Molecular Characterization of Mucus Binding - ACS Publications

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Molecular characterization of mucus binding Jacob Witten, Tahoura Samad, and Katharina Ribbeck Biomacromolecules, Just Accepted Manuscript • DOI: 10.1021/acs.biomac.8b01467 • Publication Date (Web): 19 Feb 2019 Downloaded from http://pubs.acs.org on February 20, 2019

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Biomacromolecules

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Molecular characterization of mucus binding

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Jacob Witten†,‡, Tahoura Samad†, Katharina Ribbeck†*

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AUTHOR INFORMATION

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†

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Cambridge, MA 02139, USA

6

‡

7

Technology, Cambridge, MA 02139, USA

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Corresponding Author

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* [email protected]

Department of Biological Engineering, Massachusetts Institute of Technology,

Computational and Systems Biology Initiative, Massachusetts Institute of

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ABSTRACT. Binding of small molecules to mucus membranes in the body has

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an important role in human health, as it can affect the diffusivity and activity of

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any molecule that acts in a mucosal environment. The binding of drugs, and of

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toxins and signaling molecules from mucosal pathogens, is of particular clinical

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interest. Despite the importance of mucus-small molecule binding, there is a

ACS Paragon Plus Environment

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lack of data revealing the precise chemical features of small molecules that

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lead to mucus binding. We developed a novel equilibrium dialysis assay to

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measure the binding of libraries of small molecules to mucin and other mucus

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components, substantially increasing the throughput of small molecule binding

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measurements. We validated the biological relevance of our approach by

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quantifying binding of the antibiotic colistin to mucin, and showing that this

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binding was associated with inhibition of colistin’s bioactivity. We next used a

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small molecule microarray to identify 2,4-diaminopyrimidine as a mucin binding

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motif, and confirmed the importance of this motif for mucin binding using

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equilibrium dialysis. Furthermore, we showed that for molecules with this motif,

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binding to mucins and the mucus-associated biopolymers DNA and alginate is

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modulated by differences in hydrophobicity and charge. Finally, we showed that

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molecules lacking the motif exhibited different binding trends from those

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containing the motif. These results open up the prospect of routine testing of

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small molecule binding to mucus, and optimization of drugs for clinically

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relevant mucus binding properties.


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Biomacromolecules

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KEYWORDS: mucus, mucin, drug design, drug delivery, equilibrium dialysis

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Introduction

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Mucus is a viscoelastic hydrogel layer that coats every non-keratinized

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epithelial surface of the body, including the lungs and intestines.1 Its activity

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has important ramifications for biology and medicine:2–5 the selective binding of

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small molecules to mucus may affect the activity and diffusivity of any molecule

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acting in a mucosal environment.3 Medically relevant molecules affected by

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mucus include bacterially produced toxins and signaling molecules, and inhaled

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therapeutics for diseases including cystic fibrosis (CF), chronic obstructive

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pulmonary disease (COPD), and asthma.2,3

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The main gel-forming components of mucus are large secreted glycoproteins

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called

mucins,

which

contain

alternating

regions

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domains and disordered polyanionic oligosaccharide brushes with sialic acid and

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sulfated groups.1,6 Since mucins are a major component of mucus, the ability of

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a molecule to bind to mucin is a key determinant of mucus binding. Mucin

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overproduction is a hallmark of respiratory diseases including CF, COPD, and


of

globular

hydrophobic

3


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asthma,7 suggesting that mucin binding is correspondingly more important in

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these pathologies. Synthetically crosslinked, purified mucin gels are a second

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environment in which mucin binding plays a role; they are a promising

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biomaterial for sustained drug delivery, and mucin binding helps determine the

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kinetics of drug release.8 Mucins can also nonenzymatically catalyze some

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organic reactions by binding to relevant molecules.9

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Besides mucins, lung mucus contains lipids, proteins, extracellular DNA

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(eDNA), and bacterial polysaccharides (e.g. the polyanion alginate, often present

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in infections by the Pseudomonas aeruginosa, an opportunistic pathogen10).

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Both eDNA and bacterial polysaccharides are commonly found in CF lung

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mucus and may also bind small molecules.3

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Despite the clinical importance of small molecule binding to mucus, relatively

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little is known about the chemical properties that regulate small molecule

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binding. What is known has largely been inferred from diffusion measurements:

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slower diffusion in mucus indicates increased mucus binding, since small

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molecules are smaller than the mesh size of mucus.2 In these experiments,


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Biomacromolecules

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increased hydrophobicity typically correlates with slower drug diffusion.11 Lipids,

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and the hydrophobic domains of mucins, are believed to account for this

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slowed diffusion by binding to hydrophobic drugs2,3,5,12 although the glycans

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grafted to mucins may also play a role.9 Drug charge only appears to play a

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major role for highly cationic molecules such as aminoglycoside and polymyxin

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antibiotics (+5 molecular charge in the case of the anti-P. aeruginosa antibiotics

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tobramycin and colistin).13–18 This binding is believed to be mediated by the

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polyanionic glycosylated domains of mucins and, in CF, eDNA and bacterial

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polysaccharides.3,5

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However,

beyond

an

apparent

correlation

between

diffusion

and

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hydrophobicity, and electrostatic binding of highly charged molecules, few

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details are known about which molecules can bind mucus. One primary reason

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for this knowledge gap is that studies of mucus-small molecule interactions

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typically use small datasets, with fewer than ten molecules per study (with one

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interesting exception19).20 This low volume of data is due to the difficulty of

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acquiring large amounts of mucus or mucus-like models. In addition, diffusion


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and binding measurements have been based on molecular detection techniques

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such as radiolabeling, fluorescence, or electrochemical methods,20 which cannot

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be multiplexed. These studies typically test fewer than ten molecules,11 though

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one study using radiolabeled drugs tested 19,21 and one another used mucin-

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based affinity chromatography and ultraviolet spectrometry to measure mucin

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binding of 41 molecules.19 Thus, a data-driven approach to identifying molecules

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that bind mucus has so far not been feasible.

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Our goal in this work is to overcome this limitation by analyzing the binding

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of small molecules to mucus components, including Muc5AC, DNA, and

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alginate, in a relatively high throughput manner. To do this, we developed an

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equilibrium dialysis (ED) technique to measure binding of small molecules to

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these biopolymers. This label-free technique can measure up to 20 molecules

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in a single experiment, minimizing sample volume requirements and enabling

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the generation of a large dataset (96 molecules in total) which constitutes a

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substantial increase over previous studies of drug-mucus interactions.


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First, we validated the biological relevance of our binding assay by showing

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that the cationic polymyxin antibiotic colistin binds MUC5AC, a prominent lung

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mucin,6 but not the neutral polysaccharide methylcellulose (MC), and that

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binding was associated with inhibition of antibiotic activity. Next, we used a

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small molecule microarray (SMM) approach22 to screen for MUC5AC binding of

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thousands of molecules, which revealed a previously unknown chemical motif

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associated with mucin binding. We used our novel ED method to confirm the

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importance of this motif in mucin binding, and further showed that charge and

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hydrophobicity play a context-dependent role in predicting binding. Together,

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these findings demonstrate the strength of our technique for enabling a data-

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driven approach to understanding mucus binding, and illustrate important

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insights about the effect of mucus on small molecules.

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Experimental

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Buffer preparation and specifications. 10x Phosphate-buffered saline (PBS) was purchased from AccuGene. At 1x concentration, it consisted of 1mM


7


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KH2PO4, 2mM Na2HPO4, and 150mM NaCl at pH 7.4. Phosphate-citrate buffer

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(PCB) at 1x consisted of 10mM each of phosphate and citrate, and 58mM

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sodium, at pH 7.0.

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Mucin preparation and fluorescent labeling. MUC5AC was purified from fresh

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pig stomach scrapings as described in Celli et al,23 with the exception that the

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cesium chloride density gradient ultracentrifugation step was only performed for

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mucin used in the SMM, not for mucin tested using ED. Briefly, mucus was

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scraped from fresh pig stomachs (Research 87, Inc., Boylston, MA, USA) and

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mucins were purified using size exclusion chromatography.

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To fluorescently label the mucin, 0.6mg was solubilized in 300μL of pH 9

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0.1M sodium bicarbonate buffer overnight at 4°C. Alexa Fluor 647 succinimidyl

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ester (ThermoFisher Scientific) dissolved in dimethyl sulfoxide (DMSO) at 10

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mg/mL was added to the mucin solution to a concentration of 0.02 mg/mL

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(1:100 mass ratio with respect to the 2mg/mL mucin) and the mixture was

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incubated at room temperature for 1 hour with shaking. An equal volume of


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50mM tris(hydroxymethyl)aminomethane was added to quench the reaction and

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the mixture was left at room temperature for 5 minutes with shaking. The

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volume was diluted to 20mL with PBST (PBS with Tween 20) in a Corning

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Spin-X 100 kDa molecular weight cutoff ultrafiltration concentrator (Fisher) and

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centrifuged at 3,000xg for 25min at 4°C; the flow-through was discarded. These

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steps (dilute to 20mL with PBST, centrifuge, discard flow-through) were

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repeated twice more; the volume remaining after each centrifugation was

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approximately 5mL. The labeled mucin was then diluted to the appropriate

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concentration.

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Equilibrium dialysis. ED was performed with a 12 kDa cutoff Rapid

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Equilibrium Dialysis device (Thermo Scientific) with 100μL of 5mg/mL

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biopolymer solution or gel, or buffer control, in the sample chamber and 300μL

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of matching buffer in the assay chamber. Each experiment began with equal

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concentrations of the relevant compound cocktail loaded in both chambers (“1x

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concentration”). 1x concentration was 200nM for every molecule except colistin

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(10μM). Equilibration took place over 4 hours with shaking at 235 rpm and 37


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°C, at which point aliquots were collected from the assay chamber. HPLC

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separation took place on an Acclaim PolarAdvantage column (3μm pores,

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2.1x100mm, VWR). The HPLC method is given in Table S1. The first 5

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minutes of eluent were diverted to a waste container to avoid salt

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contamination of the mass spectrometer (Agilent 6410 triple quadrupole), and

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the rest of the eluent was analyzed using multiple reaction monitoring (MRM).

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Source parameters were as follows: temperature 350 °C, gas flow 10 L/min,

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nebulizer 25 psi, capillary voltage 4000V, with molecular ions and fragments for

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all experiments given in Tables S2-S4. Multiple transitions were tracked for

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several of the molecules (including colistin) as a consistency check; only one

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was used for analysis but all gave consistent results (not shown). Peak areas

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for each molecule’s chromatogram were compared to an external standard

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curve to measure concentrations of each molecule.

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The SMM and NIH Clinical Collection (NCC) compound sets (acquired from

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ChemBridge [San Diego, CA] and NIH respectively) were tested for biopolymer

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binding in 3 and 2 groups, respectively, with one exception. The assigned


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Biomacromolecules

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groupings are given in Tables S2-S3 (“Cocktail group 1” and “Cocktail Group 2”

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respectively). There were 18 compounds in each group (cocktail) for the SMM

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runs and 20 or 21 compounds in the NCC drug cocktails, except for the

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experiments with SMM molecules binding to mucin in PCB, where 96

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compounds (see main text) were run in 4 groups of 24 molecules each

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(“Cocktail group 2” in Table S2), of which 54 molecules were successfully

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measured. These 54 in the 3 groups of 18 were used for all remaining

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experiments. The NCC consists of over 700 compounds; 41 molecules were

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selected randomly with the constraint that molecules with the same mass could

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not be included in a single cocktail, to ease qLCMS analysis. Several DAP-

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containing molecules were included in the NCC set (they did not bind mucin

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significantly more than non-DAP containing NCC molecules; not shown).

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The uptake ratio RU of a compound was calculated by (see Supporting Text for derivation):

(

𝑅𝑈 = 1 + 4

1 𝑐𝐸

1

)

(3)

― 𝑐𝐶


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where cE is the concentration in the assay chamber of the experimental

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dialysis system and cC is the concentration in assay chamber of the buffer-

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buffer dialysis control. cE and cC were both scaled with respect to the 1x

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concentration. cE and cC were each averaged over 3 wells for one full

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biological replicate of RU, which was then converted to ΔG, so n=3 represents

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9 measured wells.

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We used a biopolymer concentration of 0.5% w/v (5 mg/mL). 5 mg/mL is

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consistent with typical mucin concentrations in lung mucus (0.1-20 mg/mL,

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depending on disease state6,24–26) and DNA concentrations in CF sputum (0.5-5

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mg/mL24,25). It is higher than the bulk concentration of alginate in P.

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aeruginosa-infected CF sputum (0.004-0.1 mg/mL27), but the local concentration

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in a P. aeruginosa biofilm may approach 5 mg/mL. Alginate (MW 120-190 kDa)

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and calf thymus genomic DNA were purchased from Sigma.

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Small molecule microarray. SMM slides were prepared as previously described in Bradner et al.28 The experimental protocol was also as described


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Biomacromolecules

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in Bradner et al. using procedure steps 21A (“Dish method”), 22A (“Direct

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detection of fluor-labeled proteins”), and 23, with the following modifications:

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PBST and PBS (for the final rinse) were used instead of TBST and TBS,

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slides were incubated at room temperature rather than 4 °C, 5mL of protein

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and wash solution was used rather than 3mL, and 3 washes were performed

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instead of 2 in step 22A(i). The slides were scanned for Alexa 647-labeled

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mucin fluorescence using a GenePix scanner (Molecular Devices) and analyzed

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by GenePix Pro software (Axon Instruments). Each printed feature’s signal-

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background ratio was quantified (on a per-feature basis per Bradner et al.) and

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normalized to a z-score with mean and standard deviation measured for all

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molecules under that feature’s condition (mucin and detergent concentration).

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For the initial SMM screen, each molecule appeared 16 times: twice on each

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slide where present, and under 8 conditions. 6 conditions were experimental:

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0.1, 0.5, and 1 μg/mL mucin with 0.05% and 0.2% Tween 20, and 2 conditions

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were controls: a PBST-only blank (0.2% Tween 20) and a fluorophore-only

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control (0.05% Tween 20). Spots with a z-score of 2 or more were determined


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to indicate binding. A molecule was considered a hit if it appeared as positive

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on 6 or more of the 12 potential experimental locations but none of the 4

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potential control locations. This combination of z-score cutoff and hit calling was

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selected to yield a manageable number of hits for ED analysis. For the follow-

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up screen, there was only one condition (0.5 μg/mL mucin, 0.1% Tween 20)

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but each molecule was present on two slides. A molecule was considered a hit

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if it had a z-score of 2 or more in all 4 possible locations. For quality control,

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this screen also included 144 blanks that were DMSO-only negative controls,

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each spotted at either 8 or 16 locations. None of these negative controls had a

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z-score greater than 2 in 3 or more locations, meaning that none of the

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negative controls were false positives (data not shown).

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Computational molecular analysis. Motif analysis was performed using RDKit

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(RDKit: Open-source cheminformatics; http://www.rdkit.org) for Python. Molecules

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were checked for presence of DAP using the substructure query

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“NC1=NC=CC(N)=N1”. Fisher’s exact test was performed using the SciPy

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package.


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Biomacromolecules

Molecular charge was calculated as the average charge weighted by

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abundance over all generated protonation microspecies using the

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MajorMicrospeciesPlugin Calculator Plugin, Marvin 5.4.1, 2011, ChemAxon

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(http://www.chemaxon.com).

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ClogP was calculated using BioByte (BioByte Corp, Claremont, CA).

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Generation of plots for local and global correlations with molecular

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properties. We generated random “ΔG” values using 40 uniformly distributed

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random numbers evenly spaced between 0 and 10 (0-10 was the arbitrarily

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scaled x axis), and made the function continuous with a cubic spline

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interpolation. We generated random “Property” values using 15 normally

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distributed random numbers evenly spaced between 0 and 10, also with a

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cubic spline interpolation. The blue and red “molecules,” or locations on the x

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axis, were selected by hand to illustrate that local and global correlations

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between ΔG and a molecular property may not be consistent. All figure


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preparation was done using Matlab (2015a, The MathWorks, Inc., Natick, MA,

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USA).

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Page 16 of 49

Colistin activity in mucin. Colistin MIC was first determined using a modified

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broth microdilution assay. P. aeruginosa strain PAO1 was grown overnight in

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BBL Mueller-Hinton II broth (MHB; BD Falcon) at 37°C. Colistin (Sigma) stock

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solution was prepared at 10 mg/mL in water, then serially diluted 2-fold into

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MHB.

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approximately 3x104 CFU/mL into fresh MHB. 90 μl of this inoculum was added

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to each well of a 96-well plate with 10 μl of the colistin dilution series, such

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that the final concentrations evaluated were between 1mg/mL and 0.4 μg/mL.

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After incubation at 37°C for 24 hours, the MIC of colistin in MHB was

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determined as 8 μg/mL, by visually inspecting the plate to identify the lowest

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concentration at which there was no visible cellular growth.

The overnight culture was diluted to a final concentration of

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To compare the efficacy of colistin in mucin, methylcellulose and MHB alone,

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PAO1 was grown overnight in MHB at 37°C. The overnight culture was diluted


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to a final concentration of approximately 6 x 104 CFU/mL into fresh MHB. 45 μl

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of this inoculum was added to each well of a 96-well plate. 45 μl of either

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mucin (1% w/v, dissolved in MHB), methylcellulose (1% w/v, dissolved in MHB)

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or MHB were added to each well depending on the condition, and then 10 μl

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of colistin diluted into MHB was added to a final concentration was 8 μg/mL

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(MIC) or 16 μg/mL (2xMIC). After incubation at 37°C for 24 hours, the number

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of surviving cells in each condition was evaluated by scraping the wells,

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homogenizing the contents by pipetting up and down vigorously, serial dilution,

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plating and counting colony forming units (CFUs).

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Statistical analysis and plots. Except for the Fisher’s exact test, statistical

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analysis and plotting was performed in R (R Core Team (2017). R: A language

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and environment for statistical computing. R Foundation for Statistical

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Computing, Vienna, Austria. URL https://www.R-project.org/.) Tests of significant

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correlation were performed using F tests. Pairwise comparison of regression

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coefficients was performed by testing the ability of a property to predict the

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difference in ΔG between two different gels. For example, a test of the model:


17


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lm((Mucin_dG - DNA_dG) ~ Charge,data=all_data)

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would test the significance of a pairwise comparison between the effect of

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267

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charge on mucin and DNA binding. All tests were two-tailed.

For the ED experiments, the binding of each molecule was measured 3 times, as described in “Equilibrium dialysis” section of Methods.

Data availability. The datasets generated during the current study are available from the corresponding author on request.

Code availability. The code written and used for data analysis for the current

271

study is available from the corresponding author on request.

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Results and Discussion

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Page 18 of 49

Development and validation of equilibrium dialysis technique to measure

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biopolymer binding. In the ED assay, we measured the partitioning of

275

molecules across a dialysis membrane between an assay chamber containing

276

buffer and a sample chamber containing the biopolymer of interest in the same


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buffer. We used multiple compounds in the same experiment (Figure 1a), under

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dilute conditions to minimize competition for binding sites. We then used

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quantitative liquid chromatography-mass spectrometry (qLCMS) to measure

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molecule concentrations and quantify the equilibrium partitioning. qLCMS

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quantitation allowed us measure up to 20 molecules in a single experiment.


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a

b

*

c

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Figure 1. (a) Schematic of equilibrium dialysis (ED). Concentration in assay

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chamber of ED device is compared to that from a buffer control using qLCMS.

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Multiple compounds may be tested at once. Here, 2 are shown: a compound

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that binds mucin (blue stars) and a compound that does not (red circles). (b)

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Binding of colistin to mucin and methylcellulose (MC); negative ΔG implies


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binding. Colistin binds mucin but not MC. Error bars: SEM. *p=0.48 (t=0.85,

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df=2). (c) Surviving P. aeruginosa strain PAO1 cells (CFU = colony forming

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units) after 24 hours of growth with colistin in biopolymer-free Mueller-Hinton

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broth (MHB), in mucin (0.5% w/v dissolved in MHB), or MC (0.5% w/v

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dissolved in MHB). Colistin activity is inhibited by mucin, but not by MC. No

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colistin control shows that mucin does not increase growth in the absence of

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antibiotic. *p

Molecular Characterization of Mucus Binding - ACS Publications

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