The long noncoding RNA MALAT1 predicts human pancreatic islet isolation quality

Wilson W.K. Wong, Guochi Jiang, Anja Elaine Sørensen, Yi Vee Chew, Cody Lee-Maynard, David Liuwantara, Lindy Williams, Phillip O'Connell, Louise Torp Dalgaard, Ronald C. Ma, Wayne J. Hawthorne, Mugdha Joglekar, Anandwardhan Awadhoot Hardikar

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

Human islet isolation is a cost-/resource-intensive program generating islets for cell therapy in Type 1 diabetes. However, only a third of cadaveric pancreas get to clinical transplantation due to low quality/number of islets. There is a need to identify biomarker(s) that predict the quality of islets, prior to initiating their isolation. Here, we sequenced transcriptome from 18 human islet preparations stratified into three groups (Gr.1: Best quality/transplantable islets, Gr.2: Intermediary quality, Gr.3: Inferior quality/non-transplantable islets) based on routine measurements including islet purity/viability. Machine-learning algorithms involving penalized regression analyses identified 10 long-non-coding(lnc)RNAs significantly different across all group-wise comparisons (Gr1VsGr2, Gr2vsGr3, Gr1vsGr3). Two variants of Metastasis-Associated Lung Adenocarcinoma Transcript-1(MALAT1) lncRNA were common across all comparisons. We confirmed RNA-seq findings in a "validation set" of 75 human islet preparations. Finally, in 19 pancreas samples, we demonstrate that assessing the levels of MALAT1 variants alone (ROC curve AUC: 0.83) offers highest specificity in predicting post-isolation islet quality and improves the predictive potential for clinical islet transplantation when combined with Edmonton Donor Points/Body Mass Index(BMI)/North American Islet Donor Score(NAIDS). We present this resource of islet-quality-stratified lncRNA transcriptome data and identify MALAT1 as a biomarker that significantly enhances current selection methods for clinical (GMP)-grade islet isolation.
OriginalsprogEngelsk
Artikelnummere129299
TidsskriftJCI Insight
Vol/bind4
Udgave nummer16
DOI
StatusUdgivet - 22 aug. 2019

Bibliografisk note

This article has been found as a ’Free Version’ from the Publisher on October 2 2019. When access to the article closes, please notify rucforsk@ruc.dk

Citer dette

Wong, Wilson W.K. ; Jiang, Guochi ; Sørensen, Anja Elaine ; Chew, Yi Vee ; Lee-Maynard, Cody ; Liuwantara, David ; Williams, Lindy ; O'Connell, Phillip ; Dalgaard, Louise Torp ; Ma, Ronald C. ; Hawthorne, Wayne J. ; Joglekar, Mugdha ; Hardikar, Anandwardhan Awadhoot. / The long noncoding RNA MALAT1 predicts human pancreatic islet isolation quality. I: JCI Insight. 2019 ; Bind 4, Nr. 16.
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title = "The long noncoding RNA MALAT1 predicts human pancreatic islet isolation quality",
abstract = "Human islet isolation is a cost-/resource-intensive program generating islets for cell therapy in Type 1 diabetes. However, only a third of cadaveric pancreas get to clinical transplantation due to low quality/number of islets. There is a need to identify biomarker(s) that predict the quality of islets, prior to initiating their isolation. Here, we sequenced transcriptome from 18 human islet preparations stratified into three groups (Gr.1: Best quality/transplantable islets, Gr.2: Intermediary quality, Gr.3: Inferior quality/non-transplantable islets) based on routine measurements including islet purity/viability. Machine-learning algorithms involving penalized regression analyses identified 10 long-non-coding(lnc)RNAs significantly different across all group-wise comparisons (Gr1VsGr2, Gr2vsGr3, Gr1vsGr3). Two variants of Metastasis-Associated Lung Adenocarcinoma Transcript-1(MALAT1) lncRNA were common across all comparisons. We confirmed RNA-seq findings in a {"}validation set{"} of 75 human islet preparations. Finally, in 19 pancreas samples, we demonstrate that assessing the levels of MALAT1 variants alone (ROC curve AUC: 0.83) offers highest specificity in predicting post-isolation islet quality and improves the predictive potential for clinical islet transplantation when combined with Edmonton Donor Points/Body Mass Index(BMI)/North American Islet Donor Score(NAIDS). We present this resource of islet-quality-stratified lncRNA transcriptome data and identify MALAT1 as a biomarker that significantly enhances current selection methods for clinical (GMP)-grade islet isolation.",
author = "Wong, {Wilson W.K.} and Guochi Jiang and S{\o}rensen, {Anja Elaine} and Chew, {Yi Vee} and Cody Lee-Maynard and David Liuwantara and Lindy Williams and Phillip O'Connell and Dalgaard, {Louise Torp} and Ma, {Ronald C.} and Hawthorne, {Wayne J.} and Mugdha Joglekar and Hardikar, {Anandwardhan Awadhoot}",
note = "This article has been found as a ’Free Version’ from the Publisher on October 2 2019. When access to the article closes, please notify rucforsk@ruc.dk",
year = "2019",
month = "8",
day = "22",
doi = "10.1172/jci.insight.129299",
language = "English",
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journal = "JCI Insight",
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Wong, WWK, Jiang, G, Sørensen, AE, Chew, YV, Lee-Maynard, C, Liuwantara, D, Williams, L, O'Connell, P, Dalgaard, LT, Ma, RC, Hawthorne, WJ, Joglekar, M & Hardikar, AA 2019, 'The long noncoding RNA MALAT1 predicts human pancreatic islet isolation quality', JCI Insight, bind 4, nr. 16, e129299. https://doi.org/10.1172/jci.insight.129299

The long noncoding RNA MALAT1 predicts human pancreatic islet isolation quality. / Wong, Wilson W.K.; Jiang, Guochi; Sørensen, Anja Elaine; Chew, Yi Vee; Lee-Maynard, Cody; Liuwantara, David; Williams, Lindy; O'Connell, Phillip; Dalgaard, Louise Torp; Ma, Ronald C.; Hawthorne, Wayne J.; Joglekar, Mugdha; Hardikar, Anandwardhan Awadhoot.

I: JCI Insight, Bind 4, Nr. 16, e129299, 22.08.2019.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - The long noncoding RNA MALAT1 predicts human pancreatic islet isolation quality

AU - Wong, Wilson W.K.

AU - Jiang, Guochi

AU - Sørensen, Anja Elaine

AU - Chew, Yi Vee

AU - Lee-Maynard, Cody

AU - Liuwantara, David

AU - Williams, Lindy

AU - O'Connell, Phillip

AU - Dalgaard, Louise Torp

AU - Ma, Ronald C.

AU - Hawthorne, Wayne J.

AU - Joglekar, Mugdha

AU - Hardikar, Anandwardhan Awadhoot

N1 - This article has been found as a ’Free Version’ from the Publisher on October 2 2019. When access to the article closes, please notify rucforsk@ruc.dk

PY - 2019/8/22

Y1 - 2019/8/22

N2 - Human islet isolation is a cost-/resource-intensive program generating islets for cell therapy in Type 1 diabetes. However, only a third of cadaveric pancreas get to clinical transplantation due to low quality/number of islets. There is a need to identify biomarker(s) that predict the quality of islets, prior to initiating their isolation. Here, we sequenced transcriptome from 18 human islet preparations stratified into three groups (Gr.1: Best quality/transplantable islets, Gr.2: Intermediary quality, Gr.3: Inferior quality/non-transplantable islets) based on routine measurements including islet purity/viability. Machine-learning algorithms involving penalized regression analyses identified 10 long-non-coding(lnc)RNAs significantly different across all group-wise comparisons (Gr1VsGr2, Gr2vsGr3, Gr1vsGr3). Two variants of Metastasis-Associated Lung Adenocarcinoma Transcript-1(MALAT1) lncRNA were common across all comparisons. We confirmed RNA-seq findings in a "validation set" of 75 human islet preparations. Finally, in 19 pancreas samples, we demonstrate that assessing the levels of MALAT1 variants alone (ROC curve AUC: 0.83) offers highest specificity in predicting post-isolation islet quality and improves the predictive potential for clinical islet transplantation when combined with Edmonton Donor Points/Body Mass Index(BMI)/North American Islet Donor Score(NAIDS). We present this resource of islet-quality-stratified lncRNA transcriptome data and identify MALAT1 as a biomarker that significantly enhances current selection methods for clinical (GMP)-grade islet isolation.

AB - Human islet isolation is a cost-/resource-intensive program generating islets for cell therapy in Type 1 diabetes. However, only a third of cadaveric pancreas get to clinical transplantation due to low quality/number of islets. There is a need to identify biomarker(s) that predict the quality of islets, prior to initiating their isolation. Here, we sequenced transcriptome from 18 human islet preparations stratified into three groups (Gr.1: Best quality/transplantable islets, Gr.2: Intermediary quality, Gr.3: Inferior quality/non-transplantable islets) based on routine measurements including islet purity/viability. Machine-learning algorithms involving penalized regression analyses identified 10 long-non-coding(lnc)RNAs significantly different across all group-wise comparisons (Gr1VsGr2, Gr2vsGr3, Gr1vsGr3). Two variants of Metastasis-Associated Lung Adenocarcinoma Transcript-1(MALAT1) lncRNA were common across all comparisons. We confirmed RNA-seq findings in a "validation set" of 75 human islet preparations. Finally, in 19 pancreas samples, we demonstrate that assessing the levels of MALAT1 variants alone (ROC curve AUC: 0.83) offers highest specificity in predicting post-isolation islet quality and improves the predictive potential for clinical islet transplantation when combined with Edmonton Donor Points/Body Mass Index(BMI)/North American Islet Donor Score(NAIDS). We present this resource of islet-quality-stratified lncRNA transcriptome data and identify MALAT1 as a biomarker that significantly enhances current selection methods for clinical (GMP)-grade islet isolation.

UR - https://insight.jci.org/articles/view/129299

U2 - 10.1172/jci.insight.129299

DO - 10.1172/jci.insight.129299

M3 - Journal article

VL - 4

JO - JCI Insight

JF - JCI Insight

SN - 2379-3708

IS - 16

M1 - e129299

ER -