Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network.

Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key posttranslational modification involved in physiology and disease. The ability to robustly and rapidly predict protease-substrate specificity would also enable targeted proteolytic cleavage by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pretrained PGCN model to guide the design of protease libraries for cleaving two noncanonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.

Histologic inflammation and collagen content are not positively correlated in human BPH.

INTRODUCTION: Recent clinical studies have implicated prostate inflammation and fibrosis in the development of bladder outlet obstruction and lower urinary tract symptoms (LUTS). Studies utilizing rodent models, including work in our laboratory, have shown prostate fibrosis to occur as a consequence of inflammation. However, the relationship between collagen content and inflammation in human tissue samples obtained from surgical treatment of benign prostatic hypererplasia (BPH)/LUTS has not to our knowledge been previously examined. METHODS: Prostate tissue specimens from 53 patients (ages 47-88, mean 65.1) treated by open simple prostatectomy or transurethral resection of the prostate for BPH/LUTS were stained to quantitatively assess prostate inflammation and collagen content. Patients with prostate cancer present in greater than 5% of the surgical specimen were excluded. Prostate volume was determined from pelvic CT scan obtained within 2 years of surgery. RESULTS: Analysis of the data showed that inflammation was inversely correlated with collagen content (r = -0.28, p = 0.04). In men with prostates less than 75 cm3 inflammation increases and collagen content decreases with prostate volume (p = 0.002 and p = 0.03, respectively) while in men with prostate volume over 75 cm3 inflammation decreases and collagen content increases with prostate volume (p = 0.30 and p = 0.005, respectively). CONCLUSIONS: Our data do not support the assumed positive association of prostate inflammation with collagen content. Coordinated analysis of scatter plots of inflammation and collagen content with prostate volume revealed a subset of prostates with volumes >50 cm3 prostate characterized by intense inflammation and low collagen content and it is this subgroup that appears most responsible for the inverse correlation of inflammation and collagen.

Critical roles of PU.1/cathepsin S activation in regulating inflammatory responses of macrophages in periodontitis.

OBJECTIVE: To determine the critical roles of PU.1/cathepsin S activation in regulating inflammatory responses of macrophages during periodontitis. BACKGROUND: Cathepsin S (CatS) is a cysteine protease and exerts important roles in the immune response. Elevated CatS has been found in the gingival tissues of periodontitis patients and is involved in alveolar bone destruction. However, the underlying mechanism of CatS-driven IL-6 production in periodontitis remains unclear. METHODS: Western blot was applied to measure mature cathepsin S(mCatS) and IL-6 expression in gingival tissues from periodontitis patients and RAW264.7 cells exposed to lipopolysaccharide from Porphyromonas gingivalis (P.g. LPS). Immunofluorescence was applied to confirm the localization of PU.1, and CatS in the gingival tissues of periodontitis patients. ELISA was performed to determine IL-6 production by the P.g. LPS-exposed RAW264.7 cells. Knockdown by shRNA was used to determine the effects of PU.1 on p38/ nuclear factor (NF)-κB activation, mCatS expression and IL-6 production in RAW264.7 cells. RESULTS: The expressions mCatS and IL-6 were significantly upregulated in gingival macrophages. In cultured RAW264.7 cells, increased mCatS and IL-6 protein paralleled the activation of p38 and NF-κB after exposure to P.g. LPS. CatS knockdown by shRNA significantly decreased P.g. LPS-induced IL-6 expression and p38/NF-κB activation. PU.1 was significantly increased in P.g. LPS-exposed RAW264.7 cells, and PU.1 knockdown dramatically abolished the P.g. LPS-induced upregulation of mCatS and IL-6 and the activation of p38 and NF-κB. Furthermore, PU.1 and CatS colocalized in macrophages within the gingival tissues of periodontitis patients. CONCLUSION: PU.1-dependent CatS drives IL-6 production in macrophages by activating p38 and NF-κB in periodontitis.

Robust estimation and variable selection for the accelerated failure time model.

This article concerns robust modeling of the survival time for cancer patients. Accurate prediction of patient survival time is crucial to the development of effective therapeutic strategies. To this goal, we propose a unified Expectation-Maximization approach combined with the L1 -norm penalty to perform variable selection and parameter estimation simultaneously in the accelerated failure time model with right-censored survival data of moderate sizes. Our approach accommodates general loss functions, and reduces to the well-known Buckley-James method when the squared-error loss is used without regularization. To mitigate the effects of outliers and heavy-tailed noise in real applications, we recommend the use of robust loss functions under the general framework. Furthermore, our approach can be extended to incorporate group structure among covariates. We conduct extensive simulation studies to assess the performance of the proposed methods with different loss functions and apply them to an ovarian carcinoma study as an illustration.

Retraction Note to: Robust Model Selection and Estimation for Censored Survival Data with High Dimensional Genomic Covariates.

The authors have retracted this article [1] because they found a fundamental mistake in the methodology that is not correctable at this time. This mistake is found in the methodology and the derivation of the model with Tukey and Huber's losses. Because of the error, the findings in the article are not reliable. All authors agree to this retraction.

Flail arm syndrome patients exhibit profound abnormalities in nerve conduction: an electromyography study.

Flail arm syndrome (FAS) is a rare degenerative disease of the nervous system and a variant of amyotrophic lateral sclerosis (ALS). In the current study, we sought to further delineate electromyographic changes in sensory and motor conduction of the median nerve in four FAS patients and also described one representative case of FAS in a 63-year old Chinese male patient who was admitted because of aggravating limb myasthenia for three months. Electromyography showed that FAS patients exhibited variable electromyographic changes in sensory conduction of the median nerve. Abnormal conduction velocity of the sensory nerve in bilateral median nerves was observed in one patient but normal in two other patients. Two patients had a marked reduction in median sensory nerve action potential amplitude. In addition, one patient showed significant reduction in the conduction velocity and motor nerve action potential amplitude. The latency of motor conduction of bilateral median nerves was markedly prolonged. Furthermore, the incidence rate of the F wave in the right median nerve ranged from 5% to 100%. Furthermore, all four patients exhibited abnormalities in needle electromyography in at least three regions of the four regions examined with massive denervations in large and widened motor units and diminished recruitment of motor units, indicating the simultaneous presence of both acute denervation and chronic nerve regeneration. In conclusion, this is the first detailed study of electromyographic changes in FAS and the findings help improve clinicians' understanding of this disease and differentiating the diagnoses of FAS from ALS.

Robust Model Selection and Estimation for Censored Survival Data with High Dimensional Genomic Covariates.

When relating genomic data to survival outcomes, there are three main challenges that are the censored survival outcomes, the high-dimensionality of the genomic data, and the non-normality of data. We propose a method to tackle these challenges simultaneously and obtain a robust estimation of detecting significant genes related to survival outcomes based on Accelerated Failure Time (AFT) model. Specifically, we include a general loss function to the AFT model, adopt model regularization and shrinkage technique, cope with parameters tuning and model selection, and develop an algorithm based on unified Expectation-Maximization approach for easy implementation. Simulation results demonstrate the advantages of the proposed method compared with existing methods when the data has heavy-tailed errors and correlated covariates. Two real case studies on patients are provided to illustrate the application of the proposed method.

Inhibitory Effects of Constituents from the Aerial Parts of Rosmarinus officinalis L. on Triglyceride Accumulation.

Sixteen flavonoids (1-16) including two new ones, named officinoflavonosides A (1) and B (2) were obtained from the aerial parts of Rosmarinus officinalis. Among the known ones, 6, 10, and 13 were isolated from the rosmarinus genus for the first time. Their structures were elucidated by chemical and spectroscopic methods. Moreover, the effects on sodium oleate-induced triglyceride accumulation (TG) in HepG2 cells of the above-mentioned compounds and 16 other isolates (17-32) reported previously to have been obtained in the plant were analyzed. Results show that eight kinds of flavonoids (compounds 1, 2, 3, 6-9 and 11) and seven kinds of other known isolates (compounds 17-20, 23, 26 and 31) possessed significant inhibitory effects on intracellular TG content in HepG2 cells. Among them, the activities of compounds 1 and 20 were comparable to that of orlistat, which suggested that these compounds in this plant might be involved in lipid metabolism.

Bioactive Constituents Obtained from the Seeds of Lepidium apetalum Willd.

Three new compounds, apetalumosides C1 (1), D (2), and 1-thio--d-glucopyranosyl(1→1)-1-thio-α-d-glucopyranoside (3), together with twenty-two known ones (4-25) were obtained from the seeds of Lepidium apetalum Willd. Among the known isolates, 5-8, 10-13, 16-20, and 25 were obtained from the genus for the first time; 4, 14, 15, and 21-24 were isolated from the species for the first time. Meanwhile, the NMR data of 16 was first reported here. Their structures were determined by means of chemical and spectroscopic methods. On the other hand, their inhibitory effects on sodium oleate-induced triglyceride (TG) overloading in HepG2 cells were evaluated. As a result, two new compounds (1 and 2), together with known isolates 7-11, 13, 14, 16-18, 20, 21, and 25 possessed significant inhibitory effects in the cells.

Purification and characterization of a novel fibrinolytic enzyme from Whitmania pigra Whitman.

A fibrinolytic enzyme was purified from the dry body of Whitmania pigra Whitman. The fibrinolytic enzyme was purified to homogeneity with a yield of 0.003% and a purification of 630.7 fold. The molecular weight of the enzyme was estimated to be 26.7 kDa by reduced sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The enzyme was tested by matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) and it showed that the enzyme was a novel fibrinolytic enzyme. The optimal pH and temperature of the enzyme were 8.5 and 55°C, respectively. Enzyme activity was enhanced by Na+, Mg2+, and K+. On the contrary, the proteolytic activity was significantly inhibited by Mn2+, Fe2+, Fe3+, ethylenediaminetetraacetic acid (EDTA), and ethylenebis(oxyethylenenitrilo)tetraacetic acid (EGTA). Fibrinolytic and fibrinogenolytic assays showed that the enzyme preferentially hydrolyzed fibrinogen Aα-chains, followed by Bß- and γ-chains. The α-, ß-, and γ-γ-chains of fibrin were also degraded by the enzyme.

Pattern discovery and cancer gene identification in integrated cancer genomic data.

Large-scale integrated cancer genome characterization efforts including the cancer genome atlas and the cancer cell line encyclopedia have created unprecedented opportunities to study cancer biology in the context of knowing the entire catalog of genetic alterations. A clinically important challenge is to discover cancer subtypes and their molecular drivers in a comprehensive genetic context. Curtis et al. [Nature (2012) 486(7403):346-352] has recently shown that integrative clustering of copy number and gene expression in 2,000 breast tumors reveals novel subgroups beyond the classic expression subtypes that show distinct clinical outcomes. To extend the scope of integrative analysis for the inclusion of somatic mutation data by massively parallel sequencing, we propose a framework for joint modeling of discrete and continuous variables that arise from integrated genomic, epigenomic, and transcriptomic profiling. The core idea is motivated by the hypothesis that diverse molecular phenotypes can be predicted by a set of orthogonal latent variables that represent distinct molecular drivers, and thus can reveal tumor subgroups of biological and clinical importance. Using the cancer cell line encyclopedia dataset, we demonstrate our method can accurately group cell lines by their cell-of-origin for several cancer types, and precisely pinpoint their known and potential cancer driver genes. Our integrative analysis also demonstrates the power for revealing subgroups that are not lineage-dependent, but consist of different cancer types driven by a common genetic alteration. Application of the cancer genome atlas colorectal cancer data reveals distinct integrated tumor subtypes, suggesting different genetic pathways in colon cancer progression.

Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits.

Genetic studies often involve quantitative traits. Identifying genetic features that influence quantitative traits can help to uncover the etiology of diseases. Quantile regression method considers the conditional quantiles of the response variable, and is able to characterize the underlying regression structure in a more comprehensive manner. On the other hand, genetic studies often involve high-dimensional genomic features, and the underlying regression structure may be heterogeneous in terms of both effect sizes and sparsity. To account for the potential genetic heterogeneity, including the heterogeneous sparsity, a regularized quantile regression method is introduced. The theoretical property of the proposed method is investigated, and its performance is examined through a series of simulation studies. A real dataset is analyzed to demonstrate the application of the proposed method.

Comprehensive assessment of chamber-specific and transmural heterogeneity in myofilament protein phosphorylation by top-down mass spectrometry.

The heart is characterized by a remarkable degree of heterogeneity, the basis of which is a subject of active investigation. Myofilament protein post-translational modifications (PTMs) represent a critical mechanism regulating cardiac contractility, and emerging evidence shows that pathological cardiac conditions induce contractile heterogeneity that correlates with transmural variations in the modification status of myofilament proteins. Nevertheless, whether there exists basal heterogeneity in myofilament protein PTMs in the heart remains unclear. Here we have systematically assessed chamber-specific and transmural variations in myofilament protein PTMs, specifically, the phosphorylation of cardiac troponin I (cTnI), cardiac troponin T (cTnT), tropomyosin (Tpm), and myosin light chain 2 (MLC2). We show that the phosphorylation of cTnI and αTm vary in the different chambers of the heart, whereas the phosphorylation of MLC2 and cTnT does not. In contrast, no significant transmural differences were observed in the phosphorylation of any of the myofilament proteins analyzed. These results highlight the importance of appropriate tissue sampling-particularly for studies aimed at elucidating disease mechanisms and biomarker discovery-in order to minimize potential variations arising from basal heterogeneity in myofilament PTMs in the heart.

Simultaneous variable selection for joint models of longitudinal and survival outcomes.

Joint models of longitudinal and survival outcomes have been used with increasing frequency in clinical investigations. Correct specification of fixed and random effects is essential for practical data analysis. Simultaneous selection of variables in both longitudinal and survival components functions as a necessary safeguard against model misspecification. However, variable selection in such models has not been studied. No existing computational tools, to the best of our knowledge, have been made available to practitioners. In this article, we describe a penalized likelihood method with adaptive least absolute shrinkage and selection operator (ALASSO) penalty functions for simultaneous selection of fixed and random effects in joint models. To perform selection in variance components of random effects, we reparameterize the variance components using a Cholesky decomposition; in doing so, a penalty function of group shrinkage is introduced. To reduce the estimation bias resulted from penalization, we propose a two-stage selection procedure in which the magnitude of the bias is ameliorated in the second stage. The penalized likelihood is approximated by Gaussian quadrature and optimized by an EM algorithm. Simulation study showed excellent selection results in the first stage and small estimation biases in the second stage. To illustrate, we analyzed a longitudinally observed clinical marker and patient survival in a cohort of patients with heart failure.

Group variable selection via convex log-exp-sum penalty with application to a breast cancer survivor study.

In many scientific and engineering applications, covariates are naturally grouped. When the group structures are available among covariates, people are usually interested in identifying both important groups and important variables within the selected groups. Among existing successful group variable selection methods, some methods fail to conduct the within group selection. Some methods are able to conduct both group and within group selection, but the corresponding objective functions are non-convex. Such a non-convexity may require extra numerical effort. In this article, we propose a novel Log-Exp-Sum(LES) penalty for group variable selection. The LES penalty is strictly convex. It can identify important groups as well as select important variables within the group. We develop an efficient group-level coordinate descent algorithm to fit the model. We also derive non-asymptotic error bounds and asymptotic group selection consistency for our method in the high-dimensional setting where the number of covariates can be much larger than the sample size. Numerical results demonstrate the good performance of our method in both variable selection and prediction. We applied the proposed method to an American Cancer Society breast cancer survivor dataset. The findings are clinically meaningful and may help design intervention programs to improve the qualify of life for breast cancer survivors.

Regularized outcome weighted subgroup identification for differential treatment effects.

To facilitate comparative treatment selection when there is substantial heterogeneity of treatment effectiveness, it is important to identify subgroups that exhibit differential treatment effects. Existing approaches model outcomes directly and then define subgroups according to interactions between treatment and covariates. Because outcomes are affected by both the covariate-treatment interactions and covariate main effects, direct modeling outcomes can be hard due to model misspecification, especially in presence of many covariates. Alternatively one can directly work with differential treatment effect estimation. We propose such a method that approximates a target function whose value directly reflects correct treatment assignment for patients. The function uses patient outcomes as weights rather than modeling targets. Consequently, our method can deal with binary, continuous, time-to-event, and possibly contaminated outcomes in the same fashion. We first focus on identifying only directional estimates from linear rules that characterize important subgroups. We further consider estimation of comparative treatment effects for identified subgroups. We demonstrate the advantages of our method in simulation studies and in analyses of two real data sets.

Using distance covariance for improved variable selection with application to learning genetic risk models.

Variable selection is of increasing importance to address the difficulties of high dimensionality in many scientific areas. In this paper, we demonstrate a property for distance covariance, which is incorporated in a novel feature screening procedure together with the use of distance correlation. The approach makes no distributional assumptions for the variables and does not require the specification of a regression model and hence is especially attractive in variable selection given an enormous number of candidate attributes without much information about the true model with the response. The method is applied to two genetic risk problems, where issues including uncertainty of variable selection via cross validation, subgroup of hard-to-classify cases, and the application of a reject option are discussed.

The Influence of Organized Physical Activity (Including Gymnastics) on Young Adult Skeletal Traits: Is Maturity Phase Important?

We prospectively evaluated adolescent organized physical activity (PA) as a factor in adult female bone traits. Annual DXA scans accompanied semiannual records of anthropometry, maturity, and PA for 42 participants in this preliminary analysis (criteria: appropriately timed DXA scans at ~1 year premenarche [predictor] and ~5 years postmenarche [dependent variable]). Regression analysis evaluated total adolescent interscan PA and PA over 3 maturity subphases as predictors of young adult bone outcomes: 1) bone mineral content (BMC), geometry, and strength indices at nondominant distal radius and femoral neck; 2) subhead BMC; 3) lumbar spine BMC. Analyses accounted for baseline gynecological age (years pre- or postmenarche), baseline bone status, adult body size and interscan body size change. Gymnastics training was evaluated as a potentially independent predictor, but did not improve models for any outcomes (p > .07). Premenarcheal bone traits were strong predictors of most adult outcomes (semipartial r2 = .21-0.59, p ≤ .001). Adult 1/3 radius and subhead BMC were predicted by both total PA and PA 1-3 years postmenarche (p < .03). PA 3-5 years postmenarche predicted femoral narrow neck width, endosteal diameter, and buckling ratio (p < .05). Thus, participation in organized physical activity programs throughout middle and high school may reduce lifetime fracture risk in females.

Magnesium sulfate with lidocaine for preventing propofol injection pain: a randomized, double-blind, placebo-controlled trial.

PURPOSE: Propofol injection pain, despite various strategies, remains common and troublesome. This study aimed to test the hypothesis that pretreatment with the combination of intravenous lidocaine and magnesium would have an additive effect on reducing propofol injection pain. METHODS: After institutional review board (IRB) approval and informed consent, we performed a prospective, double-blind, placebo-controlled, randomized trial. Subjects were randomly assigned to pretreatment with either lidocaine (50 mg), magnesium sulfate (0.25 mg), lidocaine (50 mg) plus magnesium sulfate (0.25 mg), or 0.9 % sodium chloride. Following pretreatment, propofol (50 mg) was administered, and subjects were questioned regarding injection site pain and observed for behavioral signs of pain. RESULTS: Two hundred subjects were enrolled and 158 subjects (39 placebo, 38 lidocaine, 44 magnesium sulfate, and 37 lidocaine plus magnesium sulfate) received their assigned pretreatment intervention. Intergroup baseline characteristics were similar. The proportion of subjects reporting propofol injection pain was highest in those pretreated with magnesium sulfate (57 %), followed by those pretreated with placebo (46 %), lidocaine plus magnesium sulfate (41 %), and then lidocaine (29 %; p = 0.011). When adjusted for age, gender, diabetes mellitus, chronic pain, tobacco use, and selective-serotonin reuptake inhibitor use, the pain response scale scores were significantly reduced by lidocaine pretreatment compared to magnesium sulfate and placebo (p = 0.031 and p = 0.0003, respectively). CONCLUSIONS: In this double-blind, placebo-controlled, randomized trial, the combination of intravenous magnesium sulfate and lidocaine offered no additional benefit for the relief of propofol injection pain compared to intravenous lidocaine alone. An improved, receptor-based understanding of the mechanism of propofol injection pain is still needed.

Lasso tree for cancer staging with survival data.

The tumor-node-metastasis staging system has been the lynchpin of cancer diagnosis, treatment, and prognosis for many years. For meaningful clinical use, an orderly grouping of the T and N categories into a staging system needs to be defined, usually with respect to a time-to-event outcome. This can be reframed as a model selection problem with respect to features arranged on a partially ordered two-way grid, and a penalized regression method is proposed for selecting the optimal grouping. Instead of penalizing the L1-norm of the coefficients like lasso, in order to enforce the stage grouping, we place L1 constraints on the differences between neighboring coefficients. The underlying mechanism is the sparsity-enforcing property of the L1 penalty, which forces some estimated coefficients to be the same and hence leads to stage grouping. Partial ordering constraints is also required as both the T and N categories are ordinal. A series of optimal groupings with different numbers of stages can be obtained by varying the tuning parameter, which gives a tree-like structure offering a visual aid on how the groupings are progressively made. We hence call the proposed method the lasso tree. We illustrate the utility of our method by applying it to the staging of colorectal cancer using survival outcomes. Simulation studies are carried out to examine the finite sample performance of the selection procedure. We demonstrate that the lasso tree is able to give the right grouping with moderate sample size, is stable with regard to changes in the data, and is not affected by random censoring.