Using RNA Sequencing To Study B-Cell Biology
Authored by Bangyan L Stiles
Introduction
Type II Diabetes (T2D) is a complex disease that is
influenced by environmental factors such as diet and life style as well
as genetic variations that result in deviations in gene expression.
Recent genome-wide association studies (GWAS) have established the
correlation of more than 70 genetic variants (Single nucleotide
polymorphisms or SNPs) with susceptibility to T2D [1,2].
Using highly efficient RNA sequencing (RNA-Seq) approach, it is now
possible to comprehensively profile the transcriptome of the islets of
Langerhans or individualβcells to better understand how these
environmental factors and SNPs contribute to the pathogenesis of type II
Diabetes. Next generation sequencing (NGS) technology has
revolutionized the field of transcriptomics. RNA-seq is the current gold
standard which has overcome the short comings of microarray analysis by
extending its range of detection to low expressed genes, spliced
variants and novel transcripts [3,4].
This technique is now being applied to the studies of
islet biology and the understanding of T2D pathogenesis. Such studies
confirmed some of the known characters such as signature gene expression
of GCG (glucagon), DPP4 (Dipeptidyl peptidase 4) and GC (Vitamin
D-binding protein) in α cells. Also it provided information of
transcriptome profiling in rare endocrine cell type such as y and S
cells. GHSR (Growth hormone secretagogue receptor) was specifically
expressed in S cells and y cell that also express genes such as SERTM1
(serine rich and transmembrane domain containing 1) ABCC9 (ATP binding
cassette subfamily C member 9) and SLIT (slit guidance ligand). Other
interesting findings include sub clustering within α,βand acinar cells.
For example,a small subset of α cell expressing more proliferative genes
was identified and a group of acinar cells expressing more inflammatory
related genes were separate from the others.
Furthermore, new genes correlated with T2D were also
identified in α cell-specific manner. FXYD2 (FXYD domain containing
ion transport regulator 2) encodes α gamma subunit of an Na, K-ATPase
was confirmed to have a low expression only in T2D pancreatic β cells.
Other genes upregulated in T2D β cells includes GPD2
(glycerol-3-phosphate dehydrogenase 2) and LEPROTL1 (Leptin receptor
overlapping transcript-like 1). Negative regulators of glucose
stimulated insulin secretion (GSIS) - RGS4 (regulator of G-Protein
signaling 4) and CHRM3 (cholinergic receptor muscarinic 3)- were
enriched in α cells. WFS1 (Wolframin ER transmembrane glycoprotein) is
significantly decreased in T2D α cells [5].
Direct sequencing on dispersed or FACS sorted single
cells have been developed and provided new understanding on the
transcriptome differences among the cell types in islets of Langerhans. α
recent study performed single-cell RNA-seq analysis on 609
non-diabetics and 883 T2D α , β, δ and PP cells. The authors identified
245 T2D related genes with 28% of which have no previously known
functions. The authors also compared mouse α and β cells versus
humans and found similar expression profiles. This study provided one of
the first databases on singlecell transcriptomes of α , β, δ and PP
cells that can be used to study functions of the newly identified genes [6].
In another study where RNA-seq was performed on FACS
sorted endocrine cells using HIC1-2B4,a pan-endocrine marker, four
different transcriptome profiles are identified among β cells .
These four subsets are separated based on two markers: CD9 and ST8SIA1
(alpha-N-acetylneuraminide alpha-2,8-sialyltransferase), and named β1-4
as CD9-ST8SIA1-, CD9+ST8SIA1-, CD9-ST8SIA1+ and CD9+ST8SIA1+
respectively. RNA-seq on these 4 subsets of β cells indicated
shared genes such as PDX1 (pancreatic and duodenal homeobox 1), INS
(insulin) and MAFA (MAF BZIP transcription factor A) as well as unique
genes such as HCN1 (hyperpolarization activated cyclic nucleotide gated
potassium channel 1) inβ1/2 cells. In healthy human islets, β1 subset
has highest percentage among all β cells followed by β1, β3, and
β3 subgroup. Importantly, among type 2 diabetic patients, this
composition pattern was disrupted and particularly, the ST8SIA1+ β
cells (β3 and β3) are abnormally high.
The author also provide evidence showing β3 and β3
subsets are less responsive to glucose stimulation, indicating its
potential relevance to type 2 diabetes [7].
This finding is supported by another study where islets from six health
subjects and four T2D patients were sequenced at single cell level and
five different expression clusters were identified according to their
transcriptome and different expression levels of RBβ3 (retinol binding
protein 4), FFAR4/GRβ120 (free fatty acid receptor 4), ID1, ID2, and ID3
(inhibitor Of DNA binding, HLH protein) [5].
Using RNA-seq, we recently performed transcriptome analysis of mouse
islets fed high fat diet (HFD) vs. normal chow diet (NC). Our study
indicated that HFD caused enrichment of PI3K/ AKT/mTOR pathway genes
that contribute to adaptive increase in growth and proliferation in β
cells in response to HFD insult (Figure 1A).
Using mouse models lacking PTEN in the islets where
PI3K/ AKT signal is constitutively active, we have shown previously that
this pathway is important for maintaining the mass of the islet β
cells [8,9].
Furthermore, we showed that this ability of PTEN/PI3K signal to control
β cell growth is dependent on their ability to regulate β cell
senescence and how it interacts with the mesenchymal cells that supports
the growth of islets [10,11].
Moreover, our recent study discovered AKT1 deficiency increases the UPR
(unfolded protein response) signaling in β cell which potentially
poised β cell to apoptosis caused by high fat diet (Figure 1B).
Together, these studies suggest PI3K/AKT signaling is one of the key
signaling in β cells that contributes to its growth and cell
survival.
Our finding is supported by another similar RNA-Seq
experiment in cultured human islets treated with palmitic acid for 48
hours [12].
The transcriptome profile indicated strong metabolic stress upon
treatment and how β cell failure may have happened in response to this
stress. Among the 1,325 genes modified by palmitate treatment, genes
involved in fatty acid metabolism and endoplasmic reticulum (ER) stress
signaling are highly enriched, including 11 out of 59T2D candidate
genes. Whether these genes are also altered by loss of AKT or
upregulation of PTEN remains to be elucidated. B cell transcription
factors such as PDX1, MAFA, MAFB (MAF BZIP transcription factor B),
NEUROD1 (Neuronal Differentiation 1), PAX4 (Paired Box 4) and GATA6
(GATA Binding Protein 6) were found to be repressed by this treatment.
Conclusion
In summary, the use of RNA-Seq approaches has
provided new direction for diabetes research and allowed researcher to
develop novel hypothesis to explore the pathogenesis of diabetes. The
expression profile analysis also allowed more detailed classification of
β cell to be identified and linked to T2D. Using the combination of
RNA-seq with molecular pathogenesis analysis, we and have started to
unveil the molecular mechanism for how HFD contributing to the
pathogenesis of T2D.
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