Nighat Noureen

The genomic landscape of the somatic linker histone subtypes H1.1 to H1.5 in human cells.

Human cells contain five canonical, replication-dependent somatic histone H1 subtypes (H1.1, H1.2, H1.3, H1.4, and H1.5). Although they are key chromatin components, the genomic distribution of the H1 subtypes is still unknown, and their role in chromatin processes has thus far remained elusive. Here, we map the genomic localization of all somatic replication-dependent H1 subtypes in human lung fibroblasts using an integrative DNA adenine methyltransferase identification (DamID) analysis. We find in general that H1.2 to H1.5 are depleted from CpG-dense regions and active regulatory regions. H1.1 shows a DamID binding profile distinct from the other subtypes, suggesting a unique function. H1 subtypes can mark specific domains and repressive regions, pointing toward a role for H1 in three-dimensional genome organization. Our work integrates H1 subtypes into the epigenome maps of human cells and provides a valuable resource to refine our understanding of the significance of H1 and its heterogeneity in the control of genome function.

Identification of type-specific anticancer histone deacetylase inhibitors: road to success

Cancer is a serious and life-eliminating disease. Majority of anticancer agents are non-selective. Along with the cancerous cells they also target the normal ones. An important aspect is to hit the developing mechanism of the tumor, which is highlighted by in silico drug designing. On the basis of novel molecular targets, in silico (computational approach) drug discovery has emerged as today’s need. Histone deacetylases are an important therapeutic target for many human cancers. The first and only approved (in 2006) histone deacetylase inhibitors (HDACIs) is Zolinza. Depending on the types of the histone deacetylase (HDAC) enzymes, discovery of type-specific inhibitors is important. With continued research and development, in near future HDACIs are likely to figure prominently in cancer treatment plans. This review presents the overview of HDACs, their role in cancer, their structural classes, activity, catalytic domain and the inhibitors of HDACs for cancer therapy. Also it helps in understanding the open directions in this area of research and highlights the importance of computational approaches in discovering specific drugs for cancer therapy.

Antibiotic susceptibility profiling and virulence potential of Campylobacter jejuni isolates from different sources in Pakistan

OBJECTIVE To determine antibiotic resistance patterns and virulence potential of Campylobacter jejuni (C. jejuni) isolates from clinical human diarrheal infections, cattle and healthy broilers. METHODS Antibiotic sensitivity patterns of C. jejuni isolates were determined by Kirby Bauer Disc Diffusion assay. These isolates were then subjected to virulence profiling for the detection of mapA (membrane-associated protein), cadF (fibronectin binding protein), wlaN (beta-l,3-galactosyltransferase) and neuAB (sialic acid biosynthesis gene). Further C. jejuni isolates were grouped by random amplification of polymorphic DNA (RAPD) profiling. RESULTS A total of 436 samples from poultry (n=88), cattle (n=216) and humans (n=132) from different locations were collected. Results revealed percentage of C. jejuni isolates were 35.2% (31/88), 25.0% (54/216) and 11.3% (15/132) among poultry, cattle and clinical human samples respectively. Antibiotic susceptibility results showed that similar resistance patterns to cephalothin was ie. 87.0%, 87.1% and 89%among humans, poultry and cattle respectively, followed by sulfamethoxazole+trimethoprim 40.0%, 38.7% and 31.0% in humans, poultry and cattle and Ampicillin 40%, 32% and 20% in humans, poultry and cattle respectively. Beta-lactamase activity was detected in 40.00% humans, 20.37% cattle and 32.25% in poultry C. jejuni isolates. CadF and mapA were present in all poultry, cattle and human C. jejuni isolates, wlaN was not detected in any isolate and neuAB was found in 9/31 (36%) poultry isolates. RAPD profiling results suggested high diversity of C. jejuni isolates. CONCLUSIONS Detection of multidrug resistant C. jejuni strains from poultry and cattle is alarming as they can be potential hazard to humans. Moreover, predominant association of virulence factors, cadF and mapA (100% each) in C. jejuni isolates from all sources and neuAB (36%) with poultry isolates suggest the potential source of transmission of diverse types of C. jejuni to humans.

ChromClust: A semi-supervised chromatin clustering toolkit for mining histone modifications interplay.

Mining patterns of histone modifications interplay from epigenomic profiles are one of the leading research areas these days. Various methods based on clustering approaches and hidden Markov models have been presented so far with some limitations. Here we present ChromClust, a semi-supervised clustering tool for mining commonly occurring histone modifications at various locations of the genome. Applying our method to 11 chromatin marks in nine human cell types recovered 11 clusters based on distinct chromatin signatures mapping to various elements of the genome. Our approach is efficient in respect to time and space usage along with the added facility of maintaining database at the backend. It outperforms the existing methods with respect to mining patterns in a semi-supervised fashion mapping to various functional elements of the genome. It will aid in future by saving the resources of time and space along with efficiently retrieving the hidden interplay of histone combinations.

An efficient anticancer histone deacetylase inhibitor and its analogues for human HDAC8

Histone deacetylase inhibitors (HDACIs) have emerged as efficient chemotherapeutic agents. Molecular docking studies of hydroxamtes, biphenyl and benzamide derivatives using Human HDAC8 with pdb id: 1T69 have been carried out in order to find the most active anticancer HDACI. AutoDock 4.0 has been used for docking. The most active lead compound has been identified on the basis of strong interactions and IC50 value from the chosen compounds. Five structural analogues have also been designed from the active lead compound.

Draft Genome Sequence of the Enteropathogenic Bacterium Campylobacter jejuni Strain cj255

ABSTRACT The enteropathogen Campylobacter jejuni is a global health disaster, being one of the leading causes of bacterial gastroenteritis. Here, we present the draft genome sequence of C. jejuni strain cj255, isolated from a chicken source in Islamabad, Pakistan. The draft genome sequence will aid in epidemiological studies and quarantine of this broad-host-range pathogen.

BiSim: A Simple and Efficient Biclustering Algorithm

Analysis of gene expression data includes classification of the data into groups and subgroups based on similar expression patterns. Standard clustering methods for the analysis of gene expression data only identifies the global models while missing the local expression patterns. In order to identify the missed patterns biclustering approach has been introduced. Various biclustering algorithms have been proposed by scientists. Among them Binary Inclusion maximal algorithm (BiMax) forms biclusters when applied on a gene expression data through divide and conquer approach. The worst-case running-time complexity of BiMax for matrices containing disjoint biclusters is O(nmb) and for arbitrary matrices is of order O(nmb min{n, m}) where b is the number of all inclusion-maximal biclusters in matrix. In this paper we present an improved algorithm, BiSim, for biclustering which is easy and avoids complex computations as in BiMax. The complexity of our approach is O(n*m) for n genes and m conditions, i.e, a matrix of size n*m. Also it avoids extra computations within the same complexity class and avoids missing of any biclusters.

Functional and promoter enrichment based analysis of biclustering algorithms using gene expression data of yeast

Biclustering algorithms are an important tool for the analysis of gene expression data. Research on analysis of gene expression data includes identification of groups of genes with similar expression patterns. Standard clustering methods for the analysis of gene expression data only identify the global similarity while missing the local patterns of expression similarity, i.e. genes could behave similar over only a subset of the observations. In order to identify such patterns biclustering approaches have been introduced. This paper compares the performance of five important biclustering methods by applying them on a gene expression dataset of yeast. The dataset of Saccharomyces cerevisiae comprised of 5736 genes expressed in 112 strains of yeast. The performance is scored based on the algorithms ability to find functionally enriched as well as transcription factor target site enriched groups of genes. Our studies shows that among the chosen five biclustering algorithms SAMBA and ISA showed the best performance on the basis of functional enrichment. Biclusters were also obtained through remaining three algorithms also but they were not functionally enriched.

Decoding Common Features of Neurodegenerative Disorders: From Differentially Expressed Genes to Pathways

Background: Neurodegeneration is a progressive/irreversible loss of neurons, building blocks of our nervous system. Their degeneration gradually collapses the entire structural and functional system manifesting in myriads of clinical disorders categorized as Neurodegenerative Disorders (NDs) such as Alzheimer’s Disease, (AD), Parkinson’s Disease (PD), Frontotemporal Dementia (FTD) and Amyotrophic Lateral Sclerosis (ALS). NDs are characterized by a puzzling interplay of molecular and cellular defects affecting subset of neuronal populations in specific affected brain areas. Objective: In present study, comparative in silico analysis was performed by utilizing gene expression datasets of AD, PD, FTD and ALS to identify potential common features to gain insights into complex molecular pathophysiology of the selected NDs. Methods: Gene expression data of four disorders were subjected to the identification of Differential Gene Expression (DEG) and their mapping on biological processes, KEGG pathways and molecular functions. Detailed comparative analysis was performed to highlight the common grounds of these dis-orders at various stages. Results: Astoundingly, 106 DEGs were found to be common across all disorders. Alongwith in total 100 GO terms and 7 KEGG pathways were found to be significantly enriched across all disorders. EGFR, CDC42 and CREBBP have been identified as the significantly interacting nodes in gene-gene in-teraction and in Protein-Protein Interaction (PPI) network as well. Furthermore, interaction of common DEGs targets with miRNA’s has been scrutinized. Conclusion: The complex molecular underpinnings of these disorders are currently elusive. Despite heterogeneous clinical and pathological expressions, common features have been recognized in many NDs which provide evidence of their convergence.

ChromBiSim: Interactive chromatin biclustering using a simple approach

Combinatorial patterns of histone modifications sketch the epigenomic locale. Specific positions of these modifications in the genome are marked by the presence of such signals. Various methods highlight such patterns on global scale hence missing the local patterns which are the actual hidden combinatorics. We present ChromBiSim, an interactive tool for mining subsets of modifications from epigenomic profiles. ChromBiSim efficiently extracts biclusters with their genomic locations. It is the very first user interface based and multiple cell type handling tool for decoding the interplay of subsets of histone modifications combinations along their genomic locations. It displays the results in the forms of charts and heat maps in accordance with saving them in files which could be used for post analysis. ChromBiSim tested on multiple cell types produced in total 803 combinatorial patterns. It could be used to highlight variations among diseased versus normal cell types of any species. AVAILABILITY ChromBiSim is available at (http://sourceforge.net/projects/chrombisim) in C-sharp and python languages.