WO2019000017A1 - Signatures de micro-arn intracellulaires de cellules productrices d'insuline - Google Patents

Signatures de micro-arn intracellulaires de cellules productrices d'insuline Download PDF

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WO2019000017A1
WO2019000017A1 PCT/AU2018/000108 AU2018000108W WO2019000017A1 WO 2019000017 A1 WO2019000017 A1 WO 2019000017A1 AU 2018000108 W AU2018000108 W AU 2018000108W WO 2019000017 A1 WO2019000017 A1 WO 2019000017A1
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mir
hsa
cells
micrornas
insulin
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PCT/AU2018/000108
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Anandwardhan Awadhoot HARDIKAR
Mugdha Vinay JOGLEKAR
Andrzej Szczesny JANUSZEWSKI
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The University Of Sydney
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Publication of WO2019000017A1 publication Critical patent/WO2019000017A1/fr

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    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K31/00—Medicinal preparations containing organic active ingredients
    • A61K31/70—Carbohydrates; Sugars; Derivatives thereof
    • A61K31/7088—Compounds having three or more nucleosides or nucleotides
    • A61K31/7105—Natural ribonucleic acids, i.e. containing only riboses attached to adenine, guanine, cytosine or uracil and having 3'-5' phosphodiester links
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C12N15/11—DNA or RNA fragments; Modified forms thereof; Non-coding nucleic acids having a biological activity
    • C12N15/113—Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides; Antisense DNA or RNA; Triplex- forming oligonucleotides; Catalytic nucleic acids, e.g. ribozymes; Nucleic acids used in co-suppression or gene silencing
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    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K35/00—Medicinal preparations containing materials or reaction products thereof with undetermined constitution
    • A61K35/12—Materials from mammals; Compositions comprising non-specified tissues or cells; Compositions comprising non-embryonic stem cells; Genetically modified cells
    • A61K35/37—Digestive system
    • A61K35/39—Pancreas; Islets of Langerhans
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N2310/00—Structure or type of the nucleic acid
    • C12N2310/10—Type of nucleic acid
    • C12N2310/14—Type of nucleic acid interfering N.A.
    • C12N2310/141—MicroRNAs, miRNAs
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA

Definitions

  • the present invention relates generally to the field of medicine and more specifically to insulin-related diseases and conditions. Described herein are microRNA signatures of cells that naturally produce insulin which are relevant, for example, in processes involving differentiation of stem/progenitor/precursor cells to insulin-producing cells, to predicting the level of insulin in cells based on microRNA expression, and in diagnosing and/or prognosing the development of diseases and conditions associated with the loss of insulin-producing cells (e.g. diabetes).
  • microRNA signatures of cells that naturally produce insulin which are relevant, for example, in processes involving differentiation of stem/progenitor/precursor cells to insulin-producing cells, to predicting the level of insulin in cells based on microRNA expression, and in diagnosing and/or prognosing the development of diseases and conditions associated with the loss of insulin-producing cells (e.g. diabetes).
  • Insulin is a hormone generated in the pancreas. Clusters of cells within the pancreas known as the islets of Langerhans contain beta cells, which make insulin and release it into the circulation. Insulin plays a major role in metabolism, assisting cells throughout the body to absorb glucose and use it for energy. For example, it lowers blood glucose levels by assisting muscle, fat, and liver cells absorb glucose from the bloodstream, it stimulates the liver and muscle tissue to store excess glucose (glycogen), and it lowers blood glucose levels by reducing glucose production in the liver
  • diabetes is characterized by loss of beta-cell function. Inadequate production (in Type 1 diabetes/TID) or use of insulin (as in Type 2 diabetes/T2D) affects glucose-insulin metabolism resulting in abnormally higher concentrations of glucose in the blood. Insulin lowers blood glucose levels by increasing its uptake into cells of the liver, muscle or fat and storing this in form of glycogen for its use as an energy source in future.
  • Type 1 diabetes is characterized by autoimmune destruction of pancreatic islet beta-cells
  • Type 2 diabetes is characterized by insulin resistance and impaired glucose tolerance where insulin is not efficiently used (or produced). Individuals with Type 2 diabetes may eventually require exogenous insulin to regulate blood glucose levels. Individuals with diabetes need to regularly monitor their glucose and inject exogenous insulin for several times in a day so as to maintain normal circulating concentrations of glucose.
  • Hyperglycemia as well as the more life-threatening hypoglycemia, are common outcomes of uncontrolled diabetes and over time can lead to serious damage to nerves, blood vessels, heart, eyes, and kidneys.
  • WHO World Health Organization
  • IDF International Diabetes Federation
  • Standard clinical tests for diagnosing abnormalities in the production of insulin and/or insulin metabolism typically rely on measurements of glucose (e.g. AIC, FPG and OGTT tests), insulin, or c-peptide in the blood. Extended time, expense, inaccuracies in measurement, and/or poor standardisation are just a few of the issues associated with these tests. Blood glucose tests also lack predictive power due to the capacity of pancreatic ⁇ -cells to produce the desired level of insulin even when the majority of the ⁇ -cells are dead/dying.
  • Insulin therapy is a common means of treating diseases and conditions arising from or associated with low insulin production. Exogenous insulin administration therapy typically requires the regular and long-term application of insulin with patient compliance of paramount importance. Insulin therapy also carries a high risk of achieving very low blood glucose concentrations ("hypoglycemia") if over-administered, and may be fatal. Moreover, subjects are additionally required to undertake frequent blood glucose monitoring and carefully control carbohydrate intake. Although insulin therapy can in some cases manage the clinical symptoms of diabetes, cell replacement therapy, often carried out by the transplantation of pancreas or the islets of Langerhans is another therapeutic option.
  • progenitor-/stem-/pluripotent-/precursor-cells to develop new islet cells offers a potential alternative, but achieving efficient differentiation of human progenitor cells to insulin-producing cells has proven challenging.
  • Molecules capable of enhancing the production of insulin and/or promoting the development of surrogate insulin-producing cells for cell replacement therapy are clearly desirable in the context of diseases and conditions associated with loss of insulin-producing cells (e.g. diabetes).
  • the present invention addresses existing need/s in the field by providing a set of intracellular microRNA molecules that are indicative of loss of insulin-producing cells, and/or which may be used to induce the differentiation of progenitor-/precursor-cells into insulin-producing cells.
  • the intracellular microRNA signatures of the present invention may be used, without limitation,: (i) to predict the presence/absence or relative abundance (cycle threshold- / Ct- value) of insulin gene transcripts in cells and/or tissues; (ii) to induce insulin gene expression in islet progenitor/precursor cells which, for example, can be used for cell replacement therapy in diseases and conditions associated with loss of insulin-producing cells (e.g. diabetes); (iii) as a biomarker to determine the death or function of insulin-producing cells in subjects with or progressing to diseases and conditions associated with loss of insulin production (e.g. diabetes); and/or (iv) to identify the tissue of origin, based on the signature and levels of microRNA expression.
  • the intracellular microRNA signatures of the present invention may be: i) associated with insulin-producing cells (including naturally-occurring insulin-producing cells); ii) associated with different levels of insulin expression in insulin-producing cells (including naturally-occurring insulin-producing cells); iii) causal to induction of insulin expression; and/or iv) biomarkers of pancreatic beta-cell death.
  • Embodiment 1 A method for predicting a level of insulin production in cells of a subject, the method comprising:
  • microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa- miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in a sample of the cells obtained from the subject;
  • Embodiment 2 The method of embodiment 1, comprising or consisting of determining expression levels of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.
  • Embodiment s The method of embodiment 1, comprising or consisting of determining expression levels of:
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, and hsa-miR-217; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa- miR-375, hsa-miR-183#, hsa-miR-217, and hsa-miR-429; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7-2#; or(iv) nine microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR- 217, hsa-miR-429, hsa-miR-7-2#, and mmu-miR-129-3p; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, and hsa- miR-433;
  • Embodiment 4 The method of any one of embodiments 1 to 3, further comprising or consisting of determining expression levels of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271 ;
  • Embodiment 5 The method of any one of embodiments 1 to 4, comprising or consisting of determining expression levels of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a;
  • Embodiment 6 The method of any one of embodiments 1 to 5, wherein said elevated expression levels of the microRNAs in the sample of cells is indicative of production of insulin gene transcripts in the sample of cells, and
  • said reduced or absent expression level/s of the microRNA/s in the sample of cells is indicative of reduced or absent insulin production of insulin gene transcripts in the sample of cells.
  • Embodiment 7 The method of any one of embodiments 1 to 6, wherein the control cells that do not produce insulin are from the subject.
  • Embodiment 8 The method of any one of embodiments 1 to 7, wherein the sample of cells comprises any one or more of: pancreatic cells, brain cells, gall bladder cells.
  • Embodiment 9 The method of any one of embodiments 1 to 8, wherein the sample of cells comprises beta-islet cells.
  • Embodiment 10 The method of any one of embodiments 1 to 9, wherein said reduced or absent insulin production is diagnostic or prognostic of a disease or condition associated with or arising from a loss of insulin-producing cells in the subject.
  • Embodiment 11 The method of embodiment 10, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.
  • Embodiment 12 The method of any one of embodiments 1 to 11, wherein the subject has or is progressing toward a disease or condition associated with or arising from a loss of insulin-producing cells, and the expression levels of one or more microRNA/s is a marker of death and/or loss of insulin-producing function in the cells of the subject.
  • Embodiment 13 The method of any one of embodiments 1 to 12, further comprising an initial step of obtaining the sample of cells from the subject.
  • Embodiment 14 A method for inducing insulin production in pancreatic lineage cells, the method comprising treating the pancreatic lineage cells six one or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR- 433, and any combination thereof.
  • microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR
  • Embodiment 15 The method of embodiment 14, comprising or consisting of treating the pancreatic lineage cells with seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.
  • Embodiment 16 The method of embodiment 14, comprising or consisting of treating the pancreatic lineage cells with:
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, and hsa-miR-217; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa- miR-375, hsa-miR-183#, hsa-miR-217, and hsa-miR-429; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7-2#; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, and mmu-miR-129-3p; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, and hsa- miR-433.
  • Embodiment 17 The method of any one of embodiments 14 to 16, further comprising or consisting of treating the pancreatic lineage cells with any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.
  • Embodiment 18 The method of any one of embodiments 14 to 17, comprising or consisting of treating the pancreatic lineage cells with: hsa-miR-183, hsa-miR-216b, dme- miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR- 129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.
  • Embodiment 19 The method of any one of embodiments 14 to 18, wherein said treating comprises overexpressing the one or more microRNA/s in the pancreatic lineage cells.
  • Embodiment 20 The method of any one of embodiments 14 to 19, wherein the pancreatic lineage cells comprise any one or more of: pancreatic islet precursor cells, endocrine progenitor cells, pancreatic islet progenitor cells, pancreatic islet stem cells, induced pluripotent pancreatic islet cells.
  • Embodiment 21 The method of any one of embodiments 14 to 20, wherein the pancreatic lineage cells are beta-islet precursor cells, beta-islet cell pro-precursors, "betalike” cells, "islet-like” cells.
  • Embodiment 22 The method of any one of embodiments 14 to 21, further comprising differentiating the pancreatic lineage cells into mature pancreatic cells.
  • Embodiment 23 The method of embodiment 22, wherein the mature pancreatic cells are beta-islet cells.
  • Embodiment 24 The method of any one of embodiments 14 to 23, wherein the treating is conducted in vitro or ex vivo.
  • Embodiment 25 A method for preventing treating a disease or condition associated with or arising from a loss of insulin-producing cells in a subject, the method comprising treating pancreatic lineage cells according to the method of any one of embodiments 14 to 24, and transplanting the treated cells into a subject.
  • Embodiment 26 The method of embodiment 25, wherein the cells transplanted are autologous for the subject.
  • Embodiment 27 The method of embodiment 25 or embodiment 26, wherein the subject is at risk of developing the disease or condition.
  • Embodiment 28 The method of any one of embodiments 25 to 27, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.
  • Embodiment 29 The method of embodiment 11 or embodiment 28, wherein the disease is Type 1 diabetes or insulin-requiring Type 2 diabetes.
  • Embodiment 30 A method for identifying a tissue of origin of a sample of cells obtained from a subject, the method comprising:
  • microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa- miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in the sample of cells; and
  • substantially equivalent expression levels of the microRNAs in the sample of cells compared to the expression level/s of the microRNAs generated from the control cells is indicative that the sample of cells are of the same type as the control cells
  • Embodiment 31 The method of embodiment 30, comprising or consisting of determining expression levels of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.
  • Embodiment 32 The method of embodiment 31 , comprising or consisting of determining expression levels of:
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, and hsa-miR-217; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa- miR-375, hsa-miR-183#, hsa-miR-217, and hsa-miR-429; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7-2#; or(iv) nine microRNAs which are: hsa- miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR- 217, hsa-miR-429, hsa-miR-7-2#, and mmu-miR-129-3p; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, and hsa- miR-433;
  • Embodiment 33 The method of any one of embodiments 30 to 32, further comprising or consisting of determining expression levels of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271 ;
  • Embodiment 34 The method of any one of embodiments 30 to 33, comprising or consisting of determining expression levels of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a;
  • Embodiment 35 The method of any one of embodiments 30 to 34, further comprising an initial step of obtaining the sample of cells from the subject.
  • Embodiment 36 The method of any one of embodiments 30 to 35, wherein the sample of cells is from the pancreas, brain, or gall bladder of the subject.
  • Embodiment 37 The method of any one of embodiments 4, 17 or 33, wherein the six or more microRNAs comprise or consist of any: 11, 12, 13, 14, 15, 16, 17, 18, or 19 of the microRNAs.
  • Embodiment 38 A kit comprising primers, probes and/or other binding agents for use in detecting expression of at least six microRNAs selected from the group consisting of: hsa- miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR- 429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in a sample of cells.
  • Embodiment 39 The kit of embodiment 38, comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.
  • Embodiment 40 The kit of embodiment 39, comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of:
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, and hsa-miR-217; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa- miR-375, hsa-miR-183#, hsa-miR-217, and hsa-miR-429; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7-2#; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, and mmu-miR-129-3p; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, and hsa- miR-433.
  • Embodiment 41 The kit of any one of embodiments 38 to 40, further comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.
  • Embodiment 42 The kit of any one of embodiments 38 to 41, comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of: hsa-miR- 183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.
  • Embodiment 43 Embodiment 43.
  • a microRNA signature comprising at least six microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa- miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof.
  • Embodiment 44 The microRNA signature of embodiment 43, comprising or consisting of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs, in the sample of the cells obtained from the subject.
  • Embodiment 45 The microRNA signature of embodiment 44, comprising or consisting of:
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, and hsa-miR-217; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa- miR-375, hsa-miR-183#, hsa-miR-217, and hsa-miR-429; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7-2#; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, and mmu-miR-129-3p; or
  • microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR- 375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, and hsa- miR-433.
  • Embodiment 46 The microRNA signature of any one of embodiments 43 to 45, further comprising or consisting of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271 .
  • Embodiment 47 The microRNA signature of any one of embodiments 43 to 46, comprising or consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa- miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.
  • Embodiment 48 Use of the kit of any one of embodiments 38 to 42, or the microRNA signature of any one of embodiments 43 to 47, for predicting, diagnosing, and/or prognosing a disease or condition associated with or arising from a loss of insulin-producing cells in a subject, in a subject, wherein the disease or condition is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.
  • Embodiment 49 The method of any one of embodiments 11, 28 or 29, or the use of embodiment 48, wherein the disease is Type 1 diabetes.
  • Embodiment 50 Use of six or more agents for determining the expression levels of one or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu- miR-129-3p, hsa-miR-433, and any combination thereof, for the preparation of a medicament for predicting a level of insulin production in cells of a subject.
  • microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa
  • Embodiment 51 The use of embodiment 50, wherein elevated expression levels of the microRNA/s in the sample of cells compared to expression level/s of the microRNAs in control cells that do not produce insulin is indicative of insulin production in the sample of cells, and
  • Embodiment 52 Use of six or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa- miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, for the preparation of a medicament for inducing insulin production in pancreatic lineage cells.
  • microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa- miR-429, hsa-mi
  • Embodiment 53 Use of six or more agents capable of inducing overexpression of one or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu- miR-129-3p, hsa-miR-433, and any combination thereof, in cells, for the preparation of a medicament for inducing insulin production in pancreatic lineage cells.
  • microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, h
  • Embodiment 54 The use of any one of embodiments 50 to 53, wherein the cells comprise any one or more of: pancreatic cells, brain cells, gall bladder cells, beta-islet cells, pancreatic lineage cells, pancreatic islet precursor cells, endocrine progenitor cells, pancreatic islet progenitor cells, pancreatic islet stem cells, induced pluripotent pancreatic islet cells, and/or beta-islet precursor cells.
  • Embodiment 55 Use of six or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa- miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, for the preparation of a medicament for preventing treating a disease or condition associated with or arising from a loss of insulin-producing cells in a subject.
  • Embodiment 56 Use of six or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa
  • hsa-miR-183 Use of six or more agents capable of inducing overexpression of microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in cells, for the preparation of a medicament for preventing treating a disease or condition associated with or arising from a loss of insulin- producing cells in a subject.
  • Embodiment 57 The use of embodiment 55 or embodiment 56, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, pancreatic cancer, Type 1 diabetes and insulin-requiring Type 2 diabetes.
  • Embodiment 58 Use of six or more agents for determining expression levels of six or more microRNA/s selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme- miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR- 129-3p, hsa-miR-433, and any combination thereof, in cells, for the preparation of a medicament for identifying a tissue of origin of a sample of cells obtained from a subject.
  • Embodiment 59 The use of embodiment 58, the sample of cells is from: pancreas, brain, or gall bladder.
  • Embodiment 60 The use of any one of embodiments 48 to 59, comprising or consisting of the use of agents for detecting expression of seven or more of the microRNAs, eight or more of the microRNAs, nine or more of the microRNAs, or ten of the microRNAs.
  • Embodiment 61 The use of embodiment 58 or embodiment 59, comprising or consisting of the use of:
  • microRNAs which are: hsa-miR-183, hsa-miR- 216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, and hsa-miR-217; or
  • (ii) seven or more agents to detect the microRNAs which are: hsa-miR-183, hsa- miR-216b, dme-miR-7, hsa- miR-375, hsa-miR-183#, hsa-miR-217, and hsa-miR-429; or
  • microRNAs which are: hsa-miR-183, hsa-miR- 216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7- 2#; or
  • microRNAs which are: hsa-miR-183, hsa-miR- 216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, and mmu-miR-129-3p; or
  • microRNAs which are: hsa-miR-183, hsa-miR- 216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, and hsa-miR-433.
  • microRNAs which are: hsa-miR-183, hsa-miR- 216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, and hsa-miR-433.
  • any one of embodiments 58 to 61 further comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of any six or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.
  • Embodiment 63 The use of any one of embodiments 58 to 62, comprising or consisting of primers, probes and/or other binding agents for use in detecting expression of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa- miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.
  • Embodiment 1 A method for predicting a level of insulin production in cells of a subject, the method comprising:
  • microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c- 3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR-424#,
  • Embodiment 2 The method of embodiment 1 , wherein the one or more microRNA/s is or are selected from the group consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141, hsa-miR-512-3p, hsa-miR-382, hsa-miR-329, hsa-miR-217, dme-miR-7, hsa- miR-485-3p, hsa-miR-335#, hsa-miR-200c, hsa-miR-655, and any combination thereof.
  • said reduced or absent expression level/s of the microRNA/s in the sample of cells is indicative of reduced or absent insulin production of insulin gene transcripts in the sample of cells.
  • Embodiment 4 The method of any one of embodiments 1 to 3, wherein the control cells that do not produce insulin are from the subject.
  • Embodiment s The method of any one of embodiments 1 to 4, further comprising determining an expression level of hsa miR-452 microRNA in the sample of cells, wherein: an elevated expression level of the hsa miR-452 in the sample of cells compared to expression level/s of the hsa miR-452 microRNA in control cells that do not produce insulin is indicative of reduced or absent insulin production in the sample of cells, and
  • a reduced or absent expression level of the hsa miR-452 microRNA in the sample of cells compared to expression level/s of the hsa miR-452 microRNA in control cells that do not produce insulin is indicative of insulin production in the sample of cells.
  • Embodiment 6 The method of any one of embodiments 1 to 5, wherein the sample of cells comprises any one or more of: pancreatic cells, brain cells, gall bladder cells.
  • Embodiment 7 The method of any one of embodiments 1 to 6, wherein the sample of cells comprises beta-islet cells.
  • Embodiment 8 The method of any one of embodiments 1 to 7, wherein said reduced or absent insulin production is diagnostic or prognostic of a disease or condition associated with or arising from a loss of insulin-producing cells in the subject.
  • Embodiment 9 The method of embodiment 8, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.
  • Embodiment 10 The method of any one of embodiments 1 to 7, wherein the subject has or is progressing toward a disease or condition associated with or arising from a loss of insulin-producing cells, and the expression levels of one or more microRNA/s is a marker of death and/or loss of insulin-producing function in the cells of the subject.
  • Embodiment 11 The method of any one of embodiments 1 to 10, further comprising an initial step of obtaining the sample of cells from the subject.
  • Embodiment 12 A method for inducing insulin production in pancreatic lineage cells, the method comprising treating the pancreatic lineage cells with one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa- miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa- let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR- 335#, hsa-miR-429, hsa-m
  • Embodiment 13 The method of embodiment 12, wherein the one or more micro RNA/s is or are selected from the group consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141, hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa-miR-217, dme-miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa-miR-200c, hsa-
  • Embodiment 14 The method of embodiment 12 or embodiment 13, wherein said treating comprises overexpressing the one or more microRNA/s in the pancreatic lineage cells.
  • Embodiment 15 The method of embodiment 12 or embodiment 13, comprising inhibiting expression of hsa miR-452 microRNA in the pancreatic lineage cells.
  • Embodiment 16 The method of any one of embodiments 12 to 15, wherein the pancreatic lineage cells comprise any one or more of: pancreatic islet precursor cells, endocrine progenitor cells, pancreatic islet progenitor cells, pancreatic islet stem cells, induced pluripotent pancreatic islet cells.
  • Embodiment 17 The method of any one of embodiments 12 to 16, wherein the pancreatic lineage cells are beta-islet precursor cells, beta-islet cell pro-precursors, "betalike” cells, "islet-like” cells.
  • Embodiment 18 The method of any one of embodiments 12 to 17, further comprising differentiating the pancreatic lineage cells into mature pancreatic cells.
  • Embodiment 19 The method of embodiment 18, wherein the mature pancreatic cells are beta-islet cells.
  • Embodiment 20 The method of any one of embodiments 12 to 19, wherein the treating is conducted in vitro or ex vivo.
  • Embodiment 21 A method for preventing treating a disease or condition associated with or arising from a loss of insulin-producing cells in a subject, the method comprising treating pancreatic lineage cells according to the method of any one of embodiments 12 to 20, and transplanting the treated cells into a subject.
  • Embodiment 22 The method of embodiment 21, wherein the cells transplanted are autologous for the subject.
  • Embodiment 23 The method of embodiment 21 or embodiment 22, wherein the subject is at risk of developing the disease or condition.
  • Embodiment 24 The method of any one of embodiments 21 to 23, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.
  • Embodiment 25 The method of embodiment 9 or embodiment 24, wherein the disease is Type 1 diabetes or insulin-requiring Type 2 diabetes.
  • Embodiment 26 A method for identifying a tissue of origin of a sample of cells obtained from a subject, the method comprising:
  • microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c- 3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR-424#,
  • substantially equivalent expression levels of the microRNA/s in the sample of cells compared to the expression level/s of the microRNA/s generated from the control cells is indicative that the sample of cells are of the same type as the control cells
  • substantially different expression levels of the microRNA/s in the sample of cells compared to the expression level/s of the microRNA/s generated from the control cells are indicative that the sample of cells is not of the same type as the control cells.
  • Embodiment 27 The method of embodiment 26, wherein the one or more microRNA/s is or are selected from the group consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141, hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa-miR-217, dme-miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa-miR-200c, hsa-m
  • Embodiment 28 The method of embodiment 26 or embodiment 27, further comprising an initial step of obtaining the sample of cells from the subject.
  • Embodiment 29 The method of any one of embodiments 26 to 28, wherein the sample of cells is from the pancreas, brain, or gall bladder of the subject.
  • Embodiment 30 The method of any one of embodiments 1 to 29, wherein the one or more microRNA/s comprise or consist of any: 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 or 45 of the microRNA/s.
  • Embodiment 31 The method of any one of embodiments 1 to 30, wherein the one or more microRNA/s are a microRNA signature comprising or consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, and any combination thereof.
  • Embodiment 32 A kit comprising primers, probes and/or other binding agents for use in detecting expression of at least two microRNAs selected from the group consisting of: hsa- miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR- 187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-mi -183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, h
  • Embodiment 33 The kit of embodiment 32, wherein the at least two microRNA/s is or are selected from the group consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa- miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa- miR-141, hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa-miR-217, dme- miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa-miR-200c, hsa-miR
  • Embodiment 34 A microRNA signature comprising least two microRNAs selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa-miR
  • Embodiment 35 The microRNA signature of embodiment 34 wherein the at least two microRNA/s is or are selected from the group consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141, hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa- miR-217, dme-miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa-miR-200c, hsa-
  • Embodiment 36 Use of the kit of embodiment 32 or 33, or the microRNA signature of embodiment 34 or 35, for predicting, diagnosing, and/or prognosing a disease or condition associated with or arising from a loss of insulin-producing cells in a subject, in a subject, wherein the disease or condition is selected from the group consisting of: diabetes, pancreatitis, insulinoma, and pancreatic cancer.
  • Embodiment 37 The method of any one of embodiments 9, 24 or 25, or the use of embodiment 36, wherein the disease is Type 1 diabetes.
  • Embodiment 38 Use of one or more agents for determining the expression levels of one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR- 519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa- miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR- 433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98
  • Embodiment 39 The use of embodiment 38, wherein elevated expression levels of the microRNA/s in the sample of cells compared to expression level/s of the microRNA/s in control cells that do not produce insulin is indicative of insulin production in the sample of cells, and
  • Embodiment 40 Use of one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu- miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR- 183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR- 200c, hsa-miR-98, hsa-miR-424
  • Embodiment 41 Use of one or more agents capable of inducing overexpression of one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b- 3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR- 34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR
  • Embodiment 42 The use of any one of embodiments 38 to 41, wherein the cells comprise any one or more of: pancreatic cells, brain cells, gall bladder cells, beta-islet cells, pancreatic lineage cells, pancreatic islet precursor cells, endocrine progenitor cells, pancreatic islet progenitor cells, pancreatic islet stem cells, induced pluripotent pancreatic islet cells, and/or beta-islet precursor cells.
  • Embodiment 43 Use of one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu- miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR- 183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR- 200c, hsa-miR-98, hsa-miR-424
  • Embodiment 44 Use of one or more agents capable of inducing overexpression of microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa- miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa- miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hs
  • Embodiment 45 The use of embodiment 43 or embodiment 44, wherein the condition or disease is selected from the group consisting of: diabetes, pancreatitis, insulinoma, pancreatic cancer, Type 1 diabetes and insulin-requiring Type 2 diabetes.
  • Embodiment 46 Use of one or more agents for determining expression levels of one or more microRNA/s selected from the group consisting of: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa- miR-663b, hsa-miR-335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98,
  • Embodiment 48 The use of any one of embodiments 38 to 47, wherein the one or more microRNA/s is or are selected from the group consisting of: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141, hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa- miR-217, dme-miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa-miR-200c,
  • Embodiment 49 The use of any one of embodiments 38 to 48, the kit of embodiment 32 or 33, or the microRNA signature of embodiment 34 or 35, wherein the one or more microRNA/s are a microRNA signature comprising or consisting of: hsa-miR-216b, hsa- miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, and any combination thereof.
  • Embodiment 50 The use of embodiment 41 or 44, wherein the medicament further comprises one or more agents capable of inhibiting expression of has-miR-452 microRNA in the subject.
  • microRNA also includes a plurality of microRNAs.
  • composition “comprising” means “including.” Variations of the word “comprising”, such as “comprise” and “comprises,” have correspondingly varied meanings. Thus, for example, a composition “comprising" microRNA type A may consist exclusively of microRNA X or may include one or more additional components (e.g. microRNA type B).
  • terapéuticaally effective amount includes within its meaning a non-toxic but sufficient amount of an agent or composition for use in the present invention to provide the desired therapeutic effect.
  • the exact amount required will vary from subject to subject depending on factors such as the species being treated, the age and general condition of the subject, the severity of the condition being treated, the particular agent being administered, the mode of administration and so forth. Thus, it is not possible to specify an exact “effective amount” applicable to all embodiments. However, for any given case, an appropriate "effective amount” may be determined by one of ordinary skill in the art using only routine experimentation.
  • a disease or condition that is "associated with aberrant insulin production” will be understood to encompass any ailment that arises directly and/or indirectly from aberrant insulin production, and/or that causes aberrant insulin production in a subject, "aberrant insulin production” meaning levels of insulin production that lie outside of a standard range for a population of individuals of the same species as the subject. The population may also be of the same or similar: race, gender, sex, and/or age as the subject. The determination of aberrant insulin production in a given subject may be achieved using standard tests known in the art.
  • a disease or condition that is "associated with or arising from a loss of insulin-producing cells” will be understood to encompass any ailment that arises directly and/or indirectly from a reduction in the number of insulin-producing cells in a subject (e.g. a reduction in the number of insulin-producing pancreatic cells (including beta-islet cells), brain cells, and/or gall bladder cells in the subject).
  • diseases or conditions include diabetes (e.g. Type 1 , Type 2), pancreatitis, insulinoma, and some forms of pancreatic cancer).
  • a disease or condition that is "associated with aberrant insulin metabolism” will be understood to encompass any ailment that arises directly and/or indirectly from aberrant insulin metabolism, and/or that causes aberrant insulin metabolism in a subject, "aberrant insulin metabolism” encompassing insulin resistance and insulin sensitivity, as can be determined using standard tests known in the art.
  • a “subject” includes any animal of economic, social or research importance including bovine, equine, ovine, primate, avian and rodent species.
  • a “subject” may be a mammal such as, for example, a human or a non-human mammal.
  • kits refers to any delivery system for delivering materials. Such delivery systems include systems that allow for the storage, transport, or delivery of reaction reagents (for example labels, reference samples, supporting material, etc. in the appropriate containers) and/or supporting materials (for example, buffers, written instructions for performing an assay etc.) from one location to another.
  • reaction reagents for example labels, reference samples, supporting material, etc. in the appropriate containers
  • supporting materials for example, buffers, written instructions for performing an assay etc.
  • kit may include one or more enclosures, such as boxes, containing the relevant reaction reagents and/or supporting materials.
  • kit includes both fragmented and combined kits.
  • a "fragmented kit” refers to a delivery system comprising two or more separate containers that each contains a sub-portion of the total kit components. The containers may be delivered to the intended recipient together or separately.
  • a “combined kit” refers to a delivery system containing all of the components of a reaction assay in a single container (e.g. in a single box housing each of the desired components).
  • a polypeptide of between 10 residues and 20 residues in length is inclusive of a polypeptide of 10 residues in length and a polypeptide of 20 residues in length.
  • Figure One shows fluorescent microscopy images demonstrating the presence of insulin-producing cells in human pancreatic islets, gallbladder and brain tissue.
  • Figure Two is a schematic of a study design to characterize the expression of microRNAs in human islet tissues, other insulin-producing tissues (gallbladder, brain), and non-insulin-producing tissues (endothelium/hUVECs, blood/Bl, muscle, liver/Liv, skin/Sk), for expression of microRNAs.
  • Figure Three shows a series of graphs indicating the level of expression of islet hormones (Insulin, Glucagon and Somatostatin), islet-specific transcription factors (MafA, Ngn3, Pdxl) and Hesl (inhibitor of NGN3) in human islet tissues (A), gallbladder (B), brain (C) and endothelial cells (D), derived from open array analyses.
  • islet hormones Insulin, Glucagon and Somatostatin
  • islet-specific transcription factors MafA, Ngn3, Pdxl
  • Hesl inhibitor of NGN3
  • Figure Four shows the results of an unsupervised hierarchical cluster analysis designed to group tissues based on low and high insulin production.
  • Figure Five shows the results of penalized regression analyses conducted on microRNA dataset obtained after carrying out the molecular assays outlined in Figure Two. These analyses identified a signature of microRNAs that are highly associated with insulin gene transcript abundance.
  • Figure Six shows a frequency table for the microRNAs generated by resampling validation of data using bootstrapping.
  • Figure Seven shows the results of a validation analysis used to distinguish insulin positive tissues from insulin-negative tissues (A) and predict the level of insulin gene expression (in terms of the actual Cycle-threshold value/Ct-valiie) as assessed by TaqMan- based real-time PCR (B).
  • Figure Eight is a schematic outlining an assay designed to assess if the microRNAs identified via bootstrap analysis were mere associations with insulin expression or causal to insulin expression. Expression of the bootstrapped microRNAs or insulin itself was forced in human islet-derived progenitor cells (hlPCs) in two separate sets of experiments and the differentiation of these progenitor cells was assessed on day 4.
  • hlPCs human islet-derived progenitor cells
  • Figure Nine shows the levels of insulin expressed in hlPCs with forced expression of insulin (A) or the microRNAs (B) in accordance with the assay depicted in Figure Eight.
  • Figure 10 presents a Receiver Operating Characteristic (ROC) curve analysis for detecting a high level of "insulin expression” in cells (those with real-time qPCR value ⁇ 16.8) as compared to the insulin-negative tissues (Ct value >16.8).
  • True positive rate (sensitivity) is plotted as a function of the false positive rate (indicated in the figure as 100% specificity) for different miRNAs.
  • the AUC is a measure of how well a miRNA can distinguish between cell samples that produce good (Ct value ⁇ 16.8) or bad (Ct value >16.8) levels of insulin.
  • Figure 11 shows immunostaining of insulin (green), glucagon (red) and somatostatin (pink; not detected in the brain) on freshly isolated human (A) non-diabetic islet, (B) gallbladder epithelium and (C) brain neurospheres. Nuclei (DNA) are shown in blue.
  • Realtime TaqMan qPCR expression profile for pancreatic islet (pro-) hormones and transcription factors in human (D) non-diabetic islets (N 86)
  • Results are presented as cycle threshold (Ct)-values normalized to 18s rRNA.
  • Figure 13 shows Volcano plot presenting TaqMan qRT-PCR Ct-value difference between negative tissues and (A) non-diabetic islets, (B) gallbladder and (C) brain for the 754 microRNAs profiles. Volcano plots for the same 754 miRNAs presenting the difference between top quartile of high vs low/no insulin-expressing (D) islets, (E) gallbladders and (F) brains.
  • the Ct-value differences are depicted on the X-axis and the -loglO p-value is depicted on the Y-axis.
  • (G-I) Circos plots for the miRNA expression dataset are presented for each of the insulin-producing tissues. Each panel of circos plots is divided into six sections (al, a2, b, c, d, e). Section al represents the larger part of the outermost circle with each gray rectangle representing one of the measured miRNAs. Section a2 contains colored rectangles representing each of the seven (pro-) endocrine genes or transcription factors labeled next to them. Section b of the ideogram presents Ct-value data (ranges 6.5 to 39; greater values are closer to the center and smaller values closer to the periphery). The glyphs are color coded; the greater the value the darker the red, the smaller the value, the darker the green.
  • the thick white line is at the median of the data: 22.5.
  • the thin gray lines are placed at every 5% of the data.
  • Section c presents Z-scores (for each tissue; range from -3.4 to 3.5; smaller values are closer to the center of the circle).
  • the thick white line is at 0.
  • the thin gray lines are placed at every 5% of the data.
  • Section d is a histogram plot presenting Z-scores data (relative to negative tissues). The data ranges from -37 to 1 19. Lesser values are at the bottom (closer to the center of the circle).
  • the thick white line is at 0.
  • the thin gray lines are placed at every 5% of the data.
  • Section e presents correlations between miRNAs and the seven endocrine pancreas related mRNAs in the form of colored links.
  • Figure 14 shows Volcano plots for the 754 miRNAs in the human negative-insulin solid tissues (excluding blood) vs (A) non-diabetic islets, (B) gallbladders and (C) brains.
  • the Ct-value differences are depicted on the X-axis and the -log 10 P-value is depicted on the Y- axis.
  • the values for each volcano plot are presented in Table 10.
  • FIG. 15 (A) Penalized linear regression analysis on the 754 microRNAs in human insulin-producing (positive) tissues relative to the non-insulin producing (negative) tissues. Linear regression involves comparison of the actual Ct-value of insulin to the actual Ct-value of each of the 754 microRNAs in each of the discovery set samples. A penalty applied to microRNAs is represented by the lambda on the X-axis. The lambda value is increased until coefficients for all microRNAs are reduced to zero (Y-axis). MicroRNAs that withstand a high penalty were selected for further validation.
  • MicroRNAs that withstand a high penalty were selected as the signature microRNAs associated with high insulin expression.
  • C A schematic for bootstrapping workflow (a resampling validation process) that was applied to validate this set of microRNA identified through the Penalized logistic regression model is shown here. Bootstrapping involves drawing multiple random samples with replacement from the dataset (green dots). A number of samples would be duplicated (red dots) in each bootstrap analysis followed by regression analyses. At the end of several resampling (1000 in this case), a frequency table (D), representing the number of times that a microRNA was present in the bootstraps, is generated.
  • This random sampling validation workflow allows the user to validate their microRNA signature that was generated through the penalized logistic/linear regression method. A "-" sign in the table indicates higher abundance of that particular microRNA in insulin-positive tissue, whereas a "+” sign represents lower abundance.
  • FIG. 16 Penalized logistic regression (PLR) analysis on the 754 microRNAs in human non-diabetic (high insulin-producing) islets (with a normalized insulin Ct value ⁇ 16.8) (0) to (low insulin-producing) islets (with a normalized insulin Ct value >16.8) (1).
  • a penalty was applied to each microRNA as represented by the lambda on the X-axis. The lambda value was increased until the coefficient (Y-axis) obtained is Zero.
  • MicroRNAs that withstand a high penalty had a stronger association in distinguishing the two groups (1 and 0) analyzed. Similar penalized regression analyses were carried out for gallbladder and brain samples (not shown here).
  • FIG. 1 Venn diagram of the microRNA signatures obtained from analyses presented in panel A for islets, gallbladder and brain samples. The three different tissue types were analyzed within their own specific tissue sets. MicroRNAs represented from this Venn diagram were obtained from the Penalized logistic regression bootstrapped analysis. The Venn diagram contains only the miRNAs with a bootstrapped frequency score > 25%, with the exception of brain analysis. Due to the limited number of brain samples used for PLR analysis all miRNAs identified are shown in the Venn diagram.
  • C An Euler diagram illustrating that the signature of microRNAs obtained through penalized logistic regression analysis are a subset of the microRNA signature obtained from penalized linear regression analysis.
  • Figure 17 shows violin plots generated for microRNAs that were identified through penalized (linear and logistic) regression analysis.
  • the box plots show the three quartile values of the distribution with whiskers extending to points that lie within 1.5 IQRs of the lower and upper quartile.
  • Polygons represent kernel density estimates of data and extend to extreme values.
  • the width of each of the violins is scaled by the number of observations in that bin.
  • the AUC and microRNA names are presented at the top of each ROC curve.
  • Figure 20 shows the results of an investigation as to whether bootstrapped microRNAs from the analysis could drive insulin gene expression. Either i) the insulin gene, or ii) candidate mature microRNAswere overexpressed in human islet-derived progenitor cells (hIPCs).
  • hIPCs human islet-derived progenitor cells
  • the box plots show the three quartile values of the distribution with whiskers extending to points that lie within 1.5 IQRs of the lower and upper quartile.
  • Polygons represent kernel density estimates of data and extend to extreme values.
  • the width of each of the violins is scaled by the number of observations in that bin. Forced expression of either of the three insulin-associated miRNAs (or their combination; "combo") significantly increased endocrine pancreatic hormone expression in just 4 days of induction for differentiation (D-F). The expression of Hesl did not change significantly (G).
  • the brown rectangles indicate the undetectable (non-linear) part of the qPCR data.
  • the Y-axes in panels A and B are reversed.
  • Volcano plots for the 754 validated miRNAs in human C) T2D pancreas vs non-diabetic pancreas, and (D) T2D islets vs non-diabetic islets.
  • Figure 22 shows correlation plots of the bootstrapped miRNA signature within (A) high insulin expression (insulin Ct-value ⁇ 16.8) human non-diabetic islets and T2D islets, (B) human non-diabetic pancreas and T2D pancreas and (C) human non-diabetic islets (insulin Ct-value ⁇ 16.8) and splenocyte samples.
  • the correlation was computed using Pearson pair-wise (complete) analysis. Results are presented as correlation coefficient values. Dark blue/red areas bindicate highest positive and negative correlation between the two sample sets compared. White areas indicate no correlation. Circles indicate the pie chart for individual correlation coefficient value.
  • Figure 23 shows (A) Correlation analysis of total RNA (in ng, as quantitated using Nanodrop) and Small RNA content (in pg, assessed using the Agilent Bioanalyzer smallRNA chip) for human biobank samples. The line of best fit using linear regression are plotted for islets (Green line), and gallbladder (Blue line). The amount of smallRNA and the total RNA in each of the samples was higher than the amount and concentrations desired for carrying out the proposed OpenArray microRNA and the TaqMan mRNA qPCRs. (B) Unsupervised bidirectional hierarchical cluster analysis for the three pancreatic islet (pro-)hormones and four transcription factors in different human solid tissues of the discovery set.
  • the present invention relates to intracellular microRNA signatures that are biomarkers for tissue-specific insulin production capability, and which can also be used to induce insulin production in subjects in need thereof.
  • the microRNA signatures described herein may be used: (i) to predict the presence/absence or relative abundance (cycle threshold- / Ct-value) of insulin gene transcripts in cells and/or tissues; (ii) to induce insulin gene expression in islet progenitor/precursor cells which, for example, can be used for cell replacement therapy in subjects with diseases and conditions associated with aberrant insulin production and/or aberrant insulin metabolism (e.g.
  • diabetes as a biomarker to determine the death or function of insulin-producing cells in subjects with or progressing to diseases and conditions associated with aberrant insulin production and/or aberrant insulin metabolism (e.g. diabetes); and/or (iv) to identify the tissue of origin, based on the signature and levels of microRNA expression.
  • microRNA Signatures of the present invention and non-limiting examples of their applications are described in detail as follows. microRNA Signatures
  • the present invention provides microRNA signatures indicative of beta-cell insulin production capacity.
  • the microRNA signatures may be obtained from an insulin-producing tissue (e.g. pancreas, gallbladder, brain).
  • the microRNA signatures may be intracellular microRNA signatures.
  • microRNA signatures may comprise or consist of any one or more of the following microRNA/s, in any combination: hsa-miR-216b, hsa-miR-519b-3p, hsa-miR-520e, hsa-miR- 183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let- 7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, hsa-miR-433, hsa-miR-663b, hsa-miR- 335#, hsa-miR-429, hsa-miR-200c, hsa-miR-98, hsa
  • the microRNA signatures may comprise or consist of any 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 or 45 of the microRNA/s. of the identified microRNAs
  • the microRNA signatures may comprise or consist of: hsa-miR- 216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR- 7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141 , hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa-miR-217, dme-miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa-miR-200c and hsa-miR-655.
  • the microRNA signatures may comprise or consist of: hsa-miR- 216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, and hsa- miR-7-2#.
  • the microRNA signatures may comprise or consist of six or more microRNAs selected from the group consisting of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in a sample of the cells obtained from the subject;
  • the microRNA signatures may comprise or consist of six microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR- 183#, and hsa-miR-217.
  • the microRNA signatures may comprise or consist of seven microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR- 183#, hsa-miR-217, and hsa-miR-429.
  • the microRNA signatures may comprise or consist of eight microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR- 183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7-2#.
  • the microRNA signatures may comprise or consist of nine microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR- 183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, and mmu-miR-129-3p.
  • the microRNA signatures may comprise or consist of ten microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR- 183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, and hsa-miR-433.
  • microRNA signatures may further comprise or consist of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.
  • the microRNA signatures may further comprise or consist of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa- miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.
  • microRNA signatures of the present invention can be detected in a biological sample using standard methods known in the art.
  • Methods of RNA extraction suitable for use in generating microRNA signatures of the present invention are well known in the art. Without limitation, suitable methods are disclosed in the Examples of the present application, as well as standard textbooks including Ausubel et al., Ed., "Current Protocols in Molecular Biology ", John Wiley & Sons, New York 1987-1999. Methods suitable for RNA extraction from paraffin embedded tissues are disclosed, for example, in De Andres et al. (1995) Biotechniques 18: 42-44, and Rupp & Locker (1987), Lab Invest. 56: A67.
  • RNA isolation may be performed using commercially available purification kits, buffer sets and proteases according to the manufacturer's recommended instructions (see for example, commercial kits available from Thermo Fisher Scientific, Sigma-Aldrich, Roche, Promega and Qiagen).
  • suitable commercial RNA extraction kits include the masterpureTM Complete DNA and RNA Purification Kit (epicentre), Maxwell® RSC miRNA Tissue Kits (Promega), RNeasy mini- columns (Qiagen), and Paraffin Block RNA Isolation Kits (Ambion, Inc.).
  • Total RNA from Formaldehyde Fixed Paraffin Embedded samples (FFPE) can be isolated, for example, using Maxwell* CSC RNA FFPE Kit (Promega).
  • RNA from tissue samples can be isolated, for example, using RNA Stat-60 (Tel-Test). High numbers of tissue samples may be processed using methods known to those of ordinary skill in the art (e.g. by use of the single- step RNA isolation method described in US patent no. 4,843,155).
  • Expression levels of specific microRNAs that in combination make up the microRNA signatures of the present invention can be determined using conventional methods known in the art (e.g. polymerase-based assays, hybridisation-based assays, flap endonuclease-based assays, direct RNA capture with branched DNA, and the like).
  • Non-limiting methods suitable for detecting the level of expression of a given microRNA in a biological sample include microarray profiling, RT-PCR, Northern blotting, differential display, reporter gene matrix assays, nuclease protection, slot or dot blots, ICAT, 2D gel electrophoresis, SELDI-TOF, assays using MNAzymes/PlexZymes, enzyme assays, and antibody assays.
  • microRNAs under analysis for expression may be amplified using known techniques including, for example, any one or more of: the polymerase chain reaction (PCR), reverse transcription-polym erase chain reaction (RT-PCR), nucleic acid sequence-based amplification (NASBA), loop-mediated isothermal amplification (LAMP), self-sustained sequence replication (3SR), rolling circle amplification (RCA), transcription-mediated amplification (TMA), and strand displacement amplification (SDA).
  • PCR polymerase chain reaction
  • RT-PCR reverse transcription-polym erase chain reaction
  • LAMP loop-mediated isothermal amplification
  • RCA self-sustained sequence replication
  • RCA rolling circle amplification
  • TMA transcription-mediated amplification
  • SDA strand displacement amplification
  • Suitable high throughput methods suitable for microRNA quantification may include those involving physical or logical arrays.
  • Non-limiting examples include assays which utilise solid phase arrays.
  • Exemplary formats include membrane or filter arrays (e.g. nylon, nitrocellulose), bead arrays, and pin arrays.
  • the solid phase assays may utilise probes that specifically interact with (e.g. bind or hybridise to) a microRNA expression product may be immobilised, to a solid support (e.g. by indirect or direct cross-linking).
  • Any solid support compatible with assay reagents and conditions may be utilised (e.g. silicon, modified silicon, silicon dioxide, various polymers (e.g.
  • the solid support may be a chip composed wholly or partially of any one or more of silicon, modified silicon, silicon dioxide, various polymers (e.g. polystyrene, polycarbonate, (poly)tetrafluoroethylene, (poly)vinylidenedifluoride, or combinations thereof) or functionalised glass). Binding proteins (e.g. antibodies, antigen-binding fragments, or derivatives thereof) or polynucleotide probes, (e.g.
  • DNA, RNA, cDNA, synthetic oligonucleotides, and the like which specifically interact with target microRNA/s may be immobilised on the chip in an array (i.e. a logically-ordered manner) for detection of any microRNAs in a sample applied thereto.
  • Microarray expression may be detected by scanning the microarray using any of a variety of CCD-based or laser scanners, and analysing output using any suitable software, (e.g. GENEPIXTM (Axon Instruments), nCounter ®1 (NanoString Technologies), IMAGENETM (Biodiscovery), Feature Extraction Software (Agilent)).
  • GENEPIXTM Auto Instruments
  • nCounter ®1 NanoString Technologies
  • IMAGENETM Biodiscovery
  • Feature Extraction Software e.g., Feature Extraction Software
  • Non-limiting examples include assays which utilise liquid phase arrays (e.g. for hybridisation of nucleic acids, binding of antibodies or other receptors to a ligand) in microtiter or multiwell plates.
  • suitable systems include, xMAP ® (Luminex), ORCATM (Beckman-Coulter, Inc.) SECTOR* Imager with MULTI -ARRAY ® and MULTI-SPOT ® systems (Meso Scale Discovery), miRCURY LNATM microRNA Arrays (Exiqon), and ZYMATETM (Zymark Corporation).
  • Reverse transcription PCR and real-time PCR may be employed to determine levels of microRNA expression in accordance with the invention.
  • Two commonly used quantitative RT-PCR techniques are the Lightcycler assay (Roche, USA) and the TaqMan RT-PCR assay (ABI, Foster City, USA).
  • Commercial RT-PCR products for assessing microRNA levels include the TaqMan Low-Density miRNA Array card (Applied Biosystems).
  • Art-known methods of expression profiling of microRNAs using real-time quantitative PCR are described, for example, in Chen et al. (2009), BMC Genomics, 10:407, and Benes and Castaldi (2010), Methods, 50:244-249. Data indicative of microRNA expression levels may be normalised against the expression level of a suitable control RNA.
  • the normalised data may then be processed using appropriate software to generate a microRNA signature (e.g. represented by a numeric number) representative of the expression level profile of the microRNAs.
  • This signature may be compared with a reference value to assess whether it is indicative of a low expression or a high expression of the microRNAs in question.
  • the reference value can be determined based on miRNA signatures (including the same miRNA signature) obtained from control patient/s (e.g. those with non-aberrant insulin production) via computational analysis.
  • the reference value may be the middle point between the signature of subject/s determined to have aberrant insulin production and subject/s determined to have non-aberrant insulin production.
  • the reference value may be the middle point between the signature of subject/s determined to have aberrant insulin metabolism and subject/s determined to have non-aberrant insulin production.
  • Non-limiting examples include Plausible Neural Network (PNN) (see, for example, US patent no. 7,287,014), PNN Solution software (PNN Technologies Inc.), Prediction Analysis of Microarray (PAM) (see, for example, Tibshirani et al. (2002), PNAS 99(10):6567-6572,), and Significance Analysis of Microarray (SAM).
  • PNN Plausible Neural Network
  • PNN Technologies Inc. PNN Technologies Inc.
  • PAM Prediction Analysis of Microarray
  • SAM Significance Analysis of Microarray
  • the microRNA signatures of the present invention may be used to predict the presence, absence, or relative abundance of insulin gene transcripts in cells and tissues. Given the central role of reduced beta-cell insulin-production in various diseases and conditions, the microRNA signatures disclosed herein may be used as biomarkers to inform for predicting, diagnosing, and/or prognosing the development of diseases and conditions associated with reduced or excessive insulin production. Accordingly, the microRNA signatures described herein can be used, for example, to identify and/or monitor a subject suspected to be at risk of developing a disease or condition associated with reduced or excessive insulin production. Alternatively, they may be used to diagnosis a subject with a disease or condition associated with reduced or excessive insulin production.
  • the microRNA signatures may be used to predict the progression of the disease or condition associated with reduced or excessive insulin production in a subject.
  • the microRNA signatures may be used to predict, diagnose, and/or prognose the development of diseases and conditions associated with reduced insulin production including, for example, diabetes, pancreatitis, insulinoma, and pancreatic cancer.
  • the disease is diabetes (e.g. Type 1 diabetes, insulin-requiring Type 2 Diabetes).
  • the microRNA signatures may be used to predict, diagnose, and/or prognose the development of diseases and conditions associated with reduced insulin production including, for example, diabetes, pancreatitis, insulinoma, and pancreatic cancer.
  • the disease is diabetes (e.g. Type 1 diabetes, insulin-requiring Type 2 Diabetes).
  • the microRNA signatures described herein may be used to monitor the response of a subject to a treatment administered for the purpose of alleviating, curing, and/or reducing the symptoms associated with a disease or condition associated with aberrant (e.g. reduced or increased) insulin production.
  • a determination that the subject is undergoing an increased expression of a given microRNA signature described herein in response to a given treatment or therapeutic intervention may be indicative of a positive response to the treatment or therapeutic intervention by the subject.
  • a determination that the subject does not have an increased expression, or has a reduced expression, of a given microRNA signature described herein in response to a given treatment or therapeutic intervention may be indicative of a negative or absent response to the treatment or therapeutic intervention by the subject.
  • the microRNA signatures may be used to monitor the response of the subject to treatments and therapeutic interventions for diseases and conditions including, for example, diabetes, pancreatitis, insulinoma, and pancreatic cancer.
  • the disease is diabetes (e.g. Type 1 diabetes, insulin-requiring Type 2 Diabetes).
  • the treatment or therapeutic intervention may comprise any one or more of administering pharmaceutical agents (e.g. vaccines, drugs) to the subject, the grafting of cells (beta-islet cell transplantation), and the like.
  • the microRNA signatures described herein may be used to identify and/or test the efficacy of a treatment or therapeutic intervention. For example and without limitation, a determination that the subject is undergoing an increased expression of a given microRNA signature described herein in response to a given candidate treatment or therapeutic intervention may be indicative that the treatment or therapeutic intervention is effective against the targeted disease or condition associated with reduced insulin production. Alternatively, a determination that the subject does not have an increased expression, or has a reduced expression, of a given microRNA signature described herein in response to a given candidate treatment or therapeutic intervention may be indicative that the treatment or therapeutic intervention is ineffective against the targeted disease or condition associated with reduced insulin production.
  • the targeted disease or condition may include, for example, diabetes, pancreatitis, insulinoma, and pancreatic cancer. In some embodiments, the disease is diabetes (e.g. Type 1 diabetes, insulin-requiring Type 2 Diabetes).
  • the microRNA signatures described herein may be used to induce insulin gene expression in islet progenitor/precursor cells which, for example, can be used for cell replacement therapy in subjects suffering from diseases and conditions associated with aberrant insulin production and/or aberrant insulin metabolism (e.g. diabetes).
  • the islet progenitor/precursor cells may, for example, be human embryonic stem cells (hESCs), induced pluripotent cells (iPSCs), endocrine progenitor cells, pancreatic progenitor cells (e.g. Ngn3+/NeuroD+/IAl+/Isll+/Pax6+ cells), or beta cell pro-precursors (e.g.
  • pancreatic lineage cells pancreatic islet precursor cells, endocrine progenitor cells, pancreatic islet progenitor cells, pancreatic islet stem cells, induced pluripotent pancreatic islet cells, and/or beta-islet precursor cells.
  • Suitable methods for use in cell replacement therapy in subjects suffering from diseases and conditions associated with aberrant insulin production and/or aberrant insulin metabolism are known in the art, and a review of some approaches is provided in Hayek & King, (2016), Clinical Diabetes and Endocrinology, 2:4; and Niclauss et al. (2016), Novelties in Diabetes. Endocr Dev. Basel, Karger vol 31, pp 146-162 Stettler et al. (Eds)).
  • the microRNA signatures described herein may be used as a biomarker to determine the death or function of insulin-producing cells in subjects with or progressing to diseases and conditions associated with aberrant insulin production and/or aberrant insulin metabolism (e.g. diabetes). For example and without limitation, a determination that the subject is undergoing an increased expression of a given microRNA signature described herein may be indicative that the insulin-producing cells of the subject under analysis were functional in terms of insulin production. Alternatively, a determination that the subject does not have an increased expression, or has a reduced expression, of a given microRNA signature described herein may be indicative that the insulin-producing cells of the subject under analysis were not functional in terms of insulin or at least a reduced capacity to produce insulin.
  • a determination that the subject is undergoing an increased expression of a given microRNA signature described herein may be indicative that the insulin-producing cells of the subject under analysis were functional in terms of insulin production.
  • a determination that the subject does not have an increased expression, or has a reduced expression, of a given microRNA signature described herein may be indicative
  • the targeted disease or condition may include, for example, diabetes, pancreatitis, insulinoma, and pancreatic cancer.
  • the disease is diabetes (e.g. Type 1 diabetes, insulin-requiring Type 2 Diabetes).
  • the disease is diabetes (e.g. Type 1 diabetes, Type 2 Diabetes).
  • microRNA signatures described herein may be used to identify the tissue of origin, based on the signature and levels of microRNA expression.
  • detection of an increased expression of a microRNA signature as described herein is indicative of an increased abundance of insulin gene transcripts in the cells or tissue of interest.
  • the control cells may be known to not produce insulin.
  • the control subject population may be of the same or similar: race, gender, sex, and/or age as the test subject.
  • the determination of increased insulin transcript production or increased insulin production in a given subject may be achieved using standard tests known in the art.
  • more than a 2-, 3-, 4-, 5-, 6-, 7-, 8-, 9- or 10- fold increase in the expression level of a given microRNA signature in a sample from the subject tested as compared to the control is indicative of increased insulin transcript production, and a consequent indication of reduced insulin production capacity in the subject.
  • the expression microRNA signatures may be tested in a biological sample from the subject comprising cells.
  • the cells within the biological sample may be isolated from the biological sample prior to determining microRNA expression, to ensure that the microRNA measured is predominantly/substantially intracellular.
  • the biological sample may comprise cells from one or more tissue/s of the subject.
  • the tissues are capable of insulin production, non-limiting examples of which include pancreatic tissue (e.g. pancreatic tissue comprising beta-islet cells), gallbladder tissue and brain tissue.
  • the subject from which the biological sample is derived may be a mammalian subject, such as, for example, a human or a non-human mammal.
  • the human subject may be, for example, a Caucasian, an Asian, an African, or a Hispanic.
  • the subject may be of any age.
  • kits Disclosed herein are kits for performing the methods of the present invention.
  • the kits may be fragmented kits or combined kits.
  • the kits may comprise reagents sufficient for determining the level of expression of a given microRNA signature disclosed herein.
  • kits may comprise primers, probes, and/or binding agents for detecting expression of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 or 45 of any one or more of the following microRNA/s, in any combination: hsa-miR-216b, hsa-miR-519b-3p, hsa- miR-520e, hsa-miR-183, hsa-miR-520c-3p, mmu-miR-187, hsa-miR-147b, hsa-miR-34b, hsa-miR-375, hsa-let-7e#, hsa-miR-636, hsa-miR-183#, hsa-miR-30d#, h
  • kits may comprise primers, probes, and/or binding agents for detecting expression of any one or more of the following microRNA/s, in any combination: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, hsa-miR-7-2#, hsa-miR-433, hsa-miR-429, hsa-miR-141, hsa-miR-512-3p, hsa-miR-452, hsa-miR-382, hsa-miR-329, hsa-miR-217, dme-miR-7, hsa-miR-485-3p, hsa-miR-335#, hsa- miR-200c
  • kits may comprise primers, probes, and/or binding agents for detecting expression of any one or more of the following microRNA/s, in any combination: hsa-miR-216b, hsa-miR-183, hsa-miR-375, hsa-miR-183#, mmu-miR-129-3p, hsa-miR-184, and hsa-miR-7-2#.
  • kits may comprise primers, probes, and/or binding agents for detecting expression of six or more microRNAs selected from the group consisting of: hsa- miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR- 429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, and any combination thereof, in a sample of the cells obtained from the subject;
  • kits may comprise primers, probes, and/or binding agents for detecting expression of six microRNAs which are: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR-183#, and hsa-miR-217.
  • kits may comprise primers, probes, and/or binding agents for detecting expression of seven microRNAs which are: hsa-miR-183, hsa-miR-216b, dme- miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, and hsa-miR-429.
  • kits may comprise primers, probes, and/or binding agents for detecting expression of eight microRNAs which are: hsa-miR-183, hsa-miR-216b, dme- miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, and hsa-miR-7-2#.
  • kits may comprise primers, probes, and/or binding agents for detecting expression of nine microRNAs which are: hsa-miR-183, hsa-miR-216b, dme- miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, and mmu- miR-129-3p.
  • kits may comprise primers, probes, and/or binding agents for detecting expression of ten microRNAs which are: hsa-miR-183, hsa-miR-216b, dme- miR-7, hsa-miR-375, hsa-miR-183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR- 129-3p, and hsa-miR-433.
  • kits may comprise primers, probes, and/or binding agents for detecting expression of any one or more microRNAs selected from the group consisting of: miR216a, miR144#, miR124a, miR-935, miR133b, miR206, miR200b#, miR625# and miR1271.
  • kits may comprise primers, probes, and/or binding agents for detecting expression of: hsa-miR-183, hsa-miR-216b, dme-miR-7, hsa-miR-375, hsa-miR- 183#, hsa-miR-217, hsa-miR-429, hsa-miR-7-2#, mmu-miR-129-3p, hsa-miR-433, miR216a, miR144#, and miR124a.
  • kits may comprise means for extracting RNA from a biological sample.
  • kits may comprise means for reverse-transcribing RNA into cDNA and optionally means for amplifying cDNA.
  • the means for amplifying cDNA may facilitate real-time quantification of the cDNA.
  • kits may comprise control standards to allow normalisation of microRNA signature expression data and/or comparison of microRNA signature expression data to determine whether expression of the microRNA signature is increased, reduced, or in a normal/standard range.
  • kits may comprise buffers, washing reagents, and/or RNAse inhibitors.
  • Tip disposal box (Qiagen, CAT 990550)
  • RNA samples Dilute the RNA samples to ⁇ 10 ng/ ⁇ . This protocol is designed for samples with low RNA concentrations. Diluting to ⁇ 10 ng/ ⁇ (around 8.5 ng/ ⁇ is sufficient) allows for >1 ⁇ 1 to be taken in the next step, increasing accuracy.
  • cDNA can be stored at -20°C or used immediately.
  • Both diluted and undiluted preamplified cDNA can be stored at -20°C for up to 1 week or used immediately.
  • Pt3 Loading OpenArray Slides and Performing qPCR Combine 5 ⁇ of diluted, preamplified cDNA to 5 ⁇ of TaqMan OpenArray real-time PCR mastermix in a new 96-well plate. Seal with a silicon seal.
  • the samples plate It is advisable to pre-cut the seal into the required sections, so the sections may be sealed/unsealed individually to reduce evaporation. Alternatively, the plate may be sealed with an intact seal, and then sections can be individually cut out when loading.
  • press load slide While the PCR system is loading the slide, remove the clear and red plastic from the bottom of the slide lid. When finished loading, carefully remove and seal the slide within 90 sec. 26.1. Place the slide within the plate clamp. Place the slide lid onto the slide. Clamp for 30 sec. Ensure the lid is positioned so that barcode is correctly displayed. Remove the assembly from the plate clamp.
  • NDS normal donkey serum
  • hydrophobic marker draw around your tissue section. Ensure that the line is close to your sample without touching it.
  • steps 17 and 18 at least 4 more times. 20. Add enough secondary antibody (working stock diluted in 4% NDS) to cover the step, and all subsequent steps, MUST be completed in the dark to ensure that the reporter dyes to not degrade.
  • Beta coefficients of the selected microRNAses from the discovery set were then applied to the same microRNAs in the validation set to assess the accuracy of i) insulin production status and ii) the insulin mRNA transcript level.
  • ROC analysis Hajian-Tilaki . Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation . Caspian Journal of Internal Medicine. 2013;4(2):627-635, Swets JA. ROC analysis applied to the evaluation of medical imaging techniques. Invest Radiol.
  • ROC curves were based on the concept of a scale, on which the CT values of a particular microRNA for the insulin- producing/non-producing tissue formed a pair of overlapping distributions sets. The complete separation of the two sets implies that a microRNA offers perfectly discriminating ability while complete overlap implies no discrimination.
  • the ROC curve shows the trade off between the true positive fraction (TPF, sensitivity) and false positive fraction (FPF, 1- specificity) as the separation criterion is changing.
  • siPORT NeoFX was diluted in Opti-MEM 1 medium and incubated at room temperature for 10 min.
  • Synthetic microRNAs were diluted in Opti-MEM to a final concentration of 30 nM.
  • Diluted RNA and diluted siPORT NeoFX were mixed by gentle pipetting and incubated at room temperature for 10 min.
  • the RNA/siPORT NeoFX complexes were then distributed to each well and overlaid with cell suspension. Differentiation was carried out as described earlier (Hardikar AA et al. Proc Natl Acad Sci USA.
  • Example Two insulin production and microRNA expression in different human tissues
  • microRNAs in maintenance of insulin gene expression amongst tissues, a set of 754 microRNAs in 526 different human tissues was assessed - 240 non-diabetic insulin-producing tissues; including 155 normal human donor islets, over 70 human gallbladders and 14 brains all tissues naturally expressing variable levels of insulin.
  • profile of the same 754 microRNAs in 250 tissues that do not produce insulin and 36 pancreas/islets from individuals with or without Type 2 diabetes was compared.
  • Islet hormones (Insulin, Glucagon and Somatostatin) and islet-specific transcription factors (MafA, Ngn3, Pdxl ) and Hesl (inhibitor of NGN3) were observed to be expressed in islets, gallbladders and brains - all naturally occurring insulin-producing cells ( Figures 3A- 3C). Endothelial cells do not show any of these transcripts (except the negative regulator Hesl) ( Figure 3D).
  • the dotted line represents the limit of detection and the shaded area represents un-detectable transcripts.
  • the endothelial cells shown in Figure 3D are an example of the non-insulin-producing tissue.
  • Penalized regression analyses of the datasets identified a signature of microRNAs that is highly associated with insulin gene transcript abundance (measured in Figure 3 as presented on a cycle-threshold scale).
  • a logistic regression analysis was carried out to identify microRNAs that are most associated with high (low Ct value) vs. low (high Ct value) insulin gene expression (left panel below).
  • a linear regression analysis compares the actual expression (Ct value) level of microRNAs to the actual expression (Ct value) levels of insulin gene in each tissue ( Figure 5B).
  • the computing workflow eliminates five samples at a time from the entire dataset and carries out the regression analysis on the remaining set of samples. The whole process is repeated 1,000 times with five different sets of samples eliminated at each time. Finally, a frequency table is achieved for the microRNAs (Figure 6). The microRNAs occurring at higher frequency in the bootstrap analyses were selected for validation.
  • hIPCs human islet-derived progenitor cells
  • microRNAs that are identified to be highly associated with insulin gene expression can
  • iii) be used as a biomarker to determine the death (or function) of insulin-producing cells in individuals with or progressing to diabetes; and iv) identif the tissue of origin, based on the signature and levels of microRNA expression.
  • Example Four determination of a microRNA signature associated with, predictive of and necessary for insulin transcription
  • pancreatic (pro-) hormones 4.2 Expression of pancreatic (pro-) hormones, transcription factors and microRNAs in human tissues
  • the discovery set of 507 human tissues provides a unique resource to assess microRNAs in naturally occurring insulin-producing cells with high (islets), intermediate (gallbladder), low (brain) or undetectable (blood, spleen, muscle, endothelium, liver, skin) levels of insulin gene expression.
  • Pancreatic transcription factors were detectable in most of the insulin-expressing tissues ( Figures 11D-11F).
  • Transcripts of Hesl, the negative regulator of the pro-endocrine transcription factor Ngn3 were detected in endothelial cells ( Figure 11G) and in other "insulin-negative" tissues ( Figure 12B).
  • Eight one of the 698 biobank samples that did not have either sufficient (>60%) microRNA content, desired (> ⁇ g total RNA) amount/concentration or acceptable (Ct ⁇ 10) 18s rRNA were retained as a categorical validation set ("validation set 1 "), while those that met the desired high quality and required quantity were saved as the "validation set 2". All but 91 of the 698 tissue samples surpassed the desired high quality and quantity (Figure 23A), necessary for this study.
  • microRNAs were expressed in other insulin-producing tissues when all insulin-negative tissues were compared with either gallbladder (Figure 13B) 369 microRNAs vs 6 microRNAs, p ⁇ 0.05) or brain (Figure 13C; 364 microRNAs vs 7 microRNAs, p ⁇ 0.05). These data indicate that a larger number of microRNAs (listed in Table 10) are associated with higher levels of insulin gene expression. It was observed that up to seven microRNAs were expressed at higher abundance in insulin-negative tissues as compared to any of the three insulin-producing tissues ( Figures 13A-13C, Table 10). Amongst these seven microRNAs, two microRNAs (miR-326 and miR-34a) were common across all comparisons ( Figures 13A-13C, Table 10).
  • Table 10 lists all microRNAs that show significantly higher expression in the insulin- negative tissues compared to each of the insulin-producing tissues as see in the volcano plots presented in Figure 3A-C.
  • the cycle difference and P values for each of the microRNA are provided in this table.
  • Solid tissues refers to tissues other than blood while negative solid tissues refers to solid tissues other than islets, pancreas, gallbladder and the brain.
  • Figures 14A-14C When expression of microRNAs in the islets, gallbladders, and brains was compared only with those in the insulin-negative solid tissues ( Figures 14A-14C), several microRNAs were again detected at higher levels in insulin-producing tissues). The levels of insulin expression within each insulin-producing tissue were then compared.
  • microRNAs were expressed at 2- to a million-fold higher abundance (Ct value difference of 1 to 20 cycles; Figure 2A-F) and at significantly low P-value (P ⁇ 0.05; dashed line in Figures 13A-F to P ⁇ 2.8E-262 as in Figure 13A), relative to the insulin-negative tissues.
  • the normalized expression of the seven different islet (pro-) hormones and transcription factors is indicated by the colored boxes on the rim of the circos plot, while the gray color on the rim indicates each of the 754 microRNAs measured.
  • the outermost segment of the circos presents the normalized Ct-values, the adjacent inner segment their individual Z-scores and the innermost segment presents their Z-score relative to insulin-negative tissues.
  • the lines in the center link the genes (Ins, Gcg, Sst, Pdxl, Ngn3, MafA, Hesl) with the microRNAs with which they correlate.
  • Penalized regression (Goeman, J. J. LI penalized estimation in the Cox proportional hazards model. Biom J 52, 70-84, doi: 10.1002/bimj.200900028 (2010)) was used in order to derive a microRNA signature that is associated with insulin expression. Model selection was performed using the LASSO (Least Absolute Shrinkage and Selection Operator) method. Penalty applied to the regression coefficients allows for improving the predictive power and interpretability of regression models by selecting only a subset of all the available independent variables rather than using all of them.
  • LASSO Least Absolute Shrinkage and Selection Operator
  • Penalized regression analysis was carried out using a linear (actual Ct-value of pro-insulin gene and of microRNA transcripts; Figure 15A) or logistic (dependent variable defined as high level (1 ) vs. low level/none (0) of insulin expression; Figure 15B, Figure 16A) regression analysis workflow.
  • human islets insulin Ct-value ⁇ 16.8
  • all other solid tissues insulin Ct-value >39
  • Validation of the model was carried out using bootstrapping to confirm the signature of microRNAs that are highly associated with insulin expression.
  • Validation of penalized logistic regression included resampling 1000 times ( Figure 15C).
  • Table 12 shows exemplary precursor and mature microRNA sequences of 19 microRNAs identified in these studies to be associated with insulin production and relevant to insulin gene expression.
  • Insulin-associated microRNAs predict and promote insulin-sene expression
  • the coefficients derived from the penalized regression analysis were used to obtain the odds ratios that constitute the predictive formula to determine presence of insulin gene expression (penalized logistic regression), or predict the insulin Ct-value (penalized linear regression). It was first tested if the microRNA signature identified from penalized linear regression analysis (Figure 15A) could classify a set of 91 different tissues that were originally eliminated from discovery set as they did not meet the desired quality/quantity criteria; very low RNA concentration or a higher 18s rRNA Ct-value.
  • the goal of this study was to identify associations between microRNA expression and insulin gene transcription using a large biobank of 698 human tissues.
  • the strategy also involved analysis of tissues that are known to naturally produce insulin, albeit in lower amounts. Blood, as well as other insulin-negative solid tissues, were included to compare differences between the tissues, whilst within-tissue comparisons of low vs high insulin gene expression samples of microRNA profiles allowed us to eliminate tissue-specific effects.
  • the microRNA signature was able to identify the presence or absence of insulin gene transcripts irrespective of the sample quality, and in addition, was also able to mathematically estimate the Ct-value of insulin transcripts at a level that was very close to the Ct-value observed through wet lab evaluation (Figure 18C).
  • microRNAs vs insulin
  • pancreatic islet ⁇ -cell function is a major cause leading to the decline in glucose tolerance during the development of type 2 diabetes44,45.
  • All of the (pro-) endocrine hormones and transcription factors measured were mostly detectable in islets and pancreas of individuals with Type 2 diabetes ( Figures 21A and 21B).
  • microRNAs are involved in core processes associated with T2DM, such as carbohydrate and lipid metabolism, insulin signaling pathway and the adipocytokine signaling pathway.
  • T2DM carbohydrate and lipid metabolism
  • insulin signaling pathway and the adipocytokine signaling pathway.
  • This calculator can be used as a guide, optionally along with other commonly used transcription factor expression analyses, to assess the differentiation of stem cells towards an insulin-producing lineage even when insulin gene transcript cannot be detected due to sample quality/RNA quantity issues.

Abstract

La présente invention concerne d'une manière générale le domaine de la médecine et plus spécifiquement des maladies et des états liés à l'insuline. L'invention concerne des signatures de micro-ARN de cellules qui produisent naturellement de l'insuline qui sont pertinentes, par exemple, dans des processus impliquant la différenciation de cellules souches/progénitrices/précurseurs en cellules productrices d'insuline, pour prédire le niveau d'insuline dans des cellules sur la base de l'expression de micro-ARN, et pour diagnostiquer et/ou pronostiquer le développement de maladies et d'états associés à la perte de cellules productrices d'insuline (par exemple le diabète).
PCT/AU2018/000108 2017-06-29 2018-06-29 Signatures de micro-arn intracellulaires de cellules productrices d'insuline WO2019000017A1 (fr)

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