# The dawn of the revolution that will allow us to precisely describe how microbiomes function

Jean Armengaud

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## To cite this version:

Jean Armengaud. The dawn of the revolution that will allow us to precisely describe how microbiomes function. Journal of Proteomics, 2025, 316, 10.1016/j.jprot.2025.105430. hal-05194646

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# The dawn of the revolution that will allow us to precisely describe how microbiomes function

Jean Armengaud*

Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), SPI, 30200 Bagnols-sur-Cèze, France

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## ARTICLE INFO

**Keywords:**

Protein function

Metagenomics

Metaproteomics

Microbial communities

Microbiome

Microbiota

Omics

Tandem mass spectrometry

Artificial intelligence

## ABSTRACT

The community of microorganisms inhabiting a specific environment, such as the human gut - including bacteria, fungi, archaea, viruses, protozoa, and others - is known as the microbiota. A holobiont, in turn, refers to an integrated ecological unit where microbial communities function and interact with their host, thus is a more integrative concept. To understand the processes involved, the diversity of microorganisms present must be identified and their molecular components quantified, especially proteins. Indeed, proteins - through their roles as catalytic units, structural components, and signaling molecules - are the main drivers of biological processes. Metagenomics has significantly expanded what we know about the genetic material present in microbiota, revealing their functional potential; metabolomics delivers an overall snapshot of the metabolites produced by the community. But metaproteomics offers a complementary approach to explore microbiome and holobiont functionality by focusing on the active proteins and functional pathways from each taxon. Significant recent advances in high-resolution tandem mass spectrometry have greatly expanded the catalog of peptide sequences accessible in each sample, creating the conditions for unprecedented taxonomical profiling, while also providing more accurate biomass quantification, more detailed protein characterization, and a greater capacity to monitor abundance and distinguish host biomarkers. By integrating artificial intelligence into the metaproteomics pipeline, extended datasets can now be efficiently mined to gain a more comprehensive functional view of complex biological systems, paving the way for next-generation metaproteomics. In this perspective, I discuss the transformative potential of this methodology. We are on the cusp of a remarkable omic revolution that promises to uncover the intricate workings of microbiomes by producing a vast array of new knowledge with multiple applications.

**Significance:** Metaproteomics provides a powerful lens to investigate microbiome and holobiont functionality by identifying and quantifying active proteins and functional pathways within each taxon. Recent breakthroughs in high-resolution tandem mass spectrometry have dramatically expanded the repertoire of peptide sequences detectable per sample. This progress enables unprecedented taxonomic resolution for microbial identification, more precise biomass quantification, comprehensive protein characterization, abundance monitoring, and the unique identification of host biomarkers. In this commentary, I delve into the distinctive features that make metaproteomics a transformative tool. I discuss the recent advancements in tandem mass spectrometry and argue that the primary challenge in analyzing complex samples is shifting from data acquisition to data interpretation. With the integration of artificial intelligence, I believe next-generation metaproteomics is poised to become the next Big Thing in microbiome research, unlocking profound insights into microbial functionality and ecosystem dynamics.

## 1. Introduction

Microbial ecosystems and their interactions with their hosts have long been recognized to play critical roles in health, biotechnology,

agriculture, and breeding, as well as in environmental systems and the food industry. In recognition of this importance, specific terminology has emerged. Thus, the “microbiota” is the community of microorganisms residing within a specific environment, whereas the “microbiome”

<footnote>* Corresponding author at: CEA-Marcoule, DRF/Joliot/DMTS/SPI/Li2D, “Innovative Technologies for Detection and Diagnosis” Laboratory, BP 17171, F-30200 Bagnols-sur-Cèze, France.</footnote>

<footnote>E-mail address: jean.armengaud@cea.fr.</footnote>
encompasses not only the catalog of these microorganisms but also all of their genetic material and molecular products, including transcripts, proteins, lipids, and metabolites.

The composition of the human gut microbiota is shaped by several factors, including diet and lifestyle, age, genetic background, environmental conditions, and the use of antibiotics and medications. Dysbiosis in the human gut has been linked to pathologies including inflammatory bowel diseases – such as Crohn's disease and ulcerative colitis, where an overactive immune response negatively impacts the gut's lining – but also to effects far beyond its initial site. It is thus linked to obesity and other metabolic syndromes, autoimmune disorders, and mental health disorders, including depression and anxiety, highlighting the importance of the gut-brain axis [29,49].

In the environment, the microbiota in the rhizosphere is the result of a complex interplay of plant, soil, climate, and microbial parameters. In open ecosystems such as soils and oceans microbial diversity is considered to be enormous, but it can be extremely specialized in given conditions [48]. Importantly, microbial dynamics are continuously shaped by phages and viruses, as well as by external physical and chemical parameters.

To identify the bacterial and archaeal taxa present in a microbiota, the most common methodology is to amplify the 16S ribosomal RNA gene by means of polymerase chain reaction and universal primers, sequence the products and then perform sequence similarity searches. Although effective, this metataxonomic approach is generally limited to bacteria and archaea. To investigate fungi, other conserved genetic markers must be targeted, and viruses are so heterogeneous they are simply not accessible by such approaches. Consequently, this method provides some insight into microbial diversity, but is limited with respect to the full composition and function of the communities analyzed. Many researchers have attempted to predict functions from metataxonomic data based on 16S amplicon sequencing, by linking each taxon to its potential ecological roles and health impacts. To help with these predictions, platforms have been developed, the most widely used of which are PICRUST2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States, [15]) and FAPROTAX (Functional Annotation of Prokaryotic Taxa, [33]). However, these methods are highly imprecise, as entire megaplasmids may not be taken into account.

To overcome these limitations, omics methodologies have been developed. Metagenomics based on whole-genome shotgun sequencing provides a more comprehensive catalog of the taxa present as it involves sequencing all DNA molecules extracted from a sample. Although it may struggle to resolve closely related species, this methodology also allows for functional predictions through metabolic pathway mapping. Nevertheless, there is no guarantee that these pathways are actively expressed. Metatranscriptomics, on the other hand, by sequencing RNA transcripts, captures gene expression profiles to reveal which genes are actively being used. This approach is valuable for understanding microbial activity and functional responses to changes, but the transient nature of gene expression and the instability of RNAs can result in an unclear overall picture. Metabolomics, in turn, analyzes the small molecules produced by the microbiota, revealing the end products of microbial metabolism. However, the range of metabolites that can be confidently identified and quantified is currently relatively limited, and levels may vary significantly depending on sampling conditions. Moreover, it is challenging to pinpoint which organisms are responsible for specific metabolic activities without integrating data from other omics resources. Metaproteomics therefore emerges as one of the most comprehensive methods, as protein synthesis depends on specific cellular conditions and their real impact is at least partially related to their abundance. By identifying and quantifying the proteins produced by microbial communities, it provides a snapshot of the potentially active functional pathways of individual organisms [44]. The abundance of proteins associated with each metabolic pathway is assumed to reflect their activity, potentially offering a more accurate representation of how the microbiota functions. Although this assumption is not always valid,

in particular as catalytic efficiencies can differ between proteins with the same function or some proteins may be inactivated, its application does identify some interesting trends.

## 2. What makes metaproteomics so unique?

Metaproteomics is exceptionally sensitive, currently requiring 100 ng of proteins or even less to comprehensively profile a microbiome [16,18]. Such a quantity can be extracted from 50 to 100 µg of wet biological material corresponding to 100 million bacterial cells [38]. It is also fast, with the entire process from raw sample to final results being completed in just a few hours [21] and affordable in terms of cost [44]. By accurately establishing peptide sequences using high-resolution tandem mass spectrometry, researchers can identify the specific organisms present in their sample and determine their corresponding biomasses [26,42]. Indeed, the organisms that produced a set of proteins can be easily traced by the taxonomical information encoded in their polypeptide sequences. Additionally, metaproteomics can identify and quantify host proteins, yielding valuable insights into host-microbiome interactions in cases where host proteins are retained during sample preparation, as well as the dietary exposome [53]. As a result, extensive research has been conducted to characterize various microbiota, including those of the human gut [1,28,30,52], other human-associated ecosystems [45], cattle rumen [2] soil [6,23], the rhizosphere [3,32] and ocean ecosystems [10,13,37,46,65]. Additionally, metaproteomics has been employed to enhance biotechnological applications of microbiota [12,20] and to develop microbiota-based assays [9,30,31].

In the past, metaproteomics has been denigrated because of its presumed limited depth of analysis compared to metagenomics. In samples where host and microbiota proteins are mixed, nucleic acid amplification methods excel at revealing specific signals. In contrast, metaproteomics can be hampered by its limited dynamic range when host proteins predominate, unless enrichment strategies are implemented during sample preparation. The diversity of proteins to be analyzed is directly proportional to the number and nature of organisms present in the sample. For example, the human gut microbiome comprises hundreds of bacterial, fungal, and yeast species, as well as phages and viruses, along with substantial residual plant and animal food material, and host proteins [4]. The corresponding diversity of peptides presents a formidable challenge for analytical tools, as the abundance of many chemical entities may be overshadowed by the most abundant molecules. As a result, tandem mass spectrometers often focus on the most abundant peptides, typically those shared across closely related species, and are blind to; or at best randomly sample, the low-abundance entities. This type of bias can compromise analytical reproducibility when dealing with large numbers of samples for comparative metaproteomics studies. In addition, in metaproteomics, the likelihood of sequencing multiple peptides from the same protein is significantly reduced compared to single-species proteomics, due to the vast diversity of proteins in complex microbial communities. This diversity complicates the application of parsimony rules, especially when attempting to resolve isoforms and confirm the presence of specific protein sets.

Based on these challenges, it is obvious that interpretation of metaproteomics data requires an extensive and accurate protein sequence database, as well as advanced strategies to manage the challenges of a highly expanded search space due to hyper-inflated databases representing the diversity of organisms. Traditionally, a metagenomics dataset obtained from the same sample is annotated by proteogenomics to build a customized database for each sample analyzed [47]. However, generalist databases containing reference genomes or aggregating all currently sequenced genomes (approximately 2.6 million genomes are available on the NCBI platform) can be both relevant and cost-effective for large-scale analyses, though they require greater computational resources [23]. Whatever the initial database, a successful search strategy involves identifying the taxa present in the sample by means of the taxonomic information encoded by the peptide sequences, constructing
a specific protein sequence database tailored to the list of organisms identified, and interpreting the entire dataset against this reduced protein database, as recently exemplified [16]. A specific collection of peptide sequences found in a given type of samples can be a smart alternative [51]. Despite significant advances, there remains considerable potential for improvement across the three key stages of metaproteomics: sample preparation, tandem mass spectrometry, and data interpretation.

Due to its unique technical demands and the requirement for state-of-the-art tandem mass spectrometry, metaproteomics may seem challenging for newcomers, such as microbiologists or medical doctors. To help with its adoption, and to standardize conditions, an international initiative was launched to bring together specialized platforms worldwide, with the aim of improving sample preservation, sample preparation, mass spectrometry methodologies, and data science pipelines [57] and promote the dissemination of cutting-edge knowledge [54]. This initiative should also make metaproteomics technology more widely available, and researchers wishing to deepen their knowledge of specific microbiota are encouraged to reach out to these specialized technological platforms for support [55].

### 3. Heralding a new era in tandem mass spectrometry

The introduction of the Orbitrap mass/charge analyzer (Thermo) in 2005 was a true revolution in the field of proteomics, which brought high-resolution analysis of molecules to a wide range of platforms at a more accessible cost. This breakthrough significantly accelerated the field of shotgun proteomics, as demonstrated by the surge in related publications and publicly available datasets (18,536 Orbitrap-acquired datasets in the PRIDE repository, as of 9 November 2024). Over the last two decades, high-resolution tandem mass spectrometry has continued to gain in speed and sensitivity, thanks to innovations proposed by several suppliers such as enhanced spray configurations, optimized lenses, quadrupoles, and filters, which have led to reduced neutral ion noise, as well as refined selection and more efficient transfer of target ions. Peptide chromatography has also been substantially upgraded, with more efficient reverse-phase columns and smart positioning of column outlets near nano-spray devices.

Recent progress has further pushed back the boundaries of mass spectrometry, ushering in a transformative wave that is reshaping the field of proteomics [5]. For example, acoustic ejection mass spectrometry (SCIEX) has the potential to increase analytical throughput by using acoustic waves to propel sample droplets into the spectrometer [58]. Another major innovation is the integration of ion mobility with enhanced nano-spray ionization and advanced Q-TOF architecture (Bruker), offering excellent chemical separation of ions prior to high-sensitivity ion detection [36]. Most notably, the newly introduced Asymmetric Track Lossless (Astral) analyzer (Thermo) has set a new standard in the field in terms of number of peptides/proteins identified per sample. This revolutionary analyzer, coupled with an Orbitrap mass-resolving quadrupole and a rectilinear ion trap, provides unmatched sensitivity and allows faster acquisition (200 Hz) of high-resolution MS/MS spectra [50]. The Omnitrap platform, a segmented linear ion trap, allows a variety of fragmentation methods that could bring top-down characterization of proteins at the metaproteomic scale [41]. Extensive comparative analyses have demonstrated the superiority of Data-Independent Acquisition (DIA) mode over the more traditional Data-Dependent Acquisition (DDA) mode [22]. By selecting a pool of ions for fragmentation, rather than isolating them as in DDA mode, DIA mode enhances data acquisition depth. Based on this evidence, metaproteomics protocols are now adopting DIA mode [16,43,59,64], generating massive quantities of high-quality data, which provide valuable information.

The capabilities of next-generation tandem mass spectrometers, in terms of proteome coverage, throughput, and dynamic range, must be regularly tested and compared. Although complex microbiota represent

some of the most challenging samples, in my opinion, they are particularly well suited for such benchmarks. For instance, analyzing a human fecal sample with an Orbitrap Astral instrument, that produces fragmentation spectra at rates of up to 200 Hz, as many as 120,000 peptides can be identified and 38,000 proteins quantified in just 30 min [16]. This system achieves impressive depth and speed, allowing rapid and comprehensive sample characterization. The considerable pace of progress in mass spectrometry technology promises continued transformative breakthroughs. As a result, current capabilities will be quickly surpassed. Among promising avenues are high-resolution ion mobility technology with lossless ion manipulation, as introduced by MOBILion, which could lead to unprecedented speeds of fragmentation analysis, potentially reaching rates of up to 1000 Hz, and numerous other ongoing developments by several suppliers that aim at covering whole proteomes within very short gradients (<5 min). These speeds would dramatically enhance the detection of low-abundance signals in complex biological matrices, including microbiota samples. These cutting-edge methodologies, as they become widely available in the coming years, will further improve sensitivity, coverage, and throughput in mass spectrometry. This will have knock-on effects, and as instruments achieve unprecedented analytical capabilities, the bottleneck for complex sample analysis will likely shift from data acquisition to data interpretation as symbolised in Fig. 1.

### 4. Artificial intelligence (AI) as a companion for metaproteomics

Computational tools are essential to automate the interpretation of mass spectrometry signals, translating them into peptide sequences, identifying microorganisms and their proteins, quantifying the metabolic pathways, and allowing the analysis of large metaproteomics datasets to extract key insights. The use of AI in proteomics is not entirely new. Indeed, early efforts considerably predate the current hype. Thus, in 2008, support vector machines were deployed in the PatternLab software [8] to compare proteomics datasets, whereas in 2007, semi-supervised machine learning was used in the Percolator tool [25] to attempt to discriminate between correct and decoy spectrum identifications. Recent advances in the interpretation of DIA datasets have been significantly enhanced through the use of deep neural networks and advanced signal correction strategies [14]. Deep learning has notably improved the prediction of peptide properties such as retention time, collisional cross-sections, and fragment intensities, as demonstrated by AlphaPeptDeep tool [62], leading to greater confidence in peptide detection. Comprehensive reviews offer detailed insights into recent bioinformatics workflows in metaproteomics [35,54]. Notably, a new wave of tools is emerging for metaproteomics based on DIA measurements and *de novo* interpretation, providing new perspectives to characterize previously unsequenced microorganisms [56].

The recent surge in AI-driven innovations is transformative, offering solutions for virtually every step in proteomics and metaproteomics workflows. For example, rescoring peptide spectrum matches based on information from peptide property predictors is already improving the performance of database search engines [24]. Furthermore, sensitive spectral library searches are now possible, leveraging the extensive previously acquired datasets. Additionally, AI-powered tools improve direct *de novo* peptide sequencing from tandem mass spectra, bypassing the need for reference sequence databases. Graph-based and machine learning algorithms play a pivotal role in advancing this capability [17,27,39,60,61,63], that could be a gamechanger for metaproteomics. Besides improving the interpretation of mass spectrometry measurements into peptide identification, new advanced machine learning and AI tools will help discover patterns in microbial diversity, community structure, and functional roles. For example, the LineageFilter tool for metaproteomics assesses the probability of each taxon's presence by analyzing features from a tentative list of taxa, including their abundances and identification scores [19]. Metaproteomics currently faces significant challenges in fully leveraging the diversity of detected and
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** The primary bottlenecks for next-generation metaproteomics are likely to be data storage and interpretation. This AI-generated artistic representation was created by the author using Microsoft Designer.</figcaption></figure>

quantified protein functions and in accurately modeling the global ac-
tivity of complex microbiomes. A key limitation is that most protein
annotations are inferred from existing annotations and derived from one
organism to another, rather than based on direct experimental evidence,
leaving many protein functions uncertain. Furthermore, a substantial
proportion of proteins remain simply uncharacterized, with no assigned
function. AI can contribute to benchmarking methods using represen-
tative datasets, improving protein function prediction, establishing
predictive models, and ultimately identifying potential diagnostic
markers or therapeutic targets [7]. In addition, AI facilitates multi-omics
data integration for the generation and validation of microbiome
metabolic networks and models by deploying multivariate statistical
methods and correlation-based network analysis to reveal key features
and associations reflecting host-microbiome interactions. In parallel, AI-
driven approaches are being developed to rapidly characterize drug-
microbiome interactions, which can significantly influence drug effi-
cacy and toxicity. Thus, AI is revolutionizing how complex microbiome
data is dealt with, and accelerating the pace in the field of translational
microbiome research. AI-driven advances are set to turbo-charge the
ongoing revolution in metaproteomics, extending our understanding of
microbiome functionality to unprecedented levels.

5. Perspectives

Thanks to the latest advances in mass spectrometry and AI, next-
generation metaproteomics will be able to examine microbiota from
novel perspectives, achieving a previously unattainable level of detail.
This forecast sets scientists on the verge of uncovering complex micro-
bial interactions and functions, which could unlock many new oppor-
tunities and innovations across fields including medicine,
biotechnology, agriculture, breeding, and environmental science.
Species-level resolution could soon be surpassed, although achieving
finer taxonomic resolution will require further developments in taxon-
omy and data curation. In addition, new ground is being broken, with
comprehensive virome analysis now within reach, as a recent study
demonstrates [34]. The ability to investigate host-microbiome in-
teractions at such a refined level opens new avenues for applications in
personalized medicine where a patient's individual microbiome can be
profiled and monitored to tailor therapeutic interventions. This could
lead to the development of specific probiotics, prebiotics, or drugs to

precisely modulate the microbiome in targeted ways. Metaproteomics
also offers an interesting potential route for the characterization of the
elusive non-cultivable microorganisms within microbiota, by ident-
fying their proteins and inferring their functional roles. This expansion
could potentially lead to the discovery of entirely new enzymes and
therapeutic compounds, and have applications in diagnostics [40] and
forensics science [11]. In the environmental and agricultural sciences, a
greater understanding of microbial community functionality could drive
improvements in soil health, crop yields, and ecosystem sustainability.

Definitively, next-generation metaproteomics is poised to become
the next Big Thing in microbiome research.

CRediT authorship contribution statement

**Jean Armengaud:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization.

Declaration of competing interest

The author has declared no conflict of interest.

Acknowledgments

I would like to thank José-Angel Huerta Ocampo for the invitation to deliver the inaugural lecture at the 9th Symposium of the Mexican Proteomics Society, held in Oaxaca from November 9th to 13th, 2024. The concept for this commentary paper emerged during my preparation for that presentation. I also thank Maighread Gallagher-Gambarelli for her kind advices and her help for polishing the text. I acknowledge the French National Agency for Research (Agence Nationale de la Recherche, grant ANR-20-CE34-0012), the France2030 program – INBS ProFI (grant ANR-24-INBS-0015) and the Région Occitanie (grant 21023526-DeepMicro) for their support.

Data availability

No
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