Evgeni Tsivtsivadze

Technische Universiteit Eindhoven, Eindhoven, North Brabant, Netherlands

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Publications (56)55.25 Total impact

  • Source
    [Show abstract] [Hide abstract] ABSTRACT: 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. 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. 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). 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.
    Full-text · Article · Dec 2015 · BMC Infectious Diseases
  • Sultan Imangaliyev · Bart Keijser · Wim Crielaard · Evgeni Tsivtsivadze
    [Show abstract] [Hide abstract] ABSTRACT: We use Human Microbiome Project (HMP) cohort [1] 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/. Copyright © 2015. Published by Elsevier Inc.
    No preview · Article · Apr 2015 · Methods
  • [Show abstract] [Hide abstract] ABSTRACT: 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. 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). 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. 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.
    No preview · Article · Mar 2015 · Sex Transm Dis
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    [Show abstract] [Hide abstract] ABSTRACT: 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. © 2015 Eur J Oral Sci.
    Full-text · Article · Mar 2015 · European Journal Of Oral Sciences
  • [Show abstract] [Hide abstract] ABSTRACT: 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. 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. 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). 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.
    No preview · Article · Jan 2015
  • [Show abstract] [Hide abstract] ABSTRACT: 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 young age might elucidate healthy or susceptible conditions for development of respiratory disease. Objective. To study bacterial succession of the respiratory microbiota in the first two 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 8 distinct microbiota profiles in the upper respiratory tract of healthy infants. Profiles could already been identified at 1.5 months of age, and were associated with microbiota stability and change over the first two years of life. More stable patterns were marked by early presence and high abundance of Moraxella and Corynebacterium/Dolosigranulum, 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 registration available at www.clinicaltrials.gov, ID NCT00189020.
    No preview · Article · Oct 2014 · American Journal of Respiratory and Critical Care Medicine
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    [Show abstract] [Hide abstract] ABSTRACT: 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.
    Full-text · Article · Jun 2014 · BMC Microbiology
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    [Show abstract] [Hide abstract] ABSTRACT: 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. 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. 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.
    Full-text · Article · Mar 2014 · PLoS ONE
  • [Show abstract] [Hide abstract] ABSTRACT: 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.The ISME Journal advance online publication, 6 March 2014; doi:10.1038/ismej.2014.26.
    No preview · Article · Mar 2014 · The ISME Journal
  • Sultan Imangaliyev · Bart Keijser · Wim Crielaard · Evgeni Tsivtsivadze
    [Show abstract] [Hide abstract] ABSTRACT: As the amount of metagenomic data grows rapidly, online statistical learning algorithms are poised to play key role in metagenome analysis tasks. Frequently, data are only partially labeled, namely dataset contains partial information about the problem of interest. This work presents an algorithm and a learning framework that is naturally suitable for the analysis of large scale, partially labeled metagenome datasets. We propose an online multi-output algorithm that learns by sequentially co-regularizing prediction functions on unlabeled data points and provides improved performance in comparison to several supervised methods. We evaluate predictive performance of the proposed methods on NIH Human Microbiome Project dataset. In particular we address the task of predicting relative abundance of Porphyromonas species in the oral cavity. In our empirical evaluation the proposed method outperforms several supervised regression techniques as well as leads to notable computational benefits when training the predictive model.
    No preview · Conference Paper · Dec 2013
  • Evgeni Tsivtsivadze · Tom Heskes
    [Show abstract] [Hide abstract] ABSTRACT: We propose a novel sparse preference learning/ranking algorithm. Our algorithm approximates the true utility function by a weighted sum of basis functions using the squared loss on pairs of data points, and is a generalization of the kernel matching pursuit method. It can operate both in a supervised and a semi-supervised setting and allows efficient search for multiple, near-optimal solutions. Furthermore, we describe the extension of the algorithm suitable for combined ranking and regression tasks. In our experiments we demonstrate that the proposed algorithm outperforms several state-of-the-art learning methods when taking into account unlabeled data and performs comparably in a supervised learning scenario, while providing sparser solutions.
    No preview · Article · Jul 2013
  • Evgeni Tsivtsivadze · Tom Heskes · Armand Paauw
    [Show abstract] [Hide abstract] ABSTRACT: In various learning problems data can be available in different representations, often referred to as views. We propose multi-class classification method that is particularly suitable for multi-view learning setting. The algorithm uses co-regularization and error-correcting techniques to leverage information from multiple views and in our empirical evaluation notably outperforms several state-of-the-art classification methods on publicly available datasets. Furthermore, we apply the proposed algorithm for identification of the pathogenic bacterial strains from the recently collected biomedical dataset. Our algorithm gives a low classification error rate of 5%, allows rapid identification of the pathogenic microorganisms, and can aid effective response to an infectious disease outbreak.
    No preview · Chapter · May 2013
  • No preview · Chapter · May 2013
  • Tom de Ruijter · Evgeni Tsivtsivadze · Tom Heskes
    [Show abstract] [Hide abstract] ABSTRACT: We propose an online co-regularized learning algorithm for classification and regression tasks. We demonstrate that by sequentially co-regularizing prediction functions on unlabeled data points, our algorithm provides improved performance in comparison to supervised methods on several UCI benchmarks and a real world natural language processing dataset. The presented algorithm is particularly applicable to learning tasks where large amounts of (unlabeled) data are available for training. We also provide an easy to set-up and use Python implementation of our algorithm.
    No preview · Chapter · Oct 2012
  • Daniel Kühlwein · Twan van Laarhoven · Evgeni Tsivtsivadze · Josef Urban · Tom Heskes
    [Show abstract] [Hide abstract] ABSTRACT: In this paper, an overview of state-of-the-art techniques for premise selection in large theory mathematics is provided, and new premise selection techniques are introduced. Several evaluation metrics are introduced, compared and their appropriateness is discussed in the context of automated reasoning in large theory mathematics. The methods are evaluated on the MPTP2078 benchmark, a subset of the Mizar library, and a 10% improvement is obtained over the best method so far.
    No preview · Conference Paper · Jun 2012
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    Dataset: Figure S2
    [Show abstract] [Hide abstract] ABSTRACT: Analysis of correlations between experimental conditions (salt concentrations and days in vitro) on the functional variables from the 35 control experiments. In each panel we report the number of variables (because of missing values this number varies), the Pearson correlation coefficient and the p-value of the correlation. (PDF)
    Preview · Dataset · Apr 2012
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    Dataset: Figure S1
    [Show abstract] [Hide abstract] ABSTRACT: Pearson pairwise correlations among the functional variables from the 35 control experiments. The panels on the diagonal show a histogram of each of the four functional variables. The off-diagonal panels show the pairwise correlations. In each panel we report the number of variables (because of missing values this number varies), the Pearson correlation coefficient and the p-value of a linear regression (from an F-test). A and Pv showed a correlation which was significant after Bonferroni correction for multiple comparisons (p<0.0083). Correlation for other variable combinations was not significant. (PDF)
    Preview · Dataset · Apr 2012
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    Dataset: Figure S4
    [Show abstract] [Hide abstract] ABSTRACT: Cluster assignment probability entropies Ej as a function of RMS perturbation distance from control condition RMSj. The color coding of the dots is determined by the cluster for which the perturbation has the largest assignment probability qkj. (PDF)
    Preview · Dataset · Apr 2012
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    Dataset: Table S2
    [Show abstract] [Hide abstract] ABSTRACT: Excel file with clustering results for 121 genetic perturbations: file contains sheets sorted by gene name, cluster, and author. Column description: (A) name of first author on publication, (B) Pubmed ID, (C) abbreviated NCBI name of the perturbed gene or applied compound, (D) alias used in the publication if different from the NCBI gene name, (E) full NCBI gene name, (F) name of the gene family to which the perturbed gene (isoform) belongs, (G) description of the perturbation, (H) description of the control group to which the perturbation is normalized, (I) row index in the presynaptic gene database (Table S1) of the perturbation, (J) row index in the presynaptic gene database of the control group, (K) index of perturbation in the co-occurrence matrix (Figure 3), (L-O) synaptic variables normalized to control, (P-W) log2 transformed normalized synaptic variables with log2 transformed SEM of the normalized variables, (X-Z) probabilities for clusters 1–3, (AB) entropy measure for cluster specificity calculated from the cluster probabilities using eq. 13, (AD) RMS calculated from the log2 transformed normalized data using eq. 12. (XLS)
    Preview · Dataset · Apr 2012
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    Dataset: Table S4
    [Show abstract] [Hide abstract] ABSTRACT: Excel file with the average cluster probabilities per gene calculated from the probabilities of the individual perturbations. (XLS)
    Preview · Dataset · Apr 2012

Publication Stats

300 Citations
55.25 Total Impact Points

Institutions

  • 2012
    • Technische Universiteit Eindhoven
      • Department of Electrical Engineering
      Eindhoven, North Brabant, Netherlands
  • 2010-2012
    • Radboud University Nijmegen
      • Institute for Computing and Information Sciences
      Nymegen, Gelderland, Netherlands
  • 2005-2009
    • University of Turku
      • • Department of Information Technology
      • • Turku Centre for Computing Science, TUCS
      Turku, Province of Western Finland, Finland