PrimerEvalPy: a tool for in-silico evaluation of primers for targeting the microbiome.

BACKGROUND: The selection of primer pairs in sequencing-based research can greatly influence the results, highlighting the need for a tool capable of analysing their performance in-silico prior to the sequencing process. We therefore propose PrimerEvalPy, a Python-based package designed to test the performance of any primer or primer pair against any sequencing database. The package calculates a coverage metric and returns the amplicon sequences found, along with information such as their average start and end positions. It also allows the analysis of coverage for different taxonomic levels. RESULTS: As a case study, PrimerEvalPy was used to test the most commonly used primers in the literature against two oral 16S rRNA gene databases containing bacteria and archaea. The results showed that the most commonly used primer pairs in the oral cavity did not match those with the highest coverage. The best performing primer pairs were found for the detection of oral bacteria and archaea. CONCLUSIONS: This demonstrates the importance of a coverage analysis tool such as PrimerEvalPy to find the best primer pairs for specific niches. The software is available under the MIT licence at https://gitlab.citius.usc.es/lara.vazquez/PrimerEvalPy .

A laboratory ice machine as a cold oligotrophic artificial microbial niche for biodiscovery.

Microorganisms are ubiquitously distributed in nature and usually appear as biofilms attached to a variety of surfaces. Here, we report the development of a thick biofilm in the drain pipe of several standard laboratory ice machines, and we describe and characterise, through culture-dependent and -independent techniques, the composition of this oligotrophic microbial community. By using culturomics, 25 different microbial strains were isolated and taxonomically identified. The 16S rRNA high-throughput sequencing analysis revealed that Bacteroidota and Proteobacteria were the most abundant bacterial phyla in the sample, followed by Acidobacteriota and Planctomycetota, while ITS high-throughput sequencing uncovered the fungal community was clearly dominated by the presence of a yet-unidentified genus from the Didymellaceae family. Alpha and beta diversity comparisons of the ice machine microbial community against that of other similar cold oligotrophic and/or artificial environments revealed a low similarity between samples, highlighting the ice machine could be considered a cold and oligotrophic niche with a unique selective pressure for colonisation of particular microorganisms. The recovery and analysis of high-quality metagenome-assembled genomes (MAGs) yielded a strikingly high rate of new species. The functional profiling of the metagenome sequences uncovered the presence of proteins involved in extracellular polymeric substance (EPS) and fimbriae biosynthesis and also allowed us to detect the key proteins involved in the cold adaptation mechanisms and oligotrophic metabolic pathways. The metabolic functions in the recovered MAGs confirmed that all MAGs have the genes involved in psychrophilic protein biosynthesis. In addition, the highest number of genes for EPS biosynthesis was presented in MAGs associated with the genus Sphingomonas, which was also recovered by culture-based method. Further, the MAGs with the highest potential gene number for oligotrophic protein production were closely affiliated with the genera Chryseoglobus and Mycobacterium. Our results reveal the surprising potential of a cold oligotrophic microecosystem within a machine as a source of new microbial taxa and provide the scientific community with clues about which microorganisms are able to colonise this ecological niche and what physiological mechanisms they develop. These results pave the way to understand how and why certain microorganisms can colonise similar anthropogenic environments.

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis.

The multi-batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature in recent years. The large amount of 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored in public repositories and information in taxonomic databases of the same gene far exceeds that related to complete genomes. This review is intended to guide researchers new to studying microbiota, particularly the oral microbiota, using 16S rRNA gene sequencing and those who want to expand and update their knowledge to optimise their decision-making and improve their research results. First, we describe the advantages and disadvantages of using the 16S rRNA gene as a phylogenetic marker and the latest findings on the impact of primer pair selection on diversity and taxonomic assignment outcomes in oral microbiome studies. Strategies for primer selection based on these results are introduced. Second, we identified the key factors to consider in selecting the sequencing technology and platform. The process and particularities of the main steps for processing 16S rRNA gene-derived data are described in detail to enable researchers to choose the most appropriate bioinformatics pipeline and analysis methods based on the available evidence. We then produce an overview of the different types of advanced analyses, both the most widely used in the literature and the most recent approaches. Several indices, metrics and software for studying microbial communities are included, highlighting their advantages and disadvantages. Considering the principles of clinical metagenomics, we conclude that future research should focus on rigorous analytical approaches, such as developing predictive models to identify microbiome-based biomarkers to classify health and disease states. Finally, we address the batch effect concept and the microbiome-specific methods for accounting for or correcting them.

The Microbiome of Things: Appliances, Machines, and Devices Hosting Artificial Niche-Adapted Microbial Communities.

As it is the case with natural substrates, artificial surfaces of man-made devices are home to a myriad of microbial species. Artificial products are not necessarily characterized by human-associated microbiomes; instead, they can present original microbial populations shaped by specific environmental-often extreme-selection pressures. This review provides a detailed insight into the microbial ecology of a range of artificial devices, machines, and appliances, which we argue are specific microbial niches that do not necessarily fit in the "build environment" microbiome definition. Instead, we propose here the Microbiome of Things (MoT) concept analogous to the Internet of Things (IoT) because we believe it may be useful to shed light on human-made, but not necessarily human-related, unexplored microbial niches.

In silico evaluation and selection of the best 16S rRNA gene primers for use in next-generation sequencing to detect oral bacteria and archaea.

BACKGROUND: Sequencing has been widely used to study the composition of the oral microbiome present in various health conditions. The extent of the coverage of the 16S rRNA gene primers employed for this purpose has not, however, been evaluated in silico using oral-specific databases. This paper analyses these primers using two databases containing 16S rRNA sequences from bacteria and archaea found in the human mouth and describes some of the best primers for each domain. RESULTS: A total of 369 distinct individual primers were identified from sequencing studies of the oral microbiome and other ecosystems. These were evaluated against a database reported in the literature of 16S rRNA sequences obtained from oral bacteria, which was modified by our group, and a self-created oral archaea database. Both databases contained the genomic variants detected for each included species. Primers were evaluated at the variant and species levels, and those with a species coverage (SC) ≥75.00% were selected for the pair analyses. All possible combinations of the forward and reverse primers were identified, with the resulting 4638 primer pairs also evaluated using the two databases. The best bacteria-specific pairs targeted the 3-4, 4-7, and 3-7 16S rRNA gene regions, with SC levels of 98.83-97.14%; meanwhile, the optimum archaea-specific primer pairs amplified regions 5-6, 3-6, and 3-6, with SC estimates of 95.88%. Finally, the best pairs for detecting both domains targeted regions 4-5, 3-5, and 5-9, and produced SC values of 95.71-94.54% and 99.48-96.91% for bacteria and archaea, respectively. CONCLUSIONS: Given the three amplicon length categories (100-300, 301-600, and >600 base pairs), the primer pairs with the best coverage values for detecting oral bacteria were as follows: KP_F048-OP_R043 (region 3-4; primer pair position for Escherichia coli J01859.1: 342-529), KP_F051-OP_R030 (4-7; 514-1079), and KP_F048-OP_R030 (3-7; 342-1079). For detecting oral archaea, these were as follows: OP_F066-KP_R013 (5-6; 784-undefined), KP_F020-KP_R013 (3-6; 518-undefined), and OP_F114-KP_R013 (3-6; 340-undefined). Lastly, for detecting both domains jointly they were KP_F020-KP_R032 (4-5; 518-801), OP_F114-KP_R031 (3-5; 340-801), and OP_F066-OP_R121 (5-9; 784-1405). The primer pairs with the best coverage identified herein are not among those described most widely in the oral microbiome literature. Video Abstract.

Impact of 16S rRNA Gene Redundancy and Primer Pair Selection on the Quantification and Classification of Oral Microbiota in Next-Generation Sequencing.

This study aimed to evaluate the number of 16S rRNA genes in the complete genomes of the bacterial and archaeal species inhabiting the human mouth and to assess how the use of different primer pairs would affect the detection and classification of redundant amplicons and matching amplicons (MAs) from different taxa. A total of 518 oral-bacterial and 191 oral-archaeal complete genomes were downloaded from the NCBI database, and their complete 16S rRNA genes were extracted. The numbers of genes and variants per genome were calculated. Next, 39 primer pairs were used to search for matches in the genomes and obtain amplicons. For each primer, we calculated the number of gene amplicons, variants, genomes, and species detected and the percentage of coverage at the species level with no MAs (SC-NMA). The results showed that 94.09% of oral bacteria and 52.59% of oral archaea had more than one intragenomic 16S rRNA gene. From 1.29% to 46.70% of bacterial species and from 4.65% to 38.89% of archaea detected by the primers had MAs. The best primers were the following (SC-NMA; region; position for Escherichia coli [GenBank version no. J01859.1]): KP_F048-OP_R030 for bacteria (93.55%; V3 to V7; 342 to 1079), KP_F018-KP_R063 for archaea (89.63%; V3 to V9; undefined to 1506), and OP_F114-OP_R121 for both domains (92.52%; V3 to V9; 340 to 1405). In addition to 16S rRNA gene redundancy, the presence of MAs must be controlled to ensure an accurate interpretation of microbial diversity data. The SC-NMA is a more useful parameter than the conventional coverage percentage for selecting the best primer pairs. The pairs used the most in the oral microbiome literature were not among the best performers. IMPORTANCE Hundreds of publications have studied the oral microbiome through 16S rRNA gene sequencing. However, none have assessed the number of 16S rRNA genes in the genomes of oral microbes, or how the use of primer pairs targeting different regions affects the detection of MAs from different taxa. Here, we found that almost all oral bacteria and more than half of oral archaea have more than one intragenomic 16S rRNA gene. The performance of the primer pairs in not detecting MAs increases as the length of the amplicon augments. As none of those most employed in the oral literature were among the best performers, we selected a series of primers to detect bacteria and/or archaea based on their percentage of species detected without MAs. The intragenomic 16S rRNA gene redundancy and the presence of MAs between distinct taxa need to be considered to ensure an accurate interpretation of microbial diversity data.

Update on the Role of Cytokines as Oral Biomarkers in the Diagnosis of Periodontitis.

Periodontitis is one of the world's most common chronic human diseases and has a significant impact on oral health. Recent evidence has revealed a link between periodontitis and certain severe systemic conditions. Moreover, periodontal patients remain so for life, even following successful therapy, requiring ongoing supportive care to prevent the disease's recurrence. The first challenge in treating the condition is ensuring a timely and accurate diagnosis since the loss of periodontal bone and soft tissue is progressive and largely irreversible. Although current clinical and radiographic parameters are the best available for identifying and monitoring the disease, the scientific community has a particular interest in finding quantifiable biomarkers in oral fluids that can improve early detection rates of periodontitis and evaluations of its severity. It is widely accepted that periodontitis is associated with polymicrobial dysbiosis and a chronic inflammatory immune response in the host. This response causes the generation of mediators like cytokines. Higher concentrations of cytokines are involved in inflammation and disease progression, acting as a network of biological redundancy. Most of the cytokines investigated concerning the periodontitis pathogenesis are proinflammatory. Of all of them, interleukin (IL) 1beta has been studied the most, followed by tumor necrosis factor (TNF) alpha and IL6. In contrast, only a few papers have evaluated antiinflammatory cytokines, with the most researched being IL4 and IL10. Several systemic reviews have concluded that the specific cytokines present in patients with periodontitis have a distinctive profile, which may indicate their possible discriminatory potential. In this chapter, the focus is on analyzing studies that investigate the accuracy of diagnoses of periodontitis based on the cytokines present in gingival crevicular fluid and saliva. The findings of our research group are also described.

Epigenetic Effects of Healthy Foods and Lifestyle Habits from the Southern European Atlantic Diet Pattern: A Narrative Review.

Recent scientific evidence has shown the importance of diet and lifestyle habits for the proper functioning of the human body. A balanced and healthy diet, physical activity, and psychological well-being have a direct beneficial effect on health and can have a crucial role in the development and prognosis of certain diseases. The Southern European Atlantic diet, also named the Atlantic diet, is a unique dietary pattern that occurs in regions that present higher life expectancy, suggesting that this specific dietary pattern is associated with positive health effects. In fact, it is enriched with nutrients of high biological value, which, together with its cooking methods, physical activity promotion, reduction in carbon footprint, and promoting of family meals, promote these positive effects on health. The latest scientific advances in the field of nutri-epigenetics have revealed that epigenetic markers associated with food or nutrients and environmental factors modulate gene expression and, therefore, are involved with both health and disease. Thus, in this review, we evaluated the main aspects that define the Southern European Atlantic diet and the potential epigenetic changes associated with them based on recent studies regarding the main components of these dietary patterns. In conclusion, based on the information existing in the literature, we postulate that the Southern European Atlantic diet could promote healthy aging by means of epigenetic mechanisms. This review highlights the necessity of performing longitudinal studies to demonstrate this proposal.

His452Tyr polymorphism in the human 5-HT2A receptor affects clozapine-induced signaling networks revealed by quantitative phosphoproteomics.

Antipsychotic drugs remain the current standard for schizophrenia treatment. Although they directly recognize the orthosteric binding site of numerous monoaminergic G protein-coupled receptors (GPCRs), these drugs, and particularly second-generation antipsychotics such as clozapine, all have in common a very high affinity for the serotonin 5-HT2A receptor (5-HT2AR). Using classical pharmacology and targeted signaling pathway assays, previous findings suggest that clozapine and other atypical antipsychotics behave principally as 5-HT2AR neutral antagonists and/or inverse agonists. However, more recent findings showed that antipsychotics may also behave as pathway-specific agonists. Reversible phosphorylation is a common element in multiple signaling networks. Combining a quantitative phosphoproteomic method with signaling network analysis, we tested the effect of clozapine treatment on the overall level of protein phosphorylation and signal transduction cascades in vitro in mammalian cell lines induced to express either the human 5-HT2AR or the H452Y variant of the gene encoding the 5-HT2AR receptor. This naturally occurring variation within the 5-HT2AR gene was selected because it has been repeatedly associated with schizophrenia patients who do not respond to clozapine treatment. Our data show that short time exposure (5 or 10 min) to clozapine (10-5 M) led to phosphorylation of numerous signaling components of pathways involved in processes such as endocytosis, ErbB signaling, insulin signaling or estrogen signaling. Cells induced to express the H452Y variant showed a different basal phosphoproteome, with increases in the phosphorylation of mTOR signaling components as a translationally relevant example. However, the effect of clozapine on the functional landscape of the phosphoproteome was significantly reduced in cells expressing the 5-HT2AR-H452Y construct. Together, these findings suggest that clozapine behaves as an agonist inducing phosphorylation of numerous pathways downstream of the 5-HT2AR, and that the single nucleotide polymorphism encoding 5-HT2AR-H452Y affects these clozapine-induced phosphorylation-dependent signaling networks.

In-Silico Detection of Oral Prokaryotic Species With Highly Similar 16S rRNA Sequence Segments Using Different Primer Pairs.

Although clustering by operational taxonomic units (OTUs) is widely used in the oral microbial literature, no research has specifically evaluated the extent of the limitations of this sequence clustering-based method in the oral microbiome. Consequently, our objectives were to: 1) evaluate in-silico the coverage of a set of previously selected primer pairs to detect oral species having 16S rRNA sequence segments with ≥97% similarity; 2) describe oral species with highly similar sequence segments and determine whether they belong to distinct genera or other higher taxonomic ranks. Thirty-nine primer pairs were employed to obtain the in-silico amplicons from the complete genomes of 186 bacterial and 135 archaeal species. Each fasta file for the same primer pair was inserted as subject and query in BLASTN for obtaining the similarity percentage between amplicons belonging to different oral species. Amplicons with 100% alignment coverage of the query sequences and with an amplicon similarity value ≥97% (ASI97) were selected. For each primer, the species coverage with no ASI97 (SC-NASI97) was calculated. Based on the SC-NASI97 parameter, the best primer pairs were OP_F053-KP_R020 for bacteria (region V1-V3; primer pair position for Escherichia coli J01859.1: 9-356); KP_F018-KP_R002 for archaea (V4; undefined-532); and OP_F114-KP_R031 for both (V3-V5; 340-801). Around 80% of the oral-bacteria and oral-archaea species analyzed had an ASI97 with at least one other species. These very similar species play different roles in the oral microbiota and belong to bacterial genera such as Campylobacter, Rothia, Streptococcus and Tannerella, and archaeal genera such as Halovivax, Methanosarcina and Methanosalsum. Moreover, ~20% and ~30% of these two-by-two similarity relationships were established between species from different bacterial and archaeal genera, respectively. Even taxa from distinct families, orders, and classes could be grouped in the same possible OTU. Consequently, regardless of the primer pair used, sequence clustering with a 97% similarity provides an inaccurate description of oral-bacterial and oral-archaeal species, which can greatly affect microbial diversity parameters. As a result, OTU clustering conditions the credibility of associations between some oral species and certain health and disease conditions. This significantly limits the comparability of the microbial diversity findings reported in oral microbiome literature.

Out of the Abyss: Genome and Metagenome Mining Reveals Unexpected Environmental Distribution of Abyssomicins.

Natural products have traditionally been discovered through the screening of culturable microbial isolates from diverse environments. The sequencing revolution allowed the identification of dozens of biosynthetic gene clusters (BGCs) within single bacterial genomes, either from cultured or uncultured strains. However, we are still far from fully exploiting the microbial reservoir, as most of the species are non-model organisms with complex regulatory systems that can be recalcitrant to engineering approaches. Genomic and metagenomic data produced by laboratories worldwide covering the range of natural and artificial environments on Earth, are an invaluable source of raw information from which natural product biosynthesis can be accessed. In the present work, we describe the environmental distribution and evolution of the abyssomicin BGC through the analysis of publicly available genomic and metagenomic data. Our results demonstrate that the selection of a pathway-specific enzyme to direct genome mining is an excellent strategy; we identified 74 new Diels-Alderase homologs and unveiled a surprising prevalence of the abyssomicin BGC within terrestrial habitats, mainly soil and plant-associated. We also identified five complete and 12 partial new abyssomicin BGCs and 23 new potential abyssomicin BGCs. Our results strongly support the potential of genome and metagenome mining as a key preliminary tool to inform bioprospecting strategies aimed at the identification of new bioactive compounds such as -but not restricted to- abyssomicins.

Diagnostic accuracy of IL1ß in saliva: The development of predictive models for estimating the probability of the occurrence of periodontitis in non-smokers and smokers.

AIM: To obtain salivary interleukin (IL) 1ß-based models to predict the probability of the occurrence of periodontitis, differentiating by smoking habit. MATERIALS/METHODS: A total of 141 participants were recruited, 62 periodontally healthy controls and 79 subjects affected by periodontitis. Fifty of the diseased patients were given non-surgical periodontal treatment and showed significant clinical improvement in 2 months. IL1ß was measured in the salivary samples using the Luminex instrument. Binary logistic regression models were obtained to differentiate untreated periodontitis from periodontal health (first modelling) and untreated periodontitis from treated periodontitis (second modelling), distinguishing between non-smokers and smokers. The area under the curve (AUC) and classification measures were calculated. RESULTS: In the first modelling, IL1ß presented AUC values of 0.830 for non-smokers and 0.689 for smokers (accuracy = 77.6% and 70.7%, respectively). In the second, the predictive models revealed AUC values of 0.671 for non-smokers and 0.708 for smokers (accuracy = 70.0% and 75.0%, respectively). CONCLUSION: Salivary IL1ß has an excellent diagnostic capability when it comes to distinguishing systemically healthy patients with untreated periodontitis from those who are periodontally healthy, although this discriminatory potential is reduced in smokers. The diagnostic capacity of salivary IL1ß remains acceptable for differentiating between untreated and treated periodontitis.

Accuracy of single molecular biomarkers in saliva for the diagnosis of periodontitis: A systematic review and meta-analysis.

AIM: To analyse, using a meta-analytical approach, the diagnostic accuracy of single molecular biomarkers in saliva for the detection of periodontitis in systemically healthy subjects. MATERIALS AND METHODS: Articles on molecular biomarkers in saliva providing a binary contingency table (or sensitivity and specificity values and group sample sizes) in individuals with clinically diagnosed periodontitis were considered eligible. Searches for candidate articles were conducted in six electronic databases. The methodological quality was assessed through the tool Quality Assessment of Diagnostic Studies. Meta-analyses were performed using the Hierarchical Summary Receiver Operating Characteristic model. RESULTS: Meta-analysis was possible for 5 of the 32 biomarkers studied. The highest values of sensitivity for the diagnosis of periodontitis were obtained for IL1beta (78.7%), followed by MMP8 (72.5%), IL6 and haemoglobin (72.0% for both molecules); the lowest sensitivity value was for MMP9 (70.3%). In terms of specificity estimates, MMP9 had the best result (81.5%), followed by IL1beta (78.0%) and haemoglobin (75.2%); MMP8 had the lowest specificity (70.5%). CONCLUSIONS: MMP8, MMP9, IL1beta, IL6 and Hb were salivary biomarkers with good capability to detect periodontitis in systemically healthy subjects. MMP8 and IL1beta are the most researched biomarkers in the field, both showing clinically fair effectiveness for the diagnosis of periodontitis.

Accuracy of single molecular biomarkers in gingival crevicular fluid for the diagnosis of periodontitis: A systematic review and meta-analysis.

AIM: To analyse, by means of a meta-analytical approach, the diagnostic accuracy of molecular biomarkers in gingival crevicular fluid (GCF) for the detection of periodontitis in systemically healthy subjects. MATERIAL AND METHODS: Studies on GCF molecular biomarkers providing a binary classification table (or sensitivity and specificity values and group sample sizes) in individuals with clinically diagnosed periodontitis were considered eligible. The search was performed using six electronic databases. The methodological quality of studies was assessed through the tool Quality Assessment of Diagnostic Studies. Meta-analyses were performed using the Hierarchical Summary Receiver Operating Characteristic, which adjusts classification data using random effects logistic regression. RESULTS: The included papers identified 36 potential biomarkers for the detection of periodontitis and for four of them meta-analyses were performed. The median sensitivity and specificity were for MMP8, 76.7% and 92.0%; for elastase, 74.6% and 81.1%; for cathepsin, 72.8% and 67.3%, respectively. The worst estimates of sensitivity and specificity were for trypsin (71.3% and 66.1%, respectively). CONCLUSIONS: MMP8 showed good sensitivity and excellent specificity, which resulted in this biomarker being clinically the most useful or effective for the diagnosis of periodontitis in systemically healthy subjects, regardless of smoking condition.

ABC Transporters Required for Hexose Uptake by Clostridium phytofermentans.

The mechanisms by which bacteria uptake solutes across the cell membrane broadly impact their cellular energetics. Here, we use functional genomic, genetic, and biophysical approaches to reveal how Clostridium (Lachnoclostridium) phytofermentans, a model bacterium that ferments lignocellulosic biomass, uptakes plant hexoses using highly specific, nonredundant ATP-binding cassette (ABC) transporters. We analyze the transcription patterns of its 173 annotated sugar transporter genes to find those upregulated on specific carbon sources. Inactivation of these genes reveals that individual ABC transporters are required for uptake of hexoses and hexo-oligosaccharides and that distinct ABC transporters are used for oligosaccharides versus their constituent monomers. The thermodynamics of sugar binding shows that substrate specificity of these transporters is encoded by the extracellular solute-binding subunit. As sugars are not phosphorylated during ABC transport, we identify intracellular hexokinases based on in vitro activities. These mechanisms used by Clostridia to uptake plant hexoses are key to understanding soil and intestinal microbiomes and to engineer strains for industrial transformation of lignocellulose.IMPORTANCE Plant-fermenting Clostridia are anaerobic bacteria that recycle plant matter in soil and promote human health by fermenting dietary fiber in the intestine. Clostridia degrade plant biomass using extracellular enzymes and then uptake the liberated sugars for fermentation. The main sugars in plant biomass are hexoses, and here, we identify how hexoses are taken in to the cell by the model organism Clostridium phytofermentans We show that this bacterium uptakes hexoses using a set of highly specific, nonredundant ABC transporters. Once in the cell, the hexoses are phosphorylated by intracellular hexokinases. This study provides insight into the functioning of abundant members of soil and intestinal microbiomes and identifies gene targets to engineer strains for industrial lignocellulosic fermentation.

Development of Fluorescent Probes that Target Serotonin 5-HT2B Receptors.

Some 5-HT2B fluorescent probes were obtained by tagging 1-(2,5-dimethoxy-4-iodophenyl)-propan-2-amine (DOI) with a subset of fluorescent amines. Some of the resulting fluorescent ligands showed excellent affinity and selectivity profiles at the 5-HT2B receptors (e.g. 12b), while retain the agonistic functional behaviour of the model ligand (DOI). The study highlighted the most salient features of the structure-activity relationship in this series and these were substantiated by a molecular modelling study based on a receptor-driven docking model constructed on the basis of the crystal structure of the human 5-HT2B receptor. One of the fluorescent ligands developed in this work, compound 12i, specifically labelled CHO-K1 cells expressing 5-HT2B receptors and not parental CHO-K1 cells in a concentration-dependent manner. 12i enables imaging and quantification of specific 5-HT2B receptor labelling in live cells by automated fluorescence microscopy as well as quantification by measurements of fluorescence intensity using a fluorescence plate reader.

Serotonin 2A receptor disulfide bridge integrity is crucial for ligand binding to different signalling states but not for its homodimerization.

The serotonin 2A (5-HT2A) receptor is a G-protein coupled receptor (GPCR) with a conserved disulfide bridge formed by Cys148 (transmembrane helix 3, TM3) and Cys227 (extracellular loop 2, ECL-2). We hypothesized that disulfide bridges may determine serotonin 5-HT2A receptor functions such as receptor activation, functional selectivity and ligand recognition. We used the reducing agent dithiothreitol (DTT) to determine how the reduction of disulfide bridges affects radioligand binding, second messenger mobilization and receptor dimerization. A DTT-induced decrease in the number of binding sites (1190 ± 63.55 fmol/mg protein for control cells compared with 921.2 ± 60.84 fmol/mg protein for DTT-treated cells) as well as in the efficacy of both signalling pathways characterized was observed, although the affinity and potency were unchanged. Bioluminiscence resonance energy transfer (BRET) assays revealed the DTT treatment did not modify the homodimeric nature of serotonin 5-HT2A receptors. In molecular dynamic simulations, the ECL-2 of the receptor with a broken cysteine bond adopts a wider variety of conformations, some of which protrude deeper into the receptor orthosteric binding pocket leading to collapse of the pocket. A shrunken binding pocket would be incapable of accommodating lysergic acid diethylamide (LSD). Our findings suggest that the decrease of efficacy may be due to disruption of disulfide bridge between TM3 and ECL-2. This reveals the integrity of the ECL-2 epitope, which should be explored in the development of novel ligands acting as allosteric modulators of serotonin 5-HT2A receptors.

Quantification by qPCR of Pathobionts in Chronic Periodontitis: Development of Predictive Models of Disease Severity at Site-Specific Level.

Currently, there is little evidence available on the development of predictive models for the diagnosis or prognosis of chronic periodontitis based on the qPCR quantification of subgingival pathobionts. Our objectives were to: (1) analyze and internally validate pathobiont-based models that could be used to distinguish different periodontal conditions at site-specific level within the same patient with chronic periodontitis; (2) develop nomograms derived from predictive models. Subgingival plaque samples were obtained from control and periodontal sites (probing pocket depth and clinical attachment loss <4 mm and >4 mm, respectively) from 40 patients with moderate-severe generalized chronic periodontitis. The samples were analyzed by qPCR using TaqMan probes and specific primers to determine the concentrations of Actinobacillus actinomycetemcomitans (Aa), Fusobacterium nucleatum (Fn), Parvimonas micra (Pm), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Tannerella forsythia (Tf), and Treponema denticola (Td). The pathobiont-based models were obtained using multivariate binary logistic regression. The best models were selected according to specified criteria. The discrimination was assessed using receiver operating characteristic curves and numerous classification measures were thus obtained. The nomograms were built based on the best predictive models. Eight bacterial cluster-based models showed an area under the curve (AUC) ≥0.760 and a sensitivity and specificity ≥75.0%. The PiTfFn cluster showed an AUC of 0.773 (sensitivity and specificity = 75.0%). When Pm and AaPm were incorporated in the TdPiTfFn cluster, we detected the two best predictive models with an AUC of 0.788 and 0.789, respectively (sensitivity and specificity = 77.5%). The TdPiTfAa cluster had an AUC of 0.785 (sensitivity and specificity = 75.0%). When Pm was incorporated in this cluster, a new predictive model appeared with better AUC and specificity values (0.787 and 80.0%, respectively). Distinct clusters formed by species with different etiopathogenic role (belonging to different Socransky's complexes) had a good predictive accuracy for distinguishing a site with periodontal destruction in a periodontal patient. The predictive clusters with the lowest number of bacteria were PiTfFn and TdPiTfAa, while TdPiTfAaFnPm had the highest number. In all the developed nomograms, high concentrations of these clusters were associated with an increased probability of having a periodontal site in a patient with chronic periodontitis.

Serotonin-2A homodimers are needed for signalling via both phospholipase A2 and phospholipase C in transfected CHO cells.

Different ligands differentially activate phospholipase A2 (PLA2) and phospholipase C (PLC) signalling pathways that are coupled to the serotonin 2A (5-HT2A) receptor, a class-A G-protein coupled receptor (GPCR). The serotonin 5-HT2A receptor has been shown to be expressed as a homodimer displaying some ligands negative cooperativity between protomers in the PLA2 signalling pathway. We hypothesized that the homodimeric complex is the minimum functional unit required for activation of the PLA2 and PLC pathways by the serotonin 5-HT2A receptor. To investigate this hypothesis, we partially blocked the serotonin 5-HT2A receptors with ritanserin and measured PLA2 and PLC activity simultaneously. We subsequently added the competitive antagonist spiperone to release the inactivator through a crosstalk mechanism and thus allow the dimer to return to a reactive state. Partial inactivation of the homodimer by ritanserin binding decreased the activity of the receptor by 59±13% and 70±4% in the PLA2 and PLC pathways respectively (P<0.001), with no difference in the potency of the serotonin (5-HT) was observed. The subsequent binding of spiperone released ritanserin due to the crosstalk between protomers and recovery of the receptor activity to 74±7% and 72±4%. Negative cooperativity between protomers in the dimer was maintained during arachidonic acid (AA) release after blocking ritanserin, as indicated by the biphasic inhibition curves for clozapine over 1µM serotonin (5-HT) in these conditions. These findings provide evidence that serotonin 5-HT2A receptors must be expressed as homodimers in order to activate both the PLA2 and PLC signalling pathways.