Search

Combinations of cis-regulatory elements (CREs) present at the promoters facilitate the binding of several transcription factors (TFs), thereby altering the consequent gene expressions. Due to the eminent complexity of the regulatory mechanism, the combinatorics of CRE-mediated transcriptional regulation has been elusive. In this work, we have developed a new methodology that quantifies the co-occurrence tendencies of CREs present in a set of promoter sequences; these co-occurrence scores are filtered in three consecutive steps to test their statistical significance; and the significantly co-occurring CRE pairs are presented as networks. These networks of co-occurring CREs are further transformed to derive higher order of regulatory combinatorics. We have further applied this methodology on the differentially up-regulated gene-sets of rice tissues under fungal (Magnaporthe) infected conditions to demonstrate how it helps to understand the CRE-mediated combinatorial gene regulation. Our analysis includes a wide spectrum of biologically important results. The CRE pairs having a strong tendency to co-occur often exhibit very similar joint distribution patterns at the promoters of rice. We couple the network approach with experimental results of plant gene regulation and defense mechanisms and find evidences of auto and cross regulation among TF families, cross-talk among multiple hormone signaling pathways, similarities and dissimilarities in regulatory combinatorics between different tissues, etc. Our analyses have pointed a highly distributed nature of the combinatorial gene regulation facilitating an efficient alteration in response to fungal attack. All together, our proposed methodology could be an important approach in understanding the combinatorial gene regulation. It can be further applied to unravel the tissue and/or condition specific combinatorial gene regulation in other eukaryotic systems with the availability of annotated genomic sequences and suitable experimental data. [ABSTRACT FROM AUTHOR]

More than 20% of the total caloric intake of human population comes from rice. The expression of rice genes and hence, the concentration of enzymatic proteins might vary due to several biotic and abiotic stresses. It in turn, can influence the overall metabolism and survivability of rice plant. Thus, understanding the rice cellular metabolism, its plasticity and potential readjustments under different perturbations can help rice biotechnologists to design efficient rice cultivars. Here, using the flux balance analysis (FBA) method, with the help of in-silico reaction deletion strategy, we study the metabolic plasticity of genome-scale metabolic model of rice leaf. A set of 131 reactions, essential for the production of primary biomass precursors is identified; deletion of any of them can inhibit the overall biomass production. Usability Index (IU) for the rest of the reactions are estimated and based on this parameter, they are classified into three categories—maximally-favourable, quasi-favourable and unfavourable for the primary biomass production. The lower value of 1 − IU of a reaction suggests that the cell cannot easily bypass it for biomass production. While some of the alternative paths are energetically equally efficient, others demand for higher photon. The variations in (i) ATP/NADPH ratio, (ii) exchange of metabolites through chloroplastic transporters and (iii) total biomass production are also presented here. Mutual metabolic dependencies of different cellular compartments are also demonstrated. [ABSTRACT FROM AUTHOR]

Background: Among the vertebrates, teleost and urodele amphibians are capable of regenerating their central nervous system. We have used zebrafish as a model to study spinal cord injury and regeneration. Relatively little is known about the molecular mechanisms underlying spinal cord regeneration and information based on high density oligonucleotide microarray was not available. We have used a high density microarray to profile the temporal transcriptome dynamics during the entire phenomenon. Results: A total of 3842 genes expressed differentially with significant fold changes during spinal cord regeneration. Cluster analysis revealed event specific dynamic expression of genes related to inflammation, cell death, cell migration, cell proliferation, neurogenesis, neural patterning and axonal regrowth. Spatio-temporal analysis of stat3 expression suggested its possible function in controlling inflammation and cell proliferation. Genes involved in neurogenesis and their dorso-ventral patterning (sox2 and dbx2) are differentially expressed. Injury induced cell proliferation is controlled by many cell cycle regulators and some are commonly expressed in regenerating fin, heart and retina. Expression pattern of certain pathway genes are identified for the first time during regeneration of spinal cord. Several genes involved in PNS regeneration in mammals like stat3, socs3, atf3, mmp9 and sox11 are upregulated in zebrafish SCI thus creating PNS like environment after injury. Conclusion: Our study provides a comprehensive genetic blue print of diverse cellular response(s) during regeneration of zebrafish spinal cord. The data highlights the importance of different event specific gene expression that could be better understood and manipulated further to induce successful regeneration in mammals. [ABSTRACT FROM AUTHOR]

Here we compare the structural and evolutionary attributes of Thermus thermophilus and Escherichia coli small ribosomal subunits (SSU). Our results indicate that with few exceptions, thermophilic 16S ribosomal RNA (16S rRNA) is densely packed compared to that of mesophilic at most of the analogous spatial regions. In addition, we have located species-specific cavity clusters (SSCCs) in both species. E. coli SSCCs are numerous and larger compared to T. thermophilus SSCCs, which again indicates densely packed thermophilic 16S rRNA. Thermophilic ribosomal proteins (r-proteins) have longer disordered regions than their mesophilic homologs and they experience larger disorder-to-order transitions during SSU-assembly. This is reflected in the predicted higher conformational changes of thermophilic r-proteins compared to their mesophilic homologs during SSU-assembly. This high conformational change of thermophilic r-proteins may help them to associate with the 16S ribosomal RNA with high complementary interfaces, larger interface areas, and denser molecular contacts, compared to those of mesophilic. Thus, thermophilic protein-rRNA interfaces are tightly associated with 16S rRNA than their mesophilic homologs. Densely packed 16S rRNA interior and tight protein-rRNA binding of T. thermophilus (compared to those of E. coli) are likely the signatures of its thermal adaptation. We have found a linear correlation between the free energy of protein-RNA interface formation, interface size, and square of conformational changes, which is followed in both prokaryotic and eukaryotic SSU. Disorder is associated with high protein-RNA interface polarity. We have found an evolutionary tendency to maintain high polarity (thereby disorder) at protein-rRNA interfaces, than that at rest of the protein structures. However, some proteins exhibit exceptions to this general trend. [ABSTRACT FROM AUTHOR]

Risk prediction for a particular disease in a population through SNP genotyping exploits tests whose primary goal is to rank the SNPs on the basis of their disease association. This manuscript reveals a different approach of predicting the risk through network representation by using combined genotypic data (instead of a single allele/haplotype). The aim of this study is to classify diseased group and prediction of disease risk by identifying the responsible genotype. Genotypic combination is chosen from five independent loci present on platelet receptor genes P2RY1 and P2RY12. Genotype-sets constructed from combinations of genotypes served as a network input, the network architecture constituting super-nodes (e.g., case and control) and nodes representing individuals, each individual is described by a set of genotypes containing M markers (M = number of SNP). The analysis becomes further enriched when we consider a set of networks derived from the parent network. By maintaining the super-nodes identical, each network is carrying an independent combination of M-1 markers taken from M markers. For each of the network, the ratio of case specific and control specific connections vary and the ratio of super-node specific connection shows variability. This method of network has also been applied in another case-control study which includes oral cancer, precancer and control individuals to check whether it improves presentation and interpretation of data. The analyses reveal a perfect segregation between super-nodes, only a fraction of mixed state being connected to both the super-nodes (i.e. common genotype set). This kind of approach is favorable for a population to classify whether an individual with a particular genotypic combination can be in a risk group to develop disease. In addition with that we can identify the most important polymorphism whose presence or absence in a population can make a large difference in the number of case and control individuals. [ABSTRACT FROM AUTHOR]

Here we compare the structural and evolutionary attributes of Thermus thermophilus and Escherichia coli small ribosomal subunits (SSU). Our results indicate that with few exceptions, thermophilic 16S ribosomal RNA (16S rRNA) is densely packed compared to that of mesophilic at most of the analogous spatial regions. In addition, we have located species-specific cavity clusters (SSCCs) in both species. E. coli SSCCs are numerous and larger compared to T. thermophilus SSCCs, which again indicates densely packed thermophilic 16S rRNA. Thermophilic ribosomal proteins (r-proteins) have longer disordered regions than their mesophilic homologs and they experience larger disorder-to-order transitions during SSU-assembly. This is reflected in the predicted higher conformational changes of thermophilic r-proteins compared to their mesophilic homologs during SSU-assembly. This high conformational change of thermophilic r-proteins may help them to associate with the 16S ribosomal RNA with high complementary interfaces, larger interface areas, and denser molecular contacts, compared to those of mesophilic. Thus, thermophilic protein-rRNA interfaces are tightly associated with 16S rRNA than their mesophilic homologs. Densely packed 16S rRNA interior and tight protein-rRNA binding of T. thermophilus (compared to those of E. coli) are likely the signatures of its thermal adaptation. We have found a linear correlation between the free energy of protein-RNA interface formation, interface size, and square of conformational changes, which is followed in both prokaryotic and eukaryotic SSU. Disorder is associated with high protein-RNA interface polarity. We have found an evolutionary tendency to maintain high polarity (thereby disorder) at protein-rRNA interfaces, than that at rest of the protein structures. However, some proteins exhibit exceptions to this general trend.