Taxonomical composition and functional analysis of biofilms sampled from a nuclear storage pool

Revealing taxonomy, activity, and substrate assimilation in mixed bacterial communities by GroEL-proteotyping-based stable isotope probing

Protein-based stable isotope probing (protein-SIP) can link microbial taxa to substrate assimilation. Traditionally, protein-SIP requires a sample-specific metagenome-derived database for samples with unknown composition. Here, we describe GroEL-prototyping-based stable isotope probing (GroEL-SIP), that uses GroEL as a taxonomic marker protein to identify bacterial taxa (GroEL-proteotyping) coupled to SIP directly linking identified taxa to substrate consumption. GroEL-SIP's main advantages are that (1) it can be performed with a sample-independent database and (2) sample complexity can be reduced by enriching GroEL proteins, increasing sensitivity and reducing instrument time. We applied GroEL-SIP to pure cultures, synthetic bicultures, and a human gut model using 2H-, 18O-, and 13C-labeled substrates. While 2H and 18O allowed assessing general activity, 13C enabled differentiation of substrate source and utilized metabolic pathways. GroEL-SIP offers fast and straightforward protein-SIP analyses of highly abundant families in mixed bacterial communities, but further work is needed to improve sensitivity, resolution, and database coverage.

Bacterial Communities and Their Role in Bacterial Infections

Since infections associated with microbial communities threaten human health, research is increasingly focusing on the development of biofilms and strategies to combat them. Bacterial communities may include bacteria of one or several species. Therefore, examining all the microbes and identifying individual community bacteria responsible for the infectious process is important. Rapid and accurate detection of bacterial pathogens is paramount in healthcare, food safety, and environmental monitoring. Here, we analyze biofilm composition and describe the main groups of pathogens whose presence in a microbial community leads to infection (Staphylococcus aureus, Enterococcus spp., Cutibacterium spp., bacteria of the HACEK, etc.). Particular attention is paid to bacterial communities that can lead to the development of device-associated infections, damage, and disruption of the normal functioning of medical devices, such as cardiovascular implants, biliary stents, neurological, orthopedic, urological and penile implants, etc. Special consideration is given to tissue-located bacterial biofilms in the oral cavity, lungs and lower respiratory tract, upper respiratory tract, middle ear, cardiovascular system, skeletal system, wound surface, and urogenital system. We also describe methods used to analyze the bacterial composition in biofilms, such as microbiologically testing, staining, microcolony formation, cellular and extracellular biofilm components, and other methods. Finally, we present ways to reduce the incidence of biofilm-caused infections.

LineageFilter: Improved Proteotyping of Complex Samples Using Metaproteomics and Machine Learning

Metaproteomics is a powerful tool to characterize how microbiota function by analyzing their proteic content by tandem mass spectrometry. Given the complexity of these samples, accurately assessing their taxonomical composition without prior information based solely on peptide sequences remains a challenge. Here, we present LineageFilter, a new python-based AI software for refined proteotyping of complex samples using metaproteomics interpreted data and machine learning. Given a tentative list of taxa, their abundances, and the scores associated with their identified peptides, LineageFilter computes a comprehensive set of features for each identified taxon at all taxonomical ranks. Its machine-learning model then assesses the likelihood of each taxon's presence based on these features, enabling improved proteotyping and sample-specific database construction.

Potential applications of microbial genomics in nuclear non-proliferation

As nuclear technology evolves in response to increased demand for diversification and decarbonization of the energy sector, new and innovative approaches are needed to effectively identify and deter the proliferation of nuclear arms, while ensuring safe development of global nuclear energy resources. Preventing the use of nuclear material and technology for unsanctioned development of nuclear weapons has been a long-standing challenge for the International Atomic Energy Agency and signatories of the Treaty on the Non-Proliferation of Nuclear Weapons. Environmental swipe sampling has proven to be an effective technique for characterizing clandestine proliferation activities within and around known locations of nuclear facilities and sites. However, limited tools and techniques exist for detecting nuclear proliferation in unknown locations beyond the boundaries of declared nuclear fuel cycle facilities, representing a critical gap in non-proliferation safeguards. Microbiomes, defined as "characteristic communities of microorganisms" found in specific habitats with distinct physical and chemical properties, can provide valuable information about the conditions and activities occurring in the surrounding environment. Microorganisms are known to inhabit radionuclide-contaminated sites, spent nuclear fuel storage pools, and cooling systems of water-cooled nuclear reactors, where they can cause radionuclide migration and corrosion of critical structures. Microbial transformation of radionuclides is a well-established process that has been documented in numerous field and laboratory studies. These studies helped to identify key bacterial taxa and microbially-mediated processes that directly and indirectly control the transformation, mobility, and fate of radionuclides in the environment. Expanding on this work, other studies have used microbial genomics integrated with machine learning models to successfully monitor and predict the occurrence of heavy metals, radionuclides, and other process wastes in the environment, indicating the potential role of nuclear activities in shaping microbial community structure and function. Results of this previous body of work suggest fundamental geochemical-microbial interactions occurring at nuclear fuel cycle facilities could give rise to microbiomes that are characteristic of nuclear activities. These microbiomes could provide valuable information for monitoring nuclear fuel cycle facilities, planning environmental sampling campaigns, and developing biosensor technology for the detection of undisclosed fuel cycle activities and proliferation concerns.

Flash MS/MS proteotyping allows identifying microbial isolates in 36 s of mass spectrometry signal

Rapid identification of microorganisms is essential for medical diagnostics, sanitary controls, and food safety. High-throughput analytical platforms currently rely on whole-cell MALDI-TOF mass spectrometry to process hundreds of samples per day. Although this technology has become a reference method, it is unable to process most environmental isolates and opportunistic pathogens due to an incomplete experimental spectrum database. In most cases, its discriminating power is limited to the species taxonomical rank. By recording much more sequence information at the peptide level, proteotyping by tandem mass spectrometry is able to identify the taxonomic position of any microorganism in the tree of life and can be highly discriminating at the subspecies level. We propose here a methodology for ultra-fast identification of microorganisms by tandem mass spectrometry based on direct sample infusion and a highly sensitive procedure for data processing and taxonomic identification. Results obtained on reference strains and hitherto uncharacterized bacterial isolates show identification to species level in 36 s of tandem mass spectrometry signal, 102 s when including the injection procedure. Flash proteotyping is highly discriminating, as it can provide information down to strain level. The methodology enables high throughput identification of isolates, opening up new prospects, particularly in culturomics, and diagnostics.

A Simplified Label-Free Method for Proteotyping Sets of Six Isolates in a Single Liquid Chromatography-High-Resolution Tandem Mass Spectrometry Analysis

Clinical diagnostics and microbiology require high-throughput identification of microorganisms. Sample multiplexing prior to detection is an attractive means to reduce analysis costs and time-to-result. Recent studies have demonstrated the discriminative power of tandem mass spectrometry-based proteotyping. This technology can rapidly identify the most likely taxonomical position of any microorganism, even uncharacterized organisms. Here, we present a simplified label-free multiplexing method to proteotype isolates by tandem mass spectrometry that can identify six microorganisms in a single 20 min analytical run. The strategy involves the production of peptide fractions with distinct hydrophobicity profiles using spin column fractionation. Assemblages of different fractions can then be analyzed using mass spectrometry. Results are subsequently interpreted based on the hydrophobic characteristics of the peptides detected, which make it possible to link each taxon identified to the initial sample. The methodology was tested on 32 distinct sets of six organisms including several worst-scenario assemblages-with differences in sample quantities or the presence of the same organisms in multiple fractions-and proved to be robust. These results pave the way for the deployment of tandem mass spectrometry-based proteotyping in microbiology laboratories.

Exploring Andean High-Altitude Lake Extremophiles through Advanced Proteotyping

Quickly identifying and characterizing isolates from extreme environments is currently challenging while very important to explore the Earth's biodiversity. As these isolates may, in principle, be distantly related to known species, techniques are needed to reliably identify the branch of life to which they belong. Proteotyping these environmental isolates by tandem mass spectrometry offers a rapid and cost-effective option for their identification using their peptide profiles. In this study, we document the first high-throughput proteotyping approach for environmental extremophilic and halophilic isolates. Microorganisms were isolated from samples originating from high-altitude Andean lakes (3700-4300 m a.s.l.) in the Chilean Altiplano, which represent environments on Earth that resemble conditions on other planets. A total of 66 microorganisms were cultivated and identified by proteotyping and 16S rRNA gene amplicon sequencing. Both the approaches revealed the same genus identification for all isolates except for three isolates possibly representing not yet taxonomically characterized organisms based on their peptidomes. Proteotyping was able to indicate the presence of two potentially new genera from the families of Paracoccaceae and Chromatiaceae/Alteromonadaceae, which have been overlooked by 16S rRNA amplicon sequencing approach only. The paper highlights that proteotyping has the potential to discover undescribed microorganisms from extreme environments.

GroEL-Proteotyping of Bacterial Communities Using Tandem Mass Spectrometry

Profiling bacterial populations in mixed communities is a common task in microbiology. Sequencing of 16S small subunit ribosomal-RNA (16S rRNA) gene amplicons is a widely accepted and functional approach but relies on amplification primers and cannot quantify isotope incorporation. Tandem mass spectrometry proteotyping is an effective alternative for taxonomically profiling microorganisms. We suggest that targeted proteotyping approaches can complement traditional population analyses. Therefore, we describe an approach to assess bacterial community compositions at the family level using the taxonomic marker protein GroEL, which is ubiquitously found in bacteria, except a few obligate intracellular species. We refer to our method as GroEL-proteotyping. GroEL-proteotyping is based on high-resolution tandem mass spectrometry of GroEL peptides and identification of GroEL-derived taxa via a Galaxy workflow and a subsequent Python-based analysis script. Its advantage is that it can be performed with a curated and extendable sample-independent database and that GroEL can be pre-separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) to reduce sample complexity, improving GroEL identification while simultaneously decreasing the instrument time. GroEL-proteotyping was validated by employing it on a comprehensive raw dataset obtained through a metaproteome approach from synthetic microbial communities as well as real human gut samples. Our data show that GroEL-proteotyping enables fast and straightforward profiling of highly abundant taxa in bacterial communities at reasonable taxonomic resolution.

Evaluation of protein extraction methods to improve meta-proteomics analysis of treated wastewater biofilms

Metaproteomics can be used to study functionally active biofilm-based bacterial populations in reclaimed water distribution systems, which in turn result in bacterial regrowth that impacts the water quality. However, existing protein extraction methods have differences in their protein recovery and have not been evaluated for their efficacies in reclaimed water biofilm samples. In this study, we first evaluated six different protein extraction methods with diverse chemical and physical properties on a mixture of bacterial cell culture. Based on a weighting scores-based evaluation, the extraction protocols in order of decreasing performance are listed as B-PER > RIPA > PreOmics > SDS > AllPrep > Urea. The highest four optimal methods on cell culture were further tested against treated wastewater non-chlorinated and chlorinated effluent biofilms. In terms of protein yield, our findings showed that RIPA performed the best; however, the highest number of proteins were extracted from SDS and PreOmics. Furthermore, SDS and PreOmics worked best to rupture gram-positive and gram-negative bacterial cell walls. Considering the five evaluation factors, PreOmics obtained highest weighted score, indicating its potential effectiveness in extracting proteins from biofilms. This study provides the first insight into evaluating protein extraction methods to facilitate metaproteomics for complex reclaimed water matrices.

First Isolation and Characterization of Bacteria from the Core's Cooling Pool of an Operating Nuclear Reactor

Microbial life can thrive in the most inhospitable places, such as nuclear facilities with high levels of ionizing radiation. Using direct meta-analyses, we have previously highlighted the presence of bacteria belonging to twenty-five different genera in the highly radioactive water of the cooling pool of an operating nuclear reactor core. In the present study, we further characterize this specific environment by isolating and identifying some of these microorganisms and assessing their radiotolerance and their ability to decontaminate uranium. This metal is one of the major radioactive contaminants of anthropogenic origin in the environment due to the nuclear and mining industries and agricultural practices. The microorganisms isolated when sampling was performed during the reactor operation consisted mainly of Actinobacteria and Firmicutes, whereas Proteobacteria were dominant when sampling was performed during the reactor shutdown. We investigated their tolerance to gamma radiation under different conditions. Most of the bacterial strains studied were able to survive 200 Gy irradiation. Some were even able to withstand 1 kGy, with four of them showing more than 10% survival at this dose. We also assessed their uranium uptake capacity. Seven strains were able to remove almost all the uranium from a 5 µM solution. Four strains displayed high efficiency in decontaminating a 50 µM uranium solution, demonstrating promising potential for use in bioremediation processes in environments contaminated by radionuclides.

Mix24X, a Lab-Assembled Reference to Evaluate Interpretation Procedures for Tandem Mass Spectrometry Proteotyping of Complex Samples

Correct identification of the microorganisms present in a complex sample is a crucial issue. Proteotyping based on tandem mass spectrometry can help establish an inventory of organisms present in a sample. Evaluation of bioinformatics strategies and tools for mining the recorded datasets is essential to establish confidence in the results obtained and to improve these pipelines in terms of sensitivity and accuracy. Here, we propose several tandem mass spectrometry datasets recorded on an artificial reference consortium comprising 24 bacterial species. This assemblage of environmental and pathogenic bacteria covers 20 different genera and 5 bacterial phyla. The dataset comprises difficult cases, such as the Shigella flexneri species, which is closely related to Escherichia coli, and several highly sequenced clades. Different acquisition strategies simulate real-life scenarios: from rapid survey sampling to exhaustive analysis. We provide access to individual proteomes of each bacterium separately to provide a rational basis for evaluating the assignment strategy of MS/MS spectra when recorded from complex mixtures. This resource should provide an interesting common reference for developers who wish to compare their proteotyping tools and for those interested in evaluating protein assignment when dealing with complex samples, such as microbiomes.