Proceedings of the National Academy of Sciences
By F. Whipple. 1914
The structure of hyaluronic acid has been elucidated largely by the work of Meyer and collaborators,1' 2, I who have shown it to be a linear polymer of alternating units of N-acetyl-D-glucosamine and D-glucuronic acid. The monosaccharide components appear to be combined in alternating f8-1,3and p-1,4linkages, the former being those involving C1 of GA4 and the latter those involving Cl of AG. Hyaluronic acid isolated from different tissues appears to have constant chemical composition3' L 6 but varies in average molecular weight from 300,000 to 1,500,000.7 No enzymatic synthesis of hyaluronic acid by cell-free preparations has been reported. We have now been able to demonstrate the synthesis of oligosaccharide chains having the structure of hyaluronic acid by using cell-free homogenates of the Rous chicken sarcoma,8 a tissue which has been known for a long time to produce large quantities of the mucopolysaccharide6 9, 10 and to have very little if any active hyaluronidase.10 Synthesis of the oligosaccharides has been shown by incubating sarcoma homogenates with uridine diphospho-N-acetyl-glucosamine labeled with C14 in the N-acetyl group, together with uridine diphosphoglucuronic acid or with uridine diphosphoglucose and diphosphopyridine nucleotide. In some experiments the homogenates were incubated with uridine triphosphate and N-acetyl-glucosamine-6-phosphate labeled with C14 in the N-acetyl group. The preparation of the latter material with a specific yeast N-acetylase has been described.11 UDPAg was discovered by Leloir and co-workers,12' 13 but no coenzymatic function for it was demonstrated. UDPGA has been isolated and found to be the substrate for glucuronide synthesis by liver extracts.'4' 15 The formation of UDPGA by enzymatic oxidation of UDPG in the presence of DPN has been described by Kalckar and coworkers.16 Evidence discussed below indicates that UDPAg and UDPGA are the substrates from which hyaluronic acid chains are synthesized.
Subfields: Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- Robert Tibshirani
- Francis R. Bach
- Trevor J. Hastie
- Le Song
- Peter Buhlmann
- Arthur Gretton
- Jianqing Fan
- Bernhard Schölkopf
Citations in the corpus
- Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay (2019, background): “This may seem natural as these networks are the best accessible tools that mimic human nervous system [Morgenstern et al., 2014].”
- Closed-Loop Memory GAN for Continual Learning (2018, background): “∗Contact Author learning broad-species domains can aid in finer species discriminatory capability [Flesch et al., 2018].”
- Unsupervised Progressive Learning and the STAM Architecture (2019, methodology): “The nov-elty detection component of STAM is related to the problem of anomaly detection in streaming data [Dasgupta et al. , 2018].”
- Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning (2018, background): “Evidence from cognitive science suggests that people have an “intuitive physics engine” [Battaglia et al., 2013] which can be used to simulate the outcome of an action or tool manipulation [Osiurak and Badets, 2016], and have dedicated neural architectures near the motor cortex for implementing this”
- Clustering in complex directed networks. (2006, background): “The majority of such “real-world” networks have been shown to display structural properties that are neither those of a random graph [4], nor those of regular lattices.”
- Quantum repeaters based on atomic ensembles and linear optics (2009, methodology): “B can be presented as a simple variation of the protocol of (Jiang et al., 2007b) discussed in the previous subsection, in which the entanglement generation step of (Jiang et al., 2007b) is performed locally and the first entanglement swapping step of (Jiang et al., 2007b) is performed remotely.”
- Competing spreading processes on multiplex networks: awareness and epidemics (2014, background): “The challenge remains, however, making network theory predictive, that is, developing the methods that will help us turn network data into quantitative predictions for complex systems.”
- Self-weighted Multiview Clustering with Multiple Graphs (2017, methodology): “In addition, according to Ky Fan’s Theory [Fan, 1950], we have the following equation: c∑ i=1 σi (LS) = min F∈Rn×c,FTF=I Tr ( FTLSF ) .”
- Analysis of logistic growth models. (2002, background): “Others model actual physical growth of some property of interest for an organism or organisms.”
- Physics of adherent cells (2013, methodology): “A detailed FEM-model integrating mechanical and biochemical aspects has been developed that is able to explain many details of cell adhesion (Deshpande et al., 2006, 2007; McGarry et al., 2009; Pathak et al., 2008).”