A novel harmony search-K means hybrid algorithm for clustering gene expression data

Recent progress in bioinformatics research has led to the accumulation of huge quantities of biological data at various data sources. The DNA microarray technology makes it possible to simultaneously analyze large number of genes across different samples. Clustering of microarray data can reveal the hidden gene expression patterns from large quantities of expression data that in turn offers tremendous possibilities in functional genomics, comparative genomics, disease diagnosis and drug development. The k- ¬means clustering algorithm is widely used for many practical applications. But the original k-¬means algorithm has several drawbacks. It is computationally expensive and generates locally optimal solutions based on the random choice of the initial centroids. Several methods have been proposed in the literature for improving the performance of the k-¬means algorithm. A meta-heuristic optimization algorithm named harmony search helps find out near-global optimal solutions by searching the entire solution space. Low clustering accuracy of the existing algorithms limits their use in many crucial applications of life sciences. In this paper we propose a novel Harmony Search-K means Hybrid (HSKH) algorithm for clustering the gene expression data. Experimental results show that the proposed algorithm produces clusters with better accuracy in comparison with the existing algorithms.     

Peptide fingerprinting of Alzheimer's disease in cerebrospinal fluid: identification and prospective evaluation of new synaptic biomarkers

Background

Today, dementias are diagnosed late in the course of disease. Future treatments have to start earlier in the disease process to avoid disability requiring new diagnostic tools. The objective of this study is to develop a new method for the differential diagnosis and identification of new biomarkers of Alzheimer''s disease (AD) using capillary-electrophoresis coupled to mass-spectrometry (CE-MS) and to assess the potential of early diagnosis of AD.

Methods and Findings

Cerebrospinal fluid (CSF) of 159 out-patients of a memory-clinic at a University Hospital suffering from neurodegenerative disorders and 17 cognitively-healthy controls was used to create differential peptide pattern for dementias and prospective blinded-comparison of sensitivity and specificity for AD diagnosis against the Criterion standard in a naturalistic prospective sample of patients. Sensitivity and specificity of the new method compared to standard diagnostic procedures and identification of new putative biomarkers for AD was the main outcome measure. CE-MS was used to reliably detect 1104 low-molecular-weight peptides in CSF. Training-sets of patients with clinically secured sporadic Alzheimer''s disease, frontotemporal dementia, and cognitively healthy controls allowed establishing discriminative biomarker pattern for diagnosis of AD. This pattern was already detectable in patients with mild cognitive impairment (MCI). The AD-pattern was tested in a prospective sample of patients (n = 100) and AD was diagnosed with a sensitivity of 87% and a specificity of 83%. Using CSF measurements of beta-amyloid1-42, total-tau, and phospho₁₈₁-tau, AD-diagnosis had a sensitivity of 88% and a specificity of 67% in the same sample. Sequence analysis of the discriminating biomarkers identified fragments of synaptic proteins like proSAAS, apolipoprotein J, neurosecretory protein VGF, phospholemman, and chromogranin A.

Conclusions

The method may allow early differential diagnosis of various dementias using specific peptide fingerprints and identification of incipient AD in patients suffering from MCI. Identified biomarkers facilitate face validity for the use in AD diagnosis.     

The Clavibacter michiganensis subsp. michiganensis-tomato interactome reveals the perception of pathogen by the host and suggests mechanisms of infection

The Gram-positive bacterium Clavibacter michiganensis subsp. michiganensis (Cmm) causes wilt and canker disease of tomato (Solanum lycopersicum). Mechanisms of Cmm pathogenicity and tomato response to Cmm infection are not well understood. To explore the interaction between Cmm and tomato, multidimensional protein identification technology (MudPIT) and tandem mass spectrometry were used to analyze in vitro and in planta generated samples. The results show that during infection Cmm senses the plant environment, transmits signals, induces, and then secretes multiple hydrolytic enzymes, including serine proteases of the Pat-1, Ppa, and Sbt familes, the CelA, XysA, and NagA glycosyl hydrolases, and other cell wall-degrading enzymes. Tomato induction of pathogenesis-related (PR) proteins, LOX1, and other defense-related proteins during infection indicates that the plant senses the invading bacterium and mounts a basal defense response, although partial with some suppressed components including class III peroxidases and a secreted serine peptidase. The tomato ethylene-synthesizing enzyme ACC-oxidase was induced during infection with the wild-type Cmm but not during infection with an endophytic Cmm strain, identifying Cmm-triggered host synthesis of ethylene as an important factor in disease symptom development. The proteomic data were also used to improve Cmm genome annotation, and thousands of Cmm gene models were confirmed or expanded.     

Handling Permutation in Sequence Comparison: Genome-Wide Enhancer Prediction in Vertebrates by a Novel Non-Linear Alignment Scoring Principle

Enhancers have been described to evolve by permutation without changing function. This has posed the problem of how to predict enhancer elements that are hidden from alignment-based approaches due to the loss of co-linearity. Alignment-free algorithms have been proposed as one possible solution. However, this approach is hampered by several problems inherent to its underlying working principle. Here we present a new approach, which combines the power of alignment and alignment-free techniques into one algorithm. It allows the prediction of enhancers based on the query and target sequence only, no matter whether the regulatory logic is co-linear or reshuffled. To test our novel approach, we employ it for the prediction of enhancers across the evolutionary distance of ~450Myr between human and medaka. We demonstrate its efficacy by subsequent in vivo validation resulting in 82% (9/11) of the predicted medaka regions showing reporter activity. These include five candidates with partially co-linear and four with reshuffled motif patterns. Orthology in flanking genes and conservation of the detected co-linear motifs indicates that those candidates are likely functionally equivalent enhancers. In sum, our results demonstrate that the proposed principle successfully predicts mutated as well as permuted enhancer regions at an encouragingly high rate.     

A computational approach to characterization of bovine sperm chromatin alterations

We describe here a computational morphology-based approach to the investigation of possible causes of chromatin alterations in sperm. A comprehensive set of state-of-the-art and geometric measures are computationally extracted from toluidine blue stained images and analyzed to infer the possible processes leading to normal and abnormal chromatin formation while seeking a possible taxonomy of chromatin alterations and their influence on sperm head morphology. Using this methodology, we have identified higher chromatin fragility at some specific points of the sperm head. Despite the lack of correlation between morphologies of sperm head and chromatin structure, four main morphological types of chromatin alterations in bull spermatozoa have been identified and their possible causes discussed.     

Epigenetic regulation of insulin-like growth factor binding protein-3 (IGFBP-3) in cancer

Epigenetics refers to heritable changes in gene expression that are independent of alterations in DNA sequence. It is now accepted that disruption of epigenetic mechanisms plays a key role in the pathogenesis of cancer: culminating in altered gene function and malignant cellular transformation. DNA methylation and histone modifications are the most widely studied changes but non-coding RNAs such as miRNAs are also considered part of the epigenetic machinery. The insulin-like growth factor (IGF) axis is composed of two ligands, IGF-I and –II, their receptors and six high affinity IGF binding proteins (IGFBPs). The IGF axis plays a key role in cancer development and progression. As IGFBP genes have consistently been identified among the most common to be aberrantly altered in tumours, this review will focus on epigenetic regulation of IGFBP-3 in cancer for which the majority of evidence has been obtained.