A Study of the Variation in the Salivary Peptide Profiles of Young Healthy Adults Acquired Using MALDI-TOF MS.

A cross-sectional observational study was conducted to evaluate the inter-individual variation in the MALDI-TOF MS peptide profiles of unstimulated whole saliva in a population of 268 systemically healthy adults aged 18-30 yr (150 males and 118 females) with no apparent caries lesions or periodontal disease. Using Spectral Clustering, four subgroups of individuals were identified within the study population. These subgroups were delimited by the pattern of variation in 9 peaks detected in the 2-15 kDa m/z range. An Unsupervised Feature Selection algorithm showed that P-C peptide, a 44 residue-long salivary acidic proline-rich protein, and three of its fragments (Fr. 1-25, Fr. 15-35 and Fr. 15-44) play a central role in delimiting the subgroups. Significant differences were found in the salivary biochemistry of the subgroups with regard to lysozyme and chitinase, two enzymes that are part of the salivary innate defense system (p < 0.001). These results suggest that MALDI-TOF MS salivary peptide profiles may relate information on the underlying state of the oral ecosystem and may provide a useful reference for salivary disease biomarker discovery studies.

Correlates of the molecular vaginal microbiota composition of African women.

BACKGROUND: Sociodemographic, behavioral and clinical correlates of the vaginal microbiome (VMB) as characterized by molecular methods have not been adequately studied. VMB dominated by bacteria other than lactobacilli may cause inflammation, which may facilitate HIV acquisition and other adverse reproductive health outcomes. METHODS: We characterized the VMB of women in Kenya, Rwanda, South Africa and Tanzania (KRST) using a 16S rDNA phylogenetic microarray. Cytokines were quantified in cervicovaginal lavages. Potential sociodemographic, behavioral, and clinical correlates were also evaluated. RESULTS: Three hundred thirteen samples from 230 women were available for analysis. Five VMB clusters were identified: one cluster each dominated by Lactobacillus crispatus (KRST-I) and L. iners (KRST-II), and three clusters not dominated by a single species but containing multiple (facultative) anaerobes (KRST-III/IV/V). Women in clusters KRST-I and II had lower mean concentrations of interleukin (IL)-1α (p < 0.001) and Granulocyte Colony Stimulating Factor (G-CSF) (p = 0.01), but higher concentrations of interferon-γ-induced protein (IP-10) (p < 0.01) than women in clusters KRST-III/IV/V. A lower proportion of women in cluster KRST-I tested positive for bacterial sexually transmitted infections (STIs; ptrend = 0.07) and urinary tract infection (UTI; p = 0.06), and a higher proportion of women in clusters KRST-I and II had vaginal candidiasis (ptrend = 0.09), but these associations did not reach statistical significance. Women who reported unusual vaginal discharge were more likely to belong to clusters KRST-III/IV/V (p = 0.05). CONCLUSION: Vaginal dysbiosis in African women was significantly associated with vaginal inflammation; the associations with increased prevalence of STIs and UTI, and decreased prevalence of vaginal candidiasis, should be confirmed in larger studies.

Personalized microbial network inference via co-regularized spectral clustering.

We use Human Microbiome Project (HMP) cohort (Peterson et al., 2009) to infer personalized oral microbial networks of healthy individuals. To determine clustering of individuals with similar microbial profiles, co-regularized spectral clustering algorithm is applied to the dataset. For each cluster we discovered, we compute co-occurrence relationships among the microbial species that determine microbial network per cluster of individuals. The results of our study suggest that there are several differences in microbial interactions on personalized network level in healthy oral samples acquired from various niches. Based on the results of co-regularized spectral clustering we discover two groups of individuals with different topology of their microbial interaction network. The results of microbial network inference suggest that niche-wise interactions are different in these two groups. Our study shows that healthy individuals have different microbial clusters according to their oral microbiota. Such personalized microbial networks open a better understanding of the microbial ecology of healthy oral cavities and new possibilities for future targeted medication. The scripts written in scientific Python and in Matlab, which were used for network visualization, are provided for download on the website http://learning-machines.com/.

Interindividual variation, correlations, and sex-related differences in the salivary biochemistry of young healthy adults.

A cross-sectional observational study was conducted to evaluate interindividual biochemical variation in unstimulated whole saliva in a population of 268 systemically healthy young students, 18-30 yr of age, with no apparent caries lesions or periodontal disease. Salivary flow rate, protein content, pH, buffering capacity, mucins MUC5B and MUC7, albumin, secretory IgA, cystatin S, lactoferrin, chitinase, amylase, lysozyme, and proteases were measured using ELISAs and enzymatic activity assays. Significant differences were found between male and female subjects. Salivary pH, buffering capacity, protein content, MUC5B, secretory IgA, and chitinase activity were all lower in female subjects compared with male subjects, whereas MUC7 and lysozyme activity were higher in female subjects. There was no significant difference between sexes in salivary flow rate, albumin, cystatin S, amylase, and protease activity. Principal component analysis (PCA) and spectral clustering (SC) were used to assess intervariable relationships within the data set and to identify subgroups. Spectral clustering identified two clusters of participants, which were subsequently described. This study provides a comprehensive overview of the distribution and inter-relations of a set of important salivary biochemical variables in a systemically healthy young adult population, free of apparent caries lesions and periodontal disease. It highlights significant gender differences in salivary biochemistry.

The impact of hormonal contraception and pregnancy on sexually transmitted infections and on cervicovaginal microbiota in african sex workers.

BACKGROUND: The observed association between Depo-Provera injectable use and increased HIV acquisition may be caused by hormone-induced increased susceptibility to other sexually transmitted infections (STIs) or changes in the cervicovaginal microbiota (VMB), accompanied by genital immune activation and/or mucosal remodeling. METHODS: Rwandan female sex workers (n = 800) were interviewed about contraceptive use and sexual behavior and were tested for STIs, bacterial vaginosis by Nugent score and pregnancy, at baseline. A subset of 397 HIV-negative, nonpregnant women were interviewed and tested again at regular intervals for 2 years. The VMB of a subset of 174 women was characterized by phylogenetic microarray. Outcomes of STI and VMB were compared between women with hormonal exposures (reporting oral contraceptive or injectable use, or testing positive for pregnancy) and controls (not reporting hormonal contraception and not pregnant). RESULTS: Oral contraceptive use was associated with increased human papillomavirus prevalence (adjusted odds ratio [aOR], 3.10; 1.21-7.94) and Chlamydia trachomatis incidence (aOR, 6.13; 1.58-23.80), injectable use with increased herpes simplex virus-2 prevalence (aOR, 2.13; 1.26-3.59) and pregnancy with lower HIV prevalence (aOR, 0.45; 0.22-0.92) but higher candidiasis incidence (aOR, 2.14; 1.12-4.09). Hormonal status was not associated with Nugent score category or phylogenetic VMB clustering, but oral contraceptive users had lower semiquantitative vaginal abundance of Prevotella, Sneathia/Leptotrichia amnionii, and Mycoplasma species. CONCLUSIONS: Oral contraceptive and injectable use were associated with several STIs but not with VMB composition. The increased herpes simplex virus-2 prevalence among injectable users might explain the potentially higher HIV risk in these women, but more research is needed to confirm these results and elucidate biological mechanisms.

Early respiratory microbiota composition determines bacterial succession patterns and respiratory health in children.

RATIONALE: Many bacterial pathogens causing respiratory infections in children are common residents of the respiratory tract. Insight into bacterial colonization patterns and microbiota stability at a young age might elucidate healthy or susceptible conditions for development of respiratory disease. OBJECTIVES: To study bacterial succession of the respiratory microbiota in the first 2 years of life and its relation to respiratory health characteristics. METHODS: Upper respiratory microbiota profiles of 60 healthy children at the ages of 1.5, 6, 12, and 24 months were characterized by 16S-based pyrosequencing. We determined consecutive microbiota profiles by machine-learning algorithms and validated the findings cross-sectionally in an additional cohort of 140 children per age group. MEASUREMENTS AND MAIN RESULTS: Overall, we identified eight distinct microbiota profiles in the upper respiratory tract of healthy infants. Profiles could already be identified at 1.5 months of age and were associated with microbiota stability and change over the first 2 years of life. More stable patterns were marked by early presence and high abundance of Moraxella and Corynebacterium/Dolosigranulum and were positively associated with breastfeeding in the first period of life and with lower rates of parental-reported respiratory infections in the consecutive periods. Less stable profiles were marked by high abundance of Haemophilus or Streptococcus. CONCLUSIONS: These findings provide novel insights into microbial succession in the respiratory tract in infancy and link early-life profiles to microbiota stability and respiratory health characteristics. New prospective studies should elucidate potential implications of our findings for early diagnosis and prevention of respiratory infections. Clinical trial registered with www.clinicaltrials.gov (NCT00189020).

OmpU as a biomarker for rapid discrimination between toxigenic and epidemic Vibrio cholerae O1/O139 and non-epidemic Vibrio cholerae in a modified MALDI-TOF MS assay.

BACKGROUND: Cholera is an acute diarrheal disease caused by Vibrio cholerae. Outbreaks are caused by a genetically homogenous group of strains from serogroup O1 or O139 that are able to produce the cholera toxin. Rapid detection and identification of these epidemic strains is essential for an effective response to cholera outbreaks. RESULTS: The use of ferulic acid as a matrix in a new MALDI-TOF MS assay increased the measurable mass range of existing MALDI-TOF MS protocols for bacterial identification. The assay enabled rapid discrimination between epidemic V. cholerae O1/O139 strains and other less pathogenic V. cholerae strains. OmpU, an outer membrane protein whose amino acid sequence is highly conserved among epidemic strains of V. cholerae, appeared as a discriminatory marker in the novel MALDI-TOF MS assay. CONCLUSIONS: The extended mass range of MALDI-TOF MS measurements obtained by using ferulic acid improved the screening for biomarkers in complex protein mixtures. Differences in the mass of abundant homologous proteins due to variation in amino acid sequences can rapidly be examined in multiple samples. Here, a rapid MALDI-TOF MS assay was developed that could discriminate between epidemic O1/O139 strains and other less pathogenic V. cholerae strains based on differences in mass of the OmpU protein. It appeared that the amino acid sequence of OmpU from epidemic V. cholerae O1/O139 strains is unique and highly conserved.

Lactobacillus-dominated cervicovaginal microbiota associated with reduced HIV/STI prevalence and genital HIV viral load in African women.

Cervicovaginal microbiota not dominated by lactobacilli may facilitate transmission of HIV and other sexually transmitted infections (STIs), as well as miscarriages, preterm births and sepsis in pregnant women. However, little is known about the exact nature of the microbiological changes that cause these adverse outcomes. In this study, cervical samples of 174 Rwandan female sex workers were analyzed cross-sectionally using a phylogenetic microarray. Furthermore, HIV-1 RNA concentrations were measured in cervicovaginal lavages of 58 HIV-positive women among them. We identified six microbiome clusters, representing a gradient from low semi-quantitative abundance and diversity dominated by Lactobacillus crispatus (cluster R-I, with R denoting 'Rwanda') and L. iners (R-II) to intermediate (R-V) and high abundance and diversity (R-III, R-IV and R-VI) dominated by a mixture of anaerobes, including Gardnerella, Atopobium and Prevotella species. Women in cluster R-I were less likely to have HIV (P=0.03), herpes simplex virus type 2 (HSV-2; P<0.01), and high-risk human papillomavirus (HPV; P<0.01) and had no bacterial STIs (P=0.15). Statistically significant trends in prevalence of viral STIs were found from low prevalence in cluster R-I, to higher prevalence in clusters R-II and R-V, and highest prevalence in clusters R-III/R-IV/R-VI. Furthermore, only 10% of HIV-positive women in clusters R-I/R-II, compared with 40% in cluster R-V, and 42% in clusters R-III/R-IV/R-VI had detectable cervicovaginal HIV-1 RNA (Ptrend=0.03). We conclude that L. crispatus-dominated, and to a lesser extent L. iners-dominated, cervicovaginal microbiota are associated with a lower prevalence of HIV/STIs and a lower likelihood of genital HIV-1 RNA shedding.

Lipoprotein metabolism indicators improve cardiovascular risk prediction.

BACKGROUND: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. METHODS AND RESULTS: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls) from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (ΔAUC) and by risk reclassification (Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI)). Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH) improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. CONCLUSIONS: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

Molecular machines in the synapse: overlapping protein sets control distinct steps in neurosecretion.

Activity regulated neurotransmission shapes the computational properties of a neuron and involves the concerted action of many proteins. Classical, intuitive working models often assign specific proteins to specific steps in such complex cellular processes, whereas modern systems theories emphasize more integrated functions of proteins. To test how often synaptic proteins participate in multiple steps in neurotransmission we present a novel probabilistic method to analyze complex functional data from genetic perturbation studies on neuronal secretion. Our method uses a mixture of probabilistic principal component analyzers to cluster genetic perturbations on two distinct steps in synaptic secretion, vesicle priming and fusion, and accounts for the poor standardization between different studies. Clustering data from 121 perturbations revealed that different perturbations of a given protein are often assigned to different steps in the release process. Furthermore, vesicle priming and fusion are inversely correlated for most of those perturbations where a specific protein domain was mutated to create a gain-of-function variant. Finally, two different modes of vesicle release, spontaneous and action potential evoked release, were affected similarly by most perturbations. This data suggests that the presynaptic protein network has evolved as a highly integrated supramolecular machine, which is responsible for both spontaneous and activity induced release, with a group of core proteins using different domains to act on multiple steps in the release process.