EUROCarbDB(CCRC): a EUROCarbDB node for storing glycomics standard data

Abstract

Motivation:
In the field of glycomics research, several different techniques are used for structure elucidation. Although multiple techniques are often used to increase confidence in structure assignments, most glycomics databases allow storing of only a single type of experimental data. In addition, the methods used to prepare a sample for analysis is seldom recorded making it harder to reproduce the analytical data and results.

Results:
We have extended the freely available EUROCarbDB framework to allow the submission of experimental data and the reporting of several orthogonal experimental datasets. The features aim to increase the understandability and reproducibility of the reported data.

Availability and implementation:
The installation with the glycan standards is available at http://glycomics.ccrc.uga.edu/eurocarb/. The source code of the project is available at https://code.google.com/p/ucdb/.

Supplementary information:
Supplementary data are available at Bioinformatics online.

Full text

© Oxford University Press 2005 1
Bioinformatics
EUROCarbDB(CCRC): A EUROCarbDB node for storing Gly-
comics standard data
Khalifeh Al Jadda1, Melody P. Porterfield2, Robert Bridger2, Christian Heiss2,
Michael Tiemeyer2, Lance Wells2, John A. Miller1, William S. York2 and Rene Ran-
zinger2*
1Department of Computer Science, University of Georgia, Athens, GA, USA
2Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA.
Received on XXXXX; revised on XXXXX; accepted on XXXXX
Associate Editor: XXXXXXX
ABSTRACT
Motivation: In the field of glycomics research several different tech-
niques are used for structure elucidation. Although multiple tech-
niques are often used to increase confidence in structure assign-
ments most glycomics databases allow only storing of a single type
of experimental data. In addition the methods used to prepare a
sample for analysis is seldom recorded making it harder to repro-
duce the analytical data and results.
Results: We have extended the freely available EUROCarbDB
framework to allow the submission of experimental data and the
reporting of several orthogonal experimental data sets. The features
aim to increase the understandability and reproducibility of the re-
ported data.
Availability and Implementation: The installation with the glycan
standards is available at http://glycomics.ccrc.uga.edu/eurocarb/.
The source code of the project is available at
https://code.google.com/p/ucdb/.
Contact: rene@ccrc.uga.edu
1 INTRODUCTION
In the last two and a half decades several databases for storing
glycan structures and associated meta information (e.g., biological
source, species, and publications) have been developed (Campbell,
et al., 2014). Among them the CarbBank database was the first
large publicly available database of glycan structures (Doubet and
Albersheim, 1992; Doubet, et al., 1989). Although no experimental
data was stored in this database, the experimental techniques used
for the identification of glycan structures were recorded. After the
funding for CarbBank was discontinued, several independent new
databases were created, often by importing most or all of the
CarbBank data and sometimes adding experimental data. For
example, GLYCOSCIENCES.de (Lütteke, et al., 2006) and the
Bacterial Carbohydrate Structure Database (BCSDB) (Egorova and
! !
*To whom correspondence should be addressed.
Toukach, 2014) contain NMR data that has been extracted from
the literature for different glycan structures. The Consortium for
Functional Glycomics (CFG) created its own set of databases
(Raman, et al., 2006) to store the data generated by the consortium,
including mass spectrometry (MS) profiling data and Glycan Array
data revealing the binding of glycans to various biomolecules.
Following a similar goal, GlycoBase (developed by the NIBRT
group (Campbell, et al., 2008)) stores the HPLC data generated by
that group. All of these specialized databases have been
implemented for use by a specific research group or consortium
and do not allow submission of diverse types of experimental data
by outsiders.
In 2005 under the direction of Claus-Wilhelm von der Lieth, the
EUROCarbDB (von der Lieth, et al., 2011) project was started.
The aim of the project was the establishment of a publicly
available database framework for the creation of a network of
homogeneous databases, allowing research groups worldwide to
upload annotated glycan structures and data obtained by MS, NMR
and HPLC experiments. The basic idea was to enable each research
group to create and populate their own database, while facilitating
the sharing and exchange of information among databases. The
long-term goal was that by providing a free and easy-to-use
framework, the heterogeneous landscape of glycan databases
developed before 2005 could be systematically replaced by a set of
homogeneous databases that contain experimental data with
annotated glycan structures. By the end of the project in 2010 the
source code of the database prototype that had been installed at the
European Bioinformatics Institute (EBI), was released. This code
has been subsequently used as the basis for several database
projects including the UniCarb-DB project (Hayes, et al., 2011)
and the UniCarbKB (Campbell, et al., 2011) database. Although
storing experimental data along with the glycan structures,
biological annotation, and their literature references was a
fundamental goal of the EUROCarbDB project, approaches to
storing techniques for sample preparation and methods of
experimental analysis had not been developed.
Here we describe the application of different experimental
techniques to establish the structures of several glycan standards
provided by the CFG and the setup of a EUROCarbDB node

Al Jadda et al.
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providing public access to this data. We also introduce a novel way
to represent the experimental data and meta data within a
EuroCarbDB node. In the original implementation of
EUROCarbDB, each unique structure was linked with a set of
experiments. However, the information required to determine
whether the separate experiments in a set were performed using
exactly the same sample or performed using different samples that
happened to contain the same structure was unavailable. This
critical information is required to evaluate the experimental data
and assess the reliability of the resulting structural annotation.
Confidence in a structural annotation is greater when it is based on
the application of varied orthogonal methods to the same sample
rather than on the analysis of several similar samples, which may
contain different contaminants.
!
Fig. 1. Three N-glycan and eight O-glycan compounds have been provided
by the CFG as analytical standards. Each is displayed using the CFG
cartoon representation together with the name used to identify each
structure.!
Fig.2. Design of the O-glycan analysis experiment. Each glycan sample
was divided into aliquots and analyzed by NMR without further treatment
and tandem mass spectrometry (MSn). Additional aliquots were analyzed
by MSn after being subjected to reductive β-elimination to release O-
glycans as oligoglycosyl alditols, and again after permethylation and
selection of ions formed by complexation with Li+ and Na+.
2 METHODS
100 µg of each glycan standard was prepared for MS analysis. O-
linked glycan standards were released from their threonine linker
by reductive beta elimination, neutralized, and passed over a strong
cation exchange resin, as previously described (Orlando, et al.,
2009). The O-glycans were permethylated (Ciucanu and Kerek,
1984), and dissolved (0.1 µg/µL) in either 1 mM sodium hydroxide
or 1 mM lithium hydroxide, in 50% aqueous methanol, prior to
direct infusion into a linear ion trap mass spectrometer equipped
with an Orbitrap FT detector (LTQ Orbitrap XL, Thermo-Fisher)
by nanospray ionization (NSI) at 0.4 µL/minute flow rate. A full
mass spectrum was continuously recorded by the instrument for
twenty seconds and glycan peaks were then manually selected for
fragmentation using 35% collision induced dissociation (CID).
Each resulting MS/MS spectra was also continuously detected and
recorded for twenty seconds and MS3#fragmentation# was#
performed# when# necessary# using# the# same# time# and#
fragmentation# energy.# All# scans# for# each# measurement# were#
averaged#into#a#single#RAW#file#and#converted#to#mzXML#using#
MSConvert# (Chambers,# et# al.,# 2012)# and# imported# to#
GlycoWorkbench# to# facilitate# structural# annotation of
fragmentation pathways and thereby confirm structural
assignments.
100 µg of each N-glycan standard was prepared for MS analysis as
previously described (Orlando, et al., 2009) and dissolved (0.1
µg/µL) in either 1 mM sodium hydroxide or 1 mM lithium
hydroxide, in 50% aqueous methanol, prior to direct infusion into a
linear ion trap mass spectrometer equipped with an Orbitrap FT
detector (LTQ Orbitrap XL, Thermo-Fisher) by nanospray
ionization (NSI) at 0.4 µL/minute flow rate. A high resolution full
MS scan was recorded in FT mode and all peaks 3 fold above
noise level were fragmented in the ion-trap by collision induced
dissociation (CID) with 35% collision energy. Collected spectra
were converted from RAW to mzXML format using MSConvert
and imported to GlycoWorkbench (Damerell, et al., 2012) to
facilitate structural annotation fragmentation pathways and thereby
confirm structural assignments.
For NMR, the samples were deuterium exchanged by dissolution
in D2O (99.9% D, Aldrich) and lyophilization. The deuterium-
exchanged N- and O-glycans were each dissolved in D2O (80!µL,
99.96% D, Cambridge Isotope Laboratories) and a small volume of
1% acetone (5 µL for N-glycans and 2 µL for O-glycans) was
added as an internal standard before transferring the samples to a 3
mm Shigemi NMR tube. 1-D Proton, TOCSY, NOESY, and
ROESY NMR spectra were recorded with water presaturation. All
NMR data, including gradient enhanced COSY and HSQC spectra,
were acquired at a sample temperature of 25 °C using a Varian
Inova-600 MHz spectrometer. The number of transients was 32 for
1-D proton and 2-D TOCSY and NOESY experiments, 16 for
gCOSY, and 256 for gHSQC. Mixing time was 80 ms for TOCSY,
300 ms for NOESY, and 200 ms for ROESY. Chemical shifts were
measured relative to internal acetone (δH=2.218 ppm, δC=33.00
ppm)(Wishart, et al., 1995).
3 RESULTS
In this paper we introduce a novel way to represent the
experimental data within a EUROCarbDB database. The modified
database allows the data obtained by different analytical methods
using a single sample to be combined into an experiment, thereby
increasing confidence in structural annotations inferred by
combining the orthogonal data. These modifications increase the
efficacy of EUROCarbDB for scientists using diverse techniques
to assign glycan structures.

EUROCarbDB(CCRC): A EUROCarbDB node for storing Glycomics standard data
3!
!
Fig.3. Design of the N-glycan experiment. Each native glycan sample
was divided into two aliquots: one was analyzed by NMR without
modification and the other was permethylated and analyzed as the Na+
adduct by tandem MS.
The design of the experiments described in the Methods section is
shown in Figures 2 and 3. Each experiment consists of a series of
laboratory tasks, indicated by boxes in the diagram. These tasks are
defined as protocols, which are recorded in the database by storing
the protocol name, a short description and a reference to a web
page in the Glycoscience Protocol Online Database
(http://jcggdb.jp/GlycoPOD/) or in our wiki
(http://glycomics.ccrc.uga.edu/GlycomicsWiki/Main_Page). These
web pages contain step-by-step descriptions of the experimental
procedures, including the necessary materials and instrumentation.
In addition to the web page references for each protocol, a list of
protocol parameters can be stored to capture variations of each
standard procedure described in the webpages. For example, the
incubation time, temperature or pH of a chemical reaction may
vary from one experiment to another. These specific protocol
parameter values, together with the complete but more general
description in the web pages, fully describe the experimental
methods used to obtain each data set. The availability of this
detailed information increases the analyst’s capacity to understand,
evaluate and reproduce the experimental results.
To minimize the work necessary to create an experiment and
upload experimental data, we created several template mechanisms
for the creation of an experiment (Figure 4). First the user creates a
protocol by providing its name, description, web page and a list of
parameters with their units of measurement (more details can be
found in part I of the Supplementary Material). Specific values
for these parameters are assigned later. The example shown as
scheme in Figure 3 and as screenshot in Figure 5, ”N-Glycan Mass
Spectrometer Analysis” includes 4 protocols.
In the second step the user creates specific implementations of
these protocols called protocol variants, which encapsulate
explicit values for each parameter (more details available in the
supplementary on part II). In Figure 2, “Mass spectrometer
analysis Na+” and “Mass spectrometer analysis Li+” are two
protocol variants of the protocol “Mass spectrometer analysis”
specifying Li+ or Na+, respectively, as values for the protocol
parameter adduct. The advantage of these templates is that creating
a new variation of a protocol does not require the user to re-enter
all the information. Rather, a protocol variant is created as a
specific instance of the more general protocol and distinguished by
explicit protocol parameters values. The instantiated protocol
variants are placed in a user-defined sequence called an
experimental template, which fully describes a specific series of
tasks used to prepare and analyze a sample. Each actual use of the
experiment template to analyze a specific sample is documented
by instantiating a new experiment. The data produced by an
experiment is thus associated with the metadata specified by the
above procedure, facilitating the upload and archival of the
annotated data. Each new experiment is created with a few button
clicks, which retrieve and integrate all the relevant information
about the experiment and its protocols, including protocol
parameter values. Then, it is easy to associate the specific data set
or sets that were generated by the experiment with a well-defined
sequence of parameterized protocols.
Fig.4. The design of an experiment is a multistep process. It starts by
defining protocols, which are general but detailed descriptions of
laboratory tasks, including lists of any parameters that may vary. Protocol
variants are then instantiated in the context of an experiment template,
which specifies a linear or branched sequence of protocol variants. In the
Figure, arrows leading from protocol variants to protocols represent
instantiation. These steps involve the explicit specification of protocol
parameter values. An experiment template thus defined can then be
instantiated as an actual experiment (arrows from experiment to
experiment template represent instantiation). Each experiment
encapsulates all of the MS, HPLC, and/or NMR data acquired during a
discrete analysis of a specific sample along with the metadata describing
how this data was obtained.
The concept of an experiment allows users to upload different
kinds of evidence (experimental data) used to assign a structure to
the analyzed glycan. Different types of evidence for glycan
samples include quantification data, MS data, HPLC data, and
NMR data.
Figure 5 shows a screenshot of the web interface after uploading
the experimental data generated by analysis of the N-glycan CFG
121 standard. The experiment itself (diagrammed in Figure 3)

Al Jadda et al.
4!
consists of tasks displayed as a hierarchical tree in the lower part
of the image. These include the protocols used to prepare and
analyze the sample together with the experimental data and the
annotation data, which can be retrieved by the user. Expanding this
tree representation provides an increasingly detailed view of the
results of executing each protocol, ultimately showing the data set
produced by the final analytical protocol (e.g., a file containing
mass spectral data) and the annotation file for that data. Figure 6
shows an example protocol with its name, the description, the link
to the web page and the parameters with their values.
Fig.5.Screenshot of an experiment overview containing: (1) Experiment
name; (2) Description; (3) Description URI; (4) glycan structure identified
in this experiment; (5) evidence types provided to identify the glycan
structure; (6) protocol names; (7) evidence/data files (mzXML); (8)
Annotation (Glyco Workbench File); (9) NMR protocol/data. !
4 DISCUSSION
The EUROCarbDB database framework is an extremely useful
resource for scientists seeking to create their own database to store
analytical Glycomics data. We have extended the original
EUROCarbDB code to collect and associate the different types of
analytical data obtained by analyzing single samples, adding
confidence to the structural annotations deduced from that data.
The enhanced code simplifies the generation, storage, and retrieval
of complete descriptions of the experiment process, facilitating the
interpretation and reproduction of the experiment. We have
installed a EUROCarbDB node with these enhancements on our
server and populated this node with carefully annotated
experimental data generated using several standard glycans
provided by the CFG. This database and its contents are freely
available at http://glycomics.ccrc.uga.edu/eurocarb/. In addition,
we encourage users to download this easy-to-use database so they
can organize and archive their own Glycomics data sets and
associate them with metadata describing the procedures they use to
generate that data. The EUROCarbDB source code is available at
https://code.google.com/p/ucdb/.
The fundamental utility of the tools described here lies in their
capacity to dramatically increase the public availability of well-
documented glycoanalytic data. Use of these tools will allow
researchers around the globe to download and examine
experimental data sets and structural annotations created by other
scientists, rigorously evaluate those data sets and faithfully
reproduce the experimental protocols that were originally used to
generate them. To the best of our knowledge, no other publicly
available database offers this kind of representation and this level
of detail. In order to make this representation more informative, we
also link each protocol to a web page (GlycoPOD or our wiki)
where the creator of the protocol can provide a more detailed
description of the experimental methods used. These wiki pages
can be accessed via a hyperlink in the details section of the
experimental tree (Figure 6).
!
Fig. 6. Screenshot of a protocol summary. For each protocol which is part
of an experiment a similar summary is available. Each summary includes
the name of the protocol (e.g., Aliquoting), its description, a link to the web
page describing the protocol and a list of variable parameters, each
documented with a name, description, unit of measurements and value.
ACKNOWLEDGEMENTS
We would like to express our deep gratitude to the EUROCarbDB
team who created the freely available open source framework
which we built upon.
Funding:!This work was supported by Consortium for Functional
Glycomics bridging grant [5U54GM062116-10]; and National
Institute of General Medical Sciences [8P41GM103490].
REFERENCES
Campbell, M.P., et al. (2011) UniCarbKB: putting the pieces
together for glycomics research, Proteomics, 11, 4117-4121.
Campbell, M.P., et al. (2014) Toolboxes for a standardised and
systematic study of glycans, BMC Bioinformatics, 15 Suppl 1, S9.
Campbell, M.P., et al. (2008) GlycoBase and autoGU: tools for
HPLC-based glycan analysis, Bioinformatics, 24, 1214-1216.
Chambers, M.C., et al. (2012) A cross-platform toolkit for mass
spectrometry and proteomics, Nature biotechnology, 30, 918-920.
Ciucanu, I. and Kerek, F. (1984) A simple and rapid method for
the permethylation of carbohydrates, Carbohydrate Research, 131,
209-217.
Damerell, D., et al. (2012) The GlycanBuilder and
GlycoWorkbench glycoinformatics tools: updates and new
developments, Biol Chem, 393, 1357-1362.
Doubet, S. and Albersheim, P. (1992) Letter to the Glyco-Forum
CarbBank, Glycobiology, 2, 505-505.
Doubet, S., et al. (1989) The complex carbohydrate structure
database, Trends in Biochemical Sciences, 14, 475-477.

EUROCarbDB(CCRC): A EUROCarbDB node for storing Glycomics standard data
5!
Egorova, K.S. and Toukach, P.V. (2014) Expansion of coverage of
Carbohydrate Structure Database (CSDB), Carbohydr Res, 389,
112-114.
Hayes, C.A., et al. (2011) UniCarb-DB: a database resource for
glycomic discovery, Bioinformatics, 27, 1343-1344.
Lütteke, T., et al. (2006) GLYCOSCIENCES. de: an Internet
portal to support glycomics and glycobiology research,
Glycobiology, 16, 71R-81R.
Orlando, R., et al. (2009) IDAWG: Metabolic incorporation of
stable isotope labels for quantitative glycomics of cultured cells, J
Proteome Res, 8, 3816-3823.
Raman, R., et al. (2006) Advancing glycomics: implementation
strategies at the consortium for functional glycomics,
Glycobiology, 16, 82R-90R.
von der Lieth, C.-W., et al. (2011) EUROCarbDB: an open-access
platform for glycoinformatics, Glycobiology, 21, 493-502.
Wishart, D.S., et al. (1995) 1H, 13C and 15N chemical shift
referencing in biomolecular NMR, Journal of biomolecular NMR,
6, 135-140.