Effect of aqueous extracts of Ficus vogeliana Miq and Tieghemella africana Pierre in 7,12-Dimethylbenz(a)anthracene -induced skin cancer in rats.

ETHNOPHARMACOLOGICAL RELEVANCE: Skin cancer is the most common form of cancer responsible for considerable morbidity and mortality. Tieghemella africana and Ficus vogeliana are used in traditional medicine to treat cancers. AIM OF THE STUDY: Therefore, the aim of this study was to investigate the antioxidant, antiangiogenic and anti-tumor activities of these plant extracts. MATERIALS AND METHODS: To achieve it, phytochemical screening, antioxidant activity and antiangiogenic activity were assessed. Thereafter, the anti-tumor activity was determined using skin tumorigenesis induced by 7,12-dimethylbenz[a]anthracene. RESULTS: The phytochemical result analysis showed that both plant extracts were rich in polyphenols, alkaloids and terpene compounds and possessed good antioxidant activity based on DPPH radical scavenging (IC50 = 9.70 µg/mL and 4.60 µg/mL and AAI values of 5.20 and 10.88) and strong total antioxidant capacity (115.44 VtCE (mg)/g of dry plant extract and 87.37 VtCE (mg)/g of dry plant extract, respectively). Additionally, both plant extracts possessed antiangiogenic activities (IC50 = 53.43 µg/mL and 92.68 µg/mL, respectively), which correlated with significant antitumor activities when using 35 mg/kg (65.02% and 77.54%) and 70 mg/kg of extracts (81.07% and 88.18%). CONCLUSIONS: In summary, this study illustrates the promising usage of Tieghemella africana and Ficus vogeliana plant extracts in treating skin cancer. However, further characterization of the extracts must be performed to isolate the most active anticancer compound.

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach.

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.

Novel features for identifying A-minors in three-dimensional RNA molecules.

RNA tertiary interactions or tertiary motifs are conserved structural patterns formed by pairwise interactions between nucleotides. They include base-pairing, base-stacking, and base-phosphate interactions. A-minor motifs are the most common tertiary interactions in the large ribosomal subunit. The A-minor motif is a nucleotide triple in which minor groove edges of an adenine base are inserted into the minor groove of neighboring helices, leading to interaction with a stabilizing base pair. We propose here novel features for identifying and predicting A-minor motifs in a given three-dimensional RNA molecule. By utilizing the features together with machine learning algorithms including random forests and support vector machines, we show experimentally that our approach is capable of predicting A-minor motifs in the given RNA molecule effectively, demonstrating the usefulness of the proposed approach. The techniques developed from this work will be useful for molecular biologists and biochemists to analyze RNA tertiary motifs, specifically A-minor interactions.

Searching for non-coding RNAs in genomic sequences using ncRNAscout.

Recently non-coding RNA (ncRNA) genes have been found to serve many important functions in the cell such as regulation of gene expression at the transcriptional level. Potentially there are more ncRNA molecules yet to be found and their possible functions are to be revealed. The discovery of ncRNAs is a difficult task because they lack sequence indicators such as the start and stop codons displayed by protein-coding RNAs. Current methods utilize either sequence motifs or structural parameters to detect novel ncRNAs within genomes. Here, we present an ab initio ncRNA finder, named ncRNAscout, by utilizing both sequence motifs and structural parameters. Specifically, our method has three components: (i) a measure of the frequency of a sequence, (ii) a measure of the structural stability of a sequence contained in a t-score, and (iii) a measure of the frequency of certain patterns within a sequence that may indicate the presence of ncRNA. Experimental results show that, given a genome and a set of known ncRNAs, our method is able to accurately identify and locate a significant number of ncRNA sequences in the genome. The ncRNAscout tool is available for downloading at http://bioinformatics.njit.edu/ncRNAscout.

In silico prediction of noncoding RNAs using supervised learning and feature ranking methods.

We propose here a new approach for ncRNA prediction. Our approach selects features derived from RNA folding programs and ranks these features using a class separation method that measures the ability of the features to differentiate between positive and negative classes. The target feature set comprising top-ranked features is then used to construct several classifiers with different supervised learning algorithms. These classifiers are compared to the same supervised learning algorithms with the baseline feature set employed in a state-of-the-art method. Experimental results based on ncRNA families taken from the Rfam database demonstrate the good performance of the proposed approach.

Analysis of polyphenolic compounds and radical scavenging activity of four American Actaea species.

A reversed-phase high-performance liquid chromatography (RP-HPLC) method with diode array detection has been developed for analysis of the major polyphenols in the roots and rhizomes of black cohosh (Actaea racemosa), an important botanical dietary supplement for women's health, and three closely related American Actaea species, A. rubra, A. pachypoda and A. podocarpa. The method was validated with respect to sensitivity, linearity, precision, accuracy and recovery. The total content of eight major polyphenols in the dried root and rhizome of the four species was determined to be from 0.36 to 2.92% (w/w). The antioxidant activities of Actaea extracts and polyphenolic compounds isolated from A. racemosa were evaluated on 1.1-diphenyl-2-picrylhydrazyl (DPPH) free radicals scavenging assay. The radical scavenging activity of the Actaea extracts correlates to their polyphenolic composition. This validated HPLC method can be used to distinguish A. racemosa from the other major American Actaea species based on this study.

Maize cDNAs expressed in endosperm encode functional farnesyl diphosphate synthase with geranylgeranyl diphosphate synthase activity.

Isoprenoids are the most diverse and abundant group of natural products. In plants, farnesyl diphosphate (FPP) and geranylgeranyl diphosphate (GGPP) are precursors to many isoprenoids having essential functions. Terpenoids and sterols are derived from FPP, whereas gibberellins, carotenoids, casbenes, taxenes, and others originate from GGPP. The corresponding synthases (FPP synthase [FPPS] and GGPP synthase [GGPPS]) catalyze, respectively, the addition of two and three isopentenyl diphosphate molecules to dimethylallyl diphosphate. Maize (Zea mays L. cv B73) endosperm cDNAs encoding isoprenoid synthases were isolated by functional complementation of Escherichia coli cells carrying a bacterial gene cluster encoding all pathway enzymes needed for carotenoid biosynthesis, except for GGPPS. This approach indicated that the maize gene products were functional GGPPS enzymes. Yet, the predicted enzyme sequences revealed FPPS motifs and homology with FPPS enzymes. In vitro assays demonstrated that indeed these maize enzymes produced both FPP and GGPP and that the N-terminal sequence affected the ratio of FPP to GGPP. Their functionality in E. coli demonstrated that these maize enzymes can be coupled with a metabolon to provide isoprenoid substrates for pathway use, and suggests that enzyme bifunctionality can be harnessed. The maize cDNAs are encoded by a small gene family whose transcripts are prevalent in endosperm beginning mid development. These maize cDNAs will be valuable tools for assessing the critical structural properties determining prenyl transferase specificity and in metabolic engineering of isoprenoid pathways, especially in cereal crops.