# Patent

## Title
Methods, compositions, and devices utilizing MicroRNA to determine physiological conditions

## Abstract

Methods, compositions, and devices are disclosed which use microRNA to detect, predict, treat, and monitor physiological conditions such as disease or injury. microRNA are isolated and their differential expression is measured to provide diagnostic information. This information may then be utilized for evaluation and/or treatment purposes.

## Background
This application claims priority to U.S. Provisional Patent Application Ser. No. 61/112,985, filed Nov. 10, 2008. That provisional application is hereby fully incorporated by reference in its entirety.
BACKGROUND
Disclosed herein are various methods, compositions, and devices utilizing microRNA, such as microRNA-based markers, to detect, predict, treat, or monitor various physiological or pathological conditions.
The ideal diagnostic marker has to fulfill certain key requirements including being specific, sensitive, robust, and non-invasive. Current disease diagnoses are primarily based on two different but complementary approaches—physical imaging and biomolecular profiling. Both approaches currently suffer from a lack of specificity and early detection capability. Tissue-specific blood biomarkers can increase the specificity to selected organs. However, the levels of these tissue-specific biomarkers are usually low in blood. In addition, the difficulty of developing suitable capture agents for proteins makes the identification and development of new molecular diagnostic markers difficult.
It would be desirable to provide new methods, compositions, and devices for diagnosing physiological and pathological conditions.
BRIEF DESCRIPTION
The present disclosure relates, in different embodiments, to the use of the levels of microRNA sequences (miRNA) in body fluids to establish correlations with the body's pathophysiological conditions. Exemplary body fluids include, but are not limited to, serum, plasma, saliva, urine, tears, amniotic fluid, sweat, cerebrospinal fluid, seminal fluid (semen), lung mucus (e.g. from bronchial lavage), pleural fluid, peritoneal fluid, colostrums, and breast milk. These levels can then provide diagnostic and/or predictive information with regard to important issues of health and disease.
Disclosed are methods of using microRNA sequences to detect a physiological condition. The methods comprise: isolating microRNA sequences from a biological sample; generating a microRNA profile from the isolated microRNA sequences, the profile including the levels of expressed microRNA sequences in the biological sample; comparing the microRNA profile with a reference to identify differentially expressed microRNA sequences; and detecting the physiological condition based on the identity or the levels of the differentially expressed microRNA sequences.
The biological sample may be a biopsy material, tissue, or body fluid. In embodiments, the biological sample comprises a body fluid selected from the group consisting of serum, plasma, lymph, saliva, urine, tears, sweat, semen, synovial fluid, cervical mucus, amniotic fluid, cerebrospinal fluid, and breast milk.
The microRNA sequences may be isolated by extracting the biological sample with an organic solvent to obtain an aqueous phase containing the microRNA sequences; and purifying the aqueous phase through a silica membrane to isolate the microRNA sequences.
The microRNA profile can be generated using hybridization to identify a microRNA sequences; or by using a quantitative polymerase chain reaction to identify the level of a microRNA sequences.
The reference can be a table of the levels of expressed microRNA sequences in a normal person, or a reference sample.
The biological sample may be from a microbe, such as a virus, bacterium, fungus, protozoan, or parasite.
The isolated microRNA sequences may be specific to a biological pathway, a cell type, or a tissue.
The physiological condition may be a disease, injury, or infection.
Also disclosed are methods of using microRNA sequences to detect or predict a physiological condition. These methods also comprise: generating a microRNA profile from a biological sample, the profile including the levels of expressed microRNA sequences in the biological sample; and comparing the microRNA profile with a reference to identify differentially expressed microRNA sequences. The physiological condition could then be detected or predicted based on the identity or the levels of the differentially expressed microRNA sequences. Alternatively, the physiological condition can be identified, and a treatment can then be administered based on the identity of the physiological condition.
Further disclosed are methods of using microRNA sequences to monitor a physiological condition, comprising: generating a first microRNA profile from a first biological sample of a patient; administering a treatment to the patient; generating a second microRNA profile from a second biological sample of the patient; comparing the second microRNA profile with the first microRNA profile to identify differentially expressed microRNA sequences; and identifying a change in the physiological condition based on the identity or the amounts of the differentially expressed microRNA sequences.
Additionally disclosed are methods of using microRNA sequences to treat a physiological condition. The methods comprise: identifying at least one microRNA sequence based on the physiological condition; and manipulating the level of the at least one microRNA sequence to treat the physiological condition. Manipulating the level of the at least one microRNA sequence may comprise: constructing a specific DNA or RNA sequence related to the at least one microRNA sequence; and delivering the specific DNA or RNA sequence to a targeted cell, tissue, or organ.
Also disclosed are methods of using microRNA sequences to detect, predict, or treat a physiological condition. The methods comprise: generating a microRNA profile from a biological sample; identifying at least one differentially expressed microRNA sequence by comparing the microRNA profile to a reference; and detecting, predicting, or treating the physiological condition based on the identity or the levels of the at least one differentially expressed microRNA sequence. In alternative embodiments, at least two differentially expressed microRNA sequences are identified.
Other methods of detecting or predicting a physiological condition comprise generating a microRNA profile from a biological sample, wherein the microRNA profile comprises at least one specific microRNA sequence; and comparing the microRNA profile to a reference to provide information useful for detecting or predicting the physiological condition. In alternative embodiments, the microRNA profile comprises at least two specific microRNA sequences.
A differentially expressed microRNA sequence can be identified by comparing the amount of a particular microRNA sequence in the microRNA profile with the amount of that particular microRNA sequence in the reference. A differentially expressed microRNA sequence is identified when the ratio of the amount in the microRNA profile to the amount in the reference is at least 1.5, or at least 3.
When the physiological condition is related to liver disease or liver injury, in some embodiments, the microRNA profile or the specific microRNA sequence(s) may comprise at least one microRNA sequence selected from the group consisting of mmu-miR-122, mmu-miR-486, mmu-miR-125b-5p, mmu-let-7d*, mmu-miR-101a, mmu-miR-101b, mmu-miR-1224, mmu-miR-124, mmu-miR-125a-3p, mmu-miR-125a-5p, mmu-miR-127, mmu-miR-130a, mmu-miR-133a, mmu-miR-133b, mmu-miR-135a*, mmu-miR-141, mmu-miR-193, mmu-miR-193b, mmu-miR-199a-5p, mmu-miR-199b*, mmu-miR-200c, mmu-miR-202-3p, mmu-miR-205, mmu-miR-22, mmu-miR-23b, mmu-miR-26a, mmu-miR-27b, mmu-miR-291a-5p, mmu-miR-294*, mmu-miR-29b, mmu-miR-30a, mmu-miR-30c-1*, mmu-miR-30e, mmu-miR-320, mmu-miR-327, mmu-miR-339-3p, mmu-miR-342-3p, mmu-miR-370, mmu-miR-375, mmu-miR-451, mmu-miR-466f-3p, mmu-miR-483, mmu-miR-494, mmu-miR-574-5p, mmu-miR-652, mmu-miR-671-5p, mmu-miR-685, mmu-miR-710, mmu-miR-711, mmu-miR-712, mmu-miR-714, mmu-miR-720, mmu-miR-721, mmu-miR-877, mmu-miR-877*, mmu-miR-882, mmu-miR-93, mmu-miR-99a, and human orthologs thereof.
In other embodiments where the physiological condition is related to liver disease or liver injury, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of mmu-miR-122, mmu-miR-486, mmu-miR-125b-5p, mmu-let-7d*, mmu-miR-101a, mmu-miR-101b, mmu-miR-1224, mmu-miR-124, mmu-miR-125a-3p, mmu-miR-125a-5p, mmu-miR-133a, mmu-miR-133b, mmu-miR-135a*, mmu-miR-193, mmu-miR-193b, mmu-miR-199a-5p, mmu-miR-199b*, mmu-miR-202-3p, mmu-miR-291a-5p, mmu-miR-294*, mmu-miR-30c-1*, mmu-miR-30e, mmu-miR-327, mmu-miR-339-3p, mmu-miR-342-3p, mmu-miR-375, mmu-miR-466f-3p, mmu-miR-483, mmu-miR-574-5p, mmu-miR-652, mmu-miR-671-5p, mmu-miR-685, mmu-miR-710, mmu-miR-711, mmu-miR-712, mmu-miR-714, mmu-miR-720, mmu-miR-721, mmu-miR-877, mmu-miR-877*, mmu-miR-882, and human orthologs thereof.
In particular embodiments, the at least one differentially expressed microRNA sequence or the at least one specific sequence comprises hsa-miR-122. In more specific embodiments, they comprise hsa-miR-122 and either hsa-miR-486-3p or hsa-miR-486-5p (i.e. the human orthologs to mmu-miR-486). The ratio of the amount of miR-122 to the amount of miR-486 may be greater than 4.0, including greater than 6.0.
When the physiological condition is neurological disease or neurological injury, in some embodiments, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of mmu-let-7g, mmu-miR-298, mmu-miR-1, mmu-miR-101a*, mmu-miR-101b, mmu-miR-1224, mmu-miR-126-5p, mmu-miR-127, mmu-miR-128, mmu-miR-129-3p, mmu-miR-133b, mmu-miR-136, mmu-miR-138, mmu-miR-138*, mmu-miR-139-3p, mmu-miR-140, mmu-miR-140*, mmu-miR-142-3p, mmu-miR-143, mmu-miR-146a, mmu-miR-146b, mmu-miR-148b, mmu-miR-150, mmu-miR-15a*, mmu-miR-15b, mmu-miR-181b, mmu-miR-181d, mmu-miR-183, mmu-miR-185, mmu-miR-186, mmu-miR-191*, mmu-miR-194, mmu-miR-19a, mmu-miR-200a, mmu-miR-200b, mmu-miR-200b*, mmu-miR-202-3p, mmu-miR-206, mmu-miR-208a, mmu-miR-21, mmu-miR-211, mmu-miR-221, mmu-miR-222, mmu-miR-223, mmu-miR-27a, mmu-miR-27b*, mmu-miR-28*, mmu-miR-290-5p, mmu-miR-291a-5p, mmu-miR-297a, mmu-miR-299, mmu-miR-29b, mmu-miR-29c*, mmu-miR-301b, mmu-miR-302c*, mmu-miR-30c, mmu-miR-31, mmu-miR-322, mmu-miR-323-3p, mmu-miR-324-3p, mmu-miR-324-5p, mmu-miR-326, mmu-miR-328, mmu-miR-331-5p, mmu-miR-341, mmu-miR-34b-5p, mmu-miR-34c*, mmu-miR-369-3p, mmu-miR-374, mmu-miR-376b, mmu-miR-379, mmu-miR-380-3p, mmu-miR-382, mmu-miR-384-5p, mmu-miR-409-5p, mmu-miR-411, mmu-miR-411*, mmu-miR-423-5p, mmu-miR-425, mmu-miR-429, mmu-miR-434-5p, mmu-miR-450b-3p, mmu-miR-451, mmu-miR-455, mmu-miR-465c-3p, mmu-miR-466d-5p, mmu-miR-467e*, mmu-miR-484, mmu-miR-486, mmu-miR-487b, mmu-miR-497, mmu-miR-505, mmu-miR-511, mmu-miR-539, mmu-miR-540-3p, mmu-miR-551b, mmu-miR-568, mmu-miR-654-5p, mmu-miR-669a, mmu-miR-686, mmu-miR-688, mmu-miR-699, mmu-miR-701, mmu-miR-706, mmu-miR-708, mmu-miR-720, mmu-miR-721, mmu-miR-744*, mmu-miR-760, mmu-miR-770-5p, mmu-miR-7a, mmu-miR-7b, mmu-miR-881*, mmu-miR-93, mmu-miR-96, mghv-miR-M1-6, mghv-miR-M1-9, and human orthologs thereof.
In other embodiments where the physiological condition is neurological disease or neurological injury, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of mmu-let-7g, mmu-miR-298, mmu-miR-101a*, mmu-miR-101b, mmu-miR-1224, mmu-miR-126-5p, mmu-miR-128, mmu-miR-129-3p, mmu-miR-133b, mmu-miR-138*, mmu-miR-139-3p, mmu-miR-140*, mmu-miR-146a, mmu-miR-148b, mmu-miR-15a*, mmu-miR-15b, mmu-miR-181b, mmu-miR-181d, mmu-miR-185, mmu-miR-186, mmu-miR-191*, mmu-miR-19a, mmu-miR-200b*, mmu-miR-202-3p, mmu-miR-208a, mmu-miR-211, mmu-miR-27b*, mmu-miR-28*, mmu-miR-290-5p, mmu-miR-291a-5p, mmu-miR-297a, mmu-miR-299, mmu-miR-29c*, mmu-miR-301b, mmu-miR-302c*, mmu-miR-322, mmu-miR-323-3p, mmu-miR-324-3p, mmu-miR-324-5p, mmu-miR-326, mmu-miR-328, mmu-miR-331-5p, mmu-miR-341, mmu-miR-34b-5p, mmu-miR-34c*, mmu-miR-369-3p, mmu-miR-374, mmu-miR-376b, mmu-miR-379, mmu-miR-380-3p, mmu-miR-382, mmu-miR-384-5p, mmu-miR-409-5p, mmu-miR-411, mmu-miR-411*, mmu-miR-423-5p, mmu-miR-425, mmu-miR-429, mmu-miR-434-5p, mmu-miR-450b-3p, mmu-miR-465c-3p, mmu-miR-466d-5p, mmu-miR-467e*, mmu-miR-505, mmu-miR-511, mmu-miR-539, mmu-miR-540-3p, mmu-miR-551b, mmu-miR-568, mmu-miR-654-5p, mmu-miR-669a, mmu-miR-686, mmu-miR-688, mmu-miR-699, mmu-miR-701, mmu-miR-706, mmu-miR-720, mmu-miR-721, mmu-miR-744*, mmu-miR-760, mmu-miR-770-5p, mmu-miR-7a, mmu-miR-7b, mmu-miR-881*, mmu-miR-96, mghv-miR-M1-6, mghv-miR-M1-9, and human orthologs thereof.
When the physiological condition is related to lung disease or lung injury, in some embodiments, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of hsa-miR-135a*, hsa-miR-10b, hsa-miR-1224-3p, hsa-miR-1224-5p, hsa-miR-1225-3p, hsa-miR-1225-5p, hsa-miR-1226*, hsa-miR-1227, hsa-miR-1228, hsa-miR-1229, hsa-miR-1234, hsa-miR-1237, hsa-miR-1238, hsa-miR-124, hsa-miR-129*, hsa-miR-129-3p, hsa-miR-136*, hsa-miR-187*, hsa-miR-188-5p, hsa-miR-190b, hsa-miR-198, hsa-miR-22, hsa-miR-220b, hsa-miR-300, hsa-miR-301b, hsa-miR-30e, hsa-miR-338-3p, hsa-miR-33a*, hsa-miR-33b, hsa-miR-33b*, hsa-miR-34c-3p, hsa-miR-34c-5p, hsa-miR-363*, hsa-miR-371-3p, hsa-miR-371-5p, hsa-miR-375, hsa-miR-377*, hsa-miR-423-5p, hsa-miR-424, hsa-miR-424*, hsa-miR-429, hsa-miR-448, hsa-miR-449a, hsa-miR-449b, hsa-miR-450b-3p, hsa-miR-452, hsa-miR-454*, hsa-miR-455-3p, hsa-miR-483-3p, hsa-miR-483-5p, hsa-miR-491-3p, hsa-miR-491-5p, hsa-miR-493, hsa-miR-493*, hsa-miR-494, hsa-miR-497, hsa-miR-498, hsa-miR-500, hsa-miR-503, hsa-miR-505, hsa-miR-507, hsa-miR-513a-3p, hsa-miR-513a-5p, hsa-miR-513b, hsa-miR-513c, hsa-miR-515-5p, hsa-miR-518b, hsa-miR-518c*, hsa-miR-518d-3p, hsa-miR-518d-5p, hsa-miR-518e*, hsa-miR-520d-5p, hsa-miR-520h, hsa-miR-541, hsa-miR-545*, hsa-miR-548d-3p, hsa-miR-548d-5p, hsa-miR-551a, hsa-miR-551b, hsa-miR-552, hsa-miR-554, hsa-miR-556-5p, hsa-miR-557, hsa-miR-559, hsa-miR-561, hsa-miR-564, hsa-miR-572, hsa-miR-575, hsa-miR-576-3p, hsa-miR-578, hsa-miR-583, hsa-miR-586, hsa-miR-589, hsa-miR-589*, hsa-miR-591, hsa-miR-595, hsa-miR-601, hsa-miR-602, hsa-miR-609, hsa-miR-610, hsa-miR-612, hsa-miR-613, hsa-miR-614, hsa-miR-615-3p, hsa-miR-616, hsa-miR-619, hsa-miR-622, hsa-miR-623, hsa-miR-624*, hsa-miR-627, hsa-miR-633, hsa-miR-634, hsa-miR-638, hsa-miR-639, hsa-miR-640, hsa-miR-642, hsa-miR-644, hsa-miR-647, hsa-miR-648, hsa-miR-652, hsa-miR-654-5p, hsa-miR-658, hsa-miR-659, hsa-miR-662, hsa-miR-663, hsa-miR-665, hsa-miR-671-5p, hsa-miR-675, hsa-miR-708, hsa-miR-708*, hsa-miR-744*, hsa-miR-760, hsa-miR-765, hsa-miR-766, hsa-miR-767-3p, hsa-miR-802, hsa-miR-874, hsa-miR-876-3p, hsa-miR-876-5p, hsa-miR-877, hsa-miR-877*, hsa-miR-885-3p, hsa-miR-885-5p, hsa-miR-886-3p, hsa-miR-890, hsa-miR-891b, hsa-miR-892b, hsa-miR-920, hsa-miR-922, hsa-miR-923, hsa-miR-92b, hsa-miR-92b*, hsa-miR-933, hsa-miR-934, hsa-miR-935, hsa-miR-936, hsa-miR-937, hsa-miR-939, hsa-miR-940, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-12, kshv-miR-K12-3, kshv-miR-K12-3*, kshv-miR-K12-4-5p, kshv-miR-K12-6-5p, kshv-miR-K12-8, kshv-miR-K12-9, kshv-miR-K12-9*, ebv-miR-BART10*, ebv-miR-BART12, ebv-miR-BART13, ebv-miR-BART13*, ebv-miR-BART15, ebv-miR-BART1-5p, ebv-miR-BART16, ebv-miR-BART18-5p, ebv-miR-BART19-3p, ebv-miR-BART19-5p, ebv-miR-BART20-5p, ebv-miR-BART2-5p, ebv-miR-BART3*, ebv-miR-BART5, ebv-miR-BART6-5p, ebv-miR-BART7, ebv-miR-BART7*, ebv-miR-BHRF1-1, ebv-miR-BHRF1-3, hcmv-miR-UL148D, hcmv-miR-UL22A, hcmv-miR-UL22A*, hcmv-miR-UL70-3p, hcmv-miR-UL70-5p, hcmv-miR-US25-1, hcmv-miR-US25-2-3p, hcmv-miR-US25-2-5p, hcmv-miR-US4, hiv1-miR-H1, hiv1-miR-N367, and human orthologs thereof.
In other embodiments where the physiological condition is related to lung disease or lung injury, the microRNA profile or the specific microRNA sequence may comprise at least one microRNA sequence selected from the group consisting of hsa-miR-135a*, hsa-miR-1224-3p, hsa-miR-1224-5p, hsa-miR-1225-3p, hsa-miR-1225-5p, hsa-miR-1226*, hsa-miR-1227, hsa-miR-1228, hsa-miR-1229, hsa-miR-1234, hsa-miR-1237, hsa-miR-1238, hsa-miR-124, hsa-miR-129*, hsa-miR-129-3p, hsa-miR-136*, hsa-miR-187*, hsa-miR-188-5p, hsa-miR-190b, hsa-miR-220b, hsa-miR-300, hsa-miR-301b, hsa-miR-30e, hsa-miR-338-3p, hsa-miR-33a*, hsa-miR-33b, hsa-miR-33b*, hsa-miR-34c-3p, hsa-miR-34c-5p, hsa-miR-363*, hsa-miR-371-3p, hsa-miR-371-5p, hsa-miR-375, hsa-miR-377*, hsa-miR-423-5p, hsa-miR-424*, hsa-miR-429, hsa-miR-448, hsa-miR-449a, hsa-miR-449b, hsa-miR-450b-3p, hsa-miR-452, hsa-miR-454*, hsa-miR-455-3p, hsa-miR-483-3p, hsa-miR-483-5p, hsa-miR-491-3p, hsa-miR-491-5p, hsa-miR-493, hsa-miR-493*, hsa-miR-500, hsa-miR-505, hsa-miR-507, hsa-miR-513a-3p, hsa-miR-513a-5p, hsa-miR-513b, hsa-miR-513c, hsa-miR-515-5p, hsa-miR-518c*, hsa-miR-518d-3p, hsa-miR-518d-5p, hsa-miR-518e*, hsa-miR-520d-5p, hsa-miR-541, hsa-miR-545*, hsa-miR-548d-3p, hsa-miR-548d-5p, hsa-miR-551b, hsa-miR-552, hsa-miR-554, hsa-miR-556-5p, hsa-miR-557, hsa-miR-559, hsa-miR-561, hsa-miR-564, hsa-miR-575, hsa-miR-576-3p, hsa-miR-578, hsa-miR-583, hsa-miR-586, hsa-miR-589, hsa-miR-589*, hsa-miR-591, hsa-miR-595, hsa-miR-602, hsa-miR-609, hsa-miR-610, hsa-miR-612, hsa-miR-613, hsa-miR-614, hsa-miR-615-3p, hsa-miR-616, hsa-miR-619, hsa-miR-623, hsa-miR-624*, hsa-miR-633, hsa-miR-638, hsa-miR-639, hsa-miR-640, hsa-miR-642, hsa-miR-644, hsa-miR-647, hsa-miR-652, hsa-miR-654-5p, hsa-miR-658, hsa-miR-659, hsa-miR-665, hsa-miR-671-5p, hsa-miR-675, hsa-miR-708*, hsa-miR-744*, hsa-miR-760, hsa-miR-765, hsa-miR-766, hsa-miR-767-3p, hsa-miR-768-3p, hsa-miR-768-5p, hsa-miR-801, hsa-miR-802, hsa-miR-874, hsa-miR-876-3p, hsa-miR-876-5p, hsa-miR-877, hsa-miR-877*, hsa-miR-885-3p, hsa-miR-885-5p, hsa-miR-886-3p, hsa-miR-890, hsa-miR-891b, hsa-miR-892b, hsa-miR-920, hsa-miR-922, hsa-miR-923, hsa-miR-92b*, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-12, kshv-miR-K12-3, kshv-miR-K12-3*, kshv-miR-K12-4-5p, kshv-miR-K12-6-5p, kshv-miR-K12-8, kshv-miR-K12-9, kshv-miR-K12-9*, ebv-miR-BART10*, ebv-miR-BART12, ebv-miR-BART13, ebv-miR-BART13*, ebv-miR-BART15, ebv-miR-BART1-5p, ebv-miR-BART16, ebv-miR-BART18-5p, ebv-miR-BART19-3p, ebv-miR-BART19-5p, ebv-miR-BART20-5p, ebv-miR-BART2-5p, ebv-miR-BART3*, ebv-miR-BART5, ebv-miR-BART6-5p, ebv-miR-BART7, ebv-miR-BART7*, ebv-miR-BHRF1-1, ebv-miR-BHRF1-3, hcmv-miR-UL148D, hcmv-miR-UL22A, hcmv-miR-UL22A*, hcmv-miR-UL70-3p, hcmv-miR-UL70-5p, hcmv-miR-US25-1, hcmv-miR-US25-2-3p, hcmv-miR-US25-2-5p, hcmv-miR-US4, hiv1-miR-H1, hiv1-miR-N367, and human orthologs thereof.
The physiological condition may also be a lung disease or lung injury, such as chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF), also known as interstitial lung disease (ILD).
In embodiments, the at least one differentially expressed microRNA sequence or at least one specific microRNA sequence is selected from the group consisting of hsa-miR-630, hsa-miR-134, hsa-miR-1225-5p, hsa-miR-135a*, hsa-miR-150*, hsa-miR-22, hsa-miR-223, hsa-miR-448, hsa-miR-451, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsa-miR-923, hsa-miR-92a-2*, hsa-miR-939, hsa-miR-940, hsv1-miR-H1, kshv-miR-K12-3, hsv1-miR-LAT, hcmv-miR-UL70-3p, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-3, hcmv-miR-UL70-3p, and human orthologs thereof.
In other embodiments, the biological sample is plasma and the at least one differentially expressed microRNA sequence or at least one specific microRNA sequence is selected from the group consisting of hsa-miR-630, hsa-miR-134, hsa-miR-1225-5p, hsa-miR-135a*, hsa-miR-150*, hsa-miR-22, hsa-miR-223, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsa-miR-923, hsa-miR-939, hsa-miR-940, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-3, hcmv-miR-UL70-3p, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of at least two of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.
In yet other embodiments, the biological sample is plasma and the at least one differentially expressed microRNA sequence or at least one specific microRNA sequence is selected from the group consisting of hsa-miR-630, hsa-miR-134, hcmv-miR-UL70-3p, hsa-miR-1225-5p, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of at least two of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.
In some alternate embodiments, the biological sample is plasma and at least two differentially expressed microRNA sequences or specific microRNA sequences are identified. At least one of the at least two differentially expressed microRNA sequences or specific microRNA sequences is selected from the group consisting of hsa-miR-630, hcmv-miR-UL70-3p, hsa-miR-1225-5p, hsa-miR-134, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. The other one of the at least two differentially expressed microRNA sequences or specific microRNA sequences is selected from the group consisting of hsa-miR-451, hsa-miR-448, hsa-miR-92a-2*, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.
In yet other embodiments, the biological sample is lung tissue and the at least one differentially expressed microRNA sequence is selected from the group consisting of hsa-miR-451, hsa-miR-923, hsa-miR-1225-5p, hsa-miR-22, hsa-miR-223, hsa-miR-638, kshv-miR-K12-3, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of these microRNA sequences.
In still other embodiments, the biological sample is plasma and the at least one differentially expressed microRNA sequence is selected from the group consisting of hsa-miR-940, hsa-miR-134, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-939, hsv1-miR-H1, kshv-miR-K12-3, hsv1-miR-LAT, hcmv-miR-UL70-3p, and human orthologs thereof. In some particular embodiments, the microRNA profile consists of only a selection of these microRNA sequences, i.e. the microRNA profile does not look at other microRNA sequences.
Also disclosed are methods of using microRNA sequences to detect a lung condition, comprising: generating a microRNA profile from a biological sample; and detecting the lung condition based on the levels of at least one overexpressed microRNA sequence and at least one underexpressed microRNA sequence. The at least one overexpressed microRNA sequence is selected from the group consisting of hsa-miR-630, hcmv-miR-UL70-3p, hsa-miR-1225-5p, hsa-miR-134, hsa-miR-135a*, hsa-miR-150*, hsa-miR-483-5p, hsa-miR-575, hsa-miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. The at least one underexpressed microRNA sequence is selected from the group consisting of hsa-miR-451, hsa-miR-448, and hsa-miR-92a-2*, and human orthologs thereof. In some particular embodiments, the microRNA profile examines only a selection of these listed microRNA sequences.
Also disclosed are methods of detecting or predicting certain physiological conditions in a patient. Those methods comprise generating a microRNA profile from a biological sample provided by the patient; identifying at least one differentially expressed microRNA sequence by comparing the microRNA profile to a reference; and detecting or predicting the physiological condition based on the identity or the amounts of the at least one differentially expressed microRNA sequence. The biological sample comprises (i) serum or plasma; and (ii) an additional body fluid specific to a particular location of the body that is relevant to the particular physiological condition. In a first embodiment, the biological sample further comprises amniotic fluid and the physiological condition is the health status of a fetus being carried by the patient. In a second embodiment, the biological sample further comprises urine and the physiological condition is the health status of a bladder or a kidney of the patient. In a third embodiment, the biological sample further comprises breast milk and the physiological condition is the health status of a breast of the patient. In a fourth embodiment, the biological sample further comprises saliva and the physiological condition is the health status of the head and neck region of the patient. In a fifth embodiment, the biological sample further comprises tears and the physiological condition is the health status of an eye of the patient. In a sixth embodiment, the biological sample further comprises semen and the physiological condition is the health status of a prostate or male reproductive organ of the patient. In a seventh embodiment, the biological sample further comprises synovial fluid and the physiological condition is the health status of a joint of the patient. In an eighth embodiment, the biological sample further comprises sweat and the physiological condition is the health status of the skin of the patient. In a ninth embodiment, the biological sample further comprises cerebrospinal fluid and the physiological condition is the health status of the central nerve system of the patient.
Also disclosed are methods of diagnosing a physiological condition. The methods comprise taking a sample of a body fluid and a sample of a body tissue from a patient. A first microRNA profile is generated from the body fluid sample, and a second microRNA profile is generated from the body tissue sample. At least two differentially expressed microRNA sequences are identified in the first microRNA profile by comparing the first microRNA profile to a first reference. At least two differentially expressed microRNA sequences are identified in the second microRNA profile by comparing the second microRNA profile to a second reference. The physiological condition is then diagnosed based on the differentially expressed microRNA sequences identified. In particular, the differentially expressed microRNA sequences in the first microRNA profile are different from the differentially expressed microRNA sequences in the second microRNA profile. This difference in the differentially expressed microRNA sequences between the body fluid and the body tissue increases the probability of a correct diagnosis.
Also included are assays for detecting the identity and/or levels of the various combinations of microRNA sequences described above.
These and other non-limiting aspects and/or objects of the disclosure are more particularly described below.



BRIEF DESCRIPTION OF THE DRAWINGS
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
The following is a brief description of the drawings, which are presented for the purposes of illustrating the disclosure set forth herein and not for the purposes of limiting the same.
FIGS. 1A-1B are electropherograms of RNA.
FIG. 2 is a microRNA profile showing changes in specific microRNA expression levels over time in the liver after exposing the animal to a high dose of acetaminophen.
For reference, the text on the right-hand side of FIG. 2 reads, in order from top to bottom: mmu-miR-720, mmu-miR-1224, mmu-miR-122, mmu-miR-494, mmu-miR-609, mmu-miR-21, mmu-miR-22, mmu-miR-451, mmu-miR-466f-3p, mmu-miR-574-5p, mmu-let-7a, mmu-let-7f, mmu-miR-192, mmu-miR-194, mmu-miR-212, mmu-let-7g, mmu-miR-29b, mmu-miR-26a, mmu-miR-30c, mmu-miR-29a, mmu-miR-188-5p, mmu-miR-709, mmu-miR-466g, mmu-miR-574-3p, mmu-miR-125a-3p, mmu-miR-125b-5p, mmu-miR-29c, mmu-miR-483, mmu-miR-600, mmu-miR-705, mmu-miR-721, mmu-miR-376b, mmu-miR-706, mmu-miR-710, mmu-miR-711, mmu-let-7c-2*, mmu-miR-376a, mmu-miR-891, mmu-miR-452, mmu-miR-467a*, mmu-miR-718, mmu-miR-500, mmu-miR-669c, mmu-miR-714, mmu-miR-290-5p, mmu-miR-134, mmu-miR-27b, mmu-miR-671-5p, mmu-miR-135a*, and mmu-miR-877*.
FIG. 3 is a microRNA profile showing differences in specific microRNA levels between plasma samples from a treated group and a control group.
For reference, the text on the right-hand side of FIG. 3 reads, in order from top to bottom: mmu-miR-21, mmu-miR-122, mmu-miR-22, mmu-miR-192, mmu-miR-29a, mmu-miR-30a, mmu-miR-130a, mmu-miR-29c, mmu-miR-30a, mmu-miR-148a, mmu-miR-19b, mmu-miR-101b, mmu-miR-15a, mmu-miR-685, mmu-let-7g, mmu-miR-27b, mmu-miR-574-5p, mmu-miR-671-5p, mmu-miR-107, mmu-let-7d*, mmu-miR-29b, mmu-miR-193, mmu-miR-194, mmu-miR-101a, mmu-miR-185, mmu-miR-221, mmu-miR-294*, mmu-miR-877, mmu-miR-291a-5p, mmu-miR-877*, mmu-miR-339-3p, mmu-miR-466f-3p, mmu-miR-30c-1*, mmu-miR-199b, mmu-miR-199a-5p, mmu-miR-193b, mmu-miR-370, mmu-miR-882, mmu-miR-327, mmu-miR-127, mmu-miR-714, mmu-miR-150, mmu-miR-125a-5p, mmu-miR-141, mmu-miR-23b, mmu-miR-145, mmu-miR-320, mmu-miR-342-3p, mmu-miR-200c, mmu-miR-223, mmu-miR-99a, mmu-miR-202-3p, mmu-miR-494, mmu-miR-652, mmu-miR-375, mmu-miR-125a-3p, mmu-miR-124, mmu-miR-721, mmu-miR-93, mmu-miR-483, mmu-miR-205, mmu-miR-712, mmu-miR-26a, mmu-miR-710, mmu-miR-23a, mmu-miR-135a*, mmu-miR-711, mmu-miR-720, mmu-miR-125b-5p, mmu-miR-133a, mmu-miR-133b, mmu-miR-451, mmu-miR-486, and mmu-miR-1224.
FIG. 4 is a graph of intensities for two selected microRNA sequences, mir-122 and mir-486 in plasma after exposing the animal to different doses of acetaminophen.
FIG. 5 is a graph of the ratio between mir-122 and mir-486 (either median or average intensities) for the same data as FIG. 4.
FIG. 6 is a microRNA profile showing differences in microRNA expression levels between normal brain tissue and diseased brain tissue.
FIG. 7 is a microRNA profile showing differences in microRNA expression levels as a disease progressed in lung tissue.
FIG. 8 is a microRNA profile showing differences in microRNA expression levels between serum and urine samples.
FIG. 9 is a graph comparing miRNA expression levels in control plasma samples with ILD plasma samples.
FIGS. 10A-10B are graphs showing the signal strength in the ILD and control plasma samples of FIG. 9.
FIG. 11 is a graph showing the signal strength for all oligonucleotide probes used to target certain microRNA sequences.
FIG. 12 is a graph showing the difference in the signal strength for certain microRNA sequences in the ILD and control plasma samples of FIG. 9.
FIG. 13 is a graph showing the degree of overexpression in certain microRNA sequences in the ILD and control plasma samples of FIG. 9.
FIG. 14 is a graph comparing miRNA expression levels in ILD tissue samples with ILD plasma samples.
FIG. 15 is a graph comparing miRNA expression levels in control lung tissue samples with ILD lung tissue samples.
FIG. 16 is a graph showing the effect of normalization on data in a data analysis method.
FIGS. 17A-17B are graphs showing the effect of normalization on the quality of data.
FIG. 18 is a graph clustering normalized miRNA data.
FIG. 19 is a graph showing the p-value distribution of all miRNA in a sample.
FIG. 20 is a collection of charts showing the selection of panels that separates data.



DETAILED DESCRIPTION
A more complete understanding of the processes and apparatuses disclosed herein can be obtained by reference to the accompanying drawings. These figures are merely schematic representations based on convenience and the ease of demonstrating the existing art and/or the present development, and are, therefore, not intended to indicate relative size and dimensions of the assemblies or components thereof.
Although specific terms are used in the following description for the sake of clarity, these terms are intended to refer only to the particular structure of the embodiments selected for illustration in the drawings, and are not intended to define or limit the scope of the disclosure. In the drawings and the following description below, it is to be understood that like numeric designations refer to components of like function.
MicroRNAs (also known as miRNA) are small but potent regulatory non-coding ribonucleic acid (RNA) sequences first identified in C. elegans in 1993. miRNA may be about 21 to about 23 nucleotides in length. Through sequence complementation, microRNA interacts with messenger RNA (mRNA) and affects the stability of mRNA and/or the initiation and progression of protein translation. It has been estimated that over 30% of the mRNAs are regulated by microRNA. Like mRNA, some of the microRNAs also display restricted tissue distribution. The biological function of microRNA is yet to be fully understood; however, it has been shown that microRNA sequences are involved in various physiological and pathological conditions, including differentiation, development, cancer, and neurological disorders. Unlike mRNA and proteins, microRNA is reasonably well conserved across different species. Thus, a specific microRNA sequences which is shown to correlate to a particular condition, such as disease or injury, in one species, should also correlate to that particular condition in other species, particularly humans (i.e. Homo sapiens). This correlation provides useful diagnostic content.
MicroRNAs can also be manipulated with commonly used molecular biology techniques including complementary DNA (cDNA) synthesis, polymerase chain reactions, Northern blotting, and array based hybridization. This makes it possible to easily investigate the function(s) of a given microRNA sequences of interest.
A microRNA is encoded by a gene. When the DNA of the gene is transcribed into RNA, the RNA is not subsequently translated into protein. Instead each primary transcript (a pri-mir) is processed into a short stem-loop structure (a pre-mir) and finally into a mature sequence, designated miR. The primary transcript can form local hairpin structures, which ordinarily are processed such that a single microRNA sequence accumulates from one arm of a hairpin precursor molecule. Sometimes the primary transcript contains multiple hairpins, and different hairpins give rise to different microRNA sequences.
The microRNA sequences discussed herein are named according the miRBase database available at http://microrna.sanger.ac.uk/ and maintained by the Wellcome Trust Sanger Institute (now redirected to http://www.miRBase.org/). Generally speaking, microRNA sequences are assigned sequential numerical identifiers, with the numerical identifier based on sequence similarity. A 3- or 4-letter prefix designates the species from which the microRNA sequence came. For example, the hsa in hsa-miR-101 refers to homo sapiens.
Orthologous sequences, or orthologs, refer to microRNA sequences that are in different species but are similar (i.e. homologous) because they originated from a common ancestor. Generally speaking, orthologs have the same numerical identifier and are believed to serve a similar function. For example, mmu-miR-101 and hsa-miR-101 are in mouse and human, respectively, and are orthologs to each other. In this disclosure, microRNA sequences are referred to without the prefix designating the species, and should be construed as preferentially referring to the human microRNA sequence and the murine sequence. For example, miR-101 should be construed as referring to hsa-miR-101 and mmu-miR-101.
Paralogous sequences, or paralogs, are microRNA sequences that differ from each other in only a few positions. Paralogs occur within a species. Paralogs are designated with letter suffixes. For example, mmu-miR-133a and mmu-miR-133b are paralogs.
Identical microRNA sequences that originate from separate genomic loci are given numerical suffixes, such as hsa-miR-26a-1 and hsa-miR-26a-2.
Sometimes, two different mature microRNA sequences are excised from opposite arms of the same hairpin precursor. The two microRNA sequences can be designated in at least two ways. First, when it is possible to determine which arm gives rise to the predominantly expressed miRNA sequence, an asterisk has been used to denote the less predominant form, such as hsa-let-7b and hsa-let-7b*. Alternatively, they are named to designate whether they come from the 5′ or 3′ arm, such as hsa-miR-125a-3p and hsa-miR-125a-5p.
Specific microRNA sequences have been identified in the blood that are associated with liver injuries. Thus, the levels of selected microRNA sequences can be used to detect, predict, or diagnose diseases, predict and monitor therapeutic responses, and/or predict disease outcomes.
MicroRNA-based blood markers offer superior properties over existing markers. Such markers are sensitive, in part because microRNA signals can be amplified using standard polymerase chain reactions (PCR) while protein-based markers cannot be easily amplified. Because the sequence and expression profile of microRNAs are largely conserved across species, discoveries made in animal models can be easily translated to and adapted for use in humans. MicroRNA assays can be quickly performed and developed with standard PCR or array based systems; therefore, beside PCR primers, there is no need to develop special detection agents. Finally, since microRNA can be easily accessed in various body fluids, obtaining such diagnostic information can be done non-invasively.
The level of specific microRNA sequences(s) in a cell, tissue, or body fluid(s) can be used to monitor the physiopathological conditions of the body.
Sets of microRNA sequences in the tissue and the serum have been identified that are associated with liver injuries, lung injuries, and lung diseases. The combination of information from multiple microRNA expression level changes can further enhance the sensitivity and specificity of disease/injury detection, including using the ratio of paired microRNA sequences.
MicroRNA profiles, for example a microRNA profile of tissue-specific microRNA sequences, could be used to monitor the health status of that tissue. Those microRNA sequences could also be used as therapeutic targets for diseases associated with the tissue.
MicroRNA sequences from microbes or infectious agents, such as bacteria and viruses, could be used as an indication of infection. Host responses could be monitored by using the combination of microRNA sequences from infectious agents and the host as measured from the host's body fluids.
Biological processes occurring in a number of cell types or tissues could be monitored by the use of microRNA profiles specific to a process or network. These specific microRNA sequences could also be used as therapeutic targets for diseases associated with the biological processes.
The methods of the present disclosure could be used to detect, predict, monitor, or treat a physiological condition such as a disease, injury, or infection. Generally, the methods include: (a) isolating microRNA sequences from a biological sample; (b) generating a microRNA profile from the isolated microRNA sequences, the profile including the levels of expressed microRNA sequences in the biological sample; and (c) comparing the microRNA profile with a reference to identify differentially expressed microRNA sequences. Based on the identity or the levels of the differentially expressed microRNA sequences, the physiological condition could be detected, predicted, or monitored; or a treatment could be indicated, administered, or monitored accordingly.
The biological sample is generally non-invasive, and may be, for example, a biopsy material, tissue, or body fluid. Exemplary body fluids include serum, plasma, lymph, saliva, urine, tears, sweat, semen, synovial fluid, cervical mucus, amniotic fluid, cerebrospinal fluid, and breast milk.
Combinations of different biological samples are also contemplated for providing more specific diagnoses. For example, plasma and serum would provide some general indicators of health, while a specific body fluid could be included for specific information. For example, if one wanted to assess the health status of a fetus being carried by the mother, one might test the amniotic fluid along with the mother's plasma or serum. As another example, one might test the urine to assess the health status of a bladder or a kidney. Testing the breast milk would help assess the health status of a breast of the patient providing the biological sample. Testing the saliva would help assess the health status of the head and neck region. Testing the tears would help assess the health status of an eye of the patient providing the biological sample. Testing semen would help assess the health status of a prostate or male reproductive organ. Testing the synovial fluid would help assess the health status of a joint of the patient providing the biological sample. Testing the sweat would help assess the health status of the skin. Testing the cerebrospinal fluid would help assess the health status of the central nerve system. The term “health status” refers only to the physiological condition of the given body part, and has no specific meaning otherwise.
Isolating microRNA can be done by various methods. For example, the biological sample may be extracted with an organic solvent to obtain an aqueous phase containing the microRNA sequences. The aqueous phase is then purified through a silica membrane to isolate the microRNA sequences.
A microRNA profile can then be generated from the isolated microRNA sequences. Generally speaking, the microRNA profile provides the identity of specific microRNA sequences and/or the expression level (i.e. amount) of each specific microRNA sequence. An exemplary microRNA profile is seen in FIG. 2, which shows the expression levels for several microRNA sequences from several different liver samples that have been exposed to a high dose of acetaminophen. The microRNA profile of FIG. 2 has six columns, but a microRNA profile may be simply one column (along with the identifying microRNA). The expression level can be displayed either as a sliding color scale or simply as numerical values. The microRNA profile can be generated by using hybridization to identify the microRNA sequences and/or using quantitative PCR (qPCR) to identify the levels of one or more particular microRNA sequences. It should be noted that the diagnostic information may be in the identity of the microRNA sequences themselves, or in the absolute or relative levels of the microRNA sequence, either between two microRNA sequences in a given sample or between two samples for a given microRNA sequence. A reference table could be provided, for example from a reference sample taken from the patient or from a table of levels of expressed microRNA sequences in a normal (healthy) person or a table compiled from the expressed microRNA sequences over a large sample of people. Differentially expressed microRNA sequences can then be identified by comparing the microRNA profile of the biological sample with the reference sample or table to obtain diagnostic information. The term “differentially expressed” refers only to the fact that the amount or expression level has changed. The direction of change (i.e. upwards or downwards, overexpressed or underexpressed) is not significant, except as otherwise stated.
In particular embodiments, it is contemplated that identifying at least one specific microRNA sequence as being differentially expressed would be sufficient to identify a particular physiological condition as occurring. In other embodiments, at least two differentially expressed microRNA sequences are identified. This provides for an additional degree of confirmation in the identity of the physiological condition.
In using the terms “generating” and “identifying,” it is contemplated that these actions may be performed directly or indirectly. For example, a laboratory technician may perform the actions that directly “generate” a microRNA profile. The physician who ordered the microRNA profile that was directly “generated” by the laboratory technician may be considered to have indirectly “generated” the microRNA profile.
Because microRNA sequences and expression levels are generally conserved across species, it is contemplated that sequences and levels from other species would contain useful diagnostic information. For example, the biological sample may be from a microbe, such as a virus, bacterium, fungus, protozoan, or parasite.
It has been found that microRNA sequences and their expression levels can differ depending on their location in the body. In other words, they can be specific to a biological pathway, cell type, or tissue. This fact can provide powerful diagnostic information as well.
Table 1 lists some microRNA sequences which have been found to be specific to certain tissues in the human body.










TABLE 1






Human tissue specific

Human tissue


Tissue
miRNA
Tissue
specific miRNA







Adipose
hsa-miR-452
Placenta
hsa-miR-527


Adipose
hsa-miR-196a
Placenta
hsa-miR-377


Adipose
hsa-miR-224
Placenta
hsa-miR-526c


Adipose
hsa-miR-335
Placenta
hsa-miR-524*


Adipose
hsa-miR-452*
Placenta
hsa-miR-517*


Adipose
hsa-miR-432*
Placenta
hsa-miR-450


Adrenal
hsa-miR-409-5p
Placenta
hsa-miR-503


Adrenal
hsa-miR-494
Placenta
hsa-miR-526b*


Adrenal
hsa-miR-485-5p
Placenta
hsa-miR-371


Adrenal
hsa-miR-360-5p
Placenta
hsa-miR-519b


Adrenal
hsa-miR-154
Placenta
hsa-miR-516-3p


Adrenal
hsa-miR-370
Placenta
hsa-miR-526a


Adrenal
hsa-miR-381
Placenta
hsa-miR-523


Adrenal
hsa-miR-369
Placenta
hsa-miR-518a-2*


Adrenal
hsa-miR-485-3p
Placenta
hsa-miR-518c*


Adrenal
hsa-miR-134
Placenta
hsa-miR-520b


Adrenal
hsa-miR-323
Placenta
hsa-miR-518d


Adrenal
hsa-miR-7N
Placenta
hsa-miR-524


Adrenal
hsa-miR-382
Placenta
hsa-miR-519a


Adrenal
hsa-miR-7
Placenta
hsa-miR-520a


Adrenal
hsa-miR-405
Placenta
hsa-miR-521


Adrenal
hsa-miR-127
Placenta
hsa-miR-522


Adrenal
hsa-miR-493
Placenta
hsa-miR-520d


Adrenal
hsa-miR-379
Placenta
hsa-miR-525


Adrenal
hsa-miR-432
Placenta
hsa-miR-512-5p


Adrenal
hsa-miR-299
Placenta
hsa-miR-520a*


Adrenal
hsa-miR-433
Placenta
hsa-miR-519a*


Adrenal
hsa-miR-376a
Placenta
hsa-miR-517a


Adrenal
hsa-miR-202*
Placenta
hsa-miR-517b


Adrenal
hsa-miR-137
Placenta
hsa-miR-515-5p


Adrenal
hsa-miR-501
Placenta
hsa-miR-525*


Adrenal
hsa-miR-202
Placenta
hsa-miR-518


Adrenal
hsa-miR-491
Placenta
hsa-miR-512-3p


Bladder
hsa-miR-451
Placenta
hsa-miR-517c


Brain
hsa-miR-330
Placenta
hsa-miR-518a


Brain
hsa-miR-219
Placenta
hsa-miR-519d


Brain
hsa-miR-124
Placenta
hsa-miR-518c


Brain
hsa-miR-9
Placenta
hsa-miR-518e


Brain
hsa-miR-9*
Placenta
hsa-miR-520g


Brain
hsa-miR-124a
Placenta
hsa-miR-519c


Brain
hsa-miR-129
Placenta
hsa-miR-515-3p


Brain
hsa-miR-124b
Placenta
hsa-miR-520b


Brain
hsa-miR-137
Placenta
hsa-miR-372


Brain
hsa-miR-383
Placenta
hsa-miR-520a


Brain
hsa-miR-433
Placenta
hsa-miR-520c


Brain
hsa-miR-348
Placenta
hsa-miR-373


Brain
hsa-miR-323
Placenta
hsa-miR-520b


Brain
hsa-miR-153
Placenta
hsa-miR-154*


Brain
hsa-miR-128b
Placenta
hsa-miR-520c


Brain
hsa-miR-128a
Placenta
hsa-miR-493


Brain
hsa-miR-485-5p
Placenta
hsa-miR-381


Brain
hsa-miR-370
Placenta
hsa-miR-151


Brain
hsa-miR-485-3p
Placenta
hsa-miR-495


Brain
hsa-miR-181b
Placenta
hsa-miR-474


Brain
hsa-miR-338
Placenta
hsa-miR-369-5p


Brain
hsa-miR-154*
Placenta
hsa-miR-184


Brain
hsa-miR-149
Placenta
hsa-miR-489


Brain
hsa-miR-213
Placenta
hsa-miR-376a


Brain
hsa-miR-340
Placenta
hsa-miR-500


Brain
hsa-miR-181bN
Placenta
hsa-miR-369


Brain
hsa-miR-181d
Placenta
hsa-miR-135b


Brain
hsa-miR-491
Placenta
hsa-miR-432


Brain
hsa-miR-184
Placenta
hsa-miR-27aN


Brain
hsa-miR-138
Placenta
hsa-miR-198


Brain
hsa-miR-132
Placenta
hsa-miR-224


Brain
hsa-miR-181c
Placenta
hsa-miR-452*


Brain
hsa-miR-204
Placenta
hsa-miR-433


Brain
hsa-miR-328
Placenta
hsa-miR-193b


Brain
hsa-miR-181a
Placenta
hsa-miR-494


Brain
hsa-miR-432
Placenta
hsa-miR-502


Brain
hsa-miR-379
Placenta
hsa-miR-335


Brain
hsa-miR-324-5p
Placenta
hsa-miR-299


Brain
hsa-miR-122
Placenta
hsa-miR-149


Brain
hsa-miR-134
Placenta
hsa-miR-213


Brain
hsa-miR-342
Placenta
hsa-miR-30d


Breast
hsa-miR-452
Placenta
hsa-miR-141


Breast
hsa-miR-205
Placenta
hsa-miR-301


Breast
hsa-miR-489
Placenta
hsa-miR-485-3p


Colon
hsa-miR-490
Placenta
hsa-miR-141N


Colon
hsa-miR-363
Placenta
hsa-miR-379


Colon
hsa-miR-338
Placenta
hsa-miR-130a


Colon
hsa-miR-31
Placenta
hsa-miR-382


Colon
hsa-miR-215
Placenta
hsa-miR-99b


Colon
hsa-miR-200a*
Placenta
hsa-miR-370


Colon
hsa-miR-200a
Placenta
hsa-miR-130b


Colon
hsa-miR-196b
Placenta
hsa-miR-27a


Colon
hsa-miR-196a
Placenta
hsa-miR-200cN


Colon
hsa-miR-194
Placenta
hsa-miR-24


Colon
hsa-miR-192
Placenta
hsa-miR-30a-5p


Colon
hsa-miR-141N
Placenta
hsa-miR-30bN


Colon
hsa-miR-141
Placenta
hsa-miR-221


Small
hsa-miR-490
Placenta
hsa-miR-200c


Intestine


Small
hsa-miR-451
Placenta
hsa-miR-320


Intestine


Small
hsa-miR-429
Placenta
hsa-miR-127


Intestine


Small
hsa-miR-31
Placenta
hsa-miR-485-5p


Intestine


Small
hsa-miR-215
Placenta
hsa-miR-30b


Intestine


Small
hsa-miR-200bN
Placenta
hsa-miR-90a-3p


Intestine


Small
hsa-miR-200b
Placenta
hsa-miR-181a


Intestine


Small
hsa-miR-200a*
Placenta
hsa-miR-222


Intestine


Small
hsa-miR-198
Placenta
hsa-miR-362


Intestine


Small
hsa-miR-194
Placenta
hsa-miR-125a


Intestine


Small
hsa-miR-192
Placenta
hsa-miR-323


Intestine


Small
hsa-miR-138
Placenta
hsa-miR-451


Intestine


Cervix
hsa-miR-196b
Placenta
hsa-miR-409-5p


Cervix
hsa-miR-99a
Placenta
hsa-miR-452


Heart
hsa-miR-1
Placenta
hsa-miR-518b


Heart
hsa-miR-107
Placenta
hsa-miR-515-5p


Heart
hsa-miR-133a
Placenta
hsa-miR-130aN


Heart
hsa-miR-189
Skeletal
hsa-miR-206




Muscle


Heart
hsa-miR-221
Skeletal
hsa-miR-95




Muscle


Heart
hsa-miR-23bN
Skeletal
hsa-miR-133b




Muscle


Heart
hsa-miR-302a
Skeletal
hsa-miR-133a




Muscle


Heart
hsa-miR-302b
Skeletal
hsa-miR-128b




Muscle


Heart
hsa-miR-302c
Skeletal
hsa-miR-1




Muscle


Heart
hsa-miR-302d
Skeletal
hsa-miR-489




Muscle


Heart
hsa-miR-300-3p
Skeletal
hsa-miR-378




Muscle


Heart
hsa-miR-367
Skeletal
hsa-miR-422a




Muscle


Heart
hsa-miR-378
Skeletal
hsa-miR-128a




Muscle


Heart
hsa-miR-422a
Skeletal
hsa-miR-196a




Muscle


Heart
hsa-miR-422b
Skeletal
hsa-miR-502




Muscle


Heart
hsa-miR-452
Spleen
hsa-miR-223


Heart
hsa-miR-490
Spleen
hsa-miR-139


Heart
hsa-miR-491
Lymph Node
hsa-miR-150


Heart
hsa-miR-409
Lymph Node
hsa-miR-142-3p


Heart
hsa-miR-7a
Lymph Node
hsa-miR-146b


Pericardium
hsa-miR-188
Lymph Node
hsa-miR-146


Pericardium
hsa-miR-369
Lymph Node
hsa-miR-155


Pericardium
hsa-miR-305
Lymph Node
hsa-miR-363


Pericardium
hsa-miR-452
PBMC
hsa-miR-128a


Pericardium
hsa-miR-224
PBMC
hsa-miR-124b


Pericardium
hsa-miR-511
PBMC
hsa-miR-124a


Pericardium
hsa-miR-199b
PBMC
hsa-miR-137


Kidney
hsa-miR-500
PBMC
hsa-miR-431


Kidney
hsa-miR-204
PBMC
hsa-miR-129


Kidney
hsa-miR-480
PBMC
hsa-miR-128b


Kidney
hsa-miR-190
PBMC
hsa-miR-138


Kidney
hsa-miR-501
Thymus
hsa-miR-183


Kidney
hsa-miR-196a
Thymus
hsa-miR-96


Kidney
hsa-miR-211
Thymus
hsa-miR-128b


Kidney
hsa-miR-363
Thymus
hsa-miR-213


Kidney
hsa-miR-502
Thymus
hsa-miR-205


Kidney
hsa-miR-184
Thymus
hsa-miR-128a


Liver
hsa-miR-122a
Thymus
hsa-miR-181bN


Liver
hsa-miR-30a-3p
Thymus
hsa-miR-182


Lung
hsa-miR-223
Thymus
hsa-miR-181b


Esophagus
hsa-miR-203
Thymus
hsa-miR-181d


Esophagus
hsa-miR-205
Thymus
hsa-miR-181a


Esophagus
hsa-miR-145
Thymus
hsa-miR-181c


Esophagus
hsa-miR-210N
Thymus
hsa-miR-20b


Esophagus
hsa-miR-143
Thymus
hsa-miR-383


Esophagus
hsa-miR-31
Thymus
hsa-miR-17-5p


Esophagus
hsa-miR-187
Thymus
hsa-miR-142-3p


Trachea
hsa-miR-34b
Stomach
hsa-miR-211


Trachea
hsa-miR-205
Stomach
hsa-miR-188


Trachea
hsa-miR-34cN
Stomach
hsa-miR-346


Trachea
hsa-miR-34c
Stomach
hsa-miR-200a*


Prostate
hsa-miR-363
Stomach
hsa-miR-375


Prostate
hsa-miR-205
Stomach
hsa-miR-148a


Prostate
hsa-miR-196b
Stomach
hsa-miR-200a


Ovary
hsa-miR-502
Stomach
hsa-miR-200b


Ovary
hsa-miR-383
Stomach
hsa-miR-200c


Fallopian
hsa-miR-34bN
Stomach
hsa-miR-200bN


Tube


Fallopian
hsa-miR-34b
Stomach
hsa-miR-212


Tube


Fallopian
hsa-mi-34cN
Stomach
hsa-miR-31


Tube


Fallopian
hsa-miR-449
Stomach
hsa-miR-7


Tube


Fallopian
hsa-miR-34c
Stomach
hsa-miR-153


Tube


Fallopian
hsa-miR-135a
Stomach
hsa-miR-429


Tube


Pancreas
hsa-miR-217
Stomach
hsa-miR-107


Pancreas
hsa-miR-216
Stomach
hsa-miR-200cN


Pancreas
hsa-miR-375
Stomach
hsa-miR-502


Pancreas
hsa-miR-98
Stomach
hsa-miR-203


Pancreas
hsa-miR-163
Testicle
hsa-miR-202


Pancreas
hsa-miR-141N
Testicle
hsa-miR-506


Pancreas
hsa-miR-148a
Testicle
hsa-miR-507


Pancreas
hsa-miR-141
Testicle
hsa-miR-510


Pancreas
hsa-miR-7N
Testicle
hsa-miR-514


Pancreas
hsa-miR-494
Testicle
hsa-miR-513


Pancreas
hsa-miR-130b
Testicle
hsa-miR-508


Pancreas
hsa-miR-200cN
Testicle
hsa-miR-509


Pancreas
hsa-miR-148b
Testicle
hsa-miR-202*


Pancreas
hsa-miR-182
Testicle
hsa-miR-449


Pancreas
hsa-miR-200a
Testicle
hsa-miR-34c


Thyroid
hsa-miR-138
Testicle
hsa-miR-432*


Thyroid
hsa-miR-135a
Testicle
hsa-miR-184


Thyroid
hsa-miR-206
Testicle
hsa-miR-520c


Thyroid
hsa-miR-95
Testicle
hsa-miR-520I


Thyroid
hsa-miR-1
Testicle
hsa-miR-34cN


Thyroid
hsa-miR-7
Testicle
hsa-miR-34b


Uterus
hsa-miR-10b
Testicle
hsa-miR-520b


Uterus
hsa-miR-196b
Testicle
hsa-miR-135b


Uterus
hsa-miR-502
Testicle
hsa-miR-383


Testicle
hsa-miR-204
Testicle
hsa-miR-34bN









It has been found that microRNA sequences and their expression levels can differ depending on their location in different types of body fluid samples. In other words, they can be specific to a biological pathway, cell type, or tissue. This fact can provide powerful diagnostic information as well.
Table 2 lists some microRNA sequences which have been found to be highly abundant in different body fluids. The sequences in bold font are unique to the listed body fluid.












TABLE 2







Tears
Urine
Breast Milk
Seminal Fluid
Saliva
Amniotic Fluid





miR-518e
miR-515-3p
miR-518e
miR-518e
miR-335*
miR-518e


miR-335*
miR-335*
miR-26a-2*
miR-590-3p
miR-515-3p
miR-335*


miR-137
miR-892a
miR-335*
miR-588
miR-545*
miR-302c


miR-515-3p
miR-509-5p
miR-490-5p
miR-873
miR-492
miR-515-3p


miR-509-5p
miR-223*
miR-181d
miR-590-5p
miR-892a
miR-452


miR-873
miR-302d
miR-26a-1*
miR-137
miR-518e
miR-892a


miR-223*
miR-873
miR-137
miR-197
miR-27a
miR-671-5p


miR-892a
miR-923
miR-524-5p
miR-515-5p
miR-923
miR-515-5p


miR-590-3p
miR-616*
miR-509-5p
miR-515-3p
miR-509-5p
miR-590-3p


miR-302d
miR-483-5p
miR-513c
miR-218
miR-873
miR-593*


miR-616*
miR-134
miR-595
miR-20b
miR-483-5p
miR-873


miR-590-5p
miR-589
miR-515-3p
miR-410
miR-616*
miR-137


miR-101*
miR-556-3p
miR-515-5p
miR-335*
miR-580
miR-410


miR-130a
miR-101*
miR-598
miR-617
miR-609
miR-548d-5p


miR-410
miR-138
miR-130a
miR-671-5p
miR-302d
miR-223*


miR-195
miR-652
miR-181b
miR-524-5p
miR-25*
miR-590-5p


miR-675
miR-325
miR-671-5p
miR-892a
miR-134
miR-616*


miR-325
let-7i
miR-892a
miR-181d
miR-92b
miR-302d


miR-134
miR-377*
miR-578
miR-545*
miR-598
miR-509-5p


miR-29b
miR-545*
miR-580
miR-1
let-7a
miR-210















Bronchial


Peritoneal




Lavage
CSF
Pleural Fluid
Fluid
Colostrum
Plasma





miR-515-3p
miR-515-3p
miR-515-3p
miR-892a
miR-509-5p
miR-335*


miR-335*
miR-335*
miR-892a
miR-518e
miR-181d
miR-325


miR-509-5p
miR-892a
miR-509-5p
miR-515-3p
miR-335*
miR-377*


miR-483-5p
miR-223*
miR-134
miR-134
miR-518e
miR-586


miR-892a
miR-873
miR-590-5p
miR-509-5p
miR-515-5p
miR-518e


miR-223*
miR-509-5p
miR-515-5p
miR-223*
miR-223*
let-7i


miR-873
miR-302d
miR-873
miR-515-5p
miR-671-5p
miR-539


miR-1225-3p
miR-616*
miR-335*
miR-616*
miR-873
miR-616*


miR-302d
miR-134
miR-920
miR-137
miR-483-5p
miR-302d


miR-545*
miR-483-5p
miR-616*
miR-873
miR-186
miR-589


miR-324-3p
miR-325
miR-302d
miR-483-5p
miR-515-3p
miR-556-3p


miR-616*
miR-151-5p
miR-518e
miR-518c
miR-616*
miR-151-3p


miR-92b
miR-589
miR-923
miR-92b
miR-134
miR-548b-3p


miR-25*
miR-377*
miR-589
miR-923
miR-892a
miR-192


miR-539
miR-923
miR-377*
miR-302d
miR-590-5p
miR-151-5p


miR-923
miR-652
miR-410
miR-374a
miR-590-3p
miR-598


miR-192
miR-518e
miR-137
miR-598
miR-425
miR-187


miR-134
miR-556-3p
miR-671-5p
miR-937
miR-454
miR-873


miR-371-3p
miR-767-3p
miR-151-5p
miR-335*
miR-101*
miR-218


miR-580
miR-505
miR-223*
miR-885-5p
miR-132
miR-923









With the diagnostic information obtained, a physiological condition could be detected, identified, predicted, treated, and/or monitored. For example, a treatment could be administered based on the identity of the physiological condition. A particular treatment could be monitored by taking a first sample, administering the treatment, taking a second sample, and comparing the microRNA profiles of the two samples to identify and/or track changes resulting from the treatment. Those changes could include the amounts of a particular microRNA sequence, or the identity of the differentially expressed microRNA sequences that have changed between the two samples.
It is also contemplated that manipulating the levels of microRNA sequences might itself be a treatment for a physiological condition. The microRNA level could be altered by constructing a specific DNA or RNA sequence related to the microRNA sequences, then delivering that DNA or RNA sequence to a targeted cell, tissue, or organ expressing the targeted microRNA sequences.
As discussed below, specific microRNA sequences are identified that may be useful in diagnosing and/or treating liver disease or injury, lung disease or injury, and neurological disease or injury. Such conditions include chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) (also known as interstitial lung disease (ILD)).
Other methods embodied herein include generating a microRNA profile from a biological sample. The microRNA profile comprises the amounts of specific microRNA sequences. The amounts of those specific microRNA sequences are then compared to a reference to provide information for detecting or predicting the lung condition. In this regard, the microRNA profile may include those specific microRNA sequences identified below in the examples, or a subset thereof. Such microRNA profiles would be smaller, faster, and provide the same diagnostic information as larger test kits.
The following examples are provided to illustrate the devices and methods of the present disclosure. The examples are merely illustrative and are not intended to limit the disclosure to the materials, conditions, or process parameters set forth therein.
EXAMPLES
Isolation of microRNA
microRNA can be isolated using glass filter based methods to selectively bind RNA in a high salt buffer. The unwanted biomolecules can then be washed off by using high salt buffers containing at least 50% alcohol. The bound pure RNA can then eluted off the glass membrane with low salt buffer or RNAse-free water.
1). Isolating microRNA from Solid Tissues
Briefly, total RNA, including microRNA, was isolated using commercial kits such as miRNeasy mini kit (Qiagen Inc. Valencia, Calif.). Approximately 5 mg to 50 mg tissue samples were excised from flash-frozen tissue. After placing the tissue sample into a Dounce tissue grinder, 700 microliter (μl) QIAzol lysis reagent was added to the grinder and the tissue was homogenized immediately. For every 700 μl QIAzol lysis reagent used, 140 μl chloroform was added to the tissue lysate to extract the water soluble content. After mixing for 15 seconds, the lysate was placed in a centrifuge and spun at 12000×g for 15 minutes at room temperature. The upper aqueous phase (containing the RNA) was then transferred to a new collection tube, and 1.5 volumes of ethanol was added. The sample was then transferred to a cartridge containing a glass filter (i.e. silica membrane) so that RNA could attach to the glass filter. The contaminants were washed off the silica membrane by applying different high salt washing buffers included in the miRNeasy kit. The bound pure RNA was then eluted off the membrane with water or low salt buffer.
2). Isolating microRNA from Liquid Samples
Approximately, 800 μl of QIAzol lysis reagent was added to 200 μl liquid sample. The sample was mixed in a tube followed by adding 200 μl of chloroform. After mixing rigorously for 15 seconds, the sample was then centrifuged at 12,000×g for 15 minutes. The upper aqueous phase was carefully transferred to a new collection tube, and 1.5 total volumes of ethanol was added. The sample was then applied directly to a glass membrane containing column and the RNA was bound and purified by three contiguous washing to remove unwanted contamination. The immobilized RNA was then collected from the membrane with a low salt elution buffer.
The yield of microRNA from different amount of liquid samples used in these protocols was tested. The best ratio was found to be 4 volumes of lysis buffer with 1 volume of liquid sample.
The quality and quantity of RNA isolated was evaluated by RNA by NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific Inc. Waltham, Mass.) and the Agilent 2100 Bioanalyzer (Agilent Inc. Santa Clara, Calif.).
FIG. 1A shows an electropherogram of RNA isolated from solid tissue, while FIG. 1B shows an electropherogram of RNA isolated from a liquid sample. The 185 and 28S peaks are clearly visible and marked. The microRNA are located on the left of both electropherograms. This region also contains all degraded RNA.
Array Hybridization and Quantitative PCR
Agilent's human and mouse microRNA microarray kits (Agilent Inc. Santa Clara, Calif.) were used as the array platform; however, arrays from different companies including Affymetrix and Exiqon have also been used. The human microRNA microarray contained probes for 723 human and 76 human viral microRNAs from the Sanger database v 10.1. The mouse microRNA microarray contained probes for 567 mouse and 10 mouse herpes virus microRNA sequences from the Sanger database v 10.1. Cyanine 3-pCp labeled RNA (i.e. RNA labeled with Cyanine 3-Cytidine bisphosphate) for array hybridization was generated by 100 nanograms (ng) of total RNA using Agilent's microRNA complete labeling and hybridization kit. All the steps, including labeling, hybridization, washing, scanning and feature extraction were performed in accordance with the manufacturer's instructions.
In brief, 100 ng of total RNA was dephosphorylated with calf intestinal alkaline phosphatase, then heat and DMSO treated to yield denatured RNA. Cyanine 3-Cytidine bisphosphate was joined to the microRNA by T4 RNA ligase. MicroBioSpin 6 columns were used to desalt the samples and remove any unincorporated fluorophores. The samples were hybridized to 8×15K Agilent Human microRNA (V2) or Mouse microRNA microarrays in a rotating hybridization oven for 20 hours at 55° C. and 10 rpm. The arrays were washed for 5 minutes in Agilent GE Wash Buffer 1 with Triton X-102 and then for another 5 minutes in Agilent GE Wash Buffer 2 with Triton X-102.
After washing, all slides were immediately scanned using a PerkinElmer ScanArray Express at 5 micron resolution. The resulting images were quantified using Agilent's Feature Extraction software. The differentially expressed microRNA sequences were then identified using a standard protocol developed for gene array data processing. The sample or gene clustering and array hybridization heatmap were generated using MeV4 software package from The Institute for Genomic Research (TIGR) (available at http://www.tigr.org/tdb/microarray/).
Quantitative PCR (QPCR) with microRNA specific primer sets were used to confirm the results from array hybridization. In brief, a SYBR Green based method, miScript real-time PCR (Qiagen Inc. Valencia, Calif., USA), or TaqMan primer set from Apply Biosystems, was used with 50 ng of total RNA from each sample. The first strand cDNA was generated according to the manufacturer's instruction. Approximately 2.5 ng of cDNA was used in the PCR reaction. The yield of 18 to 20 base pair fragments (based on SYBR Green intensity) corresponding to the specific microRNA species was monitored with the 7900HT fast real-time PCR system from Applied Biosystems (Applied Biosystems, Foster City, Calif.). QPCR results were analyzed by SDS 2.2.2, with a manual CT threshold value of 0.2.
Example 1
This example showed that microRNA sequences could be used as a marker to detect liver injury. Mice were used as the experimental model.
6-month-old male C57/B6 mice were grouped into control and treatment groups with 4 animals in each group. The mice then fasted for 24 hours prior to a single intraperitoneal injection of either (a) 300 mg/kg of acetaminophen in phosphate buffer saline (PBS) (treatment group); (b) or PBS (control group). Mice were sacrificed at different time points post-exposure (12 hr, 24 hr, 48 hr, 72 hr, and 120 hr) and plasma and liver samples were collected. Part of the liver samples were sectioned and examined by a pathologist and the serum alanine transaminase (ALT) levels were also determined to confirm as well as assess the severity of liver injury.
Total RNA was isolated from collected samples to conduct comprehensive microRNA analyses. To assess the level of microRNA in liver tissues, a microRNA array from Agilent was used. The RNA samples were labeled and processed according to the manufacturer's recommended protocols. The data from each array were extracted, normalized and compared following a standard gene expression microarray method.
The expression levels of various microRNA sequences in the liver tissues were used to generate a microRNA profile and used to assess tissue injury. Differentially expressed microRNA sequences were clustered using the Hierarchical clustering method and the result is shown in FIG. 2. The different time points are indicated on the top, while the identity of individual microRNA sequences is listed on the right. (The identifying labels correspond to those in the miRNA Registry maintained at the Sanger Institute.) The hybridization intensity of individual microRNA sequences is represented in different colors as indicated on top of the figure (yellow representing the highest expression and blue representing the lowest expression signal). The microRNA profile clearly indicates that the levels of some microRNA sequences were changed by the exposure to acetaminophen.
Example 2
This example showed that the levels of specific microRNA sequences in the serum or plasma could be used to assess drug-induced liver injury.
The male C57/B6 mice were randomly grouped into two groups, a treatment group (3 animals) and control group (4 animals). They fasted for 24 hours prior to a single intraperitoneal injection of either (a) 300 mg/kg of acetaminophen in PBS (treatment group); or (b) PBS (control group). Mice were sacrificed at 24 hours post exposure, the plasma samples were collected and RNA was isolated.
The expression levels of microRNA sequences in the serum were used to make a microRNA profile. The differentially expressed microRNA sequences between the treatment group and the control group were clustered with the Hierarchical clustering method and is shown in FIG. 3. The result clearly indicated that the levels of certain microRNA sequences in the serum could be used as an indication of the acetaminophen toxicity.
Example 3
This example showed that the levels of specific microRNA sequences in the serum or plasma could be used as an early indication of drug-induced liver injury.
The male C57/B6 mice were randomly grouped into nine different groups with 4 animals in each group. They fasted for 24 hours prior to a single intraperitoneal injection with either (a) 75 mg/Kg of acetaminophen in PBS (treatment 1); (b) 150 mg/Kg of acetaminophen in PBS (treatment 2); (c) 300 mg/Kg of acetaminophen in PBS (treatment 3); or (d) PBS (control group). Mice were sacrificed and plasma samples were collected at 1, 3 and 24 hours post-exposure. The nine groups were: 1) 1 hour control; 2) 1 hour treatment 1; 3) 1 hour treatment 2; 4) 1 hour treatment 3; 5) 3 hour control; 6) 3 hour treatment 1; 7) 3 hour treatment 2; 8) 3 hour treatment 3; and 9) 24 hour treatment 3. The group at 24 hr post-exposure received only the highest dose (300 mg/kg) to serve as a positive control. The expression levels of two different microRNA sequences, mir-486 and mir-122, in the serum were profiled by quantitative polymerase chain reactions (Q-PCR).
The median intensities (Z-axes) from each group (X-axis) at PCR cycle number 19 were plotted. This graph is shown in FIG. 4. Both mir-486 (red bars) and mir-122 (green bars) intensities showed dose-dependent changes at 3 hr post-exposure. The intensity of mir-122 at 300 mg/kg was almost the same between 3 hr and 24 hr post-exposure. Clear changes were observed in the samples obtained at one hour post-acetaminophen injection. The results clearly indicated that the levels of selected microRNA sequences, such as mir-122 and mir-486, in the serum could be used as an early indication of tissue injury.
Next, the ratios of the median intensities (green bars) and average intensities (blue bars) from each group at PCR cycle number 19 were plotted. This graph is shown as FIG. 5. As expected, the ratios of both median and average intensities showed dose-dependent changes at 3 hr post-exposure. The ratio also clearly indicated the difference between 3 hr and 24 hr post-exposure. This result clearly indicated the ratio of selected microRNA sequences, such as mir-122 and mir-486, in the serum could be used as an early indication of tissue injury.
Example 4
This example showed that microRNA could be used in assessing neurological disorders. The microRNA expression patterns in brain tissues obtained from normal and prion infected animals were profiled as described above. The results are shown in FIG. 6. The result clearly indicated differences between normal and diseased samples.
Example 5
This example showed that microRNA could be used in assessing the health status of lungs. The microRNA expression patterns in lung tissues obtained from normal and diseased animals were profiled as described above. The results are shown in FIG. 7. The result clearly indicated there were differences on microRNA expression as the disease progressed (from 1 to 6 where 6 has the most serious disease condition) and a number of microRNA sequences are different between normal and disease samples. Thus, specific microRNA sequences or a panel of microRNA sequences could be used as a tool to assess the health status of lungs.
Example 6
This example showed that different biological pathways or compartments had very different microRNA profiles. The microRNA profiles in serum and urine samples obtained from a normal mouse were profiled as described above, then compared. The result is shown in FIG. 8. The result clearly revealed a significant difference in the microRNA composition in different body fluids. This would allow the development of different biomarkers to be used in different body fluids to assess the health status of tissues. In addition, microRNA sequences in a specific body fluid can be used as a reliable tool to assess the health status of tissues intimately associated with that body fluid, e.g. bladder and kidney tissues to the urine.
Example 7
miRNA profiles from lung tissue and plasma from ILD patients and COPD patients were compared to a control set of miRNA profiles from uninvolved lung tissue obtained from lung cancer resections (controls) and a control set of miRNA profiles from plasma samples obtained from clinically normal donors (collected by the Marsh lab). The miRNA profiles were compared in various pairwise combinations to determine which miRNA sequences were overexpressed and thus useful for diagnostic purposes. The miRNA profiles were obtained using a microarray kit available from Agilent, which generally detected a given miRNA with usually two independent oligonucleotide (oligo) targets and four or more in some cases.
FIG. 9 shows the graph comparing miRNA expression levels in control plasma with ILD plasma. Note the log scale. MiRNA with expression values that differ substantially between the two samples reside away from the diagonal line (i.e. y=x) that would represent equivalent expression in the two samples. This graph indicated that many miRNAs in ILD plasma are expressed at substantially higher levels than in the control plasma. There also appear to be a few miRNAs in the control profile that were expressed at relatively lower levels than in the ILD profile.
Next, in order to reduce the complexity of the data, the similarity of the signals returned by the different oligos that were present on the Agilent array and designed to detect a given miRNA were examined. For example, miRNA 1225-5p (i.e. mir-1225-5p) was about 3-fold over expressed in ILD plasma. For mir-1225-5p, four probe oligos were used in the Agilent array. FIG. 10A shows the signal in the ILD plasma samples (n=24). FIG. 10B shows the signal in the control plasma samples (n=6). As seen, all four probe oligos gave signals in the ILD and control plasma samples. In addition, the signal strengths were within a factor of about two to each other, even though these oligos differ slightly in sequence.
17 miRNA sequences were identified that appeared to be overexpressed in the ILD plasma samples. FIG. 11 shows the signal strength for all of the oligos that targeted these 17 sequences. The ILD plasma signals are shown as blue diamonds and the control plasma signals are shown as pink squares. As seen, the signal strengths for all of the independent oligo probes were reasonably close (i.e. within a factor of 2.5).
Since the signals from the independent probes were close, the data from all probes was combined. Then, the mean expression level for each miRNA was calculated and miRNA sequences which were relatively overexpressed in the ILD plasma samples were identified. (An alternative analysis path could have been to choose the data from one or two of the independent probes and identify overexpressed miRNA sequences based on that data.)
FIG. 12 shows the resulting graph with the mean and one standard deviation identified. Again, the ILD plasma signals are shown as blue diamonds and the control plasma signals are shown as pink squares. The expression of the displayed microRNAs was at least two-fold higher in the ILD plasma samples than in the control plasma samples (2× was an arbitrary value). Table 3 lists the specific data of FIG. 12.












TABLE 3






mean level
St. Dev.
mean level
St. Dev.
mean ILD/


microRNA sequence
(ILD)
(ILD)
(control)
(control)
mean control




















hsv1-miR-H1
371
100
207
13.9
1.8


hsa-miR-223
688
317
338
105
2.0


hsa-miR-575
416
17
198
21.4
2.1


hsa-miR-483-5p
589
179
259
70.1
2.3


hsa-miR-150*
659
142
267
35.6
2.5


hsa-miR-22
958
589
376
91
2.5


hsa-miR-1225-5p
3361
873
1201
324.4
2.8


hsa-miR-939
644
463
224
36.5
2.9


hsa-miR-135a*
499
127
172
33.2
2.9


hsa-miR-940
1316
906
341
45.6
3.9


hsa-miR-134
830
9
201
50
4.1


hcmv-miR-UL70-3p
721
129
166
25.2
4.3


hsa-miR-630
3349
65
683
100.3
4.9


hsv1-miR-LAT
1250
490
223
64.2
5.6


kshv-miR-K12-3
3542
2912
588
422.3
6.0


hsa-miR-638
18055
11123
1670
716.2
10.8


hsa-miR-923
42215
40796
310
9883.1
136.2









Based on the standard deviations, the 17 miRNAs that met this criterion can be divided into three groups. 11 miRNAs (UL70-3p, 1225-5p, 134, 135a*, 150*, 483-5p, 575, 630, 638, H1, and LAT) were likely to be differentially expressed between ILD and control with high confidence. There was intermediate confidence for 4 miRNAs (mir-22, 223, 939, and 940); and lower confidence for miRNAs 923 and K12-3.
The degree of over expression displayed by these miRNAs varied over 100-fold, as shown in FIG. 13. Note the log scale.
Next, the miRNA that were expressed at a higher level in control plasma than ILD plasma were investigated. An arbitrary expression level of 250 or greater and 3.0 fold or greater relative overexpression as used to screen out marginal miRNA candidates. Three miRNA sequences passed this screen as shown in Table 4.












TABLE 4






mean level
St. Dev.
mean level
St. Dev.
mean control/


microRNA sequence
(ILD)
(ILD)
(control)
(control)
mean ILD




















hsa-miR-451 oligo 1
729
1695.917
5274
5362.923
7


hsa-miR-451; oligo 2
487
1096.762
3527.167
3421.076
7


hsa-miR-451; oligo 1
390
375
5274

13.5


w/o outlier


hsa-miR-451; oligo 2
268
243
3527

13.1


w/o outlier


hsa-miR-448
64
38.3
253
93
4


hsa-miR-92a-2*
78
46.94568
253.6667
109.7154
3









hsa-miR-448 and hiss-miR-92-a-2* were just over the threshold for inclusion and showed low absolute expression. The standard deviation for hsa-miR-451 was rather large when all samples (n=24) were used. However, when one outlier was removed (n=23), the standard deviations improved, as did the ratios. hsa-miR-451 was expressed ten times higher in control plasma relative to ILD plasma.
One of the miRNAs that is overexpressed in control serum relative to ILD serum, in combination with a miRNA that is overexpressed in ILD serum relative to control serum, could be used in a simple “top scoring pair” test for ILD.
Example 8
Using the same data as in Example 7, the expression of miRNA in both ILD plasma and ILD tissue was examined. That graph is shown in FIG. 14. While many miRNAs that were expressed in ILD plasma had little or no expression in ILD tissue, most of those that were expressed in tissue had at least some expression in plasma. Those miRNA sequences that had signal strength of at least 1000 in both tissue and plasma (an arbitrarily chosen value) are listed in Table 5. The ratio of the expression for the miRNA sequence was also compared to the average expression of all the miRNAs in the sample and is labeled as “overexpression ratio.”











TABLE 5






mean

mean



microRNA
level
overexpression
level
overexpression


sequence
(plasma)
ratio
(tissue)
ratio



















hsa-miR-1225-5p
4043
9.8
1189.0
3.1


hsa-miR-21
455
1.1
9938.7
26.0


hsa-miR-22
1374
3.3
5555.8
14.5


hsa-miR-223
835
2.0
1303.4
3.4


hsa-miR-451
729
1.8
6564.4
17.2


hsa-miR-638
25920
62.6
1084.6
2.8


hsa-miR-923
71062
171.7
6114.7
16.0


kshv-miR-K12-3
5602
13.5
498.0
1.3









hsa-miR-21 was present here, but not in Tables 3 or 4, while the other seven were also listed in either Table 3 or 4.
Example 9
Using the same data as in Example 7, the expression of miRNA in both ILD lung tissue and control lung tissue was examined. That graph is shown in FIG. 15. The expression levels for certain miRNA sequences, as well as those overexpressed in ILD lung tissue, are listed below in Table 6.












TABLE 6







microRNA
mean level
mean level
mean ILD/



sequence
(ILD)
(control)
mean control





















hsa-miR-923
6114.7
22421.33
0.3



hsa-miR-22
5555.8
4771.83
1.2



hsa-miR-29a
4881.6
2183.67
2.2



hsa-miR-145
3759.9
1449.50
2.6



hsa-miR-26a
3187.3
1123.33
2.8



hsa-let-7c
4405.5
1336.33
3.3



hsa-miR-23a
4396.1
1144.83
3.8



hsa-miR-21
9938.7
2450.83
4.1



hsa-miR-125b
3843.2
939.17
4.1



hsa-miR-27a
3090.2
720.50
4.3



hsa-let-7a
5709.2
1039.33
5.5



hsa-let-7f
3352.5
504.83
6.6



hsa-miR-451
6564.4
383.33
17.1










miRNA hsa-miR-923 was over 130-fold over-expressed in ILD plasma relative to control plasma (Table 3), but it is under-expressed in ILD lung tissue relative to control lung tissue (Table 6). This suggests the tissue or cell of origin for this miRNA may be within the blood itself, or at least not the ILD lung. Similarly, miRNA hsa-miR-22 is expressed three times higher in ILD plasma compared to control plasma (Table 3), but is expressed at nearly the same level in ILD lung and control lung tissue (Table 6). Other miRNAs that are characteristic of ILD, such as miRNA-451, were elevated in ILD lung tissue (17× in Table 6) but not in ILD plasma (see Table 4).
In total 17 miRNA sequences were identified as containing diagnostic information related to ILD. Those 17 sequences are listed in Table 3.
Example 10
The plasma sample data (control, COPD, and ILD) was separately analyzed using the Panorama suite of tools and consisted of the following steps: (A) Normalization; (B) Quality Control; (C) Cluster Analysis; (D) Panel Selection; and (E) Comparison. Each step is explained in more detail below.
In Normalization, the following steps occurred. First, missing values were left unchanged instead of imputing a value. Second, each sample was normalized independently of other samples. Third, the natural log was applied to the values for each sample; then the values were adjusted by the median and standard deviation. FIG. 16 shows the results of normalization.
In Quality Control, the quality of the data was assessed before and after normalization. FIG. 17A shows the Pearson correlation distribution before normalization. This figure correlated the score for each miRNA across the samples to the total miRNA expression level across the samples. This figure showed that the vast majority of miRNA sequences had the same expression profile across the samples, and furthermore, this expression profile is the total miRNA level per sample—this is the dominant feature of the dataset. FIG. 17B shows the Pearson correlation distribution after normalization. Normalization improved the quality of the dataset. The distribution in FIG. 17B was much less skewed than that of FIG. 17A.
In Cluster Analysis, the normalized miRNA data was clustered using multi-dimensional scaling (MDS); the results are presented in FIG. 18. This was an unsupervised analysis without samples being identified by group or miRNA selected that differentiated the groups. There were several notable features of this plot. First, the samples within each group clustered together showing uniformity in miRNA expression. The exception was IPF tissue where a few outliers occurred, likely due to sample handling. Second, the tissue groups cluster away from the plasma groups. Third, within the plasma groups, the COPD and ILD plasma samples overlapped and were clustered away from the plasma control samples.
Performing a T-test at significance level 0.01, 194 miRNA were found to separate the plasma control samples from the ILD plasma samples. Performing 50 permutation tests revealed that the expected number of miRNA, by chance alone, was 21.5, yielding a false discovery rate of 11%. The p-value distribution of all miRNA is shown in FIG. 19. There was a uniform distribution over most p-values, except an increase below 0.05. This was consistent with the hypothesis that there are miRNA that segregate the two sample groups.
In Panel Selection, panels that segregated the control plasma samples from the ILD plasma samples were selected using Area Under the Curve (AUC). AUC is a measure of diagnostic segregation. It ranges from 0 to 1 where 1 indicates perfect segregation. The AUC of individual miRNA can be determined independently of each other allowing for straightforward selection of the best segregating miRNA. In addition, the combined AUC of panels of miRNA can be calculated to assess how well groups of miRNA work together to segregate control plasma samples from ILD plasma samples.
To calculate the combined AUC, a combination rule must be established. The combination rule used here was majority consensus: if the strict majority of miRNA classified a sample as diseased (i.e. ILD or IPF) then the sample was classified diseased, otherwise, the sample was classified as normal.
FIG. 20 is three charts showing the distribution of directional bias (upper left), the AUC distribution (upper right), and the standard deviation for the ILD group (lower left). The number of miRNA higher in the control samples than the ILD samples was essentially the same as the opposite direction. The distribution of AUC scores for all miRNA was centered about 0.6 which is expected. A small rise around 0.95 indicated the presence of miRNA that distinguish the control and ILD samples. The distribution of miRNA expression standard deviations showed that overall, variability was similar across miRNA (note that normalization is done by sample, not by miRNA).
In Comparison, the data was analyzed. Using an AUC threshold of 0.95, 57 out of 2421 (2.4%) miRNA probes were selected. Table 7 contains the oligo probe used for the miRNA, the corresponding miRNA, p-value, AUC, and number of panels of 3 miRNA above combined AUC 0.99 that each miRNA participated in. If the miRNA was expressed higher in the control sample than the ILD sample, the column “Control>ILD” was marked with a Y.













TABLE 7





Probe
miRNA
Control > ILD
P-value
AUC
St. Dev.
# Panels





















A_25_P00010804
hsa-miR-518d-3p
Y
9.96E−06
1.00
0.42
945


A_25_P00013406
hsa-miR-135a*
N
1.56E−09
1.00
0.55
681


A_25_P00013825
hiv1-miR-H1
N
4.98E−06
1.00
0.66
656


A_25_P00011724
hcmv-miR-UL70-3p
N
1.23E−13
1.00
0.62
621


A_25_P00013407
hsa-miR-135a*
N
1.41E−12
1.00
0.62
615


A_25_P00013090
hsa-miR-940
N
8.13E−09
1.00
0.88
581


A_25_P00012074
hsa-miR-139-3p
N
3.27E−07
1.00
0.63
572


A_25_P00013689
kshv-miR-K12-3
N
1.09E−11
1.00
0.60
572


A_25_P00012231
hsa-miR-134
N
9.11E−11
1.00
0.64
548


A_25_P00012230
hsa-miR-134
N
6.22E−13
1.00
0.65
539


A_25_P00010345
hsa-miR-557
N
2.47E−06
1.00
0.55
534


A_25_P00013829
hsv1-miR-LAT
N
1.52E−11
1.00
0.77
500


A_25_P00011725
hcmv-miR-UL70-3p
N
6.62E−13
1.00
0.65
463


A_25_P00013830
hsv1-miR-LAT
N
1.42E−12
1.00
0.65
449


A_25_P00013831
hsv1-miR-LAT
N
2.62E−09
1.00
0.84
449


A_25_P00013087
hsa-miR-939
N
3.19E−13
1.00
0.53
362


A_25_P00013453
hsa-miR-150*
N
1.67E−08
1.00
0.58
362


A_25_P00014907
hsa-miR-1224-5p
N
6.43E−07
1.00
0.47
344


A_25_P00013326
hsa-miR-187*
N
1.24E−06
1.00
0.70
324


A_25_P00013828
hsv1-miR-LAT
N
1.33E−12
1.00
0.80
299


A_25_P00011853
ebv-miR-BART13
N
4.09E−08
0.99
0.47
435


A_25_P00015004
hsa-miR-1226*
N
2.14E−06
0.99
0.66
362


A_25_P00010687
hsa-miR-498
N
7.82E−08
0.99
0.46
498


A_25_P00011096
hsa-miR-572
N
3.25E−08
0.99
0.81
420


A_25_P00010808
hsa-miR-575
N
2.42E−07
0.99
0.85
415


A_25_P00014908
hsa-miR-1224-5p
N
4.81E−07
0.99
0.60
344


A_25_P00014896
hsa-miR-575
N
4.23E−07
0.99
0.82
316


A_25_P00010641
hsa-miR-601
N
2.63E−08
0.99
0.49
218


A_25_P00013086
hsa-miR-939
N
1.09E−07
0.99
0.49
179


A_25_P00013450
hsa-miR-150*
N
2.91E−08
0.99
0.67
178


A_25_P00010344
hsa-miR-557
N
5.45E−06
0.98
0.65
684


A_25_P00013327
hsa-miR-187*
N
1.06E−05
0.98
0.65
347


A_25_P00015003
hsa-miR-1226*
N
2.15E−06
0.98
0.61
179


A_25_P00013451
hsa-miR-150*
N
1.39E−06
0.98
0.67
178


A_25_P00014906
hsa-miR-1224-5p
N
2.03E−07
0.97
0.55
330


A_25_P00012059
hsa-miR-198
N
1.13E−05
0.97
0.67
296


A_25_P00011799
hsv1-miR-H1
N
6.61E−07
0.97
0.98
268


A_25_P00011097
hsa-miR-572
N
6E−05
0.97
0.60
203


A_25_P00013452
hsa-miR-150*
N
8.41E−07
0.97
0.62
179


A_25_P00010669
hsa-miR-326
N
7.84E−05
0.97
0.83
177


A_25_P00014892
hsa-miR-539
N
0.000543
0.97
0.59
722


A_25_P00010444
hsa-miR-448
N
6.72E−05
0.97
0.58
581


A_25_P00012030
hsa-miR-92a
N
1.18E−05
0.97
0.86
343


A_25_P00013448
hsa-miR-149*
N
1.95E−05
0.97
0.58
260


A_25_P00014861
hsa-miR-483-5p
N
2.9E−07
0.97
0.62
144


A_25_P00010228
hsa-miR-623
N
5.7E−05
0.96
0.79
356


A_25_P00012419
hsa-miR-423-5p
N
0.000569
0.96
0.87
336


A_25_P00011796
hsv1-miR-H1
N
2.96E−06
0.96
0.69
268


A_25_P00011854
ebv-miR-BART13
N
0.000141
0.96
0.73
268


A_25_P00011719
ebv-miR-BART7
N
7.87E−05
0.96
0.57
224


A_25_P00012459
hsa-miR-483-5p
N
2.03E−07
0.96
0.66
178


A_25_P00013449
hsa-miR-149*
N
1.55E−06
0.96
0.66
164


A_25_P00012262
hsa-miR-320
N
5.41E−05
0.96
0.73
84


A_25_P00011342
hsa-miR-765
N
5.08E−06
0.96
0.38
57


A_25_P00013324
hsa-miR-187*
N
5.29E−05
0.95
0.68
477


A_25_P00010227
hsa-miR-623
N
4.89E−05
0.95
0.97
362


A_25_P00012031
hsa-miR-92a
N
2.59E−05
0.95
1.07
343









Interestingly, only 3 of the 57 miRNA were higher in the control samples than the ILD samples, despite the near equivalence of miRNA higher in control samples over ILD samples, as compared to the opposite among all miRNA (see FIG. 20). 20 of the 57 miRNA had a perfect AUG score of 1.00. Not shown here is the fact that there were also many panels of three miRNA that had a perfect AUG score of 1.00.
There were also unique miRNA among the 57 miRNA probes, which illustrated a strong redundancy among probes. This redundancy could be used as a selection criterion.
11 of the 17 miRNA sequences listed in Table 3 of Example 7 also appear in Table 7. They are shown in bold text in Table 7.
The claims refer to identifying “at least one” or “at least two” differentially expressed microRNA sequences in a microRNA profile, wherein the differentially expressed microRNA sequences are selected from a list. This language should be construed as meaning that the microRNA sequence selected from the list is identified as a differentially expressed microRNA sequence in the microRNA profile.
It is contemplated that assays or microRNA profiles would test for only specific microRNA sequences, such as those identified above.
In some embodiments, an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-630, miR-134, hcmv-miR-UL70-3p, miR-1225-5p, miR-135a*, miR-150*, miR-22, miR-223, miR-483-5p, miR-575, miR-638, miR-923, miR-939, miR-940, hsv1-miR-H1, hsv1-miR-LAT, kshv-miR-K12-3, and human orthologs thereof. In other embodiments, at least three of these sequences is tested for. In particular embodiments, all 17 of these sequences are tested for. Specific pairs of these 17 microRNA sequences include those listed in Table 8:








TABLE 8







miR-630, miR-134
miR-630, hcmv-miR-UL70-3p


miR-630, miR-1225-5p
miR-630, miR-135a*


miR-630, miR-150*
miR-630, miR-22


miR-630, miR-223
miR-630, miR-483-5p


miR-630, miR-575
miR-630, miR-638


miR-630, miR-923
miR-630, miR-939


miR-630, miR-940
miR-630, hsv1-miR-H1


miR-630, hsv1-miR-LAT
miR-630, kshv-miR-K12-3


miR-134, hcmv-miR-UL70-3p
miR-134, miR-1225-5p


miR-134, miR-135a*
miR-134, miR-150*


miR-134, miR-22
miR-134, miR-223


miR-134, miR-483-5p
miR-134, miR-575


miR-134, miR-638
miR-134, miR-923


miR-134, miR-939
miR-134, miR-940


miR-134, hsv1-miR-H1
miR-134, hsv1-miR-LAT


miR-134, kshv-miR-K12-3
hcmv-miR-UL70-3p, miR-1225-5p


hcmv-miR-UL70-3p, miR-135a*
hcmv-miR-UL70-3p, miR-150*


hcmv-miR-UL70-3p, miR-22
hcmv-miR-UL70-3p, miR-223


hcmv-miR-UL70-3p, miR-483-5p
hcmv-miR-UL70-3p, miR-575


hcmv-miR-UL70-3p, miR-638
hcmv-miR-UL70-3p, miR-923


hcmv-miR-UL70-3p, miR-939
hcmv-miR-UL70-3p, miR-940


hcmv-miR-UL70-3p, hsv1-miR-H1
hcmv-miR-UL70-3p, hsv1-miR-LAT


hcmv-miR-UL70-3p, kshv-miR-
miR-1225-5p, miR-135a*


K12-3


miR-1225-5p, miR-150*
miR-1225-5p, miR-22


miR-1225-5p, miR-223
miR-1225-5p, miR-483-5p


miR-1225-5p, miR-575
miR-1225-5p, miR-638


miR-1225-5p, miR-923
miR-1225-5p, miR-939


miR-1225-5p, miR-940
miR-1225-5p, hsv1-miR-H1


miR-1225-5p, hsv1-miR-LAT
miR-1225-5p, kshv-miR-K12-3


miR-135a*, miR-150*
miR-135a*, miR-22


miR-135a*, miR-223
miR-135a*, miR-483-5p


miR-135a*, miR-575
miR-135a*, miR-638


miR-135a*, miR-923
miR-135a*, miR-939


miR-135a*, miR-940
miR-135a*, hsv1-miR-H1


miR-135a*, hsv1-miR-LAT
miR-135a*, kshv-miR-K12-3


miR-150*, miR-22
miR-150*, miR-223


miR-150*, miR-483-5p
miR-150*, miR-575


miR-150*, miR-638
miR-150*, miR-923


miR-150*, miR-939
miR-150*, miR-940


miR-150*, hsv1-miR-H1
miR-150*, hsv1-miR-LAT


miR-150*, kshv-miR-K12-3
miR-22, miR-223


miR-22, miR-483-5p
miR-22, miR-575


miR-22, miR-638
miR-22, miR-923


miR-22, miR-939
miR-22, miR-940


miR-22, hsv1-miR-H1
miR-22, hsv1-miR-LAT


miR-22, kshv-miR-K12-3
miR-223, miR-483-5p


miR-223, miR-575
miR-223, miR-638


miR-223, miR-923
miR-223, miR-939


miR-223, miR-940
miR-223, hsv1-miR-H1


miR-223, hsv1-miR-LAT
miR-223, kshv-miR-K12-3


miR-483-5p, miR-575
miR-483-5p, miR-638


miR-483-5p, miR-923
miR-483-5p, miR-939


miR-483-5p, miR-940
miR-483-5p, hsv1-miR-H1


miR-483-5p, hsv1-miR-LAT
miR-483-5p, kshv-miR-K12-3


miR-575, miR-638
miR-575, miR-923


miR-575, miR-939
miR-575, miR-940


miR-575, hsv1-miR-H1
miR-575, hsv1-miR-LAT


miR-575, kshv-miR-K12-3
miR-638, miR-923


miR-638, miR-939
miR-638, miR-940


miR-638, hsv1-miR-H1
miR-638, hsv1-miR-LAT


miR-638, kshv-miR-K12-3
miR-923, miR-939


miR-923, miR-940
miR-923, hsv1-miR-H1


miR-923, hsv1-miR-LAT
miR-923, kshv-miR-K12-3


miR-939, miR-940
miR-939, hsv1-miR-H1


miR-939, hsv1-miR-LAT
miR-939, kshv-miR-K12-3


miR-940, hsv1-miR-H1
miR-940, hsv1-miR-LAT


miR-940, kshv-miR-K12-3
hsv1-miR-H1, hsv1-miR-LAT


hsv1-miR-H1, kshv-miR-K12-3
hsv1-miR-LAT, kshv-miR-K12-3









In other embodiments, an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-630, miR-134, hcmv-miR-UL70-3p, miR-1225-5p, miR-135a*, miR-150*, miR-483-5p, miR-575, miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. In other embodiments, at least three of these sequences is tested for. In particular embodiments, all 11 of these sequences are tested for. Specific pairs of these 11 microRNA sequences include those listed in Table 9:








TABLE 9







miR-630, miR-134
miR-630, hcmv-miR-UL70-3p


miR-630, miR-1225-5p
miR-630, miR-135a*


miR-630, miR-150*
miR-630, miR-483-5p


miR-630, miR-575
miR-630, miR-638


miR-630, hsv1-miR-H1
miR-630, hsv1-miR-LAT


miR-134, hcmv-miR-UL70-3p
miR-134, miR-1225-5p


miR-134, miR-135a*
miR-134, miR-150*


miR-134, miR-483-5p
miR-134, miR-575


miR-134, miR-638
miR-134, hsv1-miR-H1


miR-134, hsv1-miR-LAT
hcmv-miR-UL70-3p, miR-1225-5p


hcmv-miR-UL70-3p, miR-135a*
hcmv-miR-UL70-3p, miR-150*


hcmv-miR-UL70-3p, miR-483-5p
hcmv-miR-UL70-3p, miR-575


hcmv-miR-UL70-3p, miR-638
hcmv-miR-UL70-3p, hsv1-miR-H1


hcmv-miR-UL70-3p, hsv1-miR-LAT
miR-1225-5p, miR-135a*


miR-1225-5p, miR-150*
miR-1225-5p, miR-483-5p


miR-1225-5p, miR-575
miR-1225-5p, miR-638


miR-1225-5p, hsv1-miR-H1
miR-1225-5p, hsv1-miR-LAT


miR-135a*, miR-150*
miR-135a*, miR-483-5p


miR-135a*, miR-575
miR-135a*, miR-638


miR-135a*, hsv1-miR-H1
miR-135a*, hsv1-miR-LAT


miR-150*, miR-483-5p
miR-150* miR-575


miR-150*, miR-638
miR-150*, hsv1-miR-H1


miR-150*, hsv1-miR-LAT
miR-483-5p, miR-575


miR-483-5p, miR-638
miR-483-5p, hsv1-miR-H1


miR-483-5p, hsv1-miR-LAT
miR-575, miR-638


miR-575, hsv1-miR-H1
miR-575, hsv1-miR-LAT


miR-638, hsv1-miR-H1
miR-638, hsv1-miR-LAT


hsv1-miR-H1, hsv1-miR-LAT









In some embodiments, an assay or microRNA profile tests for two or more microRNA sequences. At least one of the microRNA sequences tested for is selected from the group consisting of miR-630, hcmv-miR-UL70-3p, miR-1225-5p, miR-134, miR-135a*, miR-150*, miR-483-5p, miR-575, miR-638, hsv1-miR-H1, hsv1-miR-LAT, and human orthologs thereof. At least one of the microRNA sequences tested for is selected from the group consisting of miR-451, miR-448, and miR-92a-2*. In particular embodiments, miR-451 is one of the microRNA sequences tested for. Specific pairs of these microRNA sequences include those listed in Table 10:










TABLE 10









miR-630, miR-451
miR-630, miR-448



miR-630, miR-92a-2*
hcmv-miR-UL70-3p, miR-451



hcmv-miR-UL70-3p, miR-448
hcmv-miR-UL70-3p, miR-92a-2*



miR-1225-5p, miR-451
miR-1225-5p, miR-448



miR-1225-5p, miR-92a-2*
miR-134, miR-451



miR-134, miR-448
miR-134, miR-92a-2*



miR-135a*, miR-451
miR-135a*, miR-448



miR-135a*, miR-92a-2*
miR-150*, miR-451



miR-150*, miR-448
miR-150*, miR-92a-2*



miR-483-5p, miR-451
miR-483-5p, miR-448



miR-483-5p, miR-92a-2*
miR-575, miR-451



miR-575, miR-448
miR-575, miR-92a-2*



miR-638, miR-451
miR-638, miR-448



miR-638, miR-92a-2*
hsv1-miR-H1, miR-451



hsv1-miR-H1, miR-448
hsv1-miR-H1, miR-92a-2*



hsv1-miR-LAT, miR-451
hsv1-miR-LAT, miR-448



hsv1-miR-LAT, miR-92a-2*










In some embodiments, an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-451, miR-923, miR-1225-5p, miR-22, miR-223, miR-638, kshv-miR-K12-3, and human orthologs thereof. In other embodiments, at least three of these sequences is tested for. In particular embodiments, all seven of these sequences are tested for. Specific pairs of these seven microRNA sequences include those listed in Table 11:










TABLE 11









miR-451, miR-923
miR-451, miR-1225-5p



miR-451, miR-22
miR-451, miR-223



miR-451, miR-638
miR-451, kshv-miR-K12-3



miR-923, miR-1225-5p
miR-923, miR-22



miR-923, miR-223
miR-923, miR-638



miR-923, kshv-miR-K12-3
miR-1225-5p, miR-22



miR-1225-5p, miR-223
miR-1225-5p, miR-638



miR-1225-5p, kshv-miR-K12-3
miR-22, miR-223



miR-22, miR-638
miR-22, kshv-miR-K12-3



miR-223, miR-638
miR-223, kshv-miR-K12-3



miR-638, kshv-miR-K12-3










In some embodiments, an assay or microRNA profile tests for at least two microRNA sequences selected from the group consisting of miR-940, miR-134, miR-135a*, miR-150*, miR-483-5p, miR-575, miR-939, hsv1-miR-H1, kshv-miR-K12-3, hsv1-miR-LAT, hcmv-miR-UL70-3p, and human orthologs thereof. In other embodiments, at least three of these sequences is tested for. In particular embodiments, all 11 of these sequences are tested for. Specific pairs of these 11 microRNA sequences include those listed in Table 12:








TABLE 12







miR-940, miR-134
miR-940, miR-135a*


miR-940, miR-150*
miR-940, miR-483-5p


miR-940, miR-575
miR-940, miR-939


miR-940, hsv1-miR-H1
miR-940, kshv-miR-K12-3


miR-940, hsv1-miR-LAT
miR-940, hcmv-miR-UL70-3p


miR-134, miR-135a*
miR-134, miR-150*


miR-134, miR-483-5p
miR-134, miR-575


miR-134, miR-939
miR-134, hsv1-miR-H1


miR-134, kshv-miR-K12-3
miR-134, hsv1-miR-LAT


miR-134, hcmv-miR-UL70-3p
miR-135a*, miR-150*


miR-135a*, miR-483-5p
miR-135a*, miR-575


miR-135a*, miR-939
miR-135a*, hsv1-miR-H1


miR-135a*, kshv-miR-K12-3
miR-135a*, hsv1-miR-LAT


miR-135a*, hcmv-miR-UL70-3p
miR-150*, miR-483-5


miR-150*, miR-575
miR-150*, miR-939


miR-150*, hsv1-miR-H1
miR-150*, kshv-miR-K12-3


miR-150*, hsv1-miR-LAT
miR-150*, hcmv-miR-UL70-3p


miR-483-5p, miR-575
miR-483-5p, miR-939


miR-483-5p, hsv1-miR-H1
miR-483-5p, kshv-miR-K12-3


miR-483-5p, hsv1-miR-LAT
miR-483-5p, hcmv-miR-UL70-3p


miR-575, miR-939
miR-575, hsv1-miR-H1


miR-575, kshv-miR-K12-3
miR-575, hsv1-miR-LAT


miR-575, hcmv-miR-UL70-3p
miR-939, hsv1-miR-H1


miR-939, kshv-miR-K12-3
miR-939, hsv1-miR-LAT


miR-939, hcmv-miR-UL70-3p
hsv1-miR-H1, kshv-miR-K12-3


hsv1-miR-H1, hsv1-miR-LAT
hsv1-miR-H1, hcmv-miR-UL70-3p


kshv-miR-K12-3, hsv1-miR-LAT
kshv-miR-K12-3, hcmv-miR-



UL70-3p


hsv1-miR-LAT, hcmv-miR-UL70-3p









Appendix A provides a listing of the RNA sequences for all of the microRNA discussed herein, including human orthologs thereof.










Appendix A












Accession

SEQ ID



miRNA name
Number
RNA Sequence
No:














ebv-miR-BART10*
MIMAT0004817
gccaccucuuugguucuguaca
1



 


ebv-miR-BART12
MIMAT0003423
uccugugguguuuggugugguu
2


 


ebv-miR-BART13
MIMAT0003424
uguaacuugccagggacggcuga
3


 


ebv-miR-BART13*
MIMAT0004818
aaccggcucguggcucguacag
4


 


ebv-miR-BART15
MIMAT0003713
gucagugguuuuguuuccuuga
5


 


ebv-miR-BART15p
MIMAT0000999
ucuuaguggaagugacgugcugug
6


 


ebv-miR-BART16
MIMAT0003714
uuagauagagugggugugugcucu
7


 


ebv-miR-BART18-
MIMAT0003717
ucaaguucgcacuuccuauaca
8


5p


 


ebv-miR-BART19-
MIMAT0003718
uuuuguuugcuugggaaugcu
9


3p


 


ebv-miR-BART19-
MIMAT0004836
acauuccccgcaaacaugacaug
10


5p


 


ebv-miR-BART20-
MIMAT0003719
uagcaggcaugucuucauucc
11


5p


 


ebv-miR-BART2-5p
MIMAT0001000
uauuuucugcauucgcccuugc
12


 


ebv-miR-BART3*
MIMAT0003410
accuaguguuaguguugugcu
13


 


ebv-miR-BART5
MIMAT0003413
caaggugaauauagcugcccaucg
14


 


ebv-miR-BART6-5p
MIMAT0003414
uaagguugguccaauccauagg
15


 


ebv-miR-BART7
MIMAT0003416
caucauaguccaguguccaggg
16


 


ebv-miR-BART7*
MIMAT0004815
ccuggaccuugacuaugaaaca
17


 


ebv-miR-BHRF1-1
MIMAT0000995
uaaccugaucagccccggaguu
18


 


ebv-miR-BHRF1-3
MIMAT0000998
uaacgggaaguguguaagcaca
19


 


hcmv-miR-UL148D
MIMAT0001578
ucguccuccccuucuucaccg
20


 


hcmv-miR-UL22A
MIMAT0001574
uaacuagccuucccgugaga
21


 


hcmv-miR-UL22A*
MIMAT0001575
ucaccagaaugcuaguuuguag
22


 


hcmv-miR-UL70-3p
MIMAT0003343
ggggaugggcuggcgcgcgg
23


 


hcmv-miR-UL70-5p
MIMAT0003342
ugcgucucggccucguccaga
24


 


hcmv-miR-US25-1
MIMAT0001581
aaccgcucaguggcucggacc
25


 


hcmv-miR-US25-2-
MIMAT0001583
auccacuuggagagcucccgcgg
26


3p


 


hcmv-miR-US25-2-
MIMAT0001582
agcggucuguucagguggauga
27


5p


 


hcmv-miR-US4
MIMAT0003341
cgacauggacgugcagggggau
28


 


hiv1-miR-H1
MIMAT0004480
ccagggaggcgugccugggc
29


 


hiv1-miR-N367
MIMAT0004478
acugaccuuuggauggugcuucaa
30


 


hsa-miR-1
MIMAT0000416
ggaauguaaagaaguauguau
31


 


hsa-miR-10b
MIMAT0000254
uacccuguagaaccgaauuugug
32


 


hsa-miR-122
MIMAT0000421
uggagugugacaaugguguuug
33


 


hsa-miR-1224-3p
MIMAT0005459
ccccaccuccucucuccucag
34


 


hsa-miR-1224-5p
MIMAT0005458
gugaggacucgggaggugg
35


 


hsa-miR-1225-3p
MIMAT0005573
ugagccccugugccgcccccag
36


 


hsa-miR-1225-5p
MIMAT0005572
guggguacggcccagugggggg
37


 


hsa-miR-1226*
MIMAT0005576
gugagggcaugcaggccuggaugggg
38


 


hsa-miR-1227
MIMAT0005580
cgugccacccuuuuccccag
39


 


hsa-miR-1228
MIMAT0005583
ucacaccugccucgcccccc
40


 


hsa-miR-1229
MIMAT0005584
cucucaccacugcccucccacag
41


 


hsa-miR-1234
MIMAT0005589
ucggccugaccacccaccccac
42


 


hsa-miR-1237
MIMAT0005592
uccuucugcuccgucccccag
43


 


hsa-miR-1238
MIMAT0005593
cuuccucgucugucugcccc
44


 


hsa-miR-124
MIMAT0000422
uaaggcacgcggugaaugcc
45


 


hsa-miR-125a-3p
MIMAT0004602
acaggugagguucuugggagcc
46


 


hsa-miR-125a-5p
MIMAT0000443
ucccugagacccuuuaaccuguga
47


 


hsa-miR-127-3p
MIMAT0000446
ucggauccgucugagcuuggcu
48


 


hsa-miR-127-5p
MIMAT0004604
cugaagcucagagggcucugau
49


 


hsa-miR-128
MIMAT0000424
ucacagugaaccggucucuuu
50


 


hsa-miR-129*
MIMAT0004548
aagcccuuaccccaaaaaguau
51


 


hsa-miR-129-3p
MIMAT0004605
aagcccuuaccccaaaaagcau
52


 


hsa-miR-130a
MIMAT0000425
cagugcaauguuaaaagggcau
53


 


hsa-miR-133a
MIMAT0000427
uuugguccccuucaaccagcug
54


 


hsa-miR-133b
MIMAT0000770
uuugguccccuucaaccagcua
55


 


hsa-miR-134
MIMAT0000447
ugugacugguugaccagagggg
56


 


hsa-miR-135a*
MIMAT0004595
uauagggauuggagccguggcg
57


 


hsa-miR-136
MIMAT0000448
acuccauuuguuuugaugaugga
58


 


hsa-miR-136*
MIMAT0004606
caucaucgucucaaaugagucu
59


 


hsa-miR-138
MIMAT0000430
agcugguguugugaaucaggccg
60


 


hsa-miR-139-3p
MIMAT0004552
ggagacgcggcccuguuggagu
61


 


hsa-miR-140-3p
MIMAT0004597
uaccacaggguagaaccacgg
62


 


hsa-miR-140-5p
MIMAT0000431
cagugguuuuacccuaugguag
63


 


hsa-miR-141
MIMAT0000432
uaacacugucugguaaagaugg
64


 


hsa-miR-142-3p
MIMAT0000434
uguaguguuuccuacuuuaugga
65


 


hsa-miR-143
MIMAT0000435
ugagaugaagcacuguagcuc
66


 


hsa-miR-146a
MIMAT0000449
ugagaacugaauuccauggguu
67


 


hsa-miR-146b-3p
MIMAT0004766
ugcccuguggacucaguucugg
68


 


hsa-miR-146b-5p
MIMAT0002809
ugagaacugaauuccauaggcu
69


 


hsa-miR-148b
MIMAT0000759
ucagugcaucacagaacuuugu
70


 


hsa-miR-150
MIMAT0000451
ucucccaacccuuguaccagug
71


 


hsa-miR-150*
MIMAT0004610
cugguacaggccugggggacag
72


 


hsa-miR-15a*
MIMAT0004488
caggccauauugugcugccuca
73


 


hsa-miR-15b
MIMAT0000417
uagcagcacaucaugguuuaca
74


 


hsa-miR-181b
MIMAT0000257
aacauucauugcugucggugggu
75


 


hsa-miR-181d
MIMAT0002821
aacauucauuguugucggugggu
76


 


hsa-miR-183
MIMAT0000261
uauggcacugguagaauucacu
77


 


hsa-miR-185
MIMAT0000455
uggagagaaaggcaguuccuga
78


 


hsa-miR-186
MIMAT0000456
caaagaauucuccuuuugggcu
79


 


hsa-miR-187*
MIMAT0004561
ggcuacaacacaggacccgggc
80


 


hsa-miR-188-5p
MIMAT0000457
caucccuugcaugguggaggg
81


 


hsa-miR-190b
MIMAT0004929
ugauauguuugauauuggguu
82


 


hsa-miR-191*
MIMAT0001618
gcugcgcuuggauuucgucccc
83


 


hsa-miR-193b
MIMAT0002819
aacuggcccucaaagucccgcu
84


 


hsa-miR-194
MIMAT0000460
uguaacagcaacuccaugugga
85


 


hsa-miR-198
MIMAT0000228
gguccagaggggagauagguuc
86


 


hsa-miR-199a-5p
MIMAT0000231
cccaguguucagacuaccuguuc
87


 


hsa-miR-19a
MIMAT0000073
ugugcaaaucuaugcaaaacuga
88


 


hsa-miR-200a
MIMAT0000682
uaacacugucugguaacgaugu
89


 


hsa-miR-200b
MIMAT0000318
uaauacugccugguaaugauga
90


 


hsa-miR-200b*
MIMAT0004571
caucuuacugggcagcauugga
91


 


hsa-miR-200c
MIMAT0000617
uaauacugccggguaaugaugga
92


 


hsa-miR-205
MIMAT0000266
uccuucauuccaccggagucug
93


 


hsa-miR-206
MIMAT0000462
uggaauguaaggaagugugugg
94


 


hsa-miR-208a
MIMAT0000241
auaagacgagcaaaaagcuugu
95


 


hsa-miR-21
MIMAT0000076
uagcuuaucagacugauguuga
96


 


hsa-miR-211
MIMAT0000268
uucccuuugucauccuucgccu
97


 


hsa-miR-22
MIMAT0000077
aagcugccaguugaagaacugu
98


 


hsa-miR-220b
MIMAT0004908
ccaccaccgugucugacacuu
99


 


hsa-miR-221
MIMAT0000278
agcuacauugucugcuggguuuc
100


 


hsa-miR-222
MIMAT0000279
agcuacaucuggcuacugggu
101


 


hsa-miR-223
MIMAT0000280
ugucaguuugucaaauacccca
102


 


hsa-miR-23b
MIMAT0000418
aucacauugccagggauuacc
103


 


hsa-miR-26a
MIMAT0000082
uucaaguaauccaggauaggcu
104


 


hsa-miR-27a
MIMAT0000084
uucacaguggcuaaguuccgc
105


 


hsa-miR-27b
MIMAT0000419
uucacaguggcuaaguucugc
106


 


hsa-miR-27b*
MIMAT0004588
agagcuuagcugauuggugaac
107


 


hsa-miR-299-3p
MIMAT0000687
uaugugggaugguaaaccgcuu
108


 


hsa-miR-299-5p
MIMAT0002890
ugguuuaccgucccacauacau
109


 


hsa-miR-29b
MIMAT0000100
uagcaccauuugaaaucaguguu
110


 


hsa-miR-29c*
MIMAT0004673
ugaccgauuucuccugguguuc
111


 


hsa-miR-300
MIMAT0004903
uauacaagggcagacucucucu
112


 


hsa-miR-301b
MIMAT0004958
cagugcaaugauauugucaaagc
113


 


hsa-miR-302c*
MIMAT0000716
uuuaacauggggguaccugcug
114


 


hsa-miR-30a
MIMAT0000087
uguaaacauccucgacuggaag
115


 


hsa-miR-30c
MIMAT0000244
uguaaacauccuacacucucagc
116


 


hsa-miR-30c-1*
MIMAT0004674
cugggagaggguuguuuacucc
117


 


hsa-miR-30e
MIMAT0000692
uguaaacauccuugacuggaag
118


 


hsa-miR-31
MIMAT0000089
aggcaagaugcuggcauagcu
119


 


hsa-miR-323-3p
MIMAT0000755
cacauuacacggucgaccucu
120


 


hsa-miR-324-3p
MIMAT0000762
acugccccaggugcugcugg
121


 


hsa-miR-324-5p
MIMAT0000761
cgcauccccuagggcauuggugu
122


 


hsa-miR-326
MIMAT0000756
ccucugggcccuuccuccag
123


 


hsa-miR-328
MIMAT0000752
cuggcccucucugcccuuccgu
124


 


hsa-miR-331-5p
MIMAT0004700
cuagguauggucccagggaucc
125


 


hsa-miR-338-3p
MIMAT0000763
uccagcaucagugauuuuguug
126


 


hsa-miR-339-3p
MIMAT0004702
ugagcgccucgacgacagagccg
127


 


hsa-miR-33a*
MIMAT0004506
caauguuuccacagugcaucac
128


 


hsa-miR-33b
MIMAT0003301
gugcauugcuguugcauugc
129


 


hsa-miR-33b*
MIMAT0004811
cagugccucggcagugcagccc
130


 


hsa-miR-342-3p
MIMAT0000753
ucucacacagaaaucgcacccgu
131


 


hsa-miR-34c-3p
MIMAT0004677
aaucacuaaccacacggccagg
132


 


hsa-miR-34c-5p
MIMAT0000686
aggcaguguaguuagcugauugc
133


 


hsa-miR-363*
MIMAT0003385
cggguggaucacgaugcaauuu
134


 


hsa-miR-369-3p
MIMAT0000721
aauaauacaugguugaucuuu
135


 


hsa-miR-370
MIMAT0000722
gccugcugggguggaaccuggu
136


 


hsa-miR-371-3p
MIMAT0000723
aagugccgccaucuuuugagugu
137


 


hsa-miR-371-5p
MIMAT0004687
acucaaacugugggggcacu
138


 


hsa-miR-375
MIMAT0000728
uuuguucguucggcucgcguga
139


 


hsa-miR-376b
MIMAT0002172
aucauagaggaaaauccauguu
140


 


hsa-miR-377*
MIMAT0000730
aucacacaaaggcaacuuuugu
141


 


hsa-miR-379
MIMAT0000733
ugguagacuauggaacguagg
142


 


hsa-miR-382
MIMAT0000737
gaaguuguucgugguggauucg
143


 


hsa-miR-409-5p
MIMAT0001638
agguuacccgagcaacuuugcau
144


 


hsa-miR-411
MIMAT0003329
uaguagaccguauagcguacg
145


 


hsa-miR-411*
MIMAT0004813
uauguaacacgguccacuaacc
146


 


hsa-miR-423-5p
MIMAT0004748
ugaggggcagagagcgagacuuu
147


 


hsa-miR-424
MIMAT0001341
cagcagcaauucauguuuugaa
148


 


hsa-miR-424*
MIMAT0004749
caaaacgugaggcgcugcuau
149


 


hsa-miR-425
MIMAT0003393
aaugacacgaucacucccguuga
150


 


hsa-miR-429
MIMAT0001536
uaauacugucugguaaaaccgu
151


 


hsa-miR-448
MIMAT0001532
uugcauauguaggaugucccau
152


 


hsa-miR-449a
MIMAT0001541
uggcaguguauuguuagcuggu
153


 


hsa-miR-449b
MIMAT0003327
aggcaguguauuguuagcuggc
154


 


hsa-miR-450b-3p
MIMAT0004910
uugggaucauuuugcauccaua
155


 


hsa-miR-451
MIMAT0001631
aaaccguuaccauuacugaguu
156


 


hsa-miR-452
MIMAT0001635
aacuguuugcagaggaaacuga
157


 


hsa-miR-454*
MIMAT0003884
acccuaucaauauugucucugc
158


 


hsa-miR-455-3p
MIMAT0004784
gcaguccaugggcauauacac
159


 


hsa-miR-455-5p
MIMAT0003150
uaugugccuuuggacuacaucg
160


 


hsa-miR-483-3p
MIMAT0002173
ucacuccucuccucccgucuu
161


 


hsa-miR-483-5p
MIMAT0004761
aagacgggaggaaagaagggag
162


 


hsa-miR-484
MIMAT0002174
ucaggcucaguccccucccgau
163


 


hsa-miR-486-3p
MIMAT0004762
cggggcagcucaguacaggau
164


 


hsa-miR-486-5p
MIMAT0002177
uccuguacugagcugccccgag
165


 


hsa-miR-487b
MIMAT0003180
aaucguacagggucauccacuu
166


 


hsa-miR-491-3p
MIMAT0004765
cuuaugcaagauucccuucuac
167


 


hsa-miR-491-5p
MIMAT0002807
aguggggaacccuuccaugagg
168


 


hsa-miR-493
MIMAT0003161
ugaaggucuacugugugccagg
169


 


hsa-miR-493*
MIMAT0002813
uuguacaugguaggcuuucauu
170


 


hsa-miR-494
MIMAT0002816
ugaaacauacacgggaaaccuc
171


 


hsa-miR-497
MIMAT0002820
cagcagcacacugugguuugu
172


 


hsa-miR-498
MIMAT0002824
uuucaagccagggggcguuuuuc
173


 


hsa-miR-500
MIMAT0004773
uaauccuugcuaccugggugaga
174


 


hsa-miR-503
MIMAT0002874
uagcagcgggaacaguucugcag
175


 


hsa-miR-505
MIMAT0002876
cgucaacacuugcugguuuccu
176


 


hsa-miR-507
MIMAT0002879
uuuugcaccuuuuggagugaa
177


 


hsa-miR-511
MIMAT0002808
gugucuuuugcucugcaguca
178


 


hsa-miR-513a-3p
MIMAT0004777
uaaauuucaccuuucugagaagg
179


 


hsa-miR-513a-5p
MIMAT0002877
uucacagggaggugucau
180


 


hsa-miR-513b
MIMAT0005788
uucacaaggaggugucauuuau
181


 


hsa-miR-513c
MIMAT0005789
uucucaaggaggugucguuuau
182


 


hsa-miR-515-5p
MIMAT0002826
uucuccaaaagaaagcacuuucug
183


 


hsa-miR-518b
MIMAT0002844
caaagcgcuccccuuuagaggu
184


 


hsa-miR-518c*
MIMAT0002847
ucucuggagggaagcacuuucug
185


 


hsa-miR-518d-3p
MIMAT0002864
caaagcgcuucccuuuggagc
186


 


hsa-miR-518d-5p
MIMAT0005456
cucuagagggaagcacuuucug
187


 


hsa-miR-518e*
MIMAT0005450
cucuagagggaagcgcuuucug
188


 


hsa-miR-520d-5p
MIMAT0002855
cuacaaagggaagcccuuuc
189


 


hsa-miR-520h
MIMAT0002867
acaaagugcuucccuuuagagu
190


 


hsa-miR-539
MIMAT0003163
ggagaaauuauccuuggugugu
191


 


hsa-miR-541
MIMAT0004920
uggugggcacagaaucuggacu
192


 


hsa-miR-545*
MIMAT0004785
ucaguaaauguuuauuagauga
193


 


hsa-miR-548d-3p
MIMAT0003323
caaaaaccacaguuucuuuugc
194


 


hsa-miR-548d-5p
MIMAT0004812
aaaaguaauugugguuuuugcc
195


 


hsa-miR-551a
MIMAT0003214
gcgacccacucuugguuucca
196


 


hsa-miR-551b
MIMAT0003233
gcgacccauacuugguuucag
197


 


hsa-miR-552
MIMAT0003215
aacaggugacugguuagacaa
198


 


hsa-miR-554
MIMAT0003217
gcuaguccugacucagccagu
199


 


hsa-miR-556-5p
MIMAT0003220
gaugagcucauuguaauaugag
200


 


hsa-miR-557
MIMAT0003221
guuugcacgggugggccuugucu
201


 


hsa-miR-559
MIMAT0003223
uaaaguaaauaugcaccaaaa
202


 


hsa-miR-561
MIMAT0003225
caaaguuuaagauccuugaagu
203


 


hsa-miR-564
MIMAT0003228
aggcacggugucagcaggc
204


 


hsa-miR-568
MIMAT0003232
auguauaaauguauacacac
205


 


hsa-miR-572
MIMAT0003237
guccgcucggcgguggccca
206


 


hsa-miR-574-5p
MIMAT0004795
ugagugugugugugugagugugu
207


 


hsa-miR-575
MIMAT0003240
gagccaguuggacaggagc
208


 


hsa-miR-576-3p
MIMAT0004796
aagauguggaaaaauuggaauc
209


 


hsa-miR-578
MIMAT0003243
cuucuugugcucuaggauugu
210


 


hsa-miR-583
MIMAT0003248
caaagaggaaggucccauuac
211


 


hsa-miR-586
MIMAT0003252
uaugcauuguauuuuuaggucc
212


 


hsa-miR-589
MIMAT0004799
ugagaaccacgucugcucugag
213


 


hsa-miR-589*
MIMAT0003256
ucagaacaaaugccgguucccaga
214


 


hsa-miR-591
MIMAT0003259
agaccauggguucucauugu
215


 


hsa-miR-595
MIMAT0003263
gaagugugccguggugugucu
216


 


hsa-miR-601
MIMAT0003269
uggucuaggauuguuggaggag
217


 


hsa-miR-602
MIMAT0003270
gacacgggcgacagcugcggccc
218


 


hsa-miR-609
MIMAT0003277
aggguguuucucucaucucu
219


 


hsa-miR-610
MIMAT0003278
ugagcuaaaugugugcuggga
220


 


hsa-miR-612
MIMAT0003280
gcugggcagggcuucugagcuccuu
221


 


hsa-miR-613
MIMAT0003281
aggaauguuccuucuuugcc
222


 


hsa-miR-614
MIMAT0003282
gaacgccuguucuugccaggugg
223


 


hsa-miR-615-3p
MIMAT0003283
uccgagccugggucucccucuu
224


 


hsa-miR-616
MIMAT0004805
agucauuggaggguuugagcag
225


 


hsa-miR-619
MIMAT0003288
gaccuggacauguuugugcccagu
226


 


hsa-miR-622
MIMAT0003291
acagucugcugagguuggagc
227


 


hsa-miR-623
MIMAT0003292
aucccuugcaggggcuguugggu
228


 


hsa-miR-624*
MIMAT0003293
uaguaccaguaccuuguguuca
229


 


hsa-miR-627
MIMAT0003296
gugagucucuaagaaaagagga
230


 


hsa-miR-630
MIMAT0003299
aguauucuguaccagggaaggu
231


 


hsa-miR-633
MIMAT0003303
cuaauaguaucuaccacaauaaa
232


 


hsa-miR-634
MIMAT0003304
aaccagcaccccaacuuuggac
233


 


hsa-miR-638
MIMAT0003308
agggaucgcgggcggguggcggccu
234


 


hsa-miR-639
MIMAT0003309
aucgcugcgguugcgagcgcugu
235


 


hsa-miR-640
MIMAT0003310
augauccaggaaccugccucu
236


 


hsa-miR-642
MIMAT0003312
gucccucuccaaaugugucuug
237


 


hsa-miR-644
MIMAT0003314
aguguggcuuucuuagagc
238


 


hsa-miR-647
MIMAT0003317
guggcugcacucacuuccuuc
239


 


hsa-miR-648
MIMAT0003318
aagugugcagggcacuggu
240


 


hsa-miR-652
MIMAT0003322
aauggcgccacuaggguugug
241


 


hsa-miR-654-5p
MIMAT0003330
uggugggccgcagaacaugugc
242


 


hsa-miR-658
MIMAT0003336
ggcggagggaaguagguccguuggu
243


 


hsa-miR-659
MIMAT0003337
cuugguucagggagggucccca
244


 


hsa-miR-662
MIMAT0003325
ucccacguuguggcccagcag
245


 


hsa-miR-663
MIMAT0003326
aggcggggcgccgcgggaccgc
246


 


hsa-miR-665
MIMAT0004952
accaggaggcugaggccccu
247


 


hsa-miR-671-5p
MIMAT0003880
aggaagcccuggaggggcuggag
248


 


hsa-miR-675
MIMAT0004284
uggugcggagagggcccacagug
249


 


hsa-miR-708
MIMAT0004926
aaggagcuuacaaucuagcuggg
250


 


hsa-miR-708*
MIMAT0004927
caacuagacugugagcuucuag
251


 


hsa-miR-711
MIMAT0012734
gggacccagggagagacguaag
252


 


hsa-miR-720
MIMAT0005954
ucucgcuggggccucca
253


 


hsa-miR-744*
MIMAT0004946
cuguugccacuaaccucaaccu
254


 


hsa-miR-760
MIMAT0004957
cggcucugggucugugggga
255


 


hsa-miR-765
MIMAT0003945
uggaggagaaggaaggugaug
256


 


hsa-miR-766
MIMAT0003888
acuccagccccacagccucagc
257


 


hsa-miR-767-3p
MIMAT0003883
ucugcucauaccccaugguuucu
258


 


hsa-miR-770-5p
MIMAT0003948
uccaguaccacgugucagggcca
259


 


hsa-miR-802
MIMAT0004185
caguaacaaagauucauccuugu
260


 


hsa-miR-874
MIMAT0004911
cugcccuggcccgagggaccga
261


 


hsa-miR-876-3p
MIMAT0004925
uggugguuuacaaaguaauuca
262


 


hsa-miR-876-5p
MIMAT0004924
uggauuucuuugugaaucacca
263


 


hsa-miR-877
MIMAT0004949
guagaggagauggcgcaggg
264


 


hsa-miR-877*
MIMAT0004950
uccucuucucccuccucccag
265


 


hsa-miR-885-3p
MIMAT0004948
aggcagcgggguguaguggaua
266


 


hsa-miR-885-5p
MIMAT0004947
uccauuacacuacccugccucu
267


 


hsa-miR-886-3p
MIMAT0004906
cgcgggugcuuacugacccuu
268


 


hsa-miR-890
MIMAT0004912
uacuuggaaaggcaucaguug
269


 


hsa-miR-891b
MIMAT0004913
ugcaacuuaccugagucauuga
270


 


hsa-miR-892b
MIMAT0004918
cacuggcuccuuucuggguaga
271


 


hsa-miR-920
MIMAT0004970
ggggagcuguggaagcagua
272


 


hsa-miR-922
MIMAT0004972
gcagcagagaauaggacuacguc
273


 


hsa-miR-923
none
GUCAGCGGAGGAAAAGAAACU
274


 


hsa-miR-92a-2*
MIMAT0004508
ggguggggauuuguugcauuac
275


 


hsa-miR-92b
MIMAT0003218
uauugcacucgucccggccucc
276


 


hsa-miR-92b*
MIMAT0004792
agggacgggacgcggugcagug
277


 


hsa-miR-93
MIMAT0000093
caaagugcuguucgugcagguag
278


 


hsa-miR-933
MIMAT0004976
ugugcgcagggagaccucuccc
279


 


hsa-miR-934
MIMAT0004977
ugucuacuacuggagacacugg
280


 


hsa-miR-935
MIMAT0004978
ccaguuaccgcuuccgcuaccgc
281


 


hsa-miR-936
MIMAT0004979
acaguagagggaggaaucgcag
282


 


hsa-miR-937
MIMAT0004980
auccgcgcucugacucucugcc
283


 


hsa-miR-939
MIMAT0004982
uggggagcugaggcucugggggug
284


 


hsa-miR-940
MIMAT0004983
aaggcagggcccccgcucccc
285


 


hsa-miR-96
MIMAT0000095
uuuggcacuagcacauuuuugcu
286


 


hsa-miR-99a
MIMAT0000097
aacccguagauccgaucuugug
287


 


hsv1-miR-H1
MIMAT0003744
uggaaggacgggaaguggaag
288


 


hsv1-miR-LAT
none
uggcggcccggcccggggcc
289


 


kshv-miR-K12-12
IMAT0003712
accaggccaccauuccucuccg
290


 


kshv-miR-K12-3
MIMAT0002193
ucacauucugaggacggcagcga
291


 


kshv-miR-K12-3*
MIMAT0002194
ucgcggucacagaaugugaca
292


 


kshv-miR-K12-4-5p
MIMAT0002191
agcuaaaccgcaguacucuagg
293


 


kshv-miR-K12-6-5p
MIMAT0002188
ccagcagcaccuaauccaucgg
294


 


kshv-miR-K12-8
MIMAT0002186
uaggcgcgacugagagagcacg
295


 


kshv-miR-K12-9
MIMAT0002185
cuggguauacgcagcugcguaa
296


 


kshv-miR-K12-9*
MIMAT0002184
acccagcugcguaaaccccgcu
297


 


mghv-miR-M1-6
MIMAT0001569
ugaaacugugugaggugguuuu
298


 


mghv-miR-M1-9
MIMAT0001573
ucacauuugccuggaccuuuuu
299


 


mmu-let-7d*
MIMAT0000384
cuauacgaccugcugccuuucu
300


 


mmu-let-7g
MIMAT0000121
ugagguaguaguuuguacaguu
301


 


mmu-miR-298
MIMAT0000376
ggcagaggagggcuguucuuccc
302


 


mmu-miR-1
MIMAT0000123
uggaauguaaagaaguauguau
303


 


mmu-miR-101a
MIMAT0000133
uacaguacugugauaacugaa
304


 


mmu-miR-101a*
MIMAT0004526
ucaguuaucacagugcugaugc
305


 


mmu-miR-101b
MIMAT0000616
uacaguacugugauagcugaa
306


 


mmu-miR-122
MIMAT0000246
uggagugugacaaugguguuug
307


 


mmu-miR-1224
MIMAT0005460
gugaggacuggggagguggag
308


 


mmu-miR-124
MIMAT0000134
uaaggcacgcggugaaugcc
309


 


mmu-miR-125a-3p
MIMAT0004528
acaggugagguucuugggagcc
310


 


mmu-miR-125a-5p
MIMAT0000135
ucccugagacccuuuaaccuguga
311


 


mmu-miR-125b-5p
MIMAT0000136
ucccugagacccuaacuuguga
312


 


mmu-miR-126-5p
MIMAT0000137
auuauuacuuuugguacgcg
313


 


mmu-miR-127
MIMAT0000139
ucggauccgucugagcuuggcu
314


 


mmu-miR-128
MIMAT0000140
ucacagugaaccggucucuuu
315


 


mmu-miR-129-3p
MIMAT0000544
aagcccuuaccccaaaaagcau
316


 


mmu-miR-130a
MIMAT0000141
cagugcaauguuaaaagggcau
317


 


mmu-miR-133a
MIMAT0000145
uuugguccccuucaaccagcug
318


 


mmu-miR-133b
MIMAT0000769
uuugguccccuucaaccagcua
319


 


mmu-miR-135a*
MIMAT0004531
uauagggauuggagccguggcg
320


 


mmu-miR-136
MIMAT0000148
acuccauuuguuuugaugaugg
321


 


mmu-miR-138
MIMAT0000150
agcugguguugugaaucaggccg
322


 


mmu-miR-138*
MIMAT0004668
ggcuacuucacaacaccaggg
323


 


mmu-miR-139-3p
MIMAT0004662
uggagacgcggcccuguuggag
324


 


mmu-miR-140
MIMAT0000151
cagugguuuuacccuaugguag
325


 


mmu-miR-140*
MIMAT0000152
uaccacaggguagaaccacgg
326


 


mmu-miR-141
MIMAT0000153
uaacacugucugguaaagaugg
327


 


mmu-miR-142-3p
MIMAT0000155
uguaguguuuccuacuuuaugga
328


 


mmu-miR-143
MIMAT0000247
ugagaugaagcacuguagcuc
329


 


mmu-miR-146a
MIMAT0000158
ugagaacugaauuccauggguu
330


 


mmu-miR-146b
MIMAT0003475
ugagaacugaauuccauaggcu
331


 


mmu-miR-148b
MIMAT0000580
ucagugcaucacagaacuuugu
332


 


mmu-miR-150
MIMAT0000160
ucucccaacccuuguaccagug
333


 


mmu-miR-15a*
MIMAT0004624
caggccauacugugcugccuca
334


 


mmu-miR-15b
MIMAT0000124
uagcagcacaucaugguuuaca
335


 


mmu-miR-181b
MIMAT0000673
aacauucauugcugucggugggu
336


 


mmu-miR-181d
MIMAT0004324
aacauucauuguugucggugggu
337


 


mmu-miR-183
MIMAT0000212
uauggcacugguagaauucacu
338


 


mmu-miR-185
MIMAT0000214
uggagagaaaggcaguuccuga
339


 


mmu-miR-186
MIMAT0000215
caaagaauucuccuuuugggcu
340


 


mmu-miR-191*
MIMAT0004542
gcugcacuuggauuucguuccc
341


 


mmu-miR-193
MIMAT0000223
aacuggccuacaaagucccagu
342


 


mmu-miR-193b
MIMAT0004859
aacuggcccacaaagucccgcu
343


 


mmu-miR-194
MIMAT0000224
uguaacagcaacuccaugugga
344


 


mmu-miR-199a-5p
MIMAT0000229
cccaguguucagacuaccuguuc
345


 


mmu-miR-199b*
MIMAT0000672
cccaguguuuagacuaccuguuc
346


 


mmu-miR-19a
MIMAT0000651
ugugcaaaucuaugcaaaacuga
347


 


mmu-miR-200a
MIMAT0000519
uaacacugucugguaacgaugu
348


 


mmu-miR-200b
MIMAT0000233
uaauacugccugguaaugauga
349


 


mmu-miR-200b*
MIMAT0004545
caucuuacugggcagcauugga
350


 


mmu-miR-200c
MIMAT0000657
uaauacugccggguaaugaugga
351


 


mmu-miR-202-3p
MIMAT0000235
agagguauagcgcaugggaaga
352


 


mmu-miR-205
MIMAT0000238
uccuucauuccaccggagucug
353


 


mmu-miR-206
MIMAT0000239
uggaauguaaggaagugugugg
354


 


mmu-miR-208a
MIMAT0000520
auaagacgagcaaaaagcuugu
355


 


mmu-miR-21
MIMAT0000530
uagcuuaucagacugauguuga
356


 


mmu-miR-211
MIMAT0000668
uucccuuugucauccuuugccu
357


 


mmu-miR-22
MIMAT0000531
aagcugccaguugaagaacugu
358


 


mmu-miR-221
MIMAT0000669
agcuacauugucugcuggguuuc
359


 


mmu-miR-222
MIMAT0000670
agcuacaucuggcuacugggu
360


 


mmu-miR-223
MIMAT0000665
ugucaguuugucaaauacccca
361


 


mmu-miR-23b
MIMAT0000125
aucacauugccagggauuacc
362


 


mmu-miR-26a
MIMAT0000533
uucaaguaauccaggauaggcu
363


 


mmu-miR-27a
MIMAT0000537
uucacaguggcuaaguuccgc
364


 


mmu-miR-27b
MIMAT0000126
uucacaguggcuaaguucugc
365


 


mmu-miR-27b*
MIMAT0004522
agagcuuagcugauuggugaac
366


 


mmu-miR-28*
MIMAT0004661
cacuagauugugagcugcugga
367


 


mmu-miR-290-5p
MIMAT0000366
acucaaacuaugggggcacuuu
368


 


mmu-miR-291a-5p
MIMAT0000367
caucaaaguggaggcccucucu
369


 


mmu-miR-294*
MIMAT0004574
acucaaaauggaggcccuaucu
370


 


mmu-miR-297a
MIMAT0000375
auguaugugugcaugugcaugu
371


 


mmu-miR-299
MIMAT0004577
uaugugggacgguaaaccgcuu
372


 


mmu-miR-29b
MIMAT0000127
uagcaccauuugaaaucaguguu
373


 


mmu-miR-29c*
MIMAT0004632
ugaccgauuucuccugguguuc
374


 


mmu-miR-301b
MIMAT0004186
cagugcaaugguauugucaaagc
375


 


mmu-miR-302c*
MIMAT0003375
gcuuuaacaugggguuaccugc
376


 


mmu-miR-30a
MIMAT0000128
uguaaacauccucgacuggaag
377


 


mmu-miR-30c
MIMAT0000514
uguaaacauccuacacucucagc
378


 


mmu-miR-30c-1*
MIMAT0004616
cugggagaggguuguuuacucc
379


 


mmu-miR-30e
MIMAT0000248
uguaaacauccuugacuggaag
380


 


mmu-miR-31
MIMAT0000538
aggcaagaugcuggcauagcug
381


 


mmu-miR-320
MIMAT0000666
aaaagcuggguugagagggcga
382


 


mmu-miR-322
MIMAT0000548
cagcagcaauucauguuuugga
383


 


mmu-miR-323-3p
MIMAT0000551
cacauuacacggucgaccucu
384


 


mmu-miR-324-3p
MIMAT0000556
ccacugccccaggugcugcu
385


 


mmu-miR-324-5p
MIMAT0000555
cgcauccccuagggcauuggugu
386


 


mmu-miR-326
MIMAT0000559
ccucugggcccuuccuccagu
387


 


mmu-miR-327
MIMAT0004867
acuugaggggcaugaggau
388


 


mmu-miR-328
MIMAT0000565
cuggcccucucugcccuuccgu
389


 


mmu-miR-331-5p
MIMAT0004643
cuagguauggucccagggaucc
390


 


mmu-miR-339-3p
MIMAT0004649
ugagcgccucggcgacagagccg
391


 


mmu-miR-341
MIMAT0000588
ucggucgaucggucggucggu
392


 


mmu-miR-342-3p
MIMAT0000590
ucucacacagaaaucgcacccgu
393


 


mmu-miR-34b-5p
MIMAT0000382
aggcaguguaauuagcugauugu
394


 


mmu-miR-34c*
MIMAT0004580
aaucacuaaccacacagccagg
395


 


mmu-miR-369-3p
MIMAT0003186
aauaauacaugguugaucuuu
396


 


mmu-miR-370
MIMAT0001095
gccugcugggguggaaccuggu
397


 


mmu-miR-374
MIMAT0003727
auauaauacaaccugcuaagug
398


 


mmu-miR-375
MIMAT0000739
uuuguucguucggcucgcguga
399


 


mmu-miR-376b
MIMAT0001092
aucauagaggaacauccacuu
400


 


mmu-miR-379
MIMAT0000743
ugguagacuauggaacguagg
401


 


mmu-miR-380-3p
MIMAT0000745
uauguaguaugguccacaucuu
402


 


mmu-miR-382
MIMAT0000747
gaaguuguucgugguggauucg
403


 


mmu-miR-384-5p
MIMAT0004745
uguaaacaauuccuaggcaaugu
404


 


mmu-miR-409-5p
MIMAT0004746
agguuacccgagcaacuuugcau
405


 


mmu-miR-411
MIMAT0004747
uaguagaccguauagcguacg
406


 


mmu-miR-411*
MIMAT0001093
uauguaacacgguccacuaacc
407


 


mmu-miR-423-5p
MIMAT0004825
ugaggggcagagagcgagacuuu
408


 


mmu-miR-425
MIMAT0004750
aaugacacgaucacucccguuga
409


 


mmu-miR-429
MIMAT0001537
uaauacugucugguaaugccgu
410


 


mmu-miR-434-5p
MIMAT0001421
gcucgacucaugguuugaacca
411


 


mmu-miR-450b-3p
MIMAT0003512
auugggaacauuuugcaugcau
412


 


mmu-miR-451
MIMAT0001632
aaaccguuaccauuacugaguu
413


 


mmu-miR-455
MIMAT0003742
gcaguccacgggcauauacac
414


 


mmu-miR-465c-3p
MIMAT0004874
gaucagggccuuucuaaguaga
415


 


mmu-miR-466d-5p
MIMAT0004930
ugugugugcguacauguacaug
416


 


mmu-miR-466f-3p
MIMAT0004882
cauacacacacacauacacac
417


 


mmu-miR-467e*
MIMAT0005294
auauacauacacacaccuauau
418


 


mmu-miR-483
MIMAT0004782
aagacgggagaagagaagggag
419


 


mmu-miR-484
MIMAT0003127
ucaggcucaguccccucccgau
420


 


mmu-miR-486
MIMAT0003130
uccuguacugagcugccccgag
421


 


mmu-miR-487b
MIMAT0003184
aaucguacagggucauccacuu
422


 


mmu-miR-494
MIMAT0003182
ugaaacauacacgggaaaccuc
423


 


mmu-miR-497
MIMAT0003453
cagcagcacacugugguuugua
424


 


mmu-miR-505
IMAT0003513
cgucaacacuugcugguuuucu
425


 


mmu-miR-511
MIMAT0004940
augccuuuugcucugcacuca
426


 


mmu-miR-539
MIMAT0003169
ggagaaauuauccuuggugugu
427


 


mmu-miR-540-3p
MIMAT0004786
caagggucacccucugacucugu
428


 


mmu-miR-551b
MIMAT0003890
gcgacccauacuugguuucag
429


 


mmu-miR-568
MIMAT0004892
auguauaaauguauacacac
430


 


mmu-miR-574-5p
MIMAT0004893
ugagugugugugugugagugugu
431


 


mmu-miR-652
MIMAT0003711
aauggcgccacuaggguugug
432


 


mmu-miR-654-5p
MIMAT0004897
ugguaagcugcagaacaugugu
433


 


mmu-miR-669a
MIMAT0003477
aguugugugugcauguucaugu
434


 


mmu-miR-671-5p
MIMAT0003731
aggaagcccuggaggggcuggag
435


 


mmu-miR-685
MIMAT0003463
ucaauggcugaggugaggcac
436


 


mmu-miR-686
MIMAT0003464
auugcuucccagacggugaaga
437


 


mmu-miR-688
MIMAT0003467
ucgcaggcgacuacuuauuc
438


 


mmu-miR-701
MIMAT0003491
uuagccgcugaaauagaugga
439


 


mmu-miR-706
MIMAT0003496
agagaaacccugucucaaaaaa
440


 


mmu-miR-708
MIMAT0004828
aaggagcuuacaaucuagcuggg
441


 


mmu-miR-710
MIMAT0003500
ccaagucuuggggagaguugag
442


 


mmu-miR-711
MIMAT0003501
gggacccggggagagauguaag
443


 


mmu-miR-712
MIMAT0003502
cuccuucacccgggcgguacc
444


 


mmu-miR-714
MIMAT0003505
cgacgagggccggucggucgc
445


 


mmu-miR-720
MIMAT0003484
aucucgcuggggccucca
446


 


mmu-miR-721
MIMAT0003515
cagugcaauuaaaagggggaa
447


 


mmu-miR-744*
MIMAT0004820
cuguugccacuaaccucaaccu
448


 


mmu-miR-760
MIMAT0003898
cggcucugggucugugggga
449


 


mmu-miR-770-5p
MIMAT0004822
agcaccacgugucugggccacg
450


 


mmu-miR-7a
MIMAT0000677
uggaagacuagugauuuuguugu
451


 


mmu-miR-7b
MIMAT0000678
uggaagacuugugauuuuguugu
452


 


mmu-miR-877
MIMAT0004861
guagaggagauggcgcaggg
453


 


mmu-miR-877*
MIMAT0004862
uguccucuucucccuccuccca
454


 


mmu-miR-881*
MIMAT0004845
cagagagauaacagucacaucu
455


 


mmu-miR-882
MIMAT0004847
aggagagaguuagcgcauuagu
456


 


mmu-miR-93
MIMAT0000540
caaagugcuguucgugcagguag
457


 


mmu-miR-96
MIMAT0000541
uuuggcacuagcacauuuuugcu
458


 


mmu-miR-99a
MIMAT0000131
aacccguagauccgaucuugug
459









The devices and methods of the present disclosure have been described with reference to exemplary embodiments. Obviously, modifications and alterations will occur to others upon reading and understanding the preceding detailed description. It is intended that the exemplary embodiments be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

## Claims

1. A method of detecting or predicting a liver condition, comprising:
generating a microRNA profile from a biological sample, wherein the microRNA profile comprises the amounts of at least two specific microRNA sequences, by:
receiving the biological sample, the sample being a tissue, serum, or plasma; and
isolating microRNA from the biological sample by extracting the microRNA with an organic solvent and obtaining an aqueous phase containing the microRNA, then purifying the aqueous phase through a silica membrane to isolate the microRNA;
using hybridization to identify the microRNA sequences; and
measuring the amount of at least two specific microRNA sequences; and

comparing the amounts of the at least two specific microRNA sequences to a reference to provide information for detecting or predicting the liver condition;
wherein the at least two specific microRNA sequences are hsa-miR-122 and hsa-miR-486-3p.

2. The method of claim 1, wherein the ratio of the amount of hsa-miR-122 to the amount of hsa-miR-486-3p is greater than 4.0.

3. The method of claim 1, wherein the ratio of the amount of hsa-miR-122 to the amount of hsa-miR-486-3p is greater than 6.0.

4. A method as in any of claim 1, 2, or 3, further comprising recording the output/result of the diagnostic method on a data storage medium.

5. The method of claim 1, wherein the microRNA profile further comprises the amount of at least one additional microRNA sequence selected from the group consisting of miR-486, miR-125b-5p, let-7d*, miR-101a, miR-101b, miR-1224, miR-124, miR-125a-3p, miR-125a-5p, miR-127, miR-130a, miR-133a, miR-133b, miR-135a*, miR-141, miR-193, miR-193b, miR-199a-5p, miR-199b*, miR-200c, miR-202-3p, miR-205, miR-22, miR-23b, miR-26a, miR-27b, miR-291a-5p, miR-294*, miR-29b, miR-30a, miR-30c-1*, miR-30e, miR-320, miR-327, miR-339-3p, miR-342-3p, miR-370, miR-375, miR-451, miR-466f-3p, miR-483, miR-486-5p, miR-494, miR-574-5p, miR-652, miR-671-5p, miR-685, miR-710, miR-711, miR-712, miR-714, miR-720, miR-721, miR-877, miR-877*, miR-882, miR-93, miR-99a, and human orthologs thereof.

6. The method of claim 1, wherein the amount of at least two specific microRNA sequences is measured using quantitative polymerase chain reaction (qPCR).

7. The method of claim 1, further comprising administering a treatment to a patient providing the biological sample based on the identity or amounts of the at least two specific microRNA sequences.