Ayaz Ahmad

Decoding the Epigenetic Language of Plant Development

Epigenetics refers to the study of heritable changes in gene expression or cellular phenotype without changes in DNA sequence. Epigenetic regulation of gene expression is accomplished by DNA methylation, histone modifications, histone variants, chromatin remodeling, and may involve small RNAs. DNA methylation at cytosine is carried out by enzymes called DNA Methyltransferases and is involved in many cellular processes, such as silencing of transposable elements and pericentromeric repeats, X-chromosome inactivation and genomic imprinting, etc. Histone modifications refer to posttranslational covalent attachment of chemical groups onto histones such as phosphorylation, acetylation, and methylation, etc. Histone variants, the non-canonical histones with amino acid sequences divergent from canonical histones, can have different epigenetic impacts on the genome from canonical histones. Higher-order chromatin structures maintained or modified by chromatin remodeling proteins also play important roles in regulating gene expression. Small non-coding RNAs play various roles in the regulation of gene expression at pre- as well as posttranscriptional levels. A special issue of Molecular Plant on ‘Epigenetics and Plant Development’ (Volume 4, Number 2, 2009) published a variety of articles covering many aspects of epigenetic regulation of plant development. We have tried here to present a birds-eye view of these credible efforts towards understanding the mysterious world of epigenetics. The majority of the articles are about the chromatin modifying proteins, including histone modifiers, histone variants, and chromatin remodeling proteins that regulate various developmental processes, such as flowering time, vernalization, stem cell maintenance, and response to hormonal and environmental stresses, etc. Regulation of expression of seed transcriptome, involvement of direct tandem repeat elements in the PHE1 imprinting in addition to PcG proteins activity, paramutation, and epigenetic barriers in species hybridization are described well. The last two papers are about the Pol V-mediated heterochromatin formation independent of the 24nt-siRNA and the effect of genome position and tissue type on epigenetic regulation of gene expression. These findings not only further our current understanding of epigenetic mechanisms involved in many biological phenomena, but also pave the path for the future work, by raising many new questions that are discussed in the following lines.

Arabidopsis protein arginine methyltransferase 3 is required for ribosome biogenesis by affecting precursor ribosomal RNA processing

Significance The functional relationship between protein arginine methyltransferases (PRMTs) and ribosome biogenesis was proposed a decade ago, but the underlying mechanism still remains elusive. In this work, we demonstrate for the first time to our knowledge that the coexistence of two pre-rRNA processing pathways is conserved in plants; and Arabidopsis PRMT3 is required for the balance between these pathways. These findings uncover an important link between PRMT and proper pre-rRNA processing, which not only extends our understanding of the regulatory scope of PRMTs but also reveals the complexity of regulation of ribosome biogenesis. The alternative pre-rRNA processing pathways in plants modulated by PRMT3 may be conserved in other multicellular organisms, thereby shedding light on PRMT functions and regulation of ribosome biogenesis in animals. Ribosome biogenesis is a fundamental and tightly regulated cellular process, including synthesis, processing, and assembly of rRNAs with ribosomal proteins. Protein arginine methyltransferases (PRMTs) have been implicated in many important biological processes, such as ribosome biogenesis. Two alternative precursor rRNA (pre-rRNA) processing pathways coexist in yeast and mammals; however, how PRMT affects ribosome biogenesis remains largely unknown. Here we show that Arabidopsis PRMT3 (AtPRMT3) is required for ribosome biogenesis by affecting pre-rRNA processing. Disruption of AtPRMT3 results in pleiotropic developmental defects, imbalanced polyribosome profiles, and aberrant pre-rRNA processing. We further identify an alternative pre-rRNA processing pathway in Arabidopsis and demonstrate that AtPRMT3 is required for the balance of these two pathways to promote normal growth and development. Our work uncovers a previously unidentified function of PRMT in posttranscriptional regulation of rRNA, revealing an extra layer of complexity in the regulation of ribosome biogenesis.

Gain-of-function of mutant p53: mutant p53 enhances cancer progression by inhibiting KLF17 expression in invasive breast carcinoma cells.

Kruppel-like-factor 17 (KLF17) is a negative regulator of metastasis and epithelial-mesenchymal-transition (EMT). However, its expression is downregulated in metastatic breast cancer that contains p53 mutations. Here, we show that mutant-p53 plays a key role to suppress KLF17 and thereby enhances cancer progression, which defines novel gain-of-function (GOF) of mutant-p53. Mutant-p53 interacts with KLF17 and antagonizes KLF17 mediated EMT genes transcription. Depletion of KLF17 promotes cell viability, decreases apoptosis and induces drug resistance in metastatic breast cancer cells. KLF17 suppresses cell migration and invasion by decreasing CD44, PAI-1 and Cyclin-D1 expressions. Taken together, our results show that KLF17 is important for the suppression of metastasis and could be a potential therapeutic target during chemotherapy.

Plant PRMTs broaden the scope of arginine methylation.

Post-translational methylation at arginine residues is one of the most important covalent modifications of proteins, involved in a myriad of essential cellular processes in eukaryotes, such as transcriptional regulation, RNA processing, signal transduction, and DNA repair. Methylation at arginine residues is catalyzed by a family of enzymes called protein arginine methyltransferases (PRMTs). PRMTs have been extensively studied in various taxa and there is a growing tendency to unveil their functional importance in plants. Recent studies in plants revealed that this evolutionarily conserved family of enzymes regulates essential traits including vegetative growth, flowering time, circadian cycle, and response to high medium salinity and ABA. In this review, we highlight recent advances in the field of post-translational arginine methylation with special emphasis on the roles and future prospects of this modification in plants.

Genotyping of HCV RNA Reveals That 3a Is the Most Prevalent Genotype in Mardan, Pakistan

The clinical outcomes of patients infected with hepatitis C virus (HCV) range from acute resolving hepatitis to chronic liver diseases such as liver cirrhosis or hepatocellular carcinoma. Identification of the infecting virus genotype is indispensable for the exploration of many aspects of HCV infection, including epidemiology, pathogenesis, and response to antiviral therapy. 1419 individuals were screened for anti-HCV in this study, of which 166 (11.7%) were found reactive by ICT (Immunochromatographic test). These 166 anti-HCV positive and 26 normal individuals were further analyzed. RNA was extracted from serum and reverse-transcribed to cDNA and the core region of HCV genome was targeted and amplified by multiplex PCR. HCV RNA was detected in 121 individuals, of which 87 were male and 34 were female. Genotype 3a was the most prevalent among all the genotypes observed followed by 3b. Genotypes 1a, 2a, and 2b were found in 10.89%, 13.22%, and 6.61% patients, respectively. 25.41% of the HCV RNA positive samples were not typed. 6.05% of patients were found having mixed genotypes. These findings will not only help the physicians to prescribe more appropriate treatment for the HCV infection but will also draw the attention of health-related policy makers to devise strategies to curb the disease more effectively.

Characterization of the PRMT Gene Family in Rice Reveals Conservation of Arginine Methylation

Post-translational methylation of arginine residues profoundly affects the structure and functions of protein and, hence, implicated in a myriad of essential cellular processes such as signal transduction, mRNA splicing and transcriptional regulation. Protein arginine methyltransferases (PRMTs), the enzymes catalyzing arginine methylation have been extensively studied in animals, yeast and, to some extent, in model plant Arabidopsis thaliana. Eight genes coding for the PRMTs were identified in Oryza sativa, previously. Here, we report that these genes show distinct expression patterns in various parts of the plant. In vivo targeting experiment demonstrated that GFP-tagged OsPRMT1, OsPRMT5 and OsPRMT10 were localized to both the cytoplasm and nucleus, whereas OsPRMT6a and OsPRMT6b were predominantly localized to the nucleus. OsPRMT1, OsPRMT4, OsPRMT5, OsPRMT6a, OsPRMT6b and OsPRMT10 exhibited in vitro arginine methyltransferase activity against myelin basic protein, glycine-arginine-rich domain of fibrillarin and calf thymus core histones. Furthermore, they depicted specificities for the arginine residues in histones H3 and H4 and were classified into type I and Type II PRMTs, based on the formation of type of dimethylarginine in the substrate proteins. The two homologs of OsPRMT6 showed direct interaction in vitro and further titrating different amounts of these proteins in the methyltransferase assay revealed that OsPRMT6a inhibits the methyltransferase activity of OsPRMT6b, probably, by the formation of heterodimer. The identification and characterization of PRMTs in rice suggests the conservation of arginine methylation in monocots and hold promise for gaining further insight into regulation of plant development.

Extension of SMAC scheme for variable density flows under strong temperature gradient

An extension of SMAC scheme is proposed for variable density flows under low Mach number approximation. The algorithm is based on a predictor-corrector time integration scheme that employs a projection method for the momentum equation. A constant-coefficient Poisson equation is solved for the pressure following both the predictor and corrector steps to satisfy the continuity equation at each time step. Spatial discretization is performed on a collocated grid system that offers computational simplicity and straight forward extension to curvilinear coordinate systems. To avoid the pressure odd-even decoupling that is typically encountered in such grids, a flux interpolation technique is introduced for the equations governing variable density flows. An important characteristic of the proposed algorithm is that it can be applied to flows in both open and closed domains. Its robustness and accuracy are illustrated with a non-isothermal, turbulent channel flow at temperature ratio of 1.01 and 2.

Tumor suppressive p53 signaling empowers metastatic inhibitor KLF17-dependent transcription to overcome tumorigenesis in non-small cell lung cancer.

Background: How Krüppel-like factor 17 (KLF17) controls metastasis and epithelial-mesenchymal transition (EMT) during cancer progression remains unknown. Results: Tumor-suppressive p53 signaling is critical for KLF17 to inhibit cancer metastasis in NSCLC. Conclusion: These results indicate novel insights into the anti-EMT effect of KLF17 via p53-dependent pathway. Significance: Targeting KLF17 for cancer therapy may be applicable to NSCLC tumors with TP53 status, which may improve the prognosis of NSCLC patients. Metastasis, which is controlled by concerted action of multiple genes, is a complex process and is an important cause of cancer death. Krüppel-like factor 17 (KLF17) is a negative regulator of metastasis and epithelial-mesenchymal transition (EMT) during cancer progression. However, the underlying molecular mechanism and biological relevance of KLF17 in cancer cells are poorly understood. Here, we show that tumor suppressor protein p53 plays an integral role to induce KLF17 expression in non-small cell lung cancer (NSCLC). p53 is recruited to the KLF17 promoter and results in the formation of p53-DNA complex. p53 enhances binding of p300 and favors histone acetylation on the KLF17 promoter. Mechanistically, p53 physically interacts with KLF17 and thereby enhances the anti-metastatic function of KLF17. p53 empowers KLF17-mediated EMT genes transcription via enhancing physical association of KLF17 with target gene promoters. Nutlin-3 recruits KLF17 to EMT target gene promoters and results in the formation of KLF17-DNA complex via a p53-dependent pathway. p53 depletion abrogates DNA binding affinity of KLF17 to EMT target gene promoters. KLF17 is critical for p53 cellular activities in NSCLC. Importantly, KLF17 enhances p53 transcription to generate a novel positive feedback loop. KLF17 depletion accelerates lung cancer cell growth in response to chemotherapy. Mechanistically, we found that KLF17 increases the expression of tumor suppressor genes p53, p21, and pRB. Functionally, KLF17 required p53 to suppress cancer cell invasion and migration in NSCLC. In conclusion, our study highlights a novel insight into the anti-EMT effect of KLF17 via a p53-dependent pathway in NSCLC, and KLF17 may be a new therapeutic target in NSCLC with p53 status.

A novel hybrid decision support system for thyroid disease forecasting

Diagnosis of thyroid disease requires proper interpretation of functional data of the thyroid gland, which produces hormones to regulate the metabolism of human body. The thyroid disorders are classified on the basis of quantity of hormones produced, i.e., hyperthyroidism the case in which more hormones are produced and hypothyroidism where less than the required number of hormones are produced. Thyroid disease is a critical issue in underdeveloped countries, due to lack of awareness and early diagnosis. The use of machine learning methods is increasing with the passage of time as an alternative approach for the early diagnosis of thyroid disease. In this article, we present a novel intelligent hybrid decision support system based on linear discriminant analysis (LDA), k nearest-neighbor (kNN) weighed preprocessing, and adaptive neurofuzzy inference system (ANFIS) for the diagnosis of thyroid disorders. In the first stage of the LDA–kNN–ANFIS technique, LDA reduces the dimensionality of the disease dataset and eliminates unnecessary features. In the second stage, selected attributes are preprocessed using kNN-based weighed preprocessor. In the last stage, preprocessed selected attributes are provided to adaptive neurofuzzy inference system as an input for diagnosis. The proposed approach experimented on thyroid disease dataset, retrieved from the University of California Irvin’s machine learning repository to validate the overall performance of the system. The computed classification analysis made by accuracy, sensitivity, and specificity values of this approach were 98.5, 94.7, and 99.7%, respectively. This approach can also be efficiently applied to the diagnosis of other deadly diseases to get maximum accuracy with minimum possible features of the dataset.

In Vitro Antidiabetic Effects and Antioxidant Potential of Cassia nemophila Pods

The antidiabetic and antioxidant potential of ethanolic extract of Cassia nemophila pod (EECNP) was evaluated by three in vitro assays, including yeast glucose uptake assay, glucose adsorption assay, and DPPH radical scavenging activity. The result revealed that the extracts have enhanced the uptake of glucose through the plasma membrane of yeast cells. A linear increase in glucose uptake by yeast cells was noticed with gradual increase in the concentration of the test samples. Moreover, the adsorption capacity of the EECNP was directly proportional to the molar concentration of glucose. Also, the DPPH radical scavenging capacity of the extract was increased to a maximum value of 43.3% at 80 μg/ml, which was then decreased to 41.9% at 100 μg/ml. From the results, it was concluded that EECNP possess good antidiabetic and antioxidant properties as shown by in vitro assays.