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

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

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

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(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

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.Cg­Lepob/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

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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

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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

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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] .

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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.

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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).

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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.

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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].

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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).

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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.

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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.

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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.

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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

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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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