The rfbA Gene as a High-Resolution Marker for Pathovar-Level Classification of Xanthomonas citri and Pseudomonas syringae.

Accurate and rapid identification of pathogenic microorganisms is of critical importance in disease treatment and public health. Conventional work flows are time-consuming, and procedures are multifaceted. MS can be an alternative but is limited by low efficiency for amino acid sequencing as well as low reproducibility for spectrum fingerprinting. We systematically analyzed the feasibility of applying MS for rapid and accurate bacterial identification. Directly applying bacterial colonies without further protein extraction to MALDI-TOF MS analysis revealed rich peak contents and high reproducibility. The MS spectra derived from 57 isolates comprising six human pathogenic bacterial species were analyzed using both unsupervised hierarchical clustering and supervised model construction via the Genetic Algorithm. Hierarchical clustering analysis categorized the spectra into six groups precisely corresponding to the six bacterial species. Precise classification was also maintained in an independently prepared set of bacteria even when the numbers of m/z values were reduced to six. In parallel, classification models were constructed via Genetic Algorithm analysis. A model containing 18 m/z values accurately classified independently prepared bacteria and identified those species originally not used for model construction. Moreover bacteria fewer than 10(4) cells and different species in bacterial mixtures were identified using the classification model approach. In conclusion, the application of MALDI-TOF MS in combination with a suitable model construction provides a highly accurate method for bacterial classification and identification. The approach can identify bacteria with low abundance even in mixed flora, suggesting that a rapid and accurate bacterial identification using MS techniques even before culture can be attained in the near future.

Species-specific genomic sequences for classification of bacteria

Gram-negative bacteria are surrounded by a complex cell envelope that includes two membranes. The outer membrane prevents many drugs from entering these cells and is thus a major determinant of their intrinsic antibiotic resistance. This barrier function is imparted by the asymmetric architecture of the membrane with lipopolysaccharide (LPS) in the outer leaflet and phospholipids in the inner leaflet. The LPS and phospholipid synthesis pathways share an intermediate. Proper membrane biogenesis therefore requires that the flux through each pathway be balanced. In Escherichia coli, a major control point in establishing this balance is the committed step of LPS synthesis mediated by LpxC. Levels of this enzyme are controlled through its degradation by the inner membrane protease FtsH and its presumed adapter protein LapB (YciM). How turnover of LpxC is controlled has remained unclear for many years. Here, we demonstrate that the essential protein of unknown function YejM (PbgA) participates in this regulatory pathway. Suppressors of YejM essentiality were identified in lpxC and lapB, and LpxC overproduction was shown to be sufficient to allow survival of ΔyejM mutants. Furthermore, the stability of LpxC was shown to be reduced in cells lacking YejM, and genetic and physical interactions between LapB and YejM were detected. Taken together, our results are consistent with a model in which YejM directly modulates LpxC turnover by FtsH-LapB to regulate LPS synthesis and maintain membrane homeostasis.IMPORTANCE The outer membrane is a major determinant of the intrinsic antibiotic resistance of Gram-negative bacteria. It is composed of both lipopolysaccharide (LPS) and phospholipid, and the synthesis of these lipid species must be balanced for the membrane to maintain its barrier function in blocking drug entry. In this study, we identified an essential protein of unknown function as a key new factor in modulating LPS synthesis in the model bacterium Escherichia coli Our results provide novel insight into how this organism and most likely other Gram-negative bacteria maintain membrane homeostasis and their intrinsic resistance to antibiotics.

Bacterial identification is an essential task in medical disciplines and food hygiene. The characteristics of bacteria can be examined under a microscope using culture techniques. However, traditional clinical laboratory culture methods require considerable work, primarily physical and manual effort. An automated process using deep learning technology has been widely used for increasing accuracy and decreasing working costs. In this paper, our research evaluates different types of existing deep CNN models for bacterial contamination classification when low-resource data are used. They are baseline CNN, GCNN, ResNet, and VGGNet. The performance of CNN models was also compared with the traditional machine learning method, including SIFT+SVM. The performance of the DIBaS dataset and our own collected dataset have been evaluated. The results show that VGGNet achieves the highest accuracy. In addition, data augmentation was performed to inflate the dataset. After fitting the model with augmented data, the results show that the accuracy increases significantly. This improvement is consistent in all models and both datasets.

Sixty-four strains of Neisseria, Moraxella, and Acinetobacter were screened for cellular monosaccharides by gas-liquid chromatography and other chromatographic techniques. The four sugars ribose, glucose, glucosamine, and 2-keto-3-deoxyoctonate (KDO) were detected in all strains. Heptose was detected only in "true neisseriae" (Neisseria gonorrhoeae, N. meningitidis, N. sicca, N. cinerea, N. flavescens, and N. elongata) and in the tentaively named species Moraxella urethralis. Some marked interspecies dissimilarities within groups were revealed. Thus, N. ovis and M. atlantae were characterized by the presence of mannose. Intraspecies differences were also encountered. N. meningitidis strains of serogroups B and C were distinguished from strains of serogroup A by their sialic acid content. This sugar was also detected in two out of three examined strains of M. nonliquefaciens. In Acinetobacter, heterogeneity of monosaccharide patterns was rather pronounced. The results show the applicability of gas chromatographic "monosaccharide" profiles fo whole cells or extracted carbohydrate in bacterial classification and identification, including differentiation at the subspecies level. In addition, such profiles may be useful for monitoring during purification of cellular polysaccharides.

BackgroundMembers of the genera Prevotella, Veillonella and Fusobacterium are the predominant culturable obligate anaerobic bacteria isolated from periodontal abscesses. When determining the cumulative number of clinical anaerobic isolates from periodontal abscesses, ambiguous or overlapping signals were frequently encountered in 16S rRNA gene sequencing chromatograms, resulting in ambiguous identifications. With the exception of the genus Veillonella, the high intra-chromosomal heterogeneity of rrs genes has not been reported.MethodsThe 16S rRNA genes of 138 clinical, strictly anaerobic isolates and one reference strain were directly sequenced, and the chromatograms were carefully examined. Gene cloning was performed for 22 typical isolates with doublet sequencing signals for the 16S rRNA genes, and four copies of the rrs-ITS genes of 9 Prevotella intermedia isolates were separately amplified by PCR, sequenced and compared. Five conserved housekeeping genes, hsp60, recA, dnaJ, gyrB1 and rpoB from 89 clinical isolates of Prevotella were also amplified by PCR and sequenced for identification and phylogenetic analysis along with 18 Prevotella reference strains.ResultsHeterogeneity of 16S rRNA genes was apparent in clinical, strictly anaerobic oral bacteria, particularly in the genera Prevotella and Veillonella. One hundred out of 138 anaerobic strains (72%) had intragenomic nucleotide polymorphisms (SNPs) in multiple locations, and 13 strains (9.4%) had intragenomic insertions or deletions in the 16S rRNA gene. In the genera Prevotella and Veillonella, 75% (67/89) and 100% (19/19) of the strains had SNPs in the 16S rRNA gene, respectively. Gene cloning and separate amplifications of four copies of the rrs-ITS genes confirmed that 2 to 4 heterogeneous 16S rRNA copies existed.ConclusionSequence alignment of five housekeeping genes revealed that intra-species nucleotide similarities were very high in the genera Prevotella, ranging from 94.3–100%. However, the inter-species similarities were relatively low, ranging from 68.7–97.9%. The housekeeping genes rpoB and gyrB1 were demonstrated to be alternative classification markers to the species level based on intra- and inter-species comparisons, whereas based on phylogenetic tree rpoB proved to be reliable phylogenetic marker for the genus Prevotella.

Many ecological experiments are based on the extraction and downstream analyses of microorganisms from different environmental samples. Due to its high throughput, cost-effectiveness and rapid performance, Matrix Assisted Laser Desorption/Ionization Mass Spectrometry with Time-of-Flight detector (MALDI-TOF MS), which has been proposed as a promising tool for bacterial identification and classification, could be advantageously used for dereplication of recurrent bacterial isolates. In this study, we compared whole-cell MALDI-TOF MS-based analyses of 49 bacterial cultures to two well-established bacterial identification and classification methods based on nearly complete 16S rRNA gene sequence analyses: a phylotype-based approach, using a closest type strain assignment, and a sequence similarity-based approach involving a 98.65% sequence similarity threshold, which has been found to best delineate bacterial species. Culture classification using reference-based MALDI-TOF MS was comparable to that yielded by phylotype assignment up to the genus level. At the species level, agreement between 16S rRNA gene analysis and MALDI-TOF MS was found to be limited, potentially indicating that spectral reference databases need to be improved. We also evaluated the mass spectral similarity technique for species-level delineation which can be used independently of reference databases. We established optimal mass spectral similarity thresholds which group MALDI-TOF mass spectra of common environmental isolates analogically to phylotype- and sequence similarity-based approaches. When using a mass spectrum similarity approach, we recommend a mass range of 4–10 kDa for analysis, which is populated with stable mass signals and contains the majority of phylotype-determining peaks. We show that a cosine similarity (CS) threshold of 0.79 differentiate mass spectra analogously to 98.65% species-level delineation sequence similarity threshold, with corresponding precision and recall values of 0.70 and 0.73, respectively. When matched to species-level phylotype assignment, an optimal CS threshold of 0.92 was calculated, with associated precision and recall values of 0.83 and 0.64, respectively. Overall, our research indicates that a similarity-based MALDI-TOF MS approach can be routinely used for efficient dereplication of isolates for downstream analyses, with minimal loss of unique organisms. In addition, MALDI-TOF MS analysis has further improvement potential unlike 16S rRNA gene analysis, whose methodological limits have reached a plateau.

For decades, the 16S rRNA gene has been used to taxonomically classify prokaryotic species and to taxonomically profile microbial communities. However, the 16S rRNA gene has been criticized for being too conserved to differentiate between distinct species. We argue that the inability to differentiate between species is not a unique feature of the 16S rRNA gene. Rather, we observe the gradual loss of species-level resolution for other nearly-universal prokaryotic marker genes as the number of gene sequences increases in reference databases. This trend was strongly correlated with how represented a taxonomic group was in the database and indicates that, at the gene-level, the boundaries between many species might be fuzzy. Through our study, we argue that any approach that relies on a single marker to distinguish bacterial taxa is fraught even if some markers appear to be discriminative in current databases.

Comparison of the sequences of conserved genes, most commonly those encoding 16S rRNA, is used for bacterial genotypic identification. Among some taxa, such as the Enterobacteriaceae, variation within this gene does not allow confident species identification. We investigated the usefulness of RNA polymerase beta-subunit encoding gene (rpoB) sequences as an alternative tool for universal bacterial genotypic identification. We generated a database of partial rpoB for 14 Enterobacteriaceae species and then assessed the intra- and interspecies divergence between the rpoB and the 16S rRNA genes by pairwise comparisons. We found that levels of divergence between the rpoB sequences of different strains were markedly higher than those between their 16S rRNA genes. This higher discriminatory power was further confirmed by assigning 20 blindly selected clinical isolates to the correct enteric species on the basis of rpoB sequence comparison. Comparison of rpoB sequences from Enterobacteriaceae was also used as the basis for their phylogenetic analysis and demonstrated the genus Klebsiella to be polyphyletic. The trees obtained with rpoB were more compatible with the currently accepted classification of Enterobacteriaceae than those obtained with 16S rRNA. These data indicate that rpoB is a powerful identification tool, which may be useful for universal bacterial identification.

BackgroundMachine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification.ResultsIn view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model.ConclusionsFAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.

The bactericidal/permeability-increasing protein (BPI)-inducible protein A (BipA) is a highly conserved protein in Gram-negative bacteria that is structurally similar to translational GTPases such as IF2, EF-Tu, and EF-G. Our previous research showed that deleting bipA in Escherichia coli at 20°C leads to a defect in 50S ribosomal assembly and impaired lipopolysaccharide (LPS) synthesis. This LPS defect activates the Regulator of Capsule Synthesis (Rcs) pathway, resulting in an overproduction of capsular polysaccharides, a reduction in biofilm formation, and decreased flagella-mediated motility. In this study, we aimed to elucidate the role of BipA in the pathogenicity of Salmonella enterica serovar Typhimurium. We constructed bipA deletion mutants in two pathogenic S. Typhimurium strains, SL1344 and 14028, as well as in the attenuated strain LT2. Our ribosome profiling experiments using the mutant S. Typhimurium strains revealed a defect in ribosome assembly at 20°C, with the accumulation of abnormal 50S ribosomal subunits. We further demonstrated that the absence of BipA in S. Typhimurium impaired LPS biosynthesis at 20°C, compromising membrane integrity and presumably activating the Rcs pathway. This activation altered virulence factors, including reduced biofilm formation, particularly in the 14028ΔbipA strain. Furthermore, the SL1344ΔbipA and 14028ΔbipA strains exhibited significantly decreased swimming motility at 20°C compared to 37°C, confirmed by microscopic observation showing fewer flagella at 20°C. Subsequently, both strains exhibited a significant reduction in invasion capability and cytotoxicity toward human intestinal epithelial cells (HCT116). This functional attenuation was corroborated by the decrease in virulence observed in the 14028ΔbipA strain in a mouse model. Our findings suggest that, in S. Typhimurium, BipA functions as a bacterial fitness factor, contributing to ribosome assembly, LPS synthesis, and virulence-related processes, particularly under stress conditions relevant to host environments.

BackgroundSpecies-level classification for 16S rRNA gene sequences remains a serious challenge for microbiome researchers, because existing taxonomic classification tools for 16S rRNA gene sequences either do not provide species-level classification, or their classification results are unreliable. The unreliable results are due to the limitations in the existing methods which either lack solid probabilistic-based criteria to evaluate the confidence of their taxonomic assignments, or use nucleotide k-mer frequency as the proxy for sequence similarity measurement.ResultsWe have developed a method that shows significantly improved species-level classification results over existing methods. Our method calculates true sequence similarity between query sequences and database hits using pairwise sequence alignment. Taxonomic classifications are assigned from the species to the phylum levels based on the lowest common ancestors of multiple database hits for each query sequence, and further classification reliabilities are evaluated by bootstrap confidence scores. The novelty of our method is that the contribution of each database hit to the taxonomic assignment of the query sequence is weighted by a Bayesian posterior probability based upon the degree of sequence similarity of the database hit to the query sequence. Our method does not need any training datasets specific for different taxonomic groups. Instead only a reference database is required for aligning to the query sequences, making our method easily applicable for different regions of the 16S rRNA gene or other phylogenetic marker genes.ConclusionsReliable species-level classification for 16S rRNA or other phylogenetic marker genes is critical for microbiome research. Our software shows significantly higher classification accuracy than the existing tools and we provide probabilistic-based confidence scores to evaluate the reliability of our taxonomic classification assignments based on multiple database matches to query sequences. Despite its higher computational costs, our method is still suitable for analyzing large-scale microbiome datasets for practical purposes. Furthermore, our method can be applied for taxonomic classification of any phylogenetic marker gene sequences. Our software, called BLCA, is freely available at https://github.com/qunfengdong/BLCA.

Application of Fatty Acid Methyl Esters for the Taxonomic Analysis of the Genus Xanthomonas

Gram-typing of mastitis bacteria in milk samples using flow cytometry

Laser-induced breakdown spectroscopy (LIBS) has made tremendous progress in becoming a viable technology for rapid bacterial pathogen detection and identification. The significant advantages of LIBS include speed (< 1 sec analysis), portability, robustness, lack of consumables, little to no need for sample preparation, lack of genetic amplification, and the ability to identify all bacterial pathogens without bias (including spore-forms and viable but nonculturable specimens). In this manuscript, we present the latest advances achieved in LIBS-based bacterial sensing including the ability to uniquely identify species from more than five bacterial genera with high-sensitivity and specificity. Bacterial identifications are completely unaffected by environment, nutrition media, or state of growth and accurate diagnoses can be made on autoclaved or UV-irradiated specimens. Efficient discrimination of bacteria at the strain level has been demonstrated. A rapid urinary tract infection diagnosis has been simulated with no sample preparation and a one second diagnosis of a pathogen surrogate has been demonstrated using advanced chemometric analysis with a simple stop-light user interface. Stand-off bacterial identification at a 20-m distance has been demonstrated on a field-portable instrument. This technology could be implemented in doctors' offices, clinics, or hospital laboratories for point-of-care medical specimen analysis; mounted on military medical robotic platforms for in-the- field diagnostics; or used in stand-off configuration for remote sensing and detection.