Stereochemical studies on cyclic peptides: Detailed energy minimization studies on hydrogen bonded all-trans cyclic pentapeptide backbones

Conformational studies have been carried out on hydrogenbonded all-trans cyclic pentapeptide backbone. Application of a combination of grid search and energy minimization on this system has resulted in obtaining 23 minimum energy conformations, which are characterized by unique patterns of hydrogen bonding comprising of β- and γ-turns. A study of the minimum energy conformationsvis-a-vis non-planar deviation of the peptide units reveals that non-planarity is an inherent feature in many cases. A study on conformational clustering of minimum energy conformations shows that the minimum energy conformations fall into 6 distinct conformational families. Preliminary comparison with available X-ray structures of cyclic pentapeptide indicates that only some of the minimum energy conformations have formed crystal structures. The set of minimum energy conformations worked out in the present study can form a consolidated database of prototypes for hydrogen bonded backbone and be useful for modelling cyclic pentapeptides both synthetic and bioactive in nature.

Lipid assisted membrane entry in specific phospholipid targeted protein systems

The purpose of this thesis project is to study changes in the physical state of cell membranes during cell entry, including how these changes are connected to the presence of ceramide. The role of enzymatical manipulation of lipids in bacterial internalization is also studied. A novel technique, where a single giant vesicle is chosen under the microscope and an enzyme coupled-particle attached to the micromanipulator pipette towards the vesicle, is used. Thus, the enzymatic reaction on the membrane of the giant vesicle can be followed in real-time. The first aim of this study is to develop a system where the localized sphingomyelinase membrane interaction could be observed on the surface of the giant vesicle and the effects could be monitored with microscopy. Domain formation, which resembles acid sphingomyelinase (ASMase), causes CD95 clustering in the cell membrane due to ceramide production (Grassmé et al., 2001a; Grassmé et al., 2001b) and the formation of small vesicles inside the manipulated giant vesicle is observed. Sphingomyelinase activation has also been found to be an important factor in the bacterial and viral invasion process in nonphagocytic cells (Grassmé et al., 1997; Jan et al., 2000). Accordingly, sphingomyelinase reactions in the cell membrane might also give insight into bacterial or viral cellular entry events. We found sphingomyelinase activity in Chlamydia pneumonia elementarybodies (EBs). Interestingly, the bacterium enters host cells by endocytosis but the internalization mechanism of Chlamydia is unknown. The hypothesis is that sphingomyelin is needed for host cell entry in the infection of C. pneumonia. The second project focuses on this subject. The goal of the third project is to study a role of phosphatidylserine as a target for a membrane binding protein. Phosphatidylserine is chosen because of its importance in fusion processes. This will be another example for the importance of lipids in cell targeting, internalization, and externalization.

Bustle behind the scenes of action : nemosis as a model for fibroblast inflammatory activation

Wound healing is a complex process that requires an interplay between several cell types. Classically, fibroblasts have been viewed as producers of extracellular matrix, but more recently they have been recognized as orchestrators of the healing response, promoting and directing, inflammation and neovascularization processes. Compared to those from healthy tissue, inflammation-associated fibroblasts display a dramatically altered phenotype and have been described as sentinel cells, able to switch to an immunoregulatory profile on cue. However, the activation mechanism still remains largely uncharacterized. Nemosis is a model for stromal fibroblast activation. When normal human primary fibroblasts are deprived of growth support they cluster, forming multicellular spheroids. Clustering results in upregulation of proinflammatory markers such as cyclooxygenase-2 and secretion of prostaglandins, proteinases, cytokines, and growth factors. Fibroblasts in nemosis induce wound healing and tumorigenic responses in many cell types found in inflammatory and tumor microenvironments. This study investigated the effect of nemotic fibroblasts on two components of the vascular system, leukocytes and endothelium, and characterized the inflammation-promoting responses that arose in these cell types. Fibroblasts in nemosis were found to secrete an array of chemotactic cytokines and attract leukocytes, as well as promote their adhesion to the endothelium. Nuclear factor-kB, the master regulator of many inflammatory responses, is activated in nemotic fibroblasts. Nemotic fibroblasts are known to produce large amounts of hepatocyte growth factor, a motogenic and angiogenic factor. Also, as shown in this study, they produce vascular endothelial growth factor. These two factors induced migratory and sprouting responses in endothelial cells, both required for neovascularization. Nemotic fibroblasts also caused a decrease in the expression of adherens and tight junction components on the surface of endothelial cells. The results allow the conclusion that fibroblasts in nemosis share many similarities with inflammation-associated fibroblasts. Both inflammation and stromal fibroblasts are known to be involved in tumorigenesis and tumor progression. Nemosis may be viewed as a model for stromal fibroblast activation, or it may correlate with cell-cell interactions between adjacent fibroblasts in vivo. Nevertheless, due to nemosis-derived production of proinflammatory cytokines and growth factors, fibroblast nemosis may have therapeutic potential as an inducer of controlled tissue repair. Knowledge of stromal fibroblast activation gained through studies of nemosis, could provide new strategies to control unwanted inflammation and tumor progression.

Molecular Characterization of Sclerospora graminicola, the Incitant of Pearl Millet Downy Mildew Using ISSR Markers

The DNA polymorphism among 22 isolates of Sclerospora graminicola, the causal agent of downy mildew disease of pearl millet was assessed using 20 inter simple sequence repeats (ISSR) primers. The objective of the study was to examine the effectiveness of using ISSR markers for unravelling the extent and pattern of genetic diversity in 22 S. graminicola isolates collected from different host cultivars in different states of India. The 19 functional ISSR primers generated 410 polymorphic bands and revealed 89% polymorphism and were able to distinguish all the 22 isolates. Polymorphic bands used to construct an unweighted pair group method of averages (UPGMA) dendrogram based on Jaccard's co-efficient of similarity and principal coordinate analysis resulted in the formation of four major clusters of 22 isolates. The standardized Nei genetic distance among the 22 isolates ranged from 0.0050 to 0.0206. The UPGMA clustering using the standardized genetic distance matrix resulted in the identification of four clusters of the 22 isolates with bootstrap values ranging from 15 to 100. The 3D-scale data supported the UPGMA results, which resulted into four clusters amounting to 70% variation among each other. However, comparing the two methods show that sub clustering by dendrogram and multi dimensional scaling plot is slightly different. All the S. graminicola isolates had distinct ISSR genotypes and cluster analysis origin. The results of ISSR fingerprints revealed significant level of genetic diversity among the isolates and that ISSR markers could be a powerful tool for fingerprinting and diversity analysis in fungal pathogens.

Molecular epidemiology of human rhinoviruses

The first part of this work investigates the molecular epidemiology of a human enterovirus (HEV), echovirus 30 (E-30). This project is part of a series of studies performed in our research team analyzing the molecular epidemiology of HEV-B viruses. A total of 129 virus strains had been isolated in different parts of Europe. The sequence analysis was performed in three different genomic regions: 420 nucleotides (nt) in the VP4/VP2 capsid protein coding region, the entire VP1 capsid protein coding gene of 876 nt, and 150 nt in the VP1/2A junction region. The analysis revealed a succession of dominant sublineages within a major genotype. The temporally earlier genotypes had been replaced by a genetically homogenous lineage that has been circulating in Europe since the late 1970s. The same genotype was found by other research groups in North America and Australia. Globally, other cocirculating genetic lineages also exist. The prevalence of a dominant genotype makes E-30 different from other previously studied HEVs, such as polioviruses and coxsackieviruses B4 and B5, for which several coexisting genetic lineages have been reported. The second part of this work deals with molecular epidemiology of human rhinoviruses (HRVs). A total of 61 field isolates were studied in the 420-nt stretch in the capsid coding region of VP4/VP2. The isolates were collected from children under two years of age in Tampere, Finland. Sequences from the clinical isolates clustered in the two previously known phylogenetic clades. Seasonal clustering was found. Also, several distinct serotype-like clusters were found to co-circulate during the same epidemic season. Reappearance of a cluster after disappearing for a season was observed. The molecular epidemiology of the analyzed strains turned out to be complex, and we decided to continue our studies of HRV. Only five previously published complete genome sequences of HRV prototype strains were available for analysis. Therefore, all designated HRV prototype strains (n=102) were sequenced in the VP4/VP2 region, and the possibility of genetic typing of HRV was evaluated. Seventy-six of the 102 prototype strains clustered in HRV genetic group A (HRV-A) and 25 in group B (HRV-B). Serotype 87 clustered separately from other HRVs with HEV species D. The field strains of HRV represented as many as 19 different genotypes, as judged with an approximate demarcation of a 20% nt difference in the VP4/VP2 region. The interserotypic differences of HRV were generally similar to those reported between different HEV serotypes (i.e. about 20%), but smaller differences, less than 10%, were also observed. Because some HRV serotypes are genetically so closely related, we suggest that the genetic typing be performed using the criterion "the closest prototype strain". This study is the first systematic genetic characterization of all known HRV prototype strains, providing a further taxonomic proposal for classification of HRV. We proposed to divide the genus Human rhinoviruses into HRV-A and HRV-B. The final part of the work comprises a phylogenetic analysis of a subset (48) of HRV prototype strains and field isolates (12) in the nonstructural part of the genome coding for the RNA-dependent RNA polymerase (3D). The proposed division of the HRV strains in the species HRV-A and HRV-B was also supported by 3D region. HRV-B clustered closer to HEV species B, C, and also to polioviruses than to HRV-A. Intraspecies variation within both HRV-A and HRV-B was greater in the 3D coding region than in the VP4/VP2 coding region, in contrast to HEV. Moreover, the diversity of HRV in 3D exceeded that of HEV. One group of HRV-A, designated HRV-A', formed a separate cluster outside other HRV-A in the 3D region. It formed a cluster also in the capsid region, but located within HRV-A. This may reflect a different evolutionary history of distinct genomic regions among HRV-A. Furthermore, the tree topology within HRV-A in the 3D region differed from that in the VP4/VP2, suggesting possible recombination events in the evolution of the strains. No conflicting phylogenies were observed in any of the 12 field isolates. Possible recombination was further studied using the Similarity and Bootscanning analyses of the complete genome sequences of HRV available in public databases. Evidence for recombination among HRV-A was found, as HRV2 and HRV39 showed higher similarity in the nonstructural part of the genome. Whether HRV2 and HRV39 strains - and perhaps also some other HRV-A strains not yet completely sequenced - are recombinants remains to be determined.

Multivalent ligand presentation as a central concept to study intricate carbohydrate-protein interactions

Studying the weak binding affinities between carbohydrates and proteins has been a central theme in sustained efforts to uncover intricate details of this class of biomolecular interaction. The amphiphilic nature of most carbohydrates, the competing nature of the surrounding water molecules to a given protein receptor site and the receptor binding site characteristics led to the realization that carbohydrates are required to exert favorable interactions, primarily through clustering of the ligands. The clustering of sugar ligands has been augmented using many different innovative molecular scaffolds. The synthesis of clustered ligands also facilitates fine-tuning of the spatial and topological proximities between the ligands, so as to allow the identification of optimal molecular features for significant binding affinity enhancements. The kinetic and thermodynamic parameters have been delineated in many instances, thereby allowing an ability to correlate the multivalent presentation and the observed ligand-receptor interaction profiles. This critical review presents various multivalent ligands, synthetic and semisynthetic, and mechanisms by which the weak binding affinities are overcome, and the ligand-receptor complexation leads to significantly enhanced binding affinities (157 references).

Structure-Based Phylogeny as a Diagnostic for Functional Characterization of Proteins with a Cupin Fold

Background: The members of cupin superfamily exhibit large variations in their sequences, functions, organization of domains, quaternary associations and the nature of bound metal ion, despite having a conserved beta-barrel structural scaffold. Here, an attempt has been made to understand structure-function relationships among the members of this diverse superfamily and identify the principles governing functional diversity. The cupin superfamily also contains proteins for which the structures are available through world-wide structural genomics initiatives but characterized as ``hypothetical''. We have explored the feasibility of obtaining clues to functions of such proteins by means of comparative analysis with cupins of known structure and function. Methodology/Principal Findings: A 3-D structure-based phylogenetic approach was undertaken. Interestingly, a dendrogram generated solely on the basis of structural dissimilarity measure at the level of domain folds was found to cluster functionally similar members. This clustering also reflects an independent evolution of the two domains in bicupins. Close examination of structural superposition of members across various functional clusters reveals structural variations in regions that not only form the active site pocket but are also involved in interaction with another domain in the same polypeptide or in the oligomer. Conclusions/Significance: Structure-based phylogeny of cupins can influence identification of functions of proteins of yet unknown function with cupin fold. This approach can be extended to other proteins with a common fold that show high evolutionary divergence. This approach is expected to have an influence on the function annotation in structural genomics initiatives.

A Method to Find Sequentially Separated Motifs in Biological Sequences (SSMBS)

Sequence motifs occurring in a particular order in proteins or DNA have been proved to be of biological interest. In this paper, a new method to locate the occurrences of up to five user-defined motifs in a specified order in large proteins and in nucleotide sequence databases is proposed. It has been designed using the concept of quantifiers in regular expressions and linked lists for data storage. The application of this method includes the extraction of relevant consensus regions from biological sequences. This might be useful in clustering of protein families as well as to study the correlation between positions of motifs and their functional sites in DNA sequences.

Comparison and integration of MEG and fMRI

MEG directly measures the neuronal events and has greater temporal resolution than fMRI, which has limited temporal resolution mainly due to the larger timescale of the hemodynamic response. On the other hand fMRI has advantages in spatial resolution, while the localization results with MEG can be ambiguous due to the non-uniqueness of the electromagnetic inverse problem. Thus, these methods could provide complementary information and could be used to create both spatially and temporally accurate models of brain function. We investigated the degree of overlap, revealed by the two imaging methods, in areas involved in sensory or motor processing in healthy subjects and neurosurgical patients. Furthermore, we used the spatial information from fMRI to construct a spatiotemporal model of the MEG data in order to investigate the sensorimotor system and to create a spatiotemporal model of its function. We compared the localization results from the MEG and fMRI with invasive electrophysiological cortical mapping. We used a recently introduced method, contextual clustering, for hypothesis testing of fMRI data and assessed the the effect of neighbourhood information use on the reproducibility of fMRI results. Using MEG, we identified the ipsilateral primary sensorimotor cortex (SMI) as a novel source area contributing to the somatosensory evoked fields (SEF) to median nerve stimulation. Using combined MEG and fMRI measurements we found that two separate areas in the lateral fissure may be the generators for the SEF responses from the secondary somatosensory cortex region. The two imaging methods indicated activation in corresponding locations. By using complementary information from MEG and fMRI we established a spatiotemporal model of somatosensory cortical processing. This spatiotemporal model of cerebral activity was in good agreement with results from several studies using invasive electrophysiological measurements and with anatomical studies in monkey and man concerning the connections between somatosensory areas. In neurosurgical patients, the MEG dipole model turned out to be more reliable than fMRI in the identification of the central sulcus. This was due to prominent activation in non-primary areas in fMRI, which in some cases led to erroneous or ambiguous localization of the central sulcus.

The impact of the metabolic syndrome and parental risk factors in patients with type 1 diabetes

Background: One-third of patients with type 1 diabetes develop diabetic complications, such as diabetic nephropathy. The diabetic complications are related to a high mortality from cardiovascular disease, impose a great burden on the health care system, and reduce the health-related quality of life of patients. Aims: This thesis assessed, whether parental risk factors identify subjects at a greater risk of developing diabetic complications. Another aim was to evaluate the impact of a parental history of type 2 diabetes on patients with type 1 diabetes. A third aim was to assess the role of the metabolic syndrome in patients with type 1 diabetes, both its presence and its predictive value with respect to complications. Subjects and methods: This study is part of the ongoing nationwide Finnish Diabetic Nephropathy (FinnDiane) Study. The study was initiated in 1997, and, thus far, 4,800 adult patients with type 1 diabetes have been recruited. Since 2004, follow-up data have also been collected in parallel to the recruitment of new patients. Studies I to III have a cross-sectional design, whereas Study IV has a prospective design. Information on parents was obtained from the patients with type 1 diabetes by a questionnaire. Results: Clustering of parental hypertension, cardiovascular disease, and diabetes (type 1 and type 2) was associated with diabetic nephropathy in patients with type 1 diabetes, as was paternal mortality. A parental history of type 2 diabetes was associated with a later onset of type 1 diabetes, a higher prevalence of the metabolic syndrome, and a metabolic profile related to insulin resistance, despite no difference in the distribution of human leukocyte antigen genotypes or the presence of diabetic complications. A maternal history of type 2 diabetes, seemed to contribute to a worse metabolic profile in the patients with type 1 diabetes than a paternal history. The metabolic syndrome was a frequent finding in patients with type 1 diabetes, observed in 38% of males and 40% of females. The prevalence increased with worsening of the glycemic control and more severe renal disease. The metabolic syndrome was associated with a 3.75-fold odds ratio for diabetic nephropathy, and all of the components of the syndrome were independently associated with diabetic nephropathy. The metabolic syndrome, independent of diabetic nephropathy, increased the risk of cardiovascular events and cardiovascular and diabetes-related mortality over a 5.5-year follow-up. With respect to progression of diabetic nephropathy, the role of the metabolic syndrome was less clear, playing a strong role only in the progression from macroalbuminuria to end-stage renal disease. Conclusions: Familial factors and the metabolic syndrome play an important role in patients with type 1 diabetes. Assessment of these factors is an easily applicable tool in clinical practice to identify patients at a greater risk of developing diabetic complications.

Heritability of the network architecture of intrinsic brain functional connectivity

The brain's functional network exhibits many features facilitating functional specialization, integration, and robustness to attack. Using graph theory to characterize brain networks, studies demonstrate their small-world, modular, and "rich-club" properties, with deviations reported in many common neuropathological conditions. Here we estimate the heritability of five widely used graph theoretical metrics (mean clustering coefficient (γ), modularity (Q), rich-club coefficient (ϕnorm), global efficiency (λ), small-worldness (σ)) over a range of connection densities (k=5-25%) in a large cohort of twins (N=592, 84 MZ and 89 DZ twin pairs, 246 single twins, age 23±2.5). We also considered the effects of global signal regression (GSR). We found that the graph metrics were moderately influenced by genetic factors h2 (γ=47-59%, Q=38-59%, ϕnorm=0-29%, λ=52-64%, σ=51-59%) at lower connection densities (≤15%), and when global signal regression was implemented, heritability estimates decreased substantially h2 (γ=0-26%, Q=0-28%, ϕnorm=0%, λ=23-30%, σ=0-27%). Distinct network features were phenotypically correlated (|r|=0.15-0.81), and γ, Q, and λ were found to be influenced by overlapping genetic factors. Our findings suggest that these metrics may be potential endophenotypes for psychiatric disease and suitable for genetic association studies, but that genetic effects must be interpreted with respect to methodological choices.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.

Amino acid interaction preferences in proteins

Understanding the key factors that influence the interaction preferences of amino acids in the folding of proteins have remained a challenge. Here we present a knowledge-based approach for determining the effective interactions between amino acids based on amino acid type, their secondary structure, and the contact based environment that they find themselves in the native state structure as measured by their number of neighbors. We find that the optimal information is approximately encoded in a 60 x 60 matrix describing the 20 types of amino acids in three distinct secondary structures (helix, beta strand, and loop). We carry out a clustering scheme to understand the similarity between these interactions and to elucidate a nonredundant set. We demonstrate that the inferred energy parameters can be used for assessing the fit of a given sequence into a putative native state structure.

Identification methods of key contributing factors in crashes with high numbers of fatalities and injuries in China

Objectives In China, “serious road traffic crashes” (SRTCs) are those in which there are 10-30 fatalities, 50-100 serious injuries or a total cost of 50-100 million RMB ($US8-16m), and “particularly serious road traffic crashes” (PSRTCs) are those which are more severe or costly. Due to the large number of fatalities and injuries as well as the negative public reaction they elicit, SRTCs and PSRTCs have become great concerns to China during recent years. The aim of this study is to identify the main factors contributing to these road traffic crashes and to propose preventive measures to reduce their number. Methods 49 contributing factors of the SRTCs and PSRTCs that occurred from 2007 to 2013 were collected from the database “In-depth Investigation and Analysis System for Major Road traffic crashes” (IIASMRTC) and were analyzed through the integrated use of principal component analysis and hierarchical clustering to determine the primary and secondary groups of contributing factors. Results Speeding and overloading of passengers were the primary contributing factors, featuring in up to 66.3% and 32.6% of accidents respectively. Two secondary contributing factors were road-related: lack of or nonstandard roadside safety infrastructure, and slippery roads due to rain, snow or ice. Conclusions The current approach to SRTCs and PSRTCs is focused on the attribution of responsibility and the enforcement of regulations considered relevant to particular SRTCs and PSRTCs. It would be more effective to investigate contributing factors and characteristics of SRTCs and PSRTCs as a whole, to provide adequate information for safety interventions in regions where SRTCs and PSRTCs are more common. In addition to mandating of a driver training program and publicisation of the hazards associated with traffic violations, implementation of speed cameras, speed signs, markings and vehicle-mounted GPS are suggested to reduce speeding of passenger vehicles, while increasing regular checks by traffic police and passenger station staff, and improving transportation management to increase income of contractors and drivers are feasible measures to prevent overloading of people. Other promising measures include regular inspection of roadside safety infrastructure, and improving skid resistance on dangerous road sections in mountainous areas.

Learning Feature Trajectories Using Gabor Filter Bank for Human Activity Segmentation and Recognition

We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using dynamic time warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique.