High-Throughput Screening of Streptavidin-Binding Proteins in Self-Assembled Solid Films for Directed Evolution of Materials.

Evolution has shaped the development of proteins with an incredible diversity of properties. Incorporating proteins into materials is desirable for applications including biosensing; however, high-throughput selection techniques for screening protein libraries in materials contexts is lacking. In this work, a high-throughput platform to assess the binding affinity for ordered sensing proteins was established. A library of fusion proteins, consisting of an elastin-like polypeptide block, one of 22 variants of rcSso7d, and a coiled-coil order-directing sequence, was generated. All selected variants had high binding in films, likely due to the similarity of the assay to magnetic bead sorting used for initial selection, while solution binding was more variable. From these results, both the assembly of the fusion proteins in their operating state and the functionality of the binding protein are key factors in the biosensing performance. Thus, the integration of directed evolution with assembled systems is necessary to the design of better materials.

Large scale proteomic studies create novel privacy considerations.

Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90-95% of proteomes to their correct genome and for 95-99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.

Canonicalizing BigSMILES for Polymers with Defined Backbones.

BigSMILES, a line notation for encapsulating the molecular structure of stochastic molecules such as polymers, was recently proposed as a compact and readable solution for writing macromolecules. While BigSMILES strings serve as useful identifiers for reconstructing the molecular connectivity for polymers, in general, BigSMILES allows the same polymer to be codified into multiple equally valid representations. Having a canonicalization scheme that eliminates the multiplicity would be very useful in reducing time-intensive tasks like structural comparison and molecular search into simple string-matching tasks. Motivated by this, in this work, two strategies for deriving canonical representations for linear polymers are proposed. In the first approach, a canonicalization scheme is proposed to standardize the expression of BigSMILES stochastic objects, thereby standardizing the expression of overall BigSMILES strings. In the second approach, an analogy between formal language theory and the molecular ensemble of polymer molecules is drawn. Linear polymers can be converted into regular languages, and the minimal deterministic finite automaton uniquely associated with each prescribed language is used as the basis for constructing the unique text identifier associated with each distinct polymer. Overall, this work presents algorithms to convert linear polymers into unique structure-based text identifiers. The derived identifiers can be readily applied in chemical information systems for polymers and other polymer informatics applications.

Extending BigSMILES to non-covalent bonds in supramolecular polymer assemblies.

As a machine-recognizable representation of polymer connectivity, BigSMILES line notation extends SMILES from deterministic to stochastic structures. The same framework that allows BigSMILES to accommodate stochastic covalent connectivity can be extended to non-covalent bonds, enhancing its value for polymers, supramolecular materials, and colloidal chemistry. Non-covalent bonds are captured through the inclusion of annotations to pseudo atoms serving as complementary binding pairs, minimal key/value pairs to elaborate other relevant attributes, and indexes to specify the pairing among potential donors and acceptors or bond delocalization. Incorporating these annotations into BigSMILES line notation enables the representation of four common classes of non-covalent bonds in polymer science: electrostatic interactions, hydrogen bonding, metal-ligand complexation, and π-π stacking. The principal advantage of non-covalent BigSMILES is the ability to accommodate a broad variety of non-covalent chemistry with a simple user-orientated, semi-flexible annotation formalism. This goal is achieved by encoding a universal but non-exhaustive representation of non-covalent or stochastic bonding patterns through syntax for (de)protonated and delocalized state of bonding as well as nested bonds for correlated bonding and multi-component mixture. By allowing user-defined descriptors in the annotation expression, further applications in data-driven research can be envisioned to represent chemical structures in many other fields, including polymer nanocomposite and surface chemistry.

Protein signatures of centenarians and their offspring suggest centenarians age slower than other humans.

Using samples from the New England Centenarian Study (NECS), we sought to characterize the serum proteome of 77 centenarians, 82 centenarians' offspring, and 65 age-matched controls of the offspring (mean ages: 105, 80, and 79 years). We identified 1312 proteins that significantly differ between centenarians and their offspring and controls (FDR < 1%), and two different protein signatures that predict longer survival in centenarians and in younger people. By comparing the centenarian signature with 2 independent proteomic studies of aging, we replicated the association of 484 proteins of aging and we identified two serum protein signatures that are specific of extreme old age. The data suggest that centenarians acquire similar aging signatures as seen in younger cohorts that have short survival periods, suggesting that they do not escape normal aging markers, but rather acquire them much later than usual. For example, centenarian signatures are significantly enriched for senescence-associated secretory phenotypes, consistent with those seen with younger aged individuals, and from this finding, we provide a new list of serum proteins that can be used to measure cellular senescence. Protein co-expression network analysis suggests that a small number of biological drivers may regulate aging and extreme longevity, and that changes in gene regulation may be important to reach extreme old age. This centenarian study thus provides additional signatures that can be used to measure aging and provides specific circulating biomarkers of healthy aging and longevity, suggesting potential mechanisms that could help prolong health and support longevity.

Tuning Selective Transport of Biomolecules through Site-Mutated Nucleoporin-like Protein (NLP) Hydrogels.

Natural selective filtering systems (e.g., the extracellular matrix, nuclear pores, and mucus) separate molecules selectively and efficiently, and the detailed understanding of transport mechanisms exploited in these systems provides important bioinspired design principles for selective filters. In particular, nucleoporins consist of consensus repeat sequences that are readily utilized for engineering repeat proteins. Here, the consensus repeat sequence of Nsp1, a yeast nucleoporin, is polymerized to form a nucleoporin-like protein (NLP) and mutated to understand the effect of sequence on selective transport. The hydrophilic spacers of the NLPs were redesigned considering net charge, charge distribution, and polarity. Mutations were made near to and far from the FSFG interacting domain to explore the role of highly conserved residues as a function of spatial proximity. A nuclear transport receptor-cargo complex, nuclear transport factor 2-green fluorescent protein (NTF2-GFP), was used as a model for changes in transport. For mutations of the charged spacer, some mutations of highly conserved charged residues were possible without knocking out selective transport of the NTF2, but the formation of regions of clustered negative charge has an unfavorable effect on nuclear transporter permeation. Thus, positive net charge and alternating positive and negative charge within the hydrophilic spacer are advantageous for recognition and selective transport. In the polarity panel, mutations that increased the interaction between NTF2-GFP and the gel led to decreased permeation of the NTF2-GFP due to blocking of the interface and inability of the NTF2-GFP to transport into the gel. Therefore, these results provide a strategy for tuning selective permeability of biomolecules using the artificially designed consensus repeat-based hydrogels.

Benchmarking network algorithms for contextualizing genes of interest.

Computational approaches have shown promise in contextualizing genes of interest with known molecular interactions. In this work, we evaluate seventeen previously published algorithms based on characteristics of their output and their performance in three tasks: cross validation, prediction of drug targets, and behavior with random input. Our work highlights strengths and weaknesses of each algorithm and results in a recommendation of algorithms best suited for performing different tasks.

Age-Related Gene Expression Signature in Rats Demonstrate Early, Late, and Linear Transcriptional Changes from Multiple Tissues.

To understand the changes in gene expression that occur as a result of age, which might create a permissive or causal environment for age-related diseases, we produce a multi-time point age-related gene expression signature (AGES) from liver, kidney, skeletal muscle, and hippocampus of rats, comparing 6-, 9-, 12-, 18-, 21-, 24-, and 27-month-old animals. We focus on genes that changed in one direction throughout the lifespan of the animal, either early in life (early logistic changes), at mid-age (mid-logistic), late in life (late-logistic), or linearly, throughout the lifespan of the animal. The pathways perturbed because of chronological age demonstrate organ-specific and more-global effects of aging and point to mechanisms that could potentially be counter-regulated pharmacologically to treat age-associated diseases. A small number of genes are regulated by aging in the same manner in every tissue, suggesting they may be more-universal markers of aging.

A serum protein signature of APOE genotypes in centenarians.

The discovery of treatments to prevent or delay dementia and Alzheimer's disease is a priority. The gene APOE is associated with cognitive change and late-onset Alzheimer's disease, and epidemiological studies have provided strong evidence that the e2 allele of APOE has a neuroprotective effect, it is associated with increased longevity and an extended healthy lifespan in centenarians. In this study, we correlated APOE genotype data of 222 participants of the New England Centenarian Study, including 75 centenarians, 82 centenarian offspring, and 65 controls, comprising 55 carriers of APOE e2 , with aptamer-based serum proteomics (SomaLogic technology) of 4,785 human proteins corresponding to 4,137 genes. We discovered a signature of 16 proteins that associated with different APOE genotypes and replicated the signature in three independent studies. We also show that the protein signature tracks with gene expression profiles in brains of late-onset Alzheimer's disease versus healthy controls. Finally, we show that seven of these proteins correlate with cognitive function patterns in longitudinally collected data. This analysis in particular suggests that Baculoviral IAP repeat containing two (BIRC2) is a novel biomarker of neuroprotection that associates with the neuroprotective allele of APOE. Therefore, targeting APOE e2 molecularly may preserve cognitive function.

Enhanced Conductivity via Homopolymer-Rich Pathways in Block Polymer-Blended Electrolytes.

The optimization of ionic conductivity and lithium-ion battery stability can be achieved by independently tuning the ion transport and mechanical robustness of block polymer (BP) electrolytes. However, the ionic conductivity of BP electrolytes is inherently limited by the covalent attachment of the ionically conductive block to the mechanically robust block, among other factors. Herein, the BP electrolyte polystyrene-block-poly(oligo-oxyethylene methacrylate) [PS-b-POEM] was blended with POEM homopolymers of varying molecular weights. The incorporation of a higher molecular weight homopolymer additive (α > 1 state) promoted a "dry brush-like" homopolymer distribution within the BP self-assembly and led to higher lithium salt concentrations in the more mobile homopolymer-rich region, increasing overall ionic conductivity relative to the "wet brush-like" (α < 1 state) and unblended composites, where α is the molecular weight ratio between the POEM homopolymer and the POEM block in the copolymer. Neutron and X-ray reflectometry (NR and XRR, respectively) provided additional details on the lithium salt and polymer distributions. From XRR, the α > 1 blends showed increased interfacial widths in comparison to their BP (unblended) or α < 1 counterparts because of the more central distribution of the homopolymer. This result, paired with NR data that suggested even salt concentrations across the POEM domains, implied that there was a higher salt concentration in the homopolymer POEM-rich regions in the dry brush blend than in the wet brush blend. Furthermore, using 7Li solid-state nuclear magnetic resonance spectroscopy, we found a temperature corresponding to a transition in lithium mobility (T Li mobility) that was a function of blend type. T Li mobility was found to be 39 °C above T g in all cases. Interestingly, the ionic conductivity of the blended BPs was highest in the α > 1 composites, even though these composites had higher T gs than the α < 1 composites, demonstrating that homopolymer-rich conducting pathways formed in the α > 1 assemblies had a larger influence on conductivity than the greater lithium ion mobility in the α < 1 blends.

TORC1 inhibition enhances immune function and reduces infections in the elderly.

Inhibition of the mechanistic target of rapamycin (mTOR) protein kinase extends life span and ameliorates aging-related pathologies including declining immune function in model organisms. The objective of this phase 2a randomized, placebo-controlled clinical trial was to determine whether low-dose mTOR inhibitor therapy enhanced immune function and decreased infection rates in 264 elderly subjects given the study drugs for 6 weeks. A low-dose combination of a catalytic (BEZ235) plus an allosteric (RAD001) mTOR inhibitor that selectively inhibits target of rapamycin complex 1 (TORC1) downstream of mTOR was safe and was associated with a significant (P = 0.001) decrease in the rate of infections reported by elderly subjects for a year after study drug initiation. In addition, we observed an up-regulation of antiviral gene expression and an improvement in the response to influenza vaccination in this treatment group. Thus, selective TORC1 inhibition has the potential to improve immune function and reduce infections in the elderly.

Pharmacological Inhibition of O-GlcNAcase Does Not Increase Sensitivity of Glucocorticoid Receptor-Mediated Transrepression.

Glucocorticoid signaling regulates target genes by multiple mechanisms, including the repression of transcriptional activities of nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB) though direct protein-protein interactions and subsequent O-GlcNAcylation of RNA polymerase II (pol II). Recent studies have shown that overexpression of O-linked ß-N-acetylglucosamine transferase (OGT), which adds an O-linked ß-N-acetylglucosamine (O-GlcNAc) group to the C-terminal domain of RNA pol II, increases the transrepression effects of glucocorticoids (GC). As O-GlcNAcase (OGA) is an enzyme that removes O-GlcNAc from O-GlcNAcylated proteins, we hypothesized that the potentiation of GC effects following OGT overexpression could be similarly observed via the direct inhibition of OGA, inhibiting O-GlcNAc removal from pol II. Here we show that despite pharmacological evidence of target engagement by a selective small molecule inhibitor of OGA, there is no evidence for a sensitizing effect on glucocorticoid-mediated effects on TNF-α promoter activity, or gene expression generally, in human cells. Furthermore, inhibition of OGA did not potentiate glucocorticoid-induced apoptosis in several cancer cell lines. Thus, despite evidence for O-GlcNAc modification of RNA pol II in GR-mediated transrepression, our data indicate that pharmacological inhibition of OGA does not potentiate or enhance glucocorticoid-mediated transrepression.

Genes related to emphysema are enriched for ubiquitination pathways.

BACKGROUND: Increased small airway resistance and decreased lung elasticity contribute to the airflow limitation in chronic obstructive pulmonary disease (COPD). The lesion that corresponds to loss of lung elasticity is emphysema; the small airway obstruction is due to inflammatory narrowing and obliteration. Despite their convergence in altered physiology, different mechanisms contribute to these processes. The relationships between gene expression and these specific phenotypes may be more revealing than comparison with lung function. METHODS: We measured the ratio of alveolar surface area to lung volume (SA/V) in lung tissue from 43 smokers. Two samples from 21 subjects, in which SA/V differed by >49 cm2/mL were profiled to select genes whose expression correlated with SA/V. Significant genes were tested for replication in the 22 remaining subjects. RESULTS: The level of expression of 181 transcripts was related to SA/V ( p < 0.05). When these genes were tested in the 22 remaining subjects as a replication, thirty of the 181 genes remained significantly associated with SA/V (P < 0.05) and the direction of association was the same in 164/181. Pathway and network analysis revealed enrichment of genes involved in protein ubiquitination, and western blotting showed altered expression of genes involved in protein ubiquitination in obstructed individuals. CONCLUSION: This study implicates modified protein ubiquitination and degradation as a potentially important pathway in the pathogenesis of emphysema.

Phosphoproteomics in drug discovery.

Several important aspects of the drug discovery process, including target identification, mechanism of action determination and biomarker identification as well as drug repositioning, require complete understanding of the effects of drugs on protein phosphorylation in relevant biological systems. Novel high-throughput phosphoproteomic technologies can be employed to measure these phosphorylation events. In this review, we describe the advantages and limitations of state-of-the-art phosphoproteomic approaches such as mass spectrometry and antibody-based technologies in terms of sample and data throughput as well as data quality. We then discuss how datasets from each technology can be analyzed and how the results can be and have been applied to advance different aspects of the drug discovery process.

Normalization and statistical analysis of multiplexed bead-based immunoassay data using mixed-effects modeling.

Multiplexed bead-based flow cytometric immunoassays are a powerful experimental tool for investigating cellular communication networks, yet their widespread adoption is limited in part by challenges in robust quantitative analysis of the measurements. Here we report our application of mixed-effects modeling for the normalization and statistical analysis of bead-based immunoassay data. Our data set consisted of bead-based immunoassay measurements of 16 phospho-proteins in lysates of HepG2 cells treated with ligands that regulate acute-phase protein secretion. Mixed-effects modeling provided estimates for the effects of both the technical and biological sources of variance, and normalization was achieved by subtracting the technical effects from the measured values. This approach allowed us to detect ligand effects on signaling with greater precision and sensitivity and to more accurately characterize the HepG2 cell signaling network using constrained fuzzy logic. Mixed-effects modeling analysis of our data was vital for ascertaining that IL-1α and TGF-α treatment increased the activities of more pathways than IL-6 and TNF-α and that TGF-α and TNF-α increased p38 MAPK and c-Jun N-terminal kinase (JNK) phospho-protein levels in a synergistic manner. Moreover, we used mixed-effects modeling-based technical effect estimates to reveal the substantial variance contributed by batch effects along with the absence of loading order and assay plate position effects. We conclude that mixed-effects modeling enabled additional insights to be gained from our data than would otherwise be possible and we discuss how this methodology can play an important role in enhancing the value of experiments employing multiplexed bead-based immunoassays.

Construction of cell type-specific logic models of signaling networks using CellNOpt.

Mathematical models are useful tools for understanding protein signaling networks because they provide an integrated view of pharmacological and toxicological processes at the molecular level. Here we describe an approach previously introduced based on logic modeling to generate cell-specific, mechanistic and predictive models of signal transduction. Models are derived from a network encoding prior knowledge that is trained to signaling data, and can be either binary (based on Boolean logic) or quantitative (using a recently developed formalism, constrained fuzzy logic). The approach is implemented in the freely available tool CellNetOptimizer (CellNOpt). We explain the process CellNOpt uses to train a prior knowledge network to data and illustrate its application with a toy example as well as a realistic case describing signaling networks in the HepG2 liver cancer cell line.

Non Linear Programming (NLP) formulation for quantitative modeling of protein signal transduction pathways.

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.

CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms.

BACKGROUND: Cells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce. RESULTS: Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities. CONCLUSIONS: Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.

Querying quantitative logic models (Q2LM) to study intracellular signaling networks and cell-cytokine interactions.

Mathematical models have substantially improved our ability to predict the response of a complex biological system to perturbation, but their use is typically limited by difficulties in specifying model topology and parameter values. Additionally, incorporating entities across different biological scales ranging from molecular to organismal in the same model is not trivial. Here, we present a framework called "querying quantitative logic models" (Q2LM) for building and asking questions of constrained fuzzy logic (cFL) models. cFL is a recently developed modeling formalism that uses logic gates to describe influences among entities, with transfer functions to describe quantitative dependencies. Q2LM does not rely on dedicated data to train the parameters of the transfer functions, and it permits straight-forward incorporation of entities at multiple biological scales. The Q2LM framework can be employed to ask questions such as: Which therapeutic perturbations accomplish a designated goal, and under what environmental conditions will these perturbations be effective? We demonstrate the utility of this framework for generating testable hypotheses in two examples: (i) a intracellular signaling network model; and (ii) a model for pharmacokinetics and pharmacodynamics of cell-cytokine interactions; in the latter, we validate hypotheses concerning molecular design of granulocyte colony stimulating factor.

Comparing signaling networks between normal and transformed hepatocytes using discrete logical models.

Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of "omic" data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here, we combine network analysis and functional experimentation by using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and 4 hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type, and these families clustered topologically into normal and diseased sets.