Contemporary Insights into the Biological Mechanisms of Parkia biglobosa.

For a long time, traditional medicine has relied on the use of medicinal plants and herbal products which have served as the basis for numerous pharmaceuticals. Parkia biglobosa (Jacq) R.Br.ex. G. Don., commonly called the African locust bean tree, is a perennial deciduous plant native to West Africa where it is highly esteemed for its nutritional and traditional medicinal benefits. Parkia biglobosa's ethnomedicinal uses include microbial infections such as diarrhea and chronic diseases like hypertension and type 2 diabetes mellitus. This article presents the current understanding of the molecular mechanisms underlying Parkia biglobosa's biological effects. An electronic database search was conducted using P. biglobosa and its synonyms as keywords in Scientific Electronic Library Online, ISI Web of Knowledge, PubMed, Scopus, Science Direct, and Google Scholar. Consistently, scientific research has confirmed the medicinal effects of the plant's extracts and active phytochemicals, including antimicrobial, analgesic, antidiabetic, antihypertensive, hypolipidemic, and neuroprotective properties, among others. It highlights the contributions of identified innate phytochemicals and existing limitations to therapeutic applications, as well as the need for and prospects for further research. Advancing our understanding of the medicinal plant's biological mechanisms and the contributions of the active phytochemicals would allow for more effective exploration of its vast pharmacological potential and facilitate clinical applications.

Profiling metals in Cordyceps sinensis by using inductively coupled plasma mass spectrometry.

Cordyceps sinensis (C. sinensis) is a natural product that has diverse nutritional and medicinal values. Since the availability of natural C. sinensis becomes limited its authentication and quality control is of high significance. Herein we report on profiling of metals in C. sinensis by using inductively coupled plasma mass spectrometry (ICP-MS). The analysis reveals that C. sinensis contains a wide array of essential elements, including P, Mg, Zn, Cu, Fe, etc. Toxic metals detected are Cd, Pb, and As. In all five samples analyzed Pb contents are below 2.0 ppm. Arsenic level in C. sinensis caterpillar is significantly higher than that in its mycelium and varies from 3.0 to 32 ppm likely due to soil contamination. It's for the first time demonstrated in this work that clustering analysis on the proposed metal profiles consisting of 24 elements is very useful to identify "abnormal" C. sinensis samples, thus adding another dimension to the effective means for authentication and quality assessment of this highly demanded previous natural product.

The Association between Gene-Environment Interactions and Diseases Involving the Human GST Superfamily with SNP Variants.

Exposure to environmental hazards has been associated with diseases in humans. The identification of single nucleotide polymorphisms (SNPs) in human populations exposed to different environmental hazards, is vital for detecting the genetic risks of some important human diseases. Several studies in this field have been conducted on glutathione S-transferases (GSTs), a phase II detoxification superfamily, to investigate its role in the occurrence of diseases. Human GSTs consist of cytosolic and microsomal superfamilies that are further divided into subfamilies. Based on scientific search engines and a review of the literature, we have found a large amount of published articles on human GST super- and subfamilies that have greatly assisted in our efforts to examine their role in health and disease. Because of its polymorphic variations in relation to environmental hazards such as air pollutants, cigarette smoke, pesticides, heavy metals, carcinogens, pharmaceutical drugs, and xenobiotics, GST is considered as a significant biomarker. This review examines the studies on gene-environment interactions related to various diseases with respect to single nucleotide polymorphisms (SNPs) found in the GST superfamily. Overall, it can be concluded that interactions between GST genes and environmental factors play an important role in human diseases.

Cycling Transcriptional Networks Optimize Energy Utilization on a Genome Scale.

Genes expressing circadian RNA rhythms are enriched for metabolic pathways, but the adaptive significance of cyclic gene expression remains unclear. We estimated the genome-wide synthetic and degradative cost of transcription and translation in three organisms and found that the cost of cycling genes is strikingly higher compared to non-cycling genes. Cycling genes are expressed at high levels and constitute the most costly proteins to synthesize in the genome. We demonstrate that metabolic cycling is accelerated in yeast grown under higher nutrient flux and the number of cycling genes increases ∼40%, which are achieved by increasing the amplitude and not the mean level of gene expression. These results suggest that rhythmic gene expression optimizes the metabolic cost of global gene expression and that highly expressed genes have been selected to be downregulated in a cyclic manner for energy conservation.

FoxP1 orchestration of ASD-relevant signaling pathways in the striatum.

Mutations in the transcription factor Forkhead box p1 (FOXP1) are causative for neurodevelopmental disorders such as autism. However, the function of FOXP1 within the brain remains largely uncharacterized. Here, we identify the gene expression program regulated by FoxP1 in both human neural cells and patient-relevant heterozygous Foxp1 mouse brains. We demonstrate a role for FoxP1 in the transcriptional regulation of autism-related pathways as well as genes involved in neuronal activity. We show that Foxp1 regulates the excitability of striatal medium spiny neurons and that reduction of Foxp1 correlates with defects in ultrasonic vocalizations. Finally, we demonstrate that FoxP1 has an evolutionarily conserved role in regulating pathways involved in striatal neuron identity through gene expression studies in human neural progenitors with altered FOXP1 levels. These data support an integral role for FoxP1 in regulating signaling pathways vulnerable in autism and the specific regulation of striatal pathways important for vocal communication.

ChIP-seq and RNA-seq methods to study circadian control of transcription in mammals.

Genome-wide analyses have revolutionized our ability to study the transcriptional regulation of circadian rhythms. The advent of next-generation sequencing methods has facilitated the use of two such technologies, ChIP-seq and RNA-seq. In this chapter, we describe detailed methods and protocols for these two techniques, with emphasis on their usage in circadian rhythm experiments in the mouse liver, a major target organ of the circadian clock system. Critical factors for these methods are highlighted and issues arising with time series samples for ChIP-seq and RNA-seq are discussed. Finally, detailed protocols for library preparation suitable for Illumina sequencing platforms are presented.

C57BL/6N mutation in cytoplasmic FMRP interacting protein 2 regulates cocaine response.

The inbred mouse C57BL/6J is the reference strain for genome sequence and for most behavioral and physiological phenotypes. However, the International Knockout Mouse Consortium uses an embryonic stem cell line derived from a related C57BL/6N substrain. We found that C57BL/6N has a lower acute and sensitized response to cocaine and methamphetamine. We mapped a single causative locus and identified a nonsynonymous mutation of serine to phenylalanine (S968F) in Cytoplasmic FMRP interacting protein 2 (Cyfip2) as the causative variant. The S968F mutation destabilizes CYFIP2, and deletion of the C57BL/6N mutant allele leads to acute and sensitized cocaine-response phenotypes. We propose that CYFIP2 is a key regulator of cocaine response in mammals and present a framework to use mouse substrains to identify previously unknown genes and alleles regulating behavior.

An immunological fingerprint differentiates muscular lymphatics from arteries and veins.

The principal function of the lymphatic system is to transport lymph from the interstitium to the nodes and then from the nodes to the blood. In doing so lymphatics play important roles in fluid homeostasis, macromolecular/antigen transport and immune cell trafficking. To better understand the genes that contribute to their unique physiology, we compared the transcriptional profile of muscular lymphatics (prenodal mesenteric microlymphatics and large, postnodal thoracic duct) to axillary and mesenteric arteries and veins isolated from rats. Clustering of the differentially expressed genes demonstrated that the lymph versus blood vessel differences were more profound than between blood vessels, particularly the microvessels. Gene ontology functional category analysis indicated that microlymphatics were enriched in antigen processing/presentation, IgE receptor signaling, catabolic processes, translation and ribosome; while they were diminished in oxygen transport, regulation of cell proliferation, glycolysis and inhibition of adenylate cyclase activity by G-proteins. We evaluated the differentially expressed microarray genes/products by qPCR and/or immunofluorescence. Immunofluorescence documented that multiple MHC class II antigen presentation proteins were highly expressed by an antigen-presenting cell (APC) type found resident within the lymphatic wall. These APCs also expressed CD86, a co-stimulatory protein necessary for T-cell activation. We evaluated the distribution and phenotype of APCs within the pre and postnodal lymphatic network. This study documents a novel population of APCs resident within the walls of muscular, prenodal lymphatics that indicates novel roles in antigen sampling and immune responses. In conclusion, these prenodal lymphatics exhibit a unique profile that distinguishes them from blood vessels and highlights the role of the lymphatic system as an immunovascular system linking the parenchymal interstitium, lymph nodes and the blood.

Transcriptional architecture and chromatin landscape of the core circadian clock in mammals.

The mammalian circadian clock involves a transcriptional feed back loop in which CLOCK and BMAL1 activate the Period and Cryptochrome genes, which then feedback and repress their own transcription. We have interrogated the transcriptional architecture of the circadian transcriptional regulatory loop on a genome scale in mouse liver and find a stereotyped, time-dependent pattern of transcription factor binding, RNA polymerase II (RNAPII) recruitment, RNA expression, and chromatin states. We find that the circadian transcriptional cycle of the clock consists of three distinct phases: a poised state, a coordinated de novo transcriptional activation state, and a repressed state. Only 22% of messenger RNA (mRNA) cycling genes are driven by de novo transcription, suggesting that both transcriptional and posttranscriptional mechanisms underlie the mammalian circadian clock. We also find that circadian modulation of RNAPII recruitment and chromatin remodeling occurs on a genome-wide scale far greater than that seen previously by gene expression profiling.

UNAC tetraloops: to what extent do they mimic GNRA tetraloops?

The structures of four small RNAs each containing a different version of the UNAC loop were determined in solution using NMR spectroscopy and restrained molecular dynamics. The UMAC tetraloops (where M is A or C) exhibited a typical GNRA fold including at least one hydrogen bond between the first U and fourth C. In contrast, UGAC and UUAC tetraloops have a different orientation of the first and fourth residues, such that they do not closely mimic the GNRA fold. Although the UMAC tetraloops are excellent structural mimics of the GNRA tetraloop backbone, sequence comparisons typically do not reveal co-variation between the two loop types. The limited covariation is attributed to differences in the location of potential hydrogen bond donors and acceptors as a result of the replacement of the terminal A of GNRA with C in the UMAC version. Thus, UMAC loops do not readily form the common GNRA tetraloop-receptor interaction. The loop at positions 863-866 in E. coli 16S ribosomal RNA appears to be a major exception. However, in this case the GNRA loop does not in fact engage in the usual base to backbone tertiary interactions. In summary, UMAC loops are not just an alternative sequence version of the GNRA loop family, but instead they expand the types of interactions, or lack thereof, that are possible. From a synthetic biology perspective their inclusion in an artificial RNA may allow the establishment of a stable loop structure while minimizing unwanted long range interactions or permitting alternative long-range interactions. © 2012 Wiley Periodicals, Inc. Biopolymers 97: 617-628, 2012.

Discovering disease-specific biomarker genes for cancer diagnosis and prognosis.

The large amounts of microarray data provide us a great opportunity to identify gene expression profiles (GEPs) in different tissues or disease states. Disease-specific biomarker genes likely share GEPs that are distinct in disease samples as compared with normal samples. The similarity of the GEPs may be evaluated by Pearson Correlation Coefficient (PCC) and the distinctness of GEPs may be assessed by Kolmogorov-Smirnov distance (KSD). In this study, we used the PCC and KSD metrics for GEPs to identify disease-specific (cancer-specific) biomarkers. We first analyzed and compared GEPs using microarray datasets for smoking and lung cancer. We found that the number of genes with highly different GEPs between comparing groups in smoking dataset was much larger than that in lung cancer dataset; this observation was further verified when we compared GEPs in smoking dataset with prostate cancer datasets. Moreover, our Gene Ontology analysis revealed that the top ranked biomarker candidate genes for prostate cancer were highly enriched in molecular function categories such as 'cytoskeletal protein binding' and biological process categories such as 'muscle contraction'. Finally, we used two genes, ACTC1 (encoding an actin subunit) and HPN (encoding hepsin), to demonstrate the feasibility of diagnosing and monitoring prostate cancer using the expression intensity histograms of marker genes. In summary, our results suggested that this approach might prove promising and powerful for diagnosing and monitoring the patients who come to the clinic for screening or evaluation of a disease state including cancer.

Classification of genes and putative biomarker identification using distribution metrics on expression profiles.

BACKGROUND: Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers. METHODOLOGY/PRINCIPAL FINDINGS: In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs) of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness) were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic), and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as 'brain group' and 'non-brain group'; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes. CONCLUSIONS/SIGNIFICANCE: The methodology employed here may be used to facilitate disease-specific biomarker discovery.

Cluster analysis of hydration waters around the active sites of bacterial alanine racemase using a 2-ns MD simulation.

Structural data produced by a 2-ns molecular dynamics (MD) simulation on Geobacillus alanine racemase (AlaR; PDB: 1SFT) was used to study hydration around the two AlaR active sites. AlaR is a crucial enzyme for bacterial cell wall biosynthesis. It has been shown previously that the potency of an inhibitor can be increased by incorporating a functional group or atom that displaces hydration sites close to the substrate binding pocket of its target enzyme. The complete linkage algorithm was used for cluster analysis of the active site water positions from 126 solvent configurations sampled at regular intervals from the 2-ns MD simulation. Crystal waters in the 1SFT X-ray structure occupy most of the tightly bound water sites that were discovered. We show here that tightly bound water sites can be identified by cluster analysis of MD-generated coordinates starting with data supplied by a single X-ray structure, and we predict a highly conserved hydration site close to the carboxyl oxygen of L-Ala substrate. This approach holds promise for accelerating the drug design process. We also discuss an analysis of the well-known notion of residence time and introduce a new measure called retention time.

Biomarker discovery and visualization in gene expression data with efficient generalized matrix approximations.

In most real-world gene expression data sets, there are often multiple sample classes with ordinals, which are categorized into the normal or diseased type. The traditional feature or attribute selection methods consider multiple classes equally without paying attention to the up/down regulation across the normal and diseased types of classes, while the specific gene selection methods particularly consider the differential expressions across the normal and diseased, but ignore the existence of multiple classes. In this paper, to improve the biomarker discovery, we propose to make the best use of these two aspects: the differential expressions (that can be viewed as the domain knowledge of gene expression data) and the multiple classes (that can be viewed as a kind of data set characteristic). Therefore, we simultaneously take into account these two aspects by employing the 1-rank generalized matrix approximations (GMA). Our results show that GMA cannot only improve the accuracy of classifying the samples, but also provide a visualization method to effectively analyze the gene expression data on both genes and samples. Based on the mechanism of matrix approximation, we further propose an algorithm, CBiomarker, to discover compact biomarker by reducing the redundancy.

Dynamical systems for discovering protein complexes and functional modules from biological networks.

Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the Heaviest k-Subgraph Problem (k-HSP), which itself is NP-hard. However, any method based on the k-HSP requires the parameter k and an exact solution of k-HSP may still end up as a "spurious" heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the Standard deviation and Mean Ratio (SMR), is proposed for use in "spurious" heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal.

Modeling nonlinearity in dilution design microarray data.

MOTIVATION: Dilution design (Mixed tissue RNA) has been utilized by some researchers to evaluate and assess the performance of multiple microarray platforms. Current microarray data analysis approaches assume that the quantified signal intensities are linearly related to the expression of the corresponding genes in the sample. However, there are sources of nonlinearity in microarray expression measurements. Such nonlinearity study in the expressions of the RNA mixtures provides a new way to analyze gene expression data, and we argue that the nonlinearity can reveal novel information for microarray data analysis. Therefore, we proposed a statistical model, called proportion model, which is based on the linear regression analysis. To approximately quantify the nonlinearity in the dilution design, a new calibration, beta ratio (BR) was derived from the proportion model. Furthermore, a new adjusted fold change (adj-FC) was proposed to predict the true FC without nonlinearity, in particular for large FC. RESULTS: We applied our method to one microarray dilution dataset. The experimental results indicated that, to some extent, there are global biases comparing with the linear assumption for the significant genes. Further analysis of those highly expressed genes with significant nonlinearity revealed some promising results, e.g. 'poison' effect was discovered for some genes in RNA mixtures. The adj-FCs of those genes with 'poison' effect, indicate that the nonlinearity can be also caused by the inherent feature of the genes besides signal noise and technical variation. Moreover, when percentage of overlapping genes (POG) was used as a cross-platform consistency measure, adj-FC outperformed simple fold change to show that Affymetrix and Illumina platforms are consistent. AVAILABILITY: The R codes which implements all described methods, and some Supplementary material, are freely available from http://www.utdallas.edu/~ying.liu/BetaRatio.htm

Efficient generalized matrix approximations for biomarker discovery and visualization in gene expression data.

In most real-life gene expression data sets, there are often multiple sample classes with ordinals, which are categorized into the normal or diseased type. The traditional feature or attribute selection methods consider multiple classes equally without paying attention to the up/down regulation across the normal and diseased types of classes, while the specific gene selection methods particularly consider the differential expressions across the normal and diseased, but ignore the existence of multiple classes. In this paper, for improving the biomarker discovery, we propose to make the best use of these two aspects: the differential expressions (that can be viewed as the domain knowledge of gene expression data) and the multiple classes (that can be viewed as a kind of data set characteristic). Therefore, we simultaneously take into account these two aspects by employing the 1-rank generalized matrix approximations (GMA). Our results show that the consideration of both aspects can not only improve the accuracy of classifying the samples, but also provide a visualization method to effectively analyze the gene expression data on both genes and samples. Based on the GMA mechanism, we further propose an algorithm for obtaining the compact biomarker by reducing the redundancy.

NMR structure and Mg2+ binding of an RNA segment that underlies the L7/L12 stalk in the E.coli 50S ribosomal subunit.

Helix 42 of Domain II of Escherichia coli 23S ribosomal RNA underlies the L7/L12 stalk in the ribosome and may be significant in positioning this feature relative to the rest of the 50S ribosomal subunit. Unlike the Haloarcula marismortui and Deinococcus radiodurans examples, the lower portion of helix 42 in E.coli contains two consecutive G*A oppositions with both adenines on the same side of the stem. Herein, the structure of an analog of positions 1037-1043 and 1112-1118 in the helix 42 region is reported. NMR spectra and structure calculations support a cis Watson-Crick/Watson-Crick (cis W.C.) G*A conformation for the tandem (G*A)2 in the analog and a minimally perturbed helical duplex stem. Mg2+ titration studies imply that the cis W.C. geometry of the tandem (G*A)2 probably allows O6 of G20 and N1 of A4 to coordinate with a Mg2+ ion as indicated by the largest chemical shift changes associated with the imino group of G20 and the H8 of G20 and A4. A cross-strand bridging Mg2+ coordination has also been found in a different sequence context in the crystal structure of H.marismortui 23S rRNA, and therefore it may be a rare but general motif in Mg2+ coordination.

The application of cluster analysis in the intercomparison of loop structures in RNA.

We have developed a computational approach for the comparison and classification of RNA loop structures. Hairpin or interior loops identified in atomic resolution RNA structures were intercompared by conformational matching. The root-mean-square deviation (RMSD) values between all pairs of RNA fragments of interest, even if from different molecules, are calculated. Subsequently, cluster analysis is performed on the resulting matrix of RMSD distances using the unweighted pair group method with arithmetic mean (UPGMA). The cluster analysis objectively reveals groups of folds that resemble one another. To demonstrate the utility of the approach, a comprehensive analysis of all the terminal hairpin tetraloops that have been observed in 15 RNA structures that have been determined by X-ray crystallography was undertaken. The method found major clusters corresponding to the well-known GNRA and UNCG types. In addition, two tetraloops with the unusual primary sequence UMAC (M is A or C) were successfully assigned to the GNRA cluster. Larger loop structures were also examined and the clustering results confirmed the occurrence of variations of the GNRA and UNCG tetraloops in these loops and provided a systematic means for locating them. Nineteen examples of larger loops that closely resemble either the GNRA or UNCG tetraloop were found in the large ribosomal RNAs. When the clustering approach was extended to include all structures in the SCOR database, novel relationships were detected including one between the ANYA motif and a less common folding of the GAAA tetraloop sequence.

The association between a negatively charged ligand and the electronegative binding pocket of its receptor.

Many examples exist of charged amino acids that play a role in attracting or holding a charged ligand toward or inside an oppositely charged binding pocket of the protein. For example, the enzymes superoxide dismutase, triose-phosphate isomerase, and acetylcholinesterase can steer ligands toward their oppositely charged binding pockets or gorges. Interestingly, in our Brownian dynamics simulations of a phosphate-binding protein, we discovered that negatively charged phosphate (HPO(2-)(4)) could make its way into the negatively charged binding pocket. In fact, the phosphate-binding protein exhibits counterintuitive kinetics of association. That is, one would expect that the rate of association would increase on increases to the ionic strength since the interaction between the ligand, with a charge of -2, and the electronegative binding pocket would be repulsive and greater screening should reduce this repulsion and increase the rate of association. However, the opposite is seen-i.e., the rate of association decreases on increases in the ionic strength. We used Brownian dynamics techniques to compute the diffusion limited association rate constants between the negatively charged phosphate ligand and several open forms of PBP (wild-type and several mutants based on an x-ray structure of open-form PBP, mutant T141D). With the appropriate choices of reaction criteria and molecular parameters, the ligand was able to diffuse into the binding pocket. A number of residues influence binding of the ligand within the pocket via hydrogen bonds or salt bridges. Arg135 partially neutralizes the charges on the HPO(2-)(4) ligand in the binding pocket, allowing it to enter. It is also found that the positive electrostatic patches above and below the binding entrance of PBP contribute the major attractive forces that direct the ligand toward the surface of the protein near the binding site.