DNA Packaging and Polycation Length Determine DNA Susceptibility to Free Radical Damage in Condensed DNA.

In nature, DNA exists primarily in a highly compacted form. The compaction of DNA in vivo is mediated by cationic proteins: histones in somatic nuclei and protamines in sperm chromatin. The extreme, nearly crystalline packaging of DNA by protamines in spermatozoa is thought to be essential for both efficient genetic delivery as well as DNA protection against damage by mutagens and oxidative species. The protective role of protamines is required in sperm, as they are sensitive to ROS damage due to the progressive loss of DNA repair mechanisms during maturation. The degree to which DNA packaging directly relates to DNA protection in the condensed state, however, is poorly understood. Here, we utilized different polycation condensing agents to achieve varying DNA packaging densities and quantify DNA damage by free radical oxidation within the condensates. Although we see that tighter DNA packaging generally leads to better protection, the length of the polycation also plays a significant role. Molecular dynamics simulations suggest that longer polyarginine chains offer increased protection by occupying more space on the DNA surface and forming more stable interactions. Taken together, our results suggest a complex interplay among polycation properties, DNA packaging density, and DNA protection against free radical damage within condensed states.

Map conformational landscapes of intrinsically disordered proteins with polymer physics quantities.

Disordered proteins are conformationally flexible proteins that are biologically important and have been implicated in devastating diseases such as Alzheimer's disease and cancer. Unlike stably folded structured proteins, disordered proteins sample a range of different conformations that needs to be accounted for. Here, we treat disordered proteins as polymer chains, and compute a dimensionless quantity called instantaneous shape ratio (Rs), as Rs = Ree2/Rg2, where Ree is end-to-end distance and Rg is radius of gyration. Extended protein conformations tend to have high Ree compared with Rg, and thus have high Rs values, whereas compact conformations have smaller Rs values. We use a scatter plot of Rs (representing shape) against Rg (representing size) as a simple map of conformational landscapes. We first examine the conformational landscape of simple polymer models such as Random Walk, Self-Avoiding Walk, and Gaussian Walk (GW), and we notice that all protein/polymer maps lie within the boundaries of the GW map. We thus use the GW map as a reference and, to assess conformational diversity, we compute the fraction of the GW conformations (fC) covered by each protein/polymer. Disordered proteins all have high fC scores, consistent with their disordered nature. Each disordered protein accesses a different region of the reference map, revealing differences in their conformational ensembles. We additionally examine the conformational maps of the nonviral gene delivery vector polyethyleneimine at various protonation states, and find that they resemble disordered proteins, with coverage of the reference map decreasing with increasing protonation state, indicating decreasing conformational diversity. We propose that our method of combining Rs and Rg in a scatter plot generates a simple, meaningful map of the conformational landscape of a disordered protein, which in turn can be used to assess conformational diversity of disordered proteins.

Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity.

Protein-protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein-protein complex formation and defining the structure-function relationship. Despite advancements in understanding protein-protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein-protein interface. We found that the numbers of specific amino acids at the protein-protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.

Gene network based analysis identifies a coexpression module involved in regulating plasma lipids with high-fat diet response.

Plasma lipids are modulated by gene variants and many environmental factors, including diet-associated weight gain. However, understanding how these factors jointly interact to influence molecular networks that regulate plasma lipid levels is limited. Here, we took advantage of the BXD recombinant inbred family of mice to query weight gain as an environmental stressor on plasma lipids. Coexpression networks were examined in both nonobese and obese livers, and a network was identified that specifically responded to the obesogenic diet. This obesity-associated module was significantly associated with plasma lipid levels and enriched with genes known to have functions related to inflammation and lipid homeostasis. We identified key drivers of the module, including Cidec, Cidea, Pparg, Cd36, and Apoa4. The Pparg emerged as a potential master regulator of the module as it can directly target 19 of the top 30 hub genes. Importantly, activation of this module is causally linked to lipid metabolism in humans, as illustrated by correlation analysis and inverse-variance weighed Mendelian randomization. Our findings provide novel insights into gene-by-environment interactions for plasma lipid metabolism that may ultimately contribute to new biomarkers, better diagnostics, and improved approaches to prevent or treat dyslipidemia in patients.

Exploring Structures and Dynamics of Protamine Molecules through Molecular Dynamics Simulations.

Protamines are arginine-rich proteins that condense DNA in sperm. Despite their importance in reproduction, information on protamine structure is scarce. We, therefore, used molecular dynamics to examine the structures of salmon, bull P1, and human P1 protamines. The sizes and shapes of each protamine varied widely, indicating that they were disordered with structures covering a broad conformational landscape, from hairpin loop structures to extended coils. Despite their general disorder, the protamines did form secondary structures, including helices and hairpin loops. In eutherians, hairpins may promote disulfide bonding that facilitates protamine-DNA condensation, but the specifics of this bonding is not well established. We examined inter-residue distances in the simulations to predict residue pairs likely to form intramolecular bonds, leading to the identification of bonding pairs consistent with previous results in bull and human. These results support a model for eutherian protamine structures where a highly charged center is surrounded by disulfide-bond-stabilized loops.

Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.

How lifespan and body weight vary as a function of diet and genetic differences is not well understood. Here we quantify the impact of differences in diet on lifespan in a genetically diverse family of female mice, split into matched isogenic cohorts fed a low-fat chow diet (CD, n = 663) or a high-fat diet (HFD, n = 685). We further generate key metabolic data in a parallel cohort euthanized at four time points. HFD feeding shortens lifespan by 12%: equivalent to a decade in humans. Initial body weight and early weight gains account for longevity differences of roughly 4-6 days per gram. At 500 days, animals on a HFD typically gain four times as much weight as control, but variation in weight gain does not correlate with lifespan. Classic serum metabolites, often regarded as health biomarkers, are not necessarily strong predictors of longevity. Our data indicate that responses to a HFD are substantially modulated by gene-by-environment interactions, highlighting the importance of genetic variation in making accurate individualized dietary recommendations.

Coarse-grained Simulations of the Impact of Chain Length and Stiffness on the Formation and Aggregation of Polyelectrolyte Complexes.

Polyelectrolyte complexes formed from nucleic acids and synthetic polycations have been studied because of their potential in gene delivery. Coarse-grained molecular dynamics simulations are performed to examine the impact of chain length and polyanion stiffness on polyplex formation and aggregation. Polyplexes containing single polyanion chain fall into three structural regimes depending on polyanion stiffness: flexible polyanions form collapsed complexes, semiflexible polyanions form various morphologies including toroids and hairpins, and stiff polyanions form rod-like structures. Polyplex size generally decreases as polycation length increases. Aggregation (i.e., formation of complexes containing multiple polyanions) is observed in some simulations containing multiple polyanions and an excess of short polycations. Aggregation is observed to only occur for semiflexible and stiff polyanions and is promoted by shorter polycation lengths. Simulations of short, stiff polyanions condensed by long polycations are used as a model for siRNA gene delivery complexes. These simulations show multiple polyanions are spaced out along the polycation with polyanion-polyanion interactions, usually limited to overlapping chain ends. These structures differ from aggregates of longer polyanions in which the polyanions are packed together in parallel, forming bundles.

Functional Analysis of Genetic Variants and Somatic Mutations Impacting MicroRNA-Target Recognition: Bioinformatics Resources.

MicroRNAs are small noncoding RNA molecules with great importance in regulating a large number of diverse biological processes in health and disease. MicroRNAs can bind to both coding and noncoding RNAs and regulate their stability and expression. Genetic variants and somatic mutations may alter microRNA sequences and their target sites and therefore impact microRNA-target recognition. Aberrant microRNA-target interactions have been associated with many diseases. In recent years, computational resources have been developed for retrieving, annotating, and analyzing the impact of mutations on microRNA-target recognition. In this chapter, we provide an overview on the computational analysis of mutations impacting microRNA target recognition, followed by a detailed tutorial on how to use three major Web-based bioinformatics resources: PolymiRTS ( http://compbio.uthsc.edu/miRSNP ), a database of genetic variants impacting microRNA target recognition; SomamiR ( http://compbio.uthsc.edu/SomamiR ), a database of somatic mutations affecting the interactions between microRNAs and their targets in mRNAs and noncoding RNAs; and miR2GO ( http://compbio.uthsc.edu/miR2GO ), a computational tool for knowledge-based functional analysis of genetic variants and somatic mutations in microRNA seed regions.

Structural Comparisons of PEI/DNA and PEI/siRNA Complexes Revealed with Molecular Dynamics Simulations.

Polyplexes composed of polyethyleneimine (PEI) and DNA or siRNA have attracted great attention for their use in gene therapy. Although many physicochemical characteristics of these polyplexes remain unknown, PEI/DNA complexes have been repeatedly shown to be more stable than their PEI/siRNA counterparts. Here, we examine potential causes for this difference using atomistic molecular dynamics simulations of complexation between linear PEI and DNA or siRNA duplexes containing the same number of bases. The two types of polyplexes are stabilized by similar interactions, as PEI amines primarily interact with nucleic acid phosphate groups but also occasionally interact with groove atoms of both nucleic acids. However, the number of interactions in PEI/DNA complexes is greater than in comparable PEI/siRNA complexes, with interactions between protonated PEI amines and DNA being particularly enhanced. These results indicate that structural differences between DNA and siRNA may play a role in the increased stability of PEI/DNA complexes. In addition, we investigate the binding of PEI chains to polyplexes that have a net positive charge. The binding of PEI to these overcharged complexes involves interactions between PEI and areas on the nucleic acid surface that have maintained a negative electrostatic potential and is facilitated by the release of water from the nucleic acid.

Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver.

The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

Interactions of complex polymers with nanoporous substrate.

With the advance of polymer synthesis, polymers that possess unique architectures such as stars or cyclic chains, and unique chemical composition distributions such as block copolymers or statistical copolymers have become frequently encountered. Characterization of these complex polymer systems drives the development of interactive chromatography where the adsorption of polymers on the porous substrate in chromatography columns is finely tuned. Liquid Chromatography at the Critical Condition (LCCC) in particular makes use of the existence of the Critical Adsorption Point (CAP) of polymers on solid surfaces and has been successfully applied to characterization of complex polymer systems. Interpretation and understanding of chromatography behaviour of complex polymers in interactive chromatography motivates theoretical/computational studies on the CAP of polymers and partitioning of these complex polymers near the CAP. This review article covers the theoretical questions encountered in chromatographic studies of complex polymers.

Photosensitizer-loaded gold nanorods for near infrared photodynamic and photothermal cancer therapy.

Despite the advancement of photodynamic therapy and photothermal therapy, the ability to form compact nanocomplex for combined photodynamic and photothermal cancer therapy under a single near infrared irradiation remains limited. In this work, we prepared an integrated sub-100 nm nanosystem for simultaneous near infrared photodynamic and photothermal cancer therapy. The nanosystem was formed by adsorption of silicon 2,3-naphthalocyanine dihydroxide onto gold nanorod followed by covalent stabilization with alkylthiol linked polyethylene glycol. The effects of alkylthiol chain length on drug loading, release and cell killing efficacy were examined using 6-mercaptohexanoic acid, 11-mercaptoundecanoic acid and 16-mercaptohexadecanoic acid. We found that the loading efficiency of silicon 2,3-naphthalocyanine dihydroxide increased and the release rate decreased with the increase of the alkylthiol chain length. We demonstrated that the combined near infrared photodynamic and photothermal therapy using the silicon 2,3-naphthalocyanine dihydroxide-loaded gold nanorods exhibit superior efficacy in cancer cell destruction as compared to photodynamic therapy and photothermal therapy alone. The nanocomplex stabilized with 16-mercaptohexadecanoic acid linked polyethylene glycol provided highest cell killing efficiency as compared to those stabilized with the other two stabilizers under low drug dose. This new nanosystem has potential to completely eradicate tumors via noninvasive phototherapy, preventing tumor reoccurrence and metastasis.

Segregation of a spontaneous Klrd1 (CD94) mutation in DBA/2 mouse substrains.

Current model DBA/2J (D2J) mice lack CD94 expression due to a deletion spanning the last coding exon of the Klrd1 gene that occurred in the mid- to late 1980s. In contrast, DBA/2JRj (D2Rj) mice, crosses derived from DBA/2J before 1984, and C57BL/6J (B6) mice lack the deletion and have normal CD94 expression. For example, BXD lines (BXD1-32) generated in the 1970s by crossing B6 and D2J do not segregate for the exonic deletion and have high expression, whereas BXD lines 33 and greater were generated after 1990 are segregating for the deletion and have highly variable Klrd1 expression. We performed quantitative trait locus analysis of Klrd1 expression by using BXD lines with different generation times and found that the expression difference in Klrd1 in the later BXD set is driven by a strong cis-acting expression quantitative trait locus. Although the Klrd1/CD94 locus is essential for mousepox resistance, the genetic variation among D2 substrains and the later set of BXD strains is not associated with susceptibility to the Influenza A virus PR8 strain. Substrains with nearly identical genetic backgrounds that are segregating functional variants such as the Klrd1 deletion are useful genetic tools to investigate biological function.

Interplay of Coil-Globule Transition and Surface Adsorption of a Lattice HP Protein Model.

An end-grafted hydrophobic-polar (HP) model protein chain with alternating H and P monomers is studied to examine interactions between the critical adsorption transition due to surface attraction and the collapse transition due to pairwise attractive H-H interactions. We find that the critical adsorption phenomenon can always be observed; however, the critical adsorption temperature T(CAP) is influenced by the attractive H-H interactions in some cases. When the collapse temperature T(c) is lower than T(CAP), the critical adsorption of the HP chain is similar to that of a homopolymer without intrachain attractions and T(CAP) remains unchanged, whereas the collapse transition is suppressed by the adsorption. In contrast, for cases where T(c) is close to or higher than T(CAP), T(CAP) of the HP chain is increased, indicating that a collapsed chain is more easily adsorbed on the surface. The strength of the H-H attraction also influences the statistical size and shape of the polymer, with strong H-H attractions resulting in adsorbed and collapsed chains adopting two-dimensional, circular conformations.

Comparison of monovalent and divalent ion distributions around a DNA duplex with molecular dynamics simulation and a Poisson-Boltzmann approach.

The ion atmosphere created by monovalent (Na(+) ) or divalent (Mg(2+) ) cations surrounding a B-form DNA duplex were examined using atomistic molecular dynamics (MD) simulations and the nonlinear Poisson-Boltzmann (PB) equation. The ion distributions predicted by the two methods were compared using plots of radial and two-dimensional cation concentrations and by calculating the total number of cations and net solution charge surrounding the DNA. Na(+) ion distributions near the DNA were more diffuse in PB calculations than in corresponding MD simulations, with PB calculations predicting lower concentrations near DNA groove sites and phosphate groups and a higher concentration in the region between these locations. Other than this difference, the Na(+) distributions generated by the two methods largely agreed, as both predicted similar locations of high Na(+) concentration and nearly identical values of the number of cations and the net solution charge at all distances from the DNA. In contrast, there was greater disagreement between the two methods for Mg(2+) cation concentration profiles, as both the locations and magnitudes of peaks in Mg(2+) concentration were different. Despite experimental and simulation observations that Mg(2+) typically maintains its first solvation shell when interacting with nucleic acids, modeling Mg(2+) as an unsolvated ion during PB calculations improved the agreement of the Mg(2+) ion atmosphere predicted by the two methods and allowed for values of the number of bound ions and net solution charge surrounding the DNA from PB calculations that approached the values observed in MD simulations.

PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways.

Polymorphisms in microRNAs (miRNAs) and their target sites (PolymiRTS) are known to disrupt miRNA function, leading to the development of disease and variation in physiological and behavioral phenotypes. Here, we describe recent updates to the PolymiRTS database (http://compbio.uthsc.edu/miRSNP), an integrated platform for analyzing the functional impact of genetic polymorphisms in miRNA seed regions and miRNA target sites. Recent advances in genomic technologies have made it possible to identify miRNA-mRNA binding sites from direct mapping experiments such as CLASH (cross linking, ligation and sequencing of hybrids). We have integrated data from CLASH experiments in the PolymiRTS database to provide more complete and accurate miRNA-mRNA interactions. Other significant new features include (i) small insertions and deletions in miRNA seed regions and miRNA target sites, (ii) TargetScan context + score differences for assessing the impact of polymorphic miRNA-mRNA interactions and (iii) biological pathways. The browse and search pages of PolymiRTS allow users to explore the relations between the PolymiRTSs and gene expression traits, physiological and behavioral phenotypes, human diseases and biological pathways.

Bayesian Network Webserver: a comprehensive tool for biological network modeling.

SUMMARY: The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. AVAILABILITY AND IMPLEMENTATION: BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). CONTACT: ycui2@uthsc.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SomamiR: a database for somatic mutations impacting microRNA function in cancer.

Whole-genome sequencing of cancers has begun to identify thousands of somatic mutations that distinguish the genomes of normal tissues from cancers. While many germline mutations within microRNAs (miRNAs) and their targets have been shown to alter miRNA function in cancers and have been associated with cancer risk, the impact of somatic mutations on miRNA function has received relatively little attention. Here, we have created the SomamiR database (http://compbio.uthsc.edu/SomamiR/) to provide a comprehensive resource that integrates several types of data for use in investigating the impact of somatic and germline mutations on miRNA function in cancer. The database contains somatic mutations that may create or disrupt miRNA target sites and integrates these somatic mutations with germline mutations within the same target sites, genome-wide and candidate gene association studies of cancer and functional annotations that link genes containing mutations with cancer. Additionally, the database contains a collection of germline and somatic mutations in miRNAs and their targets that have been experimentally shown to impact miRNA function and have been associated with cancer.

CTCFBSDB 2.0: a database for CTCF-binding sites and genome organization.

CTCF is a highly conserved transcriptional regulator protein that performs diverse functions such as regulating gene expression and organizing the 3D structure of the genome. Here, we describe recent updates to a database of CTCF-binding sites, CTCFBSDB (http://insulatordb.uthsc.edu/), which now contains almost 15 million CTCF-binding sequences in 10 species. Since the original publication of the database, studies of the 3D structure of the genome, such as those provided by Hi-C experiments, have suggested that CTCF plays an important role in mediating intra- and inter-chromosomal interactions. To reflect this important progress, we have integrated CTCF-binding sites with genomic topological domains defined using Hi-C data. Additionally, the updated database includes new features enabled by new CTCF-binding site data, including binding site occupancy and the ability to visualize overlapping CTCF-binding sites determined in separate experiments.

Integrative analysis of somatic mutations altering microRNA targeting in cancer genomes.

Determining the functional impact of somatic mutations is crucial to understanding tumorigenesis and metastasis. Recent sequences of several cancers have provided comprehensive lists of somatic mutations across entire genomes, enabling investigation of the functional impact of somatic mutations in non-coding regions. Here, we study somatic mutations in 3'UTRs of genes that have been identified in four cancers and computationally predict how they may alter miRNA targeting, potentially resulting in dysregulation of the expression of the genes harboring these mutations. We find that somatic mutations create or disrupt putative miRNA target sites in the 3'UTRs of many genes, including several genes, such as MITF, EPHA3, TAL1, SCG3, and GSDMA, which have been previously associated with cancer. We also integrate the somatic mutations with germline mutations and results of association studies. Specifically, we identify putative miRNA target sites in the 3'UTRs of BMPR1B, KLK3, and SPRY4 that are disrupted by both somatic and germline mutations and, also, are in linkage disequilibrium blocks with high scoring markers from cancer association studies. The somatic mutation in BMPR1B is located in a target site of miR-125b; germline mutations in this target site have previously been both shown to disrupt regulation of BMPR1B by miR-125b and linked with cancer.