Search

Background In the present study, adaptive laboratory evolution was used to stimulate antibiotic production in a Streptomyces strain JB140 (wild-type) exhibiting very little antimicrobial activity against bacterial pathogens. The seven different competition experiments utilized three serial passages (3 cycles of adaptation-selection of 15 days each) in which Streptomyces strain (wild-type) was challenged repeatedly to one (bi-culture) or two (tri-culture) or three (quadri-culture) target pathogens. The study demonstrates a simple laboratory model to study the adaptive potential of evolved phenotypes and genotypes in Streptomyces to induce antibiotic production. Results Competition experiments resulted in the evolution of the wild-type Streptomyces strain JB140 into the seven unique mutant phenotypes that acquired the ability to constitutively exhibit increased antimicrobial activity against three bacterial pathogens Salmonella Typhi (NCIM 2051), Staphylococcus aureus (NCIM 2079), and Proteus vulgaris (NCIM 2027). The mutant phenotypes not only effectively inhibited the growth of the tested pathogens but were also observed to exhibit improved antimicrobial responses against one clinical multidrug-resistant (MDR) uropathogenic Escherichia coli (UPEC 1021) isolate. In contrast to the adaptively evolved mutants, only a weak antimicrobial activity was detected in the wild-type parental strain. To get molecular evidence of evolution, RAPD profiles of the wild-type Streptomyces and its evolved mutants were compared which revealed significant polymorphism among them. Conclusion The competition-based adaptive laboratory evolution method can constitute a platform for evolutionary engineering to select improved phenotypes (mutants) with increased antibacterial profiles against targeted pathogens.

We sequenced the genome ofStreptomycessp. JB150, isolated from a unique site of the Thar desert in India. Genome mining of the JB150 genome revealed the presence of many interesting secondary metabolic biosynthetic gene clusters (BGCs). The encoded parvome of JB150 includes non-ribosomal peptides, polyketides including β-lactone, butyrolactone, ectoine, lantipeptides, lasso peptides, melanin, resorcinol, siderophores, terpenoids, thiopeptides, and other types of hybrid compounds. Among them, ~30% BGCs displayed a high degree of novelty. The genome of JB150 was enriched for a large assortment of specialized genes coding for the production of many interesting biomolecules comprising compatible solutes, multiple stress-response regulators, transport proteins, protein secretion systems, signaling molecules, chaperones and storage reserves, etc. The presence of diverse members of CAZymes enzyme families, high numbers of riboflavin, flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD), trehalose and aromatic compounds synthesis genes, putative orthologues to several of the classical fatty acid synthesis components, prototrophy for many essential amino acids exhibit metabolic versatility of JB150 to inhabit in the extreme desert environment. Besides, the genome of JB150 was observed to specifically encode thiazole-oxazole-modified thiazolemicrocin (TOMM) and ectoine. The comparison of the complete genomes ofStreptomycessp. JB150 and seven other actinomycete strains belonging to different desert ecosystems unveiled the presence of many previously undetected, distinctive, biological, and genomic signatures. We propose that these genetic traits endowed by these strains are essential for their adaptation in the highly underprivileged, extreme ecosystem of the Thar desert to cope with multiple abiotic stressors, oligotrophic nutrient conditions and to produce a huge repertoire of diverse secondary metabolites.

Background & ObjectivesCA27.29 is a breast cancer-associated glycoprotein. Many genetic variations caused by non-synonymous nucleotide polymorphisms (nsSNPs) are known to affect the functionality of the CA27.29 protein.MethodsIn the present manuscript, an in silico analysis of the genetic variations in CA27.29 was done to observe functional nsSNPs that possibly alter its stability. Among 2205 SNPs identified from the publically available SNP database (dbSNP), 213 (9.66%) synonymous SNPs, 24 (1.09%) non-synonymous SNPs, and 1351 (61.27%) noncoding intronic SNPs were observed. The function predictability tools SIFT, Provean and Polyphen2 were used to uncover variations in the analyzed nsSNPs and their functionality.ResultsA total of 16, 20 and 10 non-synonymous SNPs (nsSNPs) were predicted to be damaging or deleterious by SIFT, Polyphen, and Provean tools respectively. Intriguingly, 9 nsSNPs were predicted to be damaging by all the three tools used while 4 nsSNPs were predicted to be damaging by SIFT and Polyphen tools. The substitutions C/G->T and G->A/T were observed to be dominant in the analyzed nsSNPs that probably have damaging role to CA27.29 glycoprotein. Moreover, the validation of results using ClinVar tool revealed that all the analyzed possibly, probably and highly damaging nsSNPs are yet to be reported and studied. Besides this, we found Global minor allele frequency (Global MAF) for only 11 nsSNPs, the values of which were observed to be Interpretation & ConclusionsAmong the analyzed nsSNPs, 3 nsSNPs rs145691584, rs148332231 and rs191544901 were found to be located in 3’UTR region of CA27.29 gene that were assumed to have the possible functional roles in altering the protein stability. The present study is useful to gain useful insights into the genetic variations in nsSNPs that may playing a critical role in determining the susceptibility to breast cancer.

In the present manuscript, an adaptive evolutionary multiple sequence alignment algorithm is proposed that uses a combination of consensus and SP-score methods. The algorithm searches intermediate pairwise consensus strings that are used to identify the final consensus string for a given set of DNA/RNA/protein sequences. The proposed algorithm is an extension of MPSAGA algorithm that uses positional matrix representation of sequences. An empirical study was performed in the present work to compare the proposed algorithm with the other three contemporary ClustalW, TCOFFEE, and MUSCLE algorithms on the four datasets. The overall observations from the various experiments revealed that the proposed algorithm outperforms than the other algorithms tested in aligning multiple sequences with an average increase of 0.03% in alignment length by inserting 0.02% increased number of gaps.

Secondary metabolites are natural products produced by plants, fungi, and bacteria. After their discovery and due to the revolution in the pharmaceutical industry, secondary metabolites such as penicillin, streptomycin, and cyclosporine were manufactured in bulk for human welfare. The secondary metabolites are synthesized by the specialized enzymes encoded by coregulated genes located on the biosynthetic gene clusters (BGCs). There are two methods to identify fungal secondary metabolites and their corresponding BGCs: manual methods or in silico methods. The manual methods are time-consuming and complex; conversely, in silico tools are relatively simple, and promising. Most of the in silico tools identify previously known biosynthetic gene clusters by matching the gene sequences to the already available secondary metabolites gene data sets. Some of the in silico tools such as SMURF, ClusterFinder, and Grape can identify known and unknown biosynthetic gene clusters in the submitted genome. Due to the availability of several online tools, it is crucial to know the key functions of each tool to solve a research problem. Therefore, in this chapter, a comprehensive survey of currently available in silico tools has been made that can identify biosynthetic gene clusters in various fungal genomes. The key functions of in silico tools, namely SMURF, antiSMASH, PRISM, SMIPS, SeMPI, DEREPLICATOR +, CASSIS, MIDDAS-M, NP.searcher, ClustScan, CLUSEAN, FunGeneClusterS, ClusterFinder, EvoMining, MIPS-CG, Pep2Path, GRAPE, GARLIC, NRPSpredictor, and SMBP, are discussed, along with the official uniform resource locator (URL) for each tool. Schematic workflow for the identification of the biosynthetic gene cluster has also been proposed, which is further extended as a decision chart in this review.

Many fungi are prolific sources of polyketides and nonribosomal peptides-derived chemically diverse secondary metabolites of pharmaceutical and biotechnological interest. Nonribosomal peptides and polyketides are synthesized by multimodular nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) enzymes, respectively. Many fungal genome sequence data are now publicly available that contain numerous genetic codes of previously undetected novel secondary metabolites. Novel findings have also revealed various mechanisms of NRPS and PKS evolution in different fungal taxa. These mechanisms help us to understand the different strategies employed by fungal communities to control their metabolic potential to produce diverse metabolic molecules. These metabolites, in turn, execute many prominent cellular functions such as antagonizing various invaders, controlling different cellular functions, viz., oxidative stress, cell wall development, cell surface properties, sexual and asexual reproduction, iron scavenging and sequestering, etc. Accordingly, the present review highlights and focuses on the existence of such diversified NRPS- and PKS-derived secondary metabolites and their regulatory biosynthetic gene clusters in different fungal communities.

The genome of actinomycetes and several other microorganisms are endowed with many cryptic gene clusters that can code for previously undetected, a plethora of complex secondary metabolites. Under standard laboratory controlled conditions, the genes regulating these biosynthetic clusters are expressed at very low levels or remain phenotypically cryptic (silent). Over the past several decades, multi-drug-resistant bacteria have been observed with increased frequency, posing a significant threat to human health worldwide. The present alarming situation urgently calls for concerted global efforts for the discovery of new antimicrobials. The present situation, if not controlled, will take us again to the pre-antibiotic era. Today, in the post-genomic era, various new strategies such as the activation of cryptic gene clusters in microorganisms rejuvenate a new conviction in the field of natural product research that may lead to the identification of yet-unidentified novel secondary metabolites of therapeutic and other use. Decryptification of this versatile endogenous genetic reservoir may provide in the near future the more concrete rationale for antibiotic discovery. The present review is an attempt to provide a comprehensive detail, outlining current strategies that have been shown successful to activate cryptic biosynthetic gene clusters in microorganisms.

The efficacy of an actinomycete strain JB66 recovered from a sand dune soil from the Bikaner district of the Thar desert in inhibiting the growth of various bacterial pathogens was studied. The type strains Staphylococcus aureus, Shigella flexneri, Klebsiella pneumoniae and the clinical isolates Escherichia coli, multidrug-resistant S. aureus and P. vulgaris were included in the antimicrobial assays. Polyphasic characterization of JB66 isolate revealed its identity as Streptomyces (MH762010). It showed 88.99-89.24% sequence similarity with the other members of this genus and share the maximum (88.89%) similarity with Streptomyces sp. ATSC13. The strain JB66 was found to produce a high amount of extracellular L-asparaginase, catalase, gelatinase, protease, tyrosinase and urease enzymes. The partial chemical categorization of the methanolic crude extract of the JB66 strain led to the preliminary identification of various metabolic compounds. The thin-layer chromatography fractionation revealed the presence of prodigiosin pigment or chandramycin, cephalosporin or zeatin, daidzein, demethoxy rapamycin, 4,6-dihydroxy-7-methoxyisoflavone, munumbicins and amiclenomycin like compounds. Bio-autography revealed that the metabolites localized at the Rf values of 0.40, 0.46 and 0.53 in TLC profile had the actual bioactive fractions. UV-VIS spectrum absorbance maxima at 288 nm revealed the presence of an aromatic nucleus.

The proposed algorithm is a novel matrix-based global pair-wise sequence alignment with a de novo sequence representation. Needleman–Wunsch, noblest, Emboss-Needle, ALIGN, LALIGN, FOGSAA, DIALIGN, ACANA, MUMmer, etc. are few other algorithms that are most commonly used for global pair-wise sequence alignment. Needleman–Wunsch algorithm is one of the most popular algorithms that provides the best possible pair-wise sequence alignment, but the algorithm output is associated with high time and space complexities. To resolve these complex issues, researchers have proposed several algorithms to reduce time and space complexities in the pair-wise sequence alignment. Most of these algorithms provide solutions, but compromise the optimal result in favor of plummeting time and space complexities. An attempt has been made in the present research to develop MPSAGA and a completely unique positional matrix (PM) based sequence representation to deal with the time and space complexities without compromising sequence alignment results (MPSAGA is in public domain available at https://github.com/JyotiLakhani1/MPSAGA ). A benchmarking of the proposed algorithm has also been performed with other popular pair-wise sequence alignment algorithms with and without positional matrix-based sequence representation. The use of an integer instead of string data type and exclusive clustering method in MPSAGA with positional matrix based sequence representation resulted in a noteworthy reduction in the memory usage (space) and execution time in the pair-wise alignment of biological sequences.

Strain DH-12 exhibits broad-spectrum antibacterial activity toward Gram-positive and Gram-negative pathogens. The 7.6-Mb draft genome sequence gives insight into the complete secondary metabolite production capacity and reveals genes putatively responsible for its antibacterial activity, as well as genes which enable the survival of the organism in an extreme arid environment.

The increase in world population is stressing the need for increased production of food supplies from plants. At the same time, plant pathogens are also developing resistance to several anti-pathogen compounds. The present situation may become worse, in the near future, if not controlled. One of the solutions to the present situation is to develop disease-resistant varieties of plants. The disease resistance in plants is controlled by the products of disease-resistance genes. The plant genomes contain many disease-resistance candidate genes, activation of which can confer the natural resistance against various diseases in plants. The major step in the development of disease-resistant plant varieties is to search for the disease-resistance candidate genes in the plant genome and prioritize them. The experiments pertaining to identify disease-resistance candidate genes can be accomplished using wet lab studies but are usually time-consuming. The present chapter is a survey of the available in silico approaches to identify the candidate genes conferring disease resistance in plants. After providing a brief overview of the multilayered defense mechanism, the present article discusses different approaches for the stepwise identification of disease-resistant candidate genes in plants.

Microbes are exploited as a synthetic platform in the system biology where biochemistry of various biosynthetic pathways can be redesigned. De novo pathway biosynthesis is used to produce extremely valuable, high molecular weight compounds from renewable sources. The tools of bioinformatics and various other analytical approaches have played a significant role in in silico identification and characterization of novel chemical scaffolds of microbial origin by genome mining. However, the majority of the computational tools employ forward approach to link “genes” to their corresponding “metabolites,” while the automated computational tools based on the retrosynthetic approach, to connect chemical structures of “metabolites” to their biosynthetic “genes,” are still in their infancy. The retrobiosynthesis approach is an ingenious pathway design concept that has gained interest in the recent days because of its potential to assist in the redesigning of novel metabolic routes in a given biosynthetic pathway to produce the optimum levels of the targeted secondary metabolite. The approach employs a stepwise, backward to forward search to identify the most advantageous reactions to formulate and optimize the custom-made pathway evolution, whereas the retrosynthetic approaches are the computer-assisted framework to analyze various enzymatic reactions of the natural product biosynthetic pathways to connect and predict their corresponding genetic cluster. The main aim of the present chapter is to provide a brief overview of the retrobiosynthesis methodology and summarize about the available genome mining tools utilized in the forward and retrosynthetic approaches to envisage secondary metabolite biosynthetic gene clusters and vice versa as well as pathway design analysis and evolution. The perspectives on the further development of the genome-scale metabolic models and various other tools and databases in the field are also discussed.

Symbiotic nitrogen fixation is the main biological source of fixed nitrogen in the atmosphere. This biological process is mainly performed by prokaryotes and among these Rhizobium spp. have fascinated many researchers. Recent progress in molecular genetics has greatly contributed to our understanding of the general organization and dynamics of the Rhizobium genome. Several symbiotic genes have been identified, and their expression under different conditions has been studied. An importance is emphasized by using the symbiotically active and stable rhizobial genotype as the preliminary material for genetic manipulations. The utmost requirement for the selection and genetic construction of the stable rhizobial genotype involves the introduction of stable nitrogen fixation and nodulation genes, co-expressed in combination with symbiotically active plant host. The various accomplishments in manipulating the rhizobial genotype to improve nitrogen fixation are discussed in this chapter. A range of methods to select the stable rhizobial genotypes for an improved nitrogen fixation are available which includes the introduction of additional copies of succinate permease (dct) genes, inactivation of negative regulators of symbiosis, genetic modification of competitiveness (cmp) genes, introduction of genes involved in trifolitoxin (tfx) synthesis, protoplast fusion, nitrogenase masking, etc. In the following pages, these aspects, as well as some other characteristics of the general organization of the Rhizobium genome and its dynamics, have been presented. In addition to that other approaches such as genetic manipulations in other microorganisms for improved nitrogen fixation and the exchange of genetic information among strains in nature have also been presented.

Bacteria have evolved various mechanisms to extract utilizable substrates from available resources and consequently acquire fitness advantage over competitors. One of the strategies is the exploitation of cryptic cellular functions encoded by genetic systems that are silent under laboratory conditions, such as the bgl (β-glucoside) operon of E. coli. The bgl operon of Escherichia coli, involved in the uptake and utilization of aromatic β-glucosides salicin and arbutin, is maintained in a silent state in the wild type organism by the presence of structural elements in the regulatory region. This operon can be activated by mutations that disrupt these negative elements. The fact that the silent bgl operon is retained without accumulating deleterious mutations seems paradoxical from an evolutionary view point. Although this operon appears to be silent, specific physiological conditions might be able to regulate its expression and/or the operon might be carrying out function(s) apart from the utilization of aromatic β-glucosides. This is consistent with the observations that the activated operon confers a Growth Advantage in Stationary Phase (GASP) phenotype to Bgl+ cells and exerts its regulation on at least twelve downstream target genes.

The aim of this study was to assess the comparative efficacy of three arbuscular mycorrhizal fungi (AMF) combined with cultivar specific Bradyrhizobium japonicum (CSBJ) in soybean under greenhouse conditions. Soybean seeds of four cultivars namely JS 335, JS 71-05, NRC 2 and NRC 7 were inoculated with three AM fungi (Glomus intraradices, Acaulospora tuberculata and Gigaspora gigantea) and CSBJ isolates, individually or in combination, and were grown in pots using autoclaved alluvial soil of a non-legume cultivated field of Ajmer (Rajasthan). Assessment of the data on nodulation, plant growth and seed yield revealed that amongst the single inoculations of three AMF, G. intraradices produced the largest increases in the parameters studied followed by A. tuberculata and G. gigantea indicating that plant acted selectively on AMF symbiosis. The dual inoculation with AMF + CSBJ further improved these parameters demonstrating synergism between the two microsymbionts. Among all the dual treatments, G. intraradices + B. japonicum brought about the largest increases in the studied characteristics particularly in seed weight per plant that increased up to 115.19%, which suggested that a strong selective synergistic relationship existed between AMF and B. japonicum. The cv. JS 335 exhibited maximum positive response towards inoculation. The variations in efficacy of different treatments with different soybean cultivars indicate the specificity of the inoculation response. These results provide a basis for selection of an appropriate combination of specific AMF and Bradyrhizobium which could further be utilized for verifying the symbiotic effectiveness and competitive ability of microsymbionts under field conditions of Ajmer region.

In the present scenario there are a variety of technical tools for supporting and validating wet-lab experiments in the field of science and biotechnology. In order to analyze biological sequences it is necessary to group similar genes. Grouping of genes can be done by using various techniques like pattern matching, classification, clustering etc. In the present study clustering is used as a tool for analyzing biological data. Clustering of Biological sequences is a very interesting and fascinating area as various researchers are working on it. But simple clustering algorithms are not much suitable for sequence analysis problems. Most of the biological sequence analysis problems are NP-hard and some strong optimization algorithm are required for these types of problems. The manuscript presented here is a survey of various clustering techniques useful for analysis of biological sequences. The 3+ stage review process is adopted for the review of literature. To prepare this report 98 papers have been reviewed from year 1997 to 2014 according to the year of publish. The papers reviewed have discussed various issues related to the analysis of biological sequences. The major issues discovered in the reviewed papers were prediction, sequence alignment, motif discovery, cluster boundary prediction etc. Various solution approaches used by researchers for the biological sequence analysis are evolutionary clustering, neural networks, hierarchical clustering, k-means, Go technologies, feature selection, incremental approach, bio-inspired methods, particle swarm optimization, fuzzy techniques, rough set theory and bi-clustering etc. Researchers have applied these solution approaches on various types of datasets. In this communication we have also discussed about these datasets and the parameters used with results mentioned in papers.

Inheritance is used for re-usability in an object oriented programming language. The complete re-usability is not possible with the simple inheritance process. On inheriting parent class, the child does not get a complete access to the inherited data. Only an instance of the values of inherited data has been accessible by the child class and it is true even for the public data. The present communication is a concept paper in which a special inheritance method has been conceptualized. This proposed method is called backward accessibility inheritance. Once inherited, the public data item of the parent class can be shared by the child class in all sense. This way the inherited data item can be truly reused by child class in terms of memory space, name and value. To control the accessibility of the data during the backward accessibility inheritance, concept of dominance has also been introduced.

We report that the bgl operon of Escherichia coli, encoding the functions necessary for the uptake and metabolism of aryl-beta-glucosides, is involved in the regulation of oligopeptide transport during stationary phase. Global analysis of intracellular proteins from Bgl-positive (Bgl(+)) and Bgl-negative (Bgl(-)) strains revealed that the operon exerts regulation on at least 12 downstream target genes. Of these, oppA, which encodes an oligopeptide transporter, was confirmed to be upregulated in the Bgl(+) strain. Loss of oppA function results in a partial loss of the growth advantage in stationary-phase (GASP) phenotype of Bgl(+) cells. The regulatory effect of the bgl operon on oppA expression is indirect and is mediated via gcvA, the activator of the glycine cleavage system, and gcvB, which regulates oppA at the posttranscriptional level. We show that BglG destabilizes the gcvA mRNA in vivo, leading to reduced expression of gcvA in the stationary phase. Deletion of gcvA results in the downregulation of gcvB and upregulation of oppA and can partially rescue the loss of the GASP phenotype seen in Delta bglG strains. A possible mechanism by which oppA confers a competitive advantage to Bgl(+) cells relative to Bgl(-) cells is discussed.