Superselective Targeting Using Multivalent Polymers

Despite their importance for material and life sciences, multivalent interactions between polymers and surfaces remain poorly understood. Combining recent achievements of synthetic chemistry and surface characterization, we have developed a well-defined and highly specific model system based on host/guest interactions. We use this model to study the binding of hyaluronic acid functionalized with host molecules to tunable surfaces displaying different densities of guest molecules. Remarkably, we find that the surface density of bound polymer increases faster than linearly with the surface density of binding sites. Based on predictions from a simple analytical model, we propose that this superselective behavior arises from a combination of enthalpic and entropic effects upon binding of nanoobjects to surfaces, accentuated by the ability of polymer chains to interpenetrat

Deep sequencing of virus derived small interfering RNAs and RNA from viral particles shows highly similar mutational landscape of a plant virus population.

RNA viruses exist within a host as a population of mutant sequences, often referred to as quasispecies. Within a host, sequences of RNA viruses constitute several distinct but interconnected pools, such as RNA packed in viral particles, double-stranded RNA, and virus-derived small interfering RNAs. We aimed to test if the same representation of within-host viral population structure could be obtained by sequencing different viral sequence pools. Using ultradeep Illumina sequencing, the diversity of two coexisting Potato virus Y sequence pools present within a plant was investigated: RNA isolated from viral particles and virus-derived small interfering RNAs (the derivatives of a plant RNA silencing mechanism). The mutational landscape of the within-host virus population was highly similar between both pools, with no notable hotspots across the viral genome. Notably, all of the single-nucleotide polymorphisms with a frequency of higher than 1.6% were found in both pools. Some unique single-nucleotide polymorphisms (SNPs) with very low frequencies were found in each of the pools, with more of them occurring in the small RNA (sRNA) pool, possibly arising through genetic drift in localized virus populations within a plant and the errors introduced during the amplification of silencing signal. Sequencing of the viral particle pool enhanced the efficiency of consensus viral genome sequence reconstruction. Nonhomologous recombinations were commonly detected in the viral particle pool, with a hot spot in the 3′ untranslated and coat protein regions of the genome. We stress that they present an important but often overlooked aspect of virus population diversity. IMPORTANCE This study is the most comprehensive whole-genome characterization of a within-plant virus population to date and the first study comparing diversity of different pools of viral sequences within a host. We show that both virus-derived small RNAs and RNA from viral particles could be used for diversity assessment of within-plant virus population, since they show a highly congruent portrayal of the virus mutational landscape within a plant. The study is an important baseline for future studies of virus population dynamics, for example, during the adaptation to a new host. The comparison of the two virus sequence enrichment techniques, sequencing of virus-derived small interfering RNAs and RNA from purified viral particles, shows the strength of the latter for the detection of recombinant viral genomes and reconstruction of complete consensus viral genome sequence

Bonding interactions between ligand-decorated colloidal particles

<p>We study the interactions between particles that form reversible bonds. Theoretically, we find that the mean association constant between a pair of such particles can be well approximated by a double exponential function of the individual bond free energy when at least one of the following conditions holds: (i) when the individual bond strength is sufficiently weak and (ii) when the bond-forming ligands are randomly (Poisson) distributed among the particles in the system. In experiments with ligand-grafted colloidal particles, randomness in both the number of the ligands per colloid and the spatial distribution on the colloid is inherent to common fabrication techniques. We theoretically investigate the effect of quenched disordered ligand positions on interaction heterogeneity. Furthermore, we perform Monte Carlo simulations of colloidal particles decorated with mobile, quenched disordered, or uniformly distributed ligands to investigate the extent to which the mean association constant is a good approximation for the magnitude of the effective interactions in such systems, and to define the limits of applicability of the double exponential expression. We find that for large enough particles (100 nm–1 µm in diameter) at usual experimental parameters, the simple analytical expression provides a good description of the inter-particle interactions. The theory is finally extended beyond dimeric bonds to general multimeric complexes.</p

Identification of evolutionarily conserved exons as regulated targets for the splicing activator Tra2β in development

Alternative splicing amplifies the information content of the genome, creating multiple mRNA isoforms from single genes. The evolutionarily conserved splicing activator Tra2β (Sfrs10) is essential for mouse embryogenesis and implicated in spermatogenesis. Here we find that Tra2β is up-regulated as the mitotic stem cell containing population of male germ cells differentiate into meiotic and post-meiotic cells. Using CLIP coupled to deep sequencing, we found that Tra2β binds a high frequency of exons and identified specific G/A rich motifs as frequent targets. Significantly, for the first time we have analysed the splicing effect of Sfrs10 depletion in vivo by generating a conditional neuronal-specific Sfrs10 knock-out mouse (Sfrs10 fl/fl; Nestin-Cre tg/+). This mouse has defects in brain development and allowed correlation of genuine physiologically Tra2β regulated exons. These belonged to a novel class which were longer than average size and importantly needed multiple cooperative Tra2β binding sites for efficient splicing activation, thus explaining the observed splicing defects in the knockout mice. Regulated exons included a cassette exon which produces a meiotic isoform of the Nasp histone chaperone that helps monitor DNA double-strand breaks. We also found a previously uncharacterised poison exon identifying a new pathway of feedback control between vertebrate Tra2 proteins. Both Nasp-T and the Tra2a poison exon are evolutionarily conserved, suggesting they might control fundamental developmental processes. Tra2β protein isoforms lacking the RRM were able to activate specific target exons indicating an additional functional role as a splicing co-activator. Significantly the N-terminal RS1 domain conserved between flies and humans was essential for the splicing activator function of Tra2β. Versions of Tra2β lacking this N-terminal RS1 domain potently repressed the same target exons activated by full-length Tra2β protein. © 2011 Grellscheid et al

Determinants of Superselectivity─Practical Concepts for Application in Biology and Medicine

Conspectus Multivalent interactions are common in biological systems and are also widely deployed for targeting applications in biomedicine. A unique feature of multivalent binding is “superselectivity”. Superselectivity refers to the sharp discrimination of surfaces (e.g., on cells or cell compartments) by their comparative surface densities of a given receptor. This feature is different from the conventional “type” selectivity, which discriminates surfaces by their distinct receptor types. In a broader definition, a probe is superselective if it converts a gradual change in any one interaction parameter into a sharp on/off dependency in probe binding. This Account describes our systematic experimental and theoretical efforts over the past decade to analyze the determinants of superselective binding. It aims to offer chemical biologists, biophysicists, biologists, and biomedical scientists a set of guidelines for the interpretation of multivalent binding data, and design rules for tuning superselective targeting. We first provide a basic introduction that identifies multiple low-affinity interactions and combinatorial entropy as the minimal set of conditions required for superselective recognition. We then introduce the main experimental and theoretical tools and analyze how salient features of the multivalent probes (i.e., their concentration, size, ligand valency, and scaffold type), of the surface receptors (i.e., their affinity for ligands, surface density, and mobility), and of competitors and cofactors (i.e., their concentration and affinity for the ligands and/or receptors) influence the sharpness and the position of the threshold for superselective recognition. Emerging from this work are a set of relatively simple yet quantitative data analysis guidelines and superselectivity design rules that apply to a broad range of probe types and interaction systems. The key finding is the scaling variable xS which faithfully predicts the influence of the surface receptor density, probe ligand valency, receptor–ligand affinity, and competitor/cofactor concentrations and affinities on superselective recognition. The scaling variable is a simple yet versatile tool to quantitatively tune the on/off threshold of superselective probes. We exemplify its application by reviewing and reinterpreting literature data for selected biological and biomedical interaction systems where superselectivity clearly is important. Our guidelines can be deployed to generate a new mechanistic understanding of multivalent recognition events inside and outside cells and the downstream physiological/pathological implications. Moreover, the design rules can be harnessed to develop novel superselective probes for analytical purposes in the life sciences and for diagnostic/therapeutic intervention in biomedicine

Optimal multivalent targeting of membranes with many distinct receptors

Cells can often be recognized by the concentrations of receptors expressed on their surface. For better (targeted drug treatment) or worse (targeted infection by pathogens), it is clearly important to be able to target cells selectively. A good targeting strategy would result in strong binding to cells with the desired receptor profile and barely binding to other cells. Using a simple model, we formulate optimal design rules for multivalent particles that allow them to distinguish target cells based on their receptor profile. We find the following: (i) It is not a good idea to aim for very strong binding between the individual ligands on the guest (delivery vehicle) and the receptors on the host (cell). Rather, one should exploit multivalency: High sensitivity to the receptor density on the host can be achieved by coating the guest with many ligands that bind only weakly to the receptors on the cell surface. (ii) The concentration profile of the ligands on the guest should closely match the composition of the cognate membrane receptors on the target surface. And (iii) irrespective of all details, the effective strength of the ligand–receptor interaction should be of the order of the thermal energy $k_BT$, where $T$ is the absolute temperature and $k_B$ is Boltzmann’s constant. We present simulations that support the theoretical predictions. We speculate that, using the above design rules, it should be possible to achieve targeted drug delivery with a greatly reduced incidence of side effects

Computational Models for Prediction of Yeast Strain Potential for Winemaking from Phenotypic Profiles

Saccharomyces cerevisiae strains from diverse natural habitats harbour a vast amount of phenotypic diversity, driven by interactions between yeast and the respective environment. In grape juice fermentations, strains are exposed to a wide array of biotic and abiotic stressors, which may lead to strain selection and generate naturally arising strain diversity. Certain phenotypes are of particular interest for the winemaking industry and could be identified by screening of large number of different strains. The objective of the present work was to use data mining approaches to identify those phenotypic tests that are most useful to predict a strain's potential for winemaking. We have constituted a S. cerevisiae collection comprising 172 strains of worldwide geographical origins or technological applications. Their phenotype was screened by considering 30 physiological traits that are important from an oenological point of view. Growth in the presence of potassium bisulphite, growth at 40 degrees C, and resistance to ethanol were mostly contributing to strain variability, as shown by the principal component analysis. In the hierarchical clustering of phenotypic profiles the strains isolated from the same wines and vineyards were scattered throughout all clusters, whereas commercial winemaking strains tended to co-cluster. Mann-Whitney test revealed significant associations between phenotypic results and strain's technological application or origin. Naive Bayesian classifier identified 3 of the 30 phenotypic tests of growth in iprodion (0.05 mg/mL), cycloheximide (0.1 mu g/mL) and potassium bisulphite (150 mg/mL) that provided most information for the assignment of a strain to the group of commercial strains. The probability of a strain to be assigned to this group was 27% using the entire phenotypic profile and increased to 95%, when only results from the three tests were considered. Results show the usefulness of computational approaches to simplify strain selection procedures.Ines Mendes and Ricardo Franco-Duarte are recipients of a fellowship from the Portuguese Science Foundation, FCT (SFRH/BD/74798/2010, SFRH/BD/48591/2008, respectively) and Joao Drumonde-Neves is recipient of a fellowship from the Azores government (M3.1.2/F/006/2008 (DRCT)). Financial support was obtained from FEDER funds through the program COMPETE and by national funds through FCT by the projects FCOMP-01-0124-008775 (PTDC/AGR-ALI/103392/2008) and PTDC/AGR-ALI/121062/2010. Lan Umek and Blaz Zupan acknowledge financial support from Slovene Research Agency (P2-0209). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Structural genomic variation and migratory behavior in a wild songbird

Structural variants (SVs) are a major source of genetic variation; and descriptions in natural populations and connections with phenotypic traits are beginning to accumulate in the literature. We integrated advances in genomic sequencing and animal tracking to begin filling this knowledge gap in the Eurasian blackcap. Specifically, we (a) characterized the genome-wide distribution, frequency, and overall fitness effects of SVs using haplotype-resolved assemblies for 79 birds, and (b) used these SVs to study the genetics of seasonal migration. We detected >15 K SVs. Many SVs overlapped repetitive regions and exhibited evidence of purifying selection suggesting they have overall deleterious effects on fitness. We used estimates of genomic differentiation to identify SVs exhibiting evidence of selection in blackcaps with different migratory strategies. Insertions and deletions dominated the SVs we identified and were associated with genes that are either directly (e.g., regulatory motifs that maintain circadian rhythms) or indirectly (e.g., through immune response) related to migration. We also broke migration down into individual traits (direction, distance, and timing) using existing tracking data and tested if genetic variation at the SVs we identified could account for phenotypic variation at these traits. This was only the case for 1 trait—direction—and 1 specific SV (a deletion on chromosome 27) accounted for much of this variation. Our results highlight the evolutionary importance of SVs in natural populations and provide insight into the genetic basis of seasonal migration

Comparative genome and transcriptome analyses of the social amoeba Acytostelium subglobosum that accomplishes multicellular development without germ-soma differentiation

Background Social amoebae are lower eukaryotes that inhabit the soil. They are characterized by the construction of a starvation-induced multicellular fruiting body with a spore ball and supportive stalk. In most species, the stalk is filled with motile stalk cells, as represented by the model organism Dictyostelium discoideum, whose developmental mechanisms have been well characterized. However, in the genus Acytostelium, the stalk is acellular and all aggregated cells become spores. Phylogenetic analyses have shown that it is not an ancestral genus but has lost the ability to undergo cell differentiation. Results We performed genome and transcriptome analyses of Acytostelium subglobosum and compared our findings to other available dictyostelid genome data. Although A. subglobosum adopts a qualitatively different developmental program from other dictyostelids, its gene repertoire was largely conserved. Yet, families of polyketide synthase and extracellular matrix proteins have not expanded and a serine protease and ABC transporter B family gene, tagA, and a few other developmental genes are missing in the A. subglobosum lineage. Temporal gene expression patterns are astonishingly dissimilar from those of D. discoideum, and only a limited fraction of the ortholog pairs shared the same expression patterns, so that some signaling cascades for development seem to be disabled in A. subglobosum. Conclusions The absence of the ability to undergo cell differentiation in Acytostelium is accompanied by a small change in coding potential and extensive alterations in gene expression patterns

Reaction rate theory for supramolecular kinetics: application to protein aggregation

Probing the reaction mechanisms of supramolecular processes in soft- and biological matter, such as protein aggregation, is inherently challenging. These processes emerge from the simultaneous action of multiple molecular mechanisms, each of which is associated with the rearrangement of a large number of weak bonds, resulting in a complex free energy landscape with many kinetic barriers. Reaction rate measurements of supramolecular processes at different temperatures can offer unprecedented insights into the underlying molecular mechanisms and their thermodynamic properties. However, to be able to interpret such measurements in terms of the underlying microscopic mechanisms, a key challenge is to establish which properties of the complex free energy landscapes are probed by the reaction rate. Here, we present a reaction rate theory for supramolecular kinetics based on Kramers rate theory for diffusive reactions over multiple kinetic barriers, and apply the results to protein aggregation. Using this framework and Monte Carlo simulations, we show that reaction rates for protein aggregation are of the Arrhenius-Eyring type and that the associated activation energies probe only one relevant barrier along the respective free energy landscapes. We apply this advancement to interpret, both in experiments and in coarse-grained computer simulations, reaction rate measurements of amyloid aggregation kinetics in terms of the underlying molecular mechanisms and associated thermodynamic signatures. Our results establish a general platform for probing the mechanisms and energetics of supramolecular phenomena in soft- and biological matter using the framework of chemical kinetics