The Random Phenotype Concept, with Applications

A random phenotype is defined as a probability distribution over any given set of phenotypes. This includes as special cases the kinds of phenotypes usually considered (qualitative, quantitative, and threshold characters) and all others. Correspondingly general methods are indicated for analyzing data of all forms in terms of the classical Mendelian factor concept (as distinct from the biometrical methods usually applied to measurement and graded data, associated with the effective factor concept). These are applied in a new analysis of the data of E. L. Green (1951, 1954, 1962) on skeletal variation in the mouse. The adequacies of various classical one-factor and several-factor models are considered. Indications of an underlying scale are found from this new standpoint. The results are compared with those obtained by Green using the scaling approach. An illustrative application is also made to some of Bruell's (1962) continuous behavioural data on mice. This work was substantially completed in 1959 but not previously prepared for publication. The same approach was originated and developed independently by R. L. Collins who has treated a wider range of theoretical problems (cf. Collins 1967, 1968a, 1969b, 1970c) and a wider range of applications (cf. Collins and Fuller 1968; Collins 1968b, 1969a, 1970a). A less general independent development is that of Mode and Gasser 1972.     

Phenotype Switching and Mutations in Random Environments

Cell populations can benefit from changing phenotype when the environment changes. One mechanism for generating these changes is stochastic phenotype switching, whereby cells switch stochastically from one phenotype to another according to genetically determined rates, irrespective of the current environment, with the matching of phenotype to environment then determined by selective pressure. This mechanism has been observed in numerous contexts, but identifying the precise connection between switching rates and environmental changes remains an open problem. Here, we introduce a simple model to study the evolution of phenotype switching in a finite population subject to random environmental shocks. We compare the successes of competing genotypes with different switching rates, and analyze how the optimal switching rates depend on the frequency of environmental changes. If environmental changes are as rare as mutations, then the optimal switching rates mimic the rates of environmental changes. If the environment changes more frequently, then the optimal genotype either maximally favors fitness in the more common environment or has the maximal switching rate to each phenotype. Our results also explain why the optimum is relatively insensitive to fitness in each environment.     

Combined Use of FSR Sensor Array and SVM Classifier for Finger Motion Recognition Based on Pressure Distribution Map

For controlling dexterous prosthetic hand with a high number of active Degrees of Freedom (DOF),it is necessary to reliably extract control volitions of finger motions from the human body.In this study,a large variety of finger motions are discriminated based on the diversities of the pressure distribution produced by the mechanical actions of muscles on the forearm.The pressure distribution patterns corresponding to the motions were measured by sensor array which is composed of 32 Force Sensitive Resistor (FSR) sensors.In order to map the pressure patterns with different finger motions,a multiclass classifier was designed based on the Support Vector Machine (SVM) algorithm.The multi-subject experiments show that it is possible to identify as many as seventeen different finger motions,including individual finger motions and multi-finger grasping motions,with the accuracy above 99％ in the in-session validation.Further,the cross-session validation demonstrates that the performance of the proposed method is robust for use if the FSR array is not reset.The results suggest that the proposed method has great application prospects for the control of multi-DOF dexterous hand prosthesis.     

RFMirTarget: Predicting Human MicroRNA Target Genes with a Random Forest Classifier

MicroRNAs are key regulators of eukaryotic gene expression whose fundamental role has already been identified in many cell pathways. The correct identification of miRNAs targets is still a major challenge in bioinformatics and has motivated the development of several computational methods to overcome inherent limitations of experimental analysis. Indeed, the best results reported so far in terms of specificity and sensitivity are associated to machine learning-based methods for microRNA-target prediction. Following this trend, in the current paper we discuss and explore a microRNA-target prediction method based on a random forest classifier, namely RFMirTarget. Despite its well-known robustness regarding general classifying tasks, to the best of our knowledge, random forest have not been deeply explored for the specific context of predicting microRNAs targets. Our framework first analyzes alignments between candidate microRNA-target pairs and extracts a set of structural, thermodynamics, alignment, seed and position-based features, upon which classification is performed. Experiments have shown that RFMirTarget outperforms several well-known classifiers with statistical significance, and that its performance is not impaired by the class imbalance problem or features correlation. Moreover, comparing it against other algorithms for microRNA target prediction using independent test data sets from TarBase and starBase, we observe a very promising performance, with higher sensitivity in relation to other methods. Finally, tests performed with RFMirTarget show the benefits of feature selection even for a classifier with embedded feature importance analysis, and the consistency between relevant features identified and important biological properties for effective microRNA-target gene alignment.     

An Ensemble Method with Hybrid Features to Identify Extracellular Matrix Proteins

The extracellular matrix (ECM) is a dynamic composite of secreted proteins that play important roles in numerous biological processes such as tissue morphogenesis, differentiation and homeostasis. Furthermore, various diseases are caused by the dysfunction of ECM proteins. Therefore, identifying these important ECM proteins may assist in understanding related biological processes and drug development. In view of the serious imbalance in the training dataset, a Random Forest-based ensemble method with hybrid features is developed in this paper to identify ECM proteins. Hybrid features are employed by incorporating sequence composition, physicochemical properties, evolutionary and structural information. The Information Gain Ratio and Incremental Feature Selection (IGR-IFS) methods are adopted to select the optimal features. Finally, the resulting predictor termed IECMP (Identify ECM Proteins) achieves an balanced accuracy of 86.4% using the 10-fold cross-validation on the training dataset, which is much higher than results obtained by other methods (ECMPRED: 71.0%, ECMPP: 77.8%). Moreover, when tested on a common independent dataset, our method also achieves significantly improved performance over ECMPP and ECMPRED. These results indicate that IECMP is an effective method for ECM protein prediction, which has a more balanced prediction capability for positive and negative samples. It is anticipated that the proposed method will provide significant information to fully decipher the molecular mechanisms of ECM-related biological processes and discover candidate drug targets. For public access, we develop a user-friendly web server for ECM protein identification that is freely accessible at http://iecmp.weka.cc.     

Improving Protein Expression Prediction Using Extra Features and Ensemble Averaging

The article focus is the improvement of machine learning models capable of predicting protein expression levels based on their codon encoding. Support vector regression (SVR) and partial least squares (PLS) were used to create the models. SVR yields predictions that surpass those of PLS. It is shown that it is possible to improve the models predictive ability by using two more input features, codon identification number and codon count, besides the already used codon bias and minimum free energy. In addition, applying ensemble averaging to the SVR or PLS models also improves the results even further. The present work motivates the test of different ensembles and features with the aim of improving the prediction models whose correlation coefficients are still far from perfect. These results are relevant for the optimization of codon usage and enhancement of protein expression levels in synthetic biology problems.     

Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites

As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein malonylation. In this study, we constructed a deep learning (DL) network classifier based on long short-term memory (LSTM) with word embedding (LSTM_WE) for the prediction of mammalian malonylation sites. LSTM_WE performs better than traditional classifiers developed with common pre-defined feature encodings or a DL classifier based on LSTM with a one-hot vector. The performance of LSTM_WE is sensitive to the size of the training set, but this limitation can be overcome by integration with a traditional machine learning (ML) classifier. Accordingly, an integrated approach called LEMP was developed, which includes LSTM_WE and the random forest classifier with a novel encoding of enhanced amino acid content. LEMP performs not only better than the individual classifiers but also superior to the currently-available malonylation predictors. Additionally, it demonstrates a promising performance with a low false positive rate, which is highly useful in the prediction application. Overall, LEMP is a useful tool for easily identifying malonylation sites with high confidence. LEMP is available at http://www.bioinfogo.org/lemp.     

Image Generation Using Bidirectional Integral Features for Face Recognition with a Single Sample per Person

In face recognition, most appearance-based methods require several images of each person to construct the feature space for recognition. However, in the real world it is difficult to collect multiple images per person, and in many cases there is only a single sample per person (SSPP). In this paper, we propose a method to generate new images with various illuminations from a single image taken under frontal illumination. Motivated by the integral image, which was developed for face detection, we extract the bidirectional integral feature (BIF) to obtain the characteristics of the illumination condition at the time of the picture being taken. The experimental results for various face databases show that the proposed method results in improved recognition performance under illumination variation.     

Structural and Dynamic Features of the MutT Protein in the Recognition of Nucleotides with the Mutagenic 8-Oxoguanine Base

Escherichia coli MutT hydrolyzes 8-oxo-dGTP to 8-oxo-dGMP, an event that can prevent the misincorporation of 8-oxoguanine opposite adenine in DNA. Of the several enzymes that recognize 8-oxoguanine, MutT exhibits high substrate specificity for 8-oxoguanine nucleotides; however, the structural basis for this specificity is unknown. The crystal structures of MutT in the apo and holo forms and in the binary and ternary forms complexed with the product 8-oxo-dGMP and 8-oxo-dGMP plus Mn²⁺, respectively, were determined. MutT strictly recognizes the overall conformation of 8-oxo-dGMP through a number of hydrogen bonds. This recognition mode revealed that 8-oxoguanine nucleotides are discriminated from guanine nucleotides by not only the hydrogen bond between the N7-H and Oδ (N119) atoms but also by the syn glycosidic conformation that 8-oxoguanine nucleotides prefer. Nevertheless, these discrimination factors cannot by themselves explain the roughly 34,000-fold difference between the affinity of MutT for 8-oxo-dGMP and dGMP. When the binary complex of MutT with 8-oxo-dGMP is compared with the ligand-free form, ordering and considerable movement of the flexible loops surrounding 8-oxo-dGMP in the binary complex are observed. These results indicate that MutT specifically recognizes 8-oxoguanine nucleotides by the ligand-induced conformational change.     

Irisin and the Metabolic Phenotype of Adults with Prader-Willi Syndrome

Context

Hyperphagia, low resting energy expenditure, and abnormal body composition contribute to severe obesity in Prader Willi syndrome (PWS). Irisin, a circulating myokine, stimulates “browning” of white adipose tissue resulting in increased energy expenditure and improved insulin sensitivity. Irisin has not been previously studied in PWS.

Objectives

Compare plasma and salivary irisin in PWS adults and normal controls. Examine the relationship of irisin to insulin sensitivity and plasma lipids.

Design and Study Participants

A fasting blood sample for glucose, lipids, insulin, leptin, adinopectin, and irisin was obtained from 22 PWS adults and 54 healthy BMI-matched volunteers. Saliva was collected for irisin assay in PWS and controls.

Results

Fasting glucose (77±9 vs 83±7mg/dl, p = 0.004), insulin (4.1±2.0 vs 7.9±4.7μU/ml, p<0.001), and triglycerides (74±34 vs 109±71mg/dl, p = 0.007) were lower in PWS than in controls. Insulin resistance (HOMA-IR) was lower (0.79±0.041 vs 1.63±1.02, p<0.001) and insulin sensitivity (QUICKI) was higher (0.41±0.04 vs 0.36±0.03, p<0.001) in PWS. Plasma irisin was similar in both groups, but salivary irisin (64.5±52.0 vs 33.0±12.1ng/ml), plasma leptin (33.5±24.2 vs 19.7±19.3ng/ml) and plasma adinopectin (13.0±10.8 vs 7.6±4.5μg/ml) were significantly greater in PWS (p<0.001). In PWS, plasma irisin showed positive Pearson correlations with total cholesterol (r = 0.58, p = 0.005), LDL-cholesterol (r = 0.59, p = 0.004), and leptin (r = 0.43, p = 0.045). Salivary irisin correlated negatively with HDL-cholesterol (r = -0.50, p = 0.043) and positively with LDL-cholesterol (r = 0.51, p = 0.037) and triglycerides (r = 0.50, p = 0.041).

Conclusions

Salivary irisin was markedly elevated in PWS although plasma irisin was similar to levels in controls. Significant associations with plasma lipids suggest that irisin may contribute to the metabolic phenotype of PWS.     

Combined Ribotyping and Random Multiprimer DNA Analysis To Probe the Population Structure of Listeria monocytogenes

To improve our understanding of the genetic links between strains originating from food and strains responsible for human diseases, we studied the genetic diversity and population structure of 130 epidemiologically unrelated Listeria monocytogenes strains. Strains were isolated from different sources and ecosystems in which the bacterium is commonly found. We used rRNA gene restriction fragment length polymorphism analysis with two endonucleases and random multiprimer DNA analysis with seven oligonucleotide primers to study multiple genetic features of each strain. We used three clustering methods to identify genetic links between individual strains and to determine the precise genetic structure of the population. The combined results confirmed that L. monocytogenes strains can be divided into two major phylogenetic divisions. The method used allowed us to demonstrate that the genetic structure and diversity of the two phylogenetic divisions differ. Division I is the most homogeneous and can easily be divided into subgroups with dissimilarity distances of less than 0.30. Each of these subgroups mainly, or exclusively, contains a single serotype (1/2b, 4b, 3b, or 4a). The serotype 4a lineage appears to form a branch that is highly divergent from the phylogenetic group containing serotypes 1/2b, 4b, and 3b. Division II contains strains of serotypes 1/2a, 1/2c, and 3a. It exhibits more genetic diversity with no peculiar clustering. The fact that division II is more heterogeneous than division I suggests that division II evolved from a common ancestor earlier than division I. A significant association was found between division I and human strains, suggesting that strains from division I are better adapted to human hosts.     

Effect of Low-Magnitude Whole-Body Vibration Combined with Alendronate in Ovariectomized Rats: A Random Controlled Osteoporosis Prevention Study

Background

Alendronate (ALE) is a conventional drug used to treat osteoporosis. Low-magnitude whole-body vibration (WBV) exercise has been developed as a potential treatment for osteoporosis. The aim of this study was to investigate whether low-magnitude WBV could enhance the protective effect of ALE on bone properties in ovariectomized rats.

Methods

A total of 128 Sprague-Dawley rats were randomly divided into five groups (SHAM, OVX+VEH, OVX+WBV, OVX + ALE, OVX+WBV+ALE). The level of WBV applied was 0.3 g at 45–55 Hz for 20 min/day, 5 day/week and for 3 months. ALE was administered in dose of 1 mg/Kg once a week. Every four weeks eight rats from each group were sacrificed and their blood and both tibiae were harvested. The expression of osteocalcin and CTX in serum was measured by enzyme-linked immunosorbent assay (ELISA) and the tibiae were subjected to metaphyseal three-point bending and μCT analysis.

Results

Osteocalcin rose after ovariectomy and was not appreciably changed by either alendronate or WBV alone or in combination. Alendronate treatment significantly prevented an increase in CTX. WBV alone treatment did not alter this effect. Compared with the OVX+WBV group, nearly all tested indices such as the BV/TV, TV apparent, Tb.N, Tb.Th, and Conn.D were higher in the OVX+ALE group at week 12.Compared with the OVX+WBV group, certain tested indices such as BV/TV, TV apparent, Tb.N, and Con.D, were higher in the OVX+WBV+ALE group at week 12. At week 12, tibiae treated with WBV+ALE exhibited a significantly higher F_max compared to the OVX+VEH group, and a significant difference was also found in energy absorption between the OVX+WBV+ALE and OVX+VEH groups.

Conclusions

Compared with the WBV, ALE was more effective at preventing bone loss and improved the trabecular architecture. However, WBV enhanced the effect of alendronate in ovariectomized rats by inducing further improvements in trabecular architecture.     

Random walk with persistence and external bias

The partial differential equation of the random walk problem with persistence of direction and external bias is derived. By persistence of direction or internal bias we mean that the probability a particle will travel in a given direction need not be the same for all directions, but depends solely upon the particle's previous direction of motion. The external bias arises from an anisotropy of the medium or an external force on the particle. The problem is treated by considering that the net displacement of a particle arises from two factors, namely, that neither the probability of the particle traveling in any direction after turning nor the distance the particle travels in a given direction need be the same for all directions. A modified Fokker-Planck equation is first obtained using the assumptions that the particles have a distribution of travel times and speeds and that the average time of travel between turns need not be zero. The fional equation incopporating the assumption of a persistence of direction and an external bias is then derived. Applications to the study of diffusion and to long-chain polymers are then made. This work was done while the author was Public Health Service Research Fellow of The National Institute of Mental Health, Federal Security Agency.     

Population-Based Resequencing of APOA1 in 10,330 Individuals: Spectrum of Genetic Variation,Phenotype, and Comparison with Extreme Phenotype Approach

Rare genetic variants, identified by in-detail resequencing of loci, may contribute to complex traits. We used the apolipoprotein A-I gene (APOA1), a major high-density lipoprotein (HDL) gene, and population-based resequencing to determine the spectrum of genetic variants, the phenotypic characteristics of these variants, and how these results compared with results based on resequencing only the extremes of the apolipoprotein A-I (apoA-I) distribution. First, we resequenced APOA1 in 10,330 population-based participants in the Copenhagen City Heart Study. The spectrum and distribution of genetic variants was determined as a function of the number of individuals resequenced. Second, apoA-I and HDL cholesterol phenotypes were determined for nonsynonymous (NS) and synonymous (S) variants and were validated in the Copenhagen General Population Study (n = 45,239). Third, observed phenotypes were compared with those predicted using an extreme phenotype approach based on the apoA-I distribution. Our results are as follows: First, population-based resequencing of APOA1 identified 40 variants of which only 7 (18%) had minor allele frequencies >1%, and most were exceedingly rare. Second, 0.27% of individuals in the general population were heterozygous for NS variants which were associated with substantial reductions in apoA-I (up to 39 mg/dL) and/or HDL cholesterol (up to 0.9 mmol/L) and, surprisingly, 0.41% were heterozygous for variants predisposing to amyloidosis. NS variants associated with a hazard ratio of 1.72 (1.09–2.70) for myocardial infarction (MI), largely driven by A164S, a variant not associated with apoA-I or HDL cholesterol levels. Third, using the extreme apoA-I phenotype approach, NS variants correctly predicted the apoA-I phenotype observed in the population-based resequencing. However, using the extreme approach, between 79% (screening 0–1^st percentile) and 21% (screening 0–20^th percentile) of all variants were not identified; among these were variants previously associated with amyloidosis. Population-based resequencing of APOA1 identified a majority of rare NS variants associated with reduced apoA-1 and HDL cholesterol levels and/or predisposing to amyloidosis. In addition, NS variants associated with increased risk of MI.     

Ensemble docking and molecular dynamics identify knoevenagel curcumin derivatives with potent anti-EGFR activity

Epidermal growth factor receptor tyrosine kinase (EGFR-TK) is an attractive target for cancer therapy. Despite a number of effective EGFR inhibitors that are constantly expanding and different methods being employed to obtain novel compounds, the search for newer EGFR inhibitors is still a major scientific challenge. In the present study, a molecular docking and molecular dynamics investigation has been carried out with an ensemble of EGFR-TK structures against a synthetically feasible library of curcumin analogs to discover potent EGFR inhibitors. To resolve protein flexibility issue we have utilized 5 EGFR wild type crystal structures during docking as this gives improved possibility of identifying an active compound as compared to using a single crystal structure. We then identified five curcumin analogs representing different scaffolds that can serve as lead molecules. Finally, the 5 ns molecular dynamics simulation shows that knoevenagel condensate of curcumin specifically C29 and C30 can be used as starting blocks for developing effective leads capable of inhibiting EGFR.     

Phenotype and Genotype Analysis of Chinese Patients with Osteogenesis Imperfecta Type V

Osteogenesis imperfecta (OI) type V is an autosomal-dominant disease characterized by calcification of the forearm interosseous membrane, radial head dislocation, a subphyseal metaphyseal radiodense line, and hyperplastic callus formation. The causative mutation, c.-14C>T in the 5''-untranslated region of IFITM5, was recently discovered to be involved in this disease. However, in spite of the little genotypic variability, considerable phenotypic variability has been recognized in two cohorts of patients, the majority of whom were Caucasians. Using exome sequencing, we identified the same heterozygous mutation in four Chinese families with OI type V. This study confirms the molecular cause of OI type V and describes the phenotype of Chinese patients with this disorder. In conclusion, the phenotype of Chinese patients was generally similar to that of Caucasian patients.     

Phenotype and genotype of deaf patients with combined genomic and mitochondrial inheritance models

In most studies, sensorineural hearing loss is reported as a single-gene disease with autosomal dominant or autosomal recessive or with X-linked or maternal inheritance. It is uncommon that the hearing impairment is caused by a combined inheritance model including genomic and mitochondrial models. Here, we report six patients with sensorineural hearing loss caused by co-existing mutations in GJB2 or SLC26A4 and the mitochondrial gene. And there was no significant difference in hearing phenotypes between the six patients and the controls. The results indicate the complicated genetic etiology of, and may impact the diagnostic strategy for, hereditary hearing impairment. All patient siblings will carry mitochondrial DNA A1555G or C1494T mutations, and 25% of siblings may carry the same homozygous or compound heterozygote mutations in GJB2 or SLC26A4. Although this combined inheritance is not common in the Chinese deaf population (0.10%), our findings will have great impact in genetic counseling and risk prediction for deafness.