# Anopheles gambiae: Metabolomic Profiles in Sugar-Fed,

Authors: Cody J. Champion; Phanidhar Kukutla; Elizabeth K. K. Glennon; Bo Wang; Shirley Luckhart; Jiannong Xu
Journal: Dataset Papers in Science
 (2017)

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## Abstract

The mosquito midgut is a physiological organ essential for nutrient acquisition
as well as an interface that encounters various mosquito-borne pathogens.
Metabolomic characterization would reveal biochemical fingerprints that are
generated by various cellular processes. The metabolite profiles of the mosquito
midgut will provide an overview of the biochemical events in both physiological
states and the dynamic responses to pathogen infections. In this study, the
midgut metabolic profiles ofAnopheles gambiae mosquitoes
following feeding with sugar, human blood, mouse blood, andPlasmodium
falciparum-infected human blood were examined. A mass spectrometry
system coupled to liquid and gas chromatography produced a time series of
metabolites in the midgut at discrete conditions (sugar feeding, 24 h and
48 h post-normal blood andP. falciparum-infected blood
feeding). Triplicates were included to ensure system validity. A total of 512
individual compounds were identified; 511 were assigned to 8 superpathways and
75 subpathways. The dataset can be used for further inquiry into the metabolic
dynamics of sugar and blood digestion and of malaria parasite infection. The
dataset is accessible at the repository Dryad.


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## Body

## 1. Introduction

Malaria is caused by infection with mosquito-borne parasites of the genusPlasmodium. According to the WHO, there were 214 million new cases and
an estimated 438,000 deaths worldwide in 2015, with most deaths associated
withP. falciparum transmitted byAn. gambiae
species complex mosquitoes in Africa [1].
Understanding mosquito biology and vector competence for parasite transmission will
facilitate the development of novel intervention strategies for malaria control.The act of feeding on blood is essential for mosquito reproduction and for malaria
parasite transmission. Blood feeding, digestion, and associated physiological
responses also affect the microbial community in the gut [2]. As an additional genetic repertoire, the microbiome plays
essential roles in various mosquito phenotypes, including reproduction and immunity.
“Omics” technologies have been robustly employed in studying
metagenomic interactions in various biological systems [3]. In particular, metabolomics approaches have been used to
characterize amino acids, lipids, sugars, cofactors, and other small molecules that
are precursors, intermediates, and by-products of reactions and pathways directed by
the genetic blueprints in the metagenomic repertoire. In mosquito studies,
transcriptomic responses to blood feeding and malaria parasite infection have been
well characterized [4–9]. However, comprehensive metabolomic data are
limited for medically important arthropods, except for a few reports [10–14]. Metabolomics allows for the identification and quantification of a
range of metabolites in a system [15]. In
this study, we used a nontargeted metabolomics approach [16, 17] to examine the
midgut metabolites of sugar-fed, blood-fed, andP.
falciparum-infected mosquitoes. This dataset can connect transcriptomic
responses to biochemical fluxes related to the blood feeding and digestion and
toP. falciparum infection.


## 2. Methodology

The study design is outlined in Figure1. The
mosquito midgut samples were prepared in two laboratories. The midgut samples from
the Xu lab (NMSU) were prepared from mosquitoes fed on sugar meals and meals of
uninfected mouse blood; and the midgut samples from the Luckhart lab (UCD) were
prepared from mosquitoes fed on sugar, on uninfected human blood, and onP.
falciparum-infected human blood. Samples of uninfected human blood
andP. falciparum-infected human blood were included as well
(Table 1).Table 1
Midgut sample collections.
Description
Tissue type
Source lab
Blood source
Sample identifier
(n)
Uninfected blood
Human blood
Luckhart
Human
NB (3)
P. falciparum-infected blood
Human blood
Luckhart
Human
IB (3)
Sugar meal
Midgut
Luckhart
SM-L (3)
Sugar meal
Midgut
Xu
SM-X (3)
Uninfected blood-fed
Midgut
Luckhart
Human
NBM-L, 24 h (3)
NBM-L, 48 h (3)
Uninfected blood-fed
Midgut
Xu
Mouse
NBM-X, 24 h (3)
NBM-X, 48 h (3)
P. falciparum-infected blood-fed
Midgut
Luckhart
Human
IBM-L, 24 h (3)
IBM-L, 48 h (3)Figure 1
Study design overview. Midgut samples were collected at time points from
mosquitoes that were sugar-fed, blood-fed, andP.
falciparum-infected blood-fed. As a control, uninfected human
blood andP. falciparum-infected blood were collected as
well. Metabolites were determined by Metabolon, Inc.An. gambiaeG3 mosquitoes were used for the study. All mosquito
rearing and feeding protocols were approved and were in accordance with regulatory
guidelines and standards set by the Institutional Animal Care and Use Committee of
New Mexico State University and the University of California, Davis.In the Xu lab, mosquitoes were reared under standard insectary conditions of
27-28°C and 80% humidity and a 12 h : 12 h
light-dark cycle. Larvae were provided with a 1 : 1 mix of
Brewer’s yeast and rodent chow (Purina Laboratory Rodent Diet 5001, LabDiet,
St. Louis, MO). Adult mosquitoes were provided with 10% sucrose ad libitum. NIH
Swiss outbred mice were used as a blood source for egg production.In the Luckhart lab,An. gambiae G3 were reared and maintained at
27-28°C and 80% humidity and a 12 h : 12 h
light-dark cycle. First instar larvae were provided with a 1 : 2 mix
of Brewer’s yeast and Sera® Micron planktonic rearing food, while second
to fourth larval instars were provided with Purina® Game Fish Chow pellets.
Adult mosquitoes were provided with 10% sucrose ad libitum and allowed to feed
on CD1 outbred mice for egg production.In the Luckhart lab, the procedures for culture ofP. falciparumstrain NF54 MCB and mosquito infection were described previously [18]. The infection rate for each replicate
mosquito cohort was determined by countingP. falciparum oocysts in
the midgut of a sample of mosquitoes at day 10 after infection. The protocols
involving the culture and handling ofP. falciparum for mosquito
infection were approved and were in accordance with regulatory guidelines and
standards set by the Biological Safety Administrative Advisory Committee of the
University of California, Davis.In the Xu lab, midgut samples were collected from adult mosquitoes fed on 10%
sucrose and were collected at 24 h and 48 h after feeding on
uninfected mouse blood. In the Luckhart lab, midgut samples were collected from
mosquitoes fed on 10% sucrose, were collected at 24 h and 48 h
after feeding on a mixture of uninfected human red blood cells (RBCs) and
heat-inactivated human serum (1 : 1), and were collected at
24 h and 48 h after feeding onP. falciparum-infected
blood (1 : 1 human RBCs : serum). Each sample contained
30 midguts at each collecting point, triplicates were collected from three mosquito
cohorts, and a total of 30 samples were collected for metabolic profiling at
Metabolon, Inc. (Durham, NC). Table 1
summarizes the sample collections.The MicroLab STAR system (Hamilton Company) was used for sample preparation. For
quality control purposes, recovery standards were added before the extraction
process. Protein from the samples was removed using both organic and aqueous
extraction methods. The extract from each sample was split into two fractions, one
of which was used for gas chromatography (GC) and the other was used for liquid
chromatography (LC).For LC/MS analysis, a Waters ACQUITY UPLC and a Thermo-Finnigan LTQ-FT mass
spectrometer were used. The sample extract was divided into two identical aliquots.
These aliquots were dried and were then reconstituted in acidic or basic
LC-compatible solvents. Each of these solvent types contained 11 or more injection
standards that had fixed concentrations. The first aliquot was analysed using acidic
positive ion optimized conditions. The second aliquot was analysed using basic
negative ion optimized conditions. These aliquots were independently injected using
separate dedicated columns. If the extract was reconstituted in acidic conditions, a
gradient elution using water and methanol containing 0.1% formic acid in each
liquid was used. If the extract was reconstituted in basic conditions, a gradient
elution containing 6.5 mM ammonium bicarbonate and water/methanol was used.
During data collection, the MS analysis alternated between MS and data-dependent MS2
scans using dynamic exclusion.A Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole mass spectrometer using
electron impact ionization was used for GS/MS. Samples were redried under vacuum for
at least 24 hours and then were derivatized under dried nitrogen using
bis-trimethyl-silyl-trifluoroacetamide (BSTFA). The column used for GC/MS analysis
was 5% phenyl and used a temperature ramp from 40° to 300°C in a
16-minute period.Raw MS files were stored in a database. Data was then examined and quality control
limits were applied. Peaks identified were stored in a separate database. Compounds
were found by comparison to analytical standards or recurrent unknown entities.
Compounds registered into the Laboratory Information Management System (LIMS) were
used as a dataset for identification. Also, entities that were classified as unknown
could still be identified by their recurrent nature (using both mass spectral and
chromatographic characteristics). If a compound was found in both the extraction
blank and the sample, it was excluded, unless the signal intensity was at least
three times the intensity found in the blank.A total of 512 individual compounds were identified; 511 were assigned to 8
superpathways and 75 subpathways (Table2).
The dataset is presented as biochemical abundance in each sample (Dataset Item 1).
MetaboAnalyst tool suite [19] can be employed
for further analysis. Some compounds were detected in certain sample(s) but were not
detected in other samples, which yielded no values of the compounds in those
samples. A Bayesian PCA (BPCA) method is designed to handle datasets with missing
values [20]. Here, we used it to present the
patterns of metabolites in the midgut samples in different conditions. Then, the
data were log-transformed and followed by data scaling via Pareto scaling [21]. Figure 2 presents a PCA plot, which explains the variance within the dataset
and provides an overview of the data with regard to questions related to the
conditions under which samples were collected. In our study, blood samples,
uninfected human blood (NB), andP. falciparum-infected blood (IB)
cluster distinctively from mosquito midgut samples. In addition, samples of
24 h post-uninfected (NBM 24 h) or post-infected (IBM 24 h)
blood feeding are closer to each other, while samples of 48 h post-blood
feeding (IBM 48 h, IBM 48 h) are clustered with the samples of
sugar-fed (SM-L, SM-X) midguts. The latter is likely due to the completion of blood
digestion and the midgut environment returning to sugar-fed conditions. Midgut
samples fed on uninfected mouse blood are separated from human blood-fed samples,
which is likely attributed to the distinction between human and mouse blood.
Overall, the PCA output indicates that variation is largely attributed to the
biological conditions, with limited variation induced by nonbiological factors
present in the dataset. Figure 3 is a
graphical representation of biochemical abundance (rescaled data).Table 2
Compounds found in at least one sample mapped to a superpathway and
subpathway. Abridged to show only subpathways that contain more than
10% of the superpathway compounds.
Superpathway
Compound number
Subpathway
Compound number
Amino acid
105
Cysteine, methionine, SAM, and taurine
metabolism
11
Glutamate metabolism
11
Glycine, serine, and threonine metabolism
12
Valine, leucine, and isoleucine metabolism
14
Other
57
Carbohydrate
41
Amino sugars metabolism
5
Fructose, mannose, galactose, starch, and sucrose
metabolism
7
Glycolysis, gluconeogenesis, and pyruvate
metabolism
13
Nucleotide sugars and pentose metabolism
16
Cofactors and vitamins
22
Hemoglobin and porphyrin metabolism
2
Nicotinate and nicotinamide metabolism
7
Pantothenate and CoA metabolism
3
Other
10
Energy
8
Krebs cycle
6
Oxidative phosphorylation
2
Lipid
148
Essential fatty acid (amino, branched, dicarboxylate,
ester, monohydroxy, and BCAA metabolism)
29
Long chain fatty acid
21
Lysolipid
33
Sterol/steroid
18
Other
47
Nucleotide
31
Purine and pyrimidine metabolism
19
Pyrimidine metabolism
12
Peptide
118
Dipeptide
111
Other
7
Xenobiotics
38
Benzoate metabolism
11
Chemical/drug
12
Food component/plant
7
Xanthine metabolism
5
Other
3Figure 2
Principal component analysis of the dataset. The first two principal
components are shown. Data are labelled and coloured by treatment group.
Shaded areas indicate 95% confidence intervals. Samples (quality
controls) of uninfected and infected human blood cluster outside of the
range where the midgut samples (main) group. Additionally, replicates
cluster closely indicating that replicate-to-replicate variability was
minimal.Figure 3
Graphical representation of biochemical abundance (rescaled data). Each box
plot presents the abundance of one compound in all samples.∗
indicates compounds that have not been officially “plexed”
(based on a standard), but we are confident in their identity;
#
indicates that samples without a PROmeasure (i.e., the media) are lost by
this normalization, as well as any compounds that were only detected in
these samples.


## 3. Dataset Description

The dataset associated with this Dataset Paper consists of 3 items which are
described as follows.1
The original scale of biochemicals, pathway assignment, and abundance.
The column Superpathway shows the superpathway the biochemical is associated
with and the column Subpathway shows the subpathway the biochemical is
associated with. The column Platform presents the platform used for the
identification of the biochemical (GC/MS, LC/MS positive, or LC/MS negative);
the column Retention Index, the retention index (RI) associated with the
biochemical; the column Mass, the mass of species; the column CAS, the Chemical
Abstracts Service identifier of the biochemical; the column PubChem, the PubChem
identifier of the biochemical; the column KEGG, the KEGG identifier of the
biochemical; and the column Group HMDB, the Human Metabolome Database (HMDB)
identifier of the biochemical. Columns 12–41 present the biochemical
abundance associated with samples. Values are normalized in terms of raw area
counts.
513
41
4797
Biochemical Name
Superpathway
Subpathway
Compound ID
Platform
Retention Index
Mass
CAS
PubChem
KEGG
Group HMDB
NB-L1
NB-L2
NB-L3
IB-L1
IB-L2
IB-L3
SM-L1
SM-L2
SM-L3
NBM-L, 24H1
NBM-L, 24H2
NBM-L, 24H3
NBM-L, 48H1
NBM-L, 48H2
NBM-L, 48H3
IBM-L,24H1
IBM-L, 24H2
IBM-L, 24H3
IBM-L, 48H1
IBM-L, 48H2
IBM-L, 48H3
SM-X1
SM-X2
SM-X3
NBM-X, 24H1
NBM-X, 24H2
NBM-X, 24H3
NBM-X, 48H1
NBM-X, 48H2t
NBM-X, 48H3t2
The rescaled biochemicals, pathway assignment, and abundance. The
column Superpathway shows the superpathway the biochemical is associated with
and the column Subpathway shows the subpathway the biochemical is associated
with. The column Platform presents the platform used for the identification of
the biochemical (GC/MS, LC/MS positive, or LC/MS negative); the column Retention
Index, the retention index (RI) associated with the biochemical; the column
Mass, the mass of species; the column CAS, the Chemical Abstracts Service
identifier of the biochemical; the column PubChem, the PubChem identifier of the
biochemical; the column KEGG, the KEGG identifier of the biochemical; and the
column Group HMDB, the Human Metabolome Database (HMDB) identifier of the
biochemical. Columns 12–41 present the biochemical abundance associated
with samples as denoted in Table 1.
Each biochemical in original scale is rescaled to have median equal to 1. Then,
missing values are imputed with the minimum.
513
41
604
Biochemical Name
Superpathway
Subpathway
Compound ID
Platform
Retention Index
Mass
CAS
PubChem
KEGG
Group HMDB
NB-L1
NB-L2
NB-L3
IB-L1
IB-L2
IB-L3
SM-L1
SM-L2
SM-L3
NBM-L, 24H1
NBM-L, 24H2
NBM-L, 24H3
NBM-L, 48H1
NBM-L, 48H2
NBM-L, 48H3
IBM-L,24H1
IBM-L, 24H2
IBM-L, 24H3
IBM-L, 48H1
IBM-L, 48H2
IBM-L, 48H3
SM-X1
SM-X2
SM-X3
NBM-X, 24H1
NBM-X, 24H2
NBM-X, 24H3
NBM-X, 48H1
NBM-X, 48H2t
NBM-X, 48H3t3
The amount of protein found in each sample.
31
2
0
Sample Identifier
Bradford Protein Concentration


## 4. Concluding Remarks

In the mosquito midgut ecosystem, the metabolites can be derived from the mosquito
host, microbial organisms in the gut microbiota, and ingested pathogens. This
dataset presents a survey of the metabolomic landscapes in theAn.
gambiae midgut in response to different diets and to human malaria
parasite infection. The dataset provides a valuable metabolic reference that will
facilitate establishing a connection to transcriptome and proteome data related to
the mosquito physiology and interactions with symbiotic microbes andP.
falciparumin the midgut environment. The dataset allows users to
further compare the metabolic dynamics under these conditions in the contexts with
their own interest.



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*Source: 8091749-2017-07-24.xml*