Introduction
The gut microbiome is a reservoir of potential biologics
Meat animal biologics broadly refer to bioactive molecules, compounds, or microbial products derived primarily from livestock and poultry that are used to enhance animal health, improve productivity, or ensure product quality in meat production systems. These biologics can include proteins, antibodies, enzymes, hormones, antimicrobial peptides (AMP), and immune-modulating metabolites. Some biologics can leverage natural processes within the animal to achieve their desired effects, such as immune responses, representing an alternative to traditional feed additives or antibiotics (Zhao et al., 2012; Sethu et al., 2012). Advances in biotechnology and genomics have accelerated the discovery and application of these biologics, making them an increasingly viable tool for addressing critical challenges in livestock production, including disease management, feed efficiency, and product safety.
The gut microbiome of meat-producing animals, such as cattle, swine, and poultry, plays a pivotal role in maintaining animal health, regulating immune responses, and influencing overall productivity (Mantovani et al., 2017). The microbiome comprises a complex community of bacteria, archaea, fungi, and viruses. Due to their wide metabolic capacity, they facilitate essential functions such as nutrient digestion, energy metabolism, and pathogen exclusion. For instance, ruminal microorganisms in cattle are crucial to breaking down fibrous plant material and producing volatile fatty acids that serve as an energy source for the animal (Liu et al., 2021).
The application of genomics has revolutionized our understanding of the gut microbiome and its potential source of biologics of microbial origin. High-throughput technologies, such as metagenomics, transcriptomics, and metabolomics, allow researchers to identify and characterize novel microbial species, enzymes, and metabolites with beneficial properties (Kwa et al., 2023; Chatman et al., 2024). These genomic tools enable the detection of functional genes encoding bioactive molecules, such as AMP, signaling molecules, or enzymes with feed-hydrolyzing capabilities. The identification and harnessing of these microbial-derived compounds can lead to the development of biologics to optimize feed utilization, reduce methane emissions, and control pathogenic bacteria, ultimately improving production efficiency and sustainability.
In this review, we first discuss the implications of the gut microbiome on the health and productivity of livestock animals, with an emphasis on ruminants, and how the metabolites produced by microorganisms that colonize the gastrointestinal tract (GIT) have a potential use as biologics. We focus on the rumen microbiome as a richly diverse and largely untapped resource for novel biologics discovery. Lastly, we mention how recent advancements in high-throughput sequencing technologies and bioinformatic tools have expanded our ability to explore the vast genetic diversity of this complex ecosystem.
Gut Microbiome
Implications for animal health and productivity
The gut microbiome is tightly linked to the health status of meat animals. Dysbiosis, or disruptions in the microbial community, has been associated with gastrointestinal diseases, reduced feed efficiency, and susceptibility to infections (Han et al., 2024; Jin et al., 2024). The strategic use of beneficial microorganisms and their metabolites can help restore microbial balance and promote a stable gut ecosystem. For example, microbial strains with immunomodulatory effects can enhance the host’s immune defenses, reducing the need for antibiotics and mitigating antimicrobial resistance risks (Kober et al., 2022). Additionally, bioactive metabolites derived from microbial species colonizing the animal gut, such as short-chain fatty acids or bacteriocins, can exhibit antimicrobial properties against pathogenic organisms such as Salmonella, Staphylococcus, and Clostridium (Zhang et al., 2024; Soltani et al., 2022).
In the context of meat production, improving gut health and nutrient absorption directly impacts animal growth rates, feed conversion efficiency, and carcass quality. Microbial solutions can also target methane-producing archaea in ruminants to reduce greenhouse gas emissions, potentially redirecting reducing equivalents (H2) to enhance energy availability for the host animal. As consumer demand for antibiotic-free and sustainable meat production grows, biologics derived from the gut microbiome present a promising alternative for the discovery of bioactive molecules to promote animal welfare and enhance productivity without compromising food safety or environmental sustainability. The topics that follow will summarize advances in studying the functional potential of gut microorganisms as a source of biologics with an emphasis on the rumen microbiome as a reservoir of antimicrobial compounds.
Microbial Metabolites
Opportunities for biologics discovery
Microorganisms produce a myriad of primary metabolites during their growth, including fermentation products, amino acids, vitamins, and nucleotides. As they transition to the stationary phase, they synthesize secondary metabolites, which serve as defense mechanisms against adverse environmental conditions (Manoharan et al., 2023; Demain, 1999). These microbial-derived secondary metabolites have significant potential as biologics, as their production can be advantageous to organisms living in complex microbial communities, and their multiple functions can be exploited for various applications beneficial to human health and industry (Demain, 1999). Among their known ecological roles, secondary metabolites function as antimicrobials (Paiva et al., 2013; Lauková and Czikková, 1998), signaling molecules (Martín et al., 2005), and effectors of the host immune system, thereby contributing to niche colonization (Moreira et al., 2020), intercellular communication (Monds and O’Toole, 2008), and host-microorganism interactions (Saha et al., 2016).
Within the animal gut microbiome, genes encoding these specialized molecules are typically found in bacterial and archaeal genomes (Anderson and Fernando, 2021). However, comparatively less is known about the production of bioactive molecules by protozoa and anaerobic fungi that inhabit the GIT of ruminants and monogastric animals. The biosynthetic pathways responsible for secondary metabolite production are often organized within a biosynthetic gene cluster (BGC) in the genomes of the producer organisms (Medema et al., 2015).
Secondary metabolites produced by microorganisms encompass a wide range of bioactive compounds: for example, bacteriocins, polyketides (PK), and nonribosomal peptides (NRP) (Table 1). These molecules exhibit diverse mechanisms of action, ranging from inhibition of bacterial cell wall biosynthesis, disruption of electrochemical gradients across cell membranes, and chelation of metal ions. For instance, nisin, a bacteriocin produced by Lactococcus lactis, binds to lipid II and prevents peptidoglycan biosynthesis while inserting into the cell membrane and forming pores that dissipate protonmotive force (Wiedemann et al., 2001). NRP with occasional PK modules that perform iron-chelating functions (siderophores) are used by microorganisms to uptake minerals from the environment. This is exemplified by yersiniabactin, produced by Yersinia pestis, a siderophore that has been explored for use as a rust remover (Ahmadi et al., 2015). Additionally, some NRP exhibit toxic effects against eukaryotic cells. For example, gliotoxin produced by Aspergillus fumigatus inhibits the catalytic activity of the 20S proteasome, thereby disrupting nuclear factor-κβ and impairing cell function (Kroll et al., 1999; Cramer et al., 2006). Given their wide spectrum of biological activities, secondary metabolites with antimicrobial properties are of particular interest as alternatives to conventional antibiotics in maintaining livestock health (Wang et al., 2011).
Source, applications, and mechanisms of action of biologics produced by microorganisms.
| Biologic | Producer Microorganism | Isolation Source or Habitat of the Producer | Application | Mechanism of Action | Reference |
|---|---|---|---|---|---|
| Bacteriocins | |||||
| Nisin | L. lactis | Raw milk | Antimicrobial commonly used as a food preservative | Forms pores and prevents cell wall biosynthesis by binding to the peptidoglycan precursor | (Wiedemann et al., 2001; Hurst, 1981) |
| Albusin B | R. albus | Rumen | Narrow range of antibacterial activity. Potential use as a feed additive in poultry | Type III bacteriocin; as a potential feed additive, it modulates lipid metabolism (shows inhibition of triglycerides synthesis and cholesterol absorption) and induces gene expression of antioxidant proteins | (Chen, et al., 2004; Wang et al., 2013) |
| Enterocin CCM4231 | E. faecium | Rumen | Brad spectrum antimicrobial activity. Potential use in dairy process | Not described, but other enterocins inhibit gene expression or alter the cell envelope; enterocin CCM4231 can reduce the bacterial counts of L. monocytogenes and S. aureus in milk | (Lauková et al., 1999; Wu et al., 2022) |
| Bovicin HC5 | S. bovis | Rumen | Alternative to antibiotics used in livestock animals and in food preservation | Inhibits bacterial cell wall synthesis by binding to lipid II | (Mantovani et al., 2001; Paiva et al., 2013; Paiva et al., 2011) |
| NRP and PK | |||||
| Yersiniabactin | Y. pestis | Bubo from a patient of the bubonic plague | Rust removal. Copper removal (potential application in Wilson’s disease treatment and bioremediation) | It is a siderophore, it binds to metals like iron and copper | (Butler, 2014; Wake et al., 1975; Ahmadi et al., 2015) |
| Gliotoxin | A. fumigatus | Soil and composts | Induce apoptosis in macrophages | It is unclear but it can inhibit NF-κβ deactivation, produce reactive oxygen species and fragment DNA | (Cramer et al., 2006; Waring et al., 1988) |
| Erytromycin | S. erythraea | Soil | Macrolide antibiotic with a wide spectrum | Inhabits protein synthesis by promoting the dissociation of peptidyl-tRNA and the ribosome | (Tenson et al., 2003; Roberts, 2014) |
| Cyclosporine A | T. infantum | Soil | Immunosuppressant | Inhibits T-cell activation and the release of inflammatory cytokines | (Periman and Karpecki, 2020; Survase et al., 2011) |
| Ivermectin | S. avermectinius | Soil | Antiparasitic and anthelmintic | Prevents closure of glutamate-gated chloride channels, causing a decrease in neuronal transmission and paralysis of somatic muscles | (Õmura and Crump, 2004) |
| Tacrolimus | S. tsukubensis | Soil | Immunosuppressant | Forms a complex with an immunophilin and block the action of calcineurin; a phosphatase that regulates transcription factors related to T-cell activation | (Muramatsu and Nagai, 2013; van Dieren et al., 2006) |
| Epothilones | S. cellulosum | Riverbank | Chemotherapy | Stabilizes microtubules, disrupts the polymerization and depolymerization homeostasis, and leads to mitotic arrest of the cell | (Gerth et al., 1996; Cortes and Baselga, 2007) |
| Lovastatin | A. terreus | Soil | Reduce cholesterol levels | Inhibits the HMG-CoA (3-hydroxy-3-methylglutaryl-coenzyme A) reductase, an enzyme involved in the initial steps of cholesterol biosynthesis | (Alberts, 1988) |
| Lynronne 1-3 | P. ruminicola 23 (CP002006.1), Uncultured bacterium KC246977.1 and uncultured bacterium KC246861.1 | Rumen | AMP, active against methicillin-resistant S. aureus | Binds to bacterial membrane lipids and increases the permeability of the membrane | (Oyama et al., 2017) |
A. fumigatus, Aspergillus fumigatus; AMP, antimicrobial peptide; A. terreus, Aspergillus terreus; E. faecium, Enterococcus faecium; L. lactis, Lactococcus lactis; L. monocytogenes, Lysteria monocytogenes; NF-κβ, nuclear factor κ-light-chain-enhancer of activated B cells; NRP, nonribosomal peptides; PK, polyketides; P. ruminicola, Prevotella ruminicola; R. albus, Ruminococcus albus; S. aureus; Staphylococcus aureus; S. avermectinius, Streptomyces avermitilis; S. bovis, Streptococcus bovis; S. cellulosum, Sorangium cellulosum; S. erythraea, Saccharopolyspora erythraea; S. tsukubensis, Streptomyces tsukubensis; T. infantum, Leishmania infantum; tRNA, transfer RNA; Y. pestis, Yersinia pestis.
Bacteriocins synthesized by bacteria or archaea have been proposed as an alternative to antibiotics due to their efficacy, potency, and lower cytotoxicity against mammalian eukaryotic cells (Cotter et al., 2013). These molecules share key biochemical characteristics such as amphiphilicity, short amino acid sequences, and a net cationic charge. Bacteriocins can be classified into 4 classes (Heng and Tagg, 2006). Class I bacteriocins include peptides with post-translational modifications, such as lanthipeptides, sactipeptides, and lasso peptides (Cotter et al., 2013), which possess antimicrobial (Salomón and Farías, 1992; Kuznedelov et al., 2011), antiviral (Constantine et al., 1995), or enzyme-inhibitory properties (Katahira et al., 1996). Class II comprises a heterogeneous group of small, heat-stable peptides that lack significant post-translational modifications, including pediocin-like bacteriocins, 2-component bacteriocins, and cyclic or linear nonpediocin-like peptides (Cotter et al., 2005). Bacteriolysins (formerly class III bacteriocins) are large heat-labile antimicrobial proteins (Cotter et al., 2005). Finally, cyclic bacteriocins with post-translational modifications are grouped in class IV (Heng and Tagg, 2006). Despite their structural and functional diversity, all bacteriocins share the common feature of ribosomal synthesis (Cotter et al., 2005).
In contrast, PK and NRP are synthesized via alternative biosynthetic machinery distinct from ribosomal pathways. PK are produced by PK synthases (PKS) (Nivina et al., 2019), whereas NRP are synthesized by NRP synthases (NRPS) (Martínez-Núñez and López, 2016). These large, multifunctional enzymes generate structurally diverse bioactive compounds with antimicrobial, antiparasitic, immunosuppressive, and cholesterol-lowering properties (Durand et al., 2019). Among their most well-characterized functions, PK and NRP exhibit potent inhibitory activity against a broad range of pathogens, including bacteria, fungi, and parasites. For example, erythromycin, a PK synthesized by the Gram-positive bacteria Saccharopolyspora erythraea (Cortes et al., 1990), is a widely used antibiotic for treating skin and respiratory tract infections (Platon et al., 2022). Cyclosporine A, an NRP synthesized by various fungi like Tolypocladium inflatum and Fusarium roseum, exhibits anti-inflammatory, antiparasitic, and antifungal properties (Martínez-Núñez and López, 2016).
In addition to their well-established antimicrobial properties, PK and NRP, particularly those isolated from soil microorganisms, have demonstrated diverse applications in pharmacology and agriculture, showcasing their significance as biologically active compounds with far-reaching impacts (Nivina et al., 2019). A prominent example is ivermectin, a PK-derived antiparasitic agent produced by Streptomyces avermitili (Li and Zhang, 2023), which has been widely used to control parasitic infections in livestock, companion animals, and humans. Ivermectin plays a critical role in combating onchocerciasis (river blindness) and lymphatic filariasis in humans and is also employed in veterinary medicine to treat nematode infections and ectoparasites (Crump and Omura, 2011). Another notable PK is FK506 (tacrolimus), an immunosuppressant synthesized by Streptomyces tsukubensis, originally discovered in a soil sample from Tsukuba, Japan. Tacrolimus is widely used to prevent organ rejection in transplant patients (Muramatsu and Nagai, 2013). Similarly, epothilones, a class of PK chemotherapy drugs recovered from Sorangium cellulosum isolated from a river bank in South Africa (Gerth et al., 1996), have shown efficacy in treating various types of cancers by stabilizing microtubules, making them a promising alternative to taxanes for treating drug-resistant tumors (Cortes and Baselga, 2007). Lastly, lovastatin, a cholesterol-lowering PK initially recovered from Aspergillus terreus in Spain, was the first statin developed to reduce low-density lipoprotein cholesterol levels, thereby lowering the risk of cardiovascular disease and related complications (Alberts, 1988). Collectively, these examples highlight the immense potential of microbial secondary metabolites in medicine, agriculture, and biotechnology. The continued exploration of these natural products, particularly those derived from complex microbial communities (as previously exemplified for the soil ecosystem), holds promise for developing novel therapeutics and sustainable biotechnological solutions.
Rumen Secondary Metabolites
A potential source of biologics
The rumen, akin to the soil environment (Figure 1), is a highly complex and dynamic ecosystem that harbors a diverse array of microorganisms, including bacteria, archaea, fungi, and protozoa. However, unlike soil, ruminal microorganisms face the additional challenge of passage rate and constant dilution/turnover in the forestomach, followed by digestion in the abomasum (Hackmann and Firkins, 2015; Hungate, 1975). To survive in this competitive, anaerobic, and nutrient-rich environment, rumen microorganisms have evolved specialized strategies (Huws et al., 2018; Miron et al., 2001). Notably, many ruminal taxa produce a wide variety of secondary metabolites that not only facilitate their adaptation to the rumen ecosystem but also represent a largely untapped reservoir of bioactive compounds with potential applications in both human and animal health (Azevedo et al., 2015; Oyama et al., 2017). Despite their biotechnological promise, systematic efforts to recover, characterize, and exploit these metabolites in livestock management or animal harvest remain limited, emphasizing the need for further research in this area.
Key features of soil and rumen ecosystems as a source of biologics of microbial origin. The soil microbiome has been studied as a source of secondary metabolites with diverse applications in anthropogenic processes. Both rumen and soil ecosystems provide challenging environments for microorganisms that boost the production of these metabolites, suggesting that the rumen microbiome should be further investigated as a source of bioactive compounds. Created in BioRender. Mantovani, H. (2025) https://BioRender.com/y90e605.
A well-characterized example of a rumen-derived secondary metabolite is albusin B, a 32-kDa bacteriocin produced by Ruminococcus albus 7 (Wang et al., 2013). Albusin B can enhance broiler chicken performance by modulating lipid metabolism and activating systemic antioxidant defenses, highlighting its potential as a feed additive for improving poultry production (Wang et al. 2013). Another promising class of rumen-derived antimicrobials includes Lynronne 1, 2, and 3, a group of AMP discovered through functional metagenomic approaches (Oyama et al., 2017). These AMP exhibit potent activity against methicillin-resistant Staphylococcus aureus, a major antibiotic-resistant pathogen that poses significant challenges to global public health (Oyama et al., 2017).
Further supporting the antimicrobial potential of rumen-derived metabolites, enterocin CCM4231, a bacteriocin produced by bacteria isolated from the rumen of calves, has activity against a broad spectrum of pathogens, including Enterococcus faecium EF 26/42, Streptococcus bovis AO 24/85, Escherichia coli, and Listeria monocytogenes OHIO (Lauková et al., 1993; Lauková and Czikková, 1998). Similarly, bovicin HC5, a heat-stable 2,449 Da lanthipeptide produced by Streptococcus (bovis) equinus HC5, is one of the most extensively studied rumen-derived bacteriocins. Bovicin HC5 exhibits broad-spectrum antimicrobial activity, low toxicity both in vitro and in vivo, and potential applications in food preservation and infectious disease treatment (Paiva et al., 2013). Its stability across various environmental conditions further enhances its suitability for food and pharmaceutical applications.
Beyond antimicrobial metabolites, the rumen microbiome also serves as a valuable source of other bioactive compounds, including branched-chain amino acids (BCAA), vitamins, and organic acids, which can have applications in animal production systems. BCAA such as leucine, isoleucine, and valine are essential nutrients for monogastric animals due to their inability to synthesize them. In ruminants, these amino acids contribute to microbial protein production, intestinal function improvement, and muscle growth stimulation (An et al., 2024). Additionally, rumen microorganisms synthesize B-complex vitamins (Bechdel et al., 1928), which are essential in the diet of livestock animals for proper health and reproduction (Vijayalakshmy et al., 2018). Rumen microorganisms also produce organic acids with potential industrial applications. For instance, Megasphera elsdenii generates medium-length (C2-C7) carboxylic acids that can be used as biofuel. Moreover, M. elsdenii metabolizes lactic acid, contributing to the prevention of ruminal acidosis (Cabral and Weimer, 2024). Although the rumen metabolome contains a diverse array of molecules with potential applications in animal production, this review focuses specifically on biologics encoded by microbial BGC. The following sections will discuss strategies for identifying BGC within the genetic material of the rumen microbiome.
High-Throughput Sequencing and Bioinformatic Tools Uncover the Genetic Diversity of the Rumen Microorganisms
In recent years, advances in microbial genome mining and metagenomic approaches have unlocked unprecedented opportunities to identify novel bioactive molecules from the rumen microbiome. The development of next-generation sequencing technologies improved the exploration of the rumen microbiome genetic diversity, enabling researchers to uncover BGC responsible for producing secondary metabolites with potential applications in medicine, agriculture, and the food industry (Wu et al., 2022; Swift et al., 2021).
Historically, the discovery of biologically active compounds from natural sources, including animal tissues and gut contents, relied on extraction and purification techniques. These methods typically included solvent extraction, solid-phase extraction, and liquid-liquid extraction, followed by various chromatographic techniques for purification. Once extracted and purified, bioassays could be employed to evaluate the biological properties of these compounds, such as antimicrobial, anti-inflammatory, or antitumor activities (Arken et al., 2023; Ladha and Jeevaratnam, 2020).
With the increasing availability of sequenced genomes from livestock gut microbiota, different approaches emerged to identify and explore biologically active molecules from genomes and metagenomes (Li et al., 2021; Xie et al., 2021; Seshadri et al., 2018). Following the identification of molecules through the analysis of genetic information, these molecules can be obtained directly from natural sources (e.g., microbial cultures, animal tissues) or through organic synthesis or heterologous expression. Among the bioinformatic methods used to identify these molecules are genome mining, metagenomic screening, and functional metagenomics.
Genome mining involves analyzing individual microbial genomes (often from cultured species) to identify gene clusters involved in the biosynthesis of potential bioactive compounds (Azevedo et al., 2015). In contrast, metagenomic screening allows researchers to explore the genetic material of multiple microbes recovered directly from the GIT, animal tissues, or environmental samples, bypassing the need for culturing individual microorganisms (Cohen et al., 2018). Additionally, functional metagenomics enables the expression of DNA fragments from a defined ecosystem in a host organism (e.g., bacteria, yeast) to screen for specific functions or bioactivities (Oyama et al., 2017; Lam et al., 2015).
These strategies allow the prediction and identification of genes encoding specific proteins or biosynthetic machinery associated with desired chemical features or biological properties (Anderson and Fernando, 2021; Li et al., 2021). These bioinformatic approaches can identify genes that lead to the development of new drugs to improve human and animal health (Figure 2). The continued exploration of this rich microbial ecosystem, coupled with advances in bioinformatics and functional characterization techniques, promises to accelerate the discovery of innovative therapeutic agents and biotechnological solutions.
Strategies for discovering novel proteins and peptides from the rumen microbiome that can act as biologics. Identification of antimicrobial proteins can be performed using culturomics approaches and phenotype mining, followed by purification and characterization of active peptides. Sequence-based mining of microbial genomes and ruminal metagenomes enables the discovery of BGC associated with the production of BCN, PK, NRP, and other putative AMP. Tools commonly used for sequence-based mining of microbial genomes and metagenomes are summarized in Table 1. Recently, machine-learning models have been applied to screen human and animal gut microbiomes to predict putative sequences with biological activity. Once these proteins and peptides are identified, bioactive molecules can be synthesized and their activity confirmed in vitro and in vivo. AMP, antimicrobial peptides; BCN, bacteriocins; BGC, biosynthetic gene clusters; NRP, nonribosomal peptides; PK, polyketides; SORF, short open reading frames. Created in BioRender. Mantovani, H. (2025) https://BioRender.com/t23j132.
Notably, rumen metagenomes have been reported to encode over 14,000 BGC, revealing the immense potential of this microbial ecosystem as a source of novel biologics (Anderson and Fernando, 2021). These BGC include NRPS, representing the most abundant class (5,346 clusters), followed by aryl polyenes (2,800 clusters), sactipeptides (2,126 clusters), and bacteriocins (1,943 clusters) (Anderson and Fernando, 2021). Among archaea, NRPS were identified exclusively in Methanobrevibacter (Anderson and Fernando, 2021), indicating a narrower distribution compared to bacteria.
Genome mining analyses also revealed that bacteriocin gene clusters are widely distributed in the rumen microbiome, with 45% of 229 surveyed bacterial genomes harboring these clusters (Azevedo et al., 2015). Specifically, class I bacteriocins were frequently detected in Butyrivibrio and Ruminococcus strains as well as in members of the family Lachnospiraceae, while sactipeptides were predominantly associated with Clostridiales (Azevedo et al., 2015). Moreover, complete BGC encoding lanthipeptides have been identified in the genomes of ruminal Streptococcus (Azevedo et al., 2015; Anderson and Fernando, 2021) and Lachnospiraceae (Anderson and Fernando, 2021). Additionally, lasso peptides were found predominantly in members of the phylum Firmicutes, particularly in the genus Butyrivibrio (Sabino et al., 2020).
Another important discovery based on genome mining is the conservation of PKS genes across the genera of gut anaerobic fungi. These organisms have evolved sophisticated mechanisms to produce bioactive compounds in environments like the rumen (Swift et al., 2021). Furthermore, integrative omics approaches (a combination of transcriptomics, epigenetics, and proteomics) demonstrated that anaerobic fungi actively transcribe and translate a substantial portion of their core biosynthetic genes under specific environmental conditions (Swift et al., 2021). These findings provide evidence of their capacity to produce secondary metabolites.
These discoveries highlight the importance of using multiomics strategies to bridge the gap between genetic potential and metabolite production. While genome mining reveals the genetic blueprints for secondary metabolite biosynthesis, transcriptomics and proteomics provide insights into the regulatory mechanisms and environmental cues that trigger the expression of these genes. Moreover, epigenetic analyses can uncover how chromatin structure and histone modifications influence gene activation, further enhancing our understanding of the biosynthetic pathways in rumen microorganisms.
Furthermore, some bioactive molecules predicted by bioinformatics tools in ruminal microbial genomes have been successfully purified and their biological activity demonstrated in vitro. For example, BGC associated with the production of class II bacteriocins were identified in the genomes of 3 ruminal strains of Streptococcus lutetiensis. These bacteriocins were extracted from the cell pellets of S. lutetiensis UFV9, UFV11, and UFV58 (de Oliveira et al., 2022). Sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis confirmed that the purified peptides had identical molecular masses (5,197 Da) and a high sequence identity (68.57%) to the precursor of a class II bacteriocin (ubericin A, ubaA, 5,197 Da) (de Oliveira et al., 2022). These findings aligned well with predictions for the precursor and core peptide sequences.
Despite these promising discoveries, the full potential of the rumen microbiome remains underexplored, and significant challenges remain. For instance, many BGC in the rumen microbiome are silent under standard laboratory conditions, requiring innovative approaches such as coculture techniques, chemical elicitors, or manipulation of growth conditions to activate their expression. One area requiring further investigation is the variation in BGC expression across rumen microbial communities of different phenotypes. For example, Prevotella strains in high- and low-feed–efficient steers exhibit differences in the expression of BGC (Anderson and Fernando, 2021). Similarly, interspecies and breed-specific variations in rumen microbiomes also demand closer scrutiny. Transcriptomic analyses have revealed that the sheep rumen contains a higher abundance of active NRPS and PKS than the cattle rumen microbiome, suggesting species-specific differences in secondary metabolite production potential (Moreira et al., 2020).
Moreover, certain genera, such as Butyrivibrio, are thought to encode novel lasso peptides with as-yet-unknown functions, representing an untapped reservoir for future exploration (Sabino et al., 2020). The ecological roles of these bioactive molecules remain poorly understood, and systematic studies are needed to uncover their significance within the rumen environment. Improvements in culturomics approaches for isolating and cultivating rumen microorganisms are also critical to unlocking their metabolic potential. Additionally, advances in synthetic biology provide an opportunity to engineer heterologous hosts, such as E. coli or Saccharomyces cerevisiae, to produce these metabolites at an industrial scale, bypassing challenges associated with cultivating anaerobic rumen microorganisms (Pouresmaeil and Azizi-Dargahlou, 2023; Tippelt and Nett, 2021). Coupled with efforts to purify and characterize bioactive compounds, these advances could reveal novel strategies to enhance animal productivity, mitigate disease, and develop sustainable biologics for human and veterinary applications.
Bioinformatic Tools for the Discovery of new Biologics
Bioinformatics pipelines for characterizing microbial genomes and metagenome-assembled genomes (MAG) are accelerating the discovery of novel biologics from rumen microbiomes. These tools significantly reduce the time and economic resources required during the screening phase compared to traditional in vitro analyses (Anderson and Fernando, 2021; Moreira et al., 2020). Once putative targets are identified through bioinformatics screening, in vivo assays can be conducted to evaluate and confirm their biological activity, thereby minimizing the number of experiments needed to validate these molecules and their biological activities (Sabino et al., 2020). Culturomics studies, which focus exclusively on cultured species, can benefit from bioinformatics screening by refining the selection of targeted molecules while accounting for microorganisms that cannot be cultivated in isolation.
Multiple tools are available to screen BGC encoding new microbial-derived biologics from genomes and MAG (Table 2). A best practice is to first define the pipeline that aligns with the research question, objectives, and the type of data being analyzed. First-generation tools, such as BLAST (Altschul et al. 1990), employ sequence alignment techniques to compare a reference BGC with genome or MAG sequences. These tools rely on sequence similarity searches and serve as a simple and essential approach for initial screening. Second-generation tools, like ARTS (Mungan et al., 2020), AntiSMASH (Blin et al., 2023), BAGEL (van Heel et al., 2018), BiG-SLiCE (Kautsar et al., 2021), and BGCFlow (Nuhamunada et al., 2024), enhance prediction accuracy by employing rule-based algorithms. These tools implement a set of criteria to define BGC based on gene content and their similarity to reference databases, enabling more refined and reliable predictions (Hannigan et al., 2019). Third-generation tools, driven by Artificial Intelligence, have introduced the potential for de novo BGC prediction, like DeepBGC (Hannigan et al., 2019), GECCO (Carroll et al., 2021), and the genomic natural language processing (NLP) algorithm (Miller et al., 2022). These tools utilize machine learning, deep learning, and NLP algorithms to detect data signatures characteristic of gene-encoding biologics, moving beyond sequence alignment and rule-based criteria to uncover novel BGC with unprecedented accuracy.
Bioinformatic tools for mining biosynthetic gene cluster and antimicrobial peptides from genome and metagenome-assembled genomes sequences.
| Tool | Description | Functionality (Input File Format) | Source |
|---|---|---|---|
| BGC | |||
| ARTS | Uses reference-based genomic comparison to identify resistance markers for prioritizing BGC; supports both genomic and metagenomic inputs | Web server (GenBank/EMBL/FASTA) | https://arts.ziemertlab.com |
| AntiSMASH | Combines domain-based predictions with reference comparisons to detect and annotate BGC; optimized for genomic data | Web server (GenBank/EMBL/FASTA) | https://antismash.secondarymetabolites.org |
| BiG-SLiCE | Applies feature-based clustering to group BGC into gene cluster families across large-scale genomic datasets | Command line (GenBank) | https://github.com/medema-group/bigslice |
| BGCFlow | Integrates genome mining and pangenomic analysis for large-scale BGC exploration; supports both genomic and metagenomic data | Command line (GenBank) | https://github.com/NBChub/bgcflow |
| DeepBGC | Employs deep learning to detect and classify BGC in genomic data, enabling identification of novel and divergent cluster types | Command line (FASTA) | https://github.com/Merck/deepbgc |
| GECCO | Utilizes machine learning for de novo prediction and classification of BGC in genomic and metagenomic data | Command line (FASTA) | https://gecco.embl.de |
| Genomic-NLP | Uses NLP to model gene context and predict gene functions, including BGC, from microbial genomic and metagenomic data | Web server (FASTA) | https://gnlp.bursteinlab.org |
| AMP | |||
| AMPEP | Utilizes multiple machine learning models to predict AMP from amino acid sequences or genome-derived data | Web server (FASTA) | https://app.cbbio.online/ampep/home |
| AMPScanner | Apply deep learning to identify AMP from protein sequences | Web server (FASTA) | https://www.dveltri.com/ascan |
| Macrel | Uses machine learning to mine AMP from genomic, metagenomic, and peptide sequence data | Command line (FASTA) | https://github.com/BigDataBiology/macrel |
AMP, antimicrobial peptides; BGC, biosynthetic gene cluster; EMBL, European Molecular Biology Laboratory; NPL, natural language processing.
In contrast to BGC, short open reading frames (sORF), which encode microproteins or sORF-encoded proteins (SEP) shorter than 100 amino acid residues, have often been overlooked in genome and MAG analyses (Storz et al., 2014; Su et al., 2013; Leong et al., 2022). However, recent studies have highlighted their critical roles in various cellular processes and their potential as a valuable source of AMP, complementing the more established BGC-focused pipelines (Duan et al., 2024; Santos-Júnior et al., 2024; Torres et al., 2024). Identifying sORF encoding AMP involves the use of specialized sORF predictor pipelines, as comprehensively reviewed by Leong et al. (2022), in combination with tools designed to classify SEP as putative AMP. Examples of such tools include AMPEP (Bhadra et al., 2018), AMPScanner (Veltri et al., 2018), and Macrel (Santos-Júnior et al., 2020), which employ random forest classifiers, deep neural networks, and machine learning algorithms, respectively, to extract SEP features and calculate the likelihood of them being AMP.
The integration of these bioinformatic tools and advanced computational methods is revolutionizing the discovery of novel biologics from microbiome datasets, particularly given the increasing availability of genomic, metagenomic, transcriptomic, and proteomic data. For instance, sequence analysis of MAG has uncovered BGC encoding putative lanthipeptides that differ from those identified in cultured ruminal bacterial isolates and genome sequences of the Hungate1000 collection, the largest database of genomes from cultured rumen bacteria (Anderson and Fernando, 2021). Using a combination of first-generation tools such as BLAST and second-generation tools, such as AntiSMASH and BAGEL, new NRPS and PKS gene clusters were identified in 310 prokaryotic genomes of ruminal bacteria (Moreira et al., 2020) as well as BGC associated with the production of lasso peptides in other rumen bacteria (Sabino et al., 2020). Beyond genome and MAG mining approaches, additional data sources such as transcriptomes (Veltri et al., 2018) and proteomes (Han and Chang, 2023) are increasingly being utilized to identify bioactive molecules within microbiomes.
Multiple bioinformatic approaches can facilitate the search for new microbial-derived biologics, but the rapid development of bioinformatic tools and the increasing amount of available data can be overwhelming. To define the workflow and tools that best fit the research, one must first delineate the specific research questions and goals and then consider the type of data to be collected (Table 3 provides a list of common tools used in bioinformatic workflows aimed at identifying BGC). To advance the field, it is essential to tailor experimental designs and workflows to specific research questions, ensuring that bioinformatics-based predictions can be translated into experimentally valid outcomes. The ongoing evolution of these bioinformatic tools raises the expectations for the development of even more efficient and accurate methods for biologics discovery from rumen microbiomes and other previously underexplored environments.
Bioinformatic tools applied to different steps of a bioinformatic DNA sequence analysis for the identification of biosynthetic gene clusters.
| Step | Tool | Description | Reference |
|---|---|---|---|
| Quality control | FastQC | Analyze the quality of high-throughput sequencing raw data | (Babraham Bioinformatics, 2010) |
| Trimming | Trimmomatic | Remove adapters (used for sequencing purposes) and low-quality bases; BBduk also filters contaminants | (Bolger et al., 2014) |
| BBDuk | (Bushnell, 2015) | ||
| Assembly | SPAdes | Align the sequence reads and create contigs based on overlapping regions, followed by arranging the contigs in super-contigs or scaffolds; MEGAHIT and MetaSPAdes are particularly suited for metagenomic data analysis | (Bankevich et al., 2012) |
| MetaSPAdes | (Nurk et al., 2017) | ||
| MEGAHIT | (Li et al., 2015) | ||
| Mapping | Bowtie2 | Align the assembled contigs back to their original sample sequences; this alignment creates an index that reveals the precise contribution of each sample to the generated contigs | (Langmead and Salzberg, 2012) |
| Binning | MetaBAT2 | Cluster the contigs using the mapping information and nucleotide composition to form representative genomes | (Kang et al., 2019) |
| CONCOCT | (Alneberg et al., 2014) | ||
| Taxonomy | GTDB-Tk | Assign taxonomy to the genomes based on phylogeny | (Chaumeil et al., 2019) |
| Annotation | Prokka | Identify and label genomic features from genomes; online tools are available, like KEGG and NCBI; command line tools like Prokka and Kraken are recommended for sensitive or large data sets; EggNOG is available for the command line and also has an online version | (Seemann, 2014) |
| Kraken | (Wood and Salzberg, 2014) | ||
| EggNOG | (Cantalapiedra et al., 2021) | ||
| KEGG | (Kanehisa et al., 2016) | ||
| NCBI | (National Library of Medicine, 1988) | ||
| Genome quality | CheckM | Estimate the completeness and contamination of the genomes | (Parks et al., 2014) |
| BGC identification | See Table 2 | ||
| Other | Anvi’o | Platform for multiomics analyses with available tutorial workflows | (Eren et al., 2021) |
| JGI | Online genomic database and sequence analytical tools | (Nordberg et al., 2014) | |
BGC, biosynthetic gene cluster; JGI, Joint Genome Institute; KEGG, Kyoto Encyclopedia of Genes and Genomes; NCBI, National Center for Biotechnology Information.
Conclusions
As our understanding of gut microbial ecosystems deepens, the remarkable diversity of microbial species and their complex metabolic activities have become increasingly evident. Although the scope of these interactions may initially appear overwhelming, advancements in genomic tools have significantly enhanced our ability to elucidate these intricate processes. Traditionally, microbial organisms have been screened individually for specific targeted functions, such as the production of antimicrobial compounds against defined pathogens. While this approach has contributed to important discoveries of commercially viable products, it has also been limited by its reliance on labor-intensive, trial-and-error methods. The advent of high-throughput sequencing technologies, including metagenomics and transcriptomics, has transformed the discovery pipeline by enabling simultaneous screening of entire microbial communities. This comprehensive approach facilitates the identification of diverse metabolic capabilities encoded across microbial genomes, expanding the potential for uncovering novel bioactive compounds with practical and commercial applications. Moreover, the integration of sequencing data with advanced computational tools has greatly improved the capacity to identify and characterize functional gene clusters, allowing for targeted exploration of specific metabolic activities and their associated bioactive metabolites. Moving forward, leveraging this wealth of genomic information to develop scalable production systems for promising metabolites will be critical. This may involve optimizing fermentation processes, genetic engineering of high-yield microbial strains, or further modifying metabolic pathways to enhance product yield and functionality. The application of bioinformatics and advanced genetic tools in designing such production systems offers a path to generate commercially viable biologics in a cost-effective manner. In summary, the gut microbiome represents a rich reservoir of bioactive metabolites and biologics with immense commercial potential. Specifically, the rumen microbiome offers great potential for the discovery of new biologics due to its complex and highly adapted microbial community. The convergence of high-throughput sequencing, advanced bioinformatics, and large-scale production technologies now enables the systematic exploration and utilization of these resources. Continued integration of these approaches will likely accelerate the discovery and development of novel microbial-derived products for industrial and pharmaceutical applications.
Conflict of Interest
The authors declare no conflicts of interest regarding this manuscript.
Acknowledgments
We thank the University of Wisconsin–Madison Libraries for access to their archives and the Department of Animal and Dairy Science and Meat Science and Animal Biologics Discovery for their support. F.L.V.U. was supported with start-up funds from the Dairy Innovation Hub. P.M.P.V. was supported by a fellowship from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), grant number 200773/2024-0.
Author Contributions
Conceptualization: S.C.R. and H.C.M; Methodology: F.L.V.U., P.M.P.V., H.C.M., S.C.R.; Formal analysis: F.L.V.U., P.M.P.V. and H.C.M.; Writing -original draft preparation: F.L.V.U., P.M.P.V. and H.C.M.; Writing - review and editing: E.L-W.M, C.C., E.O., S.C.R. F.L.V.U., P.M.P.V., and H.C.M.; Visualization: F.L.V.U., and P.M.P.V.; Supervision: S.C.R. and H.C.M. Project administration: E.O., S.C.R. and H.C.M.; Resources: E.O., S.C.R. and H.C.M.; Funding acquisition: F.L.V.U., P.M.P.V., H.C.M., and S.C.R. All authors have read and agreed to the published version of the manuscript.
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