Re-annotation of protein-coding genes in 10 complete genomes of Neisseriaceae family by combining similarity-based and composition-based methods

NGS read classification using AI

Clinical metagenomics is a powerful diagnostic tool, as it offers an open view into all DNA in a patient's sample. This allows the detection of pathogens that would slip through the cracks of classical specific assays. However, due to this unspecific nature of metagenomic sequencing, a huge amount of unspecific data is generated during the sequencing itself and the diagnosis only takes place at the data analysis stage where relevant sequences are filtered out. Typically, this is done by comparison to reference databases. While this approach has been optimized over the past years and works well to detect pathogens that are represented in the used databases, a common challenge in analysing a metagenomic patient sample arises when no pathogen sequences are found: How to determine whether truly no evidence of a pathogen is present in the data or whether the pathogen's genome is simply absent from the database and the sequences in the dataset could thus not be classified? Here, we present a novel approach to this problem of detecting novel pathogens in metagenomic datasets by classifying the (segments of) proteins encoded by the sequences in the datasets. We train a neural network on the sequences of coding sequences, labeled by taxonomic domain, and use this neural network to predict the taxonomic classification of sequences that can not be classified by comparison to a reference database, thus facilitating the detection of potential novel pathogens.

High Prevalence and Mechanism Associated With Extended Spectrum Beta-Lactamase-Positive Phenotype in Laribacter hongkongensis

In this study, we reported the prevalence and mechanism associated with the extended-spectrum beta-lactamase (ESBL)-positive phenotype in Laribacter hongkongensis isolated from patients and fish. Using the inhibition zone enhancement test, 20 (95.2%) of the 21 patient strains and 8 (57.1%) of the 14 fish strains were tested ESBL-positive. However, ESBL genes, including SHV, TEM, CTX-M, GES, and PER, were not detected in all of these 28 L. hongkongensis isolates. No ESBL gene could be detected in either the complete genome of L. hongkongensis HLHK9 or the draft genome of PW3643. PCR and DNA sequencing revealed that all the 35 L. hongkongensis isolates (showing both ESBL-positive and ESBL-negative phenotypes) were positive for the ampC gene. When the AmpC deletion mutant, HLHK9ΔampC, was subject to the zone enhancement test, the difference of zone size between ceftazidime/clavulanate and ceftazidime was less than 5 mm. When boronic acid was added to the antibiotic disks, none of the 28 “ESBL-positive” isolates showed a ≥ 5 mm enhancement of inhibition zone size diameter between ceftazidime/clavulanate and ceftazidime and between cefotaxime/clavulanate and cefotaxime. A high prevalence (80%) of ESBL-positive phenotype is present in L. hongkongensis. Overall, our results suggested that the ESBL-positive phenotype in L. hongkongensis results from the expression of the intrinsic AmpC beta-lactamase. Confirmatory tests should be performed before issuing laboratory reports for L. hongkongensis isolates that are tested ESBL-positive by disk diffusion clavulanate inhibition test.

Malate-Dependent Carbon Utilization Enhances Central Metabolism and Contributes to Biological Fitness of Laribacter hongkongensis via CRP Regulation

Metabolic adaptation in various environmental niches is crucial for bacterial extracellular survival and intracellular replication during infection. However, the metabolism of carbon/nitrogen sources and related regulatory mechanisms in Laribacter hongkongensis, an asaccharolytic bacterium associated with invasive infections and gastroenteritis, are still unknown. In the present study, we demonstrated that malate can be exploited as a preferred carbon source of L. hongkongensis. Using RNA-sequencing, we compared the transcription profiles of L. hongkongensis cultivated with or without malate supplementation, and observed that malate utilization significantly inhibits the use of alternative carbon sources while enhancing respiratory chain as well as central carbon, sulfur, and urease-mediated nitrogen metabolisms. The tight connection among these important metabolic pathways indicates that L. hongkongensis is capable of integrating information from different metabolism branches to coordinate the expression of metabolic genes and thereby adapt to environmental changing. Furthermore, we identified that a transcription factor, CRP, is repressed by malate-mediated metabolism while negatively regulating the effect of malate on these central metabolic pathways. Remarkably, CRP also responds to various environmental stresses, influences the expression of other transcription factors, and contributes to the biological fitness of L. hongkongensis. The regulatory network and cross-regulation enables the bacteria to make the appropriate metabolic responses and environmental adaptation.

Investigating Potential Chromosomal Rearrangements during Laboratory Culture of Neisseria gonorrhoeae

Comparisons of genome sequence data between different strains and isolates of Neisseria spp., such as Neisseria gonorrhoeae, reveal that over the evolutionary history of these organisms, large scale chromosomal rearrangements have occurred. Factors within the genomes, such as repetitive sequences and prophage, are believed to have contributed to these observations. However, the timescale in which rearrangements occur is not clear, nor whether it might be expected for them to happen in the laboratory. In this study, N. gonorrhoeae was repeatedly passaged in the laboratory and assessed for large scale chromosomal rearrangements. Using gonococcal strain NCCP11945, for which there is a complete genome sequence, cultures were passaged for eight weeks in the laboratory. The resulting genomic DNA was assessed using Pulsed Field Gel Electrophoresis, comparing the results to the predicted results from the genome sequence data. Three cultures generated Pulsed Field Gel Electrophoresis patterns that varied from the genomic data and were further investigated for potential chromosomal rearrangements.

Genomic repeats, misassembly and reannotation: a case study with long-read resequencing of Porphyromonas gingivalis reference strains

BACKGROUND

Without knowledge of their genomic sequences, it is impossible to make functional models of the bacteria that make up human and animal microbiota. Unfortunately, the vast majority of publicly available genomes are only working drafts, an incompleteness that causes numerous problems and constitutes a major obstacle to genotypic and phenotypic interpretation. In this work, we began with an example from the class Bacteroidia in the phylum Bacteroidetes, which is preponderant among human orodigestive microbiota. We successfully identify the genetic loci responsible for assembly breaks and misassemblies and demonstrate the importance and usefulness of long-read sequencing and curated reannotation.

RESULTS

We showed that the fragmentation in Bacteroidia draft genomes assembled from massively parallel sequencing linearly correlates with genomic repeats of the same or greater size than the reads. We also demonstrated that some of these repeats, especially the long ones, correspond to misassembled loci in three reference Porphyromonas gingivalis genomes marked as circularized (thus complete or finished). We prove that even at modest coverage (30X), long-read resequencing together with PCR contiguity verification (rrn operons and an integrative and conjugative element or ICE) can be used to identify and correct the wrongly combined or assembled regions. Finally, although time-consuming and labor-intensive, consistent manual biocuration of three P. gingivalis strains allowed us to compare and correct the existing genomic annotations, resulting in a more accurate interpretation of the genomic differences among these strains.

CONCLUSIONS

In this study, we demonstrate the usefulness and importance of long-read sequencing in verifying published genomes (even when complete) and generating assemblies for new bacterial strains/species with high genomic plasticity. We also show that when combined with biological validation processes and diligent biocurated annotation, this strategy helps reduce the propagation of errors in shared databases, thus limiting false conclusions based on incomplete or misleading information.

Laribacter hongkongensis anaerobic adaptation mediated by arginine metabolism is controlled by the cooperation of FNR and ArgR

Laribacter hongkongensis is a fish-borne pathogen associated with invasive infections and gastroenteritis. Its adaptive mechanisms to oxygen-limiting conditions in various environmental niches remain unclear. In this study, we compared the transcriptional profiles of L. hongkongensis under aerobic and anaerobic conditions using RNA-sequencing. Expression of genes involved in arginine metabolism significantly increased under anoxic conditions. Arginine was exploited as the sole energy source in L. hongkongensis for anaerobic respiration via the arginine catabolism pathway: specifically via the arginine deiminase (ADI) pathway. A transcriptional regulator FNR was identified to coordinate anaerobic metabolism by tightly regulating the expression of arginine metabolism genes. FNR executed its regulatory function by binding to FNR boxes in arc operons promoters. Survival of isogenic fnr mutant in macrophages decreased significantly when compared with wild-type; and expression level of fnr increased 8 h post-infection. Remarkably, FNR directly interacted with ArgR, another regulator that influences the biological fitness and intracellular survival of L. hongkongensis by regulating arginine metabolism genes. Our results demonstrated that FNR and ArgR work in coordination to respond to oxygen changes in both extracellular and intracellular environments, by finely regulating the ADI pathway and arginine anabolism pathways, thereby optimizing bacterial fitness in various environmental niches.

Reannotation of Yersinia pestis Strain 91001 Based on Omics Data

Yersinia pestis is among the most dangerous human pathogens, and systematic research of this pathogen is important in bacterial pathogenomics research. To fully interpret the biological functions, physiological characteristics, and pathogenesis of Y. pestis, a comprehensive annotation of its entire genome is necessary. The emergence of omics-based research has brought new opportunities to better annotate the genome of this pathogen. Here, the complete genome of Y. pestis strain 91001 was reannotated using genomics and proteogenomics data. One hundred and thirty-seven unreliable coding sequences were removed, and 41 homologous genes were relocated with their translational initiation sites, while the functions of seven pseudogenes and 392 hypothetical genes were revised. Moreover, annotations of noncoding RNAs, repeat sequences, and transposable elements have also been incorporated. The reannotated results are freely available at http://tody.bmi.ac.cn.

A Transcriptome Map of Actinobacillus pleuropneumoniae at Single-Nucleotide Resolution Using Deep RNA-Seq

Actinobacillus pleuropneumoniae is the pathogen of porcine contagious pleuropneumoniae, a highly contagious respiratory disease of swine. Although the genome of A. pleuropneumoniae was sequenced several years ago, limited information is available on the genome-wide transcriptional analysis to accurately annotate the gene structures and regulatory elements. High-throughput RNA sequencing (RNA-seq) has been applied to study the transcriptional landscape of bacteria, which can efficiently and accurately identify gene expression regions and unknown transcriptional units, especially small non-coding RNAs (sRNAs), UTRs and regulatory regions. The aim of this study is to comprehensively analyze the transcriptome of A. pleuropneumoniae by RNA-seq in order to improve the existing genome annotation and promote our understanding of A. pleuropneumoniae gene structures and RNA-based regulation. In this study, we utilized RNA-seq to construct a single nucleotide resolution transcriptome map of A. pleuropneumoniae. More than 3.8 million high-quality reads (average length ~90 bp) from a cDNA library were generated and aligned to the reference genome. We identified 32 open reading frames encoding novel proteins that were mis-annotated in the previous genome annotations. The start sites for 35 genes based on the current genome annotation were corrected. Furthermore, 51 sRNAs in the A. pleuropneumoniae genome were discovered, of which 40 sRNAs were never reported in previous studies. The transcriptome map also enabled visualization of 5'- and 3'-UTR regions, in which contained 11 sRNAs. In addition, 351 operons covering 1230 genes throughout the whole genome were identified. The RNA-Seq based transcriptome map validated annotated genes and corrected annotations of open reading frames in the genome, and led to the identification of many functional elements (e.g. regions encoding novel proteins, non-coding sRNAs and operon structures). The transcriptional units described in this study provide a foundation for future studies concerning the gene functions and the transcriptional regulatory architectures of this pathogen.

Analysis of the multi-copied genes and the impact of the redundant protein coding sequences on gene annotation in prokaryotic genomes

The important roles of duplicated genes in evolutional process have been recognized in bacteria, archaebacteria and eukaryotes, while there is very little study on the multi-copied protein coding genes that share sequence identity of 100%. In this paper, the multi-copied protein coding genes in a number of prokaryotic genomes are comprehensively analyzed firstly. The results show that 0-15.93% of the protein coding genes in each genome are multi-copied genes and 0-16.49% of the protein coding genes in each genome are highly similar with the sequence identity ≥ 80%. Function and COG (Clusters of Orthologous Groups of proteins) analysis shows that 64.64% of multi-copied genes concentrate on the function of transposase and 86.28% of the COG assigned multi-copied genes concentrate on the COG code of 'L'. Furthermore, the impact of redundant protein coding sequences on the gene prediction results is studied. The results show that the problem of protein coding sequence redundancies cannot be ignored and the consistency of the gene annotation results before and after excluding the redundant sequences is negatively related with the sequences redundancy degree of the protein coding sequences in the training set.

Characterization of hypothetical protein VNG0128C from Halobacterium NRC-1 reveals GALE like activity and its involvement in Leloir pathway of galactose metabolism

VNG0128C, a hypothetical protein from Halobacterium NRC-1, was chosen for detailed insilico and experimental investigations. Computational exercises revealed that VNG0128C functions as NAD(+) binding protein. The phylogenetic analysis with the homolog sequences of VNG0128C suggested that it could act as UDP-galactose 4-epimerase. Hence, the VNG0128C sequence was modeled using a suitable template and docking studies were performed with NAD and UDP-galactose as ligands. The binding interactions strongly indicate that VNG0128C could plausibly act as UDP-galactose 4-epimerase. In order to validate these insilico results, VNG0128C was cloned in pUC57, subcloned in pET22b(+), expressed in BL21 cells and purified using nickel affinity chromatography. An assay using blue dextran was performed to confirm the presence of NAD binding domain. To corroborate the epimerase like enzymatic role of the hypothetical protein, i.e. the ability of the enzyme to convert UDP-galactose to UDP-glucose, the conversion of NAD to NADH was measured. The experimental assay significantly correlated with the insilico predictions, indicating that VNG0128C has a NAD(+) binding domain with epimerase activity. Consequently, its key role in nucleotide-sugar metabolism was thus established. Additionally, the work highlights the need for a methodical characterization of hypothetical proteins (less studied class of biopolymers) to exploit them for relevant applications in the field of biology.

Identification of novel biomass-degrading enzymes from genomic dark matter: Populating genomic sequence space with functional annotation

Although recent nucleotide sequencing technologies have significantly enhanced our understanding of microbial genomes, the function of ∼35% of genes identified in a genome currently remains unknown. To improve the understanding of microbial genomes and consequently of microbial processes it will be crucial to assign a function to this "genomic dark matter." Due to the urgent need for additional carbohydrate-active enzymes for improved production of transportation fuels from lignocellulosic biomass, we screened the genomes of more than 5,500 microorganisms for hypothetical proteins that are located in the proximity of already known cellulases. We identified, synthesized and expressed a total of 17 putative cellulase genes with insufficient sequence similarity to currently known cellulases to be identified as such using traditional sequence annotation techniques that rely on significant sequence similarity. The recombinant proteins of the newly identified putative cellulases were subjected to enzymatic activity assays to verify their hydrolytic activity towards cellulose and lignocellulosic biomass. Eleven (65%) of the tested enzymes had significant activity towards at least one of the substrates. This high success rate highlights that a gene context-based approach can be used to assign function to genes that are otherwise categorized as "genomic dark matter" and to identify biomass-degrading enzymes that have little sequence similarity to already known cellulases. The ability to assign function to genes that have no related sequence representatives with functional annotation will be important to enhance our understanding of microbial processes and to identify microbial proteins for a wide range of applications.

Recognition of Protein-coding Genes Based on Z-curve Algorithms

Recognition of protein-coding genes, a classical bioinformatics issue, is an absolutely needed step for annotating newly sequenced genomes. The Z-curve algorithm, as one of the most effective methods on this issue, has been successfully applied in annotating or re-annotating many genomes, including those of bacteria, archaea and viruses. Two Z-curve based ab initio gene-finding programs have been developed: ZCURVE (for bacteria and archaea) and ZCURVE_V (for viruses and phages). ZCURVE_C (for 57 bacteria) and Zfisher (for any bacterium) are web servers for re-annotation of bacterial and archaeal genomes. The above four tools can be used for genome annotation or re-annotation, either independently or combined with the other gene-finding programs. In addition to recognizing protein-coding genes and exons, Z-curve algorithms are also effective in recognizing promoters and translation start sites. Here, we summarize the applications of Z-curve algorithms in gene finding and genome annotation.