Development and Targeting Efficiency of Irinotecan Engineered Proniosomes

Purpose: This study is aimed at achieving improvement in the efficacy, reduced toxicity and enhancement of therapeutic index of irinotecan.Methods: Proniosomes of irinotecan hydrochloride trihydrate were prepared by slurry method using different surfactants, cholesterol and dicetyl phosphate. The formulations were then characterized with respect to shape, surface morphology, entrapment efficiency, in vitro drug release, in vivo drug targeting and stability.Results: The proniosomes were smooth in texture indicating, thin and uniform coating over maltodextrin powder. The highest entrapment efficiency was found for formulation F2 (74.9 ± 2.7 %). The highest cumulative drug release in 24 h was achieved with formulation F3 (98.2 %) In vivo results for the proniosomes reveal that the drug was preferentially targeted to liver followed by lung and spleen. Stability studies indicate that 4 ºC was the most suitable condition for the storage of formulation F2.Conclusion: Proniosomes offer a suitable alternative colloidal carrier approach to achieving drug targeting. Proniosomes containing irinotecan are retained at targeted sites and are capable of releasing drug for an extended period of time.Keywords: Irinotecan, proniosomes, Drug targeting, In vivo tissue distribution, Stability studies

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure

Single Feature Polymorphism Discovery in Rice

The discovery of nucleotide diversity captured as single feature polymorphism (SFP) by using the expression array is a high-throughput and effective method in detecting genome-wide polymorphism. The efficacy of such method was tested in rice, and the results presented in the paper indicate high sensitivity in predicting SFP. The sensitivity of polymorphism detection was further demonstrated by the fact that no biasness was observed in detecting SFP with either single or multiple nucleotide polymorphisms. The high density SFP data that can be generated quite effectively by the current method has promise for high resolution genetic mapping studies, as physical location of features are well-defined on rice genome

Development of a biosensor for urea assay based on amidase inhibition, using an ion-selective electrode

A biosensor for urea has been developed based on the observation that urea is a powerful active-site inhibitor of amidase, which catalyzes the hydrolysis of amides such as acetamide to produce ammonia and the corresponding organic acid. Cell-free extract from Pseudomonas aeruginosa was the source of amidase (acylamide hydrolase, EC 3.5.1.4) which was immobilized on a polyethersulfone membrane in the presence of glutaraldehyde; anion-selective electrode for ammonium ions was used for biosensor development. Analysis of variance was used for optimization of the biosensorresponse and showed that 30 mu L of cell-free extract containing 7.47 mg protein mL(-1), 2 mu L of glutaraldehyde (5%, v/v) and 10 mu L of gelatin (15%, w/v) exhibited the highest response. Optimization of other parameters showed that pH 7.2 and 30 min incubation time were optimum for incubation ofmembranes in urea. The biosensor exhibited a linear response in the range of 4.0-10.0 mu M urea, a detection limit of 2.0 mu M for urea, a response timeof 20 s, a sensitivity of 58.245 % per mu M urea and a storage stability of over 4 months. It was successfully used for quantification of urea in samples such as wine and milk; recovery experiments were carried out which revealed an average substrate recovery of 94.9%. The urea analogs hydroxyurea, methylurea and thiourea inhibited amidase activity by about 90%, 10% and 0%, respectively, compared with urea inhibition

Inter-relationship of plasma markers of oxidative stress and thyroid hormones in schizophrenics

<p>Abstract</p> <p>Background</p> <p>The relationship of oxidative stress to thyroid hormones has not been studied in the schizophrenics. The present study determined the status and interrelationship of plasma markers of oxidative stress, nitric oxide and thyroid hormones in thirty (17 males and 13 females) newly diagnosed patients with acute schizophrenia before initiation of chemotherapy. Twenty five (13 males and 12 females) mentally healthy individuals served as controls. Patients and controls with history of hard drugs (including alcohol and cigarette), pre-diagnosis medications (e.g. antiparkinsonian/antipsychotic drugs), chronic infections, liver disease and diabetes mellitus were excluded from the study. Plasma levels of total antioxidant potential (TAP), total plasma peroxides (TPP), nitric oxide (NO), malondialdehyde (MDA), thyroxine (T4), tri-iodothyronine (T3) and thyroid stimulating hormone (TSH) were determined in all participants using spectrophotometric and enzyme linked immunosorbent assay (ELISA) methods respectively. Oxidative stress index (OSI) was calculated as the percent ratio of total plasma peroxides and total antioxidant potential.</p> <p>Findings</p> <p>Significantly higher plasma levels of MDA (p < 0.01), TPP (p < 0.01), OSI (p < 0.01), T3 (p < 0.01) and T4 (p < 0.05) were observed in schizophrenics when compared with the controls. The mean levels of TAP, NO and TSH were significantly lower in schizophrenics (p < 0.01) when compared with the controls. The result shows that T3 values correlate significantly with MDA (p < 0.05) and TPP (p < 0.01) in schizophrenics.</p> <p>Conclusions</p> <p>Higher level of TPP may enhance thyroid hormogenesis in schizophrenics. Adjuvant antioxidant therapy may be a novel approach in the treatment of schizophrenic patients.</p

Cell-to-Cell Stochastic Variation in Gene Expression Is a Complex Genetic Trait

The genetic control of common traits is rarely deterministic, with many genes contributing only to the chance of developing a given phenotype. This incomplete penetrance is poorly understood and is usually attributed to interactions between genes or interactions between genes and environmental conditions. Because many traits such as cancer can emerge from rare events happening in one or very few cells, we speculate an alternative and complementary possibility where some genotypes could facilitate these events by increasing stochastic cell-to-cell variations (or ‘noise’). As a very first step towards investigating this possibility, we studied how natural genetic variation influences the level of noise in the expression of a single gene using the yeast S. cerevisiae as a model system. Reproducible differences in noise were observed between divergent genetic backgrounds. We found that noise was highly heritable and placed under a complex genetic control. Scanning the genome, we mapped three Quantitative Trait Loci (QTL) of noise, one locus being explained by an increase in noise when transcriptional elongation was impaired. Our results suggest that the level of stochasticity in particular molecular regulations may differ between multicellular individuals depending on their genotypic background. The complex genetic architecture of noise buffering couples genetic to non-genetic robustness and provides a molecular basis to the probabilistic nature of complex traits

Varying Herbivore Population Structure Correlates with Lack of Local Adaptation in a Geographic Variable Plant-Herbivore Interaction

Local adaptation of parasites to their hosts due to coevolution is a central prediction of many theories in evolutionary biology. However, empirical studies looking for parasite local adaptation show great variation in outcomes, and the reasons for such variation are largely unknown. In a previous study, we showed adaptive differentiation in the arctiid moth Utetheisa ornatrix to its host plant, the pyrrolizidine alkaloid-bearing legume Crotalaria pallida, at the continental scale, but found no differentiation at the regional scale. In the present study, we sampled the same sites to investigate factors that may contribute to the lack of differentiation at the regional scale. We performed field observations that show that specialist and non-specialist polyphagous herbivore incidence varies among populations at both scales. With a series of common-garden experiments we show that some plant traits that may affect herbivory (pyrrolizidine alkaloids and extrafloral nectaries) vary at the regional scale, while other traits (trichomes and nitrogen content) just vary at the continental scale. These results, combined with our previous evidence for plant population differentiation based on larval performance on fresh fruits, suggest that U. ornatrix is subjected to divergent selection even at the regional scale. Finally, with a microsatellite study we investigated population structure of U. ornatrix. We found that population structure is not stable over time: we found population differentiation at the regional scale in the first year of sampling, but not in the second year. Unstable population structure of the herbivore is the most likely cause of the lack of regional adaptation

Transforming Growth Factor β Signaling Pathway Associated Gene Polymorphisms May Explain Lower Breast Cancer Risk in Western Indian Women

Transforming growth factor β1 (TGFB1) T29C and TGF β receptor type 1 (TGFBR1) 6A/9A polymorphisms have been implicated in the modulation of risk for breast cancer in Caucasian women. We analyzed these polymorphisms and combinations of their genotypes, in pre menopausal breast cancer patients (N = 182) and healthy women (N = 236) from western India as well as in breast cancer patients and healthy women from the Parsi community (N = 48 & 171, respectively). Western Indian women were characterized by a higher frequency of TGFB1*C allele of the TGF β T29C polymorphism (0.48 vs 0.44) and a significantly lower frequency of TGFBR1*6A allele of the TGFBR1 6A/9A polymorphism (0.02 vs 0.068, p<0.01) as compared to healthy Parsi women. A strong protective effect of TGFB1*29C allele was seen in younger western Indian women (<40 yrs; OR = 0.45, 95% CI 0.25–0.81). Compared to healthy women, the strikingly higher frequencies of low or intermediate TGF β signalers in patients suggested a strong influence of the combination of these genotypes on the risk for breast cancer in Parsi women (for intermediate signalers, OR = 4.47 95%CI 1.01–19.69). The frequency of low signalers in Parsi healthy women, while comparable to that reported in Europeans and Americans, was three times higher than that in healthy women from western India (10.6% vs 3.3%, p<0.01). These observations, in conjunction with the low incidence rate of breast cancer in Indian women compared to White women, raise a possibility that the higher frequency of TGFB1*29C allele and lower frequency of TGFBR1*6A allele may represent important genetic determinants that together contribute to a lower risk of breast cancer in western Indian women

Histone Deacetylases Play a Major Role in the Transcriptional Regulation of the Plasmodium falciparum Life Cycle

The apparent paucity of molecular factors of transcriptional control in the genomes of Plasmodium parasites raises many questions about the mechanisms of life cycle regulation in these malaria parasites. Epigenetic regulation has been suggested to play a major role in the stage specific gene expression during the Plasmodium life cycle. To address some of these questions, we analyzed global transcriptional responses of Plasmodium falciparum to a potent inhibitor of histone deacetylase activities (HDAC). The inhibitor apicidin induced profound transcriptional changes in multiple stages of the P. falciparum intraerythrocytic developmental cycle (IDC) that were characterized by rapid activation and repression of a large percentage of the genome. A major component of this response was induction of genes that are otherwise suppressed during that particular stage of the IDC or specific for the exo-erythrocytic stages. In the schizont stage, apicidin induced hyperacetylation of histone lysine residues H3K9, H4K8 and the tetra-acetyl H4 (H4Ac4) and demethylation of H3K4me3. Interestingly, we observed overlapping patterns of chromosomal distributions between H4K8Ac and H3K4me3 and between H3K9Ac and H4Ac4. There was a significant but partial association between the apicidin-induced gene expression and histone modifications, which included a number of stage specific transcription factors. Taken together, inhibition of HDAC activities leads to dramatic de-regulation of the IDC transcriptional cascade, which is a result of both disruption of histone modifications and up-regulation of stage specific transcription factors. These findings suggest an important role of histone modification and chromatin remodeling in transcriptional regulation of the Plasmodium life cycle. This also emphasizes the potential of P. falciparum HDACs as drug targets for malaria chemotherapy

A Factor Graph Nested Effects Model To Identify Networks from Genetic Perturbations

Complex phenotypes such as the transformation of a normal population of cells into cancerous tissue result from a series of molecular triggers gone awry. We describe a method that searches for a genetic network consistent with expression changes observed under the knock-down of a set of genes that share a common role in the cell, such as a disease phenotype. The method extends the Nested Effects Model of Markowetz et al. (2005) by using a probabilistic factor graph to search for a network representing interactions among these silenced genes. The method also expands the network by attaching new genes at specific downstream points, providing candidates for subsequent perturbations to further characterize the pathway. We investigated an extension provided by the factor graph approach in which the model distinguishes between inhibitory and stimulatory interactions. We found that the extension yielded significant improvements in recovering the structure of simulated and Saccharomyces cerevisae networks. We applied the approach to discover a signaling network among genes involved in a human colon cancer cell invasiveness pathway. The method predicts several genes with new roles in the invasiveness process. We knocked down two genes identified by our approach and found that both knock-downs produce loss of invasive potential in a colon cancer cell line. Nested effects models may be a powerful tool for inferring regulatory connections and genes that operate in normal and disease-related processes