Genome-wide microRNA profiling in human fetal nervous tissues by oligonucleotide microarray

OBJECTS: Our objective was to develop an oligonucleotide DNA microarray (OMA) for genome-wide microRNA profiling and use this method to find miRNAs, which control organic development especially for nervous system. MATERIALS AND METHODS: Eighteen organic samples included cerebrum and spinal cord samples from two aborted human fetuses. One was 12 gestational weeks old (G12w) and the other was 24 gestational weeks old (G24w). Global miRNA expression patterns of different organs were investigated using OMA and Northern blot. CONCLUSION: The OMA revealed that 72–83% of miRNAs were expressed in human fetal organs. A series of microRNAs were found specifically and higher-expressed in the human fetal nervous system and confirmed consistently by Northern blot, which may play a critical role in nervous system development

Single Mode Lasing from Hybrid Hemispherical Microresonators

Enormous attention has been paid to optical microresonators which hold a great promise for microlasers as well as fundamental studies in cavity quantum electrodynamics. Here we demonstrate a three-dimensional (3D) hybrid microresonator combining self-assembled hemispherical structure with a planar reflector. By incorporating dye molecules into the hemisphere, optically pumped lasing phenomenon is observed at room temperature. We have studied the lasing behaviors with different cavity sizes, and particularly single longitudinal mode lasing from hemispheres with diameter ∼15 μm is achieved. Detailed characterizations indicate that the lasing modes shift under varying pump densities, which can be well-explained by frequency shift and mode hopping. This work provides a versatile approach for 3D confined microresonators and opens an opportunity to realize tunable single mode microlasers

Classifying RNA-Binding Proteins Based on Electrostatic Properties

Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures. The method is based on characterizing unique properties of electrostatic patches on the protein surface. Using an ensemble of general protein features and specific properties extracted from the electrostatic patches, we have trained a support vector machine (SVM) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids. Specifically, the method was applied on proteins possessing the RNA recognition motif (RRM) and successfully classified RNA-binding proteins from RRM domains involved in protein–protein interactions. Overall the method achieves 88% accuracy in classifying RNA-binding proteins, yet it cannot distinguish RNA from DNA binding proteins. Nevertheless, by applying a multiclass SVM approach we were able to classify the RNA-binding proteins based on their RNA targets, specifically, whether they bind a ribosomal RNA (rRNA), a transfer RNA (tRNA), or messenger RNA (mRNA). Finally, we present here an innovative approach that does not rely on sequence or structural homology and could be applied to identify novel RNA-binding proteins with unique folds and/or binding motifs

Enzyme classification with peptide programs: a comparative study

<p>Abstract</p> <p>Background</p> <p>Efficient and accurate prediction of protein function from sequence is one of the standing problems in Biology. The generalised use of sequence alignments for inferring function promotes the propagation of errors, and there are limits to its applicability. Several machine learning methods have been applied to predict protein function, but they lose much of the information encoded by protein sequences because they need to transform them to obtain data of fixed length.</p> <p>Results</p> <p>We have developed a machine learning methodology, called peptide programs (PPs), to deal directly with protein sequences and compared its performance with that of Support Vector Machines (SVMs) and BLAST in detailed enzyme classification tasks. Overall, the PPs and SVMs had a similar performance in terms of Matthews Correlation Coefficient, but the PPs had generally a higher precision. BLAST performed globally better than both methodologies, but the PPs had better results than BLAST and SVMs for the smaller datasets.</p> <p>Conclusion</p> <p>The higher precision of the PPs in comparison to the SVMs suggests that dealing with sequences is advantageous for detailed protein classification, as precision is essential to avoid annotation errors. The fact that the PPs performed better than BLAST for the smaller datasets demonstrates the potential of the methodology, but the drop in performance observed for the larger datasets indicates that further development is required.</p> <p>Possible strategies to address this issue include partitioning the datasets into smaller subsets and training individual PPs for each subset, or training several PPs for each dataset and combining them using a bagging strategy.</p

Prediction of potential drug targets based on simple sequence properties

<p>Abstract</p> <p>Background</p> <p>During the past decades, research and development in drug discovery have attracted much attention and efforts. However, only 324 drug targets are known for clinical drugs up to now. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Therefore, the identification and validation of new and effective drug targets are of great value for drug discovery in both academia and pharmaceutical industry. If a protein can be predicted in advance for its potential application as a drug target, the drug discovery process targeting this protein will be greatly speeded up. In the current study, based on the properties of known drug targets, we have developed a sequence-based drug target prediction method for fast identification of novel drug targets.</p> <p>Results</p> <p>Based on simple physicochemical properties extracted from protein sequences of known drug targets, several support vector machine models have been constructed in this study. The best model can distinguish currently known drug targets from non drug targets at an accuracy of 84%. Using this model, potential protein drug targets of human origin from Swiss-Prot were predicted, some of which have already attracted much attention as potential drug targets in pharmaceutical research.</p> <p>Conclusion</p> <p>We have developed a drug target prediction method based solely on protein sequence information without the knowledge of family/domain annotation, or the protein 3D structure. This method can be applied in novel drug target identification and validation, as well as genome scale drug target predictions.</p

Observation of a ppb mass threshoud enhancement in \psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p}) decay

The decay channel $\psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p})$ is studied using a sample of $1.06\times 10^8$ $\psi^\prime$ events collected by the BESIII experiment at BEPCII. A strong enhancement at threshold is observed in the $p\bar{p}$ invariant mass spectrum. The enhancement can be fit with an $S$-wave Breit-Wigner resonance function with a resulting peak mass of $M=1861^{+6}_{-13} {\rm (stat)}^{+7}_{-26} {\rm (syst)} {\rm MeV/}c^2$ and a narrow width that is $\Gamma<38 {\rm MeV/}c^2$ at the 90% confidence level. These results are consistent with published BESII results. These mass and width values do not match with those of any known meson resonance.Comment: 5 pages, 3 figures, submitted to Chinese Physics

Genomic Heterogeneity in a Natural Archaeal Population Suggests a Model of tRNA Gene Disruption

Understanding the mechanistic basis of the disruption of tRNA genes, as manifested in the intron-containing and split tRNAs found in Archaea, will provide considerable insight into the evolution of the tRNA molecule. However, the evolutionary processes underlying these disruptions have not yet been identified. Previously, a composite genome of the deep-branching archaeon Caldiarchaeum subterraneum was reconstructed from a community genomic library prepared from a C. subterraneum–dominated microbial mat. Here, exploration of tRNA genes from the library reveals that there are at least three types of heterogeneity at the tRNAThr(GGU) gene locus in the Caldiarchaeum population. All three involve intronic gain and splitting of the tRNA gene. Of two fosmid clones found that encode tRNAThr(GGU), one (tRNAThr-I) contains a single intron, whereas another (tRNAThr-II) contains two introns. Notably, in the clone possessing tRNAThr-II, a 5′ fragment of the tRNAThr-I (tRNAThr-F) gene was observed 1.8-kb upstream of tRNAThr-II. The composite genome contains both tRNAThr-II and tRNAThr-F, although the loci are >500 kb apart. Given that the 1.8-kb sequence flanked by tRNAThr-F and tRNAThr-II is predicted to encode a DNA recombinase and occurs in six regions of the composite genome, it may be a transposable element. Furthermore, its dinucleotide composition is most similar to that of the pNOB8-type plasmid, which is known to integrate into archaeal tRNA genes. Based on these results, we propose that the gain of the tRNA intron and the scattering of the tRNA fragment occurred within a short time frame via the integration and recombination of a mobile genetic element

Quantitative sequence-function relationships in proteins based on gene ontology

<p>Abstract</p> <p>Background</p> <p>The relationship between divergence of amino-acid sequence and divergence of function among homologous proteins is complex. The assumption that homologs share function – the basis of transfer of annotations in databases – must therefore be regarded with caution. Here, we present a quantitative study of sequence and function divergence, based on the Gene Ontology classification of function. We determined the relationship between sequence divergence and function divergence in 6828 protein families from the PFAM database. Within families there is a broad range of sequence similarity from very closely related proteins – for instance, orthologs in different mammals – to very distantly-related proteins at the limit of reliable recognition of homology.</p> <p>Results</p> <p>We correlated the divergence in sequences determined from pairwise alignments, and the divergence in function determined by path lengths in the Gene Ontology graph, taking into account the fact that many proteins have multiple functions. Our results show that, among homologous proteins, the proportion of divergent functions decreases dramatically above a threshold of sequence similarity at about 50% residue identity. For proteins with more than 50% residue identity, transfer of annotation between homologs will lead to an erroneous attribution with a totally dissimilar function in fewer than 6% of cases. This means that for very similar proteins (about 50 % identical residues) the chance of completely incorrect annotation is low; however, because of the phenomenon of recruitment, it is still non-zero.</p> <p>Conclusion</p> <p>Our results describe general features of the evolution of protein function, and serve as a guide to the reliability of annotation transfer, based on the closeness of the relationship between a new protein and its nearest annotated relative.</p

Chromothripsis in acute myeloid leukemia: Biological features and impact on survival

Chromothripsis is a one-step genome-shattering catastrophe resulting from disruption of one or few chromosomes in multiple fragments and consequent random rejoining and repair. This study defines incidence of chromothripsis in 395 newly diagnosed adult acute myeloid leukemia (AML) patients from three institutions, its impact on survival and its genomic background. SNP 6.0 or CytoscanHD Array (Affymetrix\uae) were performed on all samples. We detected chromothripsis with a custom algorithm in 26/395 patients. Patients harboring chromothripsis had higher age (p = 0.002), ELN high risk (HR) (p &lt; 0.001), lower white blood cell (WBC) count (p = 0.040), TP53 loss, and/or mutations (p &lt; 0.001) while FLT3 (p = 0.025), and NPM1 (p = 0.032) mutations were mutually exclusive with chromothripsis. Chromothripsis-positive patients showed a worse overall survival (OS) (p &lt; 0.001) compared with HR patients (p = 0.011) and a poor prognosis in a COX-HR optimal regression model. Chromothripsis presented the hallmarks of chromosome instability [i.e., TP53 alteration, 5q deletion, higher mean of copy number alteration (CNA), complex karyotype, alterations in DNA repair, and cell cycle] and focal deletions on chromosomes 4, 7, 12, 16, and 17. CBA. FISH showed that chromothripsis is associated with marker, derivative, and ring chromosomes. In conclusion, chromothripsis frequently occurs in AML (6.6%) and influences patient prognosis and disease biology