Clustering analysis of proteins from microbial genomes at multiple levels of resolution

Comparative genomics reveals distinct diversification patterns among LysR-type transcriptional regulators in the ESKAPE pathogen Pseudomonas aeruginosa

Pseudomonas aeruginosa, a harmful nosocomial pathogen associated with cystic fibrosis and burn wounds, encodes for a large number of LysR-type transcriptional regulator proteins. To understand how and why LTTR proteins evolved with such frequency and to establish whether any relationships exist within the distribution we set out to identify the patterns underpinning LTTR distribution in P. aeruginosa and to uncover cluster-based relationships within the pangenome. Comparative genomic studies revealed that in the JGI IMG database alone ~86 000 LTTRs are present across the sequenced genomes (n=699). They are widely distributed across the species, with core LTTRs present in >93 % of the genomes and accessory LTTRs present in <7 %. Analysis showed that subsets of core LTTRs can be classified as either variable (typically specific to P. aeruginosa) or conserved (and found to be distributed in other Pseudomonas species). Extending the analysis to the more extensive Pseudomonas database, PA14 rooted analysis confirmed the diversification patterns and revealed PqsR, the receptor for the Pseudomonas quinolone signal (PQS) and 2-heptyl-4-quinolone (HHQ) quorum-sensing signals, to be amongst the most variable in the dataset. Successful complementation of the PAO1 pqsR - mutant using representative variant pqsR sequences suggests a degree of structural promiscuity within the most variable of LTTRs, several of which play a prominent role in signalling and communication. These findings provide a new insight into the diversification of LTTR proteins within the P. aeruginosa species and suggests a functional significance to the cluster, conservation and distribution patterns identified.

Characterization of 29 newly isolated bacteriophages as a potential therapeutic agent against IMP-6-producing Klebsiella pneumoniae from clinical specimens

Carbapenemase-producing Enterobacteriaceae (CPE) are one of the most detrimental species of antibiotic-resistant bacteria globally. Phage therapy has emerged as an effective strategy for the treatment of CPE infections. In western Japan, the rise of Klebsiella pneumoniae strains harboring the pKPI-6 plasmid encoding bla IMP-6 is of increasing concern. To address this challenge, we isolated 29 phages from Japanese sewage, specifically targeting 31 K. pneumoniae strains and one Escherichia coli strain harboring the pKPI-6 plasmid. Electron microscopy analysis revealed that among the 29 isolated phages, 21 (72.4%), 5 (17.2%), and 3 (10.3%) phages belonged to myovirus, siphovirus, and podovirus morphotypes, respectively. Host range analysis showed that 18 Slopekvirus strains within the isolated phages infected 25-26 K. pneumoniae strains, indicating that most of the isolated phages have a broad host range. Notably, K. pneumoniae strain Kp21 was exclusively susceptible to phage øKp_21, whereas Kp22 exhibited susceptibility to over 20 phages. Upon administering a phage cocktail composed of 10 phages, we observed delayed emergence of phage-resistant bacteria in Kp21 but not in Kp22. Intriguingly, phage-resistant Kp21 exhibited heightened sensitivity to other bacteriophages, indicating a "trade-off" for resistance to phage øKp_21. Our proposed phage set has an adequate number of phages to combat the K. pneumoniae strain prevalent in Japan, underscoring the potential of a well-designed phage cocktail in mitigating the occurrence of phage-resistant bacteria. IMPORTANCE The emergence of Klebsiella pneumoniae harboring the bla IMP-6 plasmid poses an escalating threat in Japan. In this study, we found 29 newly isolated bacteriophages that infect K. pneumoniae strains carrying the pKPI-6 plasmid from clinical settings in western Japan. Our phages exhibited a broad host range. We applied a phage cocktail treatment composed of 10 phages against two host strains, Kp21 and Kp22, which displayed varying phage susceptibility patterns. Although the phage cocktail delayed the emergence of phage-resistant Kp21, it was unable to hinder the emergence of phage-resistant Kp22. Moreover, the phage-resistant Kp21 became sensitive to other phages that were originally non-infective to the wild-type Kp21 strains. Our study highlights the potential of a well-tailored phage cocktail in reducing the occurrence of phage-resistant bacteria.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Complex Prediction in Large PPI Networks Using Expansion and Stripe of Core Cliques

The widespread availability and importance of large-scale protein-protein interaction (PPI) data demand a flurry of research efforts to understand the organisation of a cell and its functionality by analysing these data at the network level. In the bioinformatics and data mining fields, network clustering acquired a lot of attraction to examine a PPI network's topological and functional aspects. The clustering of PPI networks has been proven to be an excellent method for discovering functional modules, disclosing functions of unknown proteins, and other tasks in numerous research over the last decade. This research proposes a unique graph mining approach to detect protein complexes using dense neighbourhoods (highly connected regions) in an interaction graph. Our technique first finds size-3 cliques associated with each edge (protein interaction), and then these core cliques are expanded to form high-density subgraphs. Loosely connected proteins are stripped out from these subgraphs to produce a potential protein complex. Finally, the redundancy is removed based on the Jaccard coefficient. Computational results are presented on the yeast and human protein interaction dataset to highlight our proposed technique's efficiency. Predicted protein complexes of the proposed approach have a significantly higher score of similarity to those used as gold standards in the CYC-2008 and CORUM benchmark databases than other existing approaches.

A 13CO2 Enrichment Experiment to Study the Synthesis Pathways of Polyunsaturated Fatty Acids of the Haptophyte Tisochrysis lutea

The production of polyunsaturated fatty acids (PUFA) in Tisochrysis lutea was studied using the gradual incorporation of a ¹³C-enriched isotopic marker, ¹³CO₂, for 24 h during the exponential growth of the algae. The ¹³C enrichment of eleven fatty acids was followed to understand the synthetic pathways the most likely to form the essential polyunsaturated fatty acids 20:5n-3 (EPA) and 22:6n-3 (DHA) in T. lutea. The fatty acids 16:0, 18:1n-9 + 18:3n-3, 18:2n-6, and 22:5n-6 were the most enriched in ¹³C. On the contrary, 18:4n-3 and 18:5n-3 were the least enriched in ¹³C after long chain polyunsaturated fatty acids such as 20:5n-3 or 22:5n-3. The algae appeared to use different routes in parallel to form its polyunsaturated fatty acids. The use of the PKS pathway was hypothesized for polyunsaturated fatty acids with n-6 configuration (such as 22:5n-6) but might also exist for n-3 PUFA (especially 20:5n-3). With regard to the conventional n-3 PUFA pathway, Δ6 desaturation of 18:3n-3 appeared to be the most limiting step for T. lutea, “stopping” at the synthesis of 18:4n-3 and 18:5n-3. These two fatty acids were hypothesized to not undergo any further reaction of elongation and desaturation after being formed and were therefore considered “end-products”. To circumvent this limiting synthetic route, Tisochrysis lutea seemed to have developed an alternative route via Δ8 desaturation to produce longer chain fatty acids such as 20:5n-3 and 22:5n-3. 22:6n-3 presented a lower enrichment and appeared to be produced by a combination of different pathways: the conventional n-3 PUFA pathway by desaturation of 22:5n-3, the alternative route of ω-3 desaturase using 22:5n-6 as precursor, and possibly the PKS pathway. In this study, PKS synthesis looked particularly effective for producing long chain polyunsaturated fatty acids. The rate of enrichment of these compounds hypothetically synthesized by PKS is remarkably fast, making undetectable the ¹³C incorporation into their precursors. Finally, we identified a protein cluster gathering PKS sequences of proteins that are hypothesized allowing n-3 PUFA synthesis.

Study on Gas Chromatographic Fingerprint of Essential Oil from Stellera chamaejasme Flowers and Its Repellent Activities against Three Stored Product Insects

The objective of this study was to establish the chromatographic fingerprints of the essential oil (EO) from Stellera chamaejasme flowers collected from various natural sites by gas chromatography (GC) combined with chemometric methods. The EO was obtained by hydrodistillation, and its chemical composition was analyzed by gas chromatography-mass spectrometry (GC-MS). Most components were identified as ketones and the relatively high-content components were fitone (38.973%), n-hentriacontane (5.807%), myristic acid (4.944%) and phytol (3.988%). In addition, the repellent activities of the EO from S. chamaejasme flowers and its four main chemical compounds were evaluated against three stored product pests (Tribolium castaneum, Lasioderma serricorne, Liposcelis bostrychophila) for the first time. In this work, the EO and the four chemical compounds showed a repellent effect against three storage pests after 2 and 4 h exposure. The experimental method and repellent activity of S. chamaejasme flower EO could provide a basis for the development of botanical pesticide and the utilization of the rich plant resources of S. chamaejasme in the future.

Protein complex prediction in interaction network based on network motif

The enormous size of Protein-Protein Interaction (PPI) networks demands efficient computational methods to extract biologically significant protein complexes. A wide variety of algorithms have been proposed to predict protein complexes from PPI networks. However, it is still a challenging task to detect protein complexes with high accuracy and manageable sensitivity. In this manuscript, a novel complex prediction algorithm based on Network Motif (CPNM) is proposed. This algorithm addresses the role of proteins in the embeddings of network motif. These roles are used to define feature vectors and feature weights of proteins. Based on these features, a neighborhood search technique predict the protein complexes that consider both the inherent organization of proteins as well as the dense regions in PPI networks. The performance of the proposed algorithm is evaluated using various evaluation metrics like Precision, Recall, F-measure, Sensitivity, PPV, and Accuracy. The research finding indicates that the proposed algorithm outperforms most of the competing algorithms like MCODE, DPClus, RNSC, COACH, ClusterONE, CMC and PROCODE over the PPI network of Saccharomyces cerevisiae and Homo sapiens.

PCA via joint graph Laplacian and sparse constraint: Identification of differentially expressed genes and sample clustering on gene expression data

Background

In recent years, identification of differentially expressed genes and sample clustering have become hot topics in bioinformatics. Principal Component Analysis (PCA) is a widely used method in gene expression data. However, it has two limitations: first, the geometric structure hidden in data, e.g., pair-wise distance between data points, have not been explored. This information can facilitate sample clustering; second, the Principal Components (PCs) determined by PCA are dense, leading to hard interpretation. However, only a few of genes are related to the cancer. It is of great significance for the early diagnosis and treatment of cancer to identify a handful of the differentially expressed genes and find new cancer biomarkers.

Results

In this study, a new method gLSPCA is proposed to integrate both graph Laplacian and sparse constraint into PCA. gLSPCA on the one hand improves the clustering accuracy by exploring the internal geometric structure of the data, on the other hand identifies differentially expressed genes by imposing a sparsity constraint on the PCs.

Conclusions

Experiments of gLSPCA and its comparison with existing methods, including Z-SPCA, GPower, PathSPCA, SPCArt, gLPCA, are performed on real datasets of both pancreatic cancer (PAAD) and head & neck squamous carcinoma (HNSC). The results demonstrate that gLSPCA is effective in identifying differentially expressed genes and sample clustering. In addition, the applications of gLSPCA on these datasets provide several new clues for the exploration of causative factors of PAAD and HNSC.

Computationally Guided Discovery and Experimental Validation of Indole-3-acetic Acid Synthesis Pathways

Elucidating the interaction networks associated with secondary metabolite production in microorganisms is an ongoing challenge made all the more daunting by the rate at which DNA sequencing technology reveals new genes and potential pathways. Developing the culturing methods, expression conditions, and genetic systems needed for validating pathways in newly discovered microorganisms is often not possible. Therefore, new tools and techniques are needed for defining complex metabolic pathways. Here, we describe an in vitro computationally assisted pathway description approach that employs bioinformatic searches of genome databases, protein structural modeling, and protein-ligand-docking simulations to predict the gene products most likely to be involved in a particular secondary metabolite production pathway. This information is then used to direct in vitro reconstructions of the pathway and subsequent confirmation of pathway activity using crude enzyme preparations. As a test system, we elucidated the pathway for biosynthesis of indole-3-acetic acid (IAA) in the plant-associated microbe Pantoea sp. YR343. This organism is capable of metabolizing tryptophan into the plant phytohormone IAA. BLAST analyses identified a likely three-step pathway involving an amino transferase, an indole pyruvate decarboxylase, and a dehydrogenase. However, multiple candidate enzymes were identified at each step, resulting in a large number of potential pathway reconstructions (32 different enzyme combinations). Our approach shows the effectiveness of crude extracts to rapidly elucidate enzymes leading to functional pathways. Results are compared to affinity purified enzymes for select combinations and found to yield similar relative activities. Further, in vitro testing of the pathway reconstructions revealed the "underground" nature of IAA metabolism in Pantoea sp. YR343 and the various mechanisms used to produce IAA. Importantly, our experiments illustrate the scalable integration of computational tools and cell-free enzymatic reactions to identify and validate metabolic pathways in a broadly applicable manner.

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

GGRaSP: a R-package for selecting representative genomes using Gaussian mixture models

Motivation

The vast number of available sequenced bacterial genomes occasionally exceeds the facilities of comparative genomic methods or is dominated by a single outbreak strain, and thus a diverse and representative subset is required. Generation of the reduced subset currently requires a priori supervised clustering and sequence-only selection of medoid genomic sequences, independent of any additional genome metrics or strain attributes.

Results

The Gaussian Genome Representative Selector with Prioritization (GGRaSP) R-package described below generates a reduced subset of genomes that prioritizes maintaining genomes of interest to the user as well as minimizing the loss of genetic variation. The package also allows for unsupervised clustering by modeling the genomic relationships using a Gaussian mixture model to select an appropriate cluster threshold. We demonstrate the capabilities of GGRaSP by generating a reduced list of 315 genomes from a genomic dataset of 4600 Escherichia coli genomes, prioritizing selection by type strain and by genome completeness.

Availability and implementaion

GGRaSP is available at https://github.com/JCVenterInstitute/ggrasp/.

Supplementary information

Supplementary data are available at Bioinformatics online.