Description:
Motivation: The increasing use of DNA microarray-based tumor gene expression profiles for cancer diagnosis requires mathematical methods with high accuracy for solving clustering, feature selection and classification problems of gene expression data. Results: New algorithms are developed for solving clustering, feature selection and classification problems of gene expression data. The clustering algorithm is based on optimization techniques and allows the calculation of clusters step-by-step. This approach allows us to find as many clusters as a data set contains with respect to some tolerance. Feature selection is crucial for a gene expression database. Our feature selection algorithm is based on calculating overlaps of different genes. The database used, contains over 16 000 genes and this number is considerably reduced by feature selection. We propose a classification algorithm where each tissue sample is considered as the center of a cluster which is a ball. The results of numerical experiments confirm that the classification algorithm in combination with the feature selection algorithm perform slightly better than the published results for multi-class classifiers based on support vector machines for this data set.
Description:
Motivation: The increasing use of DNA microarray-based tumor gene expression profiles for cancer diagnosis requires mathematical methods with high accuracy for solving clustering, feature selection and classification problems of gene expression data. Results: New algorithms are developed for solving clustering, feature selection and classification problems of gene expression data. The clustering algorithm is based on optimization techniques and allows the calculation of clusters step-by-step. This approach allows us to find as many clusters as a data set contains with respect to some tolerance. Feature selection is crucial for a gene expression database. Our feature selection algorithm is based on calculating overlaps of different genes. The database used, contains over 16 000 genes and this number is considerably reduced by feature selection. We propose a classification algorithm where each tissue sample is considered as the center of a cluster which is a ball. The results of numerical experiments confirm that the classification algorithm in combination with the feature selection algorithm perform slightly better than the published results for multi-class classifiers based on support vector machines for this data set.
Description:
The newly excysted juvenile (NEJ) stage of the Fasciola hepatica lifecycle occurs just prior to invasion into the wall of the gut of the host, rendering it an important target for drug development. The cathepsin B enzymes from NEJ flukes have recently been demonstrated to be crucial to invasion and migration by the parasite. Here we characterize one of the cathepsin B enzymes (recombinant FhcatB1) from NEJ flukes. FhcatB1 has biochemical properties distinct from mammalian cathepsin B enzymes, with an atypical preference for Ile over Leu or Arg residues at the P2 substrate position and an inability to act as an exopeptidase. FhcatB1 was active across a broad pH range (optimal activity at pH 5.5–7.0) and resistant to inhibition by cystatin family inhibitors from sheep and humans, suggesting that this enzyme would be able to function in extracellular environments in its mammalian hosts. It appears, however, that the FhcatB1 protease functions largely as a digestive enzyme in the gut of the parasite, due to the localization of a specific, fluorescently labeled inhibitor with an Ile at the P2 position. Molecular modelling and dynamics were used to predict the basis for the unusual substrate specificity: a P2 Ile residue positions the substrate optimally for interaction with catalytic residues of the enzyme, and the enzyme lacks an occluding loop His residue crucial for exopeptidase activity. The unique features of the enzyme, particularly with regard to its specificity and likely importance to a vital stage of the parasite's life cycle, make it an excellent target for therapeutic inhibitors or vaccination.
Description:
Cyanobacteria, a group of photosynthetic prokaryotes, oscillate between day and night time metabolisms with concomitant oscillations in gene expression in response to light/dark cycles (LD). The oscillations in gene expression have been shown to sustain in constant light (LL) with a free running period of 24 h in a model cyanobacterium Synechococcus elongatus PCC 7942. However, equivalent oscillations in metabolism are not reported under LL in this non-nitrogen fixing cyanobacterium. Here we focus on Cyanothece sp. ATCC 51142, a unicellular, nitrogen-fixing cyanobacterium known to temporally separate the processes of oxygenic photosynthesis and oxygen-sensitive nitrogen fixation. In a recent report, metabolism of Cyanothece 51142 has been shown to oscillate between photosynthetic and respiratory phases under LL with free running periods that are temperature dependent but significantly shorter than the circadian period. Further, the oscillations shift to circadian pattern at moderate cell densities that are concomitant with slower growth rates. Here we take this understanding forward and demonstrate that the ultradian rhythm under LL sustains at much higher cell densities when grown under turbulent regimes that simulate flashing light effect. Our results suggest that the ultradian rhythm in metabolism may be needed to support higher carbon and nitrogen requirements of rapidly growing cells under LL. With a comprehensive Real time PCR based gene expression analysis we account for key regulatory interactions and demonstrate the interplay between clock genes and the genes of key metabolic pathways. Further, we observe that several genes that peak at dusk in Synechococcus peak at dawn in Cyanothece and vice versa. The circadian rhythm of this organism appears to be more robust with peaking of genes in anticipation of the ensuing photosynthetic and respiratory metabolic phases.
Description:
Cyanobacteria, a group of photosynthetic prokaryotes, oscillate between day and night time metabolisms with concomitant oscillations in gene expression in response to light/dark cycles (LD). The oscillations in gene expression have been shown to sustain in constant light (LL) with a free running period of 24 h in a model cyanobacterium Synechococcus elongatus PCC 7942. However, equivalent oscillations in metabolism are not reported under LL in this non-nitrogen fixing cyanobacterium. Here we focus on Cyanothece sp. ATCC 51142, a unicellular, nitrogen-fixing cyanobacterium known to temporally separate the processes of oxygenic photosynthesis and oxygen-sensitive nitrogen fixation. In a recent report, metabolism of Cyanothece 51142 has been shown to oscillate between photosynthetic and respiratory phases under LL with free running periods that are temperature dependent but significantly shorter than the circadian period. Further, the oscillations shift to circadian pattern at moderate cell densities that are concomitant with slower growth rates. Here we take this understanding forward and demonstrate that the ultradian rhythm under LL sustains at much higher cell densities when grown under turbulent regimes that simulate flashing light effect. Our results suggest that the ultradian rhythm in metabolism may be needed to support higher carbon and nitrogen requirements of rapidly growing cells under LL. With a comprehensive Real time PCR based gene expression analysis we account for key regulatory interactions and demonstrate the interplay between clock genes and the genes of key metabolic pathways. Further, we observe that several genes that peak at dusk in Synechococcus peak at dawn in Cyanothece and vice versa. The circadian rhythm of this organism appears to be more robust with peaking of genes in anticipation of the ensuing photosynthetic and respiratory metabolic phases.
Description:
Schistosome parasites follow a complex migration path through various tissues, changing their antigenic profile as they develop. A thorough understanding of the antibody response in each tissue region could help unravel the complex immunology of these developing parasites and aid vaccine design. Here we used a novel strategy for analysing the local antibody responses induced by Schistosoma japonicum infection at each site of infection. Cells from rat lymph nodes draining the sites of larval migration (the skin and lungs), the liver-lymph nodes where adults reside and the spleens were cultured to allow the in vivo-induced antibody-secreting cells to release antibody into the media. The amount and isotype of antibodies secreted in the supernatants differed significantly in the different lymph nodes and spleen, corresponding with the migration path of the schistosome worms. In addition, there were significant differences in binding specificity, as determined by surface labelling, western blots and by screening a glycan array. Through capturing the local antibody response, this study has revealed dramatic differences in the quality and specificity of the immune response at different tissue sites, and highlighted the existence of stage-specific protein and carbohydrate antigens. This will provide a valuable tool for the isolation of novel vaccine targets against the larval stages of schistosomes.
Description:
Previous empirical works have shown the effectiveness of differential prioritization in feature selection prior to molecular classification. We now propose to determine the theoretical basis for the concept of differential prioritization through mathematical analyses of the characteristics of predictor sets found using different values of the DDP (degree of differential prioritization) from realistic toy datasets. Mathematical analyses based on analytical measures such as distance between classes are implemented on these predictor sets. We demonstrate that the optimal value of the DDP is capable of forming a predictor set which consists of classes of features which are well separated and are highly correlated to the target classes – a characteristic of a truly optimal predictor set. From these analyses, the necessity of adjusting the DDP based on the dataset of interest is confirmed in a mathematical manner, indicating that the DDP-based feature selection technique is superior to both simplistic rank-based selection and state-of-the-art equal-priorities scoring methods. Applying similar analyses to real-life multiclass microarray datasets, we obtain further proof of the theoretical significance of the DDP for practical applications
Description:
The negative-sense asp open reading frame (ORF) positioned opposite to the human immunodeficiency virus type 1 (HIV-1) env gene encodes the 189 amino acid, membrane-associated ASP protein. Negative-sense transcription, regulated by long terminal repeat sequences, has been observed early in HIV-1 infection in vitro. All subtypes of HIV-1 were scanned to detect the negative-sense asp ORF and to identify potential regulatory sequences. A series of highly conserved upstream short open reading frames (sORFs) was identified. This potential control region from HIV-1NL4-3, containing six sORFs, was cloned upstream of the reporter gene EGFP. Expression by transfection of HEK293 cells indicated that the introduction of this sORF region inhibits EGFP reporter expression; analysis of transcripts revealed no significant changes in levels of EGFP mRNA. Reverse transcriptase–polymerase chain reaction analysis (RT-PCR) further demonstrated that the upstream sORF region undergoes alternative splicing in vitro. The most abundant product is spliced to remove sORFs I to V, leaving only the in-frame sORF VI upstream of asp. Sequence analysis revealed the presence of typical splice donor- and acceptor-site motifs. Mutation of the highly conserved splice donor and acceptor sites modulates, but does not fully relieve, inhibition of EGFP production. The strong conservation of asp and its sORFs across all HIV-1 subtypes suggests that the asp gene product may have a role in the pathogenesis of HIV-1. Alternative splicing of the upstream sORF region provides a potential mechanism for controlling expression of the asp gene.
Description:
Gaining hands-on experience throughout an under graduate degree is regarded as a competitive advantage by science students. At the Gippsland campus of Monash University, the Industry Placement Program (IPP) allows students to gain industry experience throughout their entire course. Science students enrolled in the IPP undertake a practicum learning experience, equivalent to a 12-point unit, whose emphasis is on identifying and building transferable skills while gaining technical competency. The combination of academic studies, relevant paid work experience and professional development training produces capable, work-ready graduates
Description:
Prion diseases are invariably fatal and highly infectious neurodegenerative diseases that affect a wide variety of mammalian species such as sheep and goats, cattle, deer and elk, and humans. But for rabbits, studies have shown that they have a low susceptibility to be infected by prion diseases. This paper does molecular dynamics (MD) studies of rabbit NMR structures (of the wild type and its two mutants of two surface residues), in order to understand the specific mechanism of rabbit prion proteins (RaPrPC). Protein surface electrostatic charge distributions are specially focused to analyze the MD trajectories. This paper can conclude that surface electrostatic charge distributions indeed contribute to the structural stability of wild-type RaPrPC; this may be useful for the medicinal treatment of prion diseases.
Description:
Prion diseases such as Creutzfeldt-Jakob disease, variant Creutzfeldt-Jakob diseases, Gerstmann-Sträussler-Scheinker syndrome, Fatal Familial Insomnia, Kuru in humans, scrapie in sheep, bovine spongiform encephalopathy (or ‘mad-cow’ disease) and chronic wasting disease in cattle are invariably fatal and highly infectious neurodegenerative diseases affecting humans and animals. However, by now there have not been some effective therapeutic approaches to treat all these prion diseases. In 2008, canine mammals including dogs (canis familials) were the first time academically reported to be resistant to prion diseases (Vaccine 26: 2601-2614 (2008)). Thus, it is very worth studying the molecular structures of dog prion protein to obtain insights into the immunity of dogs to prion diseases. This paper studies the molecular structural dynamics of wild-type dog prion protein. The comparison analyses with rabbit prion protein show that the dog prion protein has stable molecular structures whether under neutral or low pH environments. We also find that the salt bridges such as D177-R163 contribute to the structural stability of wild-type rabbit prion protein under neutral pH environment.
Description:
MicroRNA-181a binds to the 3’ untranslated region of messenger RNA (mRNA) for renin, a rate-limiting enzyme of the renin-angiotensin system. Our objective was to determine whether this molecular interaction translates into a clinically meaningful effect on blood pressure and whether circulating miR-181a is a measurable proxy of blood pressure. In 200 human kidneys from the TRANScriptome of renaL humAn TissuE (TRANSLATE) study, renal miR-181a was the sole negative predictor of renin mRNA and a strong correlate of circulating miR-181a. Elevated miR-181a levels correlated positively with systolic and diastolic blood pressure in TRANSLATE, and this association was independent of circulating renin. The association between serum miR-181a and systolic blood pressure was replicated in 199 subjects from the Genetic Regulation of Arterial Pressure of Humans In the Community (GRAPHIC) study. Renal immunohistochemistry and in situ hybridization showed that colocalization of miR-181a and renin was most prominent in collecting ducts where renin is not released into the systemic circulation. Analysis of 69 human kidneys characterized by RNA sequencing revealed that miR-181a was associated with downregulation of four mitochondrial pathways and upregulation of 41 signaling cascades of adaptive immunity and inflammation. We conclude that renal miR-181a has pleiotropic effects on pathways relevant to blood pressure regulation and that circulating levels of miR-181a are both a measurable proxy of renal miR-181a expression and a novel biochemical correlate of blood pressure.
Description:
Human papillomavirus (HPV) is one of the most common sexually transmitted disease, transmitted through intimate skin contact or mucosal membrane. The HPV virus consists of a double-stranded circular DNA and the role of HPV virus in cervical cancer has been studied extensively. Thus it is critical to develop rapid identification method for early detection of the virus. A portable biosensing device could give rapid and reliable results for the identification and quantitative determination of the virus. The fabrication of electrochemical biosensors is one of the current techniques utilized to achieve this aim. In such electrochemical biosensors, a single-strand DNA is immobilized onto an electrically conducting surface and the changes in electrical parameters due to the hybridization on the electrode surface are measured. This review covers the recent developments in electrochemical DNA biosensors for the detection of HPV virus. Due to the several advantages of electrochemical DNA biosensors, their applications have witnessed an increased interest and research focus nowadays.
Description:
Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum-number-of-parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well known but literature on the detailed analysis of the widely used maxP technique is lacking. The optimal search methods require large computational time. We report the theoretical analysis and experimental results of the strengths and limitations of the maxP technique. Further, using an optimal search method, we combine the strengths of the maxP technique and the known GRN topology to propose two novel algorithms. These algorithms are implemented in a Bayesian network framework and tested on biological, realistic, and in silico networks of different sizes and topologies. They overcome the limitations of the maxP technique and show superior computational speed when compared to the current optimal search algorithms.