Detecting and Estimating Hormesis Using a Model-Based Approach

Hormesis is defined as a dose-response relationship that is stimulatory at low doses, but is inhibitory at higher doses. In a given experiment, it is not unusual to observe enhanced responses at low doses, however, such enhanced responses may not imply hormesis, but the random fluctuation of the data. Statistical tests can be developed to detect hormesis when enhanced responses at low concentrations are observed. We propose the use of a model-based approach to detect the presence of, and estimate the extent of, hormesis. This approach includes two steps: detection and estimation. In the detection step, we compare the full and the reduced models. The full model describes the dose-response relationship incorporating the hormetic effect; the reduced model describes the dose-response relationship without the hormetic effect. The full model is an extension of the reduced model and has an extra parameter that measures the amount of increase in response at low doses. A test of statistical significance of this extra parameter can essentially be a test for detecting hormesis. In the estimation step, we obtain the area under the best-fitted dose-response curve falling within the hormetic zone. Considering both the number of concentrations within the hormetic zone and the magnitude of the stimulatory response, we propose using the ratio of the area under the hormetic zone (AUC_H) and the area under the best-fitted curve from zero to zero equivalent point (AUC_ZEP) as an estimate of magnitude of the hormetic effect. Two numerical examples are used to illustrate the use of this model-based approach.     

Designing of a Novel Fusion Protein Vaccine Candidate Against Human Visceral Leishmaniasis (VL) Using Immunoinformatics and Structural Approaches

Leishmaniasis is caused by an obligate intracellular protozoan parasite. The clinical forms of leishmaniasis differ from cutaneous leishmaniasis, mucocutaneous leishmaniasis and visceral leishmaniasis (VL) which depend on the parasite species and the host’s immune responses. There are significant challenges to the available anti-leishmanial drug therapy, particularly in severe forms of disease, and the rise of drug resistance has made it more difficult. Currently, no licensed vaccines have been introduced to the market for the control and elimination of VL. A potential target for use in candidate vaccines against leishmaniasis has been shown to be leishmania Kinetoplastid membrane protein-11 (KMP-11) antigen. In this study, we chose KMP-11 antigen as target antigen in our vaccine construct. In addition, B-type flagellin (fliC) was used as an adjuvant for enhancing vaccine immunogenicity. The GSGSGSGSGSG linker was applied to link the KMP-11 antigen and fliC (KMP-11-fliC) to construct our fusion protein. Bioinformatics approaches such as; 3D homology modeling, CTL, B-cell, MHC class I and II epitopes prediction, allergenicity, antigenicity evaluations, molecular docking, fast simulations of flexibility of docked complex and in silico cloning were employed to analysis and evaluation of various properties of the designed fusion construct. Computational results showed that our engineered structure has the potential for proper stimulation of cellular and humoral immune responses against VL. Consequently, it could be proposed as a candidate vaccine against VL according to these data and after verifying the efficacy of the candidate vaccine through in vivo and in vitro immunological tests.

     

Protein Fragments: Functional and Structural Roles of Their Coevolution Networks

Small protein fragments, and not just residues, can be used as basic building blocks to reconstruct networks of coevolved amino acids in proteins. Fragments often enter in physical contact one with the other and play a major biological role in the protein. The nature of these interactions might be multiple and spans beyond binding specificity, allosteric regulation and folding constraints. Indeed, coevolving fragments are indicators of important information explaining folding intermediates, peptide assembly, key mutations with known roles in genetic diseases, distinguished subfamily-dependent motifs and differentiated evolutionary pressures on protein regions. Coevolution analysis detects networks of fragments interaction and highlights a high order organization of fragments demonstrating the importance of studying at a deeper level this structure. We demonstrate that it can be applied to protein families that are highly conserved or represented by few sequences, enlarging in this manner, the class of proteins where coevolution analysis can be performed and making large-scale coevolution studies a feasible goal.     

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein–protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.     

A Protein Similarity Approach For Detecting Prophage Regions In Bacterial Genomes

Background  

Numerous completely sequenced bacterial genomes harbor prophage elements. These elements have been implicated in increasing the virulence of the host and in phage immunity. The e14 element is a defective lambdoid prophage element present at 25 min in the Escherichia coli K-12 genome. e14 is a well-characterized prophage element and has been subjected to in-depth bioinformatic analysis.     

Construction of a BAC Library for a Defoliating Insect-Resistant Soybean and Identification of Candidate Clones Using a Novel Approach

Positional cloning of an insect-resistance quantitative trait locus (QTL) requires the construction of a large-insert genomic DNA library from insect-resistant genotypes. To facilitate cloning of a major defoliating insect-resistance QTL on linkage group M of the soybean genetic map, a bacterial artificial chromosome (BAC) library for PI 229358 was constructed and characterized. The HindIII BAC library contains 55,296 clones with an average insert size 131 kb. This library represents a 6-fold soybean haploid genome equivalents, allowing a 99.8% probability of recovering any specific sequence of interest in soybean. BAC filters were screened with a genomic DNA probe Sat_258sc2 obtained through genome walking from flanking sequences of a simple sequence repeat (SSR) marker, Sat_258, which links to the insect-resistance QTL. Thirteen BAC clones were identified positive for Sat_258sc2, and two of them were confirmed to carry Sat_258. The results suggest that this library is useful in positional cloning of the major insect-resistance QTL, and the approach presented here can be used to screen a BAC library for a SSR marker without requiring the creation of BAC pools.     

Discovery of New Candidate Genes Related to Brain Development Using Protein Interaction Information

Human brain development is a dramatic process composed of a series of complex and fine-tuned spatiotemporal gene expressions. A good comprehension of this process can assist us in developing the potential of our brain. However, we have only limited knowledge about the genes and gene functions that are involved in this biological process. Therefore, a substantial demand remains to discover new brain development-related genes and identify their biological functions. In this study, we aimed to discover new brain-development related genes by building a computational method. We referred to a series of computational methods used to discover new disease-related genes and developed a similar method. In this method, the shortest path algorithm was executed on a weighted graph that was constructed using protein-protein interactions. New candidate genes fell on at least one of the shortest paths connecting two known genes that are related to brain development. A randomization test was then adopted to filter positive discoveries. Of the final identified genes, several have been reported to be associated with brain development, indicating the effectiveness of the method, whereas several of the others may have potential roles in brain development.     

Delivery of HIV-1 Nef Protein in Mammalian Cells Using Cell Penetrating Peptides as a Candidate Therapeutic Vaccine

The HIV-1 Nef protein expressed early in viral life cycle has been known as a potent candidate for therapeutic vaccine development. Due to different cell barriers, various cell penetrating peptides (CPPs) such as Pep-1 and CADY-2 have been known to deliver biologically active proteins to cytoplasmic compartments via the plasma membrane. In current study, we firstly evaluated the efficiency of lentiviral vector (pCDH-CMV-MCS-EF1-cGFP-T2A-puro) and eukaryotic expression vector (pEGFP-N1) for expression of HIV-1 Nef protein in HEK-293T cells using TurboFect transfection reagent. Our results showed that both vectors can effectively express the Nef proteins within the target cell. The pEGFP-N1 was more effective than pCDH-GFP for protein expression. Furthermore, Nef protein was expressed in E. coli as GST-Nef fusion and transfected by the amphipathic CPPs including Pep-1 and CADY-2 into HEK-293T cells. The size and morphology of the GST-Nef/CPP complexes were evaluated by scanning electron microscopy, and Zetasizer. Our data indicated that the recombinant GST-Nef protein generated in BL21 strain migrated as a clear band of ~50 kDa in SDS-PAGE. The CPP/GST-Nef nanoparticles were formed with a diameter of below 200 nm and notably delivered into HEK-293T cells. Generally, the Nef protein was expressed in prokaryotic and eukaryotic expression systems using different vectors and efficiently transfected in mammalian cells using various delivery systems. The in vitro efficient delivery of HIV-1 Nef gene and also its protein supports the potential of Nef DNA constructs and CPPs as potent carriers of Nef protein for HIV vaccine design in Future.     

A Time-Series-Based Feature Extraction Approach for Prediction of Protein Structural Class

This paper presents a novel feature vector based on physicochemical property of amino acids for prediction protein structural classes. The proposed method is divided into three different stages. First, a discrete time series representation to protein sequences using physicochemical scale is provided. Later on, a wavelet-based time-series technique is proposed for extracting features from mapped amino acid sequence and a fixed length feature vector for classification is constructed. The proposed feature space summarizes the variance information of ten different biological properties of amino acids. Finally, an optimized support vector machine model is constructed for prediction of each protein structural class. The proposed approach is evaluated using leave-one-out cross-validation tests on two standard datasets. Comparison of our result with existing approaches shows that overall accuracy achieved by our approach is better than exiting methods.     

Comparison of Species Richness Estimates Obtained Using Nearly Complete Fragments and Simulated Pyrosequencing-Generated Fragments in 16S rRNA Gene-Based Environmental Surveys

Pyrosequencing-based 16S rRNA gene surveys are increasingly utilized to study highly diverse bacterial communities, with special emphasis on utilizing the large number of sequences obtained (tens to hundreds of thousands) for species richness estimation. However, it is not yet clear how the number of operational taxonomic units (OTUs) and, hence, species richness estimates determined using shorter fragments at different taxonomic cutoffs correlates with the number of OTUs assigned using longer, nearly complete 16S rRNA gene fragments. We constructed a 16S rRNA clone library from an undisturbed tallgrass prairie soil (1,132 clones) and used it to compare species richness estimates obtained using eight pyrosequencing candidate fragments (99 to 361 bp in length) and the nearly full-length fragment. Fragments encompassing the V1 and V2 (V1+V2) region and the V6 region (generated using primer pairs 8F-338R and 967F-1046R) overestimated species richness; fragments encompassing the V3, V7, and V7+V8 hypervariable regions (generated using primer pairs 338F-530R, 1046F-1220R, and 1046F-1392R) underestimated species richness; and fragments encompassing the V4, V5+V6, and V6+V7 regions (generated using primer pairs 530F-805R, 805F-1046R, and 967F-1220R) provided estimates comparable to those obtained with the nearly full-length fragment. These patterns were observed regardless of the alignment method utilized or the parameter used to gauge comparative levels of species richness (number of OTUs observed, slope of scatter plots of pairwise distance values for short and nearly complete fragments, and nonparametric and parametric species richness estimates). Similar results were obtained when analyzing three other datasets derived from soil, adult Zebrafish gut, and basaltic formations in the East Pacific Rise. Regression analysis indicated that these observed discrepancies in species richness estimates within various regions could readily be explained by the proportions of hypervariable, variable, and conserved base pairs within an examined fragment.Culture-independent 16S rRNA gene surveys are now routinely utilized to examine the microbial diversity in various environmental habitats. However, in surveys of highly diverse ecosystems, the size of clone libraries typically constructed (100 to 500 clones) allows for the identification only of members of the community that are present in high abundance (2, 13, 14, 17, 24, 51). In addition to the failure to detect the rare members of the ecosystem, these relatively small datasets provide inaccurate estimates when used for computing species richness within an ecosystem. Regardless of the approach utilized to estimate species richness, the estimates obtained are highly dependent on sample size, and smaller datasets typically result in the underestimation of species richness (14, 44, 47, 55).The use of a pyrosequencing-based approach (40) in 16S gene-based diversity surveys promises to overcome both of the above-mentioned problems associated with inadequate sampling. The large number of 16S rRNA gene sequences produced (hundreds of thousands) allows access to rare members of the community (25; J. M. Tiedje, presented at the 108th General Meeting of the American Society for Microbiology, Boston, MA, 2008), as well as a relatively more accurate estimation of species richness. However, with the introduction of this new technology, it is necessary to correlate the results obtained from newer pyrosequencing-based surveys to the extensive collection of longer, capillary sequence-generated 16S rRNA gene sequences that has been deposited in public databases during the last 2 decades. Several recent studies have examined the utility of pyrosequencing fragments in providing an accurate survey of overall community structure (36) and investigated the ability of various fragments spanning the 16S rRNA gene to accurately predict the phylogenetic affiliation of pyrosequencing-generated fragments at various taxonomic cutoffs (35, 54). As such, these admirable efforts gave useful insights into the advantages and limitations of the pyrosequencing approach in 16S-based community surveys, pinpointed specific regions that provide better phylogenetic resolution than other pyrosequencing-generated regions, and provided a quantitative assessment of binning accuracy at various empirical cutoffs.However, while issues regarding correlating phylogenies of shorter and longer fragments are actively being addressed, efforts to calibrate species richness data obtained from various pyrosequencing fragments at various taxonomic cutoffs to estimates obtained using longer 16S rRNA gene fragments are still lacking. It is unclear how pairwise distances and, hence, operational taxonomic unit (OTU) assignments and species richness estimates computed using various shorter fragments spanning various regions of the 16S rRNA gene will correlate to pairwise distances computed using the nearly complete 16S rRNA gene. Elucidating such differences between shorter and nearly complete fragments, as well as between shorter fragments representing different regions in the 16S rRNA gene, is absolutely necessary for accurate meta-analysis of species richness in previously published and future datasets constructed using various sequencing approaches.Here, we constructed, sequenced, and analyzed a 16S rRNA library of 1,132 clones generated from an undisturbed tallgrass prairie soil in central Oklahoma and compared the numbers of OTUs and species richness values obtained using the full-length data sets (with and without the application of the Lane mask filter that excludes hypervariable regions from the phylogenetic analysis) (32) and fragments simulating pyrosequencing output generated by clipping where known conserved bacterial primers are encountered in the 16S rRNA gene. The lengths of the chosen simulated-pyrosequencing fragments represent amplicons that have been generated using the original GS20 pyrosequencing platform (≈100 bp) (25, 44, 48), similar to those currently being generated using the GS FLX pyrosequencing platform (≈250 bp) (1, 20, 35) or amplicons produced using the anticipated increase in the new GS XLR pyrosequencing platform (>250 bp). We show that the choice of the pyrosequenced fragment could indeed impact the number of OTUs calculated at different taxonomic cutoffs, with some fragments underestimating and others overestimating such parameters compared to the results with longer, nearly complete 16S rRNA gene fragments. We also show that even more marked differences could be encountered when comparing two pyrosequencing fragments within the same molecule. Further, we established a regression analysis that explains the nature of the observed discrepancies using the proportions of the hypervariable, variable, and conserved bases within fragments.     

Identification of Candidate Plasma Protein Biomarkers for Cervical Cancer Using the Multiplex Proximity Extension Assay

1. Download : Download high-res image (94KB)
2. Download : Download full-size image

Highlights

• •HPV is being introduced as the primary test in cervical cancer screening programs.
• •New biomarkers are needed for co-testing of women HPV positive in screening.
• •Analysis of plasma from women with invasive cervical cancer identified a 11-marker panel.
• •This signature shows high sensitivity and specificity to identify women with cancer.

     

DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach

The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS) obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database.

Availability

The DisoMCS is available at http://cal.tongji.edu.cn/disorder/.     

Structural Alphabets for Protein Structure Classification: A Comparison Study

Finding structural similarities between proteins often helps reveal shared functionality, which otherwise might not be detected by native sequence information alone. Such similarity is usually detected and quantified by protein structure alignment. Determining the optimal alignment between two protein structures, however, remains a hard problem. An alternative approach is to approximate each three-dimensional protein structure using a sequence of motifs derived from a structural alphabet. Using this approach, structure comparison is performed by comparing the corresponding motif sequences or structural sequences. In this article, we measure the performance of such alphabets in the context of the protein structure classification problem. We consider both local and global structural sequences. Each letter of a local structural sequence corresponds to the best matching fragment to the corresponding local segment of the protein structure. The global structural sequence is designed to generate the best possible complete chain that matches the full protein structure. We use an alphabet of 20 letters, corresponding to a library of 20 motifs or protein fragments having four residues. We show that the global structural sequences approximate well the native structures of proteins, with an average coordinate root mean square of 0.69 Å over 2225 test proteins. The approximation is best for all α-proteins, while relatively poorer for all β-proteins. We then test the performance of four different sequence representations of proteins (their native sequence, the sequence of their secondary-structure elements, and the local and global structural sequences based on our fragment library) with different classifiers in their ability to classify proteins that belong to five distinct folds of CATH. Without surprise, the primary sequence alone performs poorly as a structure classifier. We show that addition of either secondary-structure information or local information from the structural sequence considerably improves the classification accuracy. The two fragment-based sequences perform better than the secondary-structure sequence but not well enough at this stage to be a viable alternative to more computationally intensive methods based on protein structure alignment.     

Denatured State Structural Property Determines Protein Stabilization by Macromolecular Crowding: A Thermodynamic and Structural Approach

Understanding of protein structure and stability gained to date has been acquired through investigations made under dilute conditions where total macromolecular concentration never surpasses 10 g l⁻¹. However, biological macromolecules are known to evolve and function under crowded intracellular environments that comprises of proteins, nucleic acids, ribosomes and carbohydrates etc. Crowded environment is known to result in altered biological properties including thermodynamic, structural and functional aspect of macromolecules as compared to the macromolecules present in our commonly used experimental dilute buffers (for example, Tris HCl or phosphate buffer). In this study, we have investigated the thermodynamic and structural consequences of synthetic crowding agent (Ficoll 70) on three different proteins (Ribonuclease-A, lysozyme and holo α-lactalbumin) at different pH values. We report here that the effect of crowding is protein dependent in terms of protein thermal stability and structure. We also observed that the structural characteristics of the denatured state determines if crowding will have an effect or not on the protein stability.     

Transgenic Lettuce Producing a Candidate Protein for Vaccine against Edema Disease

Pig edema disease is a bacterial disease caused by Shiga toxin 2e-producing Escherichia coli belonging mainly to serotypes O138, O139, and O141. The B subunit of Shiga toxin 2e (Stx2eB) is a candidate protein for use in a vaccine against edema disease. We produced this protein in transgenic lettuce (Lactuca sativa), an edible plant that can be cultivated in a factory setting. In a transient expression system, we found that NtADH 5′-untranslated region (5′-UTR) functions as a translational enhancer in lettuce cells, and that Stx2eB accumulates most efficiently in the endoplasmic reticulum (ER) of lettuce cells. Stx2eB was produced in stable transgenic lettuce plants expressing a modified Stx2eB gene fused with the NtADH 5′-UTR and sequence encoding ER localization signals.     

Proteomic Identification of a Candidate Sequence of Wheat Cytokinin-Binding Protein 1

Our knowledge on the principle mechanisms of cytokinin action has been significantly deepened over the last years, but several weakly explored areas still remain on the map of cytokinin cellular physiology. Cytokinin-binding proteins could also be included in this pending field of cytokinin research. Probably, the best explored representative of this group is the wheat cytokinin-binding protein 1 (CBP-1). The role of this germ-allocated protein as a presumable regulator of free aromatic cytokinin levels during grain germination has been discussed intensively. To dig deeper into this interesting protein, this study was aimed at the identification of the CBP-1 amino acid sequence. A combination of in silico BLAST search, classical biochemical CBP-1 purification based on isoelectric point precipitation and ion-exchange chromatography, and proteomic analysis of the isolated protein by ultra-high resolution tandem mass spectrometry allowed us to uncover and validate two CBP-1 subunit candidate sequences with molecular masses of 56.2 and 55.0 kDa, respectively. Interestingly, we found the latter sequence alternated in two amino acids in the putative cytokinin-binding motive in comparison to the composition of this domain reported in the original studies. A BLAST search for the amino acid sequence of the binding region among plant proteomes revealed several highly related protein sequences, all originating from the Poaceae family. This piece of information could give support to the elucidation of the role of CBP-1 in physiological processes mediated by aromatic cytokinins.