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.

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.

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.

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.

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.

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 and models of pancreatic cancer. Necuparanib reduced tumor cell proliferation and invasion in a three-dimensional (3D) culture model; , it extended survival and reduced metastasis.

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.

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.

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.

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.

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.

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.

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.