fusionDB: assessing microbial diversity and environmental preferences via functional similarity networks

The Diverse Mycorrizal Morphology of Rhododendron dauricum, the Fungal Communities Structure and Dynamics from the Mycorrhizosphere

It is generally believed that mycorrhiza is a microecosystem composed of mycorrhizal fungi, host plants and other microscopic organisms. The mycorrhiza of Rhododendron dauricum is more complex and the diverse morphology of our investigated results displays both typical ericoid mycorrhizal characteristics and ectomycorrhizal traits. The characteristics of ectendoomycorrhiza, where mycelial invade from the outside into the root cells, have also been observed. In order to further clarify the mycorrhizal fungi members and other fungal communities of R. dauricum mycorrhiza, and explore the effects of vegetation and soil biological factors on their community structure, we selected two woodlands in the northeast of China as samples-one is a mixed forest of R. dauricum and Quercus mongolica, and the other a mixed forest of R. dauricum, Q. mongolica, and Pinus densiflor. The sampling time was during the local growing season, from June to September. High-throughput sequencing yielded a total of 3020 fungal amplicon sequence variants (ASVs), which were based on sequencing of the internal transcribed spacer ribosomal RNA (ITS rRNA) via the Illumina NovaSeq platform. In the different habitats of R. dauricum, there are differences in the diversity of fungi obtained from mycorrhizal niches, and specifically the mycorrhizal fungal community structure in the complex vegetation of mixed forests, where R. dauricum is found, exhibits greater stability, with relatively minor changes over time. Soil fungi are identified as the primary source of fungi within the mycorrhizal niche, and the abundance of mycorrhizal fungi from mycorrhizal niches in R. dauricum is significantly influenced by soil pH, organic matter, and available nitrogen. The relationship between soil fungi and mycorrhizal fungi from mycorrhizal niches is simultaneously found to be intricate, while the genus Hydnellum emerges as a central genus among mycorrhizal fungi from mycorrhizal niches. However, there is currently a substantial gap in the foundational research of this genus, including the fact that mycorrhizal fungi from mycorrhizal niches have, compared to fungi present in the soil, proven to be more sensitive to changes in soil moisture.

Computational analysis on the dissemination of non-B DNA structural motifs in promoter regions of 1180 cellular genomes

The promoter regions of gene regulation are under evolutionary constraints and earlier studies uncovered that they are characterized by enrichment of functional non-B DNA structural signatures like curved DNA, cruciform DNA, G-quadruplex, triple-helical DNA, slipped DNA structures, and Z-DNA. However, these studies are restricted to a few model organisms, single non-B DNA motif types, or whole genomic sequences, and their comparative accumulation in promoter regions of different domains of life has not been reported comprehensively. In this study, for the first time, we investigated the preponderance of non-B DNA-prone motifs in promoter regions in 1180 genomes belonging to 28 taxonomic groups using the non-B DNA Motif Search Tool (nBMST). The trends suggest that they are predominant in promoters compared to the upstream and downstream regions of all three domains of life and variably linked to taxonomic groups. Cruciform DNA motif is the most abundant form of non-B DNA, spanning from archaea to lower eukaryotes. Curved DNA motifs are prominent in host-associated bacteria, and suppressed in mammals. Triplex-DNA and slipped DNA structure repeats are discretely dispersed in all lineages. G-quadruplex motifs are significantly enriched in mammals. We also observed that the unique enrichment of non-B DNA in promoters is strongly linked to genome GC, size, evolutionary time divergence, and ecological adaptations. Overall, our work systematically reports the unique non-B DNA structural landscape of cellular organisms from the perspective of the cis-regulatory code of genomes.

Learning from the unknown: exploring the range of bacterial functionality

Determining the repertoire of a microbe's molecular functions is a central question in microbial biology. Modern techniques achieve this goal by comparing microbial genetic material against reference databases of functionally annotated genes/proteins or known taxonomic markers such as 16S rRNA. Here, we describe a novel approach to exploring bacterial functional repertoires without reference databases. Our Fusion scheme establishes functional relationships between bacteria and assigns organisms to Fusion-taxa that differ from otherwise defined taxonomic clades. Three key findings of our work stand out. First, bacterial functional comparisons outperform marker genes in assigning taxonomic clades. Fusion profiles are also better for this task than other functional annotation schemes. Second, Fusion-taxa are robust to addition of novel organisms and are, arguably, able to capture the environment-driven bacterial diversity. Finally, our alignment-free nucleic acid-based Siamese Neural Network model, created using Fusion functions, enables finding shared functionality of very distant, possibly structurally different, microbial homologs. Our work can thus help annotate functional repertoires of bacterial organisms and further guide our understanding of microbial communities.

Functional comparison of metabolic networks across species

Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined directly. Instead, potential phenotypes are commonly inferred from genomic information, and rarely were model-predicted phenotypes employed beyond the species level. Here, we propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and thereby link genotype and environment to phenotype. We show that these correlations provide a consistent functional complement to genomic information by capturing how network context shapes gene function. This enables, for example, phylogenetic inference across all domains of life at the organism level. For 245 bacterial species, we identify conserved and variable metabolic functions, elucidate the quantitative impact of evolutionary history and ecological niche on these functions, and generate hypotheses on associated metabolic phenotypes. We expect our framework for the joint interpretation of metabolic phenotypes, evolution, and environment to help guide future empirical studies.

CDEMI: characterizing differences in microbial composition and function in microbiome data

Microbial communities influence host phenotypes through microbiota-derived metabolites and interactions between exogenous active substances (EASs) and the microbiota. Owing to the high dynamics of microbial community composition and difficulty in microbial functional analysis, the identification of mechanistic links between individual microbes and host phenotypes is complex. Thus, it is important to characterize variations in microbial composition across various conditions (for example, topographical locations, times, physiological and pathological conditions, and populations of different ethnicities) in microbiome studies. However, no web server is currently available to facilitate such characterization. Moreover, accurately annotating the functions of microbes and investigating the possible factors that shape microbial function are critical for discovering links between microbes and host phenotypes. Herein, an online tool, CDEMI, is introduced to discover microbial composition variations across different conditions, and five types of microbe libraries are provided to comprehensively characterize the functionality of microbes from different perspectives. These collective microbe libraries include (1) microbial functional pathways, (2) disease associations with microbes, (3) EASs associations with microbes, (4) bioactive microbial metabolites, and (5) human body habitats. In summary, CDEMI is unique in that it can reveal microbial patterns in distributions/compositions across different conditions and facilitate biological interpretations based on diverse microbe libraries. CDEMI is accessible at http://rdblab.cn/cdemi/.

Quantifying structural relationships of metal-binding sites suggests origins of biological electron transfer

Biological redox reactions drive planetary biogeochemical cycles. Using a novel, structure-guided sequence analysis of proteins, we explored the patterns of evolution of enzymes responsible for these reactions. Our analysis reveals that the folds that bind transition metal–containing ligands have similar structural geometry and amino acid sequences across the full diversity of proteins. Similarity across folds reflects the availability of key transition metals over geological time and strongly suggests that transition metal–ligand binding had a small number of common peptide origins. We observe that structures central to our similarity network come primarily from oxidoreductases, suggesting that ancestral peptides may have also facilitated electron transfer reactions. Last, our results reveal that the earliest biologically functional peptides were likely available before the assembly of fully functional protein domains over 3.8 billion years ago.Thus, life is a special, very complex form of motion of matter, but this form did not always exist, and it is not separated from inorganic nature by an impassable abyss; rather, it arose from inorganic nature as a new property in the process of evolution of the world. We must study the history of this evolution if we want to solve the problem of the origin of life. [A. I. Oparin (1)]

Functional prediction of environmental variables using metabolic networks

In this manuscript, we propose a novel approach to assess relationships between environment and metabolic networks. We used a comprehensive dataset of more than 5000 prokaryotic species from which we derived the metabolic networks. We compute the scope from the reconstructed graphs, which is the set of all metabolites and reactions that can potentially be synthesized when provided with external metabolites. We show using machine learning techniques that the scope is an excellent predictor of taxonomic and environmental variables, namely growth temperature, oxygen tolerance, and habitat. In the literature, metabolites and pathways are rarely used to discriminate species. We make use of the scope underlying structure—metabolites and pathways—to construct the predictive models, giving additional information on the important metabolic pathways needed to discriminate the species, which is often absent in other metabolic network properties. For example, in the particular case of growth temperature, glutathione biosynthesis pathways are specific to species growing in cold environments, whereas tungsten metabolism is specific to species in warm environments, as was hinted in current literature. From a machine learning perspective, the scope is able to reduce the dimension of our data, and can thus be considered as an interpretable graph embedding.

Biodiversity-based development and evolution: the emerging research systems in model and non-model organisms

Evolutionary developmental biology, or Evo-Devo for short, has become an established field that, broadly speaking, seeks to understand how changes in development drive major transitions and innovation in organismal evolution. It does so via integrating the principles and methods of many subdisciplines of biology. Although we have gained unprecedented knowledge from the studies on model organisms in the past decades, many fundamental and crucially essential processes remain a mystery. Considering the tremendous biodiversity of our planet, the current model organisms seem insufficient for us to understand the evolutionary and physiological processes of life and its adaptation to exterior environments. The currently increasing genomic data and the recently available gene-editing tools make it possible to extend our studies to non-model organisms. In this review, we review the recent work on the regulatory signaling of developmental and regeneration processes, environmental adaptation, and evolutionary mechanisms using both the existing model animals such as zebrafish and Drosophila, and the emerging nonstandard model organisms including amphioxus, ascidian, ciliates, single-celled phytoplankton, and marine nematode. In addition, the challenging questions and new directions in these systems are outlined as well.

Strain inheritance and neonatal gut microbiota development: A meta-analysis

As many inflammatory and metabolic disorders have been associated with structural deficits of the human gut microbiota, the principles and mechanisms that govern its initialization and development are of considerable scientific interest and clinical relevance. However, our current understanding of the developing gut microbiota dynamics remains incomplete. We carried out a large-scale, comprehensive meta-analysis of over 1900 available metagenomic shotgun samples from neonates, infants, adolescents, and their families, using our recently introduced SameStr program for strain-level microbiota profiling and the detection of microbial strain transfer and persistence. We found robust associations between fecal microbiota composition and age, as well as delivery mode, which was measurable for up to two years of life. C-section was associated with increased relative abundances of non-gut species and delayed transition from a predominantly oxygen-tolerant to intolerant microbial community. Unsupervised networks based on shared strain profiles generated family-specific clusters connecting infants, their siblings, parents and grandparents and, in one case, suggested strain transfer between neonates from the same hospital ward, but could also be used to identify potentially mislabeled metagenome samples. Vaginally delivered newborns shared more strains with their mothers than C-section infants, but strain sharing was reduced if mothers underwent antibiotic treatment. Shared strains persisted in infants throughout the first year of life and belonged to the same bacterial species as strains that were shared between adults and their parents. Irrespective of delivery type, older children shared strains with their mothers and fathers and, into adulthood, could be accurately distinguished from unrelated sample pairs. Prominent fecal commensal bacteria were both among frequently transferred (e.g. Bacteroides and Sutterella) and newly acquired taxa (e.g. Blautia, Faecalibacterium, and Ruminococcus). Our meta-analysis presents a more detailed and comprehensive picture of the highly dynamic neonatal and infant fecal microbiota development than previous studies and presents evidence for taxonomic and functional compositional differences early in life between infants born naturally or by C-section, which persist well into adolescence.

Snow microbiome functional analyses reveal novel aspects of microbial metabolism of complex organic compounds

Microbes active in extreme cold are not as well explored as those of other extreme environments. Studies have revealed a substantial microbial diversity and identified cold-specific microbiome molecular functions. We analyzed the metagenomes and metatranscriptomes of 20 snow samples collected in early and late spring in Svalbard, Norway using mi-faser, our read-based computational microbiome function annotation tool. Our results reveal a more diverse microbiome functional capacity and activity in the early- vs. late-spring samples. We also find that functional dissimilarity between the same-sample metagenomes and metatranscriptomes is significantly higher in early than late spring samples. These findings suggest that early spring samples may contain a larger fraction of DNA of dormant (or dead) organisms, while late spring samples reflect a new, metabolically active community. We further show that the abundance of sequencing reads mapping to the fatty acid synthesis-related microbial pathways in late spring metagenomes and metatranscriptomes is significantly correlated with the organic acid levels measured in these samples. Similarly, the organic acid levels correlate with the pathway read abundances of geraniol degradation and inversely correlate with those of styrene degradation, suggesting a possible nutrient change. Our study thus highlights the activity of microbial degradation pathways of complex organic compounds previously unreported at low temperatures.

Chemoreceptors with C-terminal pentapeptides for CheR and CheB binding are abundant in bacteria that maintain host interactions

Chemosensory pathways represent a major prokaryotic signal transduction mechanism that is based on signal sensing by chemoreceptors. An essential feature of chemosensory pathways is the CheR and CheB mediated control of chemoreceptor methylation causing pathway adaptation. At their C-terminal extension the Tar and Tsr model chemoreceptors contain a pentapeptide that acts as an additional CheR and CheB binding site. The relevance of this pentapeptide is poorly understood since pentapeptide removal from Tar/Tsr causes receptor inactivation, whereas many other chemoreceptors do not require this pentapeptide for correct function. We report here a bioinformatic analysis of pentapeptide containing chemoreceptors. These receptors were detected in 11 bacterial phyla and represent approximately 10% of all chemoreceptors. Pentapeptide containing chemoreceptors are mainly found in Gram-negative bacteria, are of low abundance in Gram-positive species and almost absent from archaea. Almost 50% of TarH (Tar homologue) ligand binding domain containing chemoreceptors possess pentapeptides, whereas chemoreceptor families with other ligand binding domains are devoid of pentapeptides. The abundance of chemoreceptors with C-terminal pentapeptides correlated negatively with the number of chemoreceptor genes per genome. The consensus sequence reveals a negative net charge for many pentapeptides. Pentapeptide containing chemoreceptors are very abundant in the order Enterobacterales, particularly in the families Pectobacterium and Dickeya, where they represent about 50% of the total number. In contrast, bacteria with primarily free living lifestyles have a reduced number of pentapeptides, such as approximately 1% for Pseudomonadales. It is proposed that pentapeptide function is related to mechanisms that permit host interaction.

Computational Approaches for Unraveling the Effects of Variation in the Human Genome and Microbiome

The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.

Genomic Analysis of Bacillus megaterium NCT-2 Reveals Its Genetic Basis for the Bioremediation of Secondary Salinization Soil

Bacillus megaterium NCT-2 is a nitrate-uptake bacterial, which shows high bioremediation capacity in secondary salinization soil, including nitrate-reducing capacity, phosphate solubilization, and salinity adaptation. To gain insights into the bioremediation capacity at the genetic level, the complete genome sequence was obtained by using a multiplatform strategy involving HiSeq and PacBio sequencing. The NCT-2 genome consists of a circular chromosome of 5.19 Mbp and ten indigenous plasmids, totaling 5.88 Mbp with an average GC content of 37.87%. The chromosome encodes 5,606 genes, 142 tRNAs, and 53 rRNAs. Genes involved in the features of the bioremediation in secondary salinization soil and plant growth promotion were identified in the genome, such as nitrogen metabolism, phosphate uptake, the synthesis of organic acids and phosphatase for phosphate-solubilizing ability, and Trp-dependent IAA synthetic system. Furthermore, strain NCT-2 has great ability of adaption to environments due to the genes involved in cation transporters, osmotic stress, and oxidative stress. This study sheds light on understanding the molecular basis of using B. megaterium NCT-2 in bioremediation of the secondary salinization soils.

Fingerprinting cities: differentiating subway microbiome functionality

BACKGROUND

Accumulating evidence suggests that the human microbiome impacts individual and public health. City subway systems are human-dense environments, where passengers often exchange microbes. The MetaSUB project participants collected samples from subway surfaces in different cities and performed metagenomic sequencing. Previous studies focused on taxonomic composition of these microbiomes and no explicit functional analysis had been done till now.

RESULTS

As a part of the 2018 CAMDA challenge, we functionally profiled the available ~ 400 subway metagenomes and built predictor for city origin. In cross-validation, our model reached 81% accuracy when only the top-ranked city assignment was considered and 95% accuracy if the second city was taken into account as well. Notably, this performance was only achievable if the similarity of distribution of cities in the training and testing sets was similar. To assure that our methods are applicable without such biased assumptions we balanced our training data to account for all represented cities equally well. After balancing, the performance of our method was slightly lower (76/94%, respectively, for one or two top ranked cities), but still consistently high. Here we attained an added benefit of independence of training set city representation. In testing, our unbalanced model thus reached (an over-estimated) performance of 90/97%, while our balanced model was at a more reliable 63/90% accuracy. While, by definition of our model, we were not able to predict the microbiome origins previously unseen, our balanced model correctly judged them to be NOT-from-training-cities over 80% of the time. Our function-based outlook on microbiomes also allowed us to note similarities between both regionally close and far-away cities. Curiously, we identified the depletion in mycobacterial functions as a signature of cities in New Zealand, while photosynthesis related functions fingerprinted New York, Porto and Tokyo.

CONCLUSIONS

We demonstrated the power of our high-speed function annotation method, mi-faser, by analysing ~ 400 shotgun metagenomes in 2 days, with the results recapitulating functional signals of different city subway microbiomes. We also showed the importance of balanced data in avoiding over-estimated performance. Our results revealed similarities between both geographically close (Ofa and Ilorin) and distant (Boston and Porto, Lisbon and New York) city subway microbiomes. The photosynthesis related functional signatures of NYC were previously unseen in taxonomy studies, highlighting the strength of functional analysis.

HFSP: high speed homology-driven function annotation of proteins

Motivation

The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins. The increase in the number of available sequences, however, has drastically increased the search space, thus significantly slowing down alignment methods.

Results

Here we describe homology-derived functional similarity of proteins (HFSP), a novel computational method that uses results of a high-speed alignment algorithm, MMseqs2, to infer functional similarity of proteins on the basis of their alignment length and sequence identity. We show that our method is accurate (85% precision) and fast (more than 40-fold speed increase over state-of-the-art). HFSP can help correct at least a 16% error in legacy curations, even for a resource of as high quality as Swiss-Prot. These findings suggest HFSP as an ideal resource for large-scale functional annotation efforts.

Supplementary information

Supplementary data are available at Bioinformatics online.

LifeStyle-Specific-Islands (LiSSI): Integrated Bioinformatics Platform for Genomic Island Analysis

Distinct bacteria are able to cope with highly diverse lifestyles; for instance, they can be free living or host-associated. Thus, these organisms must possess a large and varied genomic arsenal to withstand different environmental conditions. To facilitate the identification of genomic features that might influence bacterial adaptation to a specific niche, we introduce LifeStyle-Specific-Islands (LiSSI). LiSSI combines evolutionary sequence analysis with statistical learning (Random Forest with feature selection, model tuning and robustness analysis). In summary, our strategy aims to identify conserved consecutive homology sequences (islands) in genomes and to identify the most discriminant islands for each lifestyle.