Search results for: genomics

Authors: Dulanjali Ranasinghe, Isuru Supasan, Kaushalya Premachandra, Ranjan Dissanayake, Ajith Rajapaksha, Eustace Fernando

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Proteomics studies of organisms are considered to be significantly information-rich compared to their genomic counterparts because proteomes of organisms represent the expressed state of all proteins of an organism at a given time. In modern top-down and bottom-up proteomics workflows, the primary analysis methods employed are gel–based methods such as two-dimensional (2D) electrophoresis and mass spectrometry based methods. Machine learning (ML) and artificial intelligence (AI) have been used increasingly in modern biological data analyses. In particular, the fields of genomics, DNA sequencing, and bioinformatics have seen an incremental trend in the usage of ML and AI techniques in recent years. The use of aforesaid techniques in the field of proteomics studies is only beginning to be materialised now. Although there is a wealth of information available in the scientific literature pertaining to proteomics workflows, no comprehensive review addresses various aspects of the combined use of proteomics and machine learning. The objective of this review is to provide a comprehensive outlook on the application of machine learning into the known proteomics workflows in order to extract more meaningful information that could be useful in a plethora of applications such as medicine, agriculture, and biotechnology.

Keywords: proteomics, machine learning, gel-based proteomics, mass spectrometry

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Authors: Tanushree Jaitly, Shailendra Gupta, Leila Taher, Gerold Schuler, Julio Vera

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Genomics-based personalized medicine is a promising approach to fight aggressive tumors based on patient's specific tumor mutation and expression profiles. A remarkable case is, dendritic cell-based immunotherapy, in which tumor epitopes targeting patient's specific mutations are used to design a vaccine that helps in stimulating cytotoxic T cell mediated anticancer immunity. Here we present a computational pipeline for epitope-based personalized cancer vaccines using patient-specific haplotype and cancer mutation profiles. In the workflow proposed, we analyze Whole Exome Sequencing and RNA Sequencing patient data to detect patient-specific mutations and their expression level. Epitopes including the tumor mutations are computationally predicted using patient's haplotype and filtered based on their expression level, binding affinity, and immunogenicity. We calculate binding energy for each filtered major histocompatibility complex (MHC)-peptide complex using docking studies, and use this feature to select good epitope candidates further.

Keywords: cancer immunotherapy, epitope prediction, NGS data, personalized medicine

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Authors: Narin Salehiyan

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The later sequencing of the complete genomes of Mycoplasma genitalium and M. pneumoniae has pulled in significant consideration to the atomic science of mycoplasmas, the littlest self-replicating living beings. It shows up that we are presently much closer to the objective of defining, in atomic terms, the complete apparatus of a self-replicating cell. Comparative genomics based on comparison of the genomic cosmetics of mycoplasmal genomes with those of other microbes, has opened better approaches of looking at the developmental history of the mycoplasmas. There's presently strong hereditary bolster for the speculation that mycoplasmas have advanced as a department of gram-positive microbes by a handle of reductive advancement. Amid this prepare, the mycoplasmas misplaced significant parcels of their ancestors’ chromosomes but held the qualities basic for life. In this way, the mycoplasmal genomes carry a tall rate of preserved qualities, incredibly encouraging quality comment. The critical genome compaction that happened in mycoplasmas was made conceivable by receiving a parasitic mode of life. The supply of supplements from their has clearly empowered mycoplasmas to lose, amid advancement, the qualities for numerous assimilative forms. Amid their advancement and adjustment to a parasitic mode of life, the mycoplasmas have created different hereditary frameworks giving a profoundly plastic set of variable surface proteins to avoid the have safe framework.

Keywords: mycoplasma, plasma, pathogen, genome

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Authors: Tamanda Kamwendo

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The concept of benefit sharing has been a prominent global debate in the world, gaining traction in human research ethics. Despite its prevalence, the concept of benefit sharing is not without controversy over its meaning and justification. This is due to the fact that it lacks a broadly accepted definition and many proponents discuss benefit sharing by arguing for its necessity rather than engaging in critical intellectual engagement with technical issues such as what it implies. What is clear in the literature is that the underlying premise of benefit-sharing is that research involving underprivileged and marginalized people is currently unjust and inequitable because these people are denied access to these gains; thus, benefit-sharing arrangements are required for these research projects to be just and equitable. This paper, therefore, investigates the discourses and justifications behind the concept of benefit sharing to human participants, particularly when dealing with human genomics research. Furthermore, considering that benefit sharing is generally viewed as a transaction between research organizations and research participants, it raises ethical concerns concerning the commodification of human material and undermines the sanctity of the human genome. This is predicated on the idea that research sponsors would be compelled to deliver a minimum set of possible benefits to research participants and communities in exchange for their involvement in the study. There is, therefore, need to protect benefit-sharing practices in international health research by developing a governance legal framework. A legal framework of benefit sharing will also dispel the issue of commodification of human material where human genomic research is done.

Keywords: benefit sharing, human participants, human genomic research, ethical concerns

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Authors: Lamis Naddaf, Yuval Tabach

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In this research, we identified the missense genetic variants that have the potential to enhance resistance against cancer. Such field has not been widely explored, as researchers tend to investigate mutations that cause diseases, in response to the suffering of patients, rather than those mutations that protect from them. In conjunction with the genomic revolution, and the advances in genetic engineering and synthetic biology, identifying the protective variants will increase the power of genotype-phenotype predictions and can have significant implications on improved risk estimation, diagnostics, prognosis and even for personalized therapy and drug discovery. To approach our goal, we systematically investigated the sites of the coding genomes and picked up the alleles that showed a correlation with the species’ cancer resistance. We predicted 250 protecting variants (PVs) with a 0.01 false discovery rate and more than 20 thousand PVs with a 0.25 false discovery rate. Cancer resistance in Mammals and reptiles was significantly predicted by the number of PVs a species has. Moreover, Genes enriched with the protecting variants are enriched in pathways relevant to tumor suppression like pathways of Hedgehog signaling and silencing, which its improper activation is associated with the most common form of cancer malignancy. We also showed that the PVs are more abundant in healthy people compared to cancer patients within different human races.

Keywords: comparative genomics, machine learning, cancer resistance, cancer-protecting alleles

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Authors: Lamis Naddaf, Yuval Tabach

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We identified missense genetic variants with the potential to enhance resistance against cancer. Such a field has not been widely explored as researchers tend to investigate the mutations that cause diseases, in response to the suffering of patients, rather than those mutations that protect from them. In conjunction with the genomic revolution and the advances in genetic engineering and synthetic biology, identifying the protective variants will increase the power of genotype-phenotype predictions and have significant implications for improved risk estimation, diagnostics, prognosis, and even personalized therapy and drug discovery. To approach our goal, we systematically investigated the sites of the coding genomes and selected the alleles that showed a correlation with the species’ cancer resistance. Interestingly, we found several amino acids that are more generally preferred (like the Proline) or avoided (like the Cysteine) by the resistant species. Furthermore, Cancer resistance in mammals and reptiles is significantly predicted by the number of the predicted protecting variants (PVs) a species has. Moreover, PVs-enriched-genes are enriched in pathways relevant to tumor suppression. For example, they are enriched in the Hedgehog signaling and silencing pathways, which its improper activation is associated with the most common form of cancer malignancy. We also showed that the PVs are mostly more abundant in healthy people compared to cancer patients within different human races.

Keywords: cancer resistance, protecting variant, naked mole rat, comparative genomics

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Authors: Pui Shan Wong, Kosuke Tashiro, Satoru Kuhara, Sachiyo Aburatani

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Functional genomics and gene regulation inference has readily expanded our knowledge and understanding of gene interactions with regards to expression regulation. With the advancement of transcriptome sequencing in time-series comes the ability to study the sequential changes of the transcriptome. This method presented here works to augment existing regulation networks accumulated in literature with transcriptome data gathered from time-series experiments to construct a sequential representation of transcription factor activity. This method is applied on a time-series RNA-Seq data set from Escherichia coli as it transitions from growth to stationary phase over five hours. Investigations are conducted on the various metabolic activities in gene regulation processes by taking advantage of the correlation between regulatory gene pairs to examine their activity on a dynamic network. Especially, the changes in metabolic activity during phase transition are analyzed with focus on the pagP gene as well as other associated transcription factors. The visualization of the sequential transcriptional activity is used to describe the change in metabolic pathway activity originating from the pagP transcription factor, phoP. The results show a shift from amino acid and nucleic acid metabolism, to energy metabolism during the transition to stationary phase in E. coli.

Keywords: Escherichia coli, gene regulation, network, time-series

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Authors: Nieto Bernal Wilson, Carmona Suarez Edgar

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The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects.  Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.

Keywords: data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse

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Authors: Nandhana Vivek, Bhaskar Gogoi, Ayyavu Mahesh

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Breast cancer metastasis plays a key role in cancer progression and fatality. The present study examines the potential causes of metastasis in breast cancer by investigating the novel interactions between genes and their pathways. The gene expression profile of GSE99394, GSE1246464, and GSE103865 was downloaded from the GEO data repository to analyze the differentially expressed genes (DEGs). Protein-protein interactions, target factor interactions, pathways and gene relationships, and functional enrichment networks were investigated. The proliferation pathway was shown to be highly expressed in breast cancer progression and metastasis in all three datasets. Gene Ontology analysis revealed 11 DEGs as gene targets to control breast cancer metastasis: LYN, DLGAP5, CXCR4, CDC6, NANOG, IFI30, TXP2, AGTR1, MKI67, and FTH1. Upon studying the function, genomic and proteomic data, and pathway involvement of the target genes, DLGAP5 proved to be a promising candidate due to it being highly differentially expressed in all datasets. The study takes a unique perspective on the avenues through which DLGAP5 promotes metastasis. The current investigation helps pave the way in understanding the role DLGAP5 plays in metastasis, which leads to an increased incidence of death among breast cancer patients.

Keywords: genomics, metastasis, microarray, cancer

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Authors: Abdul Musaweer Habib, Habibul Hasan Mazumder, Saiful Islam, Sohel Sikder, Omar Faruk Sikder

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The plethora of genome sequence information of bacteria in recent times has ushered in many novel strategies for antibacterial drug discovery and facilitated medical science to take up the challenge of the increasing resistance of pathogenic bacteria to current antibiotics. In this study, we adopted subtractive genomics approach to analyze the whole genome sequence of the Fusobacterium nucleatum, a human oral pathogen having association with colorectal cancer. Our study divulged 1499 proteins of Fusobacterium nucleatum, which has no homolog in human genome. These proteins were subjected to screening further by using the Database of Essential Genes (DEG) that resulted in the identification of 32 vitally important proteins for the bacterium. Subsequent analysis of the identified pivotal proteins, using the KEGG Automated Annotation Server (KAAS) resulted in sorting 3 key enzymes of F. nucleatum that may be good candidates as potential drug targets, since they are unique for the bacterium and absent in humans. In addition, we have demonstrated the 3-D structure of these three proteins. Finally, determination of ligand binding sites of the key proteins as well as screening for functional inhibitors that best fitted with the ligands sites were conducted to discover effective novel therapeutic compounds against Fusobacterium nucleatum.

Keywords: colorectal cancer, drug target, Fusobacterium nucleatum, homology modeling, ligands

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Authors: Fatemeh Nokhodchi Bonab

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Bioinformatics, in its broad sense, involves application of computer processes to solve biological problems. A wide range of computational tools are needed to effectively and efficiently process large amounts of data being generated as a result of recent technological innovations in biology and medicine. A number of computational tools have been developed or adapted to deal with the experimental riches of complex and multivariate data and transition from data collection to information or knowledge. These bioinformatics tools are being evaluated and applied in various medical areas including early detection, risk assessment, classification, and prognosis of cancer. The goal of these efforts is to develop and identify bioinformatics methods with optimal sensitivity, specificity, and predictive capabilities. The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems.

Keywords: methods, applications, transcriptional regulatory systems, techniques

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Authors: R. H. Bustos, L. Martinez, J. García, F. Suárez

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MRP1 (Multi-drug resistance associated protein 1) and MRP2 (Multi-drug resistance associated protein 2) are two proteins belonging to the transporters of ABC (ATP-Binding Cassette). These transporter proteins are involved in the efflux of several biological drugs and xenobiotic and also in multiple physiological, pathological and pharmacological processes. Evidence has been found that there is a correlation among different polymorphisms found and their clinical implication in the resistance to antiepileptic, chemotherapy and anti-infectious drugs. In our study, exonic regions of MRP1/ABCC1 y MRP2/ABCC2 were studied in the Colombian population, specifically in the region of the central Savannah (Cundinamarca) to determinate SNP (Single Nucleotide Polymorphisms) and determinate its allele frequency and its genomics frequency. Results showed that for our population, SNP are found that have been previously reported for MRP1/ABCC1 (rs200647436, rs200624910, rs150214567) as well as for MRP2/ABCC2 (rs2273697, rs3740066, rs142573385, rs17216212). In addition, 13 new SNP were identified. Evidences show an important clinic correlation for polymorphisms rs3740066 and rs2273697. The study object population displays genetic variability as compared to the one reported in other populations.

Keywords: ATP-binding cassette (ABCC), Colombian population, multidrug-resistance protein (MRP), pharmacogenetic, single nucleotide polymorphism (SNP)

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Authors: Nan Deng, Zhenqiu Liu

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Thousands of patients with metastatic tumors were diagnosed with cancers of unknown primary sites each year. The inability to identify the primary cancer site may lead to inappropriate treatment and unexpected prognosis. Nowadays, a large amount of genomics and transcriptomics cancer data has been generated by next-generation sequencing (NGS) technologies, and The Cancer Genome Atlas (TCGA) database has accrued thousands of human cancer tumors and healthy controls, which provides an abundance of resource to differentiate cancer types. Meanwhile, deep convolutional neural networks (CNNs) have shown high accuracy on classification among a large number of image object categories. Here, we utilize 25 cancer primary tumors and 3 normal tissues from TCGA and convert their RNA-Seq gene expression profiling to color images; train, validate and test a CNN classifier directly from these images. The performance result shows that our CNN classifier can archive >80% test accuracy on most of the tumors and normal tissues. Since the gene expression pattern of distant metastases is similar to their primary tumors, the CNN classifier may provide a potential computational strategy on identifying the unknown primary origin of metastatic cancer in order to plan appropriate treatment for patients.

Keywords: bioinformatics, cancer, convolutional neural network, deep leaning, gene expression pattern

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Authors: Jae-Hyung Jin, Kyung-Tae Lee, Yee-Seul So, Eunji Jeong, Yeonseon Lee, Dongpil Lee, Dong-Gi Lee, Yong-Sun Bahn

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Cryptococcus neoformans causes cryptococcal meningoencephalitis mainly in immunocompromised patients as well as immunocompetent people. But therapeutic options are limited to treat cryptococcosis. Some signaling pathways including cyclic AMP pathway, MAPK pathway, and calcineurin pathway play a central role in the regulation of the growth, differentiation, and virulence of C. neoformans. To understand signaling networks regulating the virulence of C. neoformans, we selected the 114 putative phosphatase genes, one of the major components of signaling networks, in the genome of C. neoformans. We identified putative phosphatases based on annotation in C. neoformans var. grubii genome database provided by the Broad Institute and National Center for Biotechnology Information (NCBI) and performed a BLAST search of phosphatases of Saccharomyces cerevisiae, Aspergillus nidulans, Candida albicans and Fusarium graminearum to Cryptococcus neoformans. We classified putative phosphatases into 14 groups based on InterPro phosphatase domain annotation. Here, we constructed 170 signature-tagged gene-deletion strains through homologous recombination methods for 91 putative phosphatases. We examined their phenotypic traits under 30 different in vitro conditions, including growth, differentiation, stress response, antifungal resistance and virulence-factor production.

Keywords: human fungal pathogen, phosphatase, deletion library, functional genomics

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Authors: M. Moaeen-ud-Din, G. Bilal, M. Yaqoob

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Identification of genes of importance regarding production traits in buffalo is impaired by a paucity of genomic resources. Choice to fill this gap is to exploit data available for cow. The cross-species application of comparative genomics tools is potential gear to investigate the buffalo genome. However, this is dependent on nucleotide sequences similarity. In this study gene diversity between buffalo and cattle was determined by using 86 gene orthologues. There was about 3% difference in all genes in term of nucleotide diversity; and 0.267±0.134 in amino acids indicating the possibility for successfully using cross-species strategies for genomic studies. There were significantly higher non synonymous substitutions both in cattle and buffalo however, there was similar difference in term of dN – dS (4.414 vs 4.745) in buffalo and cattle respectively. Higher rate of non-synonymous substitutions at similar level in buffalo and cattle indicated a similar positive selection pressure. Results for relative rate test were assessed with the chi-squared test. There was no significance difference on unique mutations between cattle and buffalo lineages at synonymous sites. However, there was a significance difference on unique mutations for non synonymous sites indicating ongoing mutagenic process that generates substitutional mutation at approximately the same rate at silent sites. Moreover, despite of common ancestry, our results indicate a different divergent time among genes of cattle and buffalo. This is the first demonstration that variable rates of molecular evolution may be present within the family Bovidae.

Keywords: buffalo, cattle, gene diversity, molecular evolution

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Authors: James Jr. Mashiyane, Risuna Nkolele, Stephanie J. Müller, Gciniwe S. Dlamini, Rebone L. Meraba, Darlington S. Mapiye

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When performing language tasks in natural language processing (NLP), the dimensionality of word embeddings is chosen either ad-hoc or is calculated by optimizing the Pairwise Inner Product (PIP) loss. The PIP loss is a metric that measures the dissimilarity between word embeddings, and it is obtained through matrix perturbation theory by utilizing the unitary invariance of word embeddings. Unlike in natural language, in genomics, especially in genome sequence processing, unlike in natural language processing, there is no notion of a “word,” but rather, there are sequence substrings of length k called k-mers. K-mers sizes matter, and they vary depending on the goal of the task at hand. The dimensionality of word embeddings in NLP has been studied using the matrix perturbation theory and the PIP loss. In this paper, the sufficiency and reliability of applying word-embedding algorithms to various genomic sequence datasets are investigated to understand the relationship between the k-mer size and their embedding dimension. This is completed by studying the scaling capability of three embedding algorithms, namely Latent Semantic analysis (LSA), Word2Vec, and Global Vectors (GloVe), with respect to the k-mer size. Utilising the PIP loss as a metric to train embeddings on different datasets, we also show that Word2Vec outperforms LSA and GloVe in accurate computing embeddings as both the k-mer size and vocabulary increase. Finally, the shortcomings of natural language processing embedding algorithms in performing genomic tasks are discussed.

Keywords: word embeddings, k-mer embedding, dimensionality reduction

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Authors: Hossein Miri, Zhou YongQi, Chan Bormei-Suy

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The development of advanced technologies and innovative solutions has opened up exciting new possibilities for revolutionizing healthcare systems. One such emerging concept is the use of virtual and holographic health information platforms that aim to provide interactive and personalized medical information to users. This paper provides a review of notable virtual and holographic health information platforms. It begins by highlighting the need for information visualization and 3D representation in healthcare. It then proceeds to provide background knowledge on information visualization and historical developments in 3D visualization technology. Additional domain knowledge concerning holography, holographic computing, and mixed reality is then introduced, followed by highlighting some of their common applications and use cases. After setting the scene and defining the context, the need and importance of virtual and holographic visualization in medicine are discussed. Subsequently, some of the current research areas and applications of digital holography and holographic technology are explored, alongside the importance and role of virtual and holographic visualization in genetics and genomics. An analysis of the key principles and concepts underlying virtual and holographic health information systems is presented, as well as their potential implications for healthcare are pointed out. The paper concludes by examining the most notable existing mixed-reality applications and systems that help doctors visualize diagnostic and genetic data and assist in patient education and communication. This paper is intended to be a valuable resource for researchers, developers, and healthcare professionals who are interested in the use of virtual and holographic technologies to improve healthcare.

Keywords: virtual, holographic, health information platform, personalized interactive medical information

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Authors: Po-Jung Huang, Chi-Ching Lee, Bertrand Chin-Ming Tan, Yuan-Ming Yeh, Julie Lichieh Chu, Tin-Wen Chen, Cheng-Yang Lee, Ruei-Chi Gan, Hsuan Liu, Petrus Tang

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Whole-exome sequencing focuses on the protein coding regions of disease/cancer associated genes based on a priori knowledge is the most cost-effective method to study the association between genetic alterations and disease. Recent advances in high throughput sequencing technologies and proteomic techniques has provided an opportunity to integrate genomics and proteomics, allowing readily detectable mutated peptides corresponding to mutated genes. Since sequence database search is the most widely used method for protein identification using Mass spectrometry (MS)-based proteomics technology, a mutant proteome database is required to better approximate the real protein pool to improve disease-associated mutated protein identification. Large-scale whole exome/genome sequencing studies were launched by National Cancer Institute (NCI), Broad Institute, and The Cancer Genome Atlas (TCGA), which provide not only a comprehensive report on the analysis of coding variants in diverse samples cell lines but a invaluable resource for extensive research community. No existing database is available for the collection of mutant protein sequences related to the identified variants in these studies. CMPD is designed to address this issue, serving as a bridge between genomic data and proteomic studies and focusing on protein sequence-altering variations originated from both germline and cancer-associated somatic variations.

Keywords: TCGA, cancer, mutant, proteome

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Authors: Micheale Yifter Weldemichael, Hailay Mehari Gebremedhn, Teklehaimanot Hailesslasie

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The advancement of target-specific genome editing tools, including clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein9 (Cas9), mega-nucleases, base editing (BE), prime editing (PE), transcription activator-like endonucleases (TALENs), and zinc-finger nucleases (ZFNs), have paved the way for a modern era of gene editing. CRISPR/Cas9, as a versatile, simple, cost-effective and robust system for genome editing, has dominated the genome manipulation field over the last few years. The application of CRISPR/Cas9 in sorghum improvement is particularly vital in the context of ecological, environmental and agricultural challenges, as well as global climate change. In this context, gene editing using CRISPR/Cas9 can improve nutritional value, yield, resistance to pests and disease and tolerance to different abiotic stress. Moreover, CRISPR/Cas9 can potentially perform complex editing to reshape already available elite varieties and new genetic variations. However, existing research is targeted at improving even further the effectiveness of the CRISPR/Cas9 genome editing techniques to fruitfully edit endogenous sorghum genes. These findings suggest that genome editing is a feasible and successful venture in sorghum. Newer improvements and developments of CRISPR/Cas9 techniques have further qualified researchers to modify extra genes in sorghum with improved efficiency. The fruitful application and development of CRISPR techniques for genome editing in sorghum will not only help in gene discovery, creating new, improved traits in sorghum regulating gene expression sorghum functional genomics, but also in making site-specific integration events.

Keywords: CRISPR/Cas9, genome editing, quality, sorghum, stress, yield

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Authors: Mahima Dubey, Girish Chandel

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Minor millets are highly nutritious and climate resilient cereal crops. These features make them ideal candidates to excavate the physiology of the underlying mechanism. In an attempt to understand the basis of mineral nutrition in minor millets, a set of five Barnyard millet genotypes were analyzed for grain Fe and Zn content under contrasting Fe-Zn supply to identify genotypes proficient in tolerating mineral deficiency. This resulted in the identification of Melghat-1 genotype to be nutritionally superior with better ability to withstand deficiency. Expression analysis of several Nicotianamine synthase (NAS) genes showed that HvNAS1 and OsNAS2 genes were prominent in positively mediating mineral deficiency response in Barnyard millet. Further, strategic efforts were employed for fast-track identification of more effective orthologous NAS genes from Barnyard millet. This resulted in the identification of two genes namely EfNAS1 (orthologous to HvNAS1 of barley) and EfNAS2 (orthologous to OsNAS2 gene of rice). Sequencing and thorough characterization of these sequences revealed the presence of intact NAS domain and signature tyrosine and di-leucine motifs in their predicted proteins and thus established their candidature as functional NAS genes in Barnyard millet. Moreover, EfNAS1 showed structural superiority over previously known NAS genes and is anticipated to have role in more efficient metal transport. Findings of the study provide insight into Fe-Zn deficiency response and mineral nutrition in millets. This provides millets with a physiological edge over micronutrient deficient staple cereals such as rice in withstanding Fe-Zn deficiency and subsequently accumulating higher levels of Fe and Zn in millet grains.

Keywords: gene expression, micronutrient, millet, ortholog

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Authors: Yamunah Devi Apalasamy, Sanjay Rampal, Tin Tin Su, Foong Ming Moy, Hazreen Abdul Majid, Awang Bulgiba, Zahurin Mohamed

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Obesity is a growing global health issue. Obesity results from a combination of environmental and genetics factors. Brain-derived neurotrophic factor (BDNF), a gene encodes the BDNF protein and the BDNF gene have been linked to regulation of body weight and appetite. Genome-wide association studies have identified the BDNF variants to be related to obesity among Caucasians, East Asians, and Filipinos. However, the role of BDNF in other ethnic groups remains inconclusive. This case control study aims to investigate the associations of BDNF gene polymorphisms with obesity and metabolic parameters in Malaysian Malays. BDNF rs4074134, BDNF rs10501087 and BDNF rs6265 were genotyped using Sequenom MassARRAY. Anthropometric, body fat, fasting lipids and glucose levels were measured. A total of 663 subjects (194 obese and 469 non-obese) were included in this study. There were no significant associations association between BDNF SNPs and obesity. The allelic and genotype frequencies of the BDNF SNPs were similar in the obese and non-obese groups. After adjustment for age and sex, the BDNF variants were not associated with obesity, body fat, fasting lipids and glucose levels. Haplotypes at the BDNF gene region, were not significantly associated with obesity. The BDNF rs4074134 was in strong LD with BDNF rs10501087 (D'=0.98) and BDNF rs6265 (D'=0.87). The BDNF rs10501087 was also in strong LD with BDNF rs6265 (D'=0.91). Our findings suggest that the BDNF variants and the haplotypes of BDNF gene were not associated with obesity and metabolic traits in this study population. Further research is needed to explore other BDNF variants with a larger sample size with gene-environment interactions in multi ethnic Malaysian population.

Keywords: genomics of obesity, SNP, BMI, haplotypes

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Authors: Zainul A. Khan, Malik Z. Abdin, Jawaid A. Khan

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Begomoviruses belonging to the family Geminiviridae, have single-stranded circular DNA genomes that are monopartite or bipartite. The large intergenic region (LIR) of the monopartite and common region (CR) of bipartite begomoviruses possess promoter activity in their genomes. In this study, we have characterized novel bidirectional promoters from Cotton leaf curl Burewala virus (CLCuBuV) genome using high-throughput software and analyzed with PlantCARE, PLACE, Cister and PlantPAN databases. The promoters (Rep and CP promoters) were assayed both in stable and transient expression systems in tobacco as well as cotton plants. Rep and CP-based promoters from the LIR sequence of CLCuBuV and 35S promoter of Cauliflower mosaic virus (CaMV) were tagged with β-glucuronidase (GUS) and green fluorescent protein (GFP) reporter genes to check the efficacy of the promoters. Histochemical staining of GUS in transformed tobacco (Nicotiana tabacum cv. Xanthi) leaves showed higher GUS expression driven by CLCuBuV Rep (complimentary sense) promoter as compared to conventional CaMV 35S promoter and CLCuBuV CP (virion sense) promoter, respectively. GUS activity in individual plant cells driven by CLCuBuV Rep, CLCuBuV CP, and CaMV 35S promoter were quantified through fluorometric GUS assay and reverse transcription quantitative real-time PCR (RT-qPCR). The expression level of GUS tagged with CLCuBuV Rep promoter in the transformed tobacco plants was obtained 2 to 4 fold higher than CaMV 35S promoter. When CLCuBuV CP promoter was used, lower expression level was monitored than that by CaMV 35S promoter. The expression of GFP-tagged with CLCuBuV promoters was also investigated through agroinfiltration. The CLCuBuV Rep promoters showed stronger consistent transient expression in the leaves of N. benthamiana, N. tabacum and Gossypium hirsutum plants when compared with CaMV 35S and CLCuBuV CP promoter.

Keywords: Begmovirus, bidirectional promoter, CaMV 35S promoter, GFP, GUS, qPCR

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Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

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Authors: Waqas Shafqat Chattha, Muhammad Iqbal, Amir Shakeel

Abstract:

Transcriptional factors are proteins that play a vital role in regulating the transcription of target genes in different biological processes and are being widely studied in different plant species. In the current era of genomics, plant genomes sequencing has directed to the genome-wide identification, analyses and categorization of diverse transcription factor families and hence provide key insights into their structural as well as functional diversity. The DNA-binding with One Finger (DOF) proteins belongs to C2-C2-type zinc finger protein family. DOF proteins are plant-specific transcription factors implicated in diverse functions including seed maturation and germination, phytohormone signalling, light-mediated gene regulation, cotton-fiber elongation and responses of the plant to biotic as well as abiotic stresses. In this context, a genome-wide in-silico analysis of DOF TF family in diploid cotton species i.e. Gossypium raimondii has enabled us to identify 55 non-redundant genes encoding DOF proteins renamed as GrDofs (Gossypium raimondii Dof). Gene distribution studies have shown that all of the GrDof genes are unevenly distributed across 12 out of 13 G. raimondii chromosomes. The gene structure analysis illustrated that 34 out of 55 GrDof genes are intron-less while remaining 21 genes have a single intron. Protein sequence-based phylogenetic analysis of putative 55 GrDOFs has divided these proteins into 5 major groups with various paralogous gene pairs. Molecular evolutionary studies aided with the conserved domain as well as gene structure analysis suggested that segmental duplications were the principal contributors for the expansion of Dof genes in G. raimondii.

Keywords: diploid cotton , G. raimondii, phylogenetic analysis, transcription factor

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Authors: Patricia M. B. Saint-Vincent, Anna Furches, Stephanie Galanie, Erica Teixeira Prates, Piet Jones, Nancy Engle, David Kainer, Wellington Muchero, Daniel Jacobson, Timothy J. Tschaplinski

Abstract:

Uridine diphosphate-glycosyltransferases (UGTs) are enzymes that catalyze sugar transfer to a variety of plant metabolites. UGT substrates, which include plant secondary metabolites involved in lignification, demonstrate new activities and incorporation when glycosylated. Knowledge of UGT function, substrate specificity, and enzyme products is important for plant engineering efforts, especially related to increasing plant biomass through lignification. UGTs in Populus trichocarpa, a biofuel feedstock, and model woody plant, were selected from a pool of gene candidates using rapid prioritization strategies. A functional genomics workflow, consisting of a metabolite genome-wide association study (mGWAS), expression of synthetic codon-optimized genes, and high-throughput biochemical assays with mass spectrometry-based analysis, was developed for determining the substrates and products of previously-uncharacterized enzymes. A total of 40 UGTs from P. trichocarpa were profiled, and the biochemical assay results were compared to predicted mGWAS connections. Assay results confirmed seven of 11 leaf mGWAS associations and demonstrated varying levels of substrate specificity among candidate UGTs. P. trichocarpa UGT substrate processing confirms the role of these newly-characterized enzymes in lignan, flavonoid, and phytohormone metabolism, with potential implications for cell wall biosynthesis, nitrogen uptake, and biotic and abiotic stress responses.

Keywords: Populus, metabolite-gene associations, GWAS, bio feedstocks, glycosyltransferase

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Authors: Alisa Kazarina, Guntis Gerhards, Elina Petersone-Gordina, Ilva Pole, Viktorija Igumnova, Janis Kimsis, Valentina Capligina, Renate Ranka

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Human body is inhabited by a vast number of microorganisms, collectively known as the human microbiome, and there is a tremendous interest in evolutionary changes in human microbial ecology, diversity and function. The field of paleomicrobiology, study of ancient human microbiome, is powered by modern techniques of Next Generation Sequencing (NGS), which allows extracting microbial genomic data directly from archaeological sample of interest. One of the major techniques is 16S rRNA gene sequencing, by which certain 16S rRNA gene hypervariable regions are being amplified and sequenced. However, some limitations of this method exist including the taxonomic precision and efficacy of different regions used. The aim of this study was to evaluate the phylogenetic sensitivity of different 16S rRNA gene hypervariable regions for microbiome studies in the archaeological samples. Towards this aim, archaeological bone samples and corresponding soil samples from each burial environment were collected in Medieval cemeteries in Latvia. The Ion 16S™ Metagenomics Kit targeting different 16S rRNA gene hypervariable regions was used for library construction (Ion Torrent technologies). Sequenced data were analysed by using appropriate bioinformatic techniques; alignment and taxonomic representation was done using Mothur program. Sequences of most abundant genus were further aligned to E. coli 16S rRNA gene reference sequence using MEGA7 in order to identify the hypervariable region of the segment of interest. Our results showed that different hypervariable regions had different discriminatory power depending on the groups of microbes, as well as the nature of samples. On the basis of our results, we suggest that wider range of primers used can provide more accurate recapitulation of microbial communities in archaeological samples. Acknowledgements. This work was supported by the ERAF grant Nr. 1.1.1.1/16/A/101.

Keywords: 16S rRNA gene, ancient human microbiome, archaeology, bioinformatics, genomics, microbiome, molecular biology, next-generation sequencing

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Authors: Shivam Yadav, Neelam Atri

Abstract:

Cyanobacteria are photoautotrophic prokaryotes able to grow in diverse ecological habitats, originated 2.5 - 3.5 billion years ago and brought oxygenic photosynthesis. Since then superoxide dismutases (SODs) acquired great significance due to their ability to catalyze detoxification of byproducts of oxygenic photosynthesis, i.e. superoxide radicals. Sequence information from several cyanobacterial genomes offers a unique opportunity to conduct a comprehensive comparative analysis of the superoxide dismutases family. In the present study, we extracted information regarding SODs from species of sequenced cyanobacteria and investigated their diversity, conservation, domain structure, and evolution. 144 putative SOD homologues were identified. SODs are present in all cyanobacterial species reflecting their significant role in survival. However, their distribution varies, fewer in unicellular marine strains whereas abundant in filamentous nitrogen-fixing cyanobacteria. Motifs and invariant amino acids typical in eukaryotic SODs were conserved well in these proteins. These SODs were classified into three major families according to their domain structures. Interestingly, they lack additional domains as found in proteins of other family. Phylogenetic relationships correspond well with phylogenies based on 16S rRNA and clustering occurs on the basis of structural characteristics such as domain organization. Similar conserved motifs and amino acids indicate that cyanobacterial SODs make use of a similar catalytic mechanism as eukaryotic SODs. Gene gain-and-loss is insignificant during SOD evolution as evidenced by absence of additional domain. This study has not only examined an overall background of sequence-structure-function interactions for the SOD gene family but also revealed variation among SOD distribution based on ecophysiological and morphological characters.

Keywords: comparative genomics, cyanobacteria, phylogeny, superoxide dismutases

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Authors: Asma Naseer Cheema, Attya Bhatti, Jabar Ali, John Peter

Abstract:

Background: High cholesterol levels, endothelial dysfunction, inefficient coagulation cascade and hyper inflammatory response all are the basis of coronary artery disease (CAD). Several studies are carried out to see the genetic influence of these factors on disease outcome. Objective: The objective of our study was to see the association of 10 putative loci with coronary artery disease in our population. Materials & Methods: We screened our population for 10 putative loci of CAD showing significant association (p < 5x10-8) with candidate genes (regulating the cholesterol metabolism, endothelial function, coagulation cascade and inflammatory response of body). Hardy-Weinberg equilibrium and linkage disequilibrium in cases and controls s were estimated separately. Approximately 5-10 ng of dried DNA in 384 well plate format was used to genotype each sample on the Sequenom iPLEX assay at University of Pittsburgh Genomics and Proteomics Core Laboratories. It was built on single-base primer extension with the MALDI-TOF MS detection possessing high sensitivity and specificity. The SNPs were genotyped through Taqman assay. Hardy Weinberg test was applied. The 10 SNPs were selected as genetic markers for this study (rs579459, rs1561198, rs2954029, rs1122608, rs17114036, rs9515203, rs10947789, rs7173743, rs2895811, rs2075650). Results: Mean age of the patient was 52 ± 11 years. Blood pressure and positive family history was found a significant risk factor for CAD. None of the selected SNPs showed significant association with coronary artery disease in our population (p>0.05). Conclusion: rs579459, rs1561198, rs2954029, rs1122608, rs17114036, rs9515203, rs10947789, rs7173743, rs2895811, rs2075650 are not significant genetic markers for CAD in our population.

Keywords: CAD, genetic markers, loci, risk factors

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Authors: Vadanasundari Vedarethinam, Kun Qian

Abstract:

The metabolic analysis is more distal over proteomics and genomics engaging in clinics and needs rationally distinct techniques, designed materials, and device for clinical diagnosis. Conventional techniques such as spectroscopic techniques, biochemical analyzers, and electrochemical have been used for metabolic diagnosis. Currently, there are four major challenges including (I) long-term process in sample pretreatment; (II) difficulties in direct metabolic analysis of biosamples due to complexity (III) low molecular weight metabolite detection with accuracy and (IV) construction of diagnostic tools by materials and device-based platforms for real case application in biomedical applications. Development of chips with nanomaterial is promising to address these critical issues. Mass spectroscopy (MS) has displayed high sensitivity and accuracy, throughput, reproducibility, and resolution for molecular analysis. Particularly laser desorption/ ionization mass spectrometry (LDI MS) combined with devices affords desirable speed for mass measurement in seconds and high sensitivity with low cost towards large scale uses. We developed a plasmonic chip for clinical metabolic fingerprinting as a hot carrier in LDI MS by series of chips with gold nanoshells on the surface through controlled particle synthesis, dip-coating, and gold sputtering for mass production. We integrated the optimized chip with microarrays for laboratory automation and nanoscaled experiments, which afforded direct high-performance metabolic fingerprinting by LDI MS using 500 nL of serum, urine, cerebrospinal fluids (CSF) and exosomes. Further, we demonstrated on-chip direct in-vitro metabolic diagnosis of early-stage lung cancer patients using serum and exosomes without any pretreatment or purifications. To our best knowledge, this work initiates a bionanotechnology based platform for advanced metabolic analysis toward large-scale diagnostic use.

Keywords: plasmonic chip, metabolic fingerprinting, LDI MS, in-vitro diagnostics

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Authors: Parvaneh Mahmoudi, Ahmad Moeni, Seyed Mojtaba Khayam Nekoei, Mohsen Mardi, Mehrshad Zeinolabedini, Ghasem Hosseini Salekdeh

Abstract:

Saffron (Crocus sativus L.) is one of the most important spice and medicinal plants. The three-branch style of C. sativus flowers are the most important economic part of the plant and known as saffron, which has several medicinal properties. Despite the economic and biological significance of this plant, knowledge about its molecular characteristics is very limited. In the present study, we, for the first time, constructed a comprehensive dataset for C. sativus stigma through de novo transcriptome sequencing. We performed de novo transcriptome sequencing of C. sativus stigma using the Illumina paired-end sequencing technology. A total of 52075128 reads were generated and assembled into 118075 unigenes, with an average length of 629 bp and an N50 of 951 bp. A total of 66171unigenes were identified, among them, 66171 (56%) were annotated in the non-redundant National Center for Biotechnology Information (NCBI) database, 30938 (26%) were annotated in the Swiss-Prot database, 10273 (8.7%) unigenes were mapped to 141 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, while 52560 (44%) and 40756 (34%) unigenes were assigned to Gen Ontology (GO) categories and Eukaryotic Orthologous Groups of proteins (KOG), respectively. In addition, 65 candidate genes involved in three stages of crocin biosynthesis were identified. Finally, transcriptome sequencing of saffron stigma was used to identify 6779 potential microsatellites (SSRs) molecular markers. High-throughput de novo transcriptome sequencing provided a valuable resource of transcript sequences of C. sativus in public databases. In addition, most of candidate genes potentially involved in crocin biosynthesis were identified which could be further utilized in functional genomics studies. Furthermore, numerous obtained SSRs might contribute to address open questions about the origin of this amphiploid spices with probable little genetic diversity.

Keywords: saffron, transcriptome, NGS, bioinformatic

Procedia PDF Downloads 47