Structure of the K6 capsular polysaccharide from Acinetobacter baumannii isolate RBH4

The structure of the capsular polysaccharide (CPS) from an Acinetobacter baumannii global clone 2 (GC2) clinical isolate RBH4 that carries the KL6 gene cluster was elucidated by means of chemical and spectroscopical methods. The repeating unit of K6 CPS is linear and contains N-acetyl-d-galactosamine (d-GalpNAc), two d-galactose (d-Galp) residues and 5,7-di-N-acetylpseudaminic acid (Pse5Ac7Ac). The synthesis of these sugars could be attributed to genes in the KL6 capsule biosynthesis gene cluster, and the formation of the linkages between the sugars were assigned to glycosyltransferases or the Wzy polymerase encoded in KL6.

Cluster-based network model for time-course gene expression data

We propose a model-based approach to unify clustering and network modeling using time-course gene expression data. Specifically, our approach uses a mixture model to cluster genes. Genes within the same cluster share a similar expression profile. The network is built over cluster-specific expression profiles using state-space models. We discuss the application of our model to simulated data as well as to time-course gene expression data arising from animal models on prostate cancer progression. The latter application shows that with a combined statistical/bioinformatics analyses, we are able to extract gene-to-gene relationships supported by the literature as well as new plausible relationships.

Association of microRNA 17–92 cluster host gene (MIR17HG) polymorphisms with breast cancer

Breast cancer incidence and mortality rates are increasing despite our current knowledge on the disease. Ninety-five percent of breast cancer cases correspond to sporadic forms of the disease and are believed to involve an interaction between environmental and genetic determinants. The microRNA 17–92 cluster host gene (MIR17HG) has been shown to regulate expression of genes involved in breast cancer development and progression. Study of single-nucleotide polymorphisms (SNPs) located in this cluster gene could help provide a further understanding of its role in breast cancer. Therefore, this study investigated six SNPs in the MIR17HG using two independent Australian Caucasian case–control populations (GRC-BC and GU-CCQ BB populations) to determine association to breast cancer susceptibility. Genotyping was undertaken using chip-based matrix assisted laser desorption ionisation time-of-flight (MALDI-TOF) mass spectrometry (MS). We found significant association between rs4824505 and breast cancer at the allelic level in both study cohorts (GRC-BC p = 0.01 and GU-CCQ BB p = 0.03). Furthermore, haplotypic analysis of results from our combined population determined a significant association between rs4824505/rs7336610 and breast cancer susceptibility (p = 5 × 10−4). Our study is the first to show that the A allele of rs4824505 and the AC haplotype of rs4824505/rs7336610 are associated with risk of breast cancer development. However, definitive validation of this finding requires larger cohorts or populations in different ethnical backgrounds. Finally, functional studies of these SNPs could provide a deeper understanding of the role that MIR17HG plays in the pathophysiology of breast cancer.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

Molecular classification of cutaneous malignant melanoma by gene expression profiling

The most common human cancers are malignant neoplasms of the skin. Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease. Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm. Accordingly, though melanoma is thought to present with different 'taxonomic' forms, these are considered part of a continuous spectrum rather than discrete entities. Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas. Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.

Seed-based biclustering of gene expression data

Background Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental conditions. However, functionally related genes generally do not show coherent expression across all conditions since any given cellular process is active only under a subset of conditions. Biclustering finds gene clusters that have similar expression levels across a subset of conditions. This paper proposes a seed-based algorithm that identifies coherent genes in an exhaustive, but efficient manner. Methods In order to find the biclusters in a gene expression dataset, we exhaustively select combinations of genes and conditions as seeds to create candidate bicluster tables. The tables have two columns: (a) a gene set, and (b) the conditions on which the gene set have dissimilar expression levels to the seed. First, the genes with less than the maximum number of dissimilar conditions are identified and a table of these genes is created. Second, the rows that have the same dissimilar conditions are grouped together. Third, the table is sorted in ascending order based on the number of dissimilar conditions. Finally, beginning with the first row of the table, a test is run repeatedly to determine whether the cardinality of the gene set in the row is greater than the minimum threshold number of genes in a bicluster. If so, a bicluster is outputted and the corresponding row is removed from the table. Repeating this process, all biclusters in the table are systematically identified until the table becomes empty. Conclusions This paper presents a novel biclustering algorithm for the identification of additive biclusters. Since it involves exhaustively testing combinations of genes and conditions, the additive biclusters can be found more readily.

Molecular genetic analyses of RFLPs for PCR-amplified growth hormone gene, renal kallikrein gene and atrial natriuretic factor gene in essential hypertension

The present study examined polymorphisms of genes that might be involved in the onset of essential hypertension (HT). These included the (i) growth hormone gene (GH1), whose locus has recently been linked to elevated blood pressure (BP) in the stroke-prone SHR, although recent sib-pair analysis of a polymorphism near the human chorionic somatomammotropin gene (a member of the GH cluster) was unable to show linkage with HT; (ii) renal kallikrein gene (KLK1); and (iii) atrial natriuretic factor gene (ANF), where a primary defect in production or activity of kallikrein or ANF could cause NaCl retention and vasoconstriction. Association analyses were conducted to compare restriction fragment length polymorphisms (RFLPs) of each gene in 85 HT and 95 normotensive (NT) Caucasian subjects whose parents had a similar BP status at age ≥50 years. The frequency of the minor allele of (i) a RsaI RFLP in the promoter of GH1, amplified from leukocyte DNA by the polymerase chain reaction, was 0.15 in the HT group and 0.14 in the NT group (χ1=0.34, P=0.55); (ii) a TaqI RFLP for KLK1 was 0.035 in the HT group and 0.015 in the NT group (χ2=1.5, P=0.21); and (iii) a XhoI RFLP for ANF was 0.50 in HTs and 0.46 in NTs (χ2=0.20, P=0.65). Studies of HT pedigrees found one family in which the ANF locus and HT were not linked, owing to an obligate recombinant. The present data thus provide no evidence for involvement of the growth hormone, renal kallikrein, nor ANF gene in the causation of essential hypertension.

Global gene expression in skeletal muscle from well-trained strength and endurance athletes

PURPOSE: We used gene microarray analysis to compare the global expression profile of genes involved in adaptation to training in skeletal muscle from chronically strength-trained (ST), endurance-trained (ET), and untrained control subjects (Con). METHODS: Resting skeletal muscle samples were obtained from the vastus lateralis of 20 subjects (Con n = 7, ET n = 7, ST n = 6; trained [TR] groups >8 yr specific training). Total RNA was extracted from tissue for two color microarray analysis and quantative (Q)-PCR. Trained subjects were characterized by performance measures of peak oxygen uptake V?O 2peak) on a cycle ergometer and maximal concentric and eccentric leg strength on an isokinetic dynamometer. RESULTS: Two hundred and sixty-three genes were differentially expressed in trained subjects (ET + ST) compared with Con (P < 0.05), whereas 21 genes were different between ST and ET (P < 0.05). These results were validated by reverse transcriptase polymerase chain reaction for six differentially regulated genes (EIFSJ, LDHB, LMO4, MDH1, SLC16A7, and UTRN. Manual cluster analyses revealed significant regulation of genes involved in muscle structure and development in TR subjects compared with Con (P < 0.05) and expression correlated with measures of performance (P < 0.05). ET had increased whereas ST had decreased expression of gene clusters related to mitochondrial/oxidative capacity (P ?‰Currency sign 0.05). These mitochondrial gene clusters correlated with V?O2peak (P < 0.05). V?O2peak also correlated with expression of gene clusters that regulate fat and carbohydrate oxidation (P < 0.05). CONCLUSION: We demonstrate that chronic training subtly coregulates numerous genes from important functional groups that may be part of the long-term adaptive process to adapt to repeated training stimuli.

Comparison of the coat protein, movement protein and RNA polymerase gene sequences of Australian, Chinese, and American isolates of barley yellow dwarf virus transmitted by Rhopalosiphum padi

Barley yellow dwarf luteovirus-GPV (BYDV-GPV) is a common problem in Chinese wheat crops but is unrecorded elsewhere. A defining characteristic of GPV is its capacity to be transmitted efficiently by both Schizaphis graminum and Rhopaloshiphum padi. This dual aphid species transmission contrasts with those of BYDV-RPV and BYDV-SGV, globally distributed viruses, which are efficiently transmitted only by Rhopaloshiphum padi and Schizaphis graminum respectively. The viral RNA sequences encoding the coat protein (22K) gene, the movement protein (17K) gene, the region surrounding the conserved GDD motif of the polymerase gene and the intergenic sequences between these genes were determined for GPV and an Australian isolate of BYDV-RPV (RPVa). In all three genes, the sequences of GPV and RPVa were more similar to those of an American isolate of BYDV-RPV (RPVu) than to any other luteovirus for which there is data available. RPVa and RPVu were very similar, especially their coat proteins which had 97% identity at the amino acid level. The coat protein of GPV had 76% and 78% amino acid identity with RPVa and RPVu respectively. The data suggest that RPVu and RPVa are correctly named as strains of the same serotype and that GPV is sufficiently different from either RPV strain to be considered a distinct BYDV type. The coat protein and movement protein genes of GPV are very dissimilar to SGV. The polymerase sequences of RPVu, RPVa and GPV show close affinities with those of the sobemo-like luteoviruses and little similarity with those of the carmo-like luteoviruses. The sequences of the coat proteins, movement proteins and the polymerase segments of BYDV serotypes, other than RPV and GPV, form a cluster that is separate from their counterpart sequences from dicot-infecting luteoviruses. The RPV and GPV isolates consistently fall within a dicot-infecting cluster. This suggests that RPV and GPV evolved from within this group of viruses. Since these other viruses all infect dicots it seems likely that their common ancestor infected a dicot and that RPV and GPV evolved from a virus that switched hosts from a dicot to a monocot.

Gene expression profiling reveals a downregulation in immune-associated genes in patients with AS

Objective: To identify differentially expressed genes in peripheral blood mononuclear cells (PBMCs) from patients with ankylosing spondylitis (AS) compared with healthy individuals. Methods: RNA was extracted from PBMCs collected from 18 patients with active disease and 18 gender-matched and age-matched controls. Expression profiles of these cells were determined using microarray. Candidate genes with differential expressions were confirmed in the same samples using quantitative reverse transcription-PCR (qRT-PCR). These genes were then validated in a different sample cohort of 35 patients with AS and 18 controls by qRT-PCR. Results: Microarray analysis identified 452 genes detected with 485 probes which were differentially expressed between patients with AS and controls. Underexpression of NR4A2, tumour necrosis factor AIP3 (TNFAIP3) and CD69 was confirmed. These genes were further validated in a different sample group in which the patients with AS had a wider range of disease activity. Predictive algorithms were also developed from the expression data using receiver-operating characteristic curves, which demonstrated that the three candidate genes have ∼80% power to predict AS according to their expression levels. Conclusions: The findings show differences in global gene expression patterns between patients with AS and controls, suggesting an immunosuppressive phenotype in the patients. Furthermore, downregulated expression of three immune-related genes was confirmed. These candidate genes were also shown to be strong predictive markers for AS.

Mapping the regulatory sequences controlling 93 breast cancer-associated miRNA genes leads to the identification of two functional promoters of the Hsa-mir-200b cluster, methylation of which is associated with metastasis or hormone receptor status in advanced breast cancer

MicroRNAs (miRNAs) are small non-coding RNAs of 20 nt in length that are capable of modulating gene expression post-transcriptionally. Although miRNAs have been implicated in cancer, including breast cancer, the regulation of miRNA transcription and the role of defects in this process in cancer is not well understood. In this study we have mapped the promoters of 93 breast cancer-associated miRNAs, and then looked for associations between DNA methylation of 15 of these promoters and miRNA expression in breast cancer cells. The miRNA promoters with clearest association between DNA methylation and expression included a previously described and a novel promoter of the Hsa-mir-200b cluster. The novel promoter of the Hsa-mir-200b cluster, denoted P2, is located 2 kb upstream of the 5′ stemloop and maps within a CpG island. P2 has comparable promoter activity to the previously reported promoter (P1), and is able to drive the expression of miR-200b in its endogenous genomic context. DNA methylation of both P1 and P2 was inversely associated with miR-200b expression in eight out of nine breast cancer cell lines, and in vitro methylation of both promoters repressed their activity in reporter assays. In clinical samples, P1 and P2 were differentially methylated with methylation inversely associated with miR-200b expression. P1 was hypermethylated in metastatic lymph nodes compared with matched primary breast tumours whereas P2 hypermethylation was associated with loss of either oestrogen receptor or progesterone receptor. Hypomethylation of P2 was associated with gain of HER2 and androgen receptor expression. These data suggest an association between miR-200b regulation and breast cancer subtype and a potential use of DNA methylation of miRNA promoters as a component of a suite of breast cancer biomarkers.

No evidence for association of ataxia-telangiectasia mutated gene T2119C and C3161G amino acid substitution variants with risk of breast cancer

Background There is evidence that certain mutations in the double-strand break repair pathway ataxia-telangiectasia mutated gene act in a dominant-negative manner to increase the risk of breast cancer. There are also some reports to suggest that the amino acid substitution variants T2119C Ser707Pro and C3161G Pro1054Arg may be associated with breast cancer risk. We investigate the breast cancer risk associated with these two nonconservative amino acid substitution variants using a large Australian population-based case–control study. Methods The polymorphisms were genotyped in more than 1300 cases and 600 controls using 5' exonuclease assays. Case–control analyses and genotype distributions were compared by logistic regression. Results The 2119C variant was rare, occurring at frequencies of 1.4 and 1.3% in cases and controls, respectively (P = 0.8). There was no difference in genotype distribution between cases and controls (P = 0.8), and the TC genotype was not associated with increased risk of breast cancer (adjusted odds ratio = 1.08, 95% confidence interval = 0.59–1.97, P = 0.8). Similarly, the 3161G variant was no more common in cases than in controls (2.9% versus 2.2%, P = 0.2), there was no difference in genotype distribution between cases and controls (P = 0.1), and the CG genotype was not associated with an increased risk of breast cancer (adjusted odds ratio = 1.30, 95% confidence interval = 0.85–1.98, P = 0.2). This lack of evidence for an association persisted within groups defined by the family history of breast cancer or by age. Conclusion The 2119C and 3161G amino acid substitution variants are not associated with moderate or high risks of breast cancer in Australian women.

The Expanded Human Kallikrein (KLK) gene family : genomic organisation, tissue specific expression and potential functions

The tissue kallikreins are serine proteases encoded by highly conserved multigene families. The rodent kallikrein (KLK) families are particularly large, consisting of 13 26 genes clustered in one chromosomal locus. It has been recently recognised that the human KLK gene family is of a similar size (15 genes) with the identification of another 12 related genes (KLK4-KLK15) within and adjacent to the original human KLK locus (KLK1-3) on chromosome 19q13.4. The structural organisation and size of these new genes is similar to that of other KLK genes except for additional exons encoding 5 or 3 untranslated regions. Moreover, many of these genes have multiple mRNA transcripts, a trait not observed with rodent genes. Unlike all other kallikreins, the KLK4-KLK15 encoded proteases are less related (25–44%) and do not contain a conventional kallikrein loop. Clusters of genes exhibit high prostatic (KLK2-4, KLK15) or pancreatic (KLK6-13) expression, suggesting evolutionary conservation of elements conferring tissue specificity. These genes are also expressed, to varying degrees, in a wider range of tissues suggesting a functional involvement of these newer human kallikrein proteases in a diverse range of physiological processes.