The origins of the evolutionary signal used to predict protein-protein interactions

Background: The correlation of genetic distances between pairs of protein sequence alignments has been used to infer protein-protein interactions. It has been suggested that these correlations are based on the signal of co-evolution between interacting proteins. However, although mutations in different proteins associated with maintaining an interaction clearly occur (particularly in binding interfaces and neighbourhoods), many other factors contribute to correlated rates of sequence evolution. Proteins in the same genome are usually linked by shared evolutionary history and so it would be expected that there would be topological similarities in their phylogenetic trees, whether they are interacting or not. For this reason the underlying species tree is often corrected for. Moreover processes such as expression level, are known to effect evolutionary rates. However, it has been argued that the correlated rates of evolution used to predict protein interaction explicitly includes shared evolutionary history; here we test this hypothesis. Results: In order to identify the evolutionary mechanisms giving rise to the correlations between interaction proteins, we use phylogenetic methods to distinguish similarities in tree topologies from similarities in genetic distances. We use a range of datasets of interacting and non-interacting proteins from Saccharomyces cerevisiae. We find that the signal of correlated evolution between interacting proteins is predominantly a result of shared evolutionary rates, rather than similarities in tree topology, independent of evolutionary divergence. Conclusions: Since interacting proteins do not have tree topologies that are more similar than the control group of non-interacting proteins, it is likely that coevolution does not contribute much to, if any, of the observed correlations.

Combination of similarity measures for time series classification using genetic algorithms

Time series classification deals with the problem of classification of data that is multivariate in nature. This means that one or more of the attributes is in the form of a sequence. The notion of similarity or distance, used in time series data, is significant and affects the accuracy, time, and space complexity of the classification algorithm. There exist numerous similarity measures for time series data, but each of them has its own disadvantages. Instead of relying upon a single similarity measure, our aim is to find the near optimal solution to the classification problem by combining different similarity measures. In this work, we use genetic algorithms to combine the similarity measures so as to get the best performance. The weightage given to different similarity measures evolves over a number of generations so as to get the best combination. We test our approach on a number of benchmark time series datasets and present promising results.

MULTI-OBJECTIVE GENETIC ALGORITHM FOR EFFICIENT POINT MATCHING IN MULTI-SENSOR SATELLITE IMAGE

This paper investigates a new approach for point matching in multi-sensor satellite images. The feature points are matched using multi-objective optimization (angle criterion and distance condition) based on Genetic Algorithm (GA). This optimization process is more efficient as it considers both the angle criterion and distance condition to incorporate multi-objective switching in the fitness function. This optimization process helps in matching three corresponding corner points detected in the reference and sensed image and thereby using the affine transformation, the sensed image is aligned with the reference image. From the results obtained, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient.

Fast and accurate quantification using Genetic Algorithm optimized H-1-C-13 refocused constant-time INEPT

Using Genetic Algorithm, a global optimization method inspired by nature's evolutionary process, we have improved the quantitative refocused constant-time INEPT experiment (Q-INEPT-CT) of Makela et al. (JMR 204 (2010) 124-130) with various optimization constraints. The improved `average polarization transfer' and `min-max difference' of new delay sets effectively reduces the experimental time by a factor of two (compared with Q-INEPT-CT, Makela et al.) without compromising on accuracy. We also discuss a quantitative spectral editing technique based on average polarization transfer. (C) 2013 Elsevier Inc. All rights reserved.

Accurate point matching based on multi-objective Genetic Algorithm for multi-sensor satellite imagery

This paper investigates a novel approach for point matching of multi-sensor satellite imagery. The feature (corner) points extracted using an improved version of the Harris Corner Detector (HCD) is matched using multi-objective optimization based on a Genetic Algorithm (GA). An objective switching approach to optimization that incorporates an angle criterion, distance condition and point matching condition in the multi-objective fitness function is applied to match corresponding corner-points between the reference image and the sensed image. The matched points obtained in this way are used to align the sensed image with a reference image by applying an affine transformation. From the results obtained, the performance of the image registration is evaluated and compared with existing methods, namely Nearest Neighbor-Random SAmple Consensus (NN-Ran-SAC) and multi-objective Discrete Particle Swarm Optimization (DPSO). From the performed experiments it can be concluded that the proposed approach is an accurate method for registration of multi-sensor satellite imagery. (C) 2014 Elsevier Inc. All rights reserved.

Fluctuating micro-heterogeneity in water-tert-butyl alcohol mixtures and lambda-type divergence of the mean cluster size with phase transition-like multiple anomalies

Water-tert-butyl alcohol (TBA) binary mixture exhibits a large number of thermodynamic and dynamic anomalies. These anomalies are observed at surprisingly low TBA mole fraction, with x(TBA) approximate to 0.03-0.07. We demonstrate here that the origin of the anomalies lies in the local structural changes that occur due to self-aggregation of TBA molecules. We observe a percolation transition of the TBA molecules at x(TBA) approximate to 0.05. We note that ``islands'' of TBA clusters form even below this mole fraction, while a large spanning cluster emerges above that mole fraction. At this percolation threshold, we observe a lambda-type divergence in the fluctuation of the size of the largest TBA cluster, reminiscent of a critical point. Alongside, the structure of water is also perturbed, albeit weakly, by the aggregation of TBA molecules. There is a monotonic decrease in the tetrahedral order parameter of water, while the dipole moment correlation shows a weak nonlinearity. Interestingly, water molecules themselves exhibit a reverse percolation transition at higher TBA concentration, x(TBA) approximate to 0.45, where large spanning water clusters now break-up into small clusters. This is accompanied by significant divergence of the fluctuations in the size of largest water cluster. This second transition gives rise to another set of anomalies around. Both the percolation transitions can be regarded as manifestations of Janus effect at small molecular level. (C) 2014 AIP Publishing LLC.

A Genetic Analysis of the Functional Interactions within Mycobacterium tuberculosis Single-Stranded DNA Binding Protein

Single-stranded DNA binding proteins (SSBs) are vital in all organisms. SSBs of Escherichia coli (EcoSSB) and Mycobacterium tuberculosis (MtuSSB) are homotetrameric. The N-terminal domains (NTD) of these SSBs (responsible for their tetramerization and DNA binding) are structurally well defined. However, their C-terminal domains (CTD) possess undefined structures. EcoSSB NTD consists of beta 1-beta 1'-beta 2-beta 3-alpha-beta 4-beta 45(1)-beta 45(2)-beta 5 secondary structure elements. MtuSSB NTD includes an additional beta-strand (beta 6) forming a novel hook-like structure. Recently, we observed that MtuSSB complemented an E. coli Delta ssb strain. However, a chimeric SSB (m beta 4-beta 5), wherein only the terminal part of NTD (beta 4-beta 5 region possessing L-45 loop) of EcoSSB was substituted with that from MtuSSB, failed to function in E. coli in spite of its normal DNA binding and oligomerization properties. Here, we designed new chimeras by transplanting selected regions of MtuSSB into EcoSSB to understand the functional significance of the various secondary structure elements within SSB. All chimeric SSBs formed homotetramers and showed normal DNA binding. The m beta 4-beta 6 construct obtained by substitution of the region downstream of beta 5 in m beta 4-beta 5 SSB with the corresponding region (beta 6) of MtuSSB complemented the E. coli strain indicating a functional interaction between the L-45 loop and the beta 6 strand of MtuSSB.

Paralogous cAMP Receptor Proteins in Mycobacterium smegmatis Show Biochemical and Functional Divergence

The cyclic AMP receptor protein (CRP) family of transcription factors consists of global regulators of bacterial gene expression. Here, we identify two paralogous CRPs in the genome of Mycobacterium smegmatis that have 78% identical sequences and characterize them biochemically and functionally. The two proteins (MSMEG_0539 and MSMEG_6189) show differences in cAMP binding affinity, trypsin sensitivity, and binding to a CRP site that we have identified upstream of the msmeg_3781 gene. MSMEG_6189 binds to the CRP site readily in the absence of cAMP, while MSMEG_0539 binds in the presence of cAMP, albeit weakly. msmeg_6189 appears to be an essential gene, while the ?msmeg_0539 strain was readily obtained. Using promoter-reporter constructs, we show that msmeg_3781 is regulated by CRP binding, and its transcription is repressed by MSMEG_6189. Our results are the first to characterize two paralogous and functional CRPs in a single bacterial genome. This gene duplication event has subsequently led to the evolution of two proteins whose biochemical differences translate to differential gene regulation, thus catering to the specific needs of the organism.

Sun skink diversification across the Indian-Southeast Asian biogeographical interface

Aim Widespread, transcontinental vertebrate groups represent ideal systems for biogeographical studies, because they can shed light on a wide range of questions relating to species diversification across the geographical template. We combined extensive geographical and genetic sampling from across multiple biogeographical realms to examine the timing and location of diversification in Asian sun skinks, a clade characterized by problematic species boundaries and a particularly enigmatic evolutionary history. Location Indian subcontinent, the Philippines, Southeast Asia and Sundaland. Methods We sequenced one mitochondrial and nine nuclear genes for most species in the genus Eutropis, and estimated phylogenetic relationships and divergence times using coalescent methods. To investigate the location of diversification events, we also estimated ancestral geographical ranges using several methods. Finally, we explored patterns of genetic diversity within several poorly understood, but widely distributed species. Results Divergence-time estimates indicate that Eutropis began to diversify during the Eocene. Biogeographical reconstructions show that species diversification was associated with dispersal into three biogeographical realms: India, Sundaland and the Philippines. Main conclusions The results of this study clarify several questions related to the evolutionary history of Eutropis, and place them in the context of classic Southeast Asian biogeography. Our study represents one of the first to compile a heavily sampled multilocus dataset ranging across international boundaries in southern Asia that have historically prevented a unified understanding of biogeographical and evolutionary processes involving the Indian subcontinent, mainland southern Asia and the island archipelagos of Southeast Asia.

Colored polydimethylsiloxane micropillar arrays for high throughput measurements of forces applied by genetic model organisms

Measuring forces applied by multi-cellular organisms is valuable in investigating biomechanics of their locomotion. Several technologies have been developed to measure such forces, for example, strain gauges, micro-machined sensors, and calibrated cantilevers. We introduce an innovative combination of techniques as a high throughput screening tool to assess forces applied by multiple genetic model organisms. First, we fabricated colored Polydimethylsiloxane (PDMS) micropillars where the color enhances contrast making it easier to detect and track pillar displacement driven by the organism. Second, we developed a semiautomated graphical user interface to analyze the images for pillar displacement, thus reducing the analysis time for each animal to minutes. The addition of color reduced the Young's modulus of PDMS. Therefore, the dye-PDMS composite was characterized using Yeoh's hyperelastic model and the pillars were calibrated using a silicon based force sensor. We used our device to measure forces exerted by wild type and mutant Caenorhabditis elegans moving on an agarose surface. Wild type C. elegans exert an average force of similar to 1 mu N on an individual pillar and a total average force of similar to 7.68 mu N. We show that the middle of C. elegans exerts more force than its extremities. We find that C. elegans mutants with defective body wall muscles apply significantly lower force on individual pillars, while mutants defective in sensing externally applied mechanical forces still apply the same average force per pillar compared to wild type animals. Average forces applied per pillar are independent of the length, diameter, or cuticle stiffness of the animal. We also used the device to measure, for the first time, forces applied by Drosophila melanogaster larvae. Peristaltic waves occurred at 0.4Hz applying an average force of similar to 1.58 mu N on a single pillar. Our colored microfluidic device along with its displacement tracking software allows us to measure forces applied by multiple model organisms that crawl or slither to travel through their environment. (C) 2015 AIP Publishing LLC.

Genetic diversity and proviral DNA load in different neural compartments of HIV-1 subtype C infection

In India, the low prevalence of HIV-associated dementia (HAD) in the Human immunodeficiency virus type 1 (HIV-1) subtype C infection is quite paradoxical given the high-rate of macrophage infiltration into the brain. Whether the direct viral burden in individual brain compartments could be associated with the variability of the neurologic manifestations is controversial. To understand this paradox, we examined the proviral DNA load in nine different brain regions and three different peripheral tissues derived from ten human subjects at autopsy. Using a highly sensitive TaqMan probe-based real-time PCR, we determined the proviral load in multiple samples processed in parallel from each site. Unlike previously published reports, the present analysis identified uniform proviral distribution among the brain compartments examined without preferential accumulation of the DNA in any one of them. The overall viral DNA burden in the brain tissues was very low, approximately 1 viral integration per 1000 cells or less. In a subset of the tissue samples tested, the HIV DNA mostly existed in a free unintegrated form. The V3-V5 envelope sequences, demonstrated a brain-specific compartmentalization in four of the ten subjects and a phylogenetic overlap between the neural and non-neural compartments in three other subjects. The envelope sequences phylogenetically belonged to subtype C and the majority of them were R5 tropic. To the best of our knowledge, the present study represents the first analysis of the proviral burden in subtype C postmortem human brain tissues. Future studies should determine the presence of the viral antigens, the viral transcripts, and the proviral DNA, in parallel, in different brain compartments to shed more light on the significance of the viral burden on neurologic consequences of HIV infection.

Taming Control Divergence in GPUs through Control Flow Linearization

Branch divergence is a very commonly occurring performance problem in GPGPU in which the execution of diverging branches is serialized to execute only one control flow path at a time. Existing hardware mechanism to reconverge threads using a stack causes duplicate execution of code for unstructured control flow graphs. Also the stack mechanism cannot effectively utilize the available parallelism among diverging branches. Further, the amount of nested divergence allowed is also limited by depth of the branch divergence stack. In this paper we propose a simple and elegant transformation to handle all of the above mentioned problems. The transformation converts an unstructured CFG to a structured CFG without duplicating user code. It incurs only a linear increase in the number of basic blocks and also the number of instructions. Our solution linearizes the CFG using a predicate variable. This mechanism reconverges the divergent threads as early as possible. It also reduces the depth of the reconvergence stack. The available parallelism in nested branches can be effectively extracted by scheduling the basic blocks to reduce the effect of stalls due to memory accesses. It can also increase execution efficiency of nested loops with different trip counts for different threads. We implemented the proposed transformation at PTX level using the Ocelot compiler infrastructure. We evaluated the technique using various benchmarks to show that it can be effective in handling the performance problem due to divergence in unstructured CFGs.

Draft genome of a commonly misdiagnosed multidrug resistant pathogen Candida auris

Background: Candida auris is a multidrug resistant, emerging agent of fungemia in humans. Its actual global distribution remains obscure as the current commercial methods of clinical diagnosis misidentify it as C. haemulonii. Here we report the first draft genome of C. auris to explore the genomic basis of virulence and unique differences that could be employed for differential diagnosis. Results: More than 99.5 % of the C. auris genomic reads did not align to the current whole (or draft) genome sequences of Candida albicans, Candida lusitaniae, Candida glabrata and Saccharomyces cerevisiae; thereby indicating its divergence from the active Candida clade. The genome spans around 12.49 Mb with 8527 predicted genes. Functional annotation revealed that among the sequenced Candida species, it is closest to the hemiascomycete species Clavispora lusitaniae. Comparison with the well-studied species Candida albicans showed that it shares significant virulence attributes with other pathogenic Candida species such as oligopeptide transporters, mannosyl transfersases, secreted proteases and genes involved in biofilm formation. We also identified a plethora of transporters belonging to the ABC and major facilitator superfamily along with known MDR transcription factors which explained its high tolerance to antifungal drugs. Conclusions: Our study emphasizes an urgent need for accurate fungal screening methods such as PCR and electrophoretic karyotyping to ensure proper management of fungemia. Our work highlights the potential genetic mechanisms involved in virulence and pathogenicity of an important emerging human pathogen namely C. auris. Owing to its diversity at the genomic scale; we expect the genome sequence to be a useful resource to map species specific differences that will help develop accurate diagnostic markers and better drug targets.

Genetic analysis of Indian tasar silkmoth (Antheraea mylitta) populations

Indian tasar silkmoth, Antheraea mylitta is an economically important wild silkmoth species distributed across India. A number of morphologically and ethologically well-defined ecotypes are known for this species that differ in their primary food plant specificity. Most of these ecotypes do not interbreed in nature, but are able to produce offspring under captive conditions. Microsatellite markers were developed for A. mylitta, and out of these, ten well-behaved microsatellite loci were used to analyze the population structure of different ecoraces. A total of 154 individual moths belonging to eight different ecoraces, were screened at each locus. Hierarchical analysis of population structure using Analysis of MOlecular VAriance (AMOVA) revealed significant structuring (F-ST = 0.154) and considerable inbreeding (F-IS = 0.505). A significant isolation by distance was also observed. The number of possible population clusters was investigated using distance method, Bayesian algorithm and self organization maps (SOM). The first two methods revealed two distinct clusters, whereas the SOM showed the different ecoraces not to be clearly differentiated. These results suggest that although there is a large degree of phenotypic variation among the different ecoraces of A. mylitta, genetically they are not very different, and the phenotypic differences may largely be a result of their respective ecology.