Multiomic study of skin, peripheral blood, and serum: is serum proteome a reflection of disease process at the end-organ level in systemic sclerosis?

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.

Variable blood processing procedures contribute to plasma proteomic variability.

BACKGROUND: Plasma is a potentially rich source of protein biomarkers for disease progression and drug response. Large multi-center studies are often carried out to increase the number of samples analyzed in a given study. This may increase the chances of variation in blood processing and handling, leading to altered proteomic results. This study evaluates the impact of blood processing variation on LC-MS/MS proteomic analysis of plasma. METHODS: Initially two batches of patient plasma samples (120 and 204 samples, respectively) were analyzed using LC-MS/MS shotgun proteomics. Follow-up experiments were designed and carried out on healthy donor blood in order to examine the effects of different centrifugation conditions, length of delay until first centrifugation, storage temperature and anticoagulant type on results from shotgun proteomics. RESULTS: Variable levels of intracellular proteins were observed in subsets of patient plasma samples from the initial batches analyzed. This observation correlated strongly with the site of collection, implicating variability in blood processing procedures. Results from the healthy donor blood analysis did not demonstrate a significant impact of centrifugation conditions to plasma proteome variation. The time delay until first centrifugation had a major impact on variability, while storage temperature and anticoagulant showed less pronounced but still significant effects. The intracellular proteins associated with study site effect in patient plasma samples were significantly altered by delayed processing also. CONCLUSIONS: Variable blood processing procedures contribute significantly to plasma proteomic variation and may give rise to increased intracellular proteins in plasma. Accounting for these effects can be important both at study design and data analysis stages. This understanding will be valuable to incorporate in the planning of protein-based biomarker discovery efforts in the future.

Enhancing reproducibility of gene expression analysis with known protein functional relationships: The concept of well-associated protein.

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.

Molecular profiling of rheumatoid arthritis patients reveals an association between innate and adaptive cell populations and response to anti-tumor necrosis factor.

BACKGROUND: The goal of this study is to use comprehensive molecular profiling to characterize clinical response to anti-TNF therapy in a real-world setting and identify reproducible markers differentiating good responders and non-responders in rheumatoid arthritis (RA). METHODS: Whole-blood mRNA, plasma proteins, and glycopeptides were measured in two cohorts of biologic-naïve RA patients (n = 40 and n = 36) from the Corrona CERTAIN (Comparative Effectiveness Registry to study Therapies for Arthritis and Inflammatory coNditions) registry at baseline and after 3 months of anti-TNF treatment. Response to treatment was categorized by EULAR criteria. A cell type-specific data analysis was conducted to evaluate the involvement of the most common immune cell sub-populations. Findings concordant between the two cohorts were further assessed for reproducibility using selected NCBI-GEO datasets and clinical laboratory measurements available in the CERTAIN database. RESULTS: A treatment-related signature suggesting a reduction in neutrophils, independent of the status of response, was indicated by a high level of correlation (ρ = 0.62; p < 0.01) between the two cohorts. A baseline, response signature of increased innate cell types in responders compared to increased adaptive cell types in non-responders was identified in both cohorts. This result was further assessed by applying the cell type-specific analysis to five other publicly available RA datasets. Evaluation of the neutrophil-to-lymphocyte ratio at baseline in the remaining patients (n = 1962) from the CERTAIN database confirmed the observation (odds ratio of good/moderate response = 1.20 [95% CI = 1.03-1.41, p = 0.02]). CONCLUSION: Differences in innate/adaptive immune cell type composition at baseline may be a major contributor to response to anti-TNF treatment within the first 3 months of therapy.

Circulating Markers of Inflammation Persist in Children and Adults With Giant Aneurysms After Kawasaki Disease.

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.

Necuparanib, A Multitargeting Heparan Sulfate Mimetic, Targets Tumor and Stromal Compartments in Pancreatic Cancer.

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.

Data-independent oxonium ion profiling of multi-glycosylated biotherapeutics.

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.

An integrated approach using orthogonal analytical techniques to characterize heparan sulfate structure.

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.

Combining measurements to estimate properties and characterization extent of complex biochemical mixtures; applications to Heparan Sulfate.

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.

Toward the complete characterization of host cell proteins in biotherapeutics via affinity depletions, LC-MS/MS, and multivariate analysis.

Host cell protein (HCP) impurities are generated by the host organism during the production of therapeutic recombinant proteins, and are difficult to remove completely. Though commonly present in small quantities, if levels are not controlled, HCPs can potentially reduce drug efficacy and cause adverse patient reactions. A high resolution approach for thorough HCP characterization of therapeutic monoclonal antibodies is presented herein. In this method, antibody samples are first depleted via affinity enrichment (e.g., Protein A, Protein L) using milligram quantities of material. The HCP-containing flow-through is then enzymatically digested, analyzed using nano-UPLC-MS/MS, and proteins are identified through database searching. Nearly 700 HCPs were identified from samples with very low total HCP levels (< 1 ppm to ∼ 10 ppm) using this method. Quantitation of individual HCPs was performed using normalized spectral counting as the number of peptide spectrum matches (PSMs) per protein is proportional to protein abundance. Multivariate analysis tools were utilized to assess similarities between HCP profiles by: 1) quantifying overlaps between HCP identities; and 2) comparing correlations between individual protein abundances as calculated by spectral counts. Clustering analysis using these measures of dissimilarity between HCP profiles enabled high resolution differentiation of commercial grade monoclonal antibody samples generated from different cell lines, cell culture, and purification processes.

Edge-count probabilities for the identification of local protein communities and their organization.

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.

Analyzing protein lists with large networks: edge-count probabilities in random graphs with given expected degrees.

We present an analytical framework to analyze lists of proteins with large undirected graphs representing their known functional relationships. We consider edge-count variables such as the number of interactions between a protein and a list, the size of a subgraph induced by a list, and the number of interactions bridging two lists. We derive approximate analytical expressions for the probability distributions of these variables in a model of a random graph with given expected degrees. Probabilities obtained with the analytical expressions are used to mine a protein interaction network for functional modules, characterize the connectedness of protein functional categories, and measure the strength of relations between modules.

Detection of activity centers in cellular pathways using transcript profiling.

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.