Prevalence and plasma exosome-derive microRNA diagnostic biomarker screening of adolescent idiopathic scoliosis in Yunnan Province, China.

Background: Idiopathic scoliosis significantly affects the physical and mental health of children and adolescents, with varying prevalence rates in different regions. The occurrence of idiopathic scoliosis is associated with genetic regulation and biochemical factors, but the changes in exosome-derived miRNA profiles among idiopathic scoliosis patients remain unclear. This study aimed to determine the prevalence of idiopathic scoliosis in Yunnan Province, China, and identify key exosome-derived miRNAs in idiopathic scoliosis through a cohort study. Methods: From January 2018 to December 2020, a cross-sectional study on idiopathic scoliosis in children and adolescents was conducted in Yunnan Province. A total of 84,460 students from 13 cities and counties in Yunnan Province participated in a scoliosis screening program, with ages ranging from 7 to 19 years. After confirmation through screening and imaging results, patients with severe idiopathic scoliosis and normal control individuals were selected using propensity matching. Subsequently, plasma exosome-derived miRNA sequencing and RT-qPCR validation were performed separately. Based on the validation results, diagnostic performance analysis and target gene prediction were conducted for differential plasma exosome-derived miRNAs. Results: The overall prevalence of idiopathic scoliosis in children and adolescents in Yunnan Province was 1.10%, with a prevalence of 0.87% in males and 1.32% in females. The peak prevalence was observed at age 13. Among patients diagnosed with idiopathic scoliosis, approximately 12.8% had severe cases, and there were more cases of double curvature than of single curvature, with thoracolumbar curvature being the most common in the single-curvature group. Sequencing of plasma exosome-derived miRNAs associated with idiopathic scoliosis revealed 56 upregulated and 153 downregulated miRNAs. Further validation analysis confirmed that hsa-miR-27a-5p, hsa-miR-539-5p, and hsa-miR-1246 have potential diagnostic value. Conclusions: We gained insights into the epidemiological characteristics of idiopathic scoliosis in Yunnan Province and conducted further analysis of plasma exosome-derived miRNA changes in patients with severe idiopathic scoliosis. This study has provided new insights for the prevention and diagnosis of idiopathic scoliosis, paving the way for exploring clinical biomarkers and molecular regulatory mechanisms. However, further validation and elucidation of the detailed biological mechanisms underlying these findings will be required in the future.

Treatment with bone marrow mesenchymal stem cells combined with plumbagin alleviates spinal cord injury by affecting oxidative stress, inflammation, apoptotis and the activation of the Nrf2 pathway.

The aim of the present study was to investigate the protective effect exerted by bone marrow mesenchymal stem cells (BMSCs) in combination with plumbagin on spinal cord injury (SCI) and explore the mechanism behind this protective effect. Firstly, BMSCs were extracted from male Sprague-Dawley rats, cultured in vitro, and identified by hematoxylin. Sprague-Dawley rats were then randomly divided into a control group, SCI model group, BMSC-treated group, a plumbagin-treated group, and a BMSC and plumbagin-treated group. After treatment with BMSCs combined with plumbagin, a Basso, Beattie and Bresnahan (BBB) test was carried out and the spinal cord water content was examined in order to analyze the effect of BMSCs combined with plumbagin on SCI. The myeloperoxidase (MPO), superoxide dismutase (SOD), malondialdehyde (MDA), nuclear factor-κB (NF-κB) p65 unit, tumor necrosis factor-α (TNF-α) levels were also detected. Moreover, nuclear factor erythroid 2­related factor 2 (Nrf2), phosphoinositide 3-kinase (PI3K), phosphorylated (p-)Akt, p-p38 mitogen-activated protein kinase (MAPK), and p-extracellular-signal-regulated kinase (ERK) protein expression levels were measured using western blot analysis. Treatment with BMSCs combined with plumbagin significantly improved locomotor recovery and reduced the spinal cord water content after SCI. The increased MPO, MDA, NF-κB p65 and TNF-α levels were significantly suppressed and the decreased SOD was significantly increased in SCI rats. The suppression of Nrf2, p-Akt and p-ERK, as well as the promotion of p-p38 MAPK, were reversed by treatment with BMSCs combined with plumbagin. These effects suggest that treatment with BMSCs combined with plumbagin alleviates SCI through its effects on oxidative stress, inflammation, apoptotis and activation of the Nrf2 pathway.

Identification of partially linear structure in additive models with an application to gene expression prediction from sequences.

The additive model is a semiparametric class of models that has become extremely popular because it is more flexible than the linear model and can be fitted to high-dimensional data when fully nonparametric models become infeasible. We consider the problem of simultaneous variable selection and parametric component identification using spline approximation aided by two smoothly clipped absolute deviation (SCAD) penalties. The advantage of our approach is that one can automatically choose between additive models, partially linear additive models and linear models, in a single estimation step. Simulation studies are used to illustrate our method, and we also present its applications to motif regression.

Improving taxonomy-based protein fold recognition by using global and local features.

Fold recognition from amino acid sequences plays an important role in identifying protein structures and functions. The taxonomy-based method, which classifies a query protein into one of the known folds, has been shown very promising for protein fold recognition. However, extracting a set of highly discriminative features from amino acid sequences remains a challenging problem. To address this problem, we developed a new taxonomy-based protein fold recognition method called TAXFOLD. It extensively exploits the sequence evolution information from PSI-BLAST profiles and the secondary structure information from PSIPRED profiles. A comprehensive set of 137 features is constructed, which allows for the depiction of both global and local characteristics of PSI-BLAST and PSIPRED profiles. We tested TAXFOLD on four datasets and compared it with several major existing taxonomic methods for fold recognition. Its recognition accuracies range from 79.6 to 90% for 27, 95, and 194 folds, achieving an average 6.9% improvement over the best available taxonomic method. Further test on the Lindahl benchmark dataset shows that TAXFOLD is comparable with the best conventional template-based threading method at the SCOP fold level. These experimental results demonstrate that the proposed set of features is highly beneficial to protein fold recognition.

Constructing consensus genetic maps in comparative analysis.

The construction of consensus genetic maps is a very challenging problem in computational biology. Many computational approaches have been proposed on the basis of only the marker order relations provided by a given set of individual genetic maps. In this article, we propose a comparative approach to constructing consensus genetic maps for a genome, which further takes into account the order relations from a closely related genome when resolving ordering conflicts among individual genetic maps. It aims to retain as many order relations as possible from individual genetic maps while achieving the minimum rearrangement distance to the reference genome. We implement the proposed approach as an integer linear program and test it on both simulated and real biological datasets. The experimental results show that it is capable of constructing more accurate consensus genetic maps than the most recent approach called MergeMap.

An improved classification of G-protein-coupled receptors using sequence-derived features.

BACKGROUND: G-protein-coupled receptors (GPCRs) play a key role in diverse physiological processes and are the targets of almost two-thirds of the marketed drugs. The 3 D structures of GPCRs are largely unavailable; however, a large number of GPCR primary sequences are known. To facilitate the identification and characterization of novel receptors, it is therefore very valuable to develop a computational method to accurately predict GPCRs from the protein primary sequences. RESULTS: We propose a new method called PCA-GPCR, to predict GPCRs using a comprehensive set of 1497 sequence-derived features. The principal component analysis is first employed to reduce the dimension of the feature space to 32. Then, the resulting 32-dimensional feature vectors are fed into a simple yet powerful classification algorithm, called intimate sorting, to predict GPCRs at five levels. The prediction at the first level determines whether a protein is a GPCR or a non-GPCR. If it is predicted to be a GPCR, then it will be further predicted into certain family, subfamily, sub-subfamily and subtype by the classifiers at the second, third, fourth, and fifth levels, respectively. To train the classifiers applied at five levels, a non-redundant dataset is carefully constructed, which contains 3178, 1589, 4772, 4924, and 2741 protein sequences at the respective levels. Jackknife tests on this training dataset show that the overall accuracies of PCA-GPCR at five levels (from the first to the fifth) can achieve up to 99.5%, 88.8%, 80.47%, 80.3%, and 92.34%, respectively. We further perform predictions on a dataset of 1238 GPCRs at the second level, and on another two datasets of 167 and 566 GPCRs respectively at the fourth level. The overall prediction accuracies of our method are consistently higher than those of the existing methods to be compared. CONCLUSIONS: The comprehensive set of 1497 features is believed to be capable of capturing information about amino acid composition, sequence order as well as various physicochemical properties of proteins. Therefore, high accuracies are achieved when predicting GPCRs at all the five levels with our proposed method.

Prediction of protein structural classes for low-homology sequences based on predicted secondary structure.

BACKGROUND: Prediction of protein structural classes (alpha, beta, alpha + beta and alpha/beta) from amino acid sequences is of great importance, as it is beneficial to study protein function, regulation and interactions. Many methods have been developed for high-homology protein sequences, and the prediction accuracies can achieve up to 90%. However, for low-homology sequences whose average pairwise sequence identity lies between 20% and 40%, they perform relatively poorly, yielding the prediction accuracy often below 60%. RESULTS: We propose a new method to predict protein structural classes on the basis of features extracted from the predicted secondary structures of proteins rather than directly from their amino acid sequences. It first uses PSIPRED to predict the secondary structure for each protein sequence. Then, the chaos game representation is employed to represent the predicted secondary structure as two time series, from which we generate a comprehensive set of 24 features using recurrence quantification analysis, K-string based information entropy and segment-based analysis. The resulting feature vectors are finally fed into a simple yet powerful Fisher's discriminant algorithm for the prediction of protein structural classes. We tested the proposed method on three benchmark datasets in low homology and achieved the overall prediction accuracies of 82.9%, 83.1% and 81.3%, respectively. Comparisons with ten existing methods showed that our method consistently performs better for all the tested datasets and the overall accuracy improvements range from 2.3% to 27.5%. A web server that implements the proposed method is freely available at http://www1.spms.ntu.edu.sg/~chenxin/RKS_PPSC/. CONCLUSION: The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the predicted secondary structure sequences, which is capable of characterizing the sequence order information, local interactions of the secondary structural elements, and spacial arrangements of alpha helices and beta strands. Thus, it is a valuable method to predict protein structural classes particularly for low-homology amino acid sequences.

Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation.

In this paper, we intend to predict protein structural classes (alpha, beta, alpha+beta, or alpha/beta) for low-homology data sets. Two data sets were used widely, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence homology being 40% and 25%, respectively. We propose to decompose the chaos game representation of proteins into two kinds of time series. Then, a novel and powerful nonlinear analysis technique, recurrence quantification analysis (RQA), is applied to analyze these time series. For a given protein sequence, a total of 16 characteristic parameters can be calculated with RQA, which are treated as feature representation of protein sequences. Based on such feature representation, the structural class for each protein is predicted with Fisher's linear discriminant algorithm. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies with step-by-step procedure are 65.8% and 64.2% for 1189 and 25PDB data sets, respectively. With one-against-others procedure used widely, we compare our method with five other existing methods. Especially, the overall accuracies of our method are 6.3% and 4.1% higher for the two data sets, respectively. Furthermore, only 16 parameters are used in our method, which is less than that used by other methods. This suggests that the current method may play a complementary role to the existing methods and is promising to perform the prediction of protein structural classes.

Human Pol II promoter recognition based on primary sequences and free energy of dinucleotides.

BACKGROUND: Promoter region plays an important role in determining where the transcription of a particular gene should be initiated. Computational prediction of eukaryotic Pol II promoter sequences is one of the most significant problems in sequence analysis. Existing promoter prediction methods are still far from being satisfactory. RESULTS: We attempt to recognize the human Pol II promoter sequences from the non-promoter sequences which are made up of exon and intron sequences. Four methods are used: two kinds of multifractal analysis performed on the numeric sequences obtained from the dinucleotide free energy, Z curve analysis and global descriptor of the promoter/non-promoter primary sequences. A total of 141 parameters are extracted from these methods and categorized into seven groups (methods). They are used to generate certain spaces and then each promoter/non-promoter sequence is represented by a point in the corresponding space. All the 120 possible combinations of the seven methods are tested. Based on Fisher's linear discriminant algorithm, with a relatively smaller number of parameters (96 and 117), we get satisfactory discriminant accuracies. Particularly, in the case of 117 parameters, the accuracies for the training and test sets reach 90.43% and 89.79%, respectively. A comparison with five other existing methods indicates that our methods have a better performance. Using the global descriptor method (36 parameters), 17 of the 18 experimentally verified promoter sequences of human chromosome 22 are correctly identified. CONCLUSION: The high accuracies achieved suggest that the methods of this paper are useful for understanding the difficult problem of promoter prediction.

Correlations between designability and various structural characteristics of protein lattice models.

Using six kinds of lattice types (4 x 4, 5 x 5, and 6 x 6 square lattices; 3 x 3 x 3 cubic lattice; and 2+3+4+3+2 and 4+5+6+5+4 triangular lattices), three different size alphabets (HP, HNUP, and 20 letters), and two energy functions, the designability of protein structures is calculated based on random samplings of structures and common biased sampling (CBS) of protein sequence space. Then three quantities stability (average energy gap), foldability, and partnum of the structure, which are defined to elucidate the designability, are calculated. The authors find that whatever the type of lattice, alphabet size, and energy function used, there will be an emergence of highly designable (preferred) structure. For all cases considered, the local interactions reduce degeneracy and make the designability higher. The designability is sensitive to the lattice type, alphabet size, energy function, and sampling method of the sequence space. Compared with the random sampling method, both the CBS and the Metropolis Monte Carlo sampling methods make the designability higher. The correlation coefficients between the designability, stability, and foldability are mostly larger than 0.5, which demonstrate that they have strong correlation relationship. But the correlation relationship between the designability and the partnum is not so strong because the partnum is independent of the energy. The results are useful in practical use of the designability principle, such as to predict the protein tertiary structure.

Therapeutic potential of chlorotoxin-like neurotoxin from the Chinese scorpion for human gliomas.

Chlorotoxin, one of the key toxins in scorpion Leiurus quinquestriatus venom, has been shown to bind specifically to glioma cell surface as a specific chloride channel blocker. In this study, a purified, recombinant chlorotoxin-like peptide from the scorpion Buthus martensii Karsch (named rBmK CTa) was characterized by in vivo and in vitro studies. The results from cell proliferation assay with human glioma (SHG-44) cells showed that rBmK CTa inhibits the growth of glioma cells in a dose-dependent manner, with an IC(50) value of approximately 0.28microM. Under the same conditions, the IC(50) value for normal astrocytes increased to 8microM. This clearly indicated that rBmK CTa had specific toxicity against glioma cells but not astrocytes. Results from whole-cell patch-clamp recording showed that chloride current in SHG-44 was inhibited by rBmK CTa in a voltage-dependent manner and percent inhibitions for the blocking action of rBmK CTa (0.07 and 0.14microM) on I(Cl) was 17.64+/-3.06% and 55.86+/-2.83%, respectively. Histological analysis of rBmK CTa treated mice showed that brain, leg muscle and cardiac muscle were the target organs of this toxin. These results suggest that rBmK CTa may have potential therapeutic application in clinical treatment of human glioma. It represents an approach for developing a novel therapeutic agent.

Fabrication and analysis of a Fabry-Perot cavity with a micromechanical wet-etching process.

We present a novel low-cost and batch fabrication method to fabricate a Fabry-Perot (FP) cavity with a micromechanical wet-etching process, through which FP cavities can be achieved with a cavity length of from several micrometers to tens of micrometers. The parallelism of mirror elements can be well achieved without electrostatic control. The quality of an etched surface can be greatly improved by the oxidation polish process. FP cavities with a finesse of approximately 50 are achieved. Analysis shows that the effective finesse is dominated mainly by the quality of the etched surface.