Rapid and Accurate Determination of Stern-Volmer Quenching Constants

In this work, a novel system has been designed, characterized, and validated for the determination of fluorescence quenching constants. Capillary flow injection methods are used to automate the preparation and mixing of the fluorophore and quencher solutions. Because of the small diameter of the capillary (75-200 mu m), fluorescence measurements can be made without corrections for primary and secondary absorbance effects. The fluorescence spectrometer is equipped with a charge-coupled device (CCD) that has a detection limit of 3.0 X 10-9 M (2.3 ppb) and a linear dynamic range of 10 5 for integration times of 0.01-10 s. This spectrometer has a 300 nm spectral range with 1 nm resolution, allowing the fluorescence quenching constants to be calculated at single wavelengths or over integrated wavelength ranges. This system was validated by comparison to traditional methods for the determination of Stern-Volmer constants for alternant and nonalternant polycyclic aromatic hydrocarbons with nitromethane and triethylamine

The influence of pressure on solute retention in liquid chromatography

In this investigation, multiple on-column detectors are utilized to measure the retention of model solutes directly on a packed, capillary column. The absolute pressure on the column is controlled using a restrictor at the column exit, while the pressure gradient and volumetric flowrate are maintained constant. Measurements obtained under reversed-phase conditions indicate that the local capacity factor changes considerably with local pressure under typical operating conditions. These results are somewhat surprising since the mobile-phase solvents used for liquid chromatography are generally considered to be incompressible

The bipolar disorder risk allele at CACNA1C also confers risk of recurrent major depression and of schizophrenia

Molecular genetic analysis offers opportunities to advance our understanding of the nosological relationship between psychiatric diagnostic categories in general, and the mood and psychotic disorders in particular. Strong evidence (P=7.0 × 10−7) of association at the polymorphism rs1006737 (within CACNA1C, the gene encoding the α-1C subunit of the L-type voltage-gated calcium channel) with the risk of bipolar disorder (BD) has recently been reported in a meta-analysis of three genome-wide association studies of BD, including our BD sample (N=1868) studied within the Wellcome Trust Case Control Consortium. Here, we have used our UK case samples of recurrent major depression (N=1196) and schizophrenia (N=479) and UK non-psychiatric comparison groups (N=15316) to examine the spectrum of phenotypic effect of the bipolar risk allele at rs1006737. We found that the risk allele conferred increased risk for schizophrenia (P=0.034) and recurrent major depression (P=0.013) with similar effect sizes to those previously observed in BD (allelic odds ratio ∼1.15). Our findings are evidence of some degree of overlap in the biological underpinnings of susceptibility to mental illness across the clinical spectrum of mood and psychotic disorders, and show that at least some loci can have a relatively general effect on susceptibility to diagnostic categories, as currently defined. Our findings will contribute to a better understanding of the pathogenesis of major psychiatric illness, and such knowledge should be useful in providing an etiological rationale for shaping psychiatric nosology, which is currently reliant entirely on descriptive clinical data

Psychometric precision in phenotype definition is a useful step in molecular genetic investigation of psychiatric disorders

Affective disorders are highly heritable, but few genetic risk variants have been consistently replicated in molecular genetic association studies. The common method of defining psychiatric phenotypes in molecular genetic research is either a summation of symptom scores or binary threshold score representing the risk of diagnosis. Psychometric latent variable methods can improve the precision of psychiatric phenotypes, especially when the data structure is not straightforward. Using data from the British 1946 birth cohort, we compared summary scores with psychometric modeling based on the General Health Questionnaire (GHQ-28) scale for affective symptoms in an association analysis of 27 candidate genes (249 single-nucleotide polymorphisms (SNPs)). The psychometric method utilized a bi-factor model that partitioned the phenotype variances into five orthogonal latent variable factors, in accordance with the multidimensional data structure of the GHQ-28 involving somatic, social, anxiety and depression domains. Results showed that, compared with the summation approach, the affective symptoms defined by the bi-factor psychometric model had a higher number of associated SNPs of larger effect sizes. These results suggest that psychometrically defined mental health phenotypes can reflect the dimensions of complex phenotypes better than summation scores, and therefore offer a useful approach in genetic association investigations

Environmental exposures and their genetic or environmental contribution to depression and fatigue: a twin study in Sri Lanka

Background There is very little genetically informative research identifying true environmental risks for psychiatric conditions. These may be best explored in regions with diverse environmental exposures. The current study aimed to explore similarities and differences in such risks contributing to depression and fatigue. Methods Home interviews assessed depression (lifetime-ever), fatigue and environmental exposures in 4,024 randomly selected twins from a population-based register in the Colombo district of Sri Lanka. Results Early school leaving and standard of living showed environmentally-mediated effects on depression, in men. In women, life events were associated with depression partly through genetic pathways (however, the temporal order is consistent with life events being an outcome of depression, as well as the other way around). For fatigue, there were environmentally mediated effects (through early school leaving and life events) and strong suggestions of family-environmental influences. Conclusions Compared to previous studies from higher-income countries, novel environmentally-mediated risk factors for depression and fatigue were identified in Sri Lanka. But as seen elsewhere, the association between life events and depression was partially genetically mediated in women. These results have implications for understanding environmental mechanisms around the world

Eukaryote-wide sequence analysis of mitochondrial β-barrel outer membrane proteins

<p>Abstract</p> <p>Background</p> <p>The outer membranes of mitochondria are thought to be homologous to the outer membranes of Gram negative bacteria, which contain 100's of distinct families of <it>β</it>-barrel membrane proteins (BOMPs) often forming channels for transport of nutrients or drugs. However, only four families of mitochondrial BOMPs (MBOMPs) have been confirmed to date. Although estimates as high as 100 have been made in the past, the number of yet undiscovered MBOMPs is an open question. Fortunately, the recent discovery of a membrane integration signal (the <it>β</it>-signal) for MBOMPs gave us an opportunity to look for undiscovered MBOMPs.</p> <p>Results</p> <p>We present the results of a comprehensive survey of eukaryotic protein sequences intended to identify new MBOMPs. Our search employs recent results on <it>β</it>-signals as well as structural information and a novel BOMP predictor trained on both bacterial and mitochondrial BOMPs. Our principal finding is circumstantial evidence suggesting that few MBOMPs remain to be discovered, if one assumes that, like known MBOMPs, novel MBOMPs will be monomeric and <it>β</it>-signal dependent. In addition to this, our analysis of MBOMP homologs reveals some exceptions to the current model of the <it>β</it>-signal, but confirms its consistent presence in the C-terminal region of MBOMP proteins. We also report a <it>β</it>-signal independent search for MBOMPs against the yeast and Arabidopsis proteomes. We find no good candidates MBOMPs in yeast but the Arabidopsis results are less conclusive.</p> <p>Conclusions</p> <p>Our results suggest there are no remaining MBOMPs left to discover in yeast; and if one assumes all MBOMPs are <it>β</it>-signal dependent, few MBOMP families remain undiscovered in any sequenced organism.</p

Mechanism of subunit interaction at ketosynthase-dehydratase junctions in trans-AT polyketide synthases

Modular polyketide synthases (PKSs) produce numerous structurally complex natural products with diverse applications in medicine and agriculture. They typically consist of several multienzyme subunits that utilize structurally-defined docking domains (DDs) at their N- and C-termini to ensure correct assembly into functional multi-protein complexes. Here we report a fundamentally different mechanism for subunit assembly in trans-AT modular PKSs at the junction between ketosynthase (KS) and dehydratase (DH) domains. This involves direct interaction of a largely unstructured docking domain (DD) at the C-terminus of the KS with the surface of the downstream DH. Acyl transfer assays and mechanism-based cross-linking established that the DD is required for the KS to communicate with the acyl carrier protein appended to the DH. Two distinct regions for binding of the DD to the DH were identified using NMR spectroscopy, carbene foot-printing and mutagenesis, providing a foundation for future elucidation of the molecular basis for interaction specificity

Identification and comparative analysis of components from the signal recognition particle in protozoa and fungi

BACKGROUND: The signal recognition particle (SRP) is a ribonucleoprotein complex responsible for targeting proteins to the ER membrane. The SRP of metazoans is well characterized and composed of an RNA molecule and six polypeptides. The particle is organized into the S and Alu domains. The Alu domain has a translational arrest function and consists of the SRP9 and SRP14 proteins bound to the terminal regions of the SRP RNA. So far, our understanding of the SRP and its evolution in lower eukaryotes such as protozoa and yeasts has been limited. However, genome sequences of such organisms have recently become available, and we have now analyzed this information with respect to genes encoding SRP components. RESULTS: A number of SRP RNA and SRP protein genes were identified by an analysis of genomes of protozoa and fungi. The sequences and secondary structures of the Alu portion of the RNA were found to be highly variable. Furthermore, proteins SRP9/14 appeared to be absent in certain species. Comparative analysis of the SRP RNAs from different Saccharomyces species resulted in models which contain features shared between all SRP RNAs, but also a new secondary structure element in SRP RNA helix 5. Protein SRP21, previously thought to be present only in Saccharomyces, was shown to be a constituent of additional fungal genomes. Furthermore, SRP21 was found to be related to metazoan and plant SRP9, suggesting that the two proteins are functionally related. CONCLUSIONS: Analysis of a number of not previously annotated SRP components show that the SRP Alu domain is subject to a more rapid evolution than the other parts of the molecule. For instance, the RNA portion is highly variable and the protein SRP9 seems to have evolved into the SRP21 protein in fungi. In addition, we identified a secondary structure element in the Sacccharomyces RNA that has been inserted close to the Alu region. Together, these results provide important clues as to the structure, function and evolution of SRP

EL_PSSM-RT:DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation

Background: Prediction of DNA-binding residue is important for understanding the protein-DNA recognition mechanism. Many computational methods have been proposed for the prediction, but most of them do not consider the relationships of evolutionary information between residues. Results: In this paper, we first propose a novel residue encoding method, referred to as the Position Specific Score Matrix (PSSM) Relation Transformation (PSSM-RT), to encode residues by utilizing the relationships of evolutionary information between residues. PDNA-62 and PDNA-224 are used to evaluate PSSM-RT and two existing PSSM encoding methods by five-fold cross-validation. Performance evaluations indicate that PSSM-RT is more effective than previous methods. This validates the point that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction. An ensemble learning classifier (EL_PSSM-RT) is also proposed by combining ensemble learning model and PSSM-RT to better handle the imbalance between binding and non-binding residues in datasets. EL_PSSM-RT is evaluated by five-fold cross-validation using PDNA-62 and PDNA-224 as well as two independent datasets TS-72 and TS-61. Performance comparisons with existing predictors on the four datasets demonstrate that EL_PSSM-RT is the best-performing method among all the predicting methods with improvement between 0.02-0.07 for MCC, 4.18-21.47% for ST and 0.013-0.131 for AUC. Furthermore, we analyze the importance of the pair-relationships extracted by PSSM-RT and the results validates the usefulness of PSSM-RT for encoding DNA-binding residues. Conclusions: We propose a novel prediction method for the prediction of DNA-binding residue with the inclusion of relationship of evolutionary information and ensemble learning. Performance evaluation shows that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction and ensemble learning can be used to address the data imbalance issue between binding and non-binding residues. A web service of EL_PSSM-RT ( http://hlt.hitsz.edu.cn:8080/PSSM-RT_SVM/ ) is provided for free access to the biological research community