Mammalian milk glycomes: Connecting the dots between evolutionary conservation and biosynthetic pathways

Thomès, Luc; Karlsson, Viktoria; Lundstrøm, Jon; Bojar, Daniel

doi:10.1016/j.celrep.2023.112710

Cited by 8 publications

(10 citation statements)

References 36 publications

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“…Here, we use pre-calculated milk networks from Thomès et al. (2023) 1 , obtained in step 5 of the Before you begin – Installation of glycowork and setup of environment section. Load the dictionary containing pre-calculated milk networks.…”

Section: Step-by-step Methods Detailsmentioning

confidence: 99%

“…(2023). 1 The threshold value can be modified to change the stringency of the statistical comparison, and thus, the network pruning. Plot the pruned network.…”

Section: Step-by-step Methods Detailsmentioning

confidence: 99%

“…We demonstrate the protocol using the milk glycan dataset described in Thomès et al. (2023), 1 but the protocol can be adapted to any other dataset of interest, including other glycan classes. Importantly, the set of glycans used for network generation should only contain glycans of the same class (e.g., N -, O -, lipid-linked, or free glycans).…”

Section: Before You Beginmentioning

confidence: 99%

“…

> fp = 'drive/My Drive/glycan_networks/' Download the milk glycan data from Thomès et al. (2023) 1 and place in the “glycan_networks” folder. All data is available as supplementary data or through Zenodo ( https://zenodo.org/records/8016366 ).…”

Section: Before You Beginmentioning

confidence: 99%

“… 1 https://zenodo.org/record/8016366 ‘TableS2.csv’ Precomputed milk oligosaccharide networks Thomès et al. 1 https://zenodo.org/record/8016366 ‘milk_networks_exhaustive.pkl’ SugarBase Thomès et al. 3 https://zenodo.org/records/10259832 ‘BojarLab-glycowork-40b04d7/glycowork/glycan_data/v9_sugarbase.pkl’ Software and algorithms Python 3.10.12 https://www.python.org/ https://www.python.org/ glycowork 1.0.1 Thomès et al.…”

Section: Key Resources Tablementioning

confidence: 99%

See 4 more Smart Citations

Protocol for constructing glycan biosynthetic networks using glycowork

Lundstrøm,

Thomès,

Bojar

2024

STAR Protocols

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Section: Step-by-step Methods Detailsmentioning

confidence: 99%

“…(2023). 1 The threshold value can be modified to change the stringency of the statistical comparison, and thus, the network pruning. Plot the pruned network.…”

Section: Step-by-step Methods Detailsmentioning

confidence: 99%

Section: Before You Beginmentioning

confidence: 99%

“…

Section: Before You Beginmentioning

confidence: 99%

Section: Key Resources Tablementioning

confidence: 99%

See 3 more Smart Citations

Protocol for constructing glycan biosynthetic networks using glycowork

Lundstrøm,

Thomès,

Bojar

2024

STAR Protocols

Self Cite

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GlyCompute: towards the automated analysis of protein N-linked glycosylation kinetics via an open-source computational framework

Flevaris,

Kotidis,

Kontoravdi

2024

Anal Bioanal Chem

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Understanding the complex biosynthetic pathways of glycosylation is crucial for the expanding field of glycosciences. Computer-aided glycosylation analysis has greatly benefited in recent years from the development of tools found in web-based portals and open-source libraries. However, the in silico analysis of cellular glycosylation kinetics is underrepresented in current glycoscience-related tools and databases. This could be partly attributed to the limited accessibility of kinetic models developed using proprietary software and the difficulty in reliably parameterising such models. This work aims to address these challenges by proposing GlyCompute, an open-source framework demonstrating a novel, streamlined approach for the assembly, simulation, and parameterisation of kinetic models of protein N-linked glycosylation. Specifically, given one or more sets of experimentally observed N-glycan structures and their relative abundances, minimum representations of a glycosylation reaction network are generated. The topology of the resulting networks is then used to automatically assemble the material balances and kinetic mechanisms underpinning the mathematical model. To match the experimentally observed relative abundances, a sequential parameter estimation strategy using Bayesian inference is proposed, with stages determined automatically based on the underlying network topology. The proposed framework was tested on a case study involving the simultaneous fitting of the kinetic model to two protein N-linked glycoprofiles produced by the same CHO cell culture, showing good agreement with experimental observations. We envision that GlyCompute could help glycoscientists gain quantitative insights into the effect of enzyme kinetics and their perturbations on experimentally observed glycoprofiles in biomanufacturing and clinical settings. Graphical Abstract

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The evolving world of milk oligosaccharides: Biochemical diversity understood by computational advances

Lundstrøm,

Bojar

2024

Carbohydrate Research

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Mammalian milk glycomes: Connecting the dots between evolutionary conservation and biosynthetic pathways

Cited by 8 publications

References 36 publications

Protocol for constructing glycan biosynthetic networks using glycowork

Protocol for constructing glycan biosynthetic networks using glycowork

GlyCompute: towards the automated analysis of protein N-linked glycosylation kinetics via an open-source computational framework

The evolving world of milk oligosaccharides: Biochemical diversity understood by computational advances

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