Microbial Proteomics Research Articles

Ultrafast metaproteomics for quantitative assessment of strain isolates and microbiomes

BackgroundMicrobial communities are essential in human health and environmental regulation, but present a challenge for the analytical science due to their diversity and dynamic range. Tandem mass spectrometry provides functional insights on microbial life cycle, but is time-consuming. MALDI TOF excels in rapid species identification, but not functional assessment. To address critical challenges in human health and environmental sustainability, microbiology needs advanced mass spectrometry methods and bioinformatic tools enabling both rapid identification and accurate assessment of functional activity of microbial communities. ResultsWe show for the first time that both identity and functional activity of microorganisms and their communities can be accurately determined in experiments as short as 7 min per sample, using the basic Orbitrap MS configuration without peptide fragmentation. The approach was validated using strain isolates, mock microbiomes composed of bacteria spiked at known concentrations and human fecal microbiomes. Our new bioinformatic algorithm identifies the bacterial species with an accuracy of 95 %, when no prior information on the sample is available. Microbiome composition was resolved at the genus level with the mean difference between the actual and identified components of 12 %. For mock microbiomes, Pearson coefficient of up to 0.97 was achieved in estimates of strain biomass change. By the example of Rhodococcus biodegradation of n-alkanes, phenols and its derivatives, we showed the accurate assessment of functional activity of strain isolates, compared with the standard label-free and label-based approaches. SignificanceOur approach makes microbial proteomics fast, functional and insightful using the Orbitrap instruments even without employing peptide fragmentation technology. The approach can be applied to any microorganisms and can take a niche in routine functional assessment of microbial pathogens and consortiums in clinical diagnostics together with MALDI TOF MS and 16S rRNA gene sequencing.

Microbial mimics supersize the pathogenic self-response.

Microbial mimicry, the process in which a microbial antigen elicits an immune response and breaks tolerance to a structurally related self-antigen, has long been proposed as a mechanism in autoimmunity. In this issue of the JCI, Dolton et al. extend this paradigm by demonstrating that a naturally processed peptide from Klebsiella oxytoca acts as a superagonist for autoreactive T cells in type 1 diabetes (T1D). Reframing microbial mimics as superagonists that are thousands of times better at binding disease-associated autoreactive T cell receptors than self-peptides serves to narrow the search space for relevant sequences in the vast microbial proteome. Moreover, the identified superagonists have implications for the intervention and personalized monitoring of T1D that may carry over to other autoimmune diseases with microbial mimicry.

P016 METAPROTEOMICS ANALYSIS IN HEALTHY INDIVIDUALS REVEALS A HIGH CARDIOVASCULAR RISK PROFILE ACROSS BLOOD PRESSURE, DIET, HUMAN AND MICROBIAL PROTEINS

Background/Objective: The gut microbiota is a crucial link between diet and cardiovascular health by producing beneficial metabolites, such as short-chain fatty acids (SCFAs), through fibre fermentation. However, most studies have focused on microbial DNA to infer microbiota function. Here, we employed metaproteomics, a novel method to capture gut human and microbiota protein expression profiles concurrently. We aimed to investigate the crosstalk between gut microbiota, diet, host gut health, and blood pressure (BP). Methods: Mass spectrometry-based metaproteomic analysis was performed on 61 faecal samples with paired ambulatory BP monitoring, food intake (food frequency questionnaire), and demographic data. Results: We identified 21,594 microbial and 1,072 human proteins. Traditional risk factors (age, BMI, sex, BP) explained only ∼10% of the proteome variance. However, unsupervised clustering analysis of both human and microbial proteome revealed two distinct clusters (low-risk and high-risk) with significantly different 24-hour systolic BP (mean±SD, low-risk 116±13mmHg vs high-risk: 126±15mmHg, P=0.016), women prevalence (72.4% vs 40.6%, P=0.025), and diet quality (assessed as Athlete Diet Index, 61.6±6.7 vs 57.8±7.6, P=0.044). In the human proteome, the low-risk group had lower expression of the VEGFA-VEGFR2 signalling pathway and tight junction proteins and higher SCFA production (particularly propionate), suggesting an anti-inflammatory gut environment. In the microbial proteome, the low-risk group had higher activity of inorganic ion transport and metabolism-related proteins, with increased expression of acetate kinase that produces both the SCFAs acetate and propionate, particularly in primary fibre-fermenting bacteria, including Phocaeicola dorei and Blautia wexlerae. Finally, via co-expression networks, we identified profilin-1 (PFN1), which contributes to hypertension and atherosclerosis, as a crucial human protein associated with several bacteria present in the high-risk group. Conclusions: Employing an unsupervised clustering based on human and microbial proteomics, we identified two groups of participants with varying traditional and emerging cardiovascular risk factors. This was explained by complex interactions among diet, gut microbiota, and BP. This underscores the need for sophisticated models to understand multifactorial risk factors for cardiovascular disease.

Control of cyanobacterial growth with potassium; implications for bloom control in nuclear storage ponds

Microbial blooms have been reported in the First Generation Magnox Storage Pond at the Sellafield Nuclear Facility. The pond is kept alkaline with NaOH to minimise fuel rod corrosion, however alkali-tolerant microbial blooms dominated by the cyanobacterium Pseudanabaena catenata are able to thrive in this hostile environment. This study assessed the impact of alternative alkali-dosing regimens (KOH versus NaOH treatment) on biomass accumulation, using a P. catenata dominated mixed culture, which is representative of the pond environment. Optical density was reduced by 40–67 % with KOH treatment over the 3-month chemostat experiment. Microbial community analysis and proteomics demonstrated that the KOH-dependent inhibition of cell growth was mostly specific to P. catenata. The addition of KOH to nuclear storage ponds may therefore help control growth of this pioneer photosynthetic organism due to its sensitivity to potassium, while maintaining the high pH needed to inhibit the corrosion of stored nuclear fuel.

Metaproteomic analysis from cervical biopsies and cytologies identifies proteinaceous biomarkers representing both human and microbial species

The detection of HPV infection and microbial colonization in cervical lesions is currently done through PCR-based viral or bacterial DNA amplification. Our objective was to develop a methodology to expand the metaproteomic landscape of cervical disease and determine if protein biomarkers from both human and microbes could be detected in distinct cervical samples. This would lead to the development of multi-species proteomics, which includes protein-based lateral flow diagnostics that can define patterns of microbes and/or human proteins relevant to disease status. In this study, we collected both non-frozen tissue biopsy and exfoliative non-fixed cytology samples to assess the consistency of detecting human proteomic signatures between the cytology and biopsy samples. Our results show that proteomics using biopsies or cytologies can detect both human and microbial organisms. Across patients, Lumican and Galectin-1 were most highly expressed human proteins in the tissue biopsy, whilst IL-36 and IL-1RA were most highly expressed human proteins in the cytology. We also used mass spectrometry to assess microbial proteomes known to reside based on prior 16S rRNA gene signatures. Lactobacillus spp. was the most highly expressed proteome in patient samples and specific abundant Lactobacillus proteins were identified. These methodological approaches can be used in future metaproteomic clinical studies to interrogate the vaginal human and microbiome structure and metabolic diversity in cytologies or biopsies from the same patients who have pre-invasive cervical intraepithelial neoplasia, invasive cervical cancer, as well as in healthy controls to assess how human and pathogenic proteins may correlate with disease presence and severity.

Identification of proteoforms of short open reading frame-encoded peptides in Blautia producta under different cultivation conditions.

Short open reading frames (sORF) have the potential to encode small proteins, sORF-encoded peptides (SEP), which are less than or equal to 100 amino acids in length. These SEP have been associated with various physiological processes and are known for their potential roles in modulating immune responses, signaling pathways, and cellular functions. Using bottom-up and top-down proteomics, we identified 45 novel and formerly reported SEP under different growth conditions and stress exposure in single cultures of Blautia producta that plays a central role in mediating colonization resistance and dampening inflammation in the human gut microbiome. Top-down analysis improved identification confidence and allowed the detection of a number of full-length and N- or C-terminal truncated proteoforms of the SEP and of proteoforms carrying post-translational modifications such as disulfides, which provides hints for a wide functional potential of these molecules. The identification of the SEP in single cultures of this bacterium shows that, in contrast to previous results, the biosynthesis of specific SEP is not restricted to direct bacterial interactions within the microbiome but is dependent on growth and environmental conditions during cultivation. IMPORTANCE The identification of short open reading frame-encoded peptides (SEP) and different proteoforms in single cultures of gut microbes offers new insights into a largely neglected part of the microbial proteome landscape. This is of particular importance as SEP provide various predicted functions, such as acting as antimicrobial peptides, maintaining cell homeostasis under stress conditions, or even contributing to the virulence pattern. They are, thus, taking a poorly understood role in structure and function of microbial networks in the human body. A better understanding of SEP in the context of human health requires a precise understanding of the abundance of SEP both in commensal microbes as well as pathogens. For the gut beneficial B. producta, we demonstrate the importance of specific environmental conditions for biosynthesis of SEP expanding previous findings about their role in microbial interactions.

Reverse stable isotope labelling with Raman spectroscopy for microbial proteomics.

Global proteome changes in microbes affect the survival and overall production of commercially relevant metabolites through different bioprocesses. The existing methods to monitor proteome level changes are destructive in nature. Stable isotope probing (SIP) coupled with Raman spectroscopy is a relatively new approach for proteome analysis. However, applying this approach for monitoring changes in a large culture volume is not cost-effective. In this study, for the first time we are presenting a novel method of combining reverse SIP using 13 C-glucose and Deuterium to monitor the proteome changes through Raman spectroscopy. The findings of the study revealed visible changes (blue shifts) in proteome related peaks that can be used for monitoring proteome dynamics, that is, synthesis of nascent amino acids and its turnover with time in a non-destructive, cost-effective, and label-free manner.

Microbial and Viral Genome and Proteome Nitrogen Demand Varies across Multiple Spatial Scales within a Marine Oxygen Minimum Zone.

Nutrient availability can significantly influence microbial genomic and proteomic streamlining, for example, by selecting for lower nitrogen to carbon ratios. Oligotrophic open ocean microbes have streamlined genomic nitrogen requirements relative to those of their counterparts in nutrient-rich coastal waters. However, steep gradients in nutrient availability occur at meter-level, and even micron-level, spatial scales. It is unclear whether such gradients also structure genomic and proteomic stoichiometry. Focusing on the eastern tropical North Pacific oxygen minimum zone (OMZ), we use comparative metagenomics to examine how nitrogen availability shapes microbial and viral genome properties along the vertical gradient across the OMZ and between two size fractions, distinguishing free-living microbes versus particle-associated microbes. We find a substantial increase in the nitrogen content of encoded proteins in particle-associated over free-living bacteria and archaea across nitrogen availability regimes over depth. Within each size fraction, we find that bacterial and viral genomic nitrogen tends to increase with increasing nitrate concentrations with depth. In contrast to cellular genes, the nitrogen content of virus proteins does not differ between size fractions. We identified arginine as a key amino acid in the modulation of the C:N ratios of core genes for bacteria, archaea, and viruses. Functional analysis reveals that particle-associated bacterial metagenomes are enriched for genes that are involved in arginine metabolism and organic nitrogen compound catabolism. Our results are consistent with nitrogen streamlining in both cellular and viral genomes on spatial scales of meters to microns. These effects are similar in magnitude to those previously reported across scales of thousands of kilometers. IMPORTANCE The genomes of marine microbes can be shaped by nutrient cycles, with ocean-scale gradients in nitrogen availability being known to influence microbial amino acid usage. It is unclear, however, how genomic properties are shaped by nutrient changes over much smaller spatial scales, for example, along the vertical transition into oxygen minimum zones (OMZs) or from the exterior to the interior of detrital particles. Here, we measure protein nitrogen usage by marine bacteria, archaea, and viruses by using metagenomes from the nitracline of the eastern tropical North Pacific OMZ, including both particle-associated and nonassociated biomass. Our results show higher genomic and proteomic nitrogen content in particle-associated microbes and at depths with higher nitrogen availability for cellular and viral genomes. This discovery suggests that stoichiometry influences microbial and viral evolution across multiple scales, including the micrometer to millimeter scale associated with particle-associated versus free-living lifestyles.

Unlocking the microbial studies through computational approaches: how far have we reached?

The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.

Chronic Strongyloides stercoralis infection increases presence of the Ruminococcus torques group in the gut and alters the microbial proteome

We explored the impact of chronic Strongyloides stercoralis infection on the gut microbiome and microbial activity in a longitudinal study. At baseline (time-point T0), 42 fecal samples from matched individuals (21 positive for strongyloidiasis and 21 negative) were subjected to microbiome 16S-rRNA sequencing. Those positive at T0 (untreated then because of COVID19 lockdowns) were retested one year later (T1). Persistent infection in these individuals indicated chronic strongyloidiasis: they were treated with ivermectin and retested four months later (T2). Fecal samples at T1 and T2 were subjected to 16S-rRNA sequencing and LC–MS/MS to determine microbial diversity and proteomes. No significant alteration of indices of gut microbial diversity was found in chronic strongyloidiasis. However, the Ruminococcus torques group was highly over-represented in chronic infection. Metaproteome data revealed enrichment of Ruminococcus torques mucin-degrader enzymes in infection, possibly influencing the ability of the host to expel parasites. Metaproteomics indicated an increase in carbohydrate metabolism and Bacteroidaceae accounted for this change in chronic infection. STITCH interaction networks explored highly expressed microbial proteins before treatment and short-chain fatty acids involved in the synthesis of acetate. In conclusion, our data indicate that chronic S. stercoralis infection increases Ruminococcus torques group and alters the microbial proteome.

High throughput structure-guided functional annotation of microbial proteomes.

Over the past two decades, advances in rapid genomic sequencing have massively expanded the space of known protein sequences, reaching more than 200 million sequences in recent versions of the TrEMBL database. Making optimal use of this wealth of sequencing information, however, requires considerable advances in our ability to predict biomolecular function from sequence information. While computational function predictions based purely on sequence similarity are highly effective when amino acid sequence identity is high, such predictors struggle when no high-identity annotation templates are available. In such cases, we and others have shown that the integration of additional information such as expression profiles and protein structure can substantially improve annotation accuracy.

Effect of organophosphate esters on microbial community and proteomics in constructed wetlands and its removal mechanism

Organophosphorus esters (OPEs) may cause potential risks to human health and ecological environment safety when they enter water. In this study, A lab-scale vertical flow constructed wetlands (VFCWs) exposed to dichlorvos (DDVP) was carried out to reveal removal effect and mechanism of DDVP in VFCWs. The findings indicated CWs can remove DDVP with 94%–95% average removal efficiency in long-term operation. Besides, soluble phosphate (SRP) and total phosphorus (TP) were removed by VFCWs with 48.60%–64.10% average removal efficiency, and the addition of DDVP enhanced the effect of CWs on nitrogen removal. High-throughput sequencing demonstrated that Massiilia (2.67%–18.06%), Denitratisoma (2.07%–3.80%) and SM1A02 (2.85%–4.67%) played an important role in the removal process of DDVP in VFCWs. Proteomics of plant root showed that A0A3B6ARQ4 (increased 29.53 times) and LOC_Os11g38050.1 protein (decreased 0.001 times) responsed more sensitive to OPEs which suggested DDVP enhancesenhancing plants energy production and hydrolyase synthesis, thereby promoting it hydrolysis. Therefore, the use of VFCWs to treat organic phosphate wastewater has future prospects.

Tracking nitrogen allocation to proteome biosynthesis in a marine microbial community.

Microbial growth in many environments is limited by nitrogen availability, yet there is limited understanding of how complex communities compete for and allocate this resource. Here we develop a broadly applicable approach to track biosynthetic incorporation of 15N-labelled nitrogen substrates into microbial community proteomes, enabling quantification of protein turnover and N allocation to specific cellular functions in individual taxa. Application to oligotrophic ocean surface water identifies taxa-specific substrate preferences and a distinct subset of protein functions undergoing active biosynthesis. The cyanobacterium Prochlorococcus is the most effective competitor for acquisition of ammonium and urea and shifts its proteomic allocation of N over the day/night cycle. Our approach reveals that infrastructure and protein-turnover functions comprise substantial biosynthetic demand for N in Prochlorococcus and a range of other microbial taxa. The direct interrogation of the proteomic underpinnings of N limitation with 15N-tracking proteomics illuminates how nutrient stress differentially influences metabolism in co-existing microbes.

Specific biomarker mining and rapid detection of Burkholderia cepacia complex by recombinase polymerase amplification.

To mine specific proteins and their protein-coding genes as suitable molecular biomarkers for the Burkholderia cepacia Complex (BCC) bacteria detection based on mega analysis of microbial proteomic and genomic data comparisons and to develop a real-time recombinase polymerase amplification (rt-RPA) assay for rapid isothermal screening for pharmaceutical and personal care products. We constructed an automatic screening framework based on Python to compare the microbial proteomes of 78 BCC strains and 263 non-BCC strains to identify BCC-specific protein sequences. In addition, the specific protein-coding gene and its core DNA sequence were validated in silico with a self-built genome database containing 158 thousand bacteria. The appropriate methodology for BCC detection using rt-RPA was evaluated by 58 strains in pure culture and 33 batches of artificially contaminated pharmaceutical and personal care products. We identified the protein SecY and its protein-coding gene secY through the automatic comparison framework. The virtual evaluation of the conserved region of the secY gene showed more than 99.8% specificity from the genome database, and it can distinguish all known BCC species from other bacteria by phylogenetic analysis. Furthermore, the detection limit of the rt-RPA assay targeting the secY gene was 5.6 × 102 CFU of BCC bacteria in pure culture or 1.2 pg of BCC bacteria genomic DNA within 30 min. It was validated to detect <1 CFU/portion of BCC bacteria from artificially contaminated samples after a pre-enrichment process. The relative trueness and sensitivity of the rt-RPA assay were 100% in practice compared to the reference methods. The automatic comparison framework for molecular biomarker mining is straightforward, universal, applicable, and efficient. Based on recognizing the BCC-specific protein SecY and its gene, we successfully established the rt-RPA assay for rapid detection in pharmaceutical and personal care products.

Monitoring of microbial proteome dynamics using Raman stable isotope probing.

Abnormal protein kinetics could be a cause of several diseases associated with essential life processes. An accurate understanding of protein dynamics and turnover is essential for developing diagnostic or therapeutic tools to monitor these changes. Raman spectroscopy in combination with stable isotope probes (SIP) such as carbon-13, and deuterium has been a breakthrough in the qualitative and quantitative study of various metabolites. In this work, we are reporting the utility of Raman-SIP for monitoring dynamic changes in the proteome at the community level. We have used 13 C-labeled glucose as the only carbon source in the medium and verified its incorporation in the microbial biomass in a time-dependent manner. A visible redshift in the Raman spectral vibrations of major biomolecules such as nucleic acids, phenylalanine, tyrosine, amide I, and amide III were observed. Temporal changes in the intensity of these bands demonstrating the feasibility of protein turnover monitoring were also verified. Kanamycin, a protein synthesis inhibitor was used to assess the feasibility of identifying effects on protein turnover in the cells. Successful application of this work can provide an alternate/adjunct tool for monitoring proteome-level changes in an objective and nondestructive manner.

FineFDR: Fine-grained Taxonomy-specific False Discovery Rates Control in Metaproteomics.

Microbial community proteomics, also termed metaproteomics, investigates all proteins expressed by a microbiota. Tandem mass spectrometry (MS/MS) is the typical method for identifying proteins in metaproteomics, which involves searching the mass spectra against a protein sequence database. A major post-analysis step is controlling the false discovery rate (FDR), i.e., the ratio of false positives to the total number of annotations. The current popular target-decoy FDR estimation method treats all the peptides and proteins equally and overlooks that they could have varied probabilities of being identified. In this study, we report FineFDR, a framework for FDR assessment at fine-grained levels with taxonomy information considered. FineFDR groups the identified peptide-spectrum matches, peptides, and proteins from different taxonomic units and estimates the FDR in each group separately. Empirical experiments on the simulated and real-world data sets demonstrate that our FineFDR achieved higher precision and more peptide and protein identifications when compared to the state-of-the-art methods, such as Comet, Percolator, TIDD, and Tailor. FineFDR is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/FDR.

Identification of Plant Protein-Metabolite Interactions by Limited Proteolysis-Coupled Mass Spectrometry (LiP-MS).

The interactions between metabolites and proteins constitute crucial events in cell signaling and metabolism. In recent years, large-scale proteomics techniques have emerged to identify and characterize protein-metabolite interactions. However, their implementation in plants is generally lagging behind, preventing a complete understanding of the regulatory mechanisms governing plant physiology. Recently, a novel approach to identify metabolite-binding proteins, namely, limited proteolysis-coupled mass spectrometry (LiP-MS), was developed originally for microbial proteomes. Here, we present an adapted and accessible version of the LiP-MS protocol for use in plants. Plant proteomes are extracted and incubated with the metabolite of interestorcontrol treatment, followed by a limited digestion by a nonspecific/promiscuous protease. Subsequently, a conventional shotgun proteomics sample preparation is performed including a complete digestion with the sequence-specific protease trypsin. Finally, label-free proteomics analysis is applied to identify structure-dependent proteolytic patterns corresponding to protein targets of the specific metabolite and their binding sites. Given its amenability to relatively high throughput, the LiP-MS approach may open a potent avenue for the discovery of novel regulatory mechanisms in plant species.

Analysis of Human and Microbial Salivary Proteomes in Children Offers Insights on the Molecular Pathogenesis of Molar-Incisor Hypomineralization.

Molar incisor hypomineralization is a complex developmental enamel defect that affects the permanent dentition of children with significant functional and aesthetic implications. Saliva is an ideal diagnostic tool and ensures patients’ compliance by diminishing the discomfort especially in pediatric population. Lately, salivary proteome analysis has progressively evolved in various biomedical disciplines. As changes in saliva composition are associated with oral diseases, it is reasonable to assume that the saliva proteome of MIH-affected children might be altered compared to healthy children. This study analyzed the human and microbial salivary proteome in children with MIH in order to identify salivary markers indicative of the pathology. The conducted proteomic analysis generated a comprehensive dataset comprising a total of 1515 high confidence identifications and revealed a clear discrimination between the two groups. Statistical comparison identified 142 differentially expressed proteins, while the pathway analysis indicated deregulation of inflammation, immune response mechanisms, and defense response to bacteria in MIH patients. Bacterial proteome analysis showed a lower diversity for the microbial species, which highlights the dysbiotic environment established in the MIH pathology.

Evolutionary Relationships Between Dysregulated Genes in Oral Squamous Cell Carcinoma and Oral Microbiota.

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers in the world. Changes in the composition and abundance of oral microbiota are associated with the development and metastasis of OSCC. To elucidate the exact roles of the oral microbiota in OSCC, it is essential to reveal the evolutionary relationships between the dysregulated genes in OSCC progression and the oral microbiota. Thus, we interrogated the microarray and high-throughput sequencing datasets to obtain the transcriptional landscape of OSCC. After identifying differentially expressed genes (DEGs) with three different methods, pathway and functional analyses were also performed. A total of 127 genes were identified as common DEGs, which were enriched in extracellular matrix organization and cytokine related pathways. Furthermore, we established a predictive pipeline for detecting the coevolutionary of dysregulated host genes and microbial proteomes based on the homology method, and this pipeline was employed to analyze the evolutionary relations between the seven most dysregulated genes (MMP13, MMP7, MMP1, CXCL13, CRISPO3, CYP3A4, and CRNN) and microbiota obtained from the eHOMD database. We found that cytochrome P450 3A4 (CYP3A4), a member of the cytochrome P450 family of oxidizing enzymes, was associated with 45 microbes from the eHOMD database and involved in the oral habitat of Comamonas testosteroni and Arachnia rubra. The peptidase M10 family of matrix metalloproteinases (MMP13, MMP7, and MMP1) was associated with Lacticaseibacillus paracasei, Lacticaseibacillus rhamnosus, Streptococcus salivarius, Tannerella sp._HMT_286, and Streptococcus infantis in the oral cavity. Overall, this study revealed the dysregulated genes in OSCC and explored their evolutionary relationship with oral microbiota, which provides new insight for exploring the microbiota–host interactions in diseases.

Development of an Experimental Approach to Achieve Spatially Resolved Plant Root-Associated Metaproteomics Using an Agar-Plate System.

Plant-microbe interactions in the rhizosphere play a vital role in plant health and productivity. The composition and function of root-associated microbiomes is strongly influenced by their surrounding environment, which is often customized by their host. How microbiomes change with respect to space and time across plant roots remains poorly understood, and methodologies that facilitate spatiotemporal metaproteomic studies of root-associated microbiomes are yet to be realized. Here, we developed a method that provides spatially resolved metaproteome measurements along plant roots embedded in agar-plate culture systems, which have long been used to study plants. Spatially defined agar "plugs" of interest were excised and subsequently processed using a novel peptide extraction method prior to metaproteomics, which was used to infer both microbial community composition and function. As a proof-of-principle, a previously studied 10-member community constructed from a Populus root system was grown in an agar plate with a 3-week-old Populus trichocarpa plant. Metaproteomics was performed across two time points (24 and 48 h) for three distinct locations (root base, root tip, and a region distant from the root). The spatial resolution of these measurements provides evidence that microbiome composition and expression changes across the plant root interface. Interrogation of the individual microbial proteomes revealed functional profiles related to their behavioral associations with the plant root, in which chemotaxis and augmented metabolism likely supported predominance of the most abundant member. This study demonstrated a novel peptide extraction method for studying plant agar-plate culture systems, which was previously unsuitable for (meta)proteomic measurements.