Sultan Imangaliyev

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.

On the ecosystemic network of saliva in healthy young adults

A dysbiotic state is believed to be a key factor in the onset of oral disease. Although oral diseases have been studied for decades, our understanding of oral health, the boundaries of a healthy oral ecosystem and ecological shift toward dysbiosis is still limited. Here, we present the ecobiological heterogeneity of the salivary ecosystem and relations between the salivary microbiome, salivary metabolome and host-related biochemical salivary parameters in 268 healthy adults after overnight fasting. Gender-specific differences in the microbiome and metabolome were observed and were associated with salivary pH and dietary protein intake. Our analysis grouped the individuals into five microbiome and four metabolome-based clusters that significantly related to biochemical parameters of saliva. Low salivary pH and high lysozyme activity were associated with high proportions of streptococcal phylotypes and increased membrane-lipid degradation products. Samples with high salivary pH displayed increased chitinase activity, higher abundance of Veillonella and Prevotella species and higher levels of amino acid fermentation products, suggesting proteolytic adaptation. An over-specialization toward either a proteolytic or a saccharolytic ecotype may indicate a shift toward a dysbiotic state. Their prognostic value and the degree to which these ecotypes are related to increased disease risk remains to be determined.

Feature Selection via Co-regularized Sparse-Group Lasso

We propose the co-regularized sparse-group lasso algorithm: a technique that allows the incorporation of auxiliary information into the learning task in terms of “groups” and “distances” among the predictors. The proposed algorithm is particularly suitable for a wide range of biological applications where good predictive performance is required and, in addition to that, it is also important to retrieve all relevant predictors so as to deepen the understanding of the underlying biological process. Our cost function requires related groups of predictors to provide similar contributions to the final response, and thus, guides the feature selection process using auxiliary information. We evaluate the proposed method on a synthetic dataset and examine various settings where its application is beneficial in comparison to the standard lasso, elastic net, group lasso and sparse-group lasso techniques. Last but not least, we make a python implementation of our algorithm available for download and free to use (Available at www.learning-machines.com).

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.

Deep learning for classification of dental plaque images

Dental diseases such as caries or gum disease are caused by prolonged exposure to pathogenic plaque. Assessment of such plaque accumulation can be used to identify individuals at risk. In this work we present an automated dental red autofluorescence plaque image classification model based on application of Convolutional Neural Networks (CNN) on Quantitative Light-induced Fluorescence (QLF) images. CNN model outperforms other state of the art classification models providing a 0.75 ± 0.05 F1-score on test dataset. The model directly benefits from multi-channel representation of the images resulting in improved performance when all three colour channels were used.

Effect of experimental gingivitis induction and erythritol on the salivary metabolome and functional biochemistry of systemically healthy young adults

IntroductionUnderstanding the changes occurring in the oral ecosystem during development of gingivitis could help improve prevention and treatment strategies for oral health. Erythritol is a non-caloric polyol proposed to have beneficial effects on oral health.ObjectivesTo examine the effect of experimental gingivitis and the effect of erythritol on the salivary metabolome and salivary functional biochemistry.MethodsIn a two-week experimental gingivitis challenge intervention study, non-targeted, mass spectrometry-based metabolomic profiling was performed on saliva samples from 61 healthy adults, collected at five time-points. The effect of erythritol was studied in a randomized, controlled trial setting. Fourteen salivary biochemistry variables were measured with antibody- or enzymatic activity-based assays.ResultsBacterial amino acid catabolites (cadaverine, N-acetylcadaverine, and α-hydroxyisovalerate) and end-products of bacterial alkali-producing pathways (N-α-acetylornithine and γ-aminobutyrate) increased significantly during the experimental gingivitis. Significant changes were found in a set of 13 salivary metabolite ratios composed of host cell membrane lipids involved in cell signaling, host responses to bacteria, and defense against free radicals. An increase in mevalonate was also observed. There were no significant effects of erythritol. No significant changes were found in functional salivary biochemistry.ConclusionsThe findings underline a dynamic interaction between the host and the oral microbial biofilm during an experimental induction of gingivitis.

Online semi-supervised learning: Algorithm and application in metagenomics

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.

Personalized microbial network inference via co-regularized spectral clustering

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.