Biology subjects

Dalgaard, L. T.

Publications and source records attributed to Dalgaard, L. T..

3 recordsLinked to original sources

bioRxiv2022

Identifying human islet microRNAs associated with donor sex, age and body mass index

ObjectivesHuman islets are widely researched to understand pathophysiological mechanisms leading to diabetes. Sex, age, and body mass index (BMI) are key donor traits influencing insulin secretion. Islet function is also regulated by an intricate network of microRNAs. MethodsHere, we profiled 754 microRNAs and 58,190 potential targets in up to 131 different human islet donor preparations (without diabetes) and assessed their association with donor traits. We further performed mechanistical studies to observe the causal role of the age-associated key microRNAs on relative telomere length in human islets. ResultsMicroRNA discovery analyses identified miR-199a-5p and miR-214-3p associated with sex, age and BMI; miR-147b with sex and age; miR-378a-5p with sex and BMI; miR-542-3p, miR-34a-3p, miR-34a-5p, miR-497-5p and miR-99a-5p with age and BMI. There were 959 mRNA transcripts associated with sex (excluding those from sex-chromosomes), 940 with age and 418 with BMI. MicroRNA-199a-5p and miR-214-3p levels inversely associate with transcripts critical in islet function, metabolic regulation, and senescence. Our functional studies verified that inhibition of these two microRNAs (miR-199a-5p/-214-3p) slowed down telomere length shortening in human islet cells maintained in vitro and demonstrating cellular senescence. ConclusionsOur analyses identify human islet cell microRNAs influenced by donor traits. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/512222v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1666c2org.highwire.dtl.DTLVardef@b4695forg.highwire.dtl.DTLVardef@720e99org.highwire.dtl.DTLVardef@1c78ae_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

Machine learning algorithms in big data analyses identify determinants of insulin gene transcription

Machine learning (ML)-workflows enable unprejudiced/robust evaluation of complex datasets. Here, we analyzed over 490,000,000 data points to compare 10 different ML-workflows in a large (N=11,652) training dataset of human pancreatic single-cell (sc-)transcriptomes to identify genes associated with the presence or absence of insulin transcript(s). Prediction accuracy/sensitivity of each ML-workflow were tested in a separate validation dataset (N=2,913). Ensemble ML-workflows, in particular Random Forest ML-algorithm delivered high predictive power (AUC=0.83) and sensitivity (0.98), compared to other algorithms. The transcripts identified through these analyses also demonstrated significant correlation with insulin in bulk RNA-seq data from human islets. The top-10 features, (including IAPP, ADCYAP1, LDHA and SST) common to the three Ensemble ML-workflows were significantly dysregulated in scRNA-seq datasets from Ire-1{beta}-/- mice that demonstrate dedifferentiation of pancreatic {beta}-cells in a model of type 1 diabetes (T1D) and in pancreatic single cells from individuals with type 2 Diabetes (T2D). Our findings provide direct comparison of ML-workflows in big data analyses, identify key determinants of insulin transcription and provide workflows for future analyses.

genetics↗

bioRxiv2021

A pro-endocrine pancreatic transcriptional program established during development is retained in human gallbladder epithelial cells

ObjectivePancreatic islet {beta}-cells are factories for insulin production; however ectopic expression of insulin is also well recognized. The gallbladder is a next-door neighbour to the developing pancreas. Here, we wanted to understand if gallbladders contain functional insulin-producing cells. DesignWe compared developing and adult mouse as well as human gallbladder epithelial cells and islets using immunohistochemistry, flow cytometry, ELISAs, RNA-sequencing, real-time PCR, chromatin immunoprecipitation and functional studies. ResultsWe demonstrate that the epithelial lining of developing, as well as adult mouse and human gallbladders naturally contain interspersed cells that retain the capacity to actively transcribe, translate, package, and release insulin. We show for the first time that human gallbladders also contain functional insulin-secreting cells with the potential to naturally respond to glucose in vitro and in situ. Notably, in a NOD mouse model of type 1 diabetes, we observed that insulin-producing cells in the gallbladder are not targeted by autoimmune cells. Conclusion: In summary, our biochemical, transcriptomic, and functional data in human gallbladder epithelial cells collectively demonstrate their potential for insulin-production under pathophysiological conditions, and open newer areas for type 1 diabetes research and therapy. Significance of the study What is already known about this subject?O_LIDeveloping pancreas and gallbladder are next-door neighbours and share similar developmental pathways. C_LIO_LIHuman Gallbladder-derived progenitor cells were shown to differentiate into insulin-producing cells. C_LI What are the new findings?O_LIGallbladder epithelium contains interspersed cells that can transcribe, translate, package and secrete insulin. C_LIO_LIInsulin-producing cells in the gallbladder are not destroyed by immune cells in an animal model of type 1 diabetes (T1D). C_LIO_LIOur studies demonstrating the absence of insulin splice variants in human gallbladder cells, and higher splice forms in human islets, suggest a potential mechanism (via defective ribosomal products) in escaping islet autoimmunity. C_LI How might it impact clinical practice?O_LIDeciphering mechanisms of protection of insulin-producing cells from immune cells in the gallbladder could help in developing strategies to prevent islet autoimmunity in T1D. C_LI

developmental biology↗