Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering

BACKGROUND: The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell tumor (SRBCT) and thus often leads to misdiagnosis. Identification of biomarkers for distinguishing these cancers is a well studied problem. Existing methods typically evaluate each gene separately and do not take into account the nonlinear interaction between genes and the tools that are used to design the diagnostic prediction system. Consequently, more genes are usually identified as necessary for prediction. We propose a general scheme for finding a small set of biomarkers to design a diagnostic system for accurate classification of the cancer subgroups. We use multilayer networks with online gene selection ability and relational fuzzy clustering to identify a small set of biomarkers for accurate classification of the training and blind test cases of a well studied data set. RESULTS: Our method discerned just seven biomarkers that precisely categorized the four subgroups of cancer both in training and blind samples. For the same problem, others suggested 19–94 genes. These seven biomarkers include three novel genes (NAB2, LSP1 and EHD1 – not identified by others) with distinct class-specific signatures and important role in cancer biology, including cellular proliferation, transendothelial migration and trafficking of MHC class antigens. Interestingly, NAB2 is downregulated in other tumors including Non-Hodgkin lymphoma and Neuroblastoma but we observed moderate to high upregulation in a few cases of Ewing sarcoma and Rabhdomyosarcoma, suggesting that NAB2 might be mutated in these tumors. These genes can discover the subgroups correctly with unsupervised learning, can differentiate non-SRBCT samples and they perform equally well with other machine learning tools including support vector machines. These biomarkers lead to four simple human interpretable rules for the diagnostic task. CONCLUSION: Although the proposed method is tested on a SRBCT data set, it is quite general and can be applied to other cancer data sets. Our scheme takes into account the interaction between genes as well as that between genes and the tool and thus is able find a very small set and can discover novel genes. Our findings suggest the possibility of developing specialized microarray chips or use of real-time qPCR assays or antibody based methods such as ELISA and western blot analysis for an easy and low cost diagnosis of the subgroups

Discovery of Protein Phosphorylation Motifs through Exploratory Data Analysis

BACKGROUND: The need for efficient algorithms to uncover biologically relevant phosphorylation motifs has become very important with rapid expansion of the proteomic sequence database along with a plethora of new information on phosphorylation sites. Here we present a novel unsupervised method, called Motif Finder (in short, F-Motif) for identification of phosphorylation motifs. F-Motif uses clustering of sequence information represented by numerical features that exploit the statistical information hidden in some foreground data. Furthermore, these identified motifs are then filtered to find "actual" motifs with statistically significant motif scores. RESULTS AND DISCUSSION: We have applied F-Motif to several new and existing data sets and compared its performance with two well known state-of-the-art methods. In almost all cases F-Motif could identify all statistically significant motifs extracted by the state-of-the-art methods. More importantly, in addition to this, F-Motif uncovers several novel motifs. We have demonstrated using clues from the literature that most of these new motifs discovered by F-Motif are indeed novel. We have also found some interesting phenomena. For example, for CK2 kinase, the conserved sites appear only on the right side of S. However, for CDK kinase, the adjacent site on the right of S is conserved with residue P. In addition, three different encoding methods, including a novel position contrast matrix (PCM) and the simplest binary coding, are used and the ability of F-motif to discover motifs remains quite robust with respect to encoding schemes. CONCLUSIONS: An iterative algorithm proposed here uses exploratory data analysis to discover motifs from phosphorylated data. The effectiveness of F-Motif has been demonstrated using several real data sets as well as using a synthetic data set. The method is quite general in nature and can be used to find other types of motifs also. We have also provided a server for F-Motif at http://f-motif.classcloud.org/, http://bio.classcloud.org/f-motif/ or http://ymu.classcloud.org/f-motif/

Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases

Identification of molecular descriptors for design of novel Isoalloxazine derivatives as potential Acetylcholinesterase inhibitors against Alzheimer’s disease

<p>In Alzheimer’s disease (AD), the level of Acetylcholine (ACh) neurotransmitter is reduced. Since Acetylcholinesterase (AChE) cleaves ACh, inhibitors of AChE are very much sought after for AD treatment. The side effects of current inhibitors necessitate development of newer AChE inhibitors. Isoalloxazine derivatives have proved to be promising (AChE) inhibitors. However, their structure–activity relationship studies have not been reported till date. In the present work, various quantitative structure–activity relationship (QSAR) building methods such as multiple linear regression (MLR), partial least squares ,and principal component regression were employed to derive 3D-QSAR models using steric and electrostatic field descriptors. Statistically significant model was obtained using MLR coupled with stepwise selection method having <i>r</i>² = .9405, cross validated <i>r</i>² (<i>q</i>²) = .6683, and a high predictability (pred_<i>r</i>² = .6206 and standard error, pred_<i>r</i>²se = .2491). Steric and electrostatic contribution plot revealed three electrostatic fields E_496, E_386 and E_577 and one steric field S_60 contributing towards biological activity. A ligand-based 3D-pharmacophore model was generated consisting of eight pharmacophore features. Isoalloxazine derivatives were docked against human AChE, which revealed critical residues implicated in hydrogen bonds as well as hydrophobic interactions. The binding modes of docked complexes (AChE_IA1 and AChE_IA14) were validated by molecular dynamics simulation which showed their stable trajectories in terms of root mean square deviation and molecular mechanics/Poisson–Boltzmann surface area binding free energy analysis revealed key residues contributing significantly to overall binding energy. The present study may be useful in the design of more potent Isoalloxazine derivatives as AChE inhibitors.</p

The juvenile myoclonic epilepsy-related protein EFHC1 interacts with the redox-sensitive TRPM2 channel linked to cell death.

The transient receptor potential M2 channel (TRPM2) is the Ca(2+)-permeable cation channel controlled by cellular redox status via β-NAD(+) and ADP-ribose (ADPR). TRPM2 activity has been reported to underlie susceptibility to cell death and biological processes such as inflammatory cell migration and insulin secretion. However, little is known about the intracellular mechanisms that regulate oxidative stress-induced cell death via TRPM2. We report here a molecular and functional interaction between the TRPM2 channel and EF-hand motif-containing protein EFHC1, whose mutation causes juvenile myoclonic epilepsy (JME) via mechanisms including neuronal apoptosis. In situ hybridization analysis demonstrates TRPM2 and EFHC1 are coexpressed in hippocampal neurons and ventricle cells, while immunoprecipitation analysis demonstrates physical interaction of the N- and C-terminal cytoplasmic regions of TRPM2 with the EFHC1 protein. Coexpression of EFHC1 significantly potentiates hydrogen peroxide (H(2)O(2))- and ADPR-induced Ca(2+) responses and cationic currents via recombinant TRPM2 in HEK293 cells. Furthermore, EFHC1 enhances TRPM2-conferred susceptibility of HEK293 cells to H(2)O(2)-induced cell death, which is reversed by JME mutations. These results reveal a positive regulatory action of EFHC1 on TRPM2 activity, suggesting that TRPM2 contributes to the expression of JME phenotypes by mediating disruptive effects of JME mutations of EFHC1 on biological processes including cell death

Ruthenium(II) Complex-Based Luminescent Bifunctional Probe for Ag⁺ and Phosphate Ions: Ag⁺‑Assisted Detection and Imaging of rRNA

A new <i>bis</i>-heteroleptic Ru­(II) complex (<b>1</b>) of benzimidazole-substituted 1,2,3-triazole pyridine ligand has been designed and constructed for the photoluminescent detection of cationic and anionic analytes, Ag⁺ and phosphate ions. Compound, <b>1­[PF</b>_<b>6</b><b>]</b>_<b>2</b> was fully characterized by various spectroscopic techniques and the solid-state structure was determined via single-crystal X-ray diffraction. The cation and anion sensing properties in 50% aqueous buffer (pH 9.2) and pure acetonitrile were carefully examined in photoluminescence (PL) spectroscopy. The <b>1­[PF</b>_<b>6</b><b>]</b>_<b>2</b> was found to be highly selective to pyrophosphate; PPi/HP₂O₇^3– and H₂PO₄^– ions in CH₃CN. It showed ∼10-fold PL intensity enhancement at 583 nm in the presence of only 1 and 2 equiv of PPi and H₂PO₄^– ions, respectively. The PL titrations of <b>1­[PF</b>_<b>6</b><b>]</b>_<b>2</b> with PPi and H₂PO₄^– in CH₃CN furnished the association constant (<i>K</i>_a = 3.3 × 10³ M^–1 and 6.8 × 10³ M^–1) and the detection limit was as low as 5.73 and 5.19 ppb, respectively. The <b>1­[PF</b>_<b>6</b><b>]</b>_<b>2</b> also selectively detected Ag⁺ over other competitive cations through the luminescence light up in 50% aqueous buffer (pH 9.2) media. The PL titration of <b>1­[PF</b>_<b>6</b><b>]</b>_<b>2</b> with Ag⁺ showed ∼8-fold luminescence enhancement at 591 nm and yielded association constant, <i>K</i>_a = 3.5 × 10⁴ M^–1 and the detection limit was determined to be 5.05 ppb. A new cation sensing mechanism has been established where the Ag⁺ ion is detected in photoluminescence spectroscopy through the <i>unique</i> cyclometalated Ag⁺-triazolide complex formation. The high selectivity of <b>1­[PF</b>_<b>6</b><b>]</b>_<b>2</b> for phosphates and Ag⁺ was established by PL in the presence of various competing ions. Finally, for biological application, the cytotoxicity study was performed. The probe showed low cytotoxicity and was suitable for intracellular Ag⁺ imaging. The cell imaging and <i>in vitro</i> photoluminescence study revealed that the probe stained the cell nucleoli and specifically bind with ribosomal RNA (rRNA) and, therefore, it can also serve as a luminescent probe for rRNA in the presence of Ag⁺

Unleashing the binding interaction of chrysin-Cu(II) complex with the biomacromolecular targets: further studies of cell cytotoxicity and radical scavenging properties

Flavonoids are significant dietary components and have ability to coordinate with metal ions to produce novel drug discovery leads that are superior to those of the parent flavonoids. Here, in this report, we have synthesized chrysin-Cu(II) complex (as per reported article) and characterized it further with different analytical techniques. The synthesized complex was evaluated for radical scavenging and cell cytotoxicity studies where it exhibited enhanced activity as compared to bare chrysin. The interaction studies of the complex with ct-DNA (Kb ⁓ 105 M−1), human serum albumin (HSA) and ovalbumin (Kb ⁓ 104 M−1) were evaluated using multi-spectroscopic and molecular docking studies. Groove binding mode with ct-DNA was observed as confirmed from competitive displacement studies, viscosity measurement, melting temperature estimation and docking analyses. The complex exhibited comparatively higher affinity towards ct-DNA which indicated it efficient transportation by the carrier proteins and controlled release in the target DNA. Communicated by Ramaswamy H. Sarma</p

Novel coumarin derivatives as potent acetylcholinesterase inhibitors: insight into efficacy, mode and site of inhibition

<p>The inhibitory efficacy of two substituted coumarin derivatives on the activity of neurodegenerative enzyme acetylcholinesterase (AChE) was assessed in aqueous buffer as well as in the presence of human serum albumin (HSA) and compared against standard cholinergic AD drug, Donepezil (DON). The experimental data revealed the inhibition to be of non-competitive type with both the systems showing substantial inhibitory activity on AChE. In fact, one of the tested compounds Chromenyl Coumarate (CC) was found to be better inhibitor (IC₅₀ = 48.49 ± 5.6 nM) than the reference drug DON (IC₅₀ = 74.13 ± 8.3 nM), unequivocally amplifying its importance. The structure of the compound was found to play a vital role in the inhibitory efficiency, validating previous Structure Activity Relationship (SAR) reviews for coumarin. The mechanism of inhibition remained impervious when the experimental medium was switched from aqueous buffer to HSA, albeit noticeable change in the inhibition potency of the compound 3, 3′- Methylene-bis (4-hydroxy coumarin) (MHC) (38%) and CC (35%). Both the coumarin derivatives were observed to bind to the peripheral anionic site (PAS) of AChE and also found to displace the fluorescence marker thioflavinT (ThT) from AChE binding pocket. All experimental observations were seconded by molecular docking and MD simulation results. The inferences drawn in this study form a foundation for further investigation on these compounds; magnifying the probability of their usage as AD drugs and re-emphasizes the significance of drug delivery media while considering the inhibition potencies of targeted drugs.</p