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

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

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

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

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

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

Individual variability and versatility in an eco-evolutionary model of avian migration

Seasonal migration is a complex and variable behaviour with the potential to promote reproductive isolation. In Eurasian blackcaps (Sylvia atricapilla), a migratory divide in central Europe separating populations with southwest (SW) and southeast (SE) autumn routes may facilitate isolation, and individuals using new wintering areas in Britain show divergence from Mediterranean winterers. We tracked 100 blackcaps in the wild to characterize these strategies. Blackcaps to the west and east of the divide used predominantly SW and SE directions, respectively, but close to the contact zone many individuals took intermediate (S) routes. At 14.0° E, we documented a sharp transition from SW to SE migratory directions across only 27 (10–86) km, implying a strong selection gradient across the divide. Blackcaps wintering in Britain took northwesterly migration routes from continental European breeding grounds. They originated from a surprisingly extensive area, spanning 2000 km of the breeding range. British winterers bred in sympatry with SW-bound migrants but arrived 9.8 days earlier on the breeding grounds, suggesting some potential for assortative mating by timing. Overall, our data reveal complex variation in songbird migration and suggest that selection can maintain variation in migration direction across short distances while enabling the spread of a novel strategy across a wide range

Differential expression of microRNAs and other small RNAs in muscle tissue of patients with ALS and healthy age-matched controls

Amyotrophic lateral sclerosis is a late-onset disorder primarily affecting motor neurons and leading to progressive and lethal skeletal muscle atrophy. Small RNAs, including microRNAs (miRNAs), can serve as important regulators of gene expression and can act both globally and in a tissue-/cell-type-specific manner. In muscle, miRNAs called myomiRs govern important processes and are deregulated in various disorders. Several myomiRs have shown promise for therapeutic use in cellular and animal models of ALS; however, the exact miRNA species differentially expressed in muscle tissue of ALS patients remain unknown. Following small RNA-Seq, we compared the expression of small RNAs in muscle tissue of ALS patients and healthy age-matched controls. The identified snoRNAs, mtRNAs and other small RNAs provide possible molecular links between insulin signaling and ALS. Furthermore, the identified miRNAs are predicted to target proteins that are involved in both normal processes and various muscle disorders and indicate muscle tissue is undergoing active reinnervation/compensatory attempts thus providing targets for further research and therapy development in ALS

Comprehensive Identification of RNA-Binding Domains in Human Cells

Mammalian cells harbor more than a thousand RNA-binding proteins (RBPs), with half of these employing unknown modes of RNA binding. We developed RBDmap to determine the RNA-binding sites of native RBPs on a proteome-wide scale. We identified 1,174 binding sites within 529 HeLa cell RBPs, discovering numerous RNA-binding domains (RBDs). Catalytic centers or protein-protein interaction domains are in close relationship with RNA-binding sites, invoking possible effector roles of RNA in the control of protein function. Nearly half of the RNA-binding sites map to intrinsically disordered regions, uncovering unstructured domains as prevalent partners in protein-RNA interactions. RNA-binding sites represent hot spots for defined posttranslational modifications such as lysine acetylation and tyrosine phosphorylation, suggesting metabolic and signal-dependent regulation of RBP function. RBDs display a high degree of evolutionary conservation and incidence of Mendelian mutations, suggestive of important functional roles. RBDmap thus yields profound insights into native protein-RNA interactions in living cells