Association of air pollution with postmenopausal breast cancer risk in UK Biobank.

BACKGROUND: We investigated the association of several air pollution measures with postmenopausal breast cancer (BCa) risk. METHODS: This study included 155,235 postmenopausal women (of which 6146 with BCa) from UK Biobank. Cancer diagnoses were ascertained through the linkage to the UK National Health Service Central Registers. Annual exposure averages were available from 2005, 2006, 2007, and 2010 for NO2, from 2007 and 2010 for PM10, and from 2010 for PM2.5, NOX, PM2.5-10 and PM2.5 absorbance. Information on BCa risk factors was collected at baseline. Cox proportional hazards regression was used to evaluate the associations of year-specific and cumulative average exposures with BCa risk, overall and with 2-year exposure lag, while adjusting for BCa risk factors. RESULTS: PM10 in 2007 and cumulative average PM10 were positively associated with BCa risk (2007 PM10: Hazard ratio [HR] per 10 µg/m3 = 1.18, 95% CI 1.08, 1.29; cumulative average PM10: HR per 10 µg/m3 = 1.99, 95% CI 1.75, 2.27). Compared to women with low exposure, women with higher 2007 PM10 and cumulative average PM10 had greater BCa risk (4th vs. 1st quartile HR = 1.15, 95% CI 1.07, 1.24, p-trend = 0.001 and HR = 1.35, 95% CI 1.25, 1.44, p-trend < 0.0001, respectively). No significant associations were found for any other exposure measures. In the analysis with 2-year exposure lag, both 2007 PM 10 and cumulative average PM10 were positively associated with BCa risk (4th vs. 1st quartile HR = 1.19, 95% CI 1.10, 1.28 and HR = 1.29, 95% CI 1.19, 1.39, respectively). CONCLUSION: Our findings suggest a positive association of 2007 PM10 and cumulative average PM10 with postmenopausal BCa risk.

mTOR pathway candidate genes and physical activity interaction on breast cancer risk in black women from the women's circle of health study.

BACKGROUND: Physical activity has been shown to affect the mammalian target of rapamycin (mTOR) signaling pathway and consequently breast carcinogenesis. Given that Black women in the USA are less physically active, it is not well understood whether there are gene-environment interactions between mTOR pathway genes and physical activity in relation to breast cancer risk in Black women. METHODS: The study included 1398 Black women (567 incident breast cancer cases and 831 controls) from the Women's Circle of Health Study (WCHS). We examined interactions between 43 candidate single-nucleotide polymorphisms (SNPs) in 20 mTOR pathway genes with levels of vigorous physical activity in relation to breast cancer risk overall and by ER-defined subtypes using Wald test with 2-way interaction term and multivariable logistic regression. RESULTS: AKT1 rs10138227 (C > T) and AKT1 rs1130214 (C > A) were only associated with a decreased risk of ER + breast cancer among women with vigorous physical activity (odds ratio [OR] = 0.15, 95% confidence interval (CI) 0.04, 0.56, for each copy of the T allele, p-interaction = 0.007 and OR = 0.51, 95% CI 0.27, 0.96, for each copy of the A allele, p-interaction = 0.045, respectively). MTOR rs2295080 (G > T) was only associated with an increased risk of ER + breast cancer among women with vigorous physical activity (OR = 2.24, 95% CI 1.16, 4.34, for each copy of the G allele; p-interaction = 0.043). EIF4E rs141689493 (G > A) was only associated with an increased risk of ER- breast cancer among women with vigorous physical activity (OR = 20.54, 95% CI 2.29, 184.17, for each copy of the A allele; p-interaction = 0.003). These interactions became non-significant after correction for multiple testing (FDR-adjusted p-value > 0.05). CONCLUSION: Our findings suggest that mTOR genetic variants may interact with physical activity in relation to breast cancer risk in Black women. Future studies should confirm these findings.

mTOR pathway candidate genes and obesity interaction on breast cancer risk in black women from the Women's Circle of Health Study.

BACKGROUND: Obesity is known to stimulate the mammalian target of rapamycin (mTOR) signaling pathway and both obesity and the mTOR signaling pathway are implicated in breast carcinogenesis. We investigated potential gene-environment interactions between mTOR pathway genes and obesity in relation to breast cancer risk among Black women. METHODS: The study included 1,655 Black women (821 incident breast cancer cases and 834 controls) from the Women's Circle of Health Study (WCHS). Obesity measures including body mass index (BMI); central obesity i.e., waist circumference (WC) and waist/hip ratio (WHR); and body fat distribution (fat mass, fat mass index and percent body fat) were obtained by trained research staff. We examined the associations of 43 candidate single-nucleotide polymorphisms (SNPs) in 20 mTOR pathway genes with breast cancer risk using multivariable logistic regression. We next examined interactions between these SNPs and measures of obesity using Wald test with 2-way interaction term. RESULTS: The variant allele of BRAF (rs114729114 C > T) was associated with an increase in overall breast cancer risk [odds ratio (OR) = 1.81, 95% confidence interval (CI) 1.10-2.99, for each copy of the T allele] and the risk of estrogen receptor (ER)-defined subtypes (ER+ tumors: OR = 1.83, 95% CI 1.04,3.29, for each copy of the T allele; ER- tumors OR = 2.14, 95% CI 1.03,4.45, for each copy of the T allele). Genetic variants in AKT, AKT1, PGF, PRKAG2, RAPTOR, TSC2 showed suggestive associations with overall breast cancer risk and the risk of, ER+ and ER- tumors (range of p-values = 0.040-0.097). We also found interactions of several of the SNPs with BMI, WHR, WC, fat mass, fat mass index and percent body fat in relation to breast cancer risk. These associations and interactions, however, became nonsignificant after correction for multiple testing (FDR-adjusted p-value > 0.05). CONCLUSION: We found associations between mTOR genetic variants and breast cancer risk as well as gene and body fatness interactions in relation to breast cancer risk. However, these associations and interactions became nonsignificant after correction for multiple testing. Future studies with larger sample sizes are required to confirm and validate these findings.

mTOR pathway candidate genes and energy intake interaction on breast cancer risk in Black women from the Women's Circle of Health Study.

BACKGROUND: Excessive energy intake has been shown to affect the mammalian target of the rapamycin (mTOR) signaling pathway and breast cancer risk. It is not well understood whether there are gene-environment interactions between mTOR pathway genes and energy intake in relation to breast cancer risk. METHODS: The study included 1642 Black women (809 incident breast cancer cases and 833 controls) from the Women's Circle of Health Study (WCHS). We examined interactions between 43 candidate single-nucleotide polymorphisms (SNPs) in 20 mTOR pathway genes and quartiles of energy intake in relation to breast cancer risk overall and by ER- defined subtypes using Wald test with a 2-way interaction term. RESULTS: AKT1 rs10138227 (C > T) was only associated with a decreased overall breast cancer risk among women in quartile (Q)2 of energy intake, odds ratio (OR) = 0.60, 95% confidence interval (CI) 0.40, 0.91 (p-interaction = 0.042). Similar results were found in ER- tumors. AKT rs1130214 (C > A) was associated with decreased overall breast cancer risk in Q2 (OR = 0.63, 95% CI 0.44, 0.91) and Q3 (OR = 0.65, 95% CI 0.48, 0.89) (p-interaction = 0.026). HIF-1α C1772T rs11549465 (C > T) was associated with decreased overall breast cancer risk in Q4 (OR = 0.29, 95% CI 0.14, 0.59, p-interaction = 0.007); the results were similar in ER+ tumors. These interactions became non-significant after correction for multiple comparisons. CONCLUSION: Our findings suggest that mTOR genetic variants may interact with energy intake in relation to breast cancer risk, including the ER- subtype, in Black women. Future studies should confirm these findings.

Body fatness and breast cancer risk in relation to phosphorylated mTOR expression in a sample of predominately Black women.

BACKGROUND: The mechanistic target of rapamycin (mTOR) pathway promoted by positive energy imbalance and insulin-like growth factors can be a mechanism by which obesity influences breast cancer risk. We evaluated the associations of body fatness with the risk of breast cancer varied with phosphorylated (p)-mTOR protein expression, an indication of the pathway activation. METHODS: Women with newly diagnosed breast cancer (n = 715; 574 [80%] Black and 141 [20%] White) and non-cancer controls (n = 1983; 1280 [64%] Black and 713 [36%] White) were selected from the Women's Circle of Health Study. Surgical tumor samples among the cases were immunostained for p-mTOR (Ser2448) and classified as p-mTOR-overexpressed, if the expression level ≥ 75th percentile, or p-mTOR-negative/low otherwise. Anthropometrics were measured by trained staff, and body composition was determined by bioelectrical impedance analysis. Odds ratios (ORs) of p-mTOR-overexpressed tumors and p-mTOR-negative/low tumors compared to controls were estimated using polytomous logistic regression. The differences in the associations by the p-mTOR expression status were assessed by tests for heterogeneity. RESULTS: Cases with p-mTOR-overexpressed tumors, but not cases with p-mTOR-negative/low tumors, compared to controls were more likely to have higher body mass index (BMI), percent body fat, and fat mass index (P-heterogeneity < 0.05), although the OR estimates were not significant. For the measurement of central adiposity, cases with p-mTOR overexpressed tumors had a higher odds of being at the Q3 (OR = 2.52, 95% CI = 1.46 to 4.34) and Q4 (OR = 1.99, 95% CI = 1.12 to 3.50) of waist circumference (WC) compared to controls. Similarly, cases with p-mTOR overexpressed tumors had a higher odds of being at the Q3 (OR = 1.82, 95% CI = 1.11 to 2.98) and Q4 (OR = 1.81, 95% CI = 1.11 to 2.98) of WHR compared to controls. These associations of WC and waist-to-hip ratio (WHR) did not differ by tumor p-mTOR status (P-heterogeneity = 0.27 and 0.48, respectively). CONCLUSIONS: Our findings suggest that in this population composed of predominately Black women, body fatness is associated with breast cancer differently for p-mTOR overexpression and p-mTOR negative/low expression. Whether mTOR plays a role in the obesity and breast cancer association warrants confirmation by prospective studies.

A hypolipoprotein sepsis phenotype indicates reduced lipoprotein antioxidant capacity, increased endothelial dysfunction and organ failure, and worse clinical outcomes.

OBJECTIVE: Approximately one-third of sepsis patients experience poor outcomes including chronic critical illness (CCI, intensive care unit (ICU) stay > 14 days) or early death (in-hospital death within 14 days). We sought to characterize lipoprotein predictive ability for poor outcomes and contribution to sepsis heterogeneity. DESIGN: Prospective cohort study with independent replication cohort. SETTING: Emergency department and surgical ICU at two hospitals. PATIENTS: Sepsis patients presenting within 24 h. METHODS: Measures included cholesterol levels (total cholesterol, high density lipoprotein cholesterol [HDL-C], low density lipoprotein cholesterol [LDL-C]), triglycerides, paraoxonase-1 (PON-1), and apolipoprotein A-I (Apo A-I) in the first 24 h. Inflammatory and endothelial markers, and sequential organ failure assessment (SOFA) scores were also measured. LASSO selection assessed predictive ability for outcomes. Unsupervised clustering was used to investigate the contribution of lipid variation to sepsis heterogeneity. MEASUREMENTS AND MAIN RESULTS: 172 patients were enrolled. Most (~ 67%, 114/172) rapidly recovered, while ~ 23% (41/172) developed CCI, and ~ 10% (17/172) had early death. ApoA-I, LDL-C, mechanical ventilation, vasopressor use, and Charlson Comorbidity Score were significant predictors of CCI/early death in LASSO models. Unsupervised clustering yielded two discernible phenotypes. The Hypolipoprotein phenotype was characterized by lower lipoprotein levels, increased endothelial dysfunction (ICAM-1), higher SOFA scores, and worse clinical outcomes (45% rapid recovery, 40% CCI, 16% early death; 28-day mortality, 21%). The Normolipoprotein cluster patients had higher cholesterol levels, less endothelial dysfunction, lower SOFA scores and better outcomes (79% rapid recovery, 15% CCI, 6% early death; 28-day mortality, 15%). Phenotypes were validated in an independent replication cohort (N = 86) with greater sepsis severity, which similarly demonstrated lower HDL-C, ApoA-I, and higher ICAM-1 in the Hypolipoprotein cluster and worse outcomes (46% rapid recovery, 23% CCI, 31% early death; 28-day mortality, 42%). Normolipoprotein patients in the replication cohort had better outcomes (55% rapid recovery, 32% CCI, 13% early death; 28-day mortality, 28%) Top features for cluster discrimination were HDL-C, ApoA-I, total SOFA score, total cholesterol level, and ICAM-1. CONCLUSIONS: Lipoproteins predicted poor sepsis outcomes. A Hypolipoprotein sepsis phenotype was identified and characterized by lower lipoprotein levels, increased endothelial dysfunction (ICAM-1) and organ failure, and worse clinical outcomes.

Meta-analysis of cardiomyopathy-associated variants in troponin genes identifies loci and intragenic hot spots that are associated with worse clinical outcomes.

INTRODUCTION: Troponin (TNN)-encoded cardiac troponins (Tn) are critical for sensing calcium and triggering myofilament contraction. TNN variants are associated with development of cardiomyopathy; however, recent advances in genetic analysis have identified rare population variants. It is unclear how certain variants are associated with disease while others are tolerated. OBJECTIVE: To compare probands with TNNT2, TNNI3, and TNNC1 variants and utilize high-resolution variant comparison mapping of pathologic and rare population variants to identify loci associated with disease pathogenesis. METHODS: Cardiomyopathy-associated TNN variants were identified in the literature and topology mapping conducted. Clinical features were compiled and compared. Rare population variants were obtained from the gnomAD database. Signal-to-noise (S:N) normalized pathologic variant frequency against population variant frequency. Abstract review of clinical phenotypes was applied to "significant" hot spots. RESULTS: Probands were compiled (N = 70 studies, 224 probands) as were rare variants (N = 125,748 exomes; 15,708 genomes, MAF <0.001). TNNC1-positive probands demonstrated the youngest age of presentation (20.0 years; P = .016 vs TNNT2; P = .004 vs TNNI3) and the highest death, transplant, or ventricular fibrillation events (P = .093 vs TNNT2; P = .024 vs TNNI3; Kaplan Meir: P = .025). S:N analysis yielded hot spots of diagnostic significance within the tropomyosin-binding domains, α-helix 1, and the N-Terminus in TNNT2 with increased sudden cardiac death and ventricular fibrillation (P = .004). The inhibitory region and C-terminal region in TNNI3 exhibited increased restrictive cardiomyopathy (P =.008). HCM and RCM models tended to have increased calcium sensitivity and DCM decreased sensitivity (P < .001). DCM and HCM studies typically showed no differences in Hill coefficient which was decreased in RCM models (P < .001). CM models typically demonstrated no changes to Fmax (P = .239). CONCLUSION: TNNC1-positive probands had younger ages of diagnosis and poorer clinical outcomes. Mapping of TNN variants identified locations in TNNT2 and TNNI3 associated with heightened pathogenicity, RCM diagnosis, and increased risk of sudden death.

A sparse Bayesian factor model for the construction of gene co-expression networks from single-cell RNA sequencing count data.

BACKGROUND: Gene co-expression networks (GCNs) are powerful tools that enable biologists to examine associations between genes during different biological processes. With the advancement of new technologies, such as single-cell RNA sequencing (scRNA-seq), there is a need for developing novel network methods appropriate for new types of data. RESULTS: We present a novel sparse Bayesian factor model to explore the network structure associated with genes in scRNA-seq data. Latent factors impact the gene expression values for each cell and provide flexibility to account for common features of scRNA-seq: high proportions of zero values, increased cell-to-cell variability, and overdispersion due to abnormally large expression counts. From our model, we construct a GCN by analyzing the positive and negative associations of the factors that are shared between each pair of genes. CONCLUSIONS: Simulation studies demonstrate that our methodology has high power in identifying gene-gene associations while maintaining a nominal false discovery rate. In real data analyses, our model identifies more known and predicted protein-protein interactions than other competing network models.

Topical doxycycline monohydrate hydrogel 1% targeting proteases/PAR2 pathway is a novel therapeutic for atopic dermatitis.

Atopic Dermatitis (AD) is characterized by skin barrier disruption and an aberrant immune response. Doxycycline is tetracycline antibiotics broadly used systemically to treat inflammatory dermatologic conditions. Several studies have shown doxycycline has anti-inflammatory and pro-healing properties, mainly by blocking tissue proteolytic activity. It is our hypothesis that daily application of a novel doxycycline topical formulation in AD subjects will reduce severity of the disease, by blocking cutaneous proteases activity and restoring skin barrier function and inflammation. To test this hypothesis, we performed a proof of concept, open-label clinical study. Subjects enrolled in the study (n = 15) applied NanoDOX® Hydrogel 1% daily for 4 weeks on a chosen eczematous area. Investigational drug was well tolerated, and no local or systemic adverse events due to investigational drug were reported. Notably, a significant clinical improvement was observed based on a modified Eczema Area & Severity Index (EASI) score of the treated area from start of treatment to 14 and 28 days post-treatment (P < .001). A significant improvement of pruritus was also observed (P = .02). This proof of concept clinical trial is first to explore the impact of a non-systemic doxycycline treatment on AD patients. Our results provide evidence to investigate novel AD treatment strategies targeting cutaneous proteases activity.

Detection of differentially expressed genes in discrete single-cell RNA sequencing data using a hurdle model with correlated random effects.

Single-cell RNA sequencing (scRNA-seq) technologies are revolutionary tools allowing researchers to examine gene expression at the level of a single cell. Traditionally, transcriptomic data have been analyzed from bulk samples, masking the heterogeneity now seen across individual cells. Even within the same cellular population, genes can be highly expressed in some cells but not expressed (or lowly expressed) in others. Therefore, the computational approaches used to analyze bulk RNA sequencing data are not appropriate for the analysis of scRNA-seq data. Here, we present a novel statistical model for high dimensional and zero-inflated scRNA-seq count data to identify differentially expressed (DE) genes across cell types. Correlated random effects are employed based on an initial clustering of cells to capture the cell-to-cell variability within treatment groups. Moreover, this model is flexible and can be easily adapted to an independent random effect structure if needed. We apply our proposed methodology to both simulated and real data and compare results to other popular methods designed for detecting DE genes. Due to the hurdle model's ability to detect differences in the proportion of cells expressed and the average expression level (among the expressed cells), our methods naturally identify some genes as DE that other methods do not, and we demonstrate with real data that these uniquely detected genes are associated with similar biological processes and functions.

Medical schools, primary care and family medicine: clerkship directors' perceptions of the current environment.

BACKGROUND: The culture at a medical school and the positive experiences in primary care clerkships influence student specialty choice. This choice is significant if the demand for primary care physicians is to be met. The aim of this study was to examine family medicine clerkship directors' perceptions of the medical school environment. METHODS: Data were collected as part of the 2015 Council of Academic Family Medicine Educational Research Alliance Family Medicine Clerkship Director survey. Questions asked included how clerkship directors perceived the environment of their medical school towards family medicine, has the environment towards family medicine changed between 2010 and 2015, do they take action to influence student attitudes towards family medicine and whether faculty members in other departments make negative comments about family medicine. RESULTS: The response rate was 79.4%. While most respondents indicated the environment of their medical school has become more positive towards family medicine, a majority of clerkship directors perceived the environment to be either very much against, slightly against or indifferent towards family medicine. Nearly one-half (41.4%) of the clerkship directors were notified more than once a year that a faculty member of another department made a negative comment about family medicine. Results varied among regions of the USA and between schools located in the USA and Canada. CONCLUSION: Family medicine clerkship directors often perceived negativity towards family medicine, a finding that may limit the effectiveness of academic health centres in their mission to better serve their community and profession.

Improving protein identification from tandem mass spectrometry data by one-step methods and integrating data from other platforms.

MOTIVATION: Many approaches have been proposed for the protein identification problem based on tandem mass spectrometry (MS/MS) data. In these experiments, proteins are digested into peptides and the resulting peptide mixture is subjected to mass spectrometry. Some interesting putative peptide features (peaks) are selected from the mass spectra. Following that, the precursor ions undergo fragmentation and are analyzed by MS/MS. The process of identification of peptides from the mass spectra and the constituent proteins in the sample is called protein identification from MS/MS data. There are many two-step protein identification procedures, reviewed in the literature, which first attempt to identify the peptides in a separate process and then use these results to infer the proteins. However, in recent years, there have been attempts to provide a one-step solution to protein identification, which simultaneously identifies the proteins and the peptides in the sample. RESULTS: In this review, we briefly introduce the most popular two-step protein identification procedure, PeptideProphet coupled with ProteinProphet. Following that, we describe the difficulties with two-step procedures and review some recently introduced one-step protein/peptide identification procedures that do not suffer from these issues. The focus of this review is on one-step procedures that are based on statistical likelihood-based models, but some discussion of other one-step procedures is also included. We report comparative performances of one-step and two-step methods, which support the overall superiorities of one-step procedures. We also cover some recent efforts to improve protein identification by incorporating other molecular data along with MS/MS data.

The subgingival microbiome in patients with established rheumatoid arthritis.

OBJECTIVES: To profile and compare the subgingival microbiome of RA patients with OA controls. METHODS: RA (n = 260) and OA (n = 296) patients underwent full-mouth examination and subgingival samples were collected. Bacterial DNA was profiled using 16 S rRNA Illumina sequencing. Following data filtering and normalization, hierarchical clustering analysis was used to group samples. Multivariable regression was used to examine associations of patient factors with membership in the two largest clusters. Differential abundance between RA and OA was examined using voom method and linear modelling with empirical Bayes moderation (Linear Models for Microarray Analysis, limma), accounting for the effects of periodontitis, race, marital status and smoking. RESULTS: Alpha diversity indices were similar in RA and OA after accounting for periodontitis. After filtering, 286 taxa were available for analysis. Samples grouped into one of seven clusters with membership sizes of 324, 223, 3, 2, 2, 1 and 1 patients, respectively. RA-OA status was not associated with cluster membership. Factors associated with cluster 1 (vs 2) membership included periodontitis, smoking, marital status and Caucasian race. Accounting for periodontitis, 10 taxa (3.5% of those examined) were in lower abundance in RA than OA. There were no associations between lower abundance taxa or other select taxa examined with RA autoantibody concentrations. CONCLUSION: Leveraging data from a large case-control study and accounting for multiple factors known to influence oral health status, results from this study failed to identify a subgingival microbial fingerprint that could reliably discriminate RA from OA patients.

Profiling the effects of short time-course cold ischemia on tumor protein phosphorylation using a Bayesian approach.

Phosphorylated proteins provide insight into tumor etiology and are used as diagnostic, prognostic, and therapeutic markers of complex diseases. However, pre-analytic variations, such as freezing delay after biopsy acquisition, often occur in real hospital settings and potentially lead to inaccurate results. The objective of this work is to develop statistical methodology to assess the stability of phosphorylated proteins under short-time cold ischemia. We consider a hierarchical model to determine if phosphorylation abundance of a protein at a particular phosphorylation site remains constant or not during cold ischemia. When phosphorylation levels vary across time, we estimate the direction of the changes in each protein based on the maximum overall posterior probability and on the pairwise posterior probabilities, respectively. We analyze a dataset of ovarian tumor tissues that suffered cold-ischemia shock before the proteomic profiling. Gajadhar et al. (2015) applied independent clusterings for each patient because of the high heterogeneity across patients, while our proposed model shares information allowing conclusions for the entire sample population. Using the proposed model, 15 out of 32 proteins show significant changes during 1-hour cold ischemia. Through simulation studies, we conclude that our proposed methodology has a higher accuracy for detecting changes compared to an order restricted inference method. Our approach provides inference on the stability of these phosphorylated proteins, which is valuable when using these proteins as biomarkers for a disease.

A novel statistical approach for identification of the master regulator transcription factor.

BACKGROUND: Transcription factors are known to play key roles in carcinogenesis and therefore, are gaining popularity as potential therapeutic targets in drug development. A 'master regulator' transcription factor often appears to control most of the regulatory activities of the other transcription factors and the associated genes. This 'master regulator' transcription factor is at the top of the hierarchy of the transcriptomic regulation. Therefore, it is important to identify and target the master regulator transcription factor for proper understanding of the associated disease process and identifying the best therapeutic option. METHODS: We present a novel two-step computational approach for identification of master regulator transcription factor in a genome. At the first step of our method we test whether there exists any master regulator transcription factor in the system. We evaluate the concordance of two ranked lists of transcription factors using a statistical measure. In case the concordance measure is statistically significant, we conclude that there is a master regulator. At the second step, our method identifies the master regulator transcription factor, if there exists one. RESULTS: In the simulation scenario, our method performs reasonably well in validating the existence of a master regulator when the number of subjects in each treatment group is reasonably large. In application to two real datasets, our method ensures the existence of master regulators and identifies biologically meaningful master regulators. An R code for implementing our method in a sample test data can be found in http://www.somnathdatta.org/software . CONCLUSION: We have developed a screening method of identifying the 'master regulator' transcription factor just using only the gene expression data. Understanding the regulatory structure and finding the master regulator help narrowing the search space for identifying biomarkers for complex diseases such as cancer. In addition to identifying the master regulator our method provides an overview of the regulatory structure of the transcription factors which control the global gene expression profiles and consequently the cell functioning.

Monotonic single-index models to assess drug interactions.

Although single-index models have been extensively studied, the monotonicity of the link function f in the single-index model is rarely studied. In many situations, it is desirable that f is monotonic, which results in a monotonic single-index model that can be very useful in economics and biometrics. In this article, we propose a monotonic single-index model in which the link function is constructed using penalized I-splines along with constraints on coefficients to achieve monotonicity of the link function f. An algorithm to estimate the single-index parameters and the link function is developed, and the sandwich estimate of the variance of the index parameters is provided. We propose to apply this monotonic single-index model to estimate the dose-response surface and assess drug interactions while considering the variability of the observed data. An extensive simulation study was carried out to evaluate the performance of the proposed monotonic single-index model. A case study is provided to illustrate the application of the proposed model to estimate the dose-response surface and assess drug interactions. Both the simulation and case study show that the proposed monotonic single-index model works very well. Copyright © 2016 John Wiley & Sons, Ltd.

Temporal Prediction of Future State Occupation in a Multistate Model from High-Dimensional Baseline Covariates via Pseudo-Value Regression.

In many complex diseases such as cancer, a patient undergoes various disease stages before reaching a terminal state (say disease free or death). This fits a multistate model framework where a prognosis may be equivalent to predicting the state occupation at a future time t. With the advent of high throughput genomic and proteomic assays, a clinician may intent to use such high dimensional covariates in making better prediction of state occupation. In this article, we offer a practical solution to this problem by combining a useful technique, called pseudo value regression, with a latent factor or a penalized regression method such as the partial least squares (PLS) or the least absolute shrinkage and selection operator (LASSO), or their variants. We explore the predictive performances of these combinations in various high dimensional settings via extensive simulation studies. Overall, this strategy works fairly well provided the models are tuned properly. Overall, the PLS turns out to be slightly better than LASSO in most settings investigated by us, for the purpose of temporal prediction of future state occupation. We illustrate the utility of these pseudo-value based high dimensional regression methods using a lung cancer data set where we use the patients' baseline gene expression values.

Bayesian proteoform modeling improves protein quantification of global proteomic measurements.

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that, with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian Proteoform Quantification model (BP-Quant)(1) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern or the existence of multiple overexpressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab® and R packages.

Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements.

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally-driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian model (BP-Quant) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab ® and R packages at https://github.com/PNNL-Comp-Mass-Spec/BP-Quant.

Biofilm-producing antibiotic-resistant bacteria in Indian patients: a comprehensive review.

For the past few years, microbial biofilms have been emerging as a significant threat to the modern healthcare system, and their prevalence and antibiotic resistance threat gradually increase daily among the human population. The biofilm has a remarkable impact in the field of infectious diseases, in particular healthcare-associated infections related to indwelling devices such as catheters, implants, artificial heart valves, and prosthetic joints. Bacterial biofilm potentially adheres to any biotic or abiotic surfaces that give specific shelter to the microbial community, making them less susceptible to many antimicrobial agents and even resistant to the immune cells of animal hosts. Around thirty clinical research reports available in PUBMED have been considered to establish the occurrence of biofilm-forming bacteria showing resistance against several regular antibiotics prescribed against infection by clinicians among Indian patients. After the extensive literature review, our observation exhibits a high predominance of biofilm formation among bacteria such as Escherichia sp., Streptococcus sp., Staphylococcus sp., and Pseudomonas sp., those are the most common biofilm-producing antibiotic-resistant bacteria among Indian patients with urinary tract infections and/or catheter-related infections, respiratory tract infections, dental infections, skin infections, and implant-associated infections. This review demonstrates that biofilm-associated bacterial infections constantly elevate in several pathological conditions along with the enhancement of the multi-drug resistance phenomenon.