scholarly journals Functional Profiling of Saliva Microbiome is Essential for Oral Cancer Prediction

2020 ◽  
Author(s):  
Jiung-Wen Chen ◽  
Wei-Fan Chiang ◽  
Jer-Horng Wu ◽  
Yuh-Ling Chen ◽  
Wei-Sheng Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Oral Cancer ◽  
Bacterial Communities ◽  
Predictive Performance ◽  
Oral Microbiome ◽  
Oral Carcinogenesis ◽  
Verrucous Hyperplasia ◽  
Host Health ◽  
Functional Profiling ◽  
And Function

Abstract Background: The association between microbiome and host disease has been documented in oral cancer, one of the leading cancers worldwide. Huge efforts are made to use the profile of oral microbiome and distinct signature species as markers to distinguish oral cancer patients from healthy individuals. The previous results, however, remain inconclusive. The assembly mechanisms of oral microbiome and their response to changes in oral carcinogenesis also have not been characterized. Here, using 16S rRNA gene amplicon sequencing and in-silico function prediction approaches, we analyzed the saliva microbiome in cohorts of orally healthy, oral verrucous hyperplasia, and oral cancer at taxon and function levels, and compared their corresponding predictive performance of oral cancer using machine learning algorithms.Results: Analyses of diversity and phylogenetic profiles of bacterial communities in saliva showed that microbiome dysbiosis was significantly linked to oral health status. As oral health deteriorated, the number of core species (>75% prevalence) as a percentage of overall species richness declined. In line with the null model-based analysis, taxonomic and functional assemblies of saliva microbiomes were primarily governed by the stochastic processes. Correspondingly, the quantitative assessment of partitioned beta-diversity suggested extremely high species turnover but low function turnover, revealing a functional redundancy of the oral ecosystem. Functional beta-diversity in salvia microbiome shifted from turnover to nestedness during carcinogenesis of oral verrucous hyperplasia, but this pattern was not observed at the taxon level. Moreover, using both taxon and function data as training features for machine learning-aided prediction on host health status supports a superior predictive performance when using functional profiling. Similar results were also obtained and validated using publicly accessible data.Conclusions: Our results suggest that altered oral bacterial communities are highly associated with carcinogenesis of oral verrucous hyperplasia. Partly owing to high taxonomic turnover and stochastic assembly processes of the oral ecosystem, discovering oral microbial consortia as universal biomarkers for oral cancer may prove difficult and arduous. Functional profiles are relatively stable and evolve a nestedness pattern during oral carcinogenesis, serving as a new benchmark to study the interplay of the oral microbiome and host health in the future.

scholarly journals Role of Oral Microbiome Signatures in Diagnosis and Prognosis of Oral Cancer

2019 ◽  
Vol 18 ◽  
pp. 153303381986735 ◽  
Author(s):  
Indranil Chattopadhyay ◽  
Mukesh Verma ◽  
Madhusmita Panda
Keyword(s):  
Oral Cancer ◽  
Epithelial Cells ◽  
Oral Cavity ◽  
Bacterial Species ◽  
Treatment Strategies ◽  
Genetic Alterations ◽  
Oral Microbiome ◽  
Oral Carcinogenesis ◽  
Early Diagnostic

Despite advancement in cancer treatment, oral cancer has a poor prognosis and is often detected at late stage. To overcome these challenges, investigators should search for early diagnostic and prognostic biomarkers. More than 700 bacterial species reside in the oral cavity. The oral microbiome population varies by saliva and different habitats of oral cavity. Tobacco, alcohol, and betel nut, which are causative factors of oral cancer, may alter the oral microbiome composition. Both pathogenic and commensal strains of bacteria have significantly contributed to oral cancer. Numerous bacterial species in the oral cavity are involved in chronic inflammation that lead to development of oral carcinogenesis. Bacterial products and its metabolic by-products may induce permanent genetic alterations in epithelial cells of the host that drive proliferation and/or survival of epithelial cells. Porphyromonas gingivalis and Fusobacterium nucleatum induce production of inflammatory cytokines, cell proliferation, and inhibition of apoptosis, cellular invasion, and migration thorough host cell genomic alterations. Recent advancement in metagenomic technologies may be useful in identifying oral cancer–related microbiome, their genomes, virulence properties, and their interaction with host immunity. It is very important to address which bacterial species is responsible for driving oral carcinogenesis. Alteration in the oral commensal microbial communities have potential application as a diagnostic tool to predict oral squamous cell carcinoma. Clinicians should be aware that the protective properties of the resident microflora are beneficial to define treatment strategies. To develop highly precise and effective therapeutic approaches, identification of specific oral microbiomes may be required. In this review, we narrate the role of microbiome in the progression of oral cancer and its role as an early diagnostic and prognostic biomarker for oral cancer.

scholarly journals Taxonomic and Functional Dysregulation in Salivary Microbiomes During Oral Carcinogenesis

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiung-Wen Chen ◽  
Jer-Horng Wu ◽  
Wei-Fan Chiang ◽  
Yuh-Ling Chen ◽  
Wei-Sheng Wu ◽  
...  
Keyword(s):  
Health Status ◽  
Oral Health ◽  
Oral Cancer ◽  
Metabolic Pathways ◽  
Beta Diversity ◽  
Oral Health Status ◽  
Functional Gene ◽  
Gene Content ◽  
Oral Carcinogenesis ◽  
Verrucous Hyperplasia

Exploring microbial community compositions in humans with healthy versus diseased states is crucial to understand the microbe-host interplay associated with the disease progression. Although the relationship between oral cancer and microbiome was previously established, it remained controversial, and yet the ecological characteristics and their responses to oral carcinogenesis have not been well studied. Here, using the bacterial 16S rRNA gene amplicon sequencing along with the in silico function analysis by PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2), we systematically characterized the compositions and the ecological drivers of saliva microbiome in the cohorts of orally healthy, non-recurrent oral verrucous hyperplasia (a pre-cancer lesion), and oral verrucous hyperplasia–associated oral cancer at taxonomic and function levels, and compared them with the re-analysis of publicly available datasets. Diversity analyses showed that microbiome dysbiosis in saliva was significantly linked to oral health status. As oral health deteriorated, the number of core species declined, and metabolic pathways predicted by PICRUSt2 were dysregulated. Partitioned beta-diversity revealed an extremely high species turnover but low function turnover. Functional beta-diversity in saliva microbiome shifted from turnover to nestedness during oral carcinogenesis, which was not observed at taxonomic levels. Correspondingly, the quantitative analysis of stochasticity ratios showed that drivers of microbial composition and functional gene content of saliva microbiomes were primarily governed by the stochastic processes, yet the driver of functional gene content shifted toward deterministic processes as oral cancer developed. Re-analysis of publicly accessible datasets supported not only the distinctive family taxa of Veillonellaceae and Actinomycetaceae present in normal cohorts but also that Flavobacteriaceae and Peptostreptococcaceae as well as the dysregulated metabolic pathways of nucleotides, amino acids, fatty acids, and cell structure were related to oral cancer. Using predicted functional profiles to elucidate the correlations to the oral health status shows superior performance than using taxonomic data among different studies. These findings advance our understanding of the oral ecosystem in relation to oral carcinogenesis and provide a new direction to the development of microbiome-based tools to study the interplay of the oral microbiome, metabolites, and host health.

Machine Learning-Based Scoring Functions. Development and Applications with SAnDReS.

2020 ◽  
Vol 27 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Camila Rizzotto ◽  
Walter Filgueira de Azevedo Junior
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Drug Targets ◽  
Computational Models ◽  
Factor Xa ◽  
Coagulation Factor ◽  
Predictive Performance ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Molegro Virtual Docker

Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. Objective: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. Method: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding, and thermodynamic data to create targeted scoring functions. Results: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases, and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. Conclusion: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker, and AutoDock Vina.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

2020 ◽  
Vol 28 (2) ◽  
pp. 253-265 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Amauri Duarte da Silva ◽  
Walter Filgueira de Azevedo
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Cyclin Dependent Kinase ◽  
Learning Models ◽  
Learning Techniques ◽  
Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

scholarly journals Kernel methods in system identification, machine learning and function estimation: A survey

Automatica ◽  
2014 ◽  
Vol 50 (3) ◽  
pp. 657-682 ◽  
Author(s):  
Gianluigi Pillonetto ◽  
Francesco Dinuzzo ◽  
Tianshi Chen ◽  
Giuseppe De Nicolao ◽  
Lennart Ljung
Keyword(s):  
Machine Learning ◽  
System Identification ◽  
Kernel Methods ◽  
Function Estimation ◽  
And Function

scholarly journals A Degeneration Gradient of Poplar Trees Contributes to the Taxonomic, Functional, and Resistome Diversity of Bacterial Communities in Rhizosphere Soils

2021 ◽  
Vol 22 (7) ◽  
pp. 3438
Author(s):  
Juan Liu ◽  
Xiangwei He ◽  
Jingya Sun ◽  
Yuchao Ma
Keyword(s):  
Soil Fertility ◽  
Microbial Diversity ◽  
Resistance Genes ◽  
Bacterial Communities ◽  
Rhizosphere Soil ◽  
Antibiotics Resistance ◽  
Plant Growth Promoting Bacteria ◽  
Soil Microbial Diversity ◽  
Antibiotic Target ◽  
And Function

Bacterial communities associated with roots influence the health and nutrition of the host plant. However, the microbiome discrepancy are not well understood under different healthy conditions. Here, we tested the hypothesis that rhizosphere soil microbial diversity and function varies along a degeneration gradient of poplar, with a focus on plant growth promoting bacteria (PGPB) and antibiotic resistance genes. Comprehensive metagenomic analysis including taxonomic investigation, functional detection, and ARG (antibiotics resistance genes) annotation revealed that available potassium (AK) was correlated with microbial diversity and function. We proposed several microbes, Bradyrhizobium, Sphingomonas, Mesorhizobium, Nocardioides, Variovorax, Gemmatimonadetes, Rhizobacter, Pedosphaera, Candidatus Solibacter, Acidobacterium, and Phenylobacterium, as candidates to reflect the soil fertility and the plant health. The highest abundance of multidrug resistance genes and the four mainly microbial resistance mechanisms (antibiotic efflux, antibiotic target protection, antibiotic target alteration, and antibiotic target replacement) in healthy poplar rhizosphere, corroborated the relationship between soil fertility and microbial activity. This result suggested that healthy rhizosphere soil harbored microbes with a higher capacity and had more complex microbial interaction network to promote plant growing and reduce intracellular levels of antibiotics. Our findings suggested a correlation between the plant degeneration gradient and bacterial communities, and provided insight into the role of high-turnover microbial communities as well as potential PGPB as real-time indicators of forestry soil quality, and demonstrated the inner interaction contributed by the bacterial communities.

scholarly journals Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)

2021 ◽  
Author(s):  
Kazutaka Uchida ◽  
Junichi Kouno ◽  
Shinichi Yoshimura ◽  
Norito Kinjo ◽  
Fumihiro Sakakibara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Vessel Occlusion ◽  
Predictive Values ◽  
Training Cohort ◽  
Sensitivity Specificity

AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

scholarly journals Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose

2021 ◽  
Vol 18 (13) ◽  
pp. 7139
Author(s):  
Yue Wu ◽  
Jieqiang Zhu ◽  
Peter Fu ◽  
Weida Tong ◽  
Huixiao Hong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autoimmune Diseases ◽  
Odds Ratio ◽  
Area Under Curve ◽  
Predictive Performance ◽  
Drug Induced ◽  
Drug Candidates ◽  
Daily Dose ◽  
Structural Alerts ◽  
Underlying Mechanisms

An effective approach for assessing a drug’s potential to induce autoimmune diseases (ADs) is needed in drug development. Here, we aim to develop a workflow to examine the association between structural alerts and drugs-induced ADs to improve toxicological prescreening tools. Considering reactive metabolite (RM) formation as a well-documented mechanism for drug-induced ADs, we investigated whether the presence of certain RM-related structural alerts was predictive for the risk of drug-induced AD. We constructed a database containing 171 RM-related structural alerts, generated a dataset of 407 AD- and non-AD-associated drugs, and performed statistical analysis. The nitrogen-containing benzene substituent alerts were found to be significantly associated with the risk of drug-induced ADs (odds ratio = 2.95, p = 0.0036). Furthermore, we developed a machine-learning-based predictive model by using daily dose and nitrogen-containing benzene substituent alerts as the top inputs and achieved the predictive performance of area under curve (AUC) of 70%. Additionally, we confirmed the reactivity of the nitrogen-containing benzene substituent aniline and related metabolites using quantum chemistry analysis and explored the underlying mechanisms. These identified structural alerts could be helpful in identifying drug candidates that carry a potential risk of drug-induced ADs to improve their safety profiles.

Gill microbiome structure and function in the chemosymbiotic coastal lucinid Stewartia floridana

2021 ◽  
Author(s):  
Shen Jean Lim ◽  
Brenton Davis ◽  
Danielle Gill ◽  
John Swetenburg ◽  
Laurie C Anderson ◽  
...  
Keyword(s):  
Amino Acids ◽  
Nutrient Cycling ◽  
Bacterial Communities ◽  
Structure And Function ◽  
Differentially Expressed ◽  
Seagrass Species ◽  
Bare Sand ◽  
And Function

Abstract Lucinid bivalves harbor environmentally acquired, chemosynthetic, gammaproteobacterial gill endosymbionts. Lucinid gill microbiomes, which may contain other gammaproteobacterial and/or spirochete taxa, remain under-sampled. To understand inter-host variability of the lucinid gill microbiome, specifically in the bacterial communities, we analyzed the microbiome content of Stewartia floridana collected from Florida. Sampled gills contained a monospecific gammaproteobacterial endosymbiont expressing lithoautotrophic, mixotrophic, diazotrophic, and C1 compound oxidation-related functions previously characterized in similar lucinid species. Another low-abundance Spirochaeta-like species in ∼72% of the sampled gills was most closely related to Spirochaeta-like species in another lucinid Phacoides pectinatus and formed a clade with known marine Spirochaeta symbionts. The spirochete expressed genes were involved in heterotrophy and the transport of sugars, amino acids, peptides, and other substrates. Few muscular and neurofilament genes from the host and none from the gammaproteobacterial and spirochete symbionts were differentially expressed among quadrats predominantly covered with seagrass species or 80% bare sand. Our results suggest that spirochetes are facultatively associated with S. floridana, with potential scavenging and nutrient cycling roles. Expressed stress- and defense-related functions in the host and symbionts also suggest species-species communications, which highlight the need for further study of the interactions among lucinid hosts, their microbiomes, and their environment.

scholarly journals Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Imogen Schofield ◽  
David C. Brodbelt ◽  
Noel Kennedy ◽  
Stijn J. M. Niessen ◽  
David B. Church ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cushing’S Syndrome ◽  
Clinical Decision Making ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Cushing's Syndrome ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Clinical Records

AbstractCushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.

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