MicroRNA signature of the human developing pancreas
- Samuel Rosero†1,
- Valia Bravo-Egana†1,
- Zhijie Jiang2,
- Sawsan Khuri2, 3,
- Nicholas Tsinoremas2,
- Dagmar Klein1,
- Eduardo Sabates1,
- Mayrin Correa-Medina1,
- Camillo Ricordi1,
- Juan Domínguez-Bendala1,
- Juan Diez1 and
- Ricardo L Pastori1Email author
© Rosero et al; licensee BioMed Central Ltd. 2010
Received: 2 June 2010
Accepted: 22 September 2010
Published: 22 September 2010
MicroRNAs are non-coding RNAs that regulate gene expression including differentiation and development by either inhibiting translation or inducing target degradation. The aim of this study is to determine the microRNA expression signature during human pancreatic development and to identify potential microRNA gene targets calculating correlations between the signature microRNAs and their corresponding mRNA targets, predicted by bioinformatics, in genome-wide RNA microarray study.
The microRNA signature of human fetal pancreatic samples 10-22 weeks of gestational age (wga), was obtained by PCR-based high throughput screening with Taqman Low Density Arrays. This method led to identification of 212 microRNAs. The microRNAs were classified in 3 groups: Group number I contains 4 microRNAs with the increasing profile; II, 35 microRNAs with decreasing profile and III with 173 microRNAs, which remain unchanged. We calculated Pearson correlations between the expression profile of microRNAs and target mRNAs, predicted by TargetScan 5.1 and miRBase altgorithms, using genome-wide mRNA expression data. Group I correlated with the decreasing expression of 142 target mRNAs and Group II with the increasing expression of 876 target mRNAs. Most microRNAs correlate with multiple targets, just as mRNAs are targeted by multiple microRNAs. Among the identified targets are the genes and transcription factors known to play an essential role in pancreatic development.
We have determined specific groups of microRNAs in human fetal pancreas that change the degree of their expression throughout the development. A negative correlative analysis suggests an intertwined network of microRNAs and mRNAs collaborating with each other. This study provides information leading to potential two-way level of combinatorial control regulating gene expression through microRNAs targeting multiple mRNAs and, conversely, target mRNAs regulated in parallel by other microRNAs as well. This study may further the understanding of gene expression regulation in the human developing pancreas.
MicroRNAs are small non-coding RNAs [1–3] that act mostly as translational repressors through binding to partially complementary sequences of target messenger RNAs. Recent studies indicate that in some cases microRNAs might act also as positive regulators of translation and transcription [4, 5]. To date, more than 700 human microRNAs are annotated in the Wellcome Trust Sanger Institute microRNA database . MicroRNAs play a fundamental role in regulation of gene expression, consequently affecting key biological events such as embryogenesis, stem cell proliferation/differentiation and organ development . The method of conditional knockout of the microRNA-processing enzyme Dicer has been used to study the action of microRNAs in specific tissues and organs. This approach has confirmed that the expression of microRNAs is essential for morphogenesis of several systems, including the lungs, limbs, muscle, skin and neuronal development and differentiation [8–12] including the genesis of pancreatic islet cells . Dicer-null animals displayed gross defects in all pancreatic lineages; the most dramatic reduction was seen in the endocrine cells, especially the insulin-producing beta cells. Another approach utilized to assess the role of microRNAs in organs/tissues is the loss of function, i.e., silencing or knock out of a single or cluster microRNAs. For instance, genetic deletion of the miR-1/miR-133 cluster, showed its crucial role in cardiac skeletal muscle development . Several microRNAs have been described in the context of pancreatic development, pancreatic exocrine/endocrine microRNA expression or islet biology/physiology. For example, knockdown of miR-375, an islet microRNA that negatively controls insulin secretion , has a deleterious effect on the developing pancreas of zebra fish . Mice lacking miR-375 (375KO) are hyperglycemic and exhibit an increase in total pancreatic alpha-cell numbers, whereas the pancreatic beta-cell mass is decreased . Recent studies indicate that during human pancreatic development miR-7, miR-9, miR-375 and miR-376 are specific islet microRNAs expressed at high levels [18, 19]. MiR-124a is known to target FOXA2 the transcription factor crucial for early pancreatic formation  by affecting genes such as PDX1, KCNJ11 (Kir6.2) and SUR1, essential for the normal development of the pancreas, glucose metabolism and insulin secretion , while miR-15a, -15b, -16 and -195 have important roles in regulating translation of NGN3, a transcription factor affecting the adoption of an endocrine fate during embryogenesis .
Research conducted over the last decade has outlined rather comprehensive "roadmap" of the major molecular events that shape mouse islet development [23, 24]. Development of the human endocrine pancreas has not been studied as well as that of the mouse. The previous work has described expression of endocrine hormones as well as transcription factors, and presented morphological examination of islet formation during human fetal pancreatic development [25–29].
In this study we investigated the global expression profile of microRNAs in the human fetal pancreas from age 10 to 22 weeks of gestational age (wga) and found 212 microRNAs expressed throughout this entire period. Understanding the complex processes behind pancreatic development will require the identification of RNA targets that are controlled by the extensive microRNA network expressed throughout the process. To identify potential microRNA gene targets, we calculated the correlations between the expression profile of these 212 microRNAs and their corresponding mRNA targets, predicted by bioinformatic tools, that were found in the genome-wide RNA microarray study by Sarkar et al. . These studies will advance our understanding of pancreatic development, potentially unveiling prospective therapeutic targets to treat diabetes.
Results and discussion
MicroRNA Expression profile during pancreatic development
MicroRNAs expressed during human fetal pancreatic development.
Negative regulator of glucose-induced insulin secretion through myothrophin regulation . miR-375 K/O mice are hyperglycemic -more alpha cells; less beta-cells- . Regulation of PI3 pathway by regulation of PDK1 in insulinoma cells . The miR-375 gene promoter directs expression selectively to endocrine pancreas .
Expressed at high levels during islet development . Target of transcription factor Onecut-2 impairing glucose-induced insulin secretion in insulinoma cells.
miR-195; miR-16 miR-15a; miR-15b
Role in pancreatic regeneration, possibly by targeting Ngn3 .
Expressed in mouse early fetal pancreas, controls the liver and pancreatic development regulator Onecut-2 in liver embryonic cells .
miR-484; miR-107; miR-30d
High glucose down-regulates their expression in insulinoma cells .
Increased expression in islets from db/db obese mice, contributes to fatty acids-induced beta-cell dysfunction . Pro-inflammatory cytokines induce its expression in human islet and MIN6 cells .
Over-expression induced insulin resistance in 3T3 adipocytes .
miR-503 is expressed in a pattern similar to that of miR-375 in a mouse progenitor cells at e14.5 pancreas .
Expressed at high levels during islet development .
Increases mRNA and protein levels of granulophilin, a negative regulator of insulin exocytosis .
MicroRNA groups classified according to their expression profiles.
Group I (4)
Group II (35)
Group III (173)
Inverse correlation between microRNA and mRNA during human pancreatic development
Although mechanistically, microRNAs inhibit mRNA translation, the expression of a given microRNA has also been associated with the downregulation of its target mRNA levels [30, 31]. Therefore, to identify potential microRNA targets, we performed a statistical analysis looking for inverse correlations between the microRNAs in groups I and II (a total of 39 microRNAs) and the algorithm-predicted target genes that were found in a human pancreatic development genome-wide mRNA microarray gene expression study . Predictive algorithms are a powerful tool to identify potential RNA targets . The target RNAs were selected by two of the most commonly used computational microRNA predictive target programs, miRBase  and TargetScan [30, 34]. There is a limitation to this kind of analysis because it utilizes only the sequence correlation of 3'UTR mRNA domain with microRNA seed sequences. This excludes naturally occurring microRNA targets with recognition motifs within the gene coding sequence. This kind of microRNA/mRNA interaction has been recently reported as well . Computational predictions often generate long lists of target genes and these are not always concordant . Therefore, the correlations were calculated with target genes predicted by each program individually, and also by the intersection of both algorithms, as this might increase the odds of identifying the bona fide RNA targets .
Correlation between microRNAs and gene targets.
Genes Group I
Genes Group II
Genes involved in pancreatic development correlated with microRNAs
Genes involved in pancreatic development correlated with microRNAs.
miR-382 (II), miR-432 (II)
miR-181b (II), miR-181 d (II)
miR-125a (II), miR-218 (II)
miR-130b (II), miR-301 (II), miR-494 (II)
miR-187 (II), miR-214 (II), miR-342 (II)
miR-17-5p (II), miR-18a (II), miR-92 (II), miR-103 (II), miR-494 (II)
miR-99b (II), miR-345 (II), miR-494 (II)
miR-130b (II), miR-301 (II)
miR-181 d (II), miR-342 (II)
In this study we determined the microRNA expression signature during human pancreatic development, identifying a total of 212 microRNAs expressed at least once in one of the four stages evaluated (10 to 22 wga). To further our understanding of gene expression regulation in the human developing pancreas, we compared this microRNA signature with the global expression of genes during the similar period of the developing pancreas. There are potential limitations to this kind of study that should be considered: 1) the study was designed under the assumption that microRNAs are associated with downregulation of their targets. However, some microRNAs were reported as positive regulators of transcription or translation [4, 5], 2) the correlation between microRNAs and gene expression was performed with mRNAs only, but there are some microRNAs that regulate only protein translation; 3) all potential targets were identified by algorithms analyzing the 3'UTR of the mRNA, while some microRNAs can also interact with targets containing the recognition sequence in the coding region; 4) the expression of microRNAs and mRNAs was evaluated on different set of sample tissues, using different platforms by different laboratories; 5) in this study we have analyzed the pancreas as a whole, instead of looking at the cellular subsets. Therefore, it is not possible to determine if the observed changes corresponded to a specific cell population (e.g endocrine progenitors, mesenchymal tissue etc).
Despite of these caveats, the correlation between the expression of hundreds of microRNAs with the analysis of thousands of genes expressed during human pancreatic development suggests the intertwined collaborative network of microRNAs and mRNAs. It also provides broad and useful information to explore potential two-way level of gene expression control regulation in which microRNAs target multiple mRNAs, and, target mRNAs are regulated in parallel by multiple microRNAs. Ultimately, the speculation regarding the roles of all these presumptive targets will be justified only if they are experimentally validated.
Human fetal pancreas procurement
Human fetal pancreases from 10 to 22 weeks of gestational age (wga) were collected from fetal tissue immediately after elective termination of pregnancy. The healthy women admitted to local clinics were properly informed and gave their consent to use fetal tissues for research studies. The study is in compliance with US legislation and the guidelines of the University of Miami. Gestational age was determined on the basis of the last menstrual period, with ultrasonographic measurements of the Crown-Rump Length, and the biparietal diameter.
MicroRNA isolation was performed using mirVana microRNA Isolation kit from Ambion/Applied Biosystems (Foster City CA), following protocol for the total RNA isolation procedure provided by the manufacturer.
Quantitative microRNA profiling: criteria for inclusion of microRNAs in this study
We performed this study with 10 human fetal pancreases of following gestational stages: two samples (10 wga); one sample (11 wga); one sample (13 wga); three samples (14 wga); one sample (15 wga); one sample (21 wga) and one sample (22 wga). Total RNA was isolated using mirVana microRNA Isolation kit, cDNA synthesis and the PCR amplification was performed according to the manufacturer's instructions (Applied Biosystems, Foster City, CA). MicroRNA profiling was performed with micro fluidic cards TaqMan® Low Density Array (TLDA, v1.0) for human microRNAs, which allows quantitative assessment of 352 microRNAs using the AB7900 instrument (Applied Biosystems). Quantitative values were calculated as relative quantification (RQ), normalized to endogenous control small nucleolar RNA RNU48, which is expressed evenly in all samples. RQs were calculated with SDS software supplied by the manufacturer (Applied Biosystems), utilizing the equation RQ = 2-ΔCt, where Ct is the number of cycles at which the sample reaches a software-determined threshold within the exponential amplification phase. Ct ≥ 35 cycles was considered as undetermined. The Gene Expression Omnibus (GEO) accession number is GSE22026.
For the correlation analysis we arranged the samples in 3 gestational periods: 10-11 wga; 13-15 wga and 21-22 wga. Only microRNAs that amplified in 2 out 3 samples for 10-11 wga period, 4 out of 5 samples for 13-15 wga and 2 out of 2 samples in 21-22 wga were considered as positively expressed. Statistical analysis between the groups: 10-11 wga vs 13-15 wga; 10-11 wga vs 21-22 wga and 13-15 wga vs 21-22 wga was performed with two-tailed Student's t test (Additional file 1, Table S1). Values were considered significant when p < 0.05. The microRNAs were classified according to their expression profile into three groups. Group I contained microRNAs with the expression increased at least from one gestational stage to the other (p < 0.05). Group II contained microRNAs with the expression decreased at least from one gestational stage to the other (p < 0.05). In group III we included the microRNAs with the expression unchanged throughout the studied gestational stages (p > 0.05).
MicroRNA target gene databases
Two microRNA target databases were used in this analysis. One arch.v5.txt.homo_sapiens.zip, downloaded from Sanger miRBase Targets http://microrna.sanger.ac.uk/cgi-bin/targets/v5/download.pl; the other Nonconserved_Site_Context_Scores.zip, downloaded from TargetScan http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_50.
mRNA gene expression files were retrieved from Sarkar SA, Kobberup S, Wong R, Lopez AD, Quayum N, Still T, Kutchma A, Jensen JN, Gianani R, Beattie GM et al: Global gene expression profiling and histochemical analysis of the developing human fetal pancreas. 22,000 genes were measured by 54,675 probe-sets on Human Genome HG U133 Plus 2.0 microarrays (Affymetrix, Santa Clara, CA, USA). The mRNA hybridization data from genes with multiple probes were averaged prior to the correlation analysis. For the correlation analysis the Sarkar et al gene expression data were grouped in three periods: 9, 10 and 11 wga (9-11); 15 wga; and 20, 23 wga. The gene expression of these three periods was compared to the microRNA expression corresponding to the three following periods: 10-11 wga; 13-15 wga and 21-22 wga. The Pearson correlation of the expression profile between microRNA and a target gene was calculated in the R package. The correlation coefficient (r) was calculated to determine the regulatory role of the microRNA on its target genes. The correlation with r close to -1 depicts a strong negative correlation between microRNA and its target gene, indicating the suppression function of microRNA on its target gene. P-value is used to test H0: r = 0. Benjamini-Hochberg FDR was used to correct the original p-value for multiple test correction. The correlations with adjusted p-value < 0.05 are significant correlations, and among these correlations the ones with (R2≥0.8) are significantly negative correlations where microRNAs suppress the expression of target genes in terms of expression in the three gestational ages.
Abbreviations of genes referenced in this study
Insulin gene enhancer protein
homeo box HB9
- Hex (Hhex):
hematopoietically expressed homeobox protein
Homeobox prospero-like protein
hepatocyte nuclear factor 1 homeobox B
hepatocyte nuclear factor 6
Pancreas specific transcription factor, 1a
Pancreatic and duodenal homeobox 1
Pre-B-cell leukemia transcription factor 1
transcription factor sox9
transcription factor sox4
transcription factor gata-4
transcription factor gata-6
Neurogenic differentiation 1
Insulinoma-associated protein 1
Myelin transcription factor 1
Paired box gene 6: Pax4: Paired box gene 4
Homeobox protein Nkx-2.2
Homeobox protein Nkx-6.1
V-maf musculoaponeurotic fibrosarcoma oncogene homolog a (avian)
V-maf musculoaponeurotic fibrosarcoma oncogene homolog b (avian)
forkhead box A1
forkhead box A2
hepatocyte nuclear factor 1 homeobox A
Hepatocyte nuclear factor 4 alpha
Transcription factor 7-like 2 (T-cell specific, HMG-box)
ATP-sensitive K+ channel
sulfonylurea receptor 1
This work was supported by the Diabetes Research Institute Foundation, the Peacock Foundation and the Foundation for Diabetes Research.
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