Modelling the Human Immune System by Combining Bioinformatics and Systems Biology Approaches

Over the past decade a number of bioinformatics tools have been developed that use genomic sequences as input to predict to which parts of a microbe the immune system will react, the so-called epitopes. Many predicted epitopes have later been verified experimentally, demonstrating the usefulness of such predictions. At the same time, simulation models have been developed that describe the dynamics of different immune cell populations and their interactions with microbes. These models have been used to explain experimental findings where timing is of importance, such as the time between administration of a vaccine and infection with the microbe that the vaccine is intended to protect against. In this paper, we outline a framework for integration of these two approaches. As an example, we develop a model in which HIV dynamics are correlated with genomics data. For the first time, the fitness of wild type and mutated virus are assessed by means of a sequence-dependent scoring matrix, derived from a BLOSUM matrix, that links protein sequences to growth rates of the virus in the mathematical model. A combined bioinformatics and systems biology approach can lead to a better understanding of immune system-related diseases where both timing and genomic information are of importance.     

NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure

Background

Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively.

Results

We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest.

Conclusions

The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without compromise in image quality or information loss in associated spectra. These results motivate further use of label free microscopy techniques in real-time imaging of live immune cells.     

ModifiComb, a new proteomic tool for mapping substoichiometric post-translational modifications, finding novel types of modifications, and fingerprinting complex protein mixtures

A major challenge in proteomics is to fully identify and characterize the post-translational modification (PTM) patterns present at any given time in cells, tissues, and organisms. Here we present a fast and reliable method ("ModifiComb") for mapping hundreds types of PTMs at a time, including novel and unexpected PTMs. The high mass accuracy of Fourier transform mass spectrometry provides in many cases unique elemental composition of the PTM through the difference DeltaM between the molecular masses of the modified and unmodified peptides, whereas the retention time difference DeltaRT between their elution in reversed-phase liquid chromatography provides an additional dimension for PTM identification. Abundant sequence information obtained with complementary fragmentation techniques using ion-neutral collisions and electron capture often locates the modification to a single residue. The (DeltaM, DeltaRT) maps are representative of the proteome and its overall modification state and may be used for database-independent organism identification, comparative proteomic studies, and biomarker discovery. Examples of newly found modifications include +12.000 Da (+C atom) incorporation into proline residues of peptides from proline-rich proteins found in human saliva. This modification is hypothesized to increase the known activity of the peptide.     

Targeted delivery of plasmid DNA into the nucleus of cells via nuclear localization signal peptide conjugated to DNA intercalating bis- and trisacridines

Efficient nuclear targeting via nonviral delivery of DNA is still an unmet challenge in gene therapy. We have synthesized a novel 9-aminoacridine amino acid monomer that conveniently allows multiple acridines to be incorporated into peptide conjugates. In particular we have prepared bis- and trisacridine conjugates of nuclear localization signal peptide (NLS) ((Acr)2-NLS and (Acr)3-NLS) and studied these as functional transporters for the nuclear delivery of DNA. We show that these conjugates can enhance transfection efficacy as well as nuclear localization of plasmid DNA by more than 50-fold when combined with polyethylenimine at an N:P ratio of 2-3. These conjugates have high reversible affinity for double stranded DNA by intercalation and the technique provides a simple means of associating NLS with DNA of any sequence and at any ratio.     

Nutritional requirements for boron, silicon, vanadium, nickel, and arsenic: current knowledge and speculation

Definition of specific biochemical functions in higher animals (including humans) for the ultratrace elements boron, silicon, vanadium, nickel, and arsenic still has not been achieved although all of these elements have been described as being essential nutrients. Recently, many new findings from studies using molecular biology techniques, sophisticated equipment, unusual organisms, and newly defined enzymes have revealed possible sites of essential action for these five elements. Based on these findings and the response of animals and/or humans to low intakes of these elements, the following speculations have been presented: 1) Boron has a role that affects cell membrane characteristics and transmembrane signaling. 2) Silicon is necessary for the association between cells and one or more macromolecules such as osteonectin, which affects cartilage composition and ultimately cartilage calcification. 3) Vanadium reacts with hydrogen peroxide to form a pervanadate that is required to catalyze the oxidation of halide ions and/or stimulate the phosphorylation of receptor proteins. 4) Nickel is needed for the CO2-fixation to propionyl-CoA to form D-methylmalonyl-CoA. 5) Arsenic has an important role in the conversion of methionine to its metabolites taurine, labile methyl, and the polyamines. If any of these speculations are found to be true, the element involved will be firmly established as having a nutritional requirement because the body obviously cannot synthesize it. Based on animal findings, the dietary requirement is likely to be small; that is, expressed in micrograms per day.     

Contribution of Fluorophore Dynamics and Solvation to Resonant Energy Transfer in Protein-DNA Complexes: A Molecular-Dynamics Study

In Förster resonance energy transfer (FRET) experiments, extracting accurate structural information about macromolecules depends on knowing the positions and orientations of donor and acceptor fluorophores. Several approaches have been employed to reduce uncertainties in quantitative FRET distance measurements. Fluorophore-position distributions can be estimated by surface accessibility (SA) calculations, which compute the region of space explored by the fluorophore within a static macromolecular structure. However, SA models generally do not take fluorophore shape, dye transition-moment orientation, or dye-specific chemical interactions into account. We present a detailed molecular-dynamics (MD) treatment of fluorophore dynamics for an ATTO donor/acceptor dye pair and specifically consider as case studies dye-labeled protein-DNA intermediates in Cre site-specific recombination. We carried out MD simulations in both an aqueous solution and glycerol/water mixtures to assess the effects of experimental solvent systems on dye dynamics. Our results unequivocally show that MD simulations capture solvent effects and dye-dye interactions that can dramatically affect energy transfer efficiency. We also show that results from SA models and MD simulations strongly diverge in cases where donor and acceptor fluorophores are in close proximity. Although atomistic simulations are computationally more expensive than SA models, explicit MD studies are likely to give more realistic results in both homogeneous and mixed solvents. Our study underscores the model-dependent nature of FRET analyses, but also provides a starting point to develop more realistic in silico approaches for obtaining experimental ensemble and single-molecule FRET data.     

Negative relationships between species richness and evenness render common diversity indices inadequate for assessing long-term trends in butterfly diversity

Species richness and evenness, the two principle components of species diversity, are frequently used to describe variation in species assemblages in space and time. Compound indices, including variations of both the Shannon–Wiener index and Simpson’s index, are assumed to intelligibly integrate species richness and evenness into all-encompassing measures. However, the efficacy of compound indices is disputed by the possibility of inverse relationships between species richness and evenness. Past studies have assessed relationships between various diversity measures across survey locations for a variety of taxa, often finding species richness and evenness to be inversely related. Butterflies are one of the most intensively monitored taxa worldwide, but have been largely neglected in such studies. Long-term butterfly monitoring programs provide a unique opportunity for analyzing how trends in species diversity relate to habitat and environmental conditions. However, analyzing trends in butterfly diversity first requires an assessment of the applicability of common diversity measures to butterfly assemblages. To accomplish this, we quantified relationships between butterfly diversity measures estimated from 10 years of butterfly population data collected in the North Saskatchewan River Valley in Edmonton, Alberta, Canada. Species richness and evenness were inversely related within the butterfly assemblage. We conclude that species evenness may be used in conjunction with richness to deepen our understandings of assemblage organization, but combining these two components within compound indices does not produce measures that consistently align with our intuitive sense of species diversity.     

Characterization of thin gelatin hydrogel membranes with balloon properties for dynamic tissue engineering

Cell or tissue stretching and strain are present in any in vivo environment, but is difficult to reproduce in vitro. Here, we describe a simple method for casting a thin (about 500 μm) and soft (about 0.3 kPa) hydrogel of gelatin and a method for characterizing the mechanical properties of the hydrogel simply by changing pressure with a water column. The gelatin is crosslinked with mTransglutaminase and the area of the resulting hydrogel can be increased up 13-fold by increasing the radial water pressure. This is far beyond physiological stretches observed in vivo. Actuating the hydrogel with a radial force achieves both information about stiffness, stretchability, and contractability, which are relevant properties for tissue engineering purposes. Cells could be stretched and contracted using the gelatin membrane. Gelatin is a commonly used polymer for hydrogels in tissue engineering, and the discovered reversible stretching is particularly interesting for organ modeling applications.