16S rDNA sequencing analyzes differences in intestinal flora of human immunodeficiency virus (HIV) patients and association with immune activation.

To clarify the influence of HIV on the intestinal flora and the interrelationship with CD4 T cells, the present study collected stool specimens from 33 HIV patients and 28 healthy subjects to compare the differences in the intestinal flora and CD4 T cells in a 16S rDNA-sequencing approach. ELISA was used to detect the expressions of interleukin 2 (IL-2), IL-8, and tumor necrosis factor-α (TNF-α). Meanwhile, correlation analysis with the different bacterial populations in each group was carried out. The results revealed that Alpha diversity indices of the intestinal flora of HIV patients were markedly lower than that of the healthy group (p < 0.05). The top five bacterial species in the HIV group were Bacteroides (23.453%), Prevotella (19.237%), Fusobacterium (12.408%), Lachnospira (3.811%), and Escherichia-Shigella (3.126%). Spearman correlation analysis results indicated that Fusobacterium_mortiferum, Fusobacterium, and Gammaproteobacteria were positively correlated with TNF-α (p < 0.05), whereas Ruminococcaceae, Bacteroidales was negatively correlated with TNF-α (p < 0.05). Additionally, Agathobacter was positively correlated with contents of IL-2 and IL-8 (p < 0.05), whereas Prevotellaceae, and Prevotella were negatively correlated with IL-8 content (p < 0.05). Furthermore, the top five strains in the CD4 high group (≥350/mm3) included Bacteroides (23.286%), Prevotella (21.943%), Fusobacterium (10.479%), Lachnospira (4.465%), and un_f_Lachnospiraceae (2.786%). Taken together, the present study identified that Fusobacterium and Escherichia-Shigella were specific and highly abundant in the HIV group and a correlation between the different bacterial flora and the contents of IL-2, IL-8, and TNF-α was revealed.

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.

Investigation of bacterial diversity in Cajanus cajan-planted gangue soil via high-throughput sequencing.

The ecological restoration of coal gangue can be achieved by planting Cajanus cajan (pigeon pea) because of its developed root system. The close relationships soil microorganisms have with plants are crucial for improving soil composition; the soil composition affects nutrient absorption. The microbial composition and function of soil planted with C. cajan in reclaimed land were compared with soil that was not planted with C. cajan (the control). Results showed that the dominant microflora in the soil significantly changed after planting C. cajan. Before planting, the dominant microflora included members of the phyla Sulfobacteria and Acidobacteria. After planting, the dominant microflora contained bacteria from phyla and classes that included Actinobacteria, Acidimicubia, Thermoleophilia, and Anaerolineae. Additionally, there were significant differences in the bacterial composition of each layer in soils planted with C. cajan. Principal component analysis revealed that the interpretation degrees of the results for PC2 and PC3 axes were 10.46% and 3.87%, respectively. The dominant microflora were Vicinamibacterales, Nocardioides, and Arthrobacter in the surface soil; Actinophytocola and Sphingomonas in the deep soil; and Sulfobacillus and Acidimicrobium in the mixed-layer soil. Function prediction analysis using the bioinformatics software package PICRUSt revealed that the abundance of operational taxonomic units corresponding to sigma 54-specific transcriptional regulators, serine threonine protein kinase, and histidine kinase increased by 111.2%, 56.8%, and 47.4%, respectively, after planting C. cajan. This study provides a reference for interactions among microorganisms in reclaimed soils for guiding the development and restoration of waste coal gangue hills.

Development of a reference standard for the detection and quantification of Mycobacterium avium subsp. paratuberculosis by quantitative PCR.

Quantitative PCR (qPCR) has become a frequently employed direct method for the detection and quantification of Mycobacterium avium subsp. paratuberculosis (MAP). The quantity of MAP determined by qPCR, however, may be affected by the type of qPCR quantification standard used (PCR product, plasmid, genomic DNA) and the way in which standard DNA quantity is determined (absorbance, fluorescence). In practice, this can be reflected in the inability to properly compare quantitative data from the same qPCR assays in different laboratories. Thus, the aim of this study was to prepare a prototype of an international MAP reference standard, which could be used to calibrate routinely used qPCR quantification standards in various laboratories to promote clinical data comparability. Considering stability, storage and shipment issues, a lyophilised fecal suspension artificially contaminated with a MAP reference strain was chosen as the most suitable form of the standard. The effect of five types of lyophilisation matrices on standard stability was monitored on 2-weeks interval basis for 4 months by F57 qPCR. The lyophilisation matrix with 10% skimmed milk provided the best recovery and stability in time and was thus selected for subsequent comparative testing of the standard involving six diagnostic and research laboratories, where DNA isolation and qPCR assay procedures were performed with the parallel use of the identical supplied genomic DNA solution. Furthermore, the effect of storage conditions on the standard stability was tested for at least 6 months. The storage at room temperature in the dark and under light, at + 4 °C, - 20 °C and - 80 °C showed no significant changes in the stability, and also no substantial changes in MAP viability were found using phage amplification assay. The prepared MAP quantification standard provided homogeneous and reproducible results demonstrating its suitability for utilisation as an international reference qPCR standard.

Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures.

Existing models for assessing microbiome sequencing such as operational taxonomic units (OTUs) can only test predictors' effects on OTUs. There is limited work on how to estimate the correlations between multiple OTUs and incorporate such relationship into models to evaluate longitudinal OTU measures. We propose a novel approach to estimate OTU correlations based on their taxonomic structure, and apply such correlation structure in Generalized Estimating Equations (GEE) models to estimate both predictors' effects and OTU correlations. We develop a two-part Microbiome Taxonomic Longitudinal Correlation (MTLC) model for multivariate zero-inflated OTU outcomes based on the GEE framework. In addition, longitudinal and other types of repeated OTU measures are integrated in the MTLC model. Extensive simulations have been conducted to evaluate the performance of the MTLC method. Compared with the existing methods, the MTLC method shows robust and consistent estimation, and improved statistical power for testing predictors' effects. Lastly we demonstrate our proposed method by implementing it into a real human microbiome study to evaluate the obesity on twins.

Changes of saliva microbiota in the onset and after the treatment of diabetes in patients with periodontitis.

The relationship between type 2 diabetes mellitus (T2DM) and oral microbiota is still insufficiently recognized. In the present study, we compared the salivary microbiome of nondiabetic individuals, treatment-naïve diabetic patients, and diabetic patients treated with metformin or a combination of insulin and other drugs. The α- and ß-diversity demonstrated significant differences in the salivary microbiome between the nondiabetic people and patients with a history of diabetes, while little divergence was found among individuals with a history of diabetes. After characterizing the effects of periodontitis on the microbial composition of each group, the salivary microbiome of the treatment-naïve diabetic patient group was compared with that of nondiabetic people and the metformin/combined treatment groups. The results revealed changes in the contents of certain bacteria after both the onset and the treatment of diabetes; among these differential bacteria, Blautia_wexlerae, Lactobacillus_fermentum, Nocardia_coeliaca and Selenomonas_artemidis varied in all processes. A subsequent correlational analysis of the differential bacteria and clinical characteristics demonstrated that salivary microbes were related to drug treatment and certain pathological changes. Finally, the four common differential bacteria were employed for distinguishing the treatment-naïve diabetic patients from the nondiabetic people and the treated patients, with prediction accuracies of 83.3%, 75% and 75%, respectively.

PlasClass improves plasmid sequence classification.

Many bacteria contain plasmids, but separating between contigs that originate on the plasmid and those that are part of the bacterial genome can be difficult. This is especially true in metagenomic assembly, which yields many contigs of unknown origin. Existing tools for classifying sequences of plasmid origin give less reliable results for shorter sequences, are trained using a fraction of the known plasmids, and can be difficult to use in practice. We present PlasClass, a new plasmid classifier. It uses a set of standard classifiers trained on the most current set of known plasmid sequences for different sequence lengths. We tested PlasClass sequence classification on held-out data and simulations, as well as publicly available bacterial isolates and plasmidome samples and plasmids assembled from metagenomic samples. PlasClass outperforms the state-of-the-art plasmid classification tool on shorter sequences, which constitute the majority of assembly contigs, allowing it to achieve higher F1 scores in classifying sequences from a wide range of datasets. PlasClass also uses significantly less time and memory. PlasClass can be used to easily classify plasmid and bacterial genome sequences in metagenomic or isolate assemblies. It is available under the MIT license from: https://github.com/Shamir-Lab/PlasClass.

Quantification of Multiple Bacteria in Calcified Structural Valvular Heart Disease.

Genome studies of heart valve tissue (HVT) in patients with structural valvular heart disease (sVHD) and acute infective endocarditis (aIE) showed polymicrobial infections. Subject of this study is the quantification of bacterial DNA in HVT of sVHD in comparison to aIE. It will be examined whether the bacterial DNA concentration can be used as surrogate marker to differentiate chronic and acute infections. DNA was isolated from HVT of 100 patients with sVHD and 23 microbiologically positively tested patients with aIE. Selected pathogens (Cutibacterium acnes, Enterococcus faecalis, Enterococcus faecium, Staphylococcus aureus, Streptococcus pyogenes, Streptococcus agalactiae, Clostridium difficile, and Klebsiella pneumoniae) were quantified using TaqMan-qPCR. Polymicrobial infiltration of HVT was investigated by immunohistologic methods. Of 100 sVHD patients, 94 tested positive for bacteria by 16S-rDNA and 72 by TaqMan-qPCR. In 29% of the sVHD cohort and in 70% of the aIE cohort, a coinfection with more than 2 bacteria was observed as indication of a polymicrobial infection. The most common pathogens in the sVHD patients were C. acnes (59%; 5-4074 pg/mL), E. faecalis (16%, 174-2781 pg/mL), and S. aureus (15%, 8-105 pg/mL). The DNA concentration of E. faecalis (P = 0.0285) and S. aureus (P = 0.0149) is significantly lower in the sVHD cohort than in the aIE cohort. sVHD is associated with bacterial infection and infiltration of the HVT in a majority of cases. TaqMan-qPCR is a valid instrument for the specific detection of bacteria in HVT and allows discrimination between sVHD and aIE for E. faecalis and S. aureus.

Phylogeography of the second plague pandemic revealed through analysis of historical Yersinia pestis genomes.

The second plague pandemic, caused by Yersinia pestis, devastated Europe and the nearby regions between the 14th and 18th centuries AD. Here we analyse human remains from ten European archaeological sites spanning this period and reconstruct 34 ancient Y. pestis genomes. Our data support an initial entry of the bacterium through eastern Europe, the absence of genetic diversity during the Black Death, and low within-outbreak diversity thereafter. Analysis of post-Black Death genomes shows the diversification of a Y. pestis lineage into multiple genetically distinct clades that may have given rise to more than one disease reservoir in, or close to, Europe. In addition, we show the loss of a genomic region that includes virulence-related genes in strains associated with late stages of the pandemic. The deletion was also identified in genomes connected with the first plague pandemic (541-750 AD), suggesting a comparable evolutionary trajectory of Y. pestis during both events.

A single enzyme PCR-RFLP assay targeting V1-V3 region of 16S rRNA gene for direct identification of Alicyclobacillus acidoterrestris from other Alicyclobacillus species.

Alicyclobacillus acidoterrestris is considered to be one of the most important target microorganisms in quality control of heat-processed acidic foods. This species can cause spoilage by forming spores with very high heat resistance; however, no gas production or visible changes occur in the contaminated product during storage. Thus, the differentiation of A. acidoterrestris from other Alicyclobacillus species is of great importance. Τhe present study aims to find a rapid method for the identification of A. acidoterrestris. To achieve this, 78 Alicyclobacillus isolates were subjected to PCR-RFLP (restriction fragment length polymorphism). Specifically, PCR products of amplified V1-V3 region of the 16S rRNA gene were digested with restriction endonuclease HhaI. According to the obtained results, all A. acidoterrestris isolates showed similar restriction fragments of the 16S rRNA gene and different from A. acidocaldarius and A. hesperidum. In conclusion, a single enzyme PCR-RFLP assay was developed and showed rapid, inexpensive and direct identification of Alicyclobacillus isolates. The application of this method will be useful to identify this contaminant in fruit juices.

AFLP protocol comparison for microbial diversity fingerprinting.

Over the last decade, several methods based on genomic DNA have been developed for the identification and genotyping of prokaryotic and eukaryotic organisms. These genomic methods differ regarding taxonomic range, discriminatory power, reproducibility, and ease of interpretation and standardization. The amplified fragment length polymorphism (AFLP) technique is a very powerful DNA fingerprinting technique for DNA of any source or complexity, varying in both size and base composition. In addition, this method shows high discriminatory power and good reproducibility allowing it to be efficient in discriminating at both the species and strain levels. The development and application of AFLP have allowed significant progress in the study of biodiversity and taxonomy of microorganisms. In the last years, the Applied Biosystems AFLP Microbial Fingerprinting Kit, now out of production, was widely used in various studies to perform AFLP characterization of selected bacteria strains (described by Vos et al. (Nucleic Acids Res 23(21):4407-4414, 1995)). Its replacement gives the possibility for laboratories to continue the use of the previous AFLP data as a reference for bacteria genetic fingerprinting analysis in biodiversity studies. To overcome this issue a result comparison, by using an improved AFLP protocol and the AFLP commercial kit, was performed. In particular, previous results on different species (Listeria monocytogenes, Lactobacillus plantarum, and Streptococcus thermophilus) obtained with the commercial kit were compared with the improved AFLP procedure to validate the protocol. When compared with the AFLP Microbial Fingerprinting Kit, the improved protocol shows high reproducibility, resolution, and overall, is a faster method with lower costs.

The effect of prebiotic fortified infant formulas on microbiota composition and dynamics in early life.

Gastrointestinal (GI) microbiota composition differs between breastfed and formula-fed infants. Today's infant formulas are often fortified with prebiotics to better mimic properties of human milk with respect to its effect on GI microbiota composition and function. We used Illumina HiSeq sequencing of PCR-amplified 16S rRNA gene fragments to investigate the composition of faecal microbiota in 2-12 week old infants receiving either breastmilk, infant formulas fortified with prebiotics, or mixed feeding. We compared these results with results from infants fed traditional formulas used in the Netherlands in 2002-2003, which contained no added prebiotics. We showed that today's formulas supplemented with either scGOS (0.24-0.50 g/100 ml) or scGOS and lcFOS (at a 9:1 ratio; total 0.6 g/100 ml) had a strong bifidogenic effect as compared to traditional formulas, and they also resulted in altered patterns of microbial colonisation within the developing infant gastrointestinal tract. We identified three microbial states (or developmental stages) in the first 12 weeks of life, with a gradual transition pattern towards a bifidobacteria dominated state. In infants receiving only fortified formulas, this transition towards the bifidobacteria dominated state was accelerated, whereas in infants receiving mixed feeding the transition was delayed, as compared to exclusively breastfed infants.

The hindgut microbiota of praying mantids is highly variable and includes both prey-associated and host-specific microbes.

Praying mantids are predators that consume a wide variety of insects. While the gut microbiome of carnivorous mammals is distinct from that of omnivores and herbivores, the role of the gut microbiome among predatory insects is relatively understudied. Praying mantids are the closest known relatives to termites and cockroaches, which are known for their diverse gut microbiota. However, little is known about the mantid gut microbiota or their importance to host health. In this work, we report the results of a 16S rRNA gene-based study of gut microbiome composition in adults and late-instar larvae of three mantid species. We found that the praying mantis gut microbiome exhibits substantial variation in bacterial diversity and community composition. The hindgut of praying mantids were often dominated by microbes that are present in low abundance or not found in the guts of their insect prey. Future studies will explore the role of these microbes in the digestion of the dietary substrates and/or the degradation of toxins produced by their insect prey.

Monitoring of microbial dynamics in a drinking water distribution system using the culture-free, user-friendly, MYcrobiota platform.

Drinking water utilities currently rely on a range of microbiological detection techniques to evaluate the quality of their drinking water (DW). However, microbiota profiling using culture-free 16S rRNA gene next-generation sequencing (NGS) provides an opportunity for improved monitoring of the microbial ecology and quality of DW. Here, we evaluated the utility of a previously validated microbiota profiling platform (MYcrobiota) to investigate the microbial dynamics of a full-scale, non-chlorinated DW distribution system (DWDS). In contrast to conventional methods, we observed spatial and temporal bacterial genus changes (expressed as operational taxonomic units - OTUs) within the DWDS. Further, a small subset of bacterial OTUs dominated with abundances that shifted across the length of the DWDS, and were particularly affected by a post-disinfection step. We also found seasonal variation in OTUs within the DWDS and that many OTUs could not be identified, even though MYcrobiota is specifically designed to reduce potential PCR sequencing artefacts. This suggests that our current knowledge about the microbial ecology of DW communities is limited. Our findings demonstrate that the user-friendly MYcrobiota platform facilitates culture-free, standardized microbial dynamics monitoring and has the capacity to facilitate the introduction of microbiota profiling into the management of drinking water quality.

Subgingival microbiome of rheumatoid arthritis patients in relation to their disease status and periodontal health.

OBJECTIVE: Rheumatoid arthritis (RA) and periodontitis are chronic inflammatory diseases that share common risk factors. However, the bidirectional relationship between RA and periodontal disease is not fully understood. This study was undertaken to describe the bacterial component of the subgingival microbiome in RA patients and to relate this to RA disease activity and periodontal status. METHODS: Patients with chronic established RA (N = 78) were periodontally examined and their subgingival plaque samples were collected; their clinical and laboratory data on RA status and medication were obtained from medical records. Bacterial DNA was quantified by universal 16S rDNA qPCR, and Porphyromonas gingivalis by species-specific qPCR. For microbiome assessment, 16S rDNA amplicon sequencing was performed. RESULTS: Active RA was diagnosed in 58% of the patients and periodontitis in 82% (mild: 9%, moderate: 55%, severe: 18%). P. gingivalis was present in 14% of the samples. Different levels of gingival bleeding, periodontal probing depth, RA disease status, prednisolone use and smoking were associated with significantly different microbiome compositions. Two subgingival microbial community types were discerned. CONCLUSION: In RA patients with active disease, anti-inflammatory medication as part of RA therapy was associated with better oral health status and a healthier subgingival microbiome compared to that of RA patients in remission, especially those in remission who were current smokers. RA patients in remission with current smoking status may particularly benefit from a systematic periodontal treatment program. The potential role of microbial community types in patient stratification and personalized therapy should be assessed in longitudinal studies.

Bacterial promoter prediction: Selection of dynamic and static physical properties of DNA for reliable sequence classification.

Predicting promoter activity of DNA fragment is an important task for computational biology. Approaches using physical properties of DNA to predict bacterial promoters have recently gained a lot of attention. To select an adequate set of physical properties for training a classifier, various characteristics of DNA molecule should be taken into consideration. Here, we present a systematic approach that allows us to select less correlated properties for classification by means of both correlation and cophenetic coefficients as well as concordance matrices. To prove this concept, we have developed the first classifier that uses not only sequence and static physical properties of DNA fragment, but also dynamic properties of DNA open states. Therefore, the best performing models with accuracy values up to 90% for all types of sequences were obtained. Furthermore, we have demonstrated that the classifier can serve as a reliable tool enabling promoter DNA fragments to be distinguished from promoter islands despite the similarity of their nucleotide sequences.

Analysis of fecal microbiota in patients with functional constipation undergoing treatment with synbiotics.

This study was performed to identify changes to microbial composition after treatment with synbiotics in patients with functional constipation and to define the key microbiota in the pathogenesis of functional constipation. Fecal samples from 53 patients diagnosed with chronic functional constipation according to the Rome III criteria were analyzed using 16S rRNA sequencing. After treatment with synbiotics for 1 month, fecal samples were collected from 36 patients; after a total of 3 months, fecal samples were collected from 15 patients. The outcomes were compared with the intestinal microbiota profiles of 53 healthy community volunteers. The microbiota in the constipation group differed from that in the treatment group and healthy group. After synbiotic treatment for 1 and 3 months, the abundance of Escherichia/Shigella decreased, whereas that of Prevotella_9 and Lactococcus increased. Comparison of the microbiota among the three groups showed that Prevotella_9 was the characteristic bacteria that decreased in the constipation group and increased in the treatment group. Synbiotic treatment can improve the microbiota in patients with constipation. Identification of the key bacterial genus is important to reveal the mechanism and provide a reliable theoretical basis of synbiotic treatment. It will also promote relevant research of microbiota treatment and individualized treatments.

Sequential and Selective Detection of Two Molecules with a Single Solid-Contact Chronopotentiometric Ion-Selective Electrode.

A polymeric membrane ion-selective electrode (ISE) is typically designed for the determination of one specific ion using a conventional method. In this work, we demonstrate a simple, versatile, and sensitive platform for simultaneous detection of two molecules with a single ISE. Under a series of periodic galvanostatic polarization, a solid-contact ISE without ion exchanger properties under zero-current conditions has been successfully used for simultaneous detection of two opposite charged ions with high sensitivity, good selectivity, and fast reversibility. By integration of biorecognition elements with the potentiometric measurement, highly sensitive and selective detection of a broad range of different molecular targets can be predicted. As a proof of concept, a potentiometric genosensor based on magnetic beads-enzyme sandwich assay has been designed for sensitive and selective detection of pathogenic bacteria Escherichia coli O157:H7 and Staphylococcus aureus. Under optimal conditions, two bacteria nucleic acid sequences can be detected simultaneously with high sensitivity and good selectivity by using a single solid-contact potentiometric ISE. The detection limits of Escherichia coli O157:H7 DNA and Staphylococcus aureus DNA are 120 and 54 fM (3σ), respectively. Because of its simplicity, this potentiometric technique based on ISE can be an attractive tool or detector to perform two analyte measurements.