Inference of phenotype-defining functional modules of protein families for microbial plant biomass degraders

Reliable and Scalable Identification and Prioritization of Putative Cellulolytic Anaerobes With Large Genome Data

In the era of high-throughput sequencing, genetic information that is inherently whispering hints of the microbes’ functional niches is becoming easily accessible; however, properly identifying and characterizing these genetic hints to infer the microbes’ functional niches remains a challenge. Regarding genome-centric interpretation on the specific functional niche of cellulose hydrolysis for anaerobes, often encountered in practice is a lack of confidence in predicting the anaerobes’ real cellulolytic competency based solely on abundances of the varying carbohydrate-active enzyme modules annotated or on their taxonomy affiliation. Recognition of the synergy machineries that include but not limited to the cellulosome gene clusters is equally important as the annotation of individual carbohydrate-active modules or genes. In the interpretation of complete genomes of 2,768 microbe strains whose phenotypes have been well documented, with the incorporation of an automatic recognition of synergy among the carbohydrate active elements annotated, an explicit genotype–phenotype correlation was evidenced to be feasible for cellulolytic anaerobes, and a bioinformatic pipeline was developed accordingly. This genome-centric pipeline would categorize putative cellulolytic anaerobes into six genotype groups based on differential cellulose-hydrolyzing capacity and varying synergy mechanisms. Suggested in this genotype–phenotype correlation analysis was a finer categorization of the cellulosome gene clusters: although cellulosome complexes, by their nature, could enable the assembly of a number of carbohydrate-active units, they do not certainly guarantee the formation of the cellulose–enzyme–microbe complex or the cellulose-hydrolyzing activity of the corresponding anaerobe strains, for example, the well-known Clostridium acetobutylicum strains. Also, recognized in this genotype-phenotype correlation analysis was the genetic foundation of a previously unrecognized machinery that may mediate the microbe–cellulose adhesion, to be specific, enzymes encoded by genes harboring both the surface layer homology and cellulose-binding CBM modules. Applicability of this pipeline on scalable annotation of large genome datasets was further tested with the annotation of 7,902 reference genomes downloaded from NCBI, from which 14 genomes of putative paradigm cellulose-hydrolyzing anaerobes were identified. We believe the pipeline developed in this study would be a good add as a bioinformatic tool for genome-centric interpretation of uncultivated anaerobes, specifically on their functional niche of cellulose hydrolysis.

Machine learning prediction of novel pectinolytic enzymes in Aspergillus niger through integrating heterogeneous (post-) genomics data

Pectinolytic enzymes are a variety of enzymes involved in breaking down pectin, a complex and abundant plant cell-wall polysaccharide. In nature, pectinolytic enzymes play an essential role in allowing bacteria and fungi to depolymerize and utilize pectin. In addition, pectinases have been widely applied in various industries, such as the food, wine, textile, paper and pulp industries. Due to their important biological function and increasing industrial potential, discovery of novel pectinolytic enzymes has received global interest. However, traditional enzyme characterization relies heavily on biochemical experiments, which are time consuming, laborious and expensive. To accelerate identification of novel pectinolytic enzymes, an automatic approach is needed. We developed a machine learning (ML) approach for predicting pectinases in the industrial workhorse fungus, Aspergillus niger. The prediction integrated a diverse range of features, including evolutionary profile, gene expression, transcriptional regulation and biochemical characteristics. Results on both the training and the independent testing dataset showed that our method achieved over 90 % accuracy, and recalled over 60 % of pectinolytic genes. Application of the ML model on the A. niger genome led to the identification of 83 pectinases, covering both previously described pectinases and novel pectinases that do not belong to any known pectinolytic enzyme family. Our study demonstrated the tremendous potential of ML in discovery of new industrial enzymes through integrating heterogeneous (post-) genomimcs data.

Characterization of Metagenome-Assembled Genomes and Carbohydrate-Degrading Genes in the Gut Microbiota of Tibetan Pig

Tibetan pig is an important domestic mammal, providing products of high nutritional value for millions of people living in the Qinghai-Tibet Plateau. The genomes of mammalian gut microbiota encode a large number of carbohydrate-active enzymes, which are essential for the digestion of complex polysaccharides through fermentation. However, the current understanding of microbial degradation of dietary carbohydrates in the Tibetan pig gut is limited. In this study, we produced approximately 145 gigabases of metagenomic sequence data for the fecal samples from 11 Tibetan pigs. De novo assembly and binning recovered 322 metagenome-assembled genomes taxonomically assigned to 11 bacterial phyla and two archaeal phyla. Of these genomes, 191 represented the uncultivated microbes derived from novel prokaryotic taxa. Twenty-three genomes were identified as metagenomic biomarkers that were significantly abundant in the gut ecosystem of Tibetan pigs compared to the other low-altitude relatives. Further, over 13,000 carbohydrate-degrading genes were identified, and these genes were more abundant in some of the genomes within the five principal phyla: Firmicutes, Bacteroidetes, Spirochaetota, Verrucomicrobiota, and Fibrobacterota. Particularly, three genomes representing the uncultivated Verrucomicrobiota encode the most abundant degradative enzymes in the fecal microbiota of Tibetan pigs. These findings should substantially increase the phylogenetic diversity of specific taxonomic clades in the microbial tree of life and provide an expanded repertoire of biomass-degrading genes for future application to microbial production of industrial enzymes.

Microbial diversity-ecosystem function relationships across environmental gradients

In light of increasing anthropogenic pressures on ecosystems around the globe, the question how biodiversity change of organisms in the critical zone between Earth’s canopies and bedrock relates to ecosystem functions is an urgent issue, as human life relies on these functions. Particularly, soils play vital roles in nutrient cycling, promotion of plant growth, water purification, litter decomposition, and carbon storage, thereby securing food and water resources and stabilizing the climate. Soil functions are carried to a large part by complex communities of microorganisms, such as bacteria, archaea, fungi and protists. The assessment of microbial diversity and the microbiome's functional potential continues to pose significant challenges. Next generation sequencing offers some of the most promising tools to help shedding light on microbial diversity-function relationships. Studies relating microbial diversity and ecosystem functions are rare, particularly those on how this relationship is influenced by environmental gradients. The proposed project focuses on decomposition as one of the most important microbial soil ecosystem functions. The researchers from the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig combine an unparalleled range of expertise from next generation sequencing- based analysis of microbial communities (“meta-omics”) to soil ecology and biodiversity-ecosystem function research. This consortium will make use of soil samples from large international networks to assess microbial diversity both at the taxonomic and functional level and across the domains of life. By linking microbial diversity to functional measurements of decomposition and environmental gradients, the proposed project aims to achieve a comprehensive scale-independent understanding of environmental drivers and anthropogenic effects on the structural and functional diversity of microbial communities and subsequent consequences for ecosystem functioning.

"Candidatus Paraporphyromonas polyenzymogenes" encodes multi-modular cellulases linked to the type IX secretion system

BACKGROUND

In nature, obligate herbivorous ruminants have a close symbiotic relationship with their gastrointestinal microbiome, which proficiently deconstructs plant biomass. Despite decades of research, lignocellulose degradation in the rumen has thus far been attributed to a limited number of culturable microorganisms. Here, we combine meta-omics and enzymology to identify and describe a novel Bacteroidetes family ("Candidatus MH11") composed entirely of uncultivated strains that are predominant in ruminants and only distantly related to previously characterized taxa.

RESULTS

The first metabolic reconstruction of Ca. MH11-affiliated genome bins, with a particular focus on the provisionally named "Candidatus Paraporphyromonas polyenzymogenes", illustrated their capacity to degrade various lignocellulosic substrates via comprehensive inventories of singular and multi-modular carbohydrate active enzymes (CAZymes). Closer examination revealed an absence of archetypical polysaccharide utilization loci found in human gut microbiota. Instead, we identified many multi-modular CAZymes putatively secreted via the Bacteroidetes-specific type IX secretion system (T9SS). This included cellulases with two or more catalytic domains, which are modular arrangements that are unique to Bacteroidetes species studied to date. Core metabolic proteins from Ca. P. polyenzymogenes were detected in metaproteomic data and were enriched in rumen-incubated plant biomass, indicating that active saccharification and fermentation of complex carbohydrates could be assigned to members of this novel family. Biochemical analysis of selected Ca. P. polyenzymogenes CAZymes further iterated the cellulolytic activity of this hitherto uncultured bacterium towards linear polymers, such as amorphous and crystalline cellulose as well as mixed linkage β-glucans.

CONCLUSION

We propose that Ca. P. polyenzymogene genotypes and other Ca. MH11 members actively degrade plant biomass in the rumen of cows, sheep and most likely other ruminants, utilizing singular and multi-domain catalytic CAZymes secreted through the T9SS. The discovery of a prominent role of multi-modular cellulases in the Gram-negative Bacteroidetes, together with similar findings for Gram-positive cellulosomal bacteria (Ruminococcus flavefaciens) and anaerobic fungi (Orpinomyces sp.), suggests that complex enzymes are essential and have evolved within all major cellulolytic dominions inherent to the rumen.

Metagenomics and CAZyme Discovery

Microorganisms play a primary role in regulating biogeochemical cycles and are a valuable source of enzymes that have biotechnological applications, such as carbohydrate-active enzymes (CAZymes). However, the inability to culture the majority of microorganisms that exist in natural ecosystems using common culture-dependent techniques restricts access to potentially novel cellulolytic bacteria and beneficial enzymes. The development of molecular-based culture-independent methods such as metagenomics enables researchers to study microbial communities directly from environmental samples, and presents a platform from which enzymes of interest can be sourced. We outline key methodological stages that are required as well as describe specific protocols that are currently used for metagenomic projects dedicated to CAZyme discovery.

From Genomes to Phenotypes: Traitar, the Microbial Trait Analyzer

Bacteria are ubiquitous in our ecosystem and have a major impact on human health, e.g., by supporting digestion in the human gut. Bacterial communities can also aid in biotechnological processes such as wastewater treatment or decontamination of polluted soils. Diverse bacteria contribute with their unique capabilities to the functioning of such ecosystems, but lab experiments to investigate those capabilities are labor-intensive. Major advances in sequencing techniques open up the opportunity to study bacteria by their genome sequences. For this purpose, we have developed Traitar, software that predicts traits of bacteria on the basis of their genomes. It is applicable to studies with tens or hundreds of bacterial genomes. Traitar may help researchers in microbiology to pinpoint the traits of interest, reducing the amount of wet lab work required.

The number of sequenced genomes is growing exponentially, profoundly shifting the bottleneck from data generation to genome interpretation. Traits are often used to characterize and distinguish bacteria and are likely a driving factor in microbial community composition, yet little is known about the traits of most microbes. We describe Traitar, the microbial trait analyzer, which is a fully automated software package for deriving phenotypes from a genome sequence. Traitar provides phenotype classifiers to predict 67 traits related to the use of various substrates as carbon and energy sources, oxygen requirement, morphology, antibiotic susceptibility, proteolysis, and enzymatic activities. Furthermore, it suggests protein families associated with the presence of particular phenotypes. Our method uses L1-regularized L2-loss support vector machines for phenotype assignments based on phyletic patterns of protein families and their evolutionary histories across a diverse set of microbial species. We demonstrate reliable phenotype assignment for Traitar to bacterial genomes from 572 species of eight phyla, also based on incomplete single-cell genomes and simulated draft genomes. We also showcase its application in metagenomics by verifying and complementing a manual metabolic reconstruction of two novel Clostridiales species based on draft genomes recovered from commercial biogas reactors. Traitar is available at https://github.com/hzi-bifo/traitar.

IMPORTANCE Bacteria are ubiquitous in our ecosystem and have a major impact on human health, e.g., by supporting digestion in the human gut. Bacterial communities can also aid in biotechnological processes such as wastewater treatment or decontamination of polluted soils. Diverse bacteria contribute with their unique capabilities to the functioning of such ecosystems, but lab experiments to investigate those capabilities are labor-intensive. Major advances in sequencing techniques open up the opportunity to study bacteria by their genome sequences. For this purpose, we have developed Traitar, software that predicts traits of bacteria on the basis of their genomes. It is applicable to studies with tens or hundreds of bacterial genomes. Traitar may help researchers in microbiology to pinpoint the traits of interest, reducing the amount of wet lab work required.

The landscape of microbial phenotypic traits and associated genes

Bacteria and Archaea display a variety of phenotypic traits and can adapt to diverse ecological niches. However, systematic annotation of prokaryotic phenotypes is lacking. We have therefore developed ProTraits, a resource containing ∼545 000 novel phenotype inferences, spanning 424 traits assigned to 3046 bacterial and archaeal species. These annotations were assigned by a computational pipeline that associates microbes with phenotypes by text-mining the scientific literature and the broader World Wide Web, while also being able to define novel concepts from unstructured text. Moreover, the ProTraits pipeline assigns phenotypes by drawing extensively on comparative genomics, capturing patterns in gene repertoires, codon usage biases, proteome composition and co-occurrence in metagenomes. Notably, we find that gene synteny is highly predictive of many phenotypes, and highlight examples of gene neighborhoods associated with spore-forming ability. A global analysis of trait interrelatedness outlined clusters in the microbial phenotype network, suggesting common genetic underpinnings. Our extended set of phenotype annotations allows detection of 57 088 high confidence gene-trait links, which recover many known associations involving sporulation, flagella, catalase activity, aerobicity, photosynthesis and other traits. Over 99% of the commonly occurring gene families are involved in genetic interactions conditional on at least one phenotype, suggesting that epistasis has a major role in shaping microbial gene content.