation2014), and production of natural products (NPs) (Liu et al. Citation2007; Johnson et al. Citation2016) via signaling molecules called autoinducers, released based on the cell population density (Pena et al. Citation2019). QS can also occur between bacteria and eukaryotes. For example, in a study on cyanobacteria and green algae co-culture, green algae were absorbing cyanobacterial metabolites via QS which after a certain threshold turned into quorum quenching (opposite of QS, interruption of communication) (Gautam et al. Citation2019). In cell-to-cell contact, usually, not only metabolites and small molecules but also proteins and genomic content can be exchanged between adhesive cells (Zhao et al. Citation2017). Cell-to-cell contact is direct contact between two cells via gap junctions (tubular intercellular channels), tight junctions (mediating paracellular permeability), and desmosomes (adhesive molecular threads linking cells) (Kook et al. Citation2017).
2.2.2. Types of metabolic exchange
Microorganisms communicate and exchange chemicals through the exometabolome, which is vital in determining the type of interactions that occur within a microbial community. These secreted entities can have a positive, negative, or neutral effect on the microorganisms involved, and they are responsible for a range of interactions, as previously described in microbial interaction reviews (Lidicker Citation1979; Tshikantwa et al. Citation2018). The three primary types of chemical exchange described by Tshikantwa et al. between microorganisms are:: commensalism, parasitism, and mutualism. Commensalism (or facilitation) occurs when species A releases a metabolite that benefits species B but has no impact on species A. Parasitism (or predation) is when species A releases a metabolite that benefits species B but negatively affects species A. Lastly, mutualism (also called synergism or reciprocity) refers to both species exchanging metabolites that benefit each other. illustrates these three interaction types.
2.3. Extracellular signals: exometabolome and secretome
Organisms communicate with each other and regulate their interactions in the microbial community through the release of metabolites and proteins into the extracellular environment. These extracellular signals, referred to as the exometabolome and secretome, respectively, have been shown to play a crucial role in determining the type of chemical interactions that take place within the community and shaping its biodiversity (Douglas Citation2020). For example, the presence of certain metabolites in the extracellular space may promote or inhibit the growth of specific microorganisms and influence their behavior, such as changes in motility, adherence, or metabolic activity (Chagnot et al. Citation2013; Gagic et al. Citation2016). The release of extracellular proteins can also play a role in regulating microbial interactions. For instance, these proteins may regulate the adhesion of microorganisms to environmental surfaces, such as biofilms, or other organisms in the vicinity, leading to the formation of microbial communities (Tjalsma et al. Citation2000). However, it is important to acknowledge that extracellular metabolites that are exchanged among the microbes can be difficult to measure in case of immediate consumption after being released into the extracellular environment (Ponomarova and Patil Citation2015). The sections 2.3.1 and 2.3.2 describe exometabolome and secretome in detail.
2.3.1. Exometabolome analysis
Exometabolome, also known as the metabolic footprint, is the metabolites released from the cells into the surrounding medium. It provides an overview of the cellular phenotypes and the chemical environment that is maintained within a certain microbial community over minutes and hours (Vuckovic Citation2012). The exometabolome largely determines the types of interactions taking place due to the physicochemical properties of the metabolites. Exometabolomics is considered one of the robust approaches to determining the phenotype of microbial communities. Unlike the more “classic” intracellular metabolites (endometabolites), exometabolites remain integrated within the extracellular environment for a significantly longer time and keep influencing the community phenotype (Silva and Northen Citation2015). Usually, in MS exometabolomics, the metabolites are extracted before the culturing and after the culturing to see the difference in the content and quantification of the extracellular metabolites. A study done on a co-culture of two marine bacterial species revealed an exchange of vitamins B and quorum sensing-induced compounds via Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), by measuring the late precursors of vitamins B and quorum sensing-induced compounds biosynthetic pathways; these detected metabolites were also observed in phytoplankton blooms (Wienhausen et al. Citation2017). Other studies on the exometabolome have been conducted to investigate the molecular interactions between yeast-yeast pairs, bacteria-algal communities, and other microbial interactions (Piampiano et al. Citation2021; Tran et al. Citation2022). The results of such experiments have been compiled in the Web of Microbes, a curated data repository that links environments (the starting metabolic pool), metabolites (the organic compounds measured in the transformed environments), and key microbes (the transforming agent either as monoculture or a community) (Kosina et al. Citation2018). The exometabolites are heavily influenced by the environmental conditions as described in section 2.2, and also by the different microbial taxa that are living in the environment. A correlation among all three entities (metabolites, environment, and microbes) is required to make a conclusive argument (Zelezniak et al. Citation2015). The environmental samples are quite difficult because of the increase in salt and other minerals, which could impede the extraction of metabolites and the ionization process during LC-MS analysis. To address this issue, MetFish pipeline can be used which chemically tags the targeted chemical group in the exometabolites and extracts them for further downstream analysis (Xu et al. Citation2021). Due to the biodiversity in a community setting, there is a lack of knowledge on the organism responsible for releasing an exometabolome in the surroundings, and the effect of the environment on the metabolite production, release, and transformation rates over time. Despite these challenges, using the right strategy for the research interest can help shorten the knowledge gap. To analyze the exometabolites, general metabolomics workflows can be applied including pipelines, which are available in R and Python, as well as in graphical user interface (GUI) and web services, and are designed for LC-MS2 data structure prediction or NMR structure elucidation. lists some of the most widely used tools and databases in this field.
Table 1. List of tools and databases for detection, prediction, and identification of exometabolome and secretome.
2.3.2. Secretome analysis
In addition to the exometabolome, the secretome—which refers to the secreted proteins and peptides, influenced by environmental fluctuations (Zullo et al. Citation2015)—also plays a crucial role in cell to cell communication between microbes. Microbes also acquire nutrients from the environment through secretome (including peptidases and carbohydrate active enzymes or CAZymes, as observed in marine sediments), which initiate digestion outside the cell, and afterward, the digestible nutrients are absorbed back into the cell (Orsi et al. Citation2018). Secretome have also depicted a role in the protection of “public goods,” i.e. secreted products (metabolites or enzymes) that are responsible for favorable shared traits among microbial communities, such as resistance to antibiotics or predation (Lindsay et al. Citation2021). Bioinformatics tools have been developed to predict the secretome and identify protein structures released in the extracellular environment. As with exometabolome analysis, proteomics and metabolomics tools can be used to analyze secretomes (Sethupathy et al. Citation2021). One study found a correlation between secretome content and the development of biofilms in acid mine drainage (Erickson et al. Citation2010). Other studies used quantitative proteome analysis to identify lignin-degrading enzymes in bacterial and fungal strains, and plant microbiomes (Adav et al. Citation2012; Alessi et al. Citation2017; Paës et al. Citation2019). However, simply relying on proteomics analysis is not sufficient to fully identify the secretome (Agrawal et al. Citation2010). Prediction tools for secretomes are built on the idea of conserved N-terminal signal peptide architecture in bacteria and eukaryotes (von Heijne Citation1985; Lee et al. Citation2006). These tools typically take FASTA sequences as inputs, and therefore, having sequenced genomes of known organisms is crucial. There are also genome-wide secretome databases, such as LAB-Secretome, to aid the prediction process (Zhou et al. Citation2010). It is important to note that some secreted proteins lack the classical signal peptide sequence, and some transmembrane proteins contain signal peptides but are not secreted to the extracellular environment. Tools that can predict such secreted proteins and transmembrane proteins can help reduce false negative and false positive results from the signal peptide-based tools, respectively. A comprehensive review of bioinformatics tools for secretome analysis can be found in Caccia et al. (Citation2013). lists the most commonly used tools and databases for secretome analysis.
3. Elucidation of the taxonomic composition of microbial communities
Exometabolome and secretome analysis is a powerful tool for identifying the chemical profile of microbial communities, shedding light on the molecules that microbes secrete into and absorb from their environment. However, this approach does not reveal the taxonomic composition of the community, which is essential for understanding its ecological significance. The taxonomic composition of the community can be elucidated via its genomic content. Microbiome taxonomic profiling involves analyzing the taxonomic composition of microorganisms using genome sequencing, providing insights into their diversity, ecology, and functional potential. This information is crucial for understanding the role of different microorganisms in ecological and physiological processes. The integration of exometabolome, secretome analysis, and microbiome taxonomic profiling allows for a more comprehensive understanding of microbial communities, elucidating their potential interactions and contributions to the ecosystem (Quince et al. Citation2017).
3.1. Taxonomic classification using small ribosomal subunit RNA sequencing
One of the earliest culture-independent techniques for identifying prokaryotic organisms from community samples is 16s rRNA sequencing, which was first introduced in 1977 (Woese and Fox Citation1977). This method involves PCR amplification of the small ribosomal subunit sequences [16s rRNA for prokaryotes (Schmidt et al. Citation1991) and 18s rRNA for eukaryotes (James et al. Citation1994)], using the exceptionally high sequence conservation in rRNA regions embedded within the rRNA variable regions. The resulting sequences are clustered into operational taxonomic units (OTUs) that can be compared against the reference databases to taxonomically classify organisms. While this approach is widely used for taxonomic profiling and is cost-effective, it provides limited information on the strain content of the microbial community. To overcome this limitation, complete 16S rRNA gene sequencing is necessary, which provides a higher taxonomic resolution and can discriminate between different microbes based on their hypervariable regions (Wu et al. Citation2015; Jeong et al. Citation2021). In addition, the copy variants of the gene can be used for strain-level identification through 16S gene polymorphism (Johnson et al. Citation2019). However, the sequencing of only one gene is not sufficient for the functional profiling of multiple genes present in microbial genomes and to elucidate their metabolic potential.
3.2. Whole meta-genome sequencing for advanced taxonomic profiling
Whole genome sequencing (WGS) is a next-generation sequencing method that allows the elucidation of the sequence of the complete genome of an organism. Conversely, whole metagenome sequencing (WMS) is a culture-independent sequencing technique that enables the sequencing of all genetic material present in a given sample of a microbial community or microbiome. WMS has transformed the taxonomic profiling of microbes in complex communities, providing a comprehensive view of the community by enabling the identification of both known and new species, as well as new proteins, particularly enzymes, and their possible bioactivities (Rondon et al. Citation2000). The high-throughput nature of WMS has led to the discovery of an increasing number of species and strains in complex microbial communities (Liang et al. Citation2020).
WMS-based taxonomic profiling can be performed using various techniques, including assembly-based, compositional, mapping- or alignment-based, and machine learning-based methods. Each of these methods has its advantages and limitations, and the choice of method depends on the specific goals and requirements of the study. illustrates the different metagenome-based taxonomy profiling methods, while provides a list of tools for each approach.
Figure 2. Overview of taxonomy profiling methods for metagenomics, the diagram depicts the various taxonomy profiling methods for whole meta-genome sequencing (WMS), including assembly-based (ABTP), compositional (CMTP), mapping- or alignment-based (M-ABTP), and machine learning-based methods (ML-BTP). In ABTP, similar sequenced fragments are assembled into contigs; similar contigs form bins, and finally, they result in MAGs. In CMTP, a subset of sequences from WMS, like conserved marker genes, are used that are unique for a specific clade of species, which are searched in taxonomy marker databases. In M-ABTP, all the WMS are mapped or aligned against a reference database of known microbial genomes, and in ML-BTP, different advanced computational algorithms and statistical models are used. Further, downstream analyses like co-expression and expression network generation can be performed on sequences that have already been assigned to specific clades.
Table 2. List of tools for taxonomic profiling of microbial communities.
3.2.1. Assembly-based taxonomic profiling
The assembly-based taxonomic profiling (ABTP) technique involves the assembly of DNA sequencing data from a microbial community sample into larger fragments called contigs (Scholz et al. Citation2015). These contigs are then compared against a database of known microbial genomes, such as RefSeq (O’Leary et al. Citation2016), NCBI (Federhen Citation2012), RDP (Wang et al. Citation2007), Greengene (McDonald et al. Citation2012), or SILVA (Quast et al. Citation2013), which enables identification and quantification of the various taxa present in the sample (Thomas et al. Citation2012). ABTP approaches are also used to identify novel species and strains, by separating contigs into larger bins based on taxonomic alignment, codon usage, and marker genes, called metagenome-assembled genomes (MAGs). MAGs validation is achieved through two checkpoints: contamination from noise data and completeness of the assembly (Parks et al. Citation2015). MAGs can be used as a reference genome for microbes that belong to the same taxon (Yang et al. Citation2021). The use of MAGs has led to the discovery of novel microbial species, reducing the microbial dark matter present in metagenomes (Lloyd et al. Citation2018).
3.2.2. Compositional methods for taxonomic profiling
Compositional methods for taxonomic profiling (CMTP) aim to determine the relative abundance of different taxa within a community while taking into account the inherent compositional nature of the data. Compositional data analysis (CoDA) is an approach that addresses the issue of relative abundances that sum to a constant, as is the case for microbial community data (Gloor et al. Citation2017). This information is significant as the abundance of certain species can impact community metabolism (Ross et al. Citation2013). The abundance of a taxon refers to the abundance of read counts for the genome detected for that taxon. In the CMTP approach, DNA sequences from the sample are compared to reference databases to provide an estimate of the relative abundance of different taxa, based on the presence of conserved marker genes, GC content, and codon bias (Scholz et al. Citation2015). To analyze compositional data, various methods, including centered log-ratio transformation and additive log-ratio transformation, are used to transform the data into unconstrained space, where standard statistical tools can be applied (Brückner and Heethoff Citation2017). Some tools are used to perform read count correction to calculate the abundance of different taxa. These methods are relatively quick, scalable, and efficient, making them ideal for large-scale comparative studies and complex microbiome analysis.
3.2.3. Mapping and alignment methods for taxonomic profiling
Compositional methods for taxonomic profiling (CMTP) are widely used to estimate the relative abundance of different taxa in a microbial community. However, these methods have an upper limit of taxonomy resolution up to the genus level. To achieve species-level taxonomy, mapping or alignment-based methods are used in combination with compositional methods for taxonomic profiling (M-ABTP). Unlike CMTP, M-ABTP uses the entire reference genome for the alignment process, without relying on the presence of conserved marker genes or regions. These methods allow for more accurate taxonomic classification at the species level, making them ideal for more in-depth microbiome analysis. Some examples of M-ABTP methods include Kraken2 (Wood et al. Citation2019), and CLARK (Ounit and Lonardi Citation2016). However, these methods have their limitations, including a decrease in sensitivity when analyzing low-complexity or poorly assembled genomes. Therefore, the choice of method depends on the specific goals and characteristics of the study.
3.2.4. Machine learning methods for taxonomic profiling
Machine learning approaches, such as random forests and neural networks, have gained increasing attention in microbial ecology for their ability to accurately classify new, unclassified sequences based on patterns and relationships learned from reference databases (Pasolli et al. Citation2016). Machine learning methods offer several advantages over traditional taxonomic profiling methods, including improved accuracy and efficiency, and the ability to handle complex and diverse data. They can also integrate prior knowledge and information about the microbiome, enabling the detection of subtle differences and correlations that may not be detectable using other methods (Karagöz and Nalbantoglu Citation2021). Machine learning-based taxonomic profiling is well-suited for large-scale comparative studies and can provide valuable insights into the functional and metabolic potential of different microbiomes. However, these methods require a large amount of data for training and validation, and the performance may vary depending on the quality and quantity of the training data (Bokulich et al. Citation2018). Therefore, careful evaluation and validation are essential before applying machine learning methods for taxonomic profiling.
4. “Omics” data integration for understanding molecular communication
Integrating data from multiple omics disciplines, commonly referred to as multi-omics, is a powerful approach to gaining a comprehensive understanding of the biology of microbial communities. The term “omics” encompasses the large-scale analysis of various biomolecules, including DNA, RNA, proteins, and metabolites. By combining information from genomics, transcriptomics, proteomics, and metabolomics, researchers can gain a more complete picture of the biological processes and interconnections within a microbial community. This multi-omics approach provides a systems biology perspective and offers new insights into the complex interactions between microorganisms.
4.1. Single organism multi-omics
The exometabolome and secretome analyses provide a catalog of different extracellular metabolites and proteins. Taxonomic profiling registers genomes of the microbes (known and new) through metagenomics. The natural next step is therefore to find the links between the genes (genome) and their direct and indirect products (metabolome and proteome) which can be achieved by pairing different omics techniques. The omics data integration provides a complete metabolic landscape of the organism or the community. The integration of scalable and cost-efficient omics technologies is the most popular approach for the systematic characterization of chemo- and bio-diversity of microbial communities (Bhosle et al. Citation2022) . This section describes paired and multi-omics approaches and tools that have been extensively used to study single organisms (e.g. gene-metabolite link). In the next section, we will discuss how the same approaches can be extended to the study of microbial communities (e.g. species-metabolites link). illustrates the difference between single-organism and microbial community omics integration.
Figure 3. Difference between multi-omics integration and multi-meta-omics integration. Multi-omics integration for a single organism is focused on attaining the link between significant metabolites and the gene clusters producing those metabolites. Multi-meta-omics is more focused on the microbe-metabolite link, which represents the metabolic exchange between different microbes. The metabolic exchange defines the kind of metabolic interaction between key players and it is important to link to the source organism that releases the metabolites into the extracellular space and the recipient organism that receives the metabolites from the extracellular space.
4.1.1. Gene-metabolite link
The study of enzymes that catalyze the production and release of primary and secondary metabolites has a long history, dating back to the early days of experimental biochemistry in the 1950s. However, advancements in sequencing technologies have paved the way for more in-depth analysis of the genes responsible for regulating enzyme production and metabolite synthesis. The advent of high-throughput sequencing has revealed the presence of Biosynthetic Gene Clusters (BGCs) in genomes. BGCs are groups of genes that are involved in the synthesis and transport of a particular compound or group of compounds (Cimermancic et al. Citation2014). This phenomenon is especially prominent in bacteria, where enzymes are often grouped in operons and co-transcribed (Nützmann et al. Citation2018). BGCs have also been identified in eukaryotes, particularly in fungi and plants, either as co-located genes or as groups of genes located in different parts of the chromosome but controlled by similar mechanisms. BGCs play a crucial role in producing chemical diversity in biological systems, primarily by producing polymers, such as non-ribosomal peptides and polyketides. These clusters are often regulated by complex mechanisms and can be activated or suppressed in response to different environmental conditions.
The gene-metabolite relationship is intricately connected through metabolic pathways. A metabolic pathway is a series of chemical reactions that occur within an organism to perform specific functions, such as the synthesis of a particular compound (anabolism) or the breakdown of a specific molecule (catabolism). The regulation of these pathways is largely dependent on the levels of genes transcribed, which in turn determine the metabolic fate of the organism and its intake of metabolites from other organisms within the community (Carthew Citation2021). In bacteria, BGCs can play a significant role in the regulation of these metabolic pathways. Together, the gene regulation and metabolic pathways determine the production of both primary metabolites, crucial for survival, growth, and reproduction, and secondary metabolites, important for ecological interactions among microorganisms. Understanding the connections between genes, metabolic pathways, and BGCs is critical for comprehending the intricate relationships and interplay between different elements in the microbial community.
Biosynthetic gene clusters and metabolic pathways play a crucial role in chemical communication among microorganisms. These pathways are responsible for producing Natural Products (NPs), which are often the result of secondary metabolism. While the production of NPs can occur via both BGCs and metabolic pathways, there are some differences between the two. BGCs are typically composed of polyketide synthases (PKS) and non-ribosomal peptide synthases (NRPS) (Blin et al. Citation2021). These enzymes are modular, meaning that they consist of multiple domains that perform specific functions, such as the adenylation of precursor molecules. The domains assemble the metabolite by adding chemical groups to the precursor, resulting in an elongation of the chain as shown in . In contrast, metabolic pathways produce NPs through a series of chemical reactions within the organism, including anabolism and catabolism as shown in . For example, quorum sensing is a well-known phenomenon in bacteria where a biochemical reaction occurs, catalyzed by reduced riboflavin and a long-chain aldehyde, resulting in the production of oxidized riboflavin and carboxylic acid from the aldehyde, and the release of energy in the form of light (Tinikul et al. Citation2020). Most omics integration approaches aim to predict the correlation between NPs and the gene clusters responsible for their biosynthesis. This information can help to further understand the complex relationships between gene expression, metabolism, and the production of NPs in microbial communities.
Figure 4. (A) Biosynthetic gene clusters (BGCs) are generally co-located in the same chromosomal region (operons) or spread within a chromosome at different locations but are controlled by the same transcription mechanism. The most common types of enzymes regulated by BGCs are polyketide synthases (PKS) and non-ribosomal peptide synthases (NRPS). PKS and NRPS are megasynthases leading to the production of diverse natural products (NPs). The NP assembly mechanism via these enzymes involves elongation, processing, and termination once the NP precursor structure is complete. Each enzyme has multiple modules and each module consists of domains with specific functions. The diagram illustrates minimal PKS and NRPS module architecture. (B) Bioluminescence is a common quorum sensing phenomenon most commonly found in bacteria and fungi. The lux operon in bacteria induces light emission under a high extracellular autoinducer molecule presence. Under high autoinducers extracellular concentration, few autoinducers reenter the cell and attach to the lux R protein which acts as a transcription factor for the gene cluster, activating luciferase which catalyzes the bioluminescent reaction to emit high energy in the form of bioluminescence.
4.1.2. Integrating genomics with metabolomics
The connection between the chemical structure of a metabolite and the gene sequences responsible for its metabolism is a rapidly growing area in the fields of chemical communication and NP discovery. By integrating genomic and metabolomic data, researchers can gain a more complete understanding of the gene clusters and signaling pathways used by microbes to communicate and coordinate their behavior. This combination of genomic and metabolomic analysis is commonly referred to as metabologenomics, which primarily focuses on the relationship between biosynthetic gene clusters (BGCs) or, in a more general way, metabolic pathways and metabolites (Goering et al. Citation2016; Maansson et al. Citation2016).
Databases serve as sources of information for integrating genomics and metabolomics data. These databases contain information on various aspects of metabolic pathways, including the molecules, reactions, enzymes, and biochemical pathways involved. Some of the most commonly used metabolic pathway databases include BioCyc (Karp et al. Citation2019), BRENDA (Chang et al. Citation2021), KEGG Pathway (Kanehisa et al. Citation2021), Rhea (Bansal et al. Citation2022), and REACTOME (Gillespie et al. Citation2022). Of these, KEGG and BioCyc provide information on gene correlation between genes and the associated reactions and pathways. BioCyc or more specifically MetaCyc (Caspi et al. Citation2020) which is a database subset from BioCyc of experimentally curated pathways is organism-specific, while KEGG Pathway is focused on pathways.
In the field of biosynthetic gene clusters (BGCs), there are specific databases, such as Minimum Information about a Biosynthetic Gene cluster (MIBiG) (Terlouw et al. Citation2022), BiG-FAM (Kautsar et al. Citation2021), Integrated Microbial Genomes: Atlas of Biosynthetic Gene Clusters (IMG-ABC) (Palaniappan et al. Citation2020), and antiSMASH database (Blin et al. Citation2021), which contains pre-computed results from the BGC detection tool antiSMASH. MIBiG, for instance, is a standard BGC database consisting of over 2500 well-known BGCs linked to the chemical compounds they produce and the biosynthetic pathways they regulate. This database follows a standard submission protocol to ensure consistency over various experiments and data types.
NP-specific databases have also been established in recent years to assist in pairing genomics data with secondary metabolites. The efforts to make the data publicly available have contributed to an increase in the number of known NPs. The most well-known databases for NPs include COCONUT (Sorokina et al. Citation2021), LOTUS (Rutz et al. Citation2022), NPatlas (van Santen et al. Citation2019; Citation2022), and GNPS (Wang et al. Citation2016). These knowledge bases can be used to extract the link between genes and metabolites (Soldatou et al. Citation2019).
The tools for integrating genomics and metabolomics data have seen significant advancements in recent years. MetaMiner (Cao et al. Citation2019) and GRAPE-GARLIC (Johnston et al. Citation2016) are two examples of tools that aid in extracting knowledge from databases of BGCs, metabolic pathways, and NPs. Another notable platform is the Paired Omics Data Platform (PoDP) (Schorn et al. Citation2021). PoDP enables the comparison of genomics and metabolomics data from a single source, leveraging BioStudies and BioSamples from the European Bioinformatics Institute (EBI) to provide links between BGCs, synthesized NPs, and the spectral data stored in various databases. For example, PoDP provides links between BGCs and synthesized NPs together with an MIBiG BGC identifier, which can then be linked to an MS-MS spectra URL in any spectral database. PoDP is the only platform in its category that adheres to the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles.
A hypothesis that similar BGCs produce similar metabolites is another approach used to link BGCs to NPs. This hypothesis posits that molecules belonging to the same Molecular Families (MFs) are transcribed by BGCs that belong to the same Gene Cluster Families (GCFs) (Soldatou et al. Citation2019). Few BGC clustering algorithms work on this basic principle (Cimermancic et al. Citation2014; Doroghazi et al. Citation2014; Navarro-Muñoz et al. Citation2020). These algorithms define the distance between the clusters based on sequence similarity or on the score directly obtained from sequence alignment tools. Current tools used for clustering BGCs into GCFs are NP-Linker (Eldjárn et al. Citation2021), BiG-SCAPE, and CORASON (Navarro-Muñoz et al. Citation2020). The clustering of molecular families is achieved via Molecular Networking from GNPS using GNPS-based modified cosine similarity score (Wang et al. Citation2016). A pairwise correlation can be calculated by clustering BGCs into GCFs and NPs to MFs and then combining them in gene cluster networks and molecular networks to draw the metabolic ion-GCF correlation (Navarro-Muñoz et al. Citation2020). A recent study demonstrated the potential of NP-Linker for accelerating NP biodiscovery by linking BGCs to metabolic features from 25 different polar bacterial strains (Soldatou et al. Citation2021).
This concept of reciprocal modularity in metabolism can also be observed with similar metabolic pathways producing compounds from the same MFs, which is not new but is still an active research field (Barba et al. Citation2013). Reaction modules represent a set of enzymes involved in a pathway, which perform a similar function in another parallel pathway. The enzyme homology, in these reaction modules, is linked to the chemical structure similarity, and the products from these modules are hypothesized to belong to the same molecular family. The enzyme evolution models also suggest this reciprocal modularity. According to these models, the ancestral broad specific enzymes with promiscuous activity, demonstrating side functions along with the main enzyme activity, diverged into larger groups of enzymes that belong to the current same enzyme family (Glasner et al. Citation2020). Enzyme classification tools, such as ECPred (Dalkiran et al. Citation2018), DEEPre (Li et al. Citation2018), and mlDEEPre (Zou et al. Citation2018) (specifically for multi-functional enzymes), take a protein sequence, which returns enzyme commission (EC) numbers, used to classify the enzyme family based on enzyme function. These prediction tools can be used to assign enzyme classes and using the molecular families from Molecular Networking, the EC numbers can be aligned with the reactants and products, exhibiting reactions within the molecular network.
4.1.3. Integrating expression data (transcriptomics and proteomics) with metabolomics
Metabolomics provides a comprehensive view of the phenotype resulting from the integration of gene expression and protein content. The two primary omics techniques for gene expression analysis and protein quantification are transcriptomics and proteomics, respectively. Currently, the most widely adopted technology for transcriptomics analysis is mRNA sequencing, which allows for the identification of differentially expressed transcripts and their relative abundance in a given cellular sample.
On the other hand, mass spectrometry-based proteomics is the most high-throughput and efficient method for protein quantification, although it remains more challenging than metabolomics analysis due to the complex and fragile nature of proteins, therefore, a lot of information can be lost during the sampling or processing steps. Even with extensive fractionation to obtain the whole proteome, protein structures can be lost (Ma Citation2010). Despite recent advancements in proteome analysis (Malinovska et al. Citation2022), there are still significantly more studies and tools for the integration of metabolomics with transcriptomics data.
Integrating transcriptomics, proteomics, and metabolomics data has become a growing research area in recent years, with a focus on different computational approaches to achieve this integration (Cavill et al. Citation2016). Correlation-based integration is the most common approach, linking transcripts to metabolites based on their relative abundance. However, this approach is limited by the time lag between changes in transcript and metabolite levels and the complexity of the regulatory mechanisms governing gene expression (Fendt et al. Citation2010). The metabolomics and transcriptomics datasets acquired at the same time generally show low correlation. The transcripts alter the metabolic state much later in time after its initial release as the effects of transcripts and metabolites are not simultaneous (Cavill et al. Citation2013). This also holds for transcript-to-protein (to a lesser extent) and protein-to-metabolite links. Alternative methods, such as multivariate analysis, principal component analysis, and pathway analysis, have been used to overcome these limitations and provide a more comprehensive view of the relationship between transcripts, proteins, and metabolites. These approaches have been applied to various organisms and systems, including algae (Li et al. Citation2021), fungi (Zhang et al. Citation2023), plants (Chen et al. Citation2021; Rao et al. Citation2022; Zhang et al. Citation2022a), and disease models (Tucker et al. Citation2022; Wang et al. Citation2022; Yang et al. Citation2023). Some proteomics and metabolomics integration studies have also been published, e.g. in artificial microbial communities to analyze the production of vitamin C (Ma et al. Citation2011), to assess the effects of metal-resistance bacteria on the growth of maize (Li et al. Citation2014) or to study the effects of microplastics on marine animals by assessing the metabolomics and proteomics profiles of the microbiota of the marine macroorganism (Duan et al. Citation2021).
In contrast, the integration of transcriptomics and proteomics data remains a challenging task, due to the complex and dynamic nature of gene expression regulation and the inherent difficulties in acquiring high-quality proteomics data. Despite its importance in providing a comprehensive understanding of the phenotype, limited studies have been reported in the literature, particularly in microbial communities.
The correlation between transcriptomics and proteomics is influenced by various factors, including alternative splicing of mRNA, post-translational modifications (PTMs), mRNA degradation over time, and the different half-lives of mRNA and proteins (Jovanovic et al. Citation2015). mRNA sequencing has been able to address the issue of alternative splicing, yielding a better model for protein abundance analysis. However, the mRNA goes through various stages of maturation or PTMs, and its concentration at a given point cannot be directly correlated with protein abundance in the cell. Hence, the transcription induction period is crucial to consider. Additionally, some transcription factors are translated on demand, using previously present transcripts (Guantes et al. Citation2015).
The complex nature of gene expression regulation and the various factors that affect the protein-transcript correlation make it difficult to determine the extent to which transcript level affects the protein level in a cell and the phenotype. Nevertheless, recent advances in transcriptomics and proteomics techniques are providing a better understanding of the relationship between transcriptomics, proteomics, and phenotype. To accurately understand this relationship, it is essential to consider the different factors and the time lag between changes in transcript and protein levels (Du et al. Citation2019).
4.1.4. Integrating genomics, transcriptomics, proteomics, and metabolomics data
The integration of genomics, transcriptomics, proteomics, and metabolomics data provides a comprehensive understanding of the biosynthetic capacities of microbial communities, helping to reduce false positive results and uncover the true biosynthetic potential that may remain hidden for most of the life cycle (Zhang et al. Citation2022b). However, the expression of biosynthetic potential is often dependent on specific environmental conditions and the current state of the microbial community. Therefore, to design an experiment that aims to discover novel NPs, it is important to carefully study the community’s environment and select target conditions that are likely to introduce the expression of specific biosynthetic pathways. This approach leads to condition-based NP discovery, which is a powerful tool for exploring the biosynthetic capabilities of microbial communities.
To perform a detailed CABEL analysis, a reverse central dogma approach can be taken, as described in the following hypothetical experiment. The first step in such an experiment involves untargeted metabolomics, which can be used to identify different secondary metabolites from a sample under a specific condition, by incorporating metabolomics techniques, such as tandem mass spectrometry (MS/MS or MS2) and NMR. The spectral data can then be compared with public or commercial experimental spectral libraries and in silico tools to dereplicate known metabolites using appropriate workflows (Verhoeven et al. Citation2020). In the case of high-intensity (e.g. highly abundant) compounds of unknown structure, NMR can be used for structure elucidation along with other MS-based tools such MS-Novelist (Stravs et al. Citation2022). Additionally, spatial MS imaging is a promising new technology for NP detection in microbes, which can simplify the dereplication and identification processes (Fang and Dorrestein Citation2014).
The identified secondary metabolites from metabolomics data can be further investigated using transcriptomics and proteomics data analysis. Transcriptomics can provide insights into the expressed genomic content, while proteomics can validate the transcriptomics analyses and offer information on protein-protein interactions (PPI) and post-translational modifications (PTM) that are required for the biosynthesis of the measured metabolites. Moreover, combining transcriptomics and or proteomics data with genomics data can be used to identify all active and inactive genes. It is worth noting that, according to a study on actinomycetes, microbial genomes contain much more BGCs than expressed BGCs, resulting in much higher potential chemical diversity from the diversity measured with analytical methods (Challis Citation2014). This emphasizes the importance of investigating the genomes to discover new biosynthetic pathways and BGCs and subsequent novel NPs.
The integration of multi-omics data can provide a comprehensive understanding of the biosynthetic potential of a single organism and a microbial community under a specific condition. However, integrating these diverse, high-throughput, heterogeneous datasets presents significant challenges. False positive correlations can arise from differences in the number of genes, transcripts, proteins, and metabolites acquired using different techniques. Additionally, data generated from different omics platforms often have varying formats and quality control measures, making the comparison across different studies cumbersome (Tarazona et al. Citation2021). As a result, to address these integration challenges, most microbial studies resort to paired omics analysis. A list of different paired-omics integration tools is present in .
Table 3. List of tools and databases for multi-meta-omics integration and analysis.
The mixOmics software (Rohart et al. Citation2017b) enables the integration of various types of omics data, including genomics, transcriptomics, proteomics, and metabolomics. The software offers a suite of tools, comprising 19 embedded multivariate analysis techniques, such as PCA (principal component analysis), PLS (Partial least squares), PLS-DA (Partial least squares-discriminant analysis), IPCA (independent principal component analysis), MINT (Rohart et al. Citation2017a) and DIABLO (Singh et al. Citation2019), that enable the integration of omics data. MINT and DIABLO are specifically designed for multivariate analysis of omics data. Currently, the mixOmics model is based on linear combinations of the variables and does not consider time-course experiments. However, the authors are working on the development of two modules for a future version of mixOmics, making it one of the few multi-omics integration tools that can handle multiple datasets, features, and time-course data to provide a comprehensive view of one organism or multiple organisms. It is important to note that, while mixOmics can accommodate multiple organism or microbiome data, it currently works with interaction data.
4.2. Community multi-omics
Omics integration in ecological communities provides a means to understand the metabolic pathways and gene expression profiles of the individual members, as well as the metabolic interactions between them. The metabolic interdependencies of different microbial species in a community can be elucidated by integrating multiple omics data types. This approach, known as community omics, can be used to analyze multi-organism consortia. It can be specifically used to construct metabolic networks that capture the interactions between microbes and their environment. This section will present the various community multi-omics tools and techniques that enable the analysis of microbial communities and the construction of metabolic networks.
4.2.1. Species-metabolites link
In community omics, the link between metabolites and the microorganisms that produce and release them into the extracellular space, affecting other microbes, is a fundamental principle. The discovery of genomic content in the metagenome with low similarity to known microbial sequences suggests the existence of a large reservoir of microbial dark matter with uncharacterized metabolic potential (van Bergeijk et al. Citation2022). Additionally, abundant species in the community can mask the key players with low abundance due to experimental and analytical limitations (Quince et al. Citation2017). However, community omics is a growing field in microbiology and omics technology, and several tools have been developed to characterize the link between metabolites and the microorganism. These tools include MelonnPan (Mallick et al. Citation2019), mmVEC (Morton et al. Citation2019), MiMeNet (Reiman et al. Citation2021), and MIMOSA (Noecker et al. Citation2022), which integrate metabolome and microbiome data. Other tools use the hypothesis that certain compound classes or enzymes are specific to certain species or genera, which can aid in metabolite identification. For example, the R package “tima-r” (Rutz et al. Citation2019) performs taxonomically informed annotations using the LOTUS database (Rutz et al. Citation2022), while tools like Biotransformers annotate structures to spectra based on the biological knowledge source organism (Djoumbou-Feunang et al. Citation2019).
4.2.2. Multi-meta-omics
The term meta-omics refers to the analysis of omics data from a microbial community sample with a high level of biodiversity. Specifically, multi-meta-omics refers to the simultaneous use of multiple omics techniques, such as metagenomics, metatranscriptomics, metaproteomics, and meta-metabolomics, to capture a more comprehensive understanding of microbial community structure and function. This approach allows for the identification of key functional players and metabolic pathways within microbial communities, as well as the characterization of the interplay between microorganisms and their environment. Multi-meta-omics analysis can provide insights into the relationships between different omics data types, allowing for a more holistic view of microbial community function than would be possible with the analysis of individual data types alone.
Individual meta-omics studies have their benefits and limitations. For example, exometabolomics only describes the metabolic content of the community but not the source, while metagenomics provides the taxonomic profile but doesn’t allow to have insights into the metabolic content of the community. Therefore, just as multi-omics integration for a single organism provides an overview of important functional key players and metabolites, multi-omics integration for community samples (e.g. multi-meta-omics) can provide a more comprehensive understanding of the functional interactions between microorganisms and the metabolites they produce (Heintz-Buschart and Westerhuis Citation2022).
Meta-omics data can be used in several ways. Firstly, meta-metabolomics and exometabolomics can provide validation and new insights by identifying the metabolic profile of the microbial community. Secondly, the meta-genome is essential to link the produced metabolites to microorganisms, for instance, phylogenetically similar species tend to have similar functional and metabolic profiles. Furthermore, GCFs (Gene Cluster Families) with known and unknown functions can help trace back to taxonomy, based on the principle of reserved homology. However, assigning metabolic functions to the species of origin can be challenging as one metabolic function may not necessarily be attributed to a single organism (Cimermancic et al. Citation2014; Goering et al. Citation2016). In some cases, however, microorganisms of the same species but different strains can have very different metabolic functions, e.g. virulence. There is often a complex network inter-wiring several species behind a specific function. To address this, ChocoPhlAn uses a Genome-scale Metabolic Network (GMN) from a meta-omics sample to identify species-based metabolic functions (Beghini et al. Citation2021).
An ideal community for multi-omics integration is a co-culture sample, which involves the cultivation of two different species of interest, either as monocultures or cocultures, for experimental conditions (Guo et al. Citation2022). By analyzing transcriptome sequencing and metabolite data acquisition from co-culture and monoculture samples of the two species, differentially expressed metabolites in co-culture can be explored. For instance, a recent study on cider fermentation by two yeast species, both as monocultures and co-cultures, used integrative analysis of metabolomics and transcriptomics to infer that gene expression in yeast is responsible for the cider aroma (Yu et al. Citation2022).
Furthermore, combining these different approaches can provide a comprehensive understanding of the genetic makeup and metabolic processes of an organism or group of organisms. Metabolic networks are a useful tool for assessing metabolic relationships between different players, along with the genomes of each known sequenced species. This enables the tracing of a metabolite back to its origin (Shaffer et al. Citation2019).
5. Functional profiling in microbial communities using network biology
Microbial communities aim to efficiently use available resources in naturally occurring or bio-engineered ecosystems for maintaining resilience and balance (Bernstein and Carlson Citation2012; Yin et al. Citation2021). These communities are highly dynamic, and the metabolic exchange between microorganisms is influenced by environmental factors. The dynamic nature of microbial communities is reflected in the differential gene expression, leading to distinct metabolic profiles over time. This constant adaptation enables the community to maintain balance in response to environmental changes, and exhibit environment-specific phenotypes (Allison and Martiny Citation2008). Researchers seeking to understand the mechanisms underlying microbial community balance use various approaches, one of which consists of introducing imbalances to these systems. This approach is so popular that the “Balance of the Microverse” research cluster at Friedrich Schiller University Jena is driven by this research motive.
To understand the effects of specific genes or pathways on metabolism and the resulting metabolome, microbiologists use a metabologenomics approach that involves introducing genetic perturbations. Techniques, such as gene knockout (gene function completely or partially inactivated), gene overexpression (an increase of the absolute amount of gene product), or gene modification using CRISPR technology are commonly used to induce these perturbations. Additionally, reversible gene overexpression and repression can be achieved using CRISPRa (Wang et al. Citation2019a) and CRISPRi (Huang et al. Citation2016), respectively. Transcriptome profiling following any of these perturbations can help to elucidate the gene function and metabolite bioactivity.
However, applying these technologies to the microbial community comes with several challenges. Environmental samples of communities contain billions of cells belonging to different species and strains, which poses the first hurdle in adopting these technologies due to fine genetic diversity. Additionally, the response of the community to a single genetic modification can be challenging to predict (Pursey et al. Citation2018). Scientists are working on applying gene editing techniques to the study of microbial communities, for example using CRISPR-Cas transposon insertion, which is a relatively new technique that targets a specific species via a phage designed for them to understand precisely its interplay within the community (Ma et al. Citation2020; Koch Citation2022). However, performing a non-targeted approach is even more challenging to track.
The complexity of microbial communities means that solely relying on genetic modifications within a system can be not only labor-intensive and time-consuming but also practically impossible to perform and analyze. Computational approaches have been developed to simulate the introduction of system perturbations into in silico biological systems, providing an alternative avenue for exploring the dynamics of microbial communities. These methodologies rely on the use of biological networks and represent the interactions between different biological entities (Cardona et al. Citation2016). Network biology, a new standard in the field of biological data analysis, leverages different types of networks to provide insights into the workings of microbial communities (Barabási and Oltvai Citation2004). These networks include gene expression networks, protein-protein interaction networks, metabolic networks, interaction networks, and signaling networks. The following section will describe some of the specific approaches and tools used to understand microbial communities as dynamic systems using network biology.
5.1. Interactome networks integration
In microbial communities, the complete set of interactions that occur between cells including primary and secondary metabolism, is referred to as the interactome. These interactions may be either molecular or physical, occurring between two or more microbial species (Faust and Raes Citation2012). Molecular interactions are often represented as a network of spontaneous or catalyzed reactions allowing us to analyze the “nutritional connectivity” and the overall metabolic potential of the microbial community (Garcia et al. Citation2015). This interaction network can help to identify key players and connections within the community. For instance, in a study involving four bacterial species, it was found that Polynucleobacter produced all essential vitamins except for vitamin B12, for which it relied on the actinobacteria and archaea, while the actinobacteria relied on other species for cysteine synthesis (Garcia et al. Citation2015). To unravel such complex interactions, interactome provides an essential foundation for analysis.
The construction of a complete interactome network is a complex task that requires the consideration of three fundamental factors: (1) community metabolome, which represents the functional capacity of metabolites; (2) environmental factors, such as spatiotemporal variables that affect the community; and (3) microbial community structure, which comprises biodiversity and the types of interactions between different species (Larsen et al. Citation2012). These factors can be combined in different ways to construct community interactomes that are specific to the physical environment where the microbial community resides. For example, a function-based interactome can be designed using the first two factors, metabolome and environment parameters. However, these models are based on known species and their metabolic interactions, and the addition of an environmental parameter, that is alien to the model, can lead to biased results. In situations where different species have distinct responses to different environmental stimuli, building a global model for the community is not feasible. To overcome this issue, a widely used approach is to build heterogeneous network models where any of the three interactome factors can be linked together to visualize stronger or weaker associations (Larsen et al. Citation2011). Such models can provide a more comprehensive understanding of the microbial community structure and function.
Understanding microbial interactions and their complex nature requires the use of computational tools to design, build, test, and learn about community-level properties to predict interactions. By predicting interactions, we can generate an interaction network that provides a snapshot of the microbial community system, which can be further used for metabolic modeling and introducing disbalance (Liu et al. Citation2021; Matchado et al. Citation2021).
5.2. Co-expression and co-abundance networks
Genomics and transcriptomics data can be used to generate co-expression or co-abundance networks. A co-expression network is constructed by representing each gene with a single node and connecting two nodes with an edge if they exhibit similar expression patterns, indicating functional association. This type of network highlights the differences in the expression patterns of different pairs of genes across various samples and conditions (Ovens et al. Citation2021). Combining co-expression networks with interaction networks can reveal links between the genome and metabolome. Comparably, co-abundance networks are constructed using metagenomic or metatranscriptomic data from the microbial community. They are based on the concept that microbial taxa that co-occur frequently across samples tend to be functionally related or codependent on the same environmental factors. Co-abundance networks are built by assigning each microbial taxon as a node, and the abundance of each taxon is represented by a numerical value (e.g. read counts). Two nodes are connected by an edge if their abundance profiles are highly correlated across samples. These edges represent co-occurrence or mutual exclusivity patterns between microbial taxa and can provide insights into the functional relationships and ecological interactions between microbes within the community (Lahti Citation2022).
To construct a co-expression network, transcriptome data from differentially expressed genes (DEGs) can be used to generate a gene expression matrix. This matrix quantifies the mRNA levels of each gene across different samples. To determine whether two genes have similar expression patterns, correlation coefficients are calculated between pairs of genes using methods like the Pearson or Spearman correlation or Euclidean distance. A similarity matrix is generated based on these correlations, and a threshold is applied to convert this similarity matrix into an adjacency matrix. Depending on the threshold applied, the co-expression network can be either unweighted or weighted. The correlation coefficients facilitate the formation of a hierarchical module or cluster of co-expressed genes. The workflow for constructing a co-expression network is depicted in . This network can be used to correlate modules of co-expressed genes with a particular phenotype (Contreras-López et al. Citation2018). provides an overview of some of the state-of-the-art tools including WGCNA (Langfelder and Horvath Citation2008) and GWENA (Lemoine et al. Citation2021), as well as other tools for comparative analysis among different datasets, such as CoExpNetViz (Tzfadia et al. Citation2015), Juxtapose (Ovens et al. Citation2021), and CoCoCoNet (Lee et al. Citation2020a).
Figure 5. General workflow of the co-expression network. The gene expression matrix describes the intensities of genes (rows) in different samples (columns). The expression profiles are generated from these matrices and are used to generate a similarity matrix for gene co-expression. A threshold is applied to cluster similar genes together based on their expression patterns and this cluster can be represented as a network, with nodes representing genes and edges representing the correlation between these genes. Further downstream analysis can be performed based on the research objective.
Table 4. List of tools and databases network analysis of microbial communities.
The context of the data used to generate co-expression networks is crucial and often relies on transcriptome sequencing. In microbial studies, co-expression networks have been instrumental in deciphering co-expressed genes, their interactions, and unsurprisingly, interactions between the microbes. For instance, in a recent study, co-expression networks were constructed to investigate the meta-transcriptomic data of phytoplankton microbial communities in two marine ecosystems. The results indicated that 70% of the interactions were conserved in the communities, providing insights into the complex interplay between the microbial communities in different ecosystems (Sharma et al. Citation2020). Another study utilized a microbiome-gene co-expression network to assess the impact of the microbiome on host genes in cancer patients, by integrating the microbial abundance data with the gene expression matrix. This study highlighted the potential of co-expression networks in identifying key microbial taxa that may contribute to tumorigenesis (Uriarte-Navarrete et al. Citation2021).
5.3. Metabolic modeling
Metabolic modeling is a promising approach to predicting the interactive behaviors observed in microbial communities. The microbial community interactome integrates different microbial species and metabolites through meta-genomics and meta-metabolomics data analysis, to assess the metabolic capacity of the consortium. Metabolic models are mathematical representations of the genome that incorporate associated biochemical reactions and enzyme kinetics (Biggs et al. Citation2015). Interaction data can be integrated into these models to predict the metabolic behavior of organisms in microbial communities. Multiple metabolic networks from different species in the community can be also used to represent interactions on a multi-species level.
One workflow proposed for metabolic modeling is the biological Computer-Aided-Design of interactions in microbial communities (bioCADi), which can be developed using a top-down (from interactions data to genes) or bottom-up (from genes to interaction predictions) approach (Song et al. Citation2018). The next subsection will discuss different types of metabolic network approaches used for microbial community analysis.
5.3.1. Metabolic network modelling
Metabolic networks are a crucial tool for representing and understanding metabolic interactions (Sung et al. Citation2016). A metabolic network is a set of interconnected biochemical reactions that occur within a single organism, involving the conversion of metabolites into other compounds necessary for cellular function. In these networks, each reaction is connected to others in a complex web, with some reactions using substrates produced by other reactions. These connections allow for the efficient flow of metabolites through the network, enabling the organism to maintain cellular homeostasis and perform its required functions (Lacroix et al. Citation2008). The topology of these networks can provide insight into the communication and interactions between different enzymes, metabolites, and processes within the organism, as well as with other organisms in a microbial community.
5.3.1.1. Genome-scale metabolic networks
Genome-Scale Metabolic Networks (GMNs) provide a way to integrate multiple interactions within a multi-organism system. These networks incorporate the meta-genome annotation and use metabolomics data to construct an overview of pathways that evaluate the phenotype of the community (Hao et al. Citation2018). The activated pathways and processes in the microbes involved can also be predicted with transcriptomics and/or proteomics data (Wen et al. Citation2014). A GMN of an organism can be generated using MG-RAST (Keegan et al. Citation2016), a tool for the analysis of metagenomes, and can then be manually curated by filling in gaps in the network (Song et al. Citation2018). However, many microbes are uncultivable, and generating a GMN for them is not possible in a direct way. To address this, the metagenome sequence contigs can be divided into species-specific groups. This allows for the segregation of species into individual GMNs and subsequently combines the species-specific GMNs into a community metabolic network. Despite the availability of high volumes of metagenomic data, many zones in community metabolic networks remain unanalyzed, underscoring the need for effective data management and analysis to enhance our understanding of the metabolic interactions within microbial communities (Song et al. Citation2018).
5.3.1.2. Genome-scale network reconstruction
Metabolic network models are essential to understand microbial communities, however, it is difficult to generalize multi-dimensional microbial interactions in a single GMN for different groups of organisms. Efficient metabolic network reconstruction tools are therefore needed to resolve this issue. The term Genome-scale network reconstruction (GENRE) was first coined in 2004 (Price et al. Citation2004), which refers to the reconstruction of GMNs that can be extended to multiple organism consortia (Biggs et al. Citation2015). The reconstructed network stores stoichiometric information through enzyme and reaction databases and assigns compartments to metabolites, representing either cytosol (endometabolome) or extracellular space (exometabolome). The metabolic exchange between these compartments is depicted by the flow of metabolites (Payne et al. Citation2021).
5.3.1.3. Genome-scale metabolic models
GENRE can be used as a baseline for generating genome-scale metabolic models (GEMs) by adding flux of organic matter information to the biochemical reactions, to influence, in the model, the flow, direction, and amount of metabolites exchanged within compartments. GEMs are therefore a specific type of GMN that has been further refined and optimized. Constraint-based approaches like flux balance analysis (FBA) are typically used to analyze GEMs. FBA simulates metabolic processes based on an objective or metabolic goal (Gu et al. Citation2019). Integrating metabolomics data can provide strong constraints for GEMs, but this approach has not been extensively developed due to limitations in analytical and metabolomics data analysis, and an unfortunate discontinuity between disciplines working with FBA and metabolomics. Also depending on the type of metabolites, endo- or exometabolites, the integration of metabolomics data can vary. Factors that could be taken into account are the differences between the size of metabolites, the composition of the endo- or exometabolome, and the data sparsity which is indicated by the low concentration of certain metabolites. Based on these factors, endo- or exometabolites are intrinsically different. The addition of flux bounds to GMNs allows for the simulation of metabolic processes under specific conditions. In this way, GEMs can be used for a variety of applications, such as predicting the effects of genetic mutations on metabolism or identifying potential drug targets.
The terms GMN (metabolic model), GENRE (metabolic models with reactions), and GEM (metabolic models with metabolic flow) are not interchangeable, however have been used interchangeably in the literature. While they are related concepts and are used in the same field, each term refers to a distinct aspect of metabolic network modeling.
The most frequently used approach to generate a GEM for a target organism is known as the ModelSEED pipeline (Seaver et al. Citation2021) but can be generalized beyond this tool (). This semi-automated pipeline integrates genomic, metabolic, and biochemical information to construct a genome-scale metabolic model. The pipeline includes four main steps: (1) genome annotation, (2) reaction network reconstruction, (3) gap filling, and (4) model testing and validation. The first step involves the prediction of open reading frames (ORFs) and the annotation of protein-coding genes based on sequence homology searches. This process can be performed using various tools, such as RAST (Aziz et al. Citation2008; Overbeek et al. Citation2014; Brettin et al. Citation2015), Prodigal (Hyatt et al. Citation2010), and Glimmer (Delcher et al. Citation1999), among others. The second step involves the reconstruction of the metabolic network by linking the annotated genes to metabolic reactions. This process can be carried out using a variety of databases, such as KEGG (Karlsen et al. Citation2018), MetaCyc (Caspi et al. Citation2020), or ModelSEED (Seaver et al. Citation2021). The reconstruction process involves the identification of metabolic pathways (to distinguish main reaction compounds from auxiliary ones) and the formulation of stoichiometric equations that describe the mass balance of the network. The distinction between main and auxiliary compounds prevents connecting reactions on ubiquitary compounds (such as ATP, water, or H+), which in turn avoids creating small-world networks which represent a very biased version of a metabolic network and shade metabolically important connections. The third step in the creation of a GEM involves gap filling, which is the addition of reactions to the network to complete pathways and ensure network connectivity. This step can be performed using various methods, such as literature curation, comparative genomics, and database searches. The final step involves the testing and validation of the GEM. The model is tested by comparing the simulated metabolic fluxes to experimental data and by assessing its predictive capability. Various methods can be used to validate the model, such as growth phenotype analysis, gene essentiality predictions, and flux variability analysis.
Figure 6. Construction of GEM: the hypothetical microbial community that has two species (species 1 and species 2), where species 2 releases metabolite Y to the extracellular environment which is taken up by species 1. The initial construction of the genome-scale metabolic network (GMN) is generated automatically using genome sequences. Manual curation is performed via gap filling which takes information from different enzyme and reaction databases, such as MetaCyc. Once this reconstruction of GMN is carried out (GENRE), a stoichiometric matrix can be generated and the reactions and pathways involved in the production of biomass are compartmentalized, together with an objective function Z.
Using GEMs of multiple species and the interaction data enables the analysis of the metabolic flow between different microorganisms (Hanemaaijer et al. Citation2017). A study conducted on the effects of cross-feeding metabolites in microbial communities used GEMs of different species to analyze the interaction patterns among different species in a community (Pacheco et al. Citation2019).
5.3.2. Fluxomics
Fluxomics is a field of study in systems biology that aims to measure and quantify the metabolic fluxes or rates of biochemical reactions in a biological system (Emwas et al. Citation2022). This comprehensive approach enables researchers to understand how different metabolic processes are interconnected and how they respond to environmental perturbations or genetic modifications. By identifying the key regulatory nodes in the network and predicting how perturbations will affect the system’s behavior, fluxomics is a powerful tool in metabolic analysis.
5.3.2.1. Flux balance analysis
The metabolic fluxes through a particular reaction or pathway can be defined as the rate at which a specific metabolite or set of metabolites is being produced or consumed by the biological system. In the context of microbial community analysis, flux refers to the rate at which the microbial community is producing or consuming a particular metabolite or set of metabolites (Gottstein et al. Citation2016). To predict the metabolic fluxes through the network of reactions in a microbial community, FBA uses the stoichiometry of the metabolic reactions and the constraints on the available nutrients and other environmental conditions. The flux balance solution provides the optimal rates of metabolic fluxes that meet the constraints while maximizing a specific objective function, such as growth rate or substrate utilization (Orth et al. Citation2010). FBA is a static approach with the concept of minimal accumulation of intracellular metabolites, ensuring a steady flux rate of production and consumption of the intracellular metabolites.
The GEM of an organism can be used to analyze the metabolic fluxes through the defined intracellular and extracellular compartments. In the context of a community, GEMs can also be used to calculate metabolic fluxes. The community can be described in terms of steady-state (equilibrium) and dynamic-state (constantly changing) conditions, which depend on the flux change. A steady flux change indicates an equilibrium state, while an irregular flux change indicates dynamic communities. Stoichiometry represents the quantitative change of reactants into products, and computational approaches to solve FBA, use a model of metabolic fluxes and a stoichiometric matrix. The optimal solution is calculated within the allowable space to give the best outcome. While FBA generally optimizes the objective function for steady-state conditions, this approach provides an oversimplified view of the system, as it assumes that the rate of metabolite production is equal to the rate of consumption (Lewis et al. Citation2012).
provides an overview of how metabolic fluxes can be analyzed using GEMs, stoichiometry, and FBA in the context of microbial communities. The integration of fluxomics, GEMs, and FBA can provide a powerful platform for understanding the metabolic behavior of complex biological systems.
5.3.2.2. Dynamic flux balance analysis
Classical FBA cannot capture the dynamic state and the metabolic exchange occurring in a microbial community. Dynamic FBA (dFBA) models the dynamic changes of the metabolic exchange over time by incorporating time-dependent constraints into the optimization problem. In other words, the masses of extracellular metabolites, that impact the metabolism of the community, are integrated to balance the FBA equation, by covering all substrates consumed and products secreted (Mahadevan et al. Citation2002).
To use dFBA, a series of steady-state FBA models are constructed at discrete time points, and the dynamic changes in the metabolic fluxes are calculated between these time points using the concentration of extracellular substrates and products, with the assumption that the cell maintains a steady state internally (Henson and Hanly Citation2014). dFBA can be used to simulate the dynamic behavior of a microbial community under changing environmental conditions or genetic modifications and to predict the response of the community to these changes. It can also be used to identify the key metabolic pathways and enzymes that are responsible for the dynamic behavior of the community and to predict the effect of perturbations on the community’s metabolic fluxes. Overall, dFBA is a powerful tool for analyzing the dynamic behavior of microbial communities and for predicting their response to changing environmental conditions.
5.3.2.3. Steady-state and dynamic FBA for microbial communities
To model multiple species simultaneously, FBA uses a compartmentalization approach that constructs a meta-stoichiometry matrix linking each species compartment to measure the metabolic flux. While FBA is useful for measuring syntrophic interactions between two species, it has limitations in comprehending the multi-dimensional decision-making of communities and may not directly reflect the inter-species interactions taking place (De Bernardini et al. Citation2022). Community FBA (cFBA) was introduced to address these limitations by linking genotype to phenotype using the FBA approach on multi-species consortia (Khandelwal et al. Citation2013). cFBA deals with steady-state metabolic fluxes and defines a community objective function that not only depends on the metabolic fluxes but also the metabolic tradeoff between the microorganisms within systems. To integrate the extracellular changes occurring in the community, Dynamic Multispecies Metabolic Modeling (DMMM) (Zhuang et al. Citation2011), a dFBA variant, considers different species as compartments and analyses metabolic fluxes among the compartments while computing external metabolite concentrations. Ultimately, all compartments are integrated into one stoichiometric matrix (Ang et al. Citation2018). Another framework to predict microbial abundance profile, using FBA approach, is SteadyCom (Chan et al. Citation2017). SteadyCom applies linear programming to the microbial community genome-scale metabolic model (GEM) with minimum constraints and infers the abundance of various microbes in response to varying environmental factors. Predicting the composition of microbial communities within specific environments contributes significantly to the broader challenge of understanding and forecasting microbial community dynamics. SteadyCom has been integrated into the COBRA suite (check for more information on COBRA) (Heirendt et al. Citation2019).
5.3.2.4. Optimized FBA for microbial interactions
To better account for the complex integrated metabolism of microbial communities, a multi-level optimization problem was introduced in 2012, called OptCom (Zomorrodi and Maranas Citation2012). This comprehensive framework takes the metabolic fluxes as a 2-fold optimization problem, with an inner problem to understand the species-level biomass production and an outer problem to capture the metabolic exchange among different species. Inner and outer problems are linked through the optimization of community biomass and inter-organism flow constraints. As a result, OptCom allows for studying any number of species and any type of metabolic exchange. However, this approach is computationally expensive and cannot be applied to poorly defined sets of species, as it lacks a standardized fitness criterion due to varying interactions. OptCom only analyses steady-state models, so d-OptCom was introduced in 2014 (Zomorrodi et al. Citation2014). d-OptCom adds temporal variations by integrating time-dependent constraints to capture dynamic changes in biomass concentrations and the exchange of exo-metabolites via substrate uptake kinetics.
To gain more insight into microbial community metabolism, integrating meta-transcriptomics into existing approaches can help to identify activated pathways. Transcriptomics data can be directly integrated into FBA or GENRE through gene expression tables, with a threshold set for high or low gene expression to improve the accuracy of GEMs (Kim and Lun Citation2014).
5.3.2.5. Evaluation of the fluxomics approaches for microbial communities
A recent evaluation of various fluxomics approaches on microbial communities has been published, providing a comprehensive analysis of the qualitative and quantitative features of different tools (Scott et al. Citation2023). This study guides researchers to select the most suitable method for their specific microbial community of interest. However, a major limitation is the lack of GEMs for non-model organisms. To overcome this challenge, the construction of GEMs using the standardized practices of MEMOTE, a GEM quality assessment Python toolkit, is recommended (Lieven et al. Citation2020). Additionally, adding enzyme availability constraints can help reduce biased results and improve the accuracy of predicting the metabolic state of the microbial community (Moreno-Paz et al. Citation2022). The evaluation study found that the cFBA approach is the most effective for studying steady-state microbial communities, while for dynamic states, the dFBAlab tool is recommended (Gomez et al. Citation2014). For studying spatiotemporal effects, COMETS exhibited the best performance (Dukovski et al. Citation2021). gives a list of different fluxomics tools.
6. Current challenges and outlook
6.1. Genomics and meta-genomics challenges
Despite the various advancements in omics techniques, comprehending complex microbial communities on different molecular levels still poses significant challenges. One major obstacle is the lack of complete genome sequences, particularly for non-model organisms and uncultivable species. Moreover, even with genome sequences, not all genes are functionally or structurally characterized, limiting the success rate of many computational tools that rely on available information. The prediction of genome-metabolome link algorithms is mainly based on validated metabolic networks of known species, and less studied or scarcely present organisms are often overlooked (Larsen et al. Citation2011). It also suffers from misassembly, low-quality sequencing, and poor annotation, making it challenging to reassemble the sequences for meaningful interpretation. Furthermore, despite high coverage, metagenomics may still miss sequences from rare species, presenting a challenge in understanding microbial communities (Tolonen and Xavier Citation2017). To address this issue, several solutions are available, including increasing sequencing depth, using long-read sequencing technologies, employing metagenome binning, or using bioinformatic pipelines specially designed to detect rare species. Each approach has its advantages and limitations. Increasing sequencing depth can improve the chances of detecting rare species, but may not always be feasible and can be expensive. Long-read sequencing technologies can produce longer reads, improving genome assembly accuracy and rare species detection, but also come with higher costs and require high-quality DNA extraction. Metagenome binning can identify missed rare species by clustering sequencing reads into groups representing individual species but relies on sequencing depth and overall microbial community diversity. Specific bioinformatic pipelines can also detect rare species but are dependent on input sequencing data quality. In conclusion, utilizing a combination of these modern solutions can overcome the limitations of metagenomics sequencing and provide a more comprehensive understanding of microbial communities.
6.2. Challenges and opportunities in the “omics world”
Despite the availability of genome sequences, the lack of meta-transcriptomics and meta-proteomics data limits our understanding of microbial communication. Context-specific environmental conditions can cause up to 90% of gene clusters to be silent, which hinders correlation calculations between activated genes and phenotype in omics analysis (Soldatou et al. Citation2019). Furthermore, even with data obtained from meta-transcriptomics and meta-proteomics, the corresponding meta-metabolomics data can be sparse due to limitations in analytical and sampling techniques. As a result, the analysis of biosynthetic potential is affected, and up to 90% of spectral features remain unannotated without the assistance of in silico spectral matching tools (Romano et al. Citation2018). Therefore, it is essential to improve sampling and analytical techniques to capture more metabolites and to develop new computational methods that can better annotate spectral features. By doing so, we can gain a more comprehensive understanding of microbial communication and the biosynthetic capabilities of microbial communities.
The study of microbial communication requires a vast amount of meta-omics data, and the data generated from meta-omics experiments is increasing at an unprecedented rate. This results in a paradox of data scarcity and data explosion, where there is both a lack of available data and an overwhelming amount of data to analyze. As such, it is crucial to employ a dereplication strategy in all analysis pipelines to remove redundant data and ensure accurate taxonomic profiling. The NCBI taxonomy database is a valuable resource for this purpose, as it is continuously updated with new taxonomic information based on metagenomic sequence data submissions. However, as more data is generated, this strategy becomes more difficult to implement, and new approaches must be developed to handle this large amount of data. The data explosion is currently the most significant challenge in meta-omics technology (Ye et al. Citation2019), and new methods are needed to analyze, store, and integrate the vast amounts of data generated by modern omics technologies. A relatively new effort in this regard has been made for microbiome data, i.e. National Microbiome Data Collaborative (NMDC), which hosts enormous data on all four meta-omics data types, as well as, Natural Organic Matter measured with FT ICR-MS analysis results (Wood-Charlson et al. Citation2020; Eloe-Fadrosh et al. Citation2022). The storage structure for this data is designed to represent various aspects of microbiome research, including studies, samples, data objects, and the relationships among these entities. Ultimately, this sort of management of the data explosion is crucial to fully realizing the potential of meta-omics in understanding microbial communities and their interactions.
6.3. Data standardization challenges
The exponential growth of meta-omics data has led to a general lack of standardization, further complicated by the absence of complete metadata (Amos et al. Citation2020). To address this, minimum requirements for microbiome studies have been proposed in the agricultural (Dundore-Arias et al. Citation2020) and human microbiome (Mirzayi et al. Citation2021) fields. Despite this, ensuring adherence to these minimum requirements in different microbiome studies remains a significant challenge, as the checklist of requirements still requires additional inputs from a diverse range of microbiome or microbial community studies to make the standards applicable to most research (Amos et al. Citation2020). The issue of standardization becomes particularly important when attempting to compare results from different studies, as variations in sample collection, sequencing, and bioinformatics analyses can introduce significant biases and confounders. Therefore, it is essential to establish a common framework for reporting, analyzing, and sharing data to increase the reproducibility, transparency, and comparability of results, and to ensure that data generated in one study can be used to validate or complement data from another. The National Microbiome Data Collaborative (NMDC) contributes to this effort in data standardization by adhering to FAIR principles, engaging with microbiome researchers, and collaborating with the GO FAIR team for the FAIR Microbiome Implementation Network initiative. This involves following community standards for data and metadata, aligning to ensure reproducible results (Wood-Charlson et al. Citation2020; Eloe-Fadrosh et al. Citation2022).
6.4. Opportunities for omics data integration
The integration of heterogeneous omics datasets is a critical challenge in meta-omics research. While meta-omics generates vast amounts of data across various molecular categories, each category has its technical limitations (Tarazona et al. Citation2021). Moreover, different omics platforms have varying precision levels and signal-to-noise ratios, which can lead to missing features (Tarazona et al. Citation2020). Therefore, it is crucial to incorporate missing value imputation into the analysis pipelines for multi-meta-omics studies. In addition, statistical analysis combined with a visual representation of multi-meta-omics data can help identify issues that may not be immediately apparent during the data integration process. Overcoming these challenges will be critical for advancing our understanding of microbial communication and the underlying mechanisms that drive community dynamics.
6.5. Challenges in network biology
Integrating multi-omics data into biological networks has revolutionized our understanding of microbial communities (Franzosa et al. Citation2015) (see Section 5 for details). Recent exemplifications are algorithms, such as the Metabolite-Expression-Metabolic Network Integration for Pathway Identification and Selection (MEMPIS), which simultaneously integrates both metatranscriptomics and metabolomics data using metabolic networks implemented on soil microbial community data (Roy Chowdhury et al. Citation2019); netOmics, which integrates longitudinal multi-omics data into biological network to interpret the regulators behind different biological phenomena (Bodein et al. Citation2022); and INTEGRATE which also integrates metabolomics and transcriptomics data, using the stoichiometric metabolic networks to predict differential reaction expression and metabolic flux changes (Di Filippo et al. Citation2022). Metabolic networks can help to identify the biological relationships between different molecular levels, enabling the identification of key players in microbial communication and interactions. However, several limitations impact the accuracy and comprehensiveness of these networks. One major challenge is the representation of uncultivable microbes in these networks. The majority of microbial species in any given community are difficult to culture, and as a result, their genomic content is often underrepresented in multi-omics studies. This results in a bias toward the known and abundant organisms, limiting the scope of the networks. Moreover, the complex nature of microbial communities, affects metabolic modeling in different ways, depending on the community dynamics, such as spatiotemporal factors, chemo and bio-diversity of the community, types of interactions, i.e. physical or chemical, and also the types of omics measurements, which contributes to the level of information obtained about the microbes and their interactions, adding layers of complexity to the modeling of these intricate microbial systems. These complexities cannot be generalized, leading to a need for customized approaches for each community. The standardization of microbiome multi-omics dataset submission is also crucial to the success of network biology applications. A lack of standardization across multi-omics studies, including metadata, can significantly affect the integration of data into biological networks. Therefore, there is a need for standardized protocols and guidelines for data submission and integration to maximize the utility of these networks (Vasilakou et al. Citation2016).
6.6. Computational constraints and resource limitations
With the rapid advancement of high-throughput technologies, computational limitations have become a major bottleneck in multi-omics studies. While there has been an exponential increase in computational capacity, it remains insufficient for the scale of the challenge. A viable solution lies in the application of FAIR principles to datasets, metadata, computational tools, and workflows. Cloud-based computing is an emerging technology that provides a single platform to store and analyze data with the tools available on the cloud, allowing reproducible results to be shared easily (Koppad et al. Citation2021). The parallel processing of multiple datasets on multiple machines is another promising strategy to reduce computational time. In the coming years, these computationally efficient schemes will become part of every robust analysis pipeline, as many emerging and well-established tools, such as GNPS, PairedOmics, and MIBiG, are already FAIR and computationally robust (Guo et al. Citation2018). Nevertheless, it is an emerging area in multi-omics and microbial community studies, and the need for resilient, scalable, and cost-effective technologies to deal with the surge in multi-omics data is becoming increasingly urgent.
The choice of tools in research is essential for obtaining accurate and reliable results. Although computational tools have improved over the years, it is important to select the most appropriate tools based on the research hypothesis and goals. A benchmark study conducted by Valesko et al. demonstrated that taxonomy profiling tools are tailored toward specific goals and should be utilized accordingly (Velsko et al. Citation2018). It was observed that different tools generated varying taxonomic profiles, hence, it is crucial to validate the results obtained through statistical tests. In addition, the choice or priority given to omics data should be based on the research context and working hypothesis. For instance, if the function of a microbial community is the main scientific purpose of the study, proteomics, metabolomics, or fluxomics provide better insights, which could be further facilitated by metagenomics. If taxonomic profiling of the community is the main concern, metagenomics is the obvious choice. Therefore, selecting the appropriate tools and prioritizing omics data based on research context is crucial for obtaining reliable and accurate results.
The field of multi-omics in microbial communities is a multidimensional and complex area of research, with challenges ranging from data acquisition to computational analysis. Despite these challenges, multi-omics approaches are essential for the study of microbial communities and represent the future of systems biology. To overcome the challenges associated with multi-omics data, computational microbiologists are continuously working on developing new algorithms [such as MEMPIS (Roy Chowdhury et al. Citation2019)] as described in section 6.5, and techniques, such as machine learning and deep learning, to improve predictive accuracy and efficiency in community metabolic models (Zampieri et al. Citation2019). In addition, advances in the elucidation of biosynthetic activities in microbial communities are expected to lead to the discovery of new synthetic molecules that can be utilized in various areas of research, including synthetic biology and therapeutics (Hillman et al. Citation2017). To fully realize the potential of multi-omics in microbial communities, there is a need for continued research and innovation to overcome existing challenges. The application of machine learning and deep learning algorithms offers great promise for improving predictive accuracy and efficiency, which can enhance our understanding of the biological setup of microbial communities. The discovery of new synthetic molecules derived from microbial communities has the potential to revolutionize many areas of research, including synthetic biology and therapeutics. Overall, the ongoing advancements in multi-omics and related technologies are expected to play a crucial role in shaping the future of microbiology research.
7. Conclusion
Microbial communities are essential for many important biological processes, including bioremediation, nutrient cycling, and host-microbe interactions. Understanding the communication and interactions within these communities is crucial for unlocking their full potential. Computer-assisted biosynthetic capabilities elucidation (CABEL) and the integration of omics data have emerged as powerful tools for studying microbial communities and their interactions.
Future directions for CABEL include the development of computational models that can accurately predict the biosynthetic capabilities of individual microbes within a community. This will require better and more systematic production and integration of diverse data sources, including metagenomic, metatranscriptomic, and metabolomic data, to create more realistic and predictive models of microbial community interactions. Additionally, there is a need for the development of tools to analyze the complex signals and communication mechanisms that occur within microbial communities. This will require the integration of computational approaches with experimental techniques, such as imaging and single-cell genomics to identify the key signaling pathways and molecular interactions that underlie microbial community behavior. Finally, increased collaboration and sharing of data and resources among researchers in the field of microbial community communication is essential to accelerate progress and ensure the most promising approaches are widely adopted and validated, including the implementation of FAIR data and software standards.
The integration of multi-omics data and network analysis is a powerful tool that can help to uncover the complex interactions and communications within microbial communities, leading to a better understanding of the system and the discovery of novel natural products with potential therapeutic and industrial applications. The analysis of these interactions can lead to the discovery of novel NPs, which can result in the discovery of new drug leads and therapies. In conclusion, CABEL and the integration of omics data and network analysis have the potential to revolutionize our understanding of microbial community communication and lead to the discovery of new NPs with potential therapeutic and industrial applications.
Disclosure statement
We declare that we have no personal relationships with other people or organizations that can inappropriately influence our work; there is no professional or other personal interest of company that could be construed as influencing the content of this paper. No potential conflict of interest was reported by the author(s).
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