Unveiling the Biodiversity of Hyperthermophilic Archaea in Jharia Coal Mines: Potential Threat to Methanogenesis?

AIM: To examine the biodiversity of archaeal sulfate reducers and methanogens present in the underground coal mines of Jharia using metagenomics and pyrosequencing. OBJECTIVES: 1) Bioinformatical analysis of the metagenomic data related to a taxonomic analysis obtained from the coal to investigate complete archaeal taxonomic features of the coal bed methane (CBM) microbiome. 2) Bioinformatical analysis of the metagenomic data related to a functional analysis obtained from the coal to investigate functional features relating to taxonomic diversity of the CBM microbiome. 3) The functional attributes have been examined specifically for ORFs related to sulfite reduction and methanogenesis.The taxonomic and functional biodiversity related to euryarchaeota will help in a better understanding of the obstacles associated with methane production imposed by the sulfate reducers. BACKGROUND: The microbial methanogenesis in the coal microbiome is a resultant of substrate utilization by primarily fermentative bacteria and methanogens. The present work reveals the biodiversity of archaeal sulfate reducers and methanogens present in the underground coal mines of Jharia using metagenomics and pyrosequencing. METHODOLOGY: Bioinformatical analysis for structural and functional attributes was accomplished using MG-RAST. The structural analysis was accomplished using RefSeq database, whereas the functional analysis was done via CoG database with a cut off value, a sequence percent identity, and sequence alignment length cut off of 1e-5, 60% and 45, respectively. RESULTS: Attained communities revealed the dominance of hyperthermophilic archaea Pyrococcus furiosus along with Thermococcus kodakarensis in the coal metagenome.The obtained results also suggest the presence of dissimilatory sulfite reductase and formylmethanofuran dehydrogenase, formylmethanofuran: tetrahydromethanopterin formyltransferase involved in sulfite reduction and methanogenesis, respectively, in the microbiome. CONCLUSION: This report is the first attempt to showcase the existence of specific euryarchaeal diversity and their related functional attributes from Jharia coal mines through high throughput sequencing. The study helps in developing a better understanding of the presence of indigenous microbes (archaea) and their functions in the coal microbiome, which can be utilized further to resolve the energy crisis.

RNA interference: concept to reality in crop improvement.

The phenomenon of RNA interference (RNAi) is involved in sequence-specific gene regulation driven by the introduction of dsRNA resulting in inhibition of translation or transcriptional repression. Since the discovery of RNAi and its regulatory potentials, it has become evident that RNAi has immense potential in opening a new vista for crop improvement. RNAi technology is precise, efficient, stable and better than antisense technology. It has been employed successfully to alter the gene expression in plants for better quality traits. The impact of RNAi to improve the crop plants has proved to be a novel approach in combating the biotic and abiotic stresses and the nutritional improvement in terms of bio-fortification and bio-elimination. It has been employed successfully to bring about modifications of several desired traits in different plants. These modifications include nutritional improvements, reduced content of food allergens and toxic compounds, enhanced defence against biotic and abiotic stresses, alteration in morphology, crafting male sterility, enhanced secondary metabolite synthesis and seedless plant varieties. However, crop plants developed by RNAi strategy may create biosafety risks. So, there is a need for risk assessment of GM crops in order to make RNAi a better tool to develop crops with biosafety measures. This article is an attempt to review the RNAi, its biochemistry, and the achievements attributed to the application of RNAi in crop improvement.

Computational intelligence-based diagnosis tool for the detection of prediabetes and type 2 diabetes in India.

BACKGROUND: The incidence of diabetes is increasing rapidly across the globe. India has the highest proportion of diabetic patients, earning it the doubtful distinction of the 'diabetes capital of the world'. Early detection of diabetes could help to prevent or postpone its onset by taking appropriate preventive measures, including the initiation of lifestyle changes. To date, early identification of prediabetes or type 2 diabetes has proven problematic, such that there is an urgent requirement for tools enabling easy, quick, and accurate diagnosis. AIM: To develop an easy, quick, and precise tool for diagnosing early diabetes based on machine learning algorithms. METHODS: The dataset used in this study was based on the health profiles of diabetic and non-diabetic patients from hospitals in India. A novel machine learning algorithm, termed "mixture of expert", was used for the determination of a patient's diabetic state. Out of a total of 1415 subjects, 1104 were used to train the mixture of expert system. The remaining 311 data sets were reserved for validation of the algorithm. Mixture of expert was implemented in matlab to train the data for the development of the model. The model with the minimum mean square error was selected and used for the validation of the results. RESULTS: Different combinations and numbers of hidden nodes and expectation maximization (EM) iterations were used to optimize the accuracy of the algorithm. The overall best accuracy of 99.36% was achieved with an iteration of 150 and 20 hidden nodes. Sensitivity, specificity, and total classification accuracy were calculated as 99.5%, 99.07%, and 99.36%, respectively. Furthermore, a graphical user interface was developed in java script such that the user can readily enter the variables and easily use the algorithm as a tool. CONCLUSIONS: This study describes a highly precise machine learning prediction tool for identifying prediabetic, diabetic, and non-diabetic individuals with high accuracy. The tool could be used for large scale screening in hopsitals or diabetes prevention programs.

Java-based diabetes type 2 prediction tool for better diagnosis.

BACKGROUND: The concept of classification of clinical data can be utilized in the development of an effective diagnosis system by taking the advantage of computational intelligence. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important problem in neural networks. Unfortunately, although several classification studies have been carried out with significant performance, many of the current methods often fail to reach out to patients. Graphical user interface-enabled tools need to be developed through which medical practitioners can simply enter the health profiles of their patients and receive an instant diabetes prediction with an acceptable degree of confidence. METHODS: In this study, the neural network approach was used for a dataset of 768 persons from a Pima Indian population living near Phoenix, AZ. A neural network mixture of experts model was trained with these data using the expectation-minimization algorithm. RESULTS: The mixture of experts method was used to train the algorithm with 97% accuracy. A graphical user interface was developed that would work in conjunction with the trained network to provide the output in a presentable format. CONCLUSIONS: This study provides a machine-implementable approach that can be used by physicians and patients to minimize the extent of error in diagnosis. The authors are hopeful that replication of results of this study in other populations may lead to improved diagnosis. Physicians can simply enter the health profile of patients and get the diagnosis for diabetes type 2.

SWIFT MODELLER v2.0: a platform-independent GUI for homology modeling.

SWIFT MODELLER v2.0 is a platform-independent Java-based graphical user interface to MODELLER. It provides an interactive homology modeling solution by automating the formatting, scripting, and data extraction processes, meaning that the user only needs to paste in the protein target sequence as input. SWIFT MODELLER v2.0 takes a step-by-step approach where the flow of the software screens depicts steps in the homology modeling protocol. Ramachandran plots and DOPE profile graphs are sketched and displayed for in-depth model analysis, along with an embedded Jmol viewer for 3D visualization of the constructed model. SWIFT MODELLER v2.0 is functional on all Linux-based and Microsoft Windows operating systems for which MODELLER has been developed. The software is available as freeware at http://www.bitmesra.ac.in/swift-modeller/swift.htm .

Identification of single exon genes and their encoded proteins in rice (Oryza sativa L.) genome: an in silico approach.

In this study, we predicted Single Exon Genes (SEGs) distributed in whole rice genome and their expressed proteins. Complete genome of rice was retrieved from TIGR. CDS annotation in the FEATURE (GenBank format) was used to predict SEGs sequences. Organelle gene sequences, pseudogenes, tRNA genes, rRNA genes and duplicated genes were eliminated through different bioinformatics tools. A sizeable number (8.1%) of SEGs in whole rice genome were detected. Predicted SEGs were further searched for their differential response under anoxia. Out of total detected SEGs, only 39.33% were anoxia responsive. Among the total detected anoxia-responsive SEG, only 23.48% encode the known proteins.

Genetic variation among species, races, forms and inbred lines of lac insects belonging to the genus Kerria (Homoptera, Tachardiidae).

THE LAC INSECTS (HOMOPTERA: Tachardiidae), belonging to the genus Kerria, are commercially exploited for the production of lac. Kerria lacca is the most commonly used species in India. RAPD markers were used for assessing genetic variation in forty-eight lines of Kerria, especially among geographic races, infrasubspecific forms, cultivated lines, inbred lines, etc., of K. lacca. In the 48 lines studied, the 26 RAPD primers generated 173 loci, showing 97.7% polymorphism. By using neighbor-joining, the dendrogram generated from the similarity matrix resolved the lines into basically two clusters and outgroups. The major cluster, comprising 32 lines, included mainly cultivated lines of the rangeeni form, geographic races and inbred lines of K. lacca. The second cluster consisted of eight lines of K. lacca, seven of the kusmi form and one of the rangeeni from the southern state of Karnataka. The remaining eight lines formed a series of outgroups, this including a group of three yellow mutant lines of K. lacca and other species of the Kerria studied, among others. Color mutants always showed distinctive banding patterns compared to their wild-type counterparts from the same population. This study also adds support to the current status of kusmi and rangeeni, as infraspecific forms of K. lacca.

SWIFT MODELLER: a Java based GUI for molecular modeling.

MODELLER is command line argument based software which requires tedious formatting of inputs and writing of Python scripts which most people are not comfortable with. Also the visualization of output becomes cumbersome due to verbose files. This makes the whole software protocol very complex and requires extensive study of MODELLER manuals and tutorials. Here we describe SWIFT MODELLER, a GUI that automates formatting, scripting and data extraction processes and present it in an interactive way making MODELLER much easier to use than before. The screens in SWIFT MODELLER are designed keeping homology modeling in mind and their flow is a depiction of its steps. It eliminates the formatting of inputs, scripting processes and analysis of verbose output files through automation and makes pasting of the target sequence as the only prerequisite. Jmol (3D structure visualization tool) has been integrated into the GUI which opens and demonstrates the protein data bank files created by the MODELLER software. All files required and created by the software are saved in a folder named after the work instance's date and time of execution. SWIFT MODELLER lowers the skill level required for the software through automation of many of the steps in the original software protocol, thus saving an enormous amount of time per instance and making MODELLER very easy to work with.

Genetic variation among species, races, forms and inbred lines of lac insects belonging to the genus Kerria (Homoptera, Tachardiidae)

The lac insects (Homoptera: Tachardiidae), belonging to the genus Kerria, are commercially exploited for the production of lac. Kerria lacca is the most commonly used species in India. RAPD markers were used for assessing genetic variation in forty-eight lines of Kerria, especially among geographic races, infrasubspecific forms, cultivated lines, inbred lines, etc., of K. lacca. In the 48 lines studied, the 26 RAPD primers generated 173 loci, showing 97.7 percent polymorphism. By using neighbor-joining, the dendrogram generated from the similarity matrix resolved the lines into basically two clusters and outgroups. The major cluster, comprising 32 lines, included mainly cultivated lines of the rangeeni form, geographic races and inbred lines of K. lacca. The second cluster consisted of eight lines of K. lacca, seven of the kusmi form and one of the rangeeni from the southern state of Karnataka. The remaining eight lines formed a series of outgroups, this including a group of three yellow mutant lines of K. lacca and other species of the Kerria studied, among others. Color mutants always showed distinctive banding patterns compared to their wild-type counterparts from the same population. This study also adds support to the current status of kusmi and rangeeni, as infraspecific forms of K. lacca.

Evaluation of various ions and compounds on nitrilase produced from Streptomyces sp.

The nitrilase produced from a new isolate is evaluated for its activity in presence of a number of different ions and compounds at optimal conditions. It was found that the activity of nitrilase increased up to 10-20% in presence of most of the divalent ions at a concentration of 5 mM relative to the control. Silver, mercury, tin, DTT, ascorbic acid and thiourea, respectively, were observed as potential inhibitors of the enzyme catalysis. The investigation on storage stability of whole cells in presence of a number of stabilizers showed that the enzyme is stable (relative activity 50%) for more than 120 days at various temperatures.

Computational intelligence in early diabetes diagnosis: a review.

The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.