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.

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.

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.

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.