Immunoglobulin gene expression profiles and microbiome characteristics in periodontitis in nonhuman primates.

Colonization of mucosal tissues throughout the body occurs by a wide array of bacteria in the microbiome that stimulate the cells and tissues, as well as respond to changes in the local milieu. A feature of periodontitis is the detection of adaptive immune responses to members of the oral microbiome that show specificity and changes with disease and treatment. Thus, variations in antibody responses are noted across the population and affected by aging, albeit, data are still unclear as to how these differences relate to disease risk and expression. This study used a nonhuman primate model of experimental periodontitis to track local microbiome changes as they related to the use and expression of a repertoire of immunoglobulin genes in gingival tissues. Gingival tissue biopsies from healthy tissues and following ligature-placement for disease initiation and progression provided gene expression analysis. Additionally, following removal of the ligatures, clinical healing occurs with gene expression in disease resolved tissues. Groups of 9 animals (young: <3 yrs., adolescent: 3-7 yrs., adult -12 to 15 yrs.; aged: 17-22 yrs) were used in the investigation. In healthy tissues, young and adolescent animals showed levels of expression of 78 Ig genes that were uniformly less than adults. In contrast, ⅔ of the Ig genes were elevated by > 2-fold in the aged samples. Specific increases in an array of the Ig gene transcripts were detected in adults at disease initiation and throughout progression, while increases in young and adolescent animals were observed only with disease progression, and in aged samples primarily late in disease progression. Resolved lesions continued to demonstrate elevated levels of Ig gene expression in only young, adolescent and adult animals. The array of Ig genes significantly correlated with inflammatory, tissue biology and hypoxia genes in the gingival tissues, with variations associated with age. In the young group of animals, specific members of the oral microbiome positively correlated with Ig gene expression, while in the older animals, many of these correlations were negative. Significant correlations were observed with a select assortment of bacterial OTUs and multiple Ig genes in both younger and older animal samples, albeit the genera/species showed little overlap. Incorporating this array of microbes and host responses clearly discriminated the various time points in transition from health to disease and resolution in both the young and adult animals. The results support a major importance of adaptive immune responses in the kinetics of periodontal lesion formation, and support aging effects on the repertoire of Ig genes that may relate to the increased prevalence and severity of periodontitis with age.

Variations in temporal trends in non-traumatic dental condition related emergencies.

OBJECTIVE: The objective of the study was to investigate temporal trends in non-traumatic dental condition (NTDC) related emergency visits at Emergency Department (ED), urgent care (UC), and at a Federally Qualified Health Center (FQHC) that providing dental services to a mid-sized rural community. METHODS: Temporal trends over a 9-year period (2008-2016) in NTDC rates at ED, UC, FQHC and in a region around the FQHC were determined. Statistically significant changes (α = 0.05) in the proportion of NTDC visits between FQHC and UC across each of the time points were investigated. RESULTS: Proportion of NTDC ED visits was relatively stable over the study period; whereas those at FQHC exceeded those at UC site beginning 2012 and were significantly (α = 0.05) higher than that of UC subsequently (2015-2016). CONCLUSIONS: NTDCs are preventable dental conditions and the care provided in treating NTDCs in emergency settings is palliative and does not address the underlying conditions resulting in poor outcomes. The results presented elucidate the critical role of FQHCs in significantly reducing NTDC visits. These might be precursors to a potential shift in NTDC care seeking behavior and expected to favorably impact oral health outcomes.

Transcriptomic phases of periodontitis lesions using the nonhuman primate model.

We used a nonhuman primate model of ligature-induced periodontitis to identify patterns of gingival transcriptomic after changes demarcating phases of periodontitis lesions (initiation, progression, resolution). A total of 18 adult Macaca mulatta (12-22 years) had ligatures placed (premolar, 1st molar teeth) in all 4 quadrants. Gingival tissue samples were obtained (baseline, 2 weeks, 1 and 3 months during periodontitis and at 5 months resolution). Gene expression was analyzed by microarray [Rhesus Gene 1.0 ST Array (Affymetrix)]. Compared to baseline, a large array of genes were significantly altered at initiation (n = 6049), early progression (n = 4893), and late progression (n = 5078) of disease, with the preponderance being up-regulated. Additionally, 1918 genes were altered in expression with disease resolution, skewed towards down-regulation. Assessment of the genes demonstrated specific profiles of epithelial, bone/connective tissue, apoptosis/autophagy, metabolism, regulatory, immune, and inflammatory responses that were related to health, stages of disease, and tissues with resolved lesions. Unique transcriptomic profiles occured during the kinetics of the periodontitis lesion exacerbation and remission. We delineated phase specific gene expression profiles of the disease lesion. Detection of these gene products in gingival crevicular fluid samples from human disease may contribute to a better understanding of the biological dynamics of the disease to improve patient management.

Variations in Schedule III prescription patterns in a Medicaid population pre- and post-policy.

The present study investigated variations in patient movement patterns between prescribers before and after House Bill 1 (HB1) implementation in Kentucky using network abstractions (PPN: prescriber-prescriber networks) from a one-month cross-sectional Schedule III prescription data in a Medicaid population. Network characteristics such as degree centrality distribution of PPN was positively skewed and revealed Dental Practitioners to be the highly connected specialty with opioid analgesic hydrocodone-acetaminophen to be the most commonly prescribed drug. Taxonomy enrichment of the prescriber specialties in PPN using chi-square test revealed a reduction in the enriched taxonomies Post-HB1 compared to Pre-HB1 with Dental practitioners being constitutively enriched (p < 0.05). PPNs were also found to exhibit rich community structure revealing inherent clustering of prescribers as a result of patient movement, and were markedly different from those generated by random graph models. The magnitude of deviation from random graphs decreased Post-HB1 relative to Pre-HB1. The proposed network approach provides system-level insights into prescribers with potential to complement classical reductionist approaches and aggregate statistical measures used in assessing changes in prescription patterns pre- and post- policy implementation. It can provide preliminary cues into drug seeking behavior, and facilitate targeted surveillance of prescriber communities.

Oral Microbiome and Gingival Gene Expression of Inflammatory Biomolecules With Aging and Periodontitis.

Although data describe the presence and increase of inflammatory mediators in the local environment in periodontitis vs. health in humans, details regarding how these responses evolve in the transition from health to disease, changes during disease progression, and features of a resolved lesion remain unknown. This study used a nonhuman primate model of ligature-induced periodontitis in young, adolescent, adult, and aged animals to document features of inflammatory response affected by age. Rhesus monkeys had ligatures tied and provided gingival tissue biopsy specimens at baseline, 0.5, 1, and 3 months of disease and at 5 months of the study, which was 2 months post-ligature removal for clinically resolved tissues. The transcriptome was assessed using microarrays for chemokine (n = 41), cytokine (n = 45), chemokine receptor (n = 21), cytokine receptor (n = 37), and lipid mediator (n = 31) genes. Limited differences were noted in healthy tissues for chemokine expression with age; however, chemokine receptor genes were decreased in young but elevated in aged samples. IL1A, IL36A, and IL36G cytokines were decreased in the younger groups, with IL36A elevated in aged animals. IL10RA/IL10RB cytokine receptors were altered with age. Striking variation in the lipid mediator genes in health was observed with nearly 60% of these genes altered with age. A specific repertoire of chemokine and chemokine receptor genes was affected by the disease process, predominated by changes during disease initiation. Cytokine/cytokine receptor genes were also elevated with disease initiation, albeit IL36B, IL36G, and IL36RN were all significantly decreased throughout disease and resolution. Significant changes were observed in similar lipid mediator genes with disease and resolution across the age groups. Examination of the microbiome links to the inflammatory genes demonstrated that specific microbes, including Fusobacterium, P. gingivalis, F. alocis, Pasteurellaceae, and Prevotella are most frequently significantly correlated. These correlations were generally positive in older animals and negative in younger specimens. Gene expression and microbiome patterns from baseline were distinctly different from disease and resolution. These results demonstrate patterns of inflammatory gene expression throughout the phases of the induction of a periodontal disease lesion. The patterns show a very different relationship to specific members of the oral microbiome in younger compared with older animals.

Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks.

Surrogate testing techniques have been used widely to investigate the presence of dynamical nonlinearities, an essential ingredient of deterministic chaotic processes. Traditional surrogate testing subscribes to statistical hypothesis testing and investigates potential differences in discriminant statistics between the given empirical sample and its surrogate counterparts. The choice and estimation of the discriminant statistics can be challenging across short time series. Also, conclusion based on a single empirical sample is an inherent limitation. The present study proposes a recurrent neural network classification framework that uses the raw time series obviating the need for discriminant statistic while accommodating multiple time series realizations for enhanced generalizability of the findings. The results are demonstrated on short time series with lengths (L = 32, 64, 128) from continuous and discrete dynamical systems in chaotic regimes, nonlinear transform of linearly correlated noise and experimental data. Accuracy of the classifier is shown to be markedly higher than ≫50% for the processes in chaotic regimes whereas those of nonlinearly correlated noise were around ~50% similar to that of random guess from a one-sample binomial test. These results are promising and elucidate the usefulness of the proposed framework in identifying potential dynamical nonlinearities from short experimental time series.

Network Abstractions of Prescription Patterns in a Medicaid Population.

Understanding prescription patterns have relied largely on aggregate statistical measures. Evidence of doctor- shopping, inappropriate prescribing, drug diversion and patient seeking prescription drugs across multiple prescribers demand understanding the concerted working of prescribers and prescriber communities as opposed to treating them as independent entities. We model potential associations between prescribers as prescriber-prescriber network (PPN) and subsequently investigate its properties across Schedule II, III, IV drugs in a single month in a Medicaid population. Community structure detection algorithms and geo-spatial layouts revealed characteristic patterns in PPN markedly different from their random graph surrogate counterparts rejecting them as potential generative mechanism. Outlier detection with recommended thresholds also revealed a subset of prescriber specialties to be constitutively flagged across Schedule II, III, IV drugs. Presence of prescriber communities may assist in targeted monitoring and their deviation from random graphs may serve as a metric in assessing PPN evolution temporally and pre-/post- interventions.

Biologic modelling of periodontal disease progression.

AIM: To investigate the synergistic role of biologic markers from saliva, serum and plaque in modelling periodontitis disease progression. MATERIAL AND METHODS: This longitudinal study evaluated characteristics of disease progression in 114 patients with generalized moderate to severe periodontitis. The primary outcome was detection of sites with progressing attachment loss sites over 6 months in patients who received scaling and root planing or oral hygiene only. The predictive potential of 27 biomarkers in serum, whole saliva and subgingival plaque was evaluated using three classification algorithms (Support Vector Machines; Naïve Bayes Classifier; and Linear Discriminant Analysis) within an ensemble predictive modelling framework. RESULTS: Disease progression occurred in 24.6% of subjects (28/114). Predictive modelling using Naïve Bayes Classifier identified progressors best with sensitivity of ~89%. The use of the three classification algorithms revealed the concerted role of salivary matrix metalloproteinase-8, serum biomarkers (serum amyloid P, matrix metalloproteinase 1, bactericidal permeability-increasing protein, isoprostane) along with levels of Porphryomonas gingivalis and Tannerella forsythia in plaque in predicting progressors. CONCLUSIONS: Synergistic utility of baseline bacterial and inflammatory biomarkers from saliva, serum and plaque predicted disease progression.

Translational bioinformatics in mental health: open access data sources and computational biomarker discovery.

Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data.

The periodontal war: microbes and immunity.

Maintenance of periodontal health or transition to a periodontal lesion reflects the continuous and ongoing battle between the vast microbial ecology in the oral cavity and the array of resident and emigrating inflammatory/immune cells in the periodontium. This war clearly signifies many 'battlefronts' representing the interface of the mucosal-surface cells with the dynamic biofilms composed of commensal and potential pathogenic species, as well as more recent knowledge demonstrating active invasion of cells and tissues of the periodontium leading to skirmishes in connective tissue, the locality of bone and even in the local vasculature. Research in the discipline has uncovered a concerted effort of the microbiome, using an array of survival strategies, to interact with other bacteria and host cells. These strategies aid in colonization by 'ambushing, infiltrating and outflanking' host cells and molecules, responding to local environmental changes (including booby traps for host biomolecules), communicating within and between genera and species that provide MASINT (Measurement and Signature Intelligence) to enhance sustained survival, sabotage the host inflammatory and immune responses and by potentially adopting a 'Fabian strategy' with a war of attrition and resulting disease manifestations. Additionally, much has been learned regarding the ever-increasing complexity of the host-response armamentarium at both cellular and molecular levels that is addressed in this review. Knowledge regarding how these systems fully interact requires both new laboratory and clinical tools, as well as sophisticated modeling of the networks that help maintain homeostasis and are dysregulated in disease. Finally, the triggers resulting in a 'coup de main' by the microbiome (exacerbation of disease) and the characteristics of susceptible hosts that can result in 'pyrrhic victories' with collateral damage to host tissues, the hallmark of periodontitis, remains unclear. While much has been learned, substantial gaps in our understanding of the 'parameters of this war' remain elusive toward fulfilling the Sun Tzu adage: 'If you know the enemy and know yourself, you need not fear the result of a hundred battles.'

An ensemble predictive modeling framework for breast cancer classification.

Molecular changes often precede clinical presentation of diseases and can be useful surrogates with potential to assist in informed clinical decision making. Recent studies have demonstrated the usefulness of modeling approaches such as classification that can predict the clinical outcomes from molecular expression profiles. While useful, a majority of these approaches implicitly use all molecular markers as features in the classification process often resulting in sparse high-dimensional projection of the samples often comparable to that of the sample size. In this study, a variant of the recently proposed ensemble classification approach is used for predicting good and poor-prognosis breast cancer samples from their molecular expression profiles. In contrast to traditional single and ensemble classifiers, the proposed approach uses multiple base classifiers with varying feature sets obtained from two-dimensional projection of the samples in conjunction with a majority voting strategy for predicting the class labels. In contrast to our earlier implementation, base classifiers in the ensembles are chosen based on maximal sensitivity and minimal redundancy by choosing only those with low average cosine distance. The resulting ensemble sets are subsequently modeled as undirected graphs. Performance of four different classification algorithms is shown to be better within the proposed ensemble framework in contrast to using them as traditional single classifier systems. Significance of a subset of genes with high-degree centrality in the network abstractions across the poor-prognosis samples is also discussed.

Integrated biomarker profiling of smokers with periodontitis.

BACKGROUND: In the context of precision medicine, understanding patient-specific variation is an important step in developing targeted and patient-tailored treatment regimens for periodontitis. While several studies have successfully demonstrated the usefulness of molecular expression profiling in conjunction with single classifier systems in discerning distinct disease groups, the majority of these studies do not provide sufficient insights into potential variations within the disease groups. AIM: The goal of this study was to discern biological response profiles of periodontitis and non-periodontitis smoking subjects using an informed panel of biomarkers across multiple scales (salivary, oral microbiome, pathogens and other markers). MATERIAL & METHODS: The investigation uses a novel ensemble classification approach (SVA-SVM) to differentiate disease groups and patient-specific biological variation of systemic inflammatory mediators and IgG antibody to oral commensal and pathogenic bacteria within the groups. RESULTS: Sensitivity of SVA-SVM is shown to be considerably higher than several traditional independent classifier systems. Patient-specific networks generated from SVA-SVM are also shown to reveal crosstalk between biomarkers in discerning the disease groups. High-confidence classifiers in these network abstractions comprised of host responses to microbial infection elucidated their critical role in discerning the disease groups. CONCLUSIONS: Host adaptive immune responses to the oral colonization/infection contribute significantly to creating the profiles specific for periodontitis patients with potential to assist in defining patient-specific risk profiles and tailored interventions.

An approach for deciphering patient-specific variations with application to breast cancer molecular expression profiles.

Several studies have successfully used molecular expression profiling in conjunction with classification techniques for discerning distinct disease groups. However, a majority of these studies do not provide sufficient insights into potential patient-specific variations within the disease groups. Such variations are ubiquitous and manifests across multiple scales with varying resolution. There is an urgent need for novel approaches that falls within the objective of precision medicine and provide novel insights into patient-specific variations and sub-populations within disease groups while discerning the disease groups of interest so as to enable timely and targeted intervention of select subjects. This study presents a selective-voting ensemble classification approach (SVA) for discerning good and poor-prognosis breast cancer samples from their 70-gene molecular expression profile revealing patient-specific variations within the poor-prognosis group. In contrast to traditional classification, SVA adapts the feature sets in a sample-specific manner capturing the proclivity of the samples to each of the disease groups. Correlation between normalized vote counts from SVA and clinical outcomes of the subjects is elucidated. Performance of Support Vector Machine and Naïve Bayes classifier is investigated within the SVA framework and compared to established clinical criteria (Nottingham Prognostic Index, Adjuvant Online, St. Gallen) and Mammaprint approach. Weighted undirected graph abstractions of the ensemble sets of the poor-prognosis test samples is also shown to exhibit markedly different topologies with varying proclivities. These patient-specific networks may reflect inherent variations in underlying signaling mechanisms in the poor-prognosis subjects and reveal potential targets for personalized therapeutic intervention.

An informatics research agenda to support precision medicine: seven key areas.

The recent announcement of the Precision Medicine Initiative by President Obama has brought precision medicine (PM) to the forefront for healthcare providers, researchers, regulators, innovators, and funders alike. As technologies continue to evolve and datasets grow in magnitude, a strong computational infrastructure will be essential to realize PM's vision of improved healthcare derived from personal data. In addition, informatics research and innovation affords a tremendous opportunity to drive the science underlying PM. The informatics community must lead the development of technologies and methodologies that will increase the discovery and application of biomedical knowledge through close collaboration between researchers, clinicians, and patients. This perspective highlights seven key areas that are in need of further informatics research and innovation to support the realization of PM.

Targeted salivary biomarkers for discrimination of periodontal health and disease(s).

UNLABELLED: Generally, clinical parameters are used in dental practice for periodontal disease, yet several drawbacks exist with the clinical standards for addressing the needs of the public at large in determining the current status/progression of the disease, and requiring a significant amount of damage before these parameters can document disease. Therefore, a quick, easy and reliable method of assessing and monitoring periodontal disease should provide important diagnostic information that improves and speeds treatment decisions and moves the field closer to individualized point-of-care diagnostics. OBJECTIVE: This report provides results for a saliva-based diagnostic approach for periodontal health and disease based upon the abundance of salivary analytes coincident with disease, and the significant progress already made in the identification of discriminatory salivary biomarkers of periodontitis. METHODS: We evaluated biomarkers representing various phases of periodontitis initiation and progression (IL-1ß, IL-6, MMP-8, MIP-1α) in whole saliva from 209 subjects categorized with periodontal health, gingivitis, and periodontitis. RESULTS: Evaluation of the salivary analytes demonstrated utility for individual biomarkers to differentiate periodontitis from health. Inclusion of gingivitis patients into the analyses provided a more robust basis to estimate the value of each of these analytes. Various clinical and statistical approaches showed that pairs or panels of the analytes were able to increase the sensitivity and specificity for the identification of disease. CONCLUSIONS: Salivary concentrations of IL-1ß, IL-6, MMP-8, MIP-1α alone and in combination are able to distinguish health from gingivitis and periodontitis. The data clearly demonstrated a heterogeneity in response profiles of these analytes that supports the need for refinement of the standard clinical classifications if we are to move toward precision/personalized dentistry for the twenty-first century.

Patient-Specific Variations in Biomarkers across Gingivitis and Periodontitis.

This study investigates the use of saliva, as an emerging diagnostic fluid in conjunction with classification techniques to discern biological heterogeneity in clinically labelled gingivitis and periodontitis subjects (80 subjects; 40/group) A battery of classification techniques were investigated as traditional single classifier systems as well as within a novel selective voting ensemble classification approach (SVA) framework. Unlike traditional single classifiers, SVA is shown to reveal patient-specific variations within disease groups, which may be important for identifying proclivity to disease progression or disease stability. Salivary expression profiles of IL-1ß, IL-6, MMP-8, and MIP-1α from 80 patients were analyzed using four classification algorithms (LDA: Linear Discriminant Analysis [LDA], Quadratic Discriminant Analysis [QDA], Naïve Bayes Classifier [NBC] and Support Vector Machines [SVM]) as traditional single classifiers and within the SVA framework (SVA-LDA, SVA-QDA, SVA-NB and SVA-SVM). Our findings demonstrate that performance measures (sensitivity, specificity and accuracy) of traditional classification as single classifier were comparable to that of the SVA counterparts using clinical labels of the samples as ground truth. However, unlike traditional single classifier approaches, the normalized ensemble vote-counts from SVA revealed varying proclivity of the subjects for each of the disease groups. More importantly, the SVA identified a subset of gingivitis and periodontitis samples that demonstrated a biological proclivity commensurate with the other clinical group. This subset was confirmed across SVA-LDA, SVA-QDA, SVA-NB and SVA-SVM. Heatmap visualization of their ensemble sets revealed lack of consensus between these subsets and the rest of the samples within the respective disease groups indicating the unique nature of the patients in these subsets. While the source of variation is not known, the results presented clearly elucidate the need for novel approaches that accommodate inherent heterogeneity and personalized variations within disease groups in diagnostic characterization. The proposed approach falls within the scope of P4 medicine (predictive, preventive, personalized, and participatory) with the ability to identify unique patient profiles that may predict specific disease trajectories and targeted disease management.

3D tumor tissue analogs and their orthotopic implants for understanding tumor-targeting of microenvironment-responsive nanosized chemotherapy and radiation.

An appropriate representation of the tumor microenvironment in tumor models can have a pronounced impact on directing combinatorial treatment strategies and cancer nanotherapeutics. The present study develops a novel 3D co-culture spheroid model (3D TNBC) incorporating tumor cells, endothelial cells and fibroblasts as color-coded murine tumor tissue analogs (TTA) to better represent the tumor milieu of triple negative breast cancer in vitro. Implantation of TTA orthotopically in nude mice, resulted in enhanced growth and aggressive metastasis to ectopic sites. Subsequently, the utility of the model is demonstrated for preferential targeting of irradiated tumor endothelial cells via radiation-induced stromal enrichment of galectin-1 using anginex conjugated nanoparticles (nanobins) carrying arsenic trioxide and cisplatin. Demonstration of a multimodal nanotherapeutic system and inclusion of the biological response to radiation using an in vitro/in vivo tumor model incorporating characteristics of tumor microenvironment presents an advance in preclinical evaluation of existing and novel cancer nanotherapies. FROM THE CLINICAL EDITOR: Existing in-vivo tumor models are established by implanting tumor cells into nude mice. Here, the authors described their approach 3D spheres containing tumor cells, enodothelial cells and fibroblasts. This would mimic tumor micro-environment more realistically. This interesting 3D model should reflect more accurately tumor response to various drugs and would enable the design of new treatment modalities.

Social network analysis to assess the impact of the CTSA on biomedical research grant collaboration.

Success of the Clinical Translational Science Award (CTSA) program implicitly demands team science efforts and well-orchestrated collaboration across the translational silos (T1-T4). Networks have proven to be useful abstractions of research collaborations. Networks provide novel system-level insights and exhibit marked changes in response to external interventions, making them potential evaluation tools that complement more traditional approaches. This study is part of our ongoing efforts to assess the impact of the CTSA on Biomedical Research Grant Collaboration (BRGC). Collaborative research grants are a complex undertaking and an outcome of sustained interaction among researchers. In this report, BRGC networks representing collaborations among CTSA-affiliated investigators constructed from grants management system data at the University of Kentucky across a period of six years (2007-2012) corresponding to pre- and post-CTSA are investigated. Overlapping community structure detection algorithms, in conjunction with surrogate testing, revealed the presence of intricate research communities rejecting random graphs as generative mechanisms. The deviation from randomness was especially pronounced post-CTSA, reflecting an increasing trend in collaborations and team-science efforts potentially as a result of CTSA. Intercommunity cross talk was especially pronounced post-CTSA.

Impact of noise on molecular network inference.

Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms. Noise is ubiquitous in molecular data and can have a pronounced effect on the inferred network. Noise can be an outcome of several factors including: inherent stochastic mechanisms at the molecular level, variation in the abundance of molecules, heterogeneity, sensitivity of the biological assay or measurement artefacts prevalent especially in high-throughput settings. The present study investigates the impact of discrepancies in noise variance on pair-wise dependencies, conditional dependencies and constraint-based Bayesian network structure learning algorithms that incorporate conditional independence tests as a part of the learning process. Popular network motifs and fundamental connections, namely: (a) common-effect, (b) three-chain, and (c) coherent type-I feed-forward loop (FFL) are investigated. The choice of these elementary networks can be attributed to their prevalence across more complex networks. Analytical expressions elucidating the impact of discrepancies in noise variance on pairwise dependencies and conditional dependencies for special cases of these motifs are presented. Subsequently, the impact of noise on two popular constraint-based Bayesian network structure learning algorithms such as Grow-Shrink (GS) and Incremental Association Markov Blanket (IAMB) that implicitly incorporate tests for conditional independence is investigated. Finally, the impact of noise on networks inferred from publicly available single cell molecular expression profiles is investigated. While discrepancies in noise variance are overlooked in routine molecular network inference, the results presented clearly elucidate their non-trivial impact on the conclusions that in turn can challenge the biological significance of the findings. The analytical treatment and arguments presented are generic and not restricted to molecular data sets.

Identifying significant edges in graphical models of molecular networks.

OBJECTIVE: Modelling the associations from high-throughput experimental molecular data has provided unprecedented insights into biological pathways and signalling mechanisms. Graphical models and networks have especially proven to be useful abstractions in this regard. Ad hoc thresholds are often used in conjunction with structure learning algorithms to determine significant associations. The present study overcomes this limitation by proposing a statistically motivated approach for identifying significant associations in a network. METHODS AND MATERIALS: A new method that identifies significant associations in graphical models by estimating the threshold minimising the L1 norm between the cumulative distribution function (CDF) of the observed edge confidences and those of its asymptotic counterpart is proposed. The effectiveness of the proposed method is demonstrated on popular synthetic data sets as well as publicly available experimental molecular data corresponding to gene and protein expression profiles. RESULTS: The improved performance of the proposed approach is demonstrated across the synthetic data sets using sensitivity, specificity and accuracy as performance metrics. The results are also demonstrated across varying sample sizes and three different structure learning algorithms with widely varying assumptions. In all cases, the proposed approach has specificity and accuracy close to 1, while sensitivity increases linearly in the logarithm of the sample size. The estimated threshold systematically outperforms common ad hoc ones in terms of sensitivity while maintaining comparable levels of specificity and accuracy. Networks from experimental data sets are reconstructed accurately with respect to the results from the original papers. CONCLUSION: Current studies use structure learning algorithms in conjunction with ad hoc thresholds for identifying significant associations in graphical abstractions of biological pathways and signalling mechanisms. Such an ad hoc choice can have pronounced effect on attributing biological significance to the associations in the resulting network and possible downstream analysis. The statistically motivated approach presented in this study has been shown to outperform ad hoc thresholds and is expected to alleviate spurious conclusions of significant associations in such graphical abstractions.