Iodine

Among the most widespread seabirds in the world, shearwaters of the genus Puffinus are also some of the deepest diving members of the Procellariiformes. Maximum diving depths are known for several Puffinus species, but dive depths or diving behaviour have never been recorded for great shearwaters (P. gravis), the largest member of this genus. This study reports the first high sampling rate (2 s) of depth and diving behaviour for Puffinus shearwaters.

Time-depth recorders (TDRs) were deployed on two female great shearwaters nesting on Inaccessible Island in the South Atlantic Ocean, recording 10 consecutive days of diving activity. Remote sensing imagery and movement patterns of 8 males tracked by satellite telemetry over the same period were used to identify probable foraging areas used by TDR-equipped females. The deepest and longest dive was to 18.9 m and lasted 40 s, but most (>50%) dives were <2 m deep. Diving was most frequent near dawn and dusk, with <0.5% of dives occurring at night. The two individuals foraged in contrasting oceanographic conditions, one in cold (8 to 10°C) water of the Sub-Antarctic Front, likely 1000 km south of the breeding colony, and the other in warmer (10 to 16°C) water of the Sub-tropical Frontal Zone, at the same latitude as the colony, possibly on the Patagonian Shelf, 4000 km away. The cold water bird spent fewer days commuting, conducted four times as many dives as the warm water bird, dived deeper on average, and had a greater proportion of bottom time during dives.

General patterns of diving activity were consistent with those of other shearwaters foraging in cold and warm water habitats. Great shearwaters are likely adapted to forage in a wide range of oceanographic conditions, foraging mostly with shallow dives but capable of deep diving.

Characterization of the epigenetic profile of humans since the initial breakthrough on the human genome project has strongly established the key role of histone modifications and DNA methylation. These dynamic elements interact to determine the normal level of expression or methylation status of the constituent genes in the genome. Recently, considerable evidence has been put forward to demonstrate that environmental stress implicitly alters epigenetic patterns causing imbalance that can lead to cancer initiation. This chain of consequences has motivated attempts to computationally model the influence of histone modification and DNA methylation in gene expression and investigate their intrinsic interdependency. In this paper, we explore the relation between DNA methylation and transcription and characterize in detail the histone modifications for specific DNA methylation levels using a stochastic approach.

Starch is one of the major dietary energy sources for mammals. However, the nutritional value of starch largely depends on its amylose and amylopectin ratio. In this study, the overall metabolic and transcriptomic responses of weaned pigs fed with different dietary starches were assessed. Sixteen weaned pigs were randomly allotted to two experimental diets containing either of pure cassava starch (CS) or maize starch (MS) as the sole energy source (the amylose-amylopectin ratio were 0.25 and 0.43, respectively). Results indicated that the body weight gain was not affected by different dietary starches. However, a moderate fatty liver was observed in CS-fed group. Long-term ingestion of CS not only increased the total liver fat content, but significantly elevated the liver triglyceride and cholesterol content (P<0.05). In addition, the serum insulin and cholesterol concentrations were both elevated in CS-fed group (P<0.05). Microarray analysis led to the identification of 648 genes differentially expressed in liver (P<0.05), and a lot of them were involved in lipid and carbohydrate metabolism. Additionally, pathway analysis indicated that both the insulin and PPAR signaling pathways were acutely affected by dietary amylose-amylopectin ratio. Long-term ingestion of CS activated the transcription of lipogenic genes such as hmgr and fasn, but decreased the expression of lipolytic genes such as aox1, ppara and fbp. The microarray results correlated well with the measurements of several key enzymes involved in hepatic lipid metabolism. Our results suggested that both the metabolic and transcriptomic responses of weaned pigs were tightly regulated by dietary starch composition, and a high amylose ratio starch (i.e MS) may be more healthful for mammals as the long-term energy source by down-regulation of hepatic lipogenesis and steroidogenesis.

β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC = 0.50, Q_total = 82.1%, sensitivity = 75.6%, PPV = 68.8% and AUC = 0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17 – 0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively.

The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences.

Despite many attempts to establish pre-treatment prognostic markers to understand the clinical biology of esophageal adenocarcinoma (EAC), validated clinical biomarkers or parameters remain elusive. We generated and analyzed tumor transcriptome to develop a practical biomarker prognostic signature in EAC.

Untreated esophageal endoscopic biopsy specimens were obtained from 64 patients undergoing surgery and chemoradiation. Using DNA microarray technology, genome-wide gene expression profiling was performed on 75 untreated cancer specimens from 64 EAC patients. By applying various statistical and informatical methods to gene expression data, we discovered distinct subgroups of EAC with differences in overall gene expression patterns and identified potential biomarkers significantly associated with prognosis. The candidate marker genes were further explored in formalin-fixed, paraffin-embedded tissues from an independent cohort (52 patients) using quantitative RT-PCR to measure gene expression. We identified two genes whose expression was associated with overall survival in 52 EAC patients and the combined 2-gene expression signature was independently associated with poor outcome (P<0.024) in the multivariate Cox hazard regression analysis.

Our findings suggest that the molecular gene expression signatures are associated with prognosis of EAC patients and can be assessed prior to any therapy. This signature could provide important improvement for the management of EAC patients.

C-di-GMP, a bacterial second messenger plays a key role in survival and adaptation of bacteria under different environmental conditions. The level of c-di-GMP is regulated by two opposing activities, namely diguanylate cyclase (DGC) and phosphodiesterase (PDE-A) exhibited by GGDEF and EAL domain, respectively in the same protein. Previously, we reported a bifunctional GGDEF–EAL domain protein, MSDGC-1 from Mycobacterium smegmatis showing both these activities (Kumar and Chatterji, 2008). In this current report, we have identified and characterized the homologous protein from Mycobacterium tuberculosis (Rv 1354c) named as MtbDGC. MtbDGC is also a bifunctional protein, which can synthesize and degrade c-di-GMP in vitro. Further we expressed Mtbdgc in M. smegmatis and it was able to complement the MSDGC-1 knock out strain by restoring the long term survival of M. smegmatis. Another protein Rv 1357c, named as MtbPDE, is an EAL domain protein and degrades c-di-GMP to pGpG in vitro. Rv1354c and 1357c have seven cysteine amino acids in their sequence, distributed along the full length of the protein. Disulfide bonds play an important role in stabilizing protein structure and regulating protein function. By proteolytic digestion and mass spectrometric analysis of MtbDGC, connectivity between cysteine pairs Cys⁹⁴-Cys⁵⁸⁴, Cys²-Cys⁴⁷⁹ and Cys⁴²⁹-Cys⁶¹⁴ was determined, whereas the third cysteine (Cys⁴⁰⁶) from N terminal was found to be free in MtbDGC protein, which was further confirmed by alkylation with iodoacetamide labeling. Bioinformatics modeling investigations also supported the pattern of disulfide connectivity obtained by Mass spectrometric analysis. Cys⁴⁰⁶ was mutated to serine by site directed mutagenesis and the mutant MtbC406S was not found to be active and was not able to synthesize or degrade c-di-GMP. The disulfide connectivity established here would help further in understanding the structure – function relationship in MtbDGC.

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies.

The organization of protein structures in protein genotype space is well studied. The same does not hold for protein functions, whose organization is important to understand how novel protein functions can arise through blind evolutionary searches of sequence space. In systems other than proteins, two organizational features of genotype space facilitate phenotypic innovation. The first is that genotypes with the same phenotype form vast and connected genotype networks. The second is that different neighborhoods in this space contain different novel phenotypes. We here characterize the organization of enzymatic functions in protein genotype space, using a data set of more than 30,000 proteins with known structure and function. We show that different neighborhoods of genotype space contain proteins with very different functions. This property both facilitates evolutionary innovation through exploration of a genotype network, and it constrains the evolution of novel phenotypes. The phenotypic diversity of different neighborhoods is caused by the fact that some functions can be carried out by multiple structures. We show that the space of protein functions is not homogeneous, and different genotype neighborhoods tend to contain a different spectrum of functions, whose diversity increases with increasing distance of these neighborhoods in sequence space. Whether a protein with a given function can evolve specific new functions is thus determined by the protein's location in sequence space.

Phenotypic behavior of a group of organisms can be studied using a range of molecular evolutionary tools that help to determine evolutionary relationships. Traditionally a gene or a set of gene sequences was used for generating phylogenetic trees. Incomplete evolutionary information in few selected genes causes problems in phylogenetic tree construction. Whole genomes are used as remedy. Now, the task is to identify the suitable parameters to extract the hidden information from whole genome sequences that truly represent evolutionary information. In this study we explored a random anchor (a stretch of 100 nucleotides) based approach (ABWGP) for finding distance between any two genomes, and used the distance estimates to compute evolutionary trees. A number of strains and species of Mycobacteria were used for this study. Anchor-derived parameters, such as cumulative normalized score, anchor order and indels were computed in a pair-wise manner, and the scores were used to compute distance/phylogenetic trees. The strength of branching was determined by bootstrap analysis. The terminal branches are clearly discernable using the distance estimates described here. In general, different measures gave similar trees except the trees based on indels. Overall the tree topology reflected the known biology of the organisms. This was also true for different strains of Escherichia coli. A new whole genome-based approach has been described here for studying evolutionary relationships among bacterial strains and species.

To understand how symmetric structures of many proteins are formed from asymmetric sequences, the proteins with two repeated beta-trefoil domains in Plant Cytotoxin B-chain family and all presently known beta-trefoil proteins are analyzed by structure-based multi-sequence alignments. The results show that all these proteins have similar key structural residues that are distributed symmetrically in their structures. These symmetric key structural residues are further analyzed in terms of inter-residues interaction numbers and B-factors. It is found that they can be distinguished from other residues and have significant propensities for structural framework. This indicates that these key structural residues may conduct the formation of symmetric structures although the sequences are asymmetric.