Renin inhibitors for the treatment of hypertension: design and optimization of a novel series of tertiary alcohol-bearing piperidines

The design and optimization of a novel series of renin inhibitor is described herein. Strategically, by committing the necessary resources to the development of synthetic sequences and scaffolds that were most amenable for late stage structural diversification, even as the focus of the SAR campaign moved from one end of the molecule to another, highly potent renin inhibitors could be rapidly identified and profiled.     

A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data

MOTIVATION: Microarray experiments are affected by numerous sources of non-biological variation that contribute systematic bias to the resulting data. In a dual-label (two-color) cDNA or long-oligonucleotide microarray, these systematic biases are often manifested as an imbalance of measured fluorescent intensities corresponding to Sample A versus those corresponding to Sample B. Systematic biases also affect between-slide comparisons. Making effective corrections for these systematic biases is a requisite for detecting the underlying biological variation between samples. Effective data normalization is therefore an essential step in the confident identification of biologically relevant differences in gene expression profiles. Several normalization methods for the correction of systemic bias have been described. While many of these methods have addressed intensity-dependent bias, few have addressed both intensity-dependent and spatiality-dependent bias. RESULTS: We present a neural network-based normalization method for correcting the intensity- and spatiality-dependent bias in cDNA microarray datasets. In this normalization method, the dependence of the log-intensity ratio (M) on the average log-intensity (A) as well as on the spatial coordinates (X,Y) of spots is approximated with a feed-forward neural network function. Resistance to outliers is provided by assigning weights to each spot based on how distant their M values is from the median over the spots whose A values are similar, as well as by using pseudospatial coordinates instead of spot row and column indices. A comparison of the robust neural network method with other published methods demonstrates its potential in reducing both intensity-dependent bias and spatial-dependent bias, which translates to more reliable identification of truly regulated genes.     

Quantitative trait loci affecting the difference in pigmentation between Drosophila yakuba and D. santomea

Using quantitative trait locus (QTL) mapping, we studied the genetic basis of the difference in pigmentation between two sister species of Drosophila: Drosophila yakuba, which, like other members of the D. melanogaster subgroup, shows heavy black pigmentation on the abdomen of males and females, and D. santomea, an endemic to the African island of S?o Tomé, which has virtually no pigmentation. Here we mapped four QTL with large effects on this interspecific difference in pigmentation: two on the X chromosome and one each on the second and third chromosomes. The same four QTL were detected in male hybrids in the backcrosses to both D. santomea and D. yakuba and in the female D. yakuba backcross hybrids. All four QTL exhibited strong epistatic interactions in male backcross hybrids, but only one pair of QTL interacted in females from the backcross to D. yabuka. All QTL from each species affected pigmentation in the same direction, consistent with adaptive evolution driven by directional natural selection. The regions delimited by the QTL included many positional candidate loci in the pigmentation pathway, including genes affecting catecholamine biosynthesis, melanization of the cuticle, and many additional pleiotropic effects.     

Structural studies on a protein-binding zinc-finger domain of Eos reveal both similarities and differences to classical zinc fingers

The oligomerization domain that is present at the C terminus of Ikaros-family proteins and the protein Trps-1 is important for the proper regulation of developmental processes such as hematopoiesis. Remarkably, this domain is predicted to contain two classical zinc fingers (ZnFs), domains normally associated with the recognition of nucleic acids. The preference for protein binding by these predicted ZnFs is not well-understood. We have used a range of methods to gain insight into the structure of this domain. Circular dichroism, UV-vis, and NMR experiments carried out on the C-terminal domain of Eos (EosC) revealed that the two putative ZnFs (C1 and C2) are separable, i.e., capable of folding independently in the presence of Zn(II). We next determined the structure of EosC2 using NMR spectroscopy, revealing that, although the overall fold of EosC2 is similar to other classical ZnFs, a number of differences exist. For example, the conformation of the C terminus of EosC2 appears to be flexible and may result in a major rearrangement of the zinc ligands. Finally, alanine-scanning mutagenesis was used to identify the residues that are involved in the homo- and hetero-oligomerization of Eos, and these results are discussed in the context of the structure of EosC. These studies provide the first structural insights into how EosC mediates protein-protein interactions and contributes to our understanding of why it does not exhibit high-affinity DNA binding.     

Quantitative genomics of aggressive behavior in Drosophila melanogaster

Aggressive behavior is important for animal survival and reproduction, and excessive aggression is an enormous social and economic burden for human society. Although the role of biogenic amines in modulating aggressive behavior is well characterized, other genetic mechanisms affecting this complex behavior remain elusive. Here, we developed an assay to rapidly quantify aggressive behavior in Drosophila melanogaster, and generated replicate selection lines with divergent levels of aggression. The realized heritability of aggressive behavior was approximately 0.10, and the phenotypic response to selection specifically affected aggression. We used whole-genome expression analysis to identify 1,539 probe sets with different expression levels between the selection lines when pooled across replicates, at a false discovery rate of 0.001. We quantified the aggressive behavior of 19 mutations in candidate genes that were generated in a common co-isogenic background, and identified 15 novel genes affecting aggressive behavior. Expression profiling of genetically divergent lines is an effective strategy for identifying genes affecting complex traits.     

Epimutation of the TNDM locus and the Beckwith–Wiedemann syndrome centromeric locus in individuals with transient neonatal diabetes mellitus

Transient neonatal diabetes mellitus (TNDM) is characterised by intra-uterine growth retardation, while Beckwith–Wiedemann syndrome (BWS) is a clinically heterogeneous overgrowth syndrome. Both TNDM and BWS may be caused by aberrant loss of methylation (LOM) at imprinted loci on chromosomes 6q24 and 11p15.5 respectively. Here we describe two patients with a clinical diagnosis of TNDM caused by LOM at the maternally methylated imprinted domain on 6q24; in addition, these patients had LOM at the centromeric differentially methylated region of 11p15.5. This shows that imprinting anomalies can affect more than one imprinted locus and may alter the clinical presentation of imprinted disease.     

Development and Application of a Predictive Model of Freshwater Fish Assemblage Composition to Evaluate River Health in Eastern Australia

Multivariate predictive models are widely used tools for assessment of aquatic ecosystem health and models have been successfully developed for the prediction and assessment of aquatic macroinvertebrates, diatoms, local stream habitat features and fish. We evaluated the ability of a modelling method based on the River InVertebrate Prediction and Classification System (RIVPACS) to accurately predict freshwater fish assemblage composition and assess aquatic ecosystem health in rivers and streams of south-eastern Queensland, Australia. The predictive model was developed, validated and tested in a region of comparatively high environmental variability due to the unpredictable nature of rainfall and river discharge. The model was concluded to provide sufficiently accurate and precise predictions of species composition and was sensitive enough to distinguish test sites impacted by several common types of human disturbance (particularly impacts associated with catchment land use and associated local riparian, in-stream habitat and water quality degradation). The total number of fish species available for prediction was low in comparison to similar applications of multivariate predictive models based on other indicator groups, yet the accuracy and precision of our model was comparable to outcomes from such studies. In addition, our model developed for sites sampled on one occasion and in one season only (winter), was able to accurately predict fish assemblage composition at sites sampled during other seasons and years, provided that they were not subject to unusually extreme environmental conditions (e.g. extended periods of low flow that restricted fish movement or resulted in habitat desiccation and local fish extinctions).