Metaproteomics: harnessing the power of high performance mass spectrometry to identify the suite of proteins that control metabolic activities in microbial communities

Microbial regulation of plant secondary metabolites: Impact, mechanisms and prospects

Plant secondary metabolites possess a wide range of pharmacological activities and play crucial biological roles. They serve as both a defense response during pathogen attack and a valuable drug resource. The role of microorganisms in the regulation of plant secondary metabolism has been widely recognized. The addition of specific microorganisms can increase the synthesis of secondary metabolites, and their beneficial effects depend on environmental factors and plant-related microorganisms. This article summarizes the impact and regulatory mechanisms of different microorganisms on the main secondary metabolic products of plants. We emphasize the mechanisms by which microorganisms regulate hormone levels, nutrient absorption, the supply of precursor substances, and enzyme and gene expression to promote the accumulation of plant secondary metabolites. In addition, the possible negative feedback regulation of microorganisms is discussed. The identification of additional unknown microbes and other driving factors affecting plant secondary metabolism is essential. The prospects for further analysis of medicinal plant genomes and the establishment of a genetic operation system for plant secondary metabolism research are proposed. This study provides new ideas for the use of microbial resources for biological synthesis research and the improvement of crop anti-inverse traits for the use of microbial resources.

Microbial diagnostics in periodontal diseases

Microbial analytical methods have been instrumental in elucidating the complex microbial etiology of periodontal diseases, by shaping our understanding of subgingival community dynamics. Certain pathobionts can orchestrate the establishment of dysbiotic communities that can subvert the host immune system, triggering inflammation and tissue destruction. Yet, diagnosis and management of periodontal conditions still rely on clinical and radiographic examinations, overlooking the well-established microbial etiology. This review summarizes the chronological emergence of periodontal etiological models and the co-evolution with technological advances in microbial detection. We additionally review the microbial analytical approaches currently accessible to clinicians, highlighting their value in broadening the periodontal assessment. The epidemiological importance of obtaining culture-based antimicrobial susceptibility profiles of periodontal taxa for antibiotic resistance surveillance is also underscored, together with clinically relevant analytical approaches to guide antibiotherapy choices, when necessary. Furthermore, the importance of 16S-based community and shotgun metagenomic profiling is discussed in outlining dysbiotic microbial signatures. Because dysbiosis precedes periodontal damage, biomarker identification offers early diagnostic possibilities to forestall disease relapses during maintenance. Altogether, this review highlights the underutilized potential of clinical microbiology in periodontology, spotlighting the clinical areas most conductive to its diagnostic implementation for enhancing prevention, treatment predictability, and addressing global antibiotic resistance.

Core microbiome-associated proteins associated with ulcerative colitis interact with cytokines for synergistic or antagonistic effects on gut bacteria

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is associated with a loss or an imbalance of host-microorganism interactions. However, such interactions at protein levels remain largely unknown. Here, we applied a depletion-assisted metaproteomics approach to obtain in-depth host-microbiome association networks of IBD, where the core host proteins shifted from those maintaining mucosal homeostasis in controls to those involved in inflammation, proteolysis, and intestinal barrier in IBD. Microbial nodes such as short-chain fatty-acid producer-related host-microbial crosstalk were lost or suppressed by inflammatory proteins in IBD. Guided by protein-protein association networks, we employed proteomics and lipidomics to investigate the effects of UC-related core proteins S100A8, S100A9, and cytokines (IL-1β, IL-6, and TNF-α) on gut bacteria. These proteins suppressed purine nucleotide biosynthesis in stool-derived in vitro communities, which was also reduced in IBD stool samples. Single species study revealed that S100A8, S100A9, and cytokines can synergistically or antagonistically alter gut bacteria intracellular and secreted proteome, with combined S100A8 and S100A9 potently inhibiting beneficial Bifidobacterium adolescentis. Furthermore, these inflammatory proteins only altered the extracellular but not intracellular proteins of Ruminococcus gnavus. Generally, S100A8 induced more significant bacterial proteome changes than S100A9, IL-1β, IL-6, and TNF-α but gut bacteria degrade significantly more S100A8 than S100A9 in the presence of both proteins. Among the investigated species, distinct lipid alterations were only observed in Bacteroides vulgatus treated with combined S100A8, S100A9, and cytokines. These results provided a valuable resource of inflammatory protein-centric host-microbial molecular interactions.

Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome

Metaproteomics, a method for untargeted, high-throughput identification of proteins in complex samples, provides functional information about microbial communities and can tie functions to specific taxa. Metaproteomics often generates less data than other omics techniques, but analytical workflows can be improved to increase usable data in metaproteomic outputs. Identification of peptides in the metaproteomic analysis is performed by comparing mass spectra of sample peptides to a reference database of protein sequences. Although these protein databases are an integral part of the metaproteomic analysis, few studies have explored how database composition impacts peptide identification. Here, we used cervicovaginal lavage (CVL) samples from a study of bacterial vaginosis (BV) to compare the performance of databases built using six different strategies. We evaluated broad versus sample-matched databases, as well as databases populated with proteins translated from metagenomic sequencing of the same samples versus sequences from public repositories. Smaller sample-matched databases performed significantly better, driven by the statistical constraints on large databases. Additionally, large databases attributed up to 34% of significant bacterial hits to taxa absent from the sample, as determined orthogonally by 16S rRNA gene sequencing. We also tested a set of hybrid databases which included bacterial proteins from NCBI RefSeq and translated bacterial genes from the samples. These hybrid databases had the best overall performance, identifying 1,068 unique human and 1,418 unique bacterial proteins, ~30% more than a database populated with proteins from typical vaginal bacteria and fungi. Our findings can help guide the optimal identification of proteins while maintaining statistical power for reaching biological conclusions. IMPORTANCE Metaproteomic analysis can provide valuable insights into the functions of microbial and cellular communities by identifying a broad, untargeted set of proteins. The databases used in the analysis of metaproteomic data influence results by defining what proteins can be identified. Moreover, the size of the database impacts the number of identifications after accounting for false discovery rates (FDRs). Few studies have tested the performance of different strategies for building a protein database to identify proteins from metaproteomic data and those that have largely focused on highly diverse microbial communities. We tested a range of databases on CVL samples and found that a hybrid sample-matched approach, using publicly available proteins from organisms present in the samples, as well as proteins translated from metagenomic sequencing of the samples, had the best performance. However, our results also suggest that public sequence databases will continue to improve as more bacterial genomes are published.

Mistle: bringing spectral library predictions to metaproteomics with an efficient search index

MOTIVATION

Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics.

RESULTS

In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step. We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with a 4- to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes.

AVAILABILITY AND IMPLEMENTATION

Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle.

MetaProD: A Highly-Configurable Mass Spectrometry Analyzer for Multiplexed Proteomic and Metaproteomic Data

The microbiome has been shown to be important for human health because of its influence on disease and the immune response. Mass spectrometry is an important tool for evaluating protein expression and species composition in the microbiome but is technically challenging and time-consuming. Multiplexing has emerged as a way to make spectrometry workflows faster while improving results. Here, we present MetaProD (MetaProteomics in Django) as a highly configurable metaproteomic data analysis pipeline supporting label-free and multiplexed mass spectrometry. The pipeline is open-source, uses fully open-source tools, and is integrated with Django to offer a web-based interface for configuration and data access. Benchmarking of MetaProD using multiple metaproteomics data sets showed that MetaProD achieved fast and efficient identification of peptides and proteins. Application of MetaProD to a multiplexed cancer data set resulted in identification of more differentially expressed human proteins in cancer tissues versus healthy tissues as compared to previous studies; in addition, MetaProD identified bacterial proteins in those samples, some of which are differentially abundant.

Interrogating the role of the milk microbiome in mastitis in the multi-omics era

There is growing interest in a functional understanding of milk-associated microbiota as there is ample evidence that host-associated microbial communities play an active role in host health and phenotype. Mastitis, characterized by painful inflammation of the mammary gland, is prevalent among lactating humans and agricultural animals and is associated with significant clinical and economic consequences. The etiology of mastitis is complex and polymicrobial and correlative studies have indicated alterations in milk microbial community composition. Recent evidence is beginning to suggest that a causal relationship may exist between the milk microbiota and host phenotype in mastitis. Multi-omic approaches can be leveraged to gain a mechanistic, molecular level understanding of how the milk microbiome might modulate host physiology, thereby informing strategies to prevent and ameliorate mastitis. In this paper, we review existing studies that have utilized omics approaches to investigate the role of the milk microbiome in mastitis. We also summarize the strengths and challenges associated with the different omics techniques including metagenomics, metatranscriptomics, metaproteomics, metabolomics and lipidomics and provide perspective on the integration of multiple omics technologies for a better functional understanding of the milk microbiome.

Forages and pastures symposium: forage biodegradation: advances in ruminal microbial ecology

The rumen microbial ecosystem provides ruminants a selective advantage, the ability to utilize forages, allowing them to flourish worldwide in various environments. For many years, our understanding of the ruminal microbial ecosystem was limited to understanding the microbes (usually only laboratory-amenable bacteria) grown in pure culture, meaning that much of our understanding of ruminal function remained a "black box." However, the ruminal degradation of plant cell walls is performed by a consortium of bacteria, archaea, protozoa, and fungi that produces a wide variety of carbohydrate-active enzymes (CAZymes) that are responsible for the catabolism of cellulose, hemicellulose, and pectin. The past 15 years have seen the development and implementation of numerous next-generation sequencing (NGS) approaches (e.g., pyrosequencing, Illumina, and shotgun sequencing), which have contributed significantly to a greater level of insight regarding the microbial ecology of ruminants fed a variety of forages. There has also been an increase in the utilization of liquid chromatography and mass spectrometry that revolutionized transcriptomic approaches, and further improvements in the measurement of fermentation intermediates and end products have advanced with metabolomics. These advanced NGS techniques along with other analytic approaches, such as metaproteomics, have been utilized to elucidate the specific role of microbial CAZymes in forage degradation. Other methods have provided new insights into dynamic changes in the ruminal microbial population fed different diets and how these changes impact the assortment of products presented to the host animal. As more omics-based data has accumulated on forage-fed ruminants, the sequence of events that occur during fiber colonization by the microbial consortium has become more apparent, with fungal populations and fibrolytic bacterial populations working in conjunction, as well as expanding understanding of the individual microbial contributions to degradation of plant cell walls and polysaccharide components. In the future, the ability to predict microbial population and enzymatic activity and end products will be able to support the development of dynamic predictive models of rumen forage degradation and fermentation. Consequently, it is imperative to understand the rumen's microbial population better to improve fiber degradation in ruminants and, thus, stimulate more sustainable production systems.

Metaproteomics to understand how microbiota function: The crystal ball predicts a promising future

In the medical, environmental, and biotechnological fields, microbial communities have attracted much attention due to their roles and numerous possible applications. The study of these communities is challenging due to their diversity and complexity. Innovative methods are needed to identify the taxonomic components of individual microbiota, their changes over time, and to determine how microoorganisms interact and function. Metaproteomics is based on the identification and quantification of proteins, and can potentially provide this full picture. Due to the wide molecular panorama and functional insights it provides, metaproteomics is gaining momentum in microbiome and holobiont research. Its full potential should be unleashed in the coming years with progress in speed and cost of analyses. In this exploratory crystal ball exercise, I discuss the technical and conceptual advances in metaproteomics that I expect to drive innovative research over the next few years in microbiology. I also debate the concepts of 'microbial dark matter' and 'Metaproteomics-Assembled Proteomes (MAPs)' and present some long-term prospects for metaproteomics in clinical diagnostics and personalized medicine, environmental monitoring, agriculture, and biotechnology.

Tool and techniques study to plant microbiome current understanding and future needs: an overview

Microorganisms are present in the universe and they play role in beneficial and harmful to human life, society, and environments. Plant microbiome is a broad term in which microbes are present in the rhizo, phyllo, or endophytic region and play several beneficial and harmful roles with the plant. To know of these microorganisms, it is essential to be able to isolate purification and identify them quickly under laboratory conditions. So, to improve the microbial study, several tools and techniques such as microscopy, rRNA, or rDNA sequencing, fingerprinting, probing, clone libraries, chips, and metagenomics have been developed. The major benefits of these techniques are the identification of microbial community through direct analysis as well as it can apply in situ. Without tools and techniques, we cannot understand the roles of microbiomes. This review explains the tools and their roles in the understanding of microbiomes and their ecological diversity in environments.

Current State and Challenges of the Global Outcomes of Dental Caries Research in the Meta-Omics Era

Despite significant healthcare advances in the 21st century, the exact etiology of dental caries remains unsolved. The past two decades have witnessed a tremendous growth in our understanding of dental caries amid the advent of revolutionary omics technologies. Accordingly, a consensus has been reached that dental caries is a community-scale metabolic disorder, and its etiology is beyond a single causative organism. This conclusion was based on a variety of microbiome studies following the flow of information along the central dogma of biology from genomic data to the end products of metabolism. These studies were facilitated by the unprecedented growth of the next- generation sequencing tools and omics techniques, such as metagenomics and metatranscriptomics, to estimate the community composition of oral microbiome and its functional potential. Furthermore, the rapidly evolving proteomics and metabolomics platforms, including nuclear magnetic resonance spectroscopy and/or mass spectrometry coupled with chromatography, have enabled precise quantification of the translational outcomes. Although the majority supports 'conserved functional changes' as indicators of dysbiosis, it remains unclear how caries dynamics impact the microbiota functions and vice versa, over the course of disease onset and progression. What compounds the situation is the host-microbiota crosstalk. Genome-wide association studies have been undertaken to elucidate the interaction of host genetic variation with the microbiome. However, these studies are challenged by the complex interaction of host genetics and environmental factors. All these complementary approaches need to be orchestrated to capture the key players in this multifactorial disease. Herein, we critically review the milestones in caries research focusing on the state-of-art singular and integrative omics studies, supplemented with a bibliographic network analysis to address the oral microbiome, the host factors, and their interactions. Additionally, we highlight gaps in the dental literature and shed light on critical future research questions and study designs that could unravel the complexities of dental caries, the most globally widespread disease.

An integrated workflow for enhanced taxonomic and functional coverage of the mouse fecal metaproteome

Intestinal microbiota plays a key role in shaping host homeostasis by regulating metabolism, immune responses and behavior. Its dysregulation has been associated with metabolic, immune and neuropsychiatric disorders and is accompanied by changes in bacterial metabolic regulation. Although proteomics is well suited for analysis of individual microbes, metaproteomics of fecal samples is challenging due to the physical structure of the sample, presence of contaminating host proteins and coexistence of hundreds of taxa. Furthermore, there is a lack of consensus regarding preparation of fecal samples, as well as downstream bioinformatic analyses following metaproteomics data acquisition. Here we assess sample preparation and data analysis strategies applied to mouse feces in a typical mass spectrometry-based metaproteomic experiment. We show that subtle changes in sample preparation protocols may influence interpretation of biological findings. Two-step database search strategies led to significant underestimation of false positive protein identifications. Unipept software provided the highest sensitivity and specificity in taxonomic annotation of the identified peptides of unknown origin. Comparison of matching metaproteome and metagenome data revealed a positive correlation between protein and gene abundances. Notably, nearly all functional categories of detected protein groups were differentially abundant in the metaproteome compared to what would be expected from the metagenome, highlighting the need to perform metaproteomics when studying complex microbiome samples.

Using integrated meta-omics to appreciate the role of the gut microbiota in epilepsy

The way the human microbiota may modulate neurological pathologies is a fascinating matter of research. Epilepsy is a common neurological disorder, which has been largely investigated in correlation with microbiota health and function. However, the mechanisms that regulate this apparent connection are scarcely defined, and extensive effort has been conducted to understand the role of microbiota in preventing and reducing epileptic seizures. Intestinal bacteria seem to modulate the seizure frequency mainly by releasing neurotransmitters and inflammatory mediators. In order to elucidate the complex microbial contribution to epilepsy pathophysiology, integrated meta-omics could be pivotal. In fact, the combination of two or more meta-omics approaches allows a multifactorial study of microbial activity within the frame of disease or drug treatments. In this review, we provide information depicting and supporting the use of multi-omics to study the microbiota-epilepsy connection. We described different meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics), focusing on current technical challenges in stool collection procedures, sample extraction methods and data processing. We further discussed the current advantages and limitations of using the integrative approach of multi-omics in epilepsy investigations.

Evaluation of Sample Preservation and Storage Methods for Metaproteomics Analysis of Intestinal Microbiomes

A critical step in studies of the intestinal microbiome using meta-omics approaches is the preservation of samples before analysis. Preservation is essential for approaches that measure gene expression, such as metaproteomics, which is used to identify and quantify proteins in microbiomes. Intestinal microbiome samples are typically stored by flash-freezing and storage at -80°C, but some experimental setups do not allow for immediate freezing of samples. In this study, we evaluated methods to preserve fecal microbiome samples for metaproteomics analyses when flash-freezing is not possible. We collected fecal samples from C57BL/6 mice and stored them for 1 and 4 weeks using the following methods: flash-freezing in liquid nitrogen, immersion in RNAlater, immersion in 95% ethanol, immersion in a RNAlater-like buffer, and combinations of these methods. After storage, we extracted protein and prepared peptides for liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis to identify and quantify peptides and proteins. All samples produced highly similar metaproteomes, except for ethanol-preserved samples that were distinct from all other samples in terms of protein identifications and protein abundance profiles. Flash-freezing and RNAlater (or RNAlater-like treatments) produced metaproteomes that differed only slightly, with less than 0.7% of identified proteins differing in abundance. In contrast, ethanol preservation resulted in an average of 9.5% of the identified proteins differing in abundance between ethanol and the other treatments. Our results suggest that preservation at room temperature in RNAlater or an RNAlater-like solution performs as well as freezing for the preservation of intestinal microbiome samples before metaproteomics analyses. IMPORTANCE Metaproteomics is a powerful tool to study the intestinal microbiome. By identifying and quantifying a large number of microbial, dietary, and host proteins in microbiome samples, metaproteomics provides direct evidence of the activities and functions of microbial community members. A critical step for metaproteomics workflows is preserving samples before analysis because protein profiles are susceptible to fast changes in response to changes in environmental conditions (air exposure, temperature changes, etc.). This study evaluated the effects of different preservation treatments on the metaproteomes of intestinal microbiome samples. In contrast to prior work on preservation of fecal samples for metaproteomics analyses, we ensured that all steps of sample preservation were identical so that all differences could be attributed to the preservation method.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies

A number of studies have shown that the microbial communities of the human body are integral for the maintenance of human health. Advances in next-generation sequencing have enabled rapid and large-scale quantification of the composition of microbial communities in health and disease. Microorganisms mediate diverse host responses including metabolic pathways and immune responses. Using a system biology approach to further understand the underlying alterations of the microbiota in physiological and pathological states can help reveal potential novel therapeutic and diagnostic interventions within the field of synthetic biology. Tools such as biosensors, memory arrays, and engineered bacteria can rewire the microbiome environment. In this article, we review the computational tools used to study microbiome communities and the current limitations of these methods. We evaluate how genome-scale metabolic models (GEMs) can advance our understanding of the microbe-microbe and microbe-host interactions. Moreover, we present how synergies between these system biology approaches and synthetic biology can be harnessed in human microbiome studies to improve future therapeutics and diagnostics and highlight important knowledge gaps for future research in these rapidly evolving fields.

Implication on selection and replacement of granular activated carbon used in biologically activated carbon filters through meta-omics analysis

Biologically activated carbon (BAC) filters are widely used in China and worldwide as an essential part of advanced water treatment. However, it is unclear how to properly select the granular activated carbon (GAC) used in BAC filters and to determine when GAC should be replaced. In this study, five BAC filters, each filled with a different coconut- or coal-based GAC with different physicochemical properties, were run continuously for 400 days. The structure and function of the microbial community and the quantity of specific enzymes in the BAC filters were investigated through an integrated metagenomic/metaproteomic analysis. The results indicated that GAC adsorption still played a major role in removing organic matter once the filters reached a steady-state, which was attributed to bioregeneration, and the contribution of adsorption might be relatively greater than that of biodegradation. GAC with strong adsorption capacity and high bioregeneration potential selected bacterial communities more phylogenetically closely-related than others. The iodine value could be used as an indicator of BAC performance in terms of organic matter removal in the initial stage of the filters, which is dominated by adsorption. However, it could not be used to assess performance at a later stage when adsorption and biodegradation occurred simultaneously. Pore-size distribution characteristics could be chosen as a potential better indicator compared with the current adsorption indicators, dually representing the adsorption performance and the microbial activity, and the proportion of important pore-size of GAC that is more suitable for BAC filter is suggested. GAC with strongly polar terminal groups is more conducive to the removal of ammonium-nitrogen.

Network analyses in microbiome based on high-throughput multi-omics data

Together with various hosts and environments, ubiquitous microbes interact closely with each other forming an intertwined system or community. Of interest, shifts of the relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. While advances in high-throughput Omics technologies offer a great opportunity for understanding the structures and functions of microbiome, it is still challenging to analyse and interpret the omics data. Specifically, the heterogeneity and diversity of microbial communities, compounded with the large size of the datasets, impose a tremendous challenge to mechanistically elucidate the complex communities. Fortunately, network analyses provide an efficient way to tackle this problem, and several network approaches have been proposed to improve this understanding recently. Here, we systemically illustrate these network theories that have been used in biological and biomedical research. Then, we review existing network modelling methods of microbial studies at multiple layers from metagenomics to metabolomics and further to multi-omics. Lastly, we discuss the limitations of present studies and provide a perspective for further directions in support of the understanding of microbial communities.

Updates on metaQuantome Software for Quantitative Metaproteomics

metaQuantome is a software suite that enables the quantitative analysis, statistical evaluation. and visualization of mass-spectrometry-based metaproteomics data. In the latest update of this software, we have provided several extensions, including a step-by-step training guide, the ability to perform statistical analysis on samples from multiple conditions, and a comparative analysis of metatranscriptomics data. The training module, accessed via the Galaxy Training Network, will help users to use the suite effectively both for functional as well as for taxonomic analysis. We extend the ability of metaQuantome to now perform multi-data-point quantitative and statistical analyses so that studies with measurements across multiple conditions, such as time-course studies, can be analyzed. With an eye on the multiomics analysis of microbial communities, we have also initiated the use of metaQuantome statistical and visualization tools on outputs from metatranscriptomics data, which complements the metagenomic and metaproteomic analyses already available. For this, we have developed a tool named MT2MQ ("metatranscriptomics to metaQuantome"), which takes in outputs from the ASaiM metatranscriptomics workflow and transforms them so that the data can be used as an input for comparative statistical analysis and visualization via metaQuantome. We believe that these improvements to metaQuantome will facilitate the use of the software for quantitative metaproteomics and metatranscriptomics and will enable multipoint data analysis. These improvements will take us a step toward integrative multiomic microbiome analysis so as to understand dynamic taxonomic and functional responses of these complex systems in a variety of biological contexts. The updated metaQuantome and MT2MQ are open-source software and are available via the Galaxy Toolshed and GitHub.

Recent Advanced Technologies for the Characterization of Xenobiotic-Degrading Microorganisms and Microbial Communities

Global environmental contamination with a complex mixture of xenobiotics has become a major environmental issue worldwide. Many xenobiotic compounds severely impact the environment due to their high toxicity, prolonged persistence, and limited biodegradability. Microbial-assisted degradation of xenobiotic compounds is considered to be the most effective and beneficial approach. Microorganisms have remarkable catabolic potential, with genes, enzymes, and degradation pathways implicated in the process of biodegradation. A number of microbes, including Alcaligenes, Cellulosimicrobium, Microbacterium, Micrococcus, Methanospirillum, Aeromonas, Sphingobium, Flavobacterium, Rhodococcus, Aspergillus, Penecillium, Trichoderma, Streptomyces, Rhodotorula, Candida, and Aureobasidium, have been isolated and characterized, and have shown exceptional biodegradation potential for a variety of xenobiotic contaminants from soil/water environments. Microorganisms potentially utilize xenobiotic contaminants as carbon or nitrogen sources to sustain their growth and metabolic activities. Diverse microbial populations survive in harsh contaminated environments, exhibiting a significant biodegradation potential to degrade and transform pollutants. However, the study of such microbial populations requires a more advanced and multifaceted approach. Currently, multiple advanced approaches, including metagenomics, proteomics, transcriptomics, and metabolomics, are successfully employed for the characterization of pollutant-degrading microorganisms, their metabolic machinery, novel proteins, and catabolic genes involved in the degradation process. These technologies are highly sophisticated, and efficient for obtaining information about the genetic diversity and community structures of microorganisms. Advanced molecular technologies used for the characterization of complex microbial communities give an in-depth understanding of their structural and functional aspects, and help to resolve issues related to the biodegradation potential of microorganisms. This review article discusses the biodegradation potential of microorganisms and provides insights into recent advances and omics approaches employed for the specific characterization of xenobiotic-degrading microorganisms from contaminated environments.

A semi-tryptic peptide centric metaproteomic mining approach and its potential utility in capturing signatures of gut microbial proteolysis

BACKGROUND

Proteolysis regulation allows gut microbes to respond rapidly to dynamic intestinal environments by fast degradation of misfolded proteins and activation of regulatory proteins. However, alterations of gut microbial proteolytic signatures under complex disease status such as inflammatory bowel disease (IBD, including Crohn's disease (CD) and ulcerative colitis (UC)), have not been investigated. Metaproteomics holds the potential to investigate gut microbial proteolysis because semi-tryptic peptides mainly derive from endogenous proteolysis.

RESULTS

We have developed a semi-tryptic peptide centric metaproteomic mining approach to obtain a snapshot of human gut microbial proteolysis signatures. This approach employed a comprehensive meta-database, two-step multiengine database search, and datasets with high-resolution fragmentation spectra to increase the confidence of semi-tryptic peptide identification. The approach was validated by discovering altered proteolysis signatures of Escherichia coli heat shock response. Utilizing two published large-scale metaproteomics datasets containing 623 metaproteomes from 447 fecal and 176 mucosal luminal interface (MLI) samples from IBD patients and healthy individuals, we obtain potential signatures of altered gut microbial proteolysis at taxonomic, functional, and cleavage site motif levels. The functional alterations mainly involved microbial carbohydrate transport and metabolism, oxidative stress, cell motility, protein synthesis, and maturation. Altered microbial proteolysis signatures of CD and UC mainly occurred in terminal ileum and descending colon, respectively. Microbial proteolysis patterns exhibited low correlations with β-diversity and moderate correlations with microbial protease and chaperones levels, respectively. Human protease inhibitors and immunoglobulins were mainly negatively associated with microbial proteolysis patterns, probably because of the inhibitory effects of these host factors on gut microbial proteolysis events.

CONCLUSIONS

This semi-tryptic peptide centric mining strategy offers a label-free approach to discover signatures of in vivo gut microbial proteolysis events if experimental conditions are well controlled. It can also capture in vitro proteolysis signatures to facilitate the evaluation and optimization of experimental conditions. Our findings highlight the complex and diverse proteolytic events of gut microbiome, providing a unique layer of information beyond taxonomic and proteomic abundance. Video abstract.

Metaproteome and metabolome of oral microbial communities

The emergence of high-throughput technologies for the comprehensive measurement of biomolecules, also referred to as "omics" technologies, has helped us gather "big data" and characterize microbial communities. In this article, we focus on metaproteomic and metabolomic approaches that support hypothesis-driven investigations on various oral biologic samples. Proteomics reveals the working units of the oral milieu and metabolomics unveils the reactions taking place; and so these complementary techniques can unravel the functionality and underlying regulatory processes within various oral microbial communities. Current knowledge of the proteomic interplay and metabolic interactions of microorganisms within oral biofilm and salivary microbiome communities is presented and discussed, from both clinical and basic research perspectives. Communities indicative of, or from, health, caries, periodontal diseases, and endodontic lesions are represented. Challenges, future prospects, and examples of best practice are given.

Modulation of gut microbiota in healthy rats after exposure to nutritional supplements

INTRODUCTION

Rats are experimental animals, frequently used as model organisms in the biomedical studies, and increasingly used to study the gut microbiota. Specifically, the aim of latter studies is either the elucidation of relationship between intestinal dysbiosis and diseases or the determination of nutrients or pharmaceutical agents which can cause the modulation in the presence or abundance of gut microbiota.

AIM

Herein, the research studies conducted on the gut microbiota of healthy rats are presented in a summarized and concise overview. The focus is on studies aimed to reveal the shifts in microbial composition and functional changes after exposure to various types of nutritional supplements.

METHODS

We performed the search of PubMed database using the term "rat gut microbiome microbiota" and examined studies aimed to assess the composition of gut microbiota in physiological homeostasis as well as the effect of various nutritional supplements on the gut microbiota of healthy rats.

A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane

Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.

Proteomics and Metaproteomics Add Functional, Taxonomic and Biomass Dimensions to Modeling the Ecosystem at the Mucosal-luminal Interface

Recent efforts in gut microbiome studies have highlighted the importance of explicitly describing the ecological processes beyond correlative analysis. However, we are still at the early stage of understanding the organizational principles of the gut ecosystem, partially because of the limited information provided by currently used analytical tools in ecological modeling practices. Proteomics and metaproteomics can provide a number of insights for ecological studies, including biomass, matter and energy flow, and functional diversity. In this Mini Review, we discuss proteomics and metaproteomics-based experimental strategies that can contribute to studying the ecology, in particular at the mucosal-luminal interface (MLI) where the direct host-microbiome interaction happens. These strategies include isolation protocols for different MLI components, enrichment methods to obtain designated array of proteins, probing for specific pathways, and isotopic labeling for tracking nutrient flow. Integration of these technologies can generate spatiotemporal and site-specific biological information that supports mathematical modeling of the ecosystem at the MLI.

pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework

MOTIVATION

De novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing.

RESULTS

In order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum. Three metrics for measuring the similarity between each experimental spectrum and its corresponding theoretical spectrum were used as important features, in which the theoretical spectra can be precisely predicted by the pDeep algorithm using deep learning. On seven benchmark datasets from six diverse species, pNovo 3 recalled 29-102% more correct spectra, and the precision was 11-89% higher than three other state-of-the-art de novo sequencing algorithms. Furthermore, compared with the newly developed DeepNovo, which also used the deep learning approach, pNovo 3 still identified 21-50% more spectra on the nine datasets used in the study of DeepNovo. In summary, the deep learning and learning-to-rank techniques implemented in pNovo 3 significantly improve the precision of de novo sequencing, and such machine learning framework is worth extending to other related research fields to distinguish the similar sequences.

AVAILABILITY AND IMPLEMENTATION

pNovo 3 can be freely downloaded from http://pfind.ict.ac.cn/software/pNovo/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Gut-Brain Axis and Neurodegeneration: State-of-the-Art of Meta-Omics Sciences for Microbiota Characterization

Recent advances in the field of meta-omics sciences and related bioinformatics tools have allowed a comprehensive investigation of human-associated microbiota and its contribution to achieving and maintaining the homeostatic balance. Bioactive compounds from the microbial community harboring the human gut are involved in a finely tuned network of interconnections with the host, orchestrating a wide variety of physiological processes. These includes the bi-directional crosstalk between the central nervous system, the enteric nervous system, and the gastrointestinal tract (i.e., gut-brain axis). The increasing accumulation of evidence suggest a pivotal role of the composition and activity of the gut microbiota in neurodegeneration. In the present review we aim to provide an overview of the state-of-the-art of meta-omics sciences including metagenomics for the study of microbial genomes and taxa strains, metatranscriptomics for gene expression, metaproteomics and metabolomics to identify and/or quantify microbial proteins and metabolites, respectively. The potential and limitations of each discipline were highlighted, as well as the advantages of an integrated approach (multi-omics) to predict microbial functions and molecular mechanisms related to human diseases. Particular emphasis is given to the latest results obtained with these approaches in an attempt to elucidate the link between the gut microbiota and the most common neurodegenerative diseases, such as multiple sclerosis (MS), Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS).

pepFunk: a tool for peptide-centric functional analysis of metaproteomic human gut microbiome studies

Motivation

Enzymatic digestion of proteins before mass spectrometry analysis is a key process in metaproteomic workflows. Canonical metaproteomic data processing pipelines typically involve matching spectra produced by the mass spectrometer to a theoretical spectra database, followed by matching the identified peptides back to parent-proteins. However, the nature of enzymatic digestion produces peptides that can be found in multiple proteins due to conservation or chance, presenting difficulties with protein and functional assignment.

Results

To combat this challenge, we developed pepFunk, a peptide-centric metaproteomic workflow focused on the analysis of human gut microbiome samples. Our workflow includes a curated peptide database annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG) terms and a gene set variation analysis-inspired pathway enrichment adapted for peptide-level data. Analysis using our peptide-centric workflow is fast and highly correlated to a protein-centric analysis, and can identify more enriched KEGG pathways than analysis using protein-level data. Our workflow is open source and available as a web application or source code to be run locally.

Availability and implementation

pepFunk is available online as a web application at https://shiny.imetalab.ca/pepFunk/ with open-source code available from https://github.com/northomics/pepFunk.

Contact

dfigeys@uottawa.ca

Supplementary information

Supplementary data are available at Bioinformatics online.

Function is what counts: how microbial community complexity affects species, proteome and pathway coverage in metaproteomics

Introduction: Metaproteomics is an established method to obtain a comprehensive taxonomic and functional view of microbial communities. After more than a decade, we are now able to describe the promise, reality, and perspectives of metaproteomics and provide useful information about the choice of method, applications, and potential improvement strategies.Areas covered: In this article, we will discuss current challenges of species and proteome coverage, and also highlight functional aspects of metaproteomics analysis of microbial communities with different levels of complexity. To do this, we re-analyzed data from microbial communities with low to high complexity (8, 72, 200 and >300 species). High species diversity leads to a reduced number of protein group identifications in a complex community, and thus the number of species resolved is underestimated. Ultimately, low abundance species remain undiscovered in complex communities. However, we observed that the main functional categories were better represented within complex microbiomes when compared to species coverage.Expert opinion: Our findings showed that even with low species coverage, metaproteomics has the potential to reveal habitat-specific functional features. Finally, we exploit this information to highlight future research avenues that are urgently needed to enhance our understanding of taxonomic composition and functions of complex microbiomes.

Antimicrobial Resistance in Veterinary Medicine: An Overview

Antimicrobial resistance (AMR) represents one of the most important human- and animal health-threatening issues worldwide. Bacterial capability to face antimicrobial compounds is an ancient feature, enabling bacterial survival over time and the dynamic surrounding. Moreover, bacteria make use of their evolutionary machinery to adapt to the selective pressure exerted by antibiotic treatments, resulting in reduced efficacy of the therapeutic intervention against human and animal infections. The mechanisms responsible for both innate and acquired AMR are thoroughly investigated. Commonly, AMR traits are included in mobilizable genetic elements enabling the homogeneous diffusion of the AMR traits pool between the ecosystems of diverse sectors, such as human medicine, veterinary medicine, and the environment. Thus, a coordinated multisectoral approach, such as One-Health, provides a detailed comprehensive picture of the AMR onset and diffusion. Following a general revision of the molecular mechanisms responsible for both innate and acquired AMR, the present manuscript focuses on reviewing the contribution of veterinary medicine to the overall issue of AMR. The main sources of AMR amenable to veterinary medicine are described, driving the attention towards the indissoluble cross-talk existing between the diverse ecosystems and sectors and their cumulative cooperation to this warning phenomenon.

Emerging Priorities for Microbiome Research

Microbiome research has increased dramatically in recent years, driven by advances in technology and significant reductions in the cost of analysis. Such research has unlocked a wealth of data, which has yielded tremendous insight into the nature of the microbial communities, including their interactions and effects, both within a host and in an external environment as part of an ecological community. Understanding the role of microbiota, including their dynamic interactions with their hosts and other microbes, can enable the engineering of new diagnostic techniques and interventional strategies that can be used in a diverse spectrum of fields, spanning from ecology and agriculture to medicine and from forensics to exobiology. From June 19-23 in 2017, the NIH and NSF jointly held an Innovation Lab on Quantitative Approaches to Biomedical Data Science Challenges in our Understanding of the Microbiome. This review is inspired by some of the topics that arose as priority areas from this unique, interactive workshop. The goal of this review is to summarize the Innovation Lab's findings by introducing the reader to emerging challenges, exciting potential, and current directions in microbiome research. The review is broken into five key topic areas: (1) interactions between microbes and the human body, (2) evolution and ecology of microbes, including the role played by the environment and microbe-microbe interactions, (3) analytical and mathematical methods currently used in microbiome research, (4) leveraging knowledge of microbial composition and interactions to develop engineering solutions, and (5) interventional approaches and engineered microbiota that may be enabled by selectively altering microbial composition. As such, this review seeks to arm the reader with a broad understanding of the priorities and challenges in microbiome research today and provide inspiration for future investigation and multi-disciplinary collaboration.

Robust Accurate Identification and Biomass Estimates of Microorganisms via Tandem Mass Spectrometry

Rapid and accurate identification of microorganisms and estimation of their biomasses are of extreme importance to public health. Mass spectrometry has become an important technique for these purposes. Previously we published a workflow named Microorganism Classification and Identification (MiCId v.12.26.2017) that was shown to perform no worse than other workflows. This manuscript presents MiCId v.12.13.2018 that, in comparison with the earlier version v.12.26.2017, allows for biomass estimates, provides more accurate microorganism identifications (better controls the number of false positives), and is robust against database size increase. This significant advance is made possible by several new ingredients introduced: first, we apply a modified expectation-maximization method to compute for each taxon considered a prior probability, which can be used for biomass estimate; second, we introduce a new concept called ownership, through which the participation ratio is computed and use it as the number of taxa to be kept within a cluster of closely related taxa; third, based on confidently identified peptides, we calculate for each taxon its degree of independence from the rest of taxa considered to determine whether or not to split this taxon off the cluster. Using 270 data files, each containing a large number of MS/MS spectra, we show that, in comparison with v.12.26.2017, version v.12.13.2018 yields superior retrieval results. We also show that MiCId v.12.13.2018 can estimate species biomass reasonably well. The new MiCId v.12.13.2018, designed to run in Linux environment, is freely available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.

A Bioinformatics Guide to Plant Microbiome Analysis

Recent evidence for intimate relationship of plants with their microbiota shows that plants host individual and diverse microbial communities that are essential for their survival. Understanding their relatedness using genome-based and high-throughput techniques remains a hot topic in microbiome research. Molecular analysis of the plant holobiont necessitates the application of specific sampling and preparatory steps that also consider sources of unwanted information, such as soil, co-amplified plant organelles, human DNA, and other contaminations. Here, we review state-of-the-art and present practical guidelines regarding experimental and computational aspects to be considered in molecular plant-microbiome studies. We discuss sequencing and "omics" techniques with a focus on the requirements needed to adapt these methods to individual research approaches. The choice of primers and sequence databases is of utmost importance for amplicon sequencing, while the assembly and binning of shotgun metagenomic sequences is crucial to obtain quality data. We discuss specific bioinformatic workflows to overcome the limitation of genome database resources and for covering large eukaryotic genomes such as fungi. In transcriptomics, it is necessary to account for the separation of host mRNA or dual-RNAseq data. Metaproteomics approaches provide a snapshot of the protein abundances within a plant tissue which requires the knowledge of complete and well-annotated plant genomes, as well as microbial genomes. Metabolomics offers a powerful tool to detect and quantify small molecules and molecular changes at the plant-bacteria interface if the necessary requirements with regard to (secondary) metabolite databases are considered. We highlight data integration and complementarity which should help to widen our understanding of the interactions among individual players of the plant holobiont in the future.

Milk microbiota: Characterization methods and role in cheese production

Milk is a complex body fluid aimed at addressing the nutritional and defensive needs of the mammal's newborns. Harbored microbiota plays a pivotal role throughout the cheesemaking process and contributes to the development of flavor and texture typical of different type of cheeses. Understanding the dairy microbiota dynamics is of paramount importance for controlling the qualitative, sensorial and biosafety features of the dairy products. Although many studies investigated the contribution of single or few microorganisms, still there is some information lacking about microbial communities. The widespread of the omics platforms and bioinformatic tools enable the investigation of the cheese-associated microbial community in both phylogenetical and functional terms, highlighting the effects of the diverse cheesemaking variables. In this review, the most relevant literature is revised to provide an introduction of the milk- and cheese-associated microbiota, along with their structural and functional dynamics in relation to the diverse cheesemaking technologies and influencing variables. Also, we focus our attention on the latest omics technologies adopted in dairy microbiota investigation. Discussion on the key-steps and major drawbacks of each omics discipline is provided along with a collection of results from the latest research studies performed to unravel the fascinating world of the dairy-associated microbiota. SIGNIFICANCE: Understanding the milk- and cheese- associated microbial community is nowadays considered a key factor in the dairy industry, since it allows a comprehensive knowledge on how all phases of the cheesemaking process impact the harbored microflora; thus, predict the consequences in the finished products in terms of texture, organoleptic characteristics, palatability and biosafety. This review, collect the pioneering and milestones works so far performed in the field of dairy microbiota, and provide the basic guidance to whom approaching the cheese microbiota investigation by means of the latest omics technologies. Also, the review emphasizes the benefits and drawbacks of the omics disciplines, and underline how the integration of diverse omics sciences enhance a comprehensive depiction of the cheese microbiota. In turn, a better consciousness of the dairy microbiota might results in the application of improved starter cultures, cheesemaking practices and technologies; supporting a bio-safe and standardized production of cheese, with a strong economic benefit for both large-scale industries and local traditional dairy farms.

Response of Microbial Communities and Their Metabolic Functions to Drying⁻Rewetting Stress in a Temperate Forest Soil

Global climate change is predicted to alter drought-precipitation patterns, which will likely affect soil microbial communities and their functions, ultimately shifting microbially-mediated biogeochemical cycles. The present study aims to investigate the simultaneous variation of microbial community compositions and functions in response to drought and following rewetting events, using a soil metaproteomics approach. For this, an established field experiment located in an Austrian forest with two levels (moderate and severe stress) of precipitation manipulation was evaluated. The results showed that fungi were more strongly influenced by drying and rewetting (DRW) than bacteria, and that there was a drastic shift in the fungal community towards a more Ascomycota-dominated community. In terms of functional responses, a larger number of proteins and a higher functional diversity were observed in both moderate and severe DRW treatments compared to the control. Furthermore, in both DRW treatments a rise in proteins assigned to "translation, ribosomal structure, and biogenesis" and "protein synthesis" suggests a boost in microbial cell growth after rewetting. We also found that the changes within intracellular functions were associated to specific phyla, indicating that responses of microbial communities to DRW primarily shifted microbial functions. Microbial communities seem to respond to different levels of DRW stress by changing their functional potential, which may feed back to biogeochemical cycles.

Identification of dominant sulfamethoxazole-degraders in pig farm-impacted soil by DNA and protein stable isotope probing

Increasing introduction of antibiotic residues from humans and animal farming into the environment impacts the functioning of natural ecosystems and significantly contributes to the propagation of antibiotic resistance. Microbial degradation is the major sink of antibiotics in soil but the identification of in situ degrading populations is challenging. Here, we investigated sulfamethoxazole-degrading bacteria in soil microcosms by culture-independent DNA and protein stable isotope probing. 0.5% of the carbon from ¹³C₆-labeled sulfamethoxazole amended to soil microcosms was transformed to ¹³CO₂ demonstrating partial mineralization of the antibiotic. DNA stable isotope probing revealed incorporation of ¹³C from ¹³C₆-labeled sulfamethoxazole into Actinobacteria and among them into the families Intrasporangiaceae, Nocardioidaceae, and Gaiellaceae and the order Solirubrobacterales. Protein stable isotope probing demonstrated the incorporation of ¹³C from ¹³C₆-labeled sulfamethoxazole into proteins of bacteria of the families Intrasporangiaceae, Nocardioidaceae and the order Solirubrobacterales, which is consistent with the results of DNA stable isotope probing. The ¹³C abundance of 60 to 80% in several taxonomically relevant proteins indicated that Intrasporangiaceae directly acquired carbon from ¹³C₆-labeled sulfamethoxazole. The results highlight the crucial role of yet-uncultivated indigenous bacteria for antibiotics degradation, and the potential of cultivation-independent stable isotope based molecular approaches to elucidate the structure of antibiotic-degrading populations in complex microbial communities under natural conditions.

Challenges and promise at the interface of metaproteomics and genomics: an overview of recent progress in metaproteogenomic data analysis

INTRODUCTION

The study of microbial communities based on the combined analysis of genomic and proteomic data - called metaproteogenomics - has gained increased research attention in recent years. This relatively young field aims to elucidate the functional and taxonomic interplay of proteins in microbiomes and its implications on human health and the environment. Areas covered: This article reviews bioinformatics methods and software tools dedicated to the analysis of data from metaproteomics and metaproteogenomics experiments. In particular, it focuses on the creation of tailored protein sequence databases, on the optimal use of database search algorithms including methods of error rate estimation, and finally on taxonomic and functional annotation of peptide and protein identifications. Expert opinion: Recently, various promising strategies and software tools have been proposed for handling typical data analysis issues in metaproteomics. However, severe challenges remain that are highlighted and discussed in this article; these include: (i) robust false-positive assessment of peptide and protein identifications, (ii) complex protein inference against a background of highly redundant data, (iii) taxonomic and functional post-processing of identification data, and finally, (iv) the assessment and provision of metrics and tools for quantitative analysis.

Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification?

While peptide identifications in mass spectrometry (MS)-based shotgun proteomics are mostly obtained using database search methods, high-resolution spectrum data from modern MS instruments nowadays offer the prospect of improving the performance of computational de novo peptide sequencing. The major benefit of de novo sequencing is that it does not require a reference database to deduce full-length or partial tag-based peptide sequences directly from experimental tandem mass spectrometry spectra. Although various algorithms have been developed for automated de novo sequencing, the prediction accuracy of proposed solutions has been rarely evaluated in independent benchmarking studies. The main objective of this work is to provide a detailed evaluation on the performance of de novo sequencing algorithms on high-resolution data. For this purpose, we processed four experimental data sets acquired from different instrument types from collision-induced dissociation and higher energy collisional dissociation (HCD) fragmentation mode using the software packages Novor, PEAKS and PepNovo. Moreover, the accuracy of these algorithms is also tested on ground truth data based on simulated spectra generated from peak intensity prediction software. We found that Novor shows the overall best performance compared with PEAKS and PepNovo with respect to the accuracy of correct full peptide, tag-based and single-residue predictions. In addition, the same tool outpaced the commercial competitor PEAKS in terms of running time speedup by factors of around 12-17. Despite around 35% prediction accuracy for complete peptide sequences on HCD data sets, taken as a whole, the evaluated algorithms perform moderately on experimental data but show a significantly better performance on simulated data (up to 84% accuracy). Further, we describe the most frequently occurring de novo sequencing errors and evaluate the influence of missing fragment ion peaks and spectral noise on the accuracy. Finally, we discuss the potential of de novo sequencing for now becoming more widely used in the field.

Comparative Metagenomics

Thanks in large part to newer, better, and cheaper DNA sequencing technologies, an enormous number of metagenomic sequence datasets have been and continue to be generated, covering a huge variety of environmental niches, including several different human body sites. Comparing these metagenomes and identifying their commonalities and differences is a challenging task, due not only to the large amounts of data, but also because there are several methodological considerations that need to be taken into account to ensure an appropriate and sound comparison between datasets. In this chapter, we describe current techniques aimed at comparing metagenomes generated by 16S ribosomal RNA and shotgun DNA sequencing, emphasizing methodological issues that arise in these comparative studies. We provide a detailed case study to illustrate some of these techniques using data from the Human Microbiome Project comparing the microbial communities from ten buccal mucosa samples with ten tongue dorsum samples in terms of alpha diversity, beta diversity, and their taxonomic and functional profiles.

Climate shapes the protein abundance of dominant soil bacteria

Sensitive models of climate change impacts would require a better integration of multi-omics approaches that connect the abundance and activity of microbial populations. Here, we show that climate is a fundamental driver of the protein abundance of Actinobacteria, Planctomycetes and Proteobacteria, supporting the hypothesis that metabolic activity of some dominant phyla may be closely linked to climate. These results may improve our capacity to construct microbial models that better predict the impact of climate change in ecosystem processes.

Improve your Galaxy text life: The Query Tabular Tool

Galaxy provides an accessible platform where multi-step data analysis workflows integrating disparate software can be run, even by researchers with limited programming expertise. Applications of such sophisticated workflows are many, including those which integrate software from different ‘omic domains (e.g. genomics, proteomics, metabolomics). In these complex workflows, intermediate outputs are often generated as tabular text files, which must be transformed into customized formats which are compatible with the next software tools in the pipeline. Consequently, many text manipulation steps are added to an already complex workflow, overly complicating the process. In some cases, limitations to existing text manipulation are such that desired analyses can only be carried out using highly sophisticated processing steps beyond the reach of even advanced users and developers. For users with some SQL knowledge, these text operations could be combined into single, concise query on a relational database. As a solution, we have developed the Query Tabular Galaxy tool, which leverages a SQLite database generated from tabular input data. This database can be queried and manipulated to produce transformed and customized tabular outputs compatible with downstream processing steps. Regular expressions can also be utilized for even more sophisticated manipulations, such as find and replace and other filtering actions. Using several Galaxy-based multi-omic workflows as an example, we demonstrate how the Query Tabular tool dramatically streamlines and simplifies the creation of multi-step analyses, efficiently enabling complicated textual manipulations and processing. This tool should find broad utility for users of the Galaxy platform seeking to develop and use sophisticated workflows involving text manipulation on tabular outputs.

Improve your Galaxy text life: The Query Tabular Tool

Galaxy provides an accessible platform where multi-step data analysis workflows integrating disparate software can be run, even by researchers with limited programming expertise. Applications of such sophisticated workflows are many, including those which integrate software from different 'omic domains (e.g. genomics, proteomics, metabolomics). In these complex workflows, intermediate outputs are often generated as tabular text files, which must be transformed into customized formats which are compatible with the next software tools in the pipeline. Consequently, many text manipulation steps are added to an already complex workflow, overly complicating the process. In some cases, limitations to existing text manipulation are such that desired analyses can only be carried out using highly sophisticated processing steps beyond the reach of even advanced users and developers. For users with some SQL knowledge, these text operations could be combined into single, concise query on a relational database. As a solution, we have developed the Query Tabular Galaxy tool, which leverages a SQLite database generated from tabular input data. This database can be queried and manipulated to produce transformed and customized tabular outputs compatible with downstream processing steps. Regular expressions can also be utilized for even more sophisticated manipulations, such as find and replace and other filtering actions. Using several Galaxy-based multi-omic workflows as an example, we demonstrate how the Query Tabular tool dramatically streamlines and simplifies the creation of multi-step analyses, efficiently enabling complicated textual manipulations and processing. This tool should find broad utility for users of the Galaxy platform seeking to develop and use sophisticated workflows involving text manipulation on tabular outputs.

A Potential Golden Age to Come-Current Tools, Recent Use Cases, and Future Avenues for De Novo Sequencing in Proteomics

In shotgun proteomics, peptide and protein identification is most commonly conducted using database search engines, the method of choice when reference protein sequences are available. Despite its widespread use the database-driven approach is limited, mainly because of its static search space. In contrast, de novo sequencing derives peptide sequence information in an unbiased manner, using only the fragment ion information from the tandem mass spectra. In recent years, with the improvements in MS instrumentation, various new methods have been proposed for de novo sequencing. This review article provides an overview of existing de novo sequencing algorithms and software tools ranging from peptide sequencing to sequence-to-protein mapping. Various use cases are described for which de novo sequencing was successfully applied. Finally, limitations of current methods are highlighted and new directions are discussed for a wider acceptance of de novo sequencing in the community.