1.
The role of pancreatic ​Slc19a2​ expression in
diabetes and thiamine­responsive megaloblastic
anemia
Julia Huesa, Edward Zhuang, Justin Zhang
The North Carolina School of Science and Mathematics
Robert Gotwals, M.S., Gary Churchill, Ph. D. and Susan McClatchy,
M.S.
Independent Studies in Computational Biology Program 2015­2016

2. Abstract
As Type II Diabetes Mellitus, or non­insulin­dependent diabetes mellitus (NIDDM), is
one of the most prevalent, widespread diseases in the world and remains one of the leading
causes of death in many countries, with over 400 million affected individuals worldwide, the
development of effective treatments and therapies is crucial [16]. While the disease shows signs
of heritability in many aspects, our knowledge of the disease’s genetic basis is limited. Several
genes are associated with the disease; however, the exact mechanism by which they trigger
disease is not fully understood. The thiamine transporter ​Slc19a2​ and corresponding
thiamine­responsive megaloblastic anemia (TRMA/Rogers’ syndrome) has previously been
found to be associated with Type II Diabetes Mellitus, but the mechanisms of this association are
not clear [2]. A mutated ​Slc19a2​ gene results in decreased levels of absorbed thiamine and
ultimately in a condition of TRMA, a disorder that leads to diabetes. Utilizing data supplied by
the Jackson Laboratory’s Center for Genome Dynamics to conduct statistical analysis in R, this
study identifies downstream targets of ​Slc19a2​ in order to reveal a network of genes connecting
Slc19a2​ to diabetes. Using the R/qtl and qtlnet packages of R, this study establishes ​Dusp23​ and
Zfp790​ as upstream regulators of ​Slc19a2​, and ​Slc19a2​, ​Prrg2​ and ​Dusp23​ as upstream
regulators of insulin at 10 weeks, thus indicating that ​Slc19a2​ expression, driven by interactions
with ​Dusp23​ and ​Zfp790​, affects the regulation of insulin and ultimately diabetes. By applying
this knowledge of investigated causal relationships between genes, their downstream targets, and
disease phenotypes, future studies might provide insight to more effective diabetic treatments, as
well as ways to identify and avoid environmental triggers for diabetes.
2

3. Introduction
Type II Diabetes Mellitus is widespread, affecting approximately 90% of diabetics in the
Western world and 9% of the United States population [15, 17]. It is a disease characterized by
high glucose levels in the blood due to insulin resistance, and can lead to various complications
such as damaged blood vessels, frequent urination, unusual weight patterns, and fatigue. Patients
with Type II Diabetes can usually control their symptoms by controlling their weight, eating
healthily and exercising, and monitoring their blood glucose levels. However, the goal of many
future researchers is to investigate the mechanisms of diabetes on a deeper genetic level so that
this disease can someday be cured instead of merely treated. [8].
Type II Diabetes Mellitus (TRMA) is one outcome of thiamine­responsive megaloblastic anemia
which is a lesser known disease which occurs from mutation in the ​Slc19a2​ gene. TRMA is a
disorder that leads to hearing loss, diabetes, and megaloblastic anemia. Some mutations in the
Slc19a2​ gene cause the production of short, non­functional thiamine transporters, disrupting
thiamine transport in cells, while others change single amino acids in thiamine­transporter
protein, preventing proper protein folding and thus negatively impacting function. This prevents
thiamine from being brought into cells [1], and inhibits the normal intestinal thiamine absorption
process [6].
Thiamine (vitamin B1), found in various foods, is extremely important for numerous body
functions, namely for the conversion of carbohydrates into glucose and the formation of ATP. In
glucose metabolism, which occurs in the cell cytoplasms of the liver, thiamine pyrophosphate
3

4. (TPP or ThDP), a derivative of thiamine that is produced by the enzyme thiamine
diphosphokinase, is a coenzyme in the transketolase reaction of the pentose phosphate pathway
and feeds excess sugar phosphates into the main carbohydrate metabolic pathways. The pentose
phosphate cycle, an alternative pathway to glycolysis, produces NADPH, a molecule that
donates its H+ ion to the electron transport chain to produce ATP [14]. ​TPP is also required for
the oxidation reaction of pyruvate to form acetyl­coenzyme A, driving the ​Krebs cycle​ for the
generation of energy [14]. ​Because thiamine is involved in tissue respiration, when it is not
absorbed properly due to malfunctions in the thiamine­transporter proteins, the breakdown of
glucose into ATP is negatively affected. As insulin is a regulator of glucose and influences the
way one processes carbohydrates, and thiamine is involved in glucose metabolism, it was
speculated that thiamine could be associated with insulin expression, which is further
emphasized by the link between obesity and diabetes.
In addition to diabetes, TRMA includes two other primary traits, deafness and megaloblastic
anemia. As of now, it is known that pharmacologic doses of thiamine help to alleviate TRMA
and its symptoms. Experiments conducted on ​Slc19a2​ knock­out mice show that lack of thiamine
caused diabetes mellitus within 6 weeks, and that the diabetes disappeared after 6 weeks of
thiamine repletion. It has been discovered that there is ​Slc19a2​ expression in rat pancreatic cells,
and inhibition of thiamine uptake led to significantly reduced expression [7].
As noted before, TRMA is associated closely with the acquisition of Type II Diabetes. However,
the mechanisms for this process are unclear. The mutations in ​Slc19a2​, aside from creating
4

5. nonfunctional thiamine transporters, may have some direct effect that causes diabetes, such as
insulin resistance, complications in the pancreas with downstream proteins, or cell signaling
malfunctions. The primary goal of this research proposal is to investigate all of these possible
relationships.
Methods
Dataset
This study uses the BTBR dataset, which involves BTBR.Cg­Lep​ob​
/Wisc and C57BL/6 mice.
These inbred mice exhibit elevated plasma insulin, glucose intolerance, and other
diabetes­related traits. The dataset includes marker data, one hundred and forty­four clinical
phenotypes, and gene expression data for 16,677 genes in the hypothalamus, islet, liver, adipose,
kidney, and gastrocnemius tissues.
R/qtl
QTL are quantitative trait loci: specific locations or positions of genes that contribute to variation
in a quantitative trait [21]. Quantitative trait locus (QTL) analysis is a statistical method that
links phenotypic data (trait measurements) and genotypic data (molecular markers) to explain the
genetic basis of variation in complex traits [18­20]. QTL mapping is the effort to identify QTL
through an experimental cross [21]. The experimental cross that is the focus of this study is that
of the ​BTBR.Cg­Lep​ob​
/Wisc and C57BL/6 mice. Because these two inbred lines demonstrate
phenotypic differences despite having been raised in a common environment, it can be deduced
that there is a genetic basis underlying this difference, which can be investigated by examining
5

6. crosses between the strains, revealing underlying QTL [21]. ​This study will implement QTL
mapping using the R/qtl package of R, ​an add­on package for the freely available and widely
used statistical software R [21].
A mainscan plots logarithm­of­odds (LOD) scores across all chromosomes, which indicates the
statistical probability that two genes are linked, and therefore inherited together, allowing for the
identification of the general QTLs. The statistical significance of the QTL being considered is
established, taking into account the genome­wide scan, generally by a permutation test, or
permuting (i.e., randomizing or shuffling) the phenotypes relative to the genotype data [21]. The
QTL mapping method is applied to this shuffled version of the data to obtain a set of LOD
curves, and the genome­wide maximum LOD score is derived [21]. The process is repeated a
number of times, generally for 1000 permutations, and the 95th percentile of the is used as an
estimated genome­wide LOD threshold [21]. From these scans, it can be inferred which
chromosomes possess significant QTL, giving an initial overview of which chromosomes we
may want to investigate further for our genes of interest. Based on preliminary mainscans, at
least four chromosomes possess significant QTL’s: chromosomes 1, 7, 11, 16.
To look at interactions between expression of different genes or phenotypes and their respective
QTLs, conditional scans were conducted, which take into account other significant genes related
to TRMA and diabetes that likely interact with ​Slc19a2​ in its function of the absorption of
vitamin B1. In this way, it can be verified whether genotypes and phenotypes are independent of
the objects they are being conditioned on by seeing whether the LOD score peaks on certain
6

7. chromosomes are dropped for a gene or phenotype when it is conditioned on another gene. In
doing this, a target gene or phenotypic expression can be isolated, giving a more accurate
representation of which chromosomes are significant and narrowing the search for significant
genes in the network of ​Slc19a2​.
Correlation and Scatterplot Matrices
A pairwise scan, a combination of correlation analyses in R and generated scatterplot matrices
between the relevant genotypic and phenotypic data, with all of the phenotypes in the BTBR
dataset was conducted using Rstudio. A cross object was created by binding clinical phenotypes
with all of the phenotypes being tested. With the pairs function in the qtl library, pairs tables
showing the correlation between phenotypes and the target gene, ​Slc19a2​, were generated. This
allows for the investigation of the relationships between the Slc19a2 and the phenotypes of
interest, which included glucose at 10 weeks and insulin at ten weeks.
Bayesian Information Criterion (BIC)
The Bayesian Information Criterion (BIC), a model that selects for the most favorable model
(either independent, reactive, causal, or complex) from a finite set of models based on a
likelihood function, was used to develop the best fitting models for a set of gene expression traits
and phenotypes [22]. This will evaluate the relationship between the selected parent phenotypes
of our genes of interest. The model with a BIC score that differs from the scores of other models
by at least 10, was considered statistically significant, indicating a causality between gene
7

8. expression traits, genes, or QTLs. This causality will then be expressed in a structure as it relates
to causalities between other QTLs and phenotypes ultimately regarding diabetes.
R/qtlnet
R/qtlnet, an R package, was used to develop a statistical network that concludes causality
between different genes and their related expression levels of phenotypes. Once specific genes of
interest were found (Dusp23, Zfp790, etc.) with conditional scans and verified using BIC
modelling, qtlnet was utilized to map their relationship through a phenotype network structure,
using the causal relationships operating under the assumption of conditional independence found
through BIC modeling [22]. The package fulfills this by way of four steps: defining how the
computations break up by designating parent phenotypes and dividing them into groups,
computing BIC scores for the selected parent phenotypes, performing Markov chain Monte
Carlo (MCMC) methods, and combining results from both to construct statistical networks [23].
This technique allowed for the development of two networks in which​ a QTL, ​Q​, and certain
phenotypes, Y1 and Y2, for instance, mapping to ​Q​ have a true causal relationship ​Q​ → ​Y​1​ →
Y​2 [11]​.
8

9. Results and Discussion
Slc19a2 Expression in Islet and Liver
a
Islet
b
Liver
Figure 1: ​Genome­wide scans of ​Slc19a2 ​expression ​(​a) Scan of ​Slc19a2​ in Islet tissue. (b) Scan
of in ​Slc19a2 ​in Liver tissue
With Jackson Laboratories’ eQTL viewer, genome­wide scans of ​Slc19a2​ in islet and liver tissue
were conducted. The scan for ​Slc19a2​ in the islet shows a significant peak on chromosome 1,
with smaller peaks on chromosomes 7, 11 and 16. The scan for ​Slc19a2​ in liver, meanwhile, has
a significant peak on chromosome 17 and smaller peaks on chromosomes 2 and 11. These
genome scans thus show that there is a distant gene or group of genes on chromosomes 1, 7, 11,
and 16 or 2, 11 and 17 that affect ​Slc19a2 ​expression in islet and liver tissue, respectively.
9

10. Slc19a2 and Phenotypes
To find the phenotypes most highly correlated with ​Slc19a2 ​expression, pairwise scans were run
between ​Slc19a2​ expression and diabetes­related clinical phenotypes in the data set, including
glucose at 10 weeks, insulin at 10 weeks, triglycerides at 10 weeks, and high­density lipoprotein
(HDL). After seeing that all of the diabetes­related phenotypes being investigated had very low
correlations with ​Slc19a2​ expression in the liver, the search for downstream gene targets of
Slc19a2 ​to was pointed to genes expressed in the pancreas rather than in the liver. Thus, the
pancreas was designated as the target tissue for studying ​Slc19a2 ​expression and the most highly
correlated clinical phenotypes were GLU.10wk, INS.10wk, and Insulin.rbm (Fig. 2).
Figure 2: ​Pairwise scans for ​Slc19a2 ​expression
in the islet, including distributions, correlations,
and scatter plots. (a) GLU.10wk is a measurement
of glucose levels 10 weeks after birth. This had a
fairly significant correlation of 0.26 with ​Slc19a2
expression. (b) INS.10wk is a measurement of
insulin levels 10 weeks after birth. This had a
fairly significant correlation of ­0.18 with
Slc19a2 ​expression. (c) Insulin.rbm as it
somewhat significantly correlates to Slc19a2
expression in the islet (correlation of ­0.20).
10

11. In order to further investigate the clinical phenotypes that may be correlated with pancreatic
Slc19a2 ​expression, conditional scans of ​Slc19a2 ​conditioned on these phenotypes were
conducted (Fig. 3). INS.10wk was used over Insulin.rbm in the conditional scans because it is a
more tangible measurement of insulin and 10 weeks is about the age where insulin measurements
are most telling.
a b
c d
Figure 3: ​Main­scans and conditional scans for several phenotypes in relation to ​Slc19a2
expression. (a) Mainscan of Glucose expression with and without ​Slc19a2​ as a covariate. There
is a significant drop on chromosome 1 with ​Slc19a2​ as a covariate. (b) Mainscan of HDL
expression with and without ​Slc19a2​ as a covariate. There are no significant drops with ​Slc19a2
as a covariate. (c) Mainscan of Insulin expression with and without ​Slc19a2​ as a covariate. There
is a significant drop on chromosome 1 with ​Slc19a2​ as a covariate. (d) Mainscan of Triglyceride
expression with and without ​Slc19a2​ as a covariate. There are no significant drops with ​Slc19a2
as a covariate.
11

12. A scan of GLU.10wk conditioned on ​Slc19a2​ revealed that the normal QTL peaks on
chromosome 1 for GLU.10wk decrease from a LOD score of about 7 to a score of about 4 just
above the threshold of significance, and for INS.10wk, the LOD score decreased from the
threshold value to a score under the threshold. In other words, the significance of the QTL peaks
for these phenotypes decreased when pancreatic ​Slc19a2​ expression was accounted for. These
results indicate that a portion of the QTL peaks of the glucose and insulin phenotypes on
chromosomes 1 and 7 is driven by the pancreatic ​Slc19a2​ expression. It should be noted that
pancreatic ​Slc19a2 ​expression has the same chromosome 1 QTL peak as the glucose phenotype.
The chromosome 1 peak shared between ​Slc19a2 ​expression and the glucose phenotype could be
caused by the same gene or group of genes.
Insulin and Correlated Genes
After seeing on Gene Cloud that ​Slc19a2​ was connected with the gene ​Apoe​, and through ​Apoe​,
to the gene ​Apoa2​ ­ both of which had been noticed in previous literature for being
HDL­associated apolipoproteins ­ correlation tests of these genes with these four diabetes­related
phenotypes were run [25]. ​Apoa2​ is the second most abundant structural protein in HDL,
constituting 20% of its protein [25]. The protein encoded by Apoe is a major apoprotein (a
protein that forms a part of a biochemical molecule) of the chylomicron (a lipoprotein particle
that transport lipids from the intestine to other tissues). This protein binds to a specific liver and
peripheral cell receptor, and is essential for the breakdown of triglyceride­rich lipoprotein
constituents [26].
12

13.
Figure 4:​ Scatterplot, correlation, and distribution scatterplot between pancreatic Slc19a2
expression, glucose at 10 weeks (GLU.10wk), insulin at 10 weeks (INS.10wk), triglycerides at
10 weeks (TRIG.10wk), high density lipoprotein (HDL), pancreatic Apoe expression, and
pancreatic Apoa2 expression.
Although ​Apoe​ and ​Apoa2​ were highly correlated with some of the phenotypes, further pursuit of
their role in Slc19a2’s genetic network was decided against since neither of the genes were
correlated with pancreatic Slc19a2 expression (Fig. 4).
13

14.
Establishing a Network between Genes of Interest using Conditional Scans
Conditional scans were utilized to establish a network between other gene targets and ​Slc19a2
expression (Fig. 5). In these conditional scans, genome scans on Slc19a2 were run and the effect
of each of gene on a certain chromosome interval was removed in order to compare the change
in the QTL peaks. Five genes were found to drop the pancreatic ​Slc19a2 ​expression peak on
chromosome 1 and another five dropped the ​Slc19a2 ​peak on chromosome 7.
14

15. Figure 5​: Conditional scans for ​Slc19a2​ expression in the islet. There are significant drops on
chromosome 1 for the genes​ Rsg5​,​ Opn3​,​ Dusp23​,​ Dusp12​,​ ​and ​Fmn2​ and on chromosome 7 for
Lim2, Mrgprx2, Prrg2, Upk1a, ​and​ Zfp790.
The raw summary showed that there was a peak overlap for insulin and ​Slc19a2​ expression on
chromosome 1, from 58.2 cM to 85.6 cM, and on chromosome 7, from 49.27 cM to 87.4 cM. In
order to determine which genes in the interval drive ​Slc19a2​ expression, conditional scans were
performed. First, a list of all genes from 58.2 cM to 85.6 cM in chromosome one was generated
using Ensembl. This gene list was inputted into the genome scan function so that each gene was
tested as a covariate, and all these scans were compiled into a large pdf. It was found that ​Rsg5​,
Opn3​,​ Dusp23​,​ Dusp12​,​ ​and ​Fmn2​ significantly dropped the peak on chromosome 1, so these
genes may drive the relationship between ​Slc19a2 ​and insulin.
This process was repeated with chromosome 7. Similarly to chromosome 1, the previous
mainscan summaries suggested that chromosome 7 contained possible relationships to insulin
expression. After reviewing the list of compiled conditional scans, it was found that ​Lim2,
Mrgprx2, Prrg2, Upk1a, ​and​ Zfp790​ considerably lowered the peak on chromosome 7; however,
these drops are less prominent than those of chromosome 1, suggesting that the relationship
between these genes, ​Slc19a2​, and insulin is less significant.
The conditional scans indicate that these genes are upstream of ​Slc19a2​ expression, as they drop
the peak on chromosome 1or 7 below the significance threshold. After the two sets of conditional
scans were completed, BIC modeling was used to further understand the relationship between
Slc19a2​, insulin expression, and the various genes selected.
15

16. Establishing a Network between Genes of Interest using BIC and qtlnet
Using conditional scans, ​Rsg5​,​ Opn3​,​ Dusp23​,​ Dusp12​,​ ​and ​Fmn2​, as well as ​Lim2, Mrgprx2,
Prrg2, Upk1a, ​and​ Zfp790 ​were established as potential downstream or upstream targets of
Slc19a2​. In order to further investigate their relationships with ​Slc19a2​ statistically, Bayesian
Information Criterion (BIC) and R/qtlnet were utilized to develop the most favorable statistical
models. With the triple fit function on R, it was found that the best fitting model for ​Slc19a2​ vs
Insulin was causal, since its score of 2770 was lower than all the other models by at least 10.
This was repeated for the 5 genes that dropped the peak on chromosome 1, and the only gene
with a significant causal model was ​Dusp23​. After​ Slc19a2​, Insulin, and ​Dusp23 ​were connected
with a causal model, R/qtlnet was used to construct a visual graph of this relationship (Fig. 7).
Another R/qtlnet model was created using ​Zfp790​ and ​Prrg2​, which both dropped ​Slc19a2​’s
peak on chromosome 7 and had significant causal models from the BIC modelling (Fig. 6).
Figure 6:​ qtlnet plot with
Prrg2, Zfp790​, ​Slc19a2​, and
INS.10wk. ​Zfp790 ​is
upstream of ​Slc19a2​,
Slc19a2​ is upstream of
INS.10wk​ ​and ​Prrg2​ is
upstream of INS.10wk.
16

17.
Figure 7: ​qtlnet plot with
Dusp23​, ​Slc19a2​, and
INS.10wk. ​Dusp23 ​is
upstream of ​Slc19a2​ and
INS.10wk​ ​and ​Slc19a2​ is
upstream of INS.10wk.
Insulin in the Established Gene Network
From the qtlnet and BIC models, it can be seen that insulin has a complex network of
relationships with ​Slc19a2​, ​Dusp23​, and other upstream genes. As of now, these genes remain
uninvestigated because of their sheer abundance and because their importance is unknown.
Conclusion
As of now, it is known that one of the symptoms of thiamine responsive megaloblastic anemia is
Type II diabetes. In addition, it is known that the driver behind TRMA is the gene ​Slc19a2​,
which causes thiamine transporter proteins to become deficient and fail to deliver important
vitamins to the cell. The goal of this research was to determine the mechanisms behind how
17

18. mutations in ​Slc19a2​ cause Type II diabetes through TRMA. It was found that ​Slc19a2
expression in the islet is strongly correlated with chromosome 1, which is significant because
lack of insulin production in the pancreas is what causes Type II diabetes. It was found that
Rsg5​,​ Opn3​,​ Dusp23​,​ Dusp12​,​ ​and ​Fmn2 ​are genes that may drive the interactions between
Slc19a2 ​and insulin expression in chromosome 1. In particular, ​Slc19a2​ and insulin and ​Dusp23
and insulin both have causal relationships according to the Bayesian Information Criteria model.
The next step in this study would be to further investigate the causal network model from qtlnet
in order to further interrogate the mechanisms behind how ​Slc19a2​ is correlated to insulin
production.
Acknowledgements
We would like to thank Susan McClatchy and Gary Churchill of Jackson Laboratory’s Churchill
lab for teaching us the fundamentals of computational biology and for providing extremely
useful feedback and guidance during the research process. We would also like to thank Robert
Gotwals for his invaluable mentorship and support through this research experience.
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