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Supplemental Figure 1 shows Pancreatic stellate cells (PSCs) promoted tumor cell invasion through close cell-cell interactions.

Pancreatic cancer has an abysmal 5-year survival rate of 8%, making it a deadly disease with a need for novel therapies. Here we describe a multitargeting heparin-based mimetic, necuparanib, and its antitumor activity in both in vitro and in vivo models of pancreatic cancer. Necuparanib reduced tumor cell proliferation and invasion in a three-dimensional (3D) culture model; in vivo, it extended survival and reduced metastasis. Furthermore, proteomic analysis demonstrated that necuparanib altered the expression levels of multiple proteins involved in cancer-driving pathways including organ development, angiogenesis, proliferation, genomic stability, cellular energetics, and invasion and metastasis. One protein family known to be involved in invasion and metastasis and altered by necuparanib treatment was the matrix metalloprotease (MMP) family. Necuparanib reduced metalloproteinase 1 (MMP1) and increased tissue inhibitor of metalloproteinase 3 (TIMP3) protein levels and was found to increase RNA expression of TIMP3. MMP enzymatic activity was also found to be reduced in the 3D model. Finally, we confirmed necuparanib's in vivo activity by analyzing plasma samples of patients enrolled in a phase I/II study in patients with metastatic pancreatic cancer; treatment with necuparanib plus standard of care significantly increased TIMP3 plasma protein levels. Together, these results demonstrate necuparanib acts as a broad multitargeting therapeutic with in vitro and in vivo anti-invasive and antimetastatic activity.

Supplementary Table 2 lists GO Categories significantly altered by necuparanib treatment, assigned to cancer-driving pathways

Supplementary Table 1 lists proteins significantly altered by necuparanib treatment

Supplemental Figure 3 shows MMP1 and MMP7 protein levels were unchanged in the plasma of patients.

Supplemental Figure 2 shows GM6001 reduced spheroid growth and invasion in the 3D co-culture model.

Background Serum proteins can be readily assessed during routine clinical care. However, it is unclear to what extent serum proteins reflect the molecular dysregulations of peripheral blood cells (PBCs) or affected end-organs in systemic sclerosis (SSc). We conducted a multiomic comparative analysis of SSc serum profile, PBC, and skin gene expression in concurrently collected samples. Methods Global gene expression profiling was carried out in skin and PBC samples obtained from 49 SSc patients enrolled in the GENISOS observational cohort and 25 unaffected controls. Levels of 911 proteins were determined by Olink Proximity Extension Assay in concurrently collected serum samples. Results Both SSc PBC and skin transcriptomes showed a prominent type I interferon signature. The examination of SSc serum profile revealed an upregulation of proteins involved in pro-fibrotic homing and extravasation, as well as extracellular matrix components/modulators. Notably, several soluble receptor proteins such as EGFR, ERBB2, ERBB3, VEGFR2, TGFBR3, and PDGF-Rα were downregulated. Thirty-nine proteins correlated with severity of SSc skin disease. The differential expression of serum protein in SSc vs. control comparison significantly correlated with the differential expression of corresponding transcripts in skin but not in PBCs. Moreover, the differentially expressed serum proteins were significantly more connected to the Well-Associated-Proteins in the skin than PBC gene expression dataset. The assessment of the concordance of between-sample similarities revealed that the molecular profile of serum proteins and skin gene expression data were significantly concordant in patients with SSc but not in healthy controls. Conclusions SSc serum protein profile shows an upregulation of profibrotic cytokines and a downregulation of soluble EGF and other key receptors. Our multilevel comparative analysis indicates that the serum protein profile in SSc correlates more closely with molecular dysregulations of skin than PBCs and might serve as a reflection of disease severity at the end-organ level.

The characterization of glycosylation is required for many protein therapeutics. The emergence of antibody and antibody-like molecules with multiple glycan attachment sites has rendered glycan analysis increasingly more complicated. Reliance on site-specific glycopeptide analysis is therefore necessary to fully analyze multi-glycosylated biotherapeutics. Established glycopeptide methodologies have generally utilized a priori knowledge of the glycosylation states of the investigated protein(s), database searching of results generated from data-dependent liquid chromatography–tandem mass spectrometry workflows, and extracted ion quantitation of the individual identified species. However, the inherent complexity of glycosylation makes predicting all glycoforms on all glycosylation sites extremely challenging, if not impossible. That is, only the “knowns” are assessed. Here, we describe an agnostic methodology to qualitatively and quantitatively assess both “known” and “unknown” site-specific glycosylation for biotherapeutics that contain multiple glycosylation sites. The workflow uses data-independent, all ion fragmentation to generate glycan oxonium ions, which are then extracted across the entirety of the chromatographic timeline to produce a glycan-specific “fingerprint” of the glycoprotein sample. We utilized both HexNAc and sialic acid oxonium ion profiles to quickly assess the presence of Fab glycosylation in a therapeutic monoclonal antibody, as well as for high-throughput comparisons of multi-glycosylated protein drugs derived from different clones to a reference product. An automated method was created to rapidly assess oxonium profiles between samples, and to provide a quantitative assessment of similarity.

Identification of differentially expressed genes (DEGs) is well recognized to be variable across independent replications of genome-wide transcriptional studies. These are often employed to characterize disease state early in the process of discovery and prioritize novel targets aimed at addressing unmet medical need. Increasing reproducibility of biological findings from these studies could potentially positively impact the success rate of new clinical interventions. This work demonstrates that statistically sound combination of gene expression data with prior knowledge about biology in the form of large protein interaction networks can yield quantitatively more reproducible observations from studies characterizing human disease. The novel concept of Well-Associated Proteins (WAPs) introduced herein—gene products significantly associated on protein interaction networks with the differences in transcript levels between control and disease—does not require choosing a differential expression threshold and can be computed efficiently enough to enable false discovery rate estimation via permutation. Reproducibility of WAPs is shown to be on average superior to that of DEGs under easily-quantifiable conditions suggesting that they can yield a significantly more robust description of disease. Enhanced reproducibility of WAPs versus DEGs is first demonstrated with four independent data sets focused on systemic sclerosis. This finding is then validated over thousands of pairs of data sets obtained by random partitions of large studies in several other diseases. Conditions that individual data sets must satisfy to yield robust WAP scores are examined. Reproducible identification of WAPs can potentially benefit drug target selection and precision medicine studies., Author summary Gene expression studies are commonly used to characterize biological systems. Genes identified in such experiments as expressed at different levels between conditions (e.g. healthy vs. disease) can indicate biological functions that are important in this context. However, it is well-recognized that such findings can vary substantially across independent investigations. We quantified reproducibility here under a conservative control scenario that partitions a given data set in two, independently of experimental conditions, for multiple data sets characterizing several diseases in humans. Furthermore, we have shown that it is possible to obtain more reproducible findings than DEGs, which we term Well-Associated Proteins, characterizing differences in gene expression between healthy and disease states. This was accomplished by combining gene expression and prior knowledge of functional relationships between gene products accumulated over many studies and publications. Resulting Well-Associated Proteins can be computed efficiently enough to enable permutation controls and demonstrate on average higher reproducibility than differentially expressed genes, both within and across data sets. This suggests that Well-Associated Proteins may better reflect differences in biology when comparing disease and healthy states than DEGs, thus representing an important step towards identification of key disease drivers.

Background: The sequelae of Kawasaki disease (KD) vary widely with the greatest risk for future cardiovascular events among those who develop giant coronary artery aneurysms (CAA). We sought to define the molecular signature associated with different outcomes in pediatric and adult KD patients. Methods: Molecular profiling was conducted using mass spectrometry–based shotgun proteomics, transcriptomics, and glycomics methods on 8 pediatric KD patients at the acute, subacute, and convalescent time points. Shotgun proteomics was performed on 9 KD adults with giant CAA and matched healthy controls. Plasma calprotectin was measured by ELISA in 28 pediatric KD patients 1 year post-KD, 70 adult KD patients, and 86 healthy adult volunteers. Results: A characteristic molecular profile was seen in pediatric patients during the acute disease, which resolved at the subacute and convalescent periods in patients with no coronary artery sequelae but persisted in 2 patients who developed giant CAA. We, therefore, investigated persistence of inflammation in KD adults with giant CAA by shotgun proteomics that revealed a signature of active inflammation, immune regulation, and cell trafficking. Correlating results obtained using shotgun proteomics in the pediatric and adult KD cohorts identified elevated calprotectin levels in the plasma of patients with CAA. Investigation of expanded pediatric and adult KD cohorts revealed elevated levels of calprotectin in pediatric patients with giant CAA 1 year post-KD and in adult KD patients who developed giant CAA in childhood. Conclusions: Complex patterns of biomarkers of inflammation and cell trafficking can persist long after the acute phase of KD in patients with giant CAA. Elevated levels of plasma calprotectin months to decades after acute KD and infiltration of cells expressing S100A8 and A9 in vascular tissues suggest ongoing, subclinical inflammation. Calprotectin may serve as a biomarker to inform the management of KD patients following the acute illness.

Heparan sulfate (HS), a glycosaminoglycan present on the surface of cells, has been postulated to have important roles in driving both normal and pathological physiologies. The chemical structure and sulfation pattern (domain structure) of HS is believed to determine its biological function, to vary across tissue types, and to be modified in the context of disease. Characterization of HS requires isolation and purification of cell surface HS as a complex mixture. This process may introduce additional chemical modification of the native residues. In this study, we describe an approach towards thorough characterization of bovine kidney heparan sulfate (BKHS) that utilizes a variety of orthogonal analytical techniques (e.g. NMR, IP-RPHPLC, LC-MS). These techniques are applied to characterize this mixture at various levels including composition, fragment level, and overall chain properties. The combination of these techniques in many instances provides orthogonal views into the fine structure of HS, and in other instances provides overlapping / confirmatory information from different perspectives. Specifically, this approach enables quantitative determination of natural and modified saccharide residues in the HS chains, and identifies unusual structures. Analysis of partially digested HS chains allows for a better understanding of the domain structures within this mixture, and yields specific insights into the non-reducing end and reducing end structures of the chains. This approach outlines a useful framework that can be applied to elucidate HS structure and thereby provides means to advance understanding of its biological role and potential involvement in disease progression. In addition, the techniques described here can be applied to characterization of heparin from different sources. Electronic supplementary material The online version of this article (doi:10.1007/s10719-016-9734-7) contains supplementary material, which is available to authorized users.

Pancreatic cancer has an abysmal 5-year survival rate of 8%, making it a deadly disease with a need for novel therapies. Here we describe a multitargeting heparin-based mimetic, necuparanib, and its antitumor activity in both in vitro and in vivo models of pancreatic cancer. Necuparanib reduced tumor cell proliferation and invasion in a three-dimensional (3D) culture model; in vivo, it extended survival and reduced metastasis. Furthermore, proteomic analysis demonstrated that necuparanib altered the expression levels of multiple proteins involved in cancer-driving pathways including organ development, angiogenesis, proliferation, genomic stability, cellular energetics, and invasion and metastasis. One protein family known to be involved in invasion and metastasis and altered by necuparanib treatment was the matrix metalloprotease (MMP) family. Necuparanib reduced metalloproteinase 1 (MMP1) and increased tissue inhibitor of metalloproteinase 3 (TIMP3) protein levels and was found to increase RNA expression of TIMP3. MMP enzymatic activity was also found to be reduced in the 3D model. Finally, we confirmed necuparanib's in vivo activity by analyzing plasma samples of patients enrolled in a phase I/II study in patients with metastatic pancreatic cancer; treatment with necuparanib plus standard of care significantly increased TIMP3 plasma protein levels. Together, these results demonstrate necuparanib acts as a broad multitargeting therapeutic with in vitro and in vivo anti-invasive and antimetastatic activity.

Complex mixtures of molecular species, such as glycoproteins and glycosaminoglycans, have important biological and therapeutic functions. Characterization of these mixtures with analytical chemistry measurements is an important step when developing generic drugs such as biosimilars. Recent developments have focused on analytical methods and statistical approaches to test similarity between mixtures. The question of how much uncertainty on mixture composition is reduced by combining several measurements still remains mostly unexplored. Mathematical frameworks to combine measurements, estimate mixture properties and quantify remaining uncertainty, i.e. a characterization extent, are introduced here. Constrained optimization and mathematical modeling are applied to a set of twenty-three experimental measurements on heparan sulfate, a mixture of linear chains of disaccharides having different levels of sulfation. While this mixture has potentially over two million molecular species, mathematical modeling and the small set of measurements establish the existence of nonhomogeneity of sulfate level along chains and the presence of abundant sulfate repeats. Constrained optimization yields not only estimations of sulfate repeats and sulfate level at each position in the chains but also bounds on these levels, thereby estimating the extent of characterization of the sulfation pattern which is achieved by the set of measurements.

A novel workflow was designed for the large-scale identification of protein N-terminal sequences. The workflow started with converting lysine to homoarginine, followed by reaction with sulfonation of N-termini by 4-sulfophenyl isothiocyanate (SPITC). Upon trypsin digestion, the SPITC modified N-terminal peptides were enriched by electrostatic repulsion hydrophilic interaction (ERLIC) chromatography in which the internal and C-terminal peptides eluted at the void volume, and the SPITC peptides were retained in the column due to the hydrophilicity and electrostatic attraction of the sulfonyl group to the stationary phase. Upon higher-energy collisional induced dissociation (HCD) SPITC peptides in ESI generated predominately y-type ion series; such simplification of spectra enables the identification of N-termini with high confidence. The appearance of b1 + SPITC product ions upon HCD further boosts the confidence for N-terminal identifications. This method was applied to an Escherichia coli cell lysate, thus allowing the identification of 358 high confidence N-terminal peptides representing 274 distinct E. coli proteins as certain proteins were found to have multiple N-terminal peptides due to cleavage from various cellular enzymes. Confidence in N-terminal assignments is further heightened since 224 of the unique proteins identified from N-terminal peptides were also identified in the analysis of flow-through fractions.

In the presence of 1 mM phosphate, 2-amino-6-mercapto-7-methylpurine ribonucleoside (MESG) is a well-behaved substrate for calf spleen purine nucleoside phosphorylase (PNP). In the absence of phosphate, calf spleen PNP catalyzes a slow hydrolysis of MESG, which is accompanied by inactivation of the enzyme, analogous to the previously observed PNP-catalyzed hydrolysis of inosine and guanosine with formation, in the former case, of a stable PNP·hypoxan thine complex (P. C. Kline and V. L. Schramm (1992) Biochemistry 31 , 5964–5973). Qualitative and semiquantitative features of calf spleen PNP-catalyzed hydrolysis of MESG are accounted for by the following model. First, in the absence of phosphate and at pH 7.4, the enzyme exists as an equilibrium mixture of monomer and trimer with a dissociation constant for the trimer of 3 × 10 14 M 2 . Second, a stoichiometric reaction between three molecules of MESG and the PNP trimer results in the formation of a stable PNP·purinethiol complex. Third, the PNP·purinethiol complex initially formed with the monomeric enzyme partitions between product release and formation of a stable complex with 55 turnovers per inactivation event. Fourth, the stable PNP·purinethiol complexes are rapidly dissociated by phosphate to regenerate active enzyme. This dissociation is accompanied by an increase in absorbance at 356 nm consistent with a p K a for the purinethiol base on the enzyme of 8.1, compared to a corresponding value of 8.8 in aqueous solution.

Values of inhibition constants, Ki, for a family of structurally related, competitive inhibitors of calf spleen purine nucleoside phosphorylase (PNP) have been determined employing both inosine as substrate and a manual assay and 2-amino-6-mercapto-7-methylpurine ribonucleoside (MESG) as substrate and a robot-based enzyme kinetics facility. Several of the values determined robotically were confirmed employing the same substrate and a manual assay. Surprisingly, for many of the inhibitors examined, values of Ki determined with MESG as substrate are smaller than those obtained employing inosine as substrate by a factor that varies from less than 2 to 10. Values of concentrations required for 50% inhibition of PNP, IC50, have also been determined for the same family of inhibitors employing inosine as substrate. Values of IC50ino and those for Kiino and Kimesg for subsets of the inhibitors have been employed as training sets to create quantitative structure-activity relationships (QSAR) which have substantial power to predict values of IC50 and Ki for inhibitors outside the training set. These QSAR models should be useful in guiding future medicinal chemistry efforts designed to discover inhibitors of PNP having increased potency.

We present a computational approach based on a local search strategy that discovers sets of proteins that preferentially interact with each other. Such sets are referred to as protein communities and are likely to represent functional modules. Preferential interaction between module members is quantified via an analytical framework based on a network null model known as the random graph with given expected degrees. Based on this framework, the concept of local protein community is generalized to that of community of communities. Protein communities and higher-level structures are extracted from two yeast protein interaction data sets and a network of published interactions between human proteins. The high level structures obtained with the human network correspond to broad biological concepts such as signal transduction, regulation of gene expression, and intercellular communication. Many of the obtained human communities are enriched, in a statistically significant way, for proteins having no clear orthologs in lower organisms. This indicates that the extracted modules are quite coherent in terms of function.

We present a new computational method for identifying regulated pathway components in transcript profiling (TP) experiments by evaluating transcriptional activity in the context of known biological pathways. We construct a graph representing thousands of protein functional relationships by integrating knowledge from public databases and review articles. We use the notion of distance in a graph to define pathway neighborhoods. The pathways perturbed in an experiment are then identified as the subgraph induced by the genes, referred to as activity centers, having significant density of transcriptional activity in their functional neighborhoods. We illustrate the predictive power of this approach by performing and analyzing an experiment of TP53 overexpression in NCI-H125 cells. The detected activity centers are in agreement with the known TP53 activation effects and our independent experimental results. We also apply the method to a serum starvation experiment using HEY cells and investigate the predicted activity of the transcription factor MYC. Finally, we discuss interesting properties of the activity center approach and its possible applications beyond the comparison of two experiments.

Earlier a three-dimensional model for a new unusual DNA conformation referred to as Slipped Loop Structure (SLS) has been suggested by us (1). The same type of folding could occur with RNA as well which means that one must use the A-form of the double helix rather than the B-one. The present paper discusses the creation of an all-atom stereochemically sound model for SLS-RNA. This calculated model, while possessing the same folding topology as the SLS-DNA, differs dramatically from the SLS-DNA by an overall folding geometry. It also differs radically from the RNA-pseudoknot and can thus be regarded as a new type of an RNA folding.