Enhancing the sialylation of recombinant EPO produced in CHO cells via the inhibition of glycosphingolipid biosynthesis

Sialylation regulates the in vivo half-life of recombinant therapeutic glycoproteins, affecting their therapeutic efficacy. Levels of the precursor molecule cytidine monophospho-N-acetylneuraminic acid (CMP-Neu5Ac) are considered a limiting factor in the sialylation of glycoproteins. Here, we show that by reducing the amount of intracellular CMP-Neu5Ac consumed for glycosphingolipid (GSL) biosynthesis, we can increase the sialylation of recombinant human erythropoietin (rhEPO) produced in CHO cells. Initially, we found that treating CHO cells with a potent inhibitor of GSL biosynthesis increases the sialylation of the rhEPO they produce. Then, we established a stable CHO cell line that produces rhEPO in the context of repression of the key GSL biosynthetic enzyme UDP-glucose ceramide glucosyltransferase (UGCG). These UGCG-depleted cells show reduced levels of gangliosides and significantly elevated levels of rhEPO sialylation. Upon further analysis of the resulting N-glycosylation pattern, we discovered that the enhanced rhEPO sialylation could be attributed to a decrease in neutral and mono-sialylated N-glycans and an increase in di-sialylated N-glycans. Our results suggest that the therapeutic efficacy of rhEPO produced in CHO cells can be improved by shunting intracellular CMP-Neu5Ac away from GSL biosynthesis and toward glycoprotein sialylation

Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

Penetrance of variants in monogenic disease and clinical utility of common polygenic variation has not been well explored on a large-scale. Here, the authors use exome sequencing data from 77,184 individuals to generate penetrance estimates and assess the utility of polygenic variation in risk prediction of monogenic variants

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Artificial Intelligence in Pathology

As in other domains, artificial intelligence is becoming increasingly important in medicine. In particular, deep learning-based pattern recognition methods can advance the field of pathology by incorporating clinical, radiologic, and genomic data to accurately diagnose diseases and predict patient prognoses. In this review, we present an overview of artificial intelligence, the brief history of artificial intelligence in the medical domain, recent advances in artificial intelligence applied to pathology, and future prospects of pathology driven by artificial intelligence

Preference and Demand for Digital Pathology and Computer-Aided Diagnosis among Korean Pathologists: A Survey Study Focused on Prostate Needle Biopsy

Digital pathology systems (DPSs) have been globally implemented, and computer-assisted diagnosis (CAD) software has been actively developed in recent years. This study aimed to investigate perceptions of digital pathology and the demand for CAD. An online survey involving members of the Korean Society of Pathologists was conducted, and a demonstration clip of the diagnostic assistant software for a prostate needle biopsy was shown to them to provide a simple experience with CAD. One hundred sixty-four Korean pathologists (13.6% of 1210 Korean pathologists) participated. The majority (77.4%) answered affirmatively regarding the necessity of implementing a DPS, and 26.8% had plans to implement or increase the use of DPSs in the following 2–3 years at their medical institutions. Pathologists felt that multidisciplinary care or conference accessibility (56.7%), remote consultation (49.4%), and big data building (32.9%) were useful parts of DPSs. Most pathologists (81.7%) responded that CAD software would assist with the diagnostic process. In a prostate needle biopsy, pathologists used the software to improve the measurement of tumor volume and/or length and core length but not to suggest a diagnostic name or Gleason grade. Korean pathologists who participated in the survey had highly positive perceptions of digital pathology and maintained a positive attitude toward the use of CAD software

Measuring the Accessible Surface Area within the Nanoparticle Corona using Molecular Probe Adsorption

Copyright © 2019 American Chemical Society. The corona phase - the adsorbed layer of polymer, surfactant, or stabilizer molecules around a nanoparticle - is typically utilized to disperse nanoparticles into a solution or solid phase. However, this phase also controls molecular access to the nanoparticle surface, a property important for catalytic activity and sensor applications. Unfortunately, few methods can directly probe the structure of this corona phase, which is subcategorized as either a hard, immobile corona or a soft, transient corona in exchange with components in the bulk solution. In this work, we introduce a molecular probe adsorption (MPA) method for measuring the accessible nanoparticle surface area using a titration of a quenchable fluorescent molecule. For example, riboflavin is utilized to measure the surface area of gold nanoparticle standards, as well as corona phases on dispersed single-walled carbon nanotubes and graphene sheets. A material balance on the titration yields certain surface coverage parameters, including the ratio of the surface area to dissociation constant of the fluorophore, q/KD, as well as KD itself. Uncertainty, precision, and the correlation of these parameters across different experimental systems, preparations, and modalities are all discussed. Using MPA across a series of corona phases, we find that the Gibbs free energy of probe binding scales inversely with the cube root of surface area, q. In this way, MPA is the only technique to date capable of discerning critical structure-property relationships for such nanoparticle surface phases. Hence, MPA is a rapid quantitative technique that should prove useful for elucidating corona structure for nanoparticles across different systems