miRNAs, compositions and methods of use thereof

Abstract

A method for identifying a patient as having a marker correlated with systemic lupus erythematosus (SLE) comprises obtaining a body fluid sample from a patient suspected of having SLE, analyzing miRNA expression in the obtained body fluid sample, and identifying the patient as having a marker correlated with SLE if an increase in the expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in the patient sample compared to a body fluid sample obtained from a healthy individual, or as not having a marker correlated with SLE if an increase in the expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is not detectable in the patient sample compared to a body fluid sample obtained from a healthy individual.

Description

[Technical field]

[0001] The present disclosure provides methods for diagnosing systemic lupus erythematosus (SLE) and systems for detecting miRNAs associated with SLE. [Background technology]

[0002] Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder with multiple organ involvement and diverse clinical manifestations. It has one of the highest mortality rates among rheumatic diseases and is the most common form of lupus. Clinical features of SLE range from mild lesions of the skin and joints to severe debilitating complications at later stages, such as infections and renal, cardiovascular and central nervous system problems, which cause significant morbidity and mortality. SLE, an autoimmune disease, is characterized by the presence of antibodies against self-antigens. Deposition of autoantibodies and immune complexes in tissues leads to inflammatory damage in various organ systems of the body. CDC website "Systemic lupus erythematosus (SLE)" (2020). There is a need in the art for a rapid and efficient method to diagnose SLE. Summary of the Invention [Means for solving the problem]

[0003] In one aspect, the present disclosure provides a method for producing a method for manufacturing a semiconductor device comprising: (a) obtaining a body fluid sample from a patient suspected of having SLE; (b) analyzing miRNA expression in the obtained bodily fluid sample; and (c) (i) identifying the patient as having a marker correlated with SLE if an increase in the expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in the patient sample compared to a body fluid sample obtained from a healthy individual, or (ii) identifying the patient as not having a marker correlated with SLE if an increase in the expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is not detectable in the patient sample compared to a body fluid sample obtained from a healthy individual.

[0004] In another aspect, the analyzing step comprises: (a) introducing the obtained bodily fluid sample into a fluidic device containing nanowires; (b) capturing extracellular vesicles in a body fluid sample on the nanowire; (c) disrupting the captured extracellular vesicles; (d) extracting miRNA from the disrupted extracellular vesicles; (e) hybridizing the extracted miRNA to an miRNA array; (f) generating an miRNA profile from the bodily fluid sample, comprising measuring miRNA hybridization to the array.

[0005] In another aspect, the methods of the present disclosure may further comprise a step of comparing miRNA expression in a bodily fluid sample obtained from a patient suspected of having SLE with miRNA expression in a bodily fluid sample obtained from a healthy individual.

[0006] In another embodiment, the bodily fluid may be blood, urine, saliva, peritoneal fluid, bronchoalveolar lavage fluid, plasma, cerebrospinal fluid, or a combination thereof.

[0007] In another embodiment, the nanowires may have at least one positively charged surface selected from the group consisting of ZnO, SiO2, Li2O, MgO, Al2O3, CaO, TiO2, Mn2O3, Fe2O3, CoO, NiO, CuO, Ga2O3, SrO, In2O3, SnO2, Sm2O3, EuO, and combinations thereof.

[0008] In another embodiment, the nanowires may be porous and / or magnetic.

[0009] In another embodiment, the captured extracellular vesicles may be disrupted by the use of a cell lysis buffer. The extracellular vesicles may be disrupted by alkaline / detergent pretreatment, storage at about -25°C for about 1-10 days, optionally about 7 days, or a combination thereof.

[0010] In another embodiment, the step of extracting miRNA may be performed in situ.

[0011] In one aspect, the present disclosure provides a method for producing a method for manufacturing a semiconductor device comprising: a) obtaining a body fluid sample from a patient suspected of having SLE; b) analyzing miRNA expression in the obtained body fluid sample; c) i) as having a marker that correlates with moderate SLE if a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in a patient sample compared to a body fluid sample obtained from a healthy individual; or ii) identifying the patient as not having a marker correlated with moderate SLE if a decrease in expression of at least one miRNA selected from SEQ ID NOs: 161 to 242 and 403 to 484 cannot be detected compared to a body fluid sample obtained from a healthy individual.

[0012] In one aspect, the present disclosure provides a method for producing a method for manufacturing a semiconductor device comprising: a) obtaining a body fluid sample from a patient suspected of having SLE; b) analyzing miRNA expression in the obtained body fluid sample; and b) identifying the patient i) as having a marker correlated with SLE comorbidity if an increase in the expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in the patient sample compared to a body fluid sample obtained from a healthy individual, or ii) as not having a marker correlated with SLE comorbidity if an increase in the expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is not detectable in the patient sample compared to a body fluid sample obtained from a healthy individual.

[0013] In another aspect, the comorbidity may be A if there is increased expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402, and / or decreased expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484.

[0014] In another aspect, the comorbidity may be B if there is increased expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402, and / or decreased expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484.

[0015] In another aspect, the comorbidity may be C if there is increased expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or decreased expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484.

[0016] In another aspect, if there is increased expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402, and / or decreased expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484, the comorbidity may be D.

[0017] In one aspect, the disclosure relates to a method of treating SLE, comprising identifying a patient as having a marker that correlates with SLE and administering to the patient an effective amount of a compound selected from the group consisting of nonsteroidal anti-inflammatory drugs (NSAIDs), immunosuppressants, and anti-BLyS antibodies.

[0018] In one aspect, the disclosure relates to a method of treating SLE, comprising identifying a patient as having markers that correlate with moderate SLE and administering to the patient an effective amount of a compound selected from the group consisting of hysteroidal anti-inflammatory drugs (NSAIDs), immunosuppressants, and anti-BLyS antibodies.

[0019] In one aspect, the disclosure relates to a method of treating SLE, comprising identifying a patient as having a marker that correlates with SLE comorbidity A and administering to the patient an effective amount of a compound selected from the group consisting of nonsteroidal anti-inflammatory drugs (NSAIDs), immunosuppressants, and anti-BLyS antibodies.

[0020] In one aspect, the disclosure relates to a method of treating SLE, comprising identifying a patient as having a marker that correlates with SLE comorbidity B, and administering to the patient an effective amount of a compound selected from the group consisting of nonsteroidal anti-inflammatory drugs (NSAIDs), immunosuppressants, and anti-BLyS antibodies.

[0021] In one aspect, the disclosure relates to a method of treating SLE, comprising identifying a patient as having a marker that correlates with SLE comorbidity C and administering to the patient an effective amount of a compound selected from the group consisting of nonsteroidal anti-inflammatory drugs (NSAIDs), immunosuppressants, and anti-BLyS antibodies.

[0022] In one aspect, the disclosure relates to a method of treating SLE, comprising identifying a patient as having a marker that correlates with SLE comorbidity D and administering to the patient an effective amount of a compound selected from the group consisting of nonsteroidal anti-inflammatory drugs (NSAIDs), immunosuppressants, and anti-BLyS antibodies.

[0023] 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.

[0024] The advantages and features of the present invention will be better understood with reference to the following more detailed description taken in conjunction with the accompanying drawings. [Brief description of the drawings]

[0025] [Figure 1]1 shows an exemplary procedure for miRNA analysis.

[0026] [Diagram 2] Figure 1 shows differential expression analysis performed by comparing each miRNA signal from SLE patients and healthy donors according to one embodiment of the present disclosure. Fold change between cohorts for each miRNA was plotted against p-value of t-test, and statistically significant miRNAs (p-value < 0.05) were selected as biomarker candidates.

[0027] [Figure 3A] The expression levels of the top 10 upregulated miRNAs shown in FIG. 2 are shown.

[0028] [Figure 3B] The expression levels of the top 10 down-regulated miRNAs shown in FIG. 2 are shown.

[0029] [Figure 4] FIG. 1 shows the correlation of expression levels of each miRNA with SLE severity according to one embodiment of the present disclosure. Scatter plot of fold change of each miRNA shows x-axis: SLE vs. non-SLE, and y-axis: moderate SLE vs. mild SLE).

[0030] [Figure 5A] Box plots of expression levels of the top 10 upregulated miRNAs in mild SLE patients (mild), moderate SLE patients (moderate), and healthy individuals (none) are depicted.

[0031] [Figure 5B] Box plots of expression levels of the top 10 downregulated miRNAs in mild SLE patients (mild), moderate SLE patients (moderate), and healthy individuals (none) are shown.

[0032] [Figure 6]Figure 1 shows a comparison of miRNA expression levels in SLE patients with or without comorbidity A according to one embodiment of the present disclosure. miRNAs with p<0.05 by t-test were selected as biomarkers.

[0033] [Figure 7] Figure 1 shows a comparison of miRNA expression levels in SLE patients with or without comorbidity B according to one embodiment of the present disclosure. miRNAs with p<0.05 by t-test were selected as biomarkers.

[0034] [Figure 8] Figure 1 shows a comparison of miRNA expression levels in SLE patients with or without comorbidity C according to one embodiment of the present disclosure. miRNAs with p<0.05 by t-test were selected as biomarkers.

[0035] [Figure 9] 1 shows a comparison of miRNA expression levels in SLE patients with or without comorbidity D according to one embodiment of the present disclosure. miRNAs with p<0.05 by t-test were selected as biomarkers. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0036] Before the present disclosure is further described, it should be understood that the present disclosure is not limited to the specific embodiments of the present disclosure described below, and that the specific embodiments may be varied and still be within the scope of the appended claims. It should also be understood that the terminology employed is for the purpose of describing the specific embodiments and is not intended to be limiting. Instead, the scope of the present disclosure is established by the appended claims. definition

[0037] Unless otherwise specified, all terms used herein have the same meaning as they would to one of ordinary skill in the art.

[0038] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0039] As used herein, "about" refers broadly to up to a 5% variation of a given numerical value.

[0040] As used herein, "array" refers broadly to a population of targets, such as miRNAs, that can be attached to a surface in a spatially distinguishable manner. Each feature of the array can contain a single copy of a target, such as miRNA, or a population of targets, such as multiple miRNAs, in each feature of the array. The population of miRNAs in each feature is usually homogenous, with a single species of a particular target. However, in some embodiments, a heterogeneous population of miRNAs can be present in a feature. Thus, a feature need not contain only a single miRNA, but can instead contain multiple different miRNAs.

[0041] "Body fluid" as used herein broadly refers to any of the various types of fluids found within an animal's body. Body fluids may be in a liquid state or in a solid state, e.g., frozen state. The solution may or may not contain the substance to be collected, such as a biomolecule, and may contain a substance for measuring the substance to be collected. The body fluid may be an animal's body fluid. The animal may be a reptile, a mammal, or an amphibian. The mammal may be a dog, a cat, a cow, a horse, a sheep, a pig, a hamster, a mouse, a squirrel, and a monkey, a gorilla, a chimpanzee, or a human. The body fluid may be tissue fluid, such as lymphatic fluid, interstitial fluid, intercellular fluid, or interstitial fluid, or may be body cavity fluid, serous fluid, pleural fluid, peritoneal fluid, bursal fluid, cerebrospinal fluid (cerebrospinal fluid), joint fluid (synovial fluid), or aqueous humor (aqueous humor). The bodily fluid may be a digestive fluid such as saliva, gastric juice, bile, pancreatic juice, intestinal fluid, etc., or may be sweat, tears, nasal mucus, urine, semen, vaginal fluid, amniotic fluid, milk, etc. The bodily fluid may be invasively collected, extracted, collected, etc. (hereinafter simply referred to as collected) or may be collected non-invasively.

[0042] "Classifier" as used herein refers broadly to machine learning algorithms such as support vector machine(s), AdaBoost classifier(s), penalized logistic regression, elastic net, regression tree(s), gradient tree boosting system(s), logistic regression, naive Bayes classifier(s), neural net, Bayesian neural net, k-nearest neighbor classifier(s), deep learning systems, and random forests. The present invention contemplates methods of using any of the listed classifiers, as well as the use of multiple classifiers in combination.

[0043] As used herein, "Classification and Regression Trees (CART)" refers broadly to a method of creating decision trees based on recursively partitioning a data space to optimize some metric, usually model performance.

[0044] As used herein, a "classification system" refers broadly to a machine learning system that implements at least one classifier.

[0045] A "device" as used herein broadly refers to a device used to separate and collect solutes from a solution. In some embodiments, a "device" may be a device used to analyze substances in a solution. In some embodiments, a "device" may be used to separate organic molecules from a solution. In some embodiments, a "device" may be used to separate biomolecules from a solution. A "device" may be a fluidic device, a flow path device, a combination thereof, or a device including any of the above.

[0046] "Elastic net," as used herein, refers broadly to a method for performing constrained linear regression that involves a linear combination of the L1 and L2 norms of the vector of regression coefficients.

[0047] As used herein, "extracellular vesicles (EVs)" refers broadly to vesicles released from cells, including those released from cells during apoptosis and those released from healthy cells. van Niel G et al., "Shedding light on the cell biology of extracellular vesicles," Nature Reviews Molecular Cell Biology (Nat Rev Mol Cell Biol) (2018) 19(4):213-228. Extracellular vesicles may be broadly divided into exosomes, microvesicles (MVs), and apoptotic bodies (apoptotic bodies) depending on size and surface markers. Exosomes typically have a diameter of 40-120 nanometers and may be capable of expressing one or more or all of the molecules selected from the group consisting of Alix, Tsg101, CD9, CD63, CD81, and flotillin. Exosomes may contain proteins and nucleic acids such as mRNA, miRNA, ncRNA. Microvesicles typically have a diameter of 50-1,000 nanometers and may express one or more or all of the molecules selected from the group consisting of integrins, selectins, and CD40. Microvesicles may contain proteins and nucleic acids such as mRNA, miRNA, ncRNA. Apoptotic bodies typically have a diameter of 500-2,000 nm and may be capable of expressing one or more molecules selected from the group consisting of annexin V and phosphatidylserine. Apoptotic bodies may contain fragmented nuclei and organelles.

[0048] As used herein, an "effective amount" refers to an amount of a composition described herein sufficient to produce a desired effect, which may be a therapeutic effect. The exact amount of a composition required for an effective amount will vary from subject to subject, depending on the species, age, weight and general condition of the subject, the severity of the condition being treated, the particular composition used, its mode of administration, the duration of the treatment, the nature of any concurrent treatment, the pharmaceutically acceptable carrier used, and similar factors within the knowledge and expertise of the skilled artisan. Thus, it is not possible to specify an exact amount for each composition of the present invention. However, an effective amount can be determined in each individual case by one of ordinary skill in the art using only routine experimentation given the teachings of this specification, with reference to the relevant texts and literature, and / or with routine experimentation. (See, for example, Remington: Science and Practice of Pharmacy, 21st ed. sup.st (2005), Lippincott Williams & Wilkins, Philadelphia, PA).

[0049] As used herein, "false positive" (FP) and "false positive identification" refer broadly to an error in which an algorithmic test result indicates the presence of a disease when in fact the disease is not present.

[0050] As used herein, "false negative" (FN) refers broadly to an error in which an algorithmic test result indicates the absence of a disease when in fact the disease is present.

[0051] As used herein, "free" refers broadly to biomolecules present in bodily fluids that are not encapsulated in extracellular vesicles and are not associated with extracellular vesicles. For example, miRNA in urine or urine extracts that are not encapsulated in extracellular vesicles and are not associated with extracellular vesicles.

[0052] "Homology" as used herein broadly refers to the degree of identity (see above, identity) between two amino acid sequences, i.e., peptide or polypeptide sequences. Said "homology" is determined by comparing two sequences aligned under optimal conditions across the sequences being compared. Such sequence homology can be calculated, for example, by creating an alignment using the ClustalW algorithm. Publicly available sequence analysis software, particularly Vector NTI, GENETYX or other tools, are provided by public databases.

[0053] The terms "sequence homology" and "sequence identity" used may be used interchangeably and refer broadly to the percentage of sequence homology or sequence identity of amino acid or nucleotide sequences. Sequences may be aligned by computer-based methods known in the art for optimal comparison purposes. Gaps may be introduced into either of the two sequences being compared to optimize the alignment between the two sequences. Such alignment may be performed over the entire length of the sequences being compared. Alternatively, alignment may be performed over a shorter length, for example, over about 5, about 10, about 20, about 50, about 100 or more nucleotides or amino acids. Sequence identity is the percentage of identical matches between two sequences over the reported alignment region.

[0054] Comparison of sequences between two sequences and determination of the percentage of sequence identity between sequences may be accomplished using a mathematical algorithm. The skilled artisan will recognize that several different computer programs are available for aligning two sequences and determining the identity between two sequences (Kruskal JB (1983) An overview of sequence comparison, in D. Sankoff and JB Kruskal (eds.), Time warps, string edits and macromolecules: the theory and practice of sequence comparison, Addison Wesley). The degree of sequence identity between two amino acid sequences or between two nucleotide sequences may be determined using the Needleman-Wunsch algorithm for alignment of two sequences. (Needleman SB and Wunsch CD (1970) J. Mal. Biol. 48, 443-453). Both amino acid and nucleotide sequences can be aligned by this algorithm. The Needleman-Wunsch algorithm is implemented in the computer program NEEDLE. For the purposes of the present invention, the NEEDLE program from the EMBOSS package was used (version 2.8.0 and later, EMBOSS: The European Molecular Biology Open Software Suite (2000) Rice, Longden and Bleasby, Trends in Genetics 16, (6) 276-277, emboss.bioinformatics.nl / ). In the case of amino acid sequences, EBLOSUM62 is used for the substitution matrix. In the case of nucleotide sequences, EDNAFULL is used.The arbitrary parameters used are a gap opening penalty of 10 and a gap extension penalty of 0.5. The artisan will recognize that all these different parameters will produce slightly different results, but that the overall match of the two sequences will not change significantly using the different algorithms.

[0055] After alignment by the program NEEDLE as described above, the percentage of sequence identity between the query sequence and the sequence of the invention is calculated as follows: the number of corresponding positions in the alignment that show identical amino acids or identical nucleotides in both sequences is divided by the total length of the alignment after subtracting the total number of gaps in the alignment. Identity as defined herein is obtained from NEEDLE using the NOBRIEF option and is labeled as "longest identity" in the program output. The nucleotide and amino acid sequences of the invention can further be used as "query sequences" to perform searches against sequence databases, for example to identify other family members or related sequences. Such searches can be performed using the NBLAST and XBLAST programs (version 2.0) of Altschul et al. (1990) J. Mal. Biol. 215:403-10. BLAST nucleotide searches can be performed with the NBLAST program, score=100, wordlength=12, to obtain nucleotide sequences homologous to the polynucleotides of the invention. To obtain amino acid sequences homologous to the polypeptides of the invention, BLAST protein searches can be performed with the XBLAST program, score=50, wordlength=3. To obtain gapped alignments for comparison purposes, Gapped BLAST can be utilized as described in Altschul et al. (1997) Nucleic Acids Res. 25(17):3389-3402. When utilizing BLAST and Gapped BLAST programs, the default parameters of the respective programs (e.g., XBLAST and NBLAST) can be used.

[0056] As used herein, "encapsulation" refers broadly to the form of a biomolecule that is incorporated into an extracellular vesicle, such as a microRNA (either fully or partially encapsulated) that is incorporated into an extracellular vesicle.

[0057] As used herein, "in situ extraction" refers broadly to the use of a microfluidic device incorporating nanowires to disrupt EVs captured on the nanowires to extract small RNAs (e.g., microRNAs) in situ, or to the extraction of small RNAs (e.g., microRNAs) captured on the nanowires from the nanowires into solution.

[0058] As used herein, "LASSO" refers broadly to a method for performing linear regression with constraints on the L1 norm of the vector of regression coefficients.

[0059] As used herein, the "L1 norm" is the sum of the absolute values ​​of the elements of a vector.

[0060] As used herein, the "L2 norm" is the square root of the sum of the squares of the elements of a vector.

[0061] As used herein, "Negative Predictive Value (NPV)" is the number of true negatives (TN) divided by the sum of the number of true negatives (TN) and the number of false negatives (FP), TP / (TN+FN).

[0062] As used herein, "neural net" refers broadly to a classification methodology that chains perceptron-like objects to create a classifier.

[0063] As used herein, "performance score" refers broadly to the distance between predicted and actual values ​​in the training data. It is expressed as a number between 0 and 100%, with higher values ​​indicating that the predicted values ​​are closer to the actual values. Typically, a higher score means that the model is performing better.

[0064] "Positive predictive value (PPV)" is the number of true positives (TP) divided by the sum of the number of true positives (TP) and the number of false positives (FP), TP / (TP+FP).

[0065] As used herein, "random forest" refers broadly to a bagging method that adapts a CART based on samples from the dataset on which the model is trained.

[0066] As used herein, "label" refers broadly to any atom or molecule that can be used to provide a detectable (preferably quantifiable) signal. The label can be attached to a molecule of interest, such as a secondary reagent. The label may provide a signal that is detectable by techniques that include, but are not limited to, fluorescence, radioactivity, colorimetry, gravimetry, X-ray diffraction or absorption, magnetism, enzymatic activity, and combinations thereof.

[0067] As used herein, the term "nanowire" broadly refers to a rod- or wire-like structure having a size, such as a cross-sectional shape or diameter, on the order of nanometers (for example, a diameter of one to several hundred nanometers).

[0068] "Autoimmune disease" as used herein refers to disease caused by the reaction of the immune system of one's own with healthy cells and tissues of one's own.Examples of autoimmune disease may include SLE, multiple sclerosis, rheumatoid arthritis, psoriasis, Crohn's disease, vitiligo, Behcet's disease, collagen disease, type I diabetes, uveitis, Sjogren's syndrome, autoimmune myocarditis, autoimmune liver disease (e.g. autoimmune hepatitis), autoimmune gastritis, autoimmune thyroid disease, pemphigus, Guillain-Barre syndrome, chronic inflammatory demyelinating polyneuropathy, and HTLV-1-associated myelopathy.

[0069] As used herein, "mild SLE" broadly refers to mild to moderate flares that commonly present as rash, oral ulcers, and arthritis. These flares may often be limited to the skin and joints and may also be accompanied by fever and fatigue. Treatment options for mild flares (e.g., malar rash, fatigue, and joint pain) may include antimalarials (such as hydroxychloroquine 200-400 mg), nonsteroidal anti-inflammatory drugs (NSAIDs), and low-dose steroids.

[0070] "Moderate SLE" as used herein broadly refers to moderate flares (e.g., more severe skin, rash, alopecia) and moderate doses of steroids may be used. Immunosuppressants such as methotrexate or azathioprine may be added for a "steroid-sparing" effect in patients who required prednisone >10 mg / day to control symptoms. Antimalarial adjustment options for moderate flares may include maximizing hydroxychloroquine, adding or substituting quinacrine, or switching to chloroquine. These drugs can help reduce symptoms, improve disease signs, and sometimes induce remission, but they can also have significant negative side effects. Steroids in particular commonly cause insomnia, osteoporosis, muscle weakness, and many others. Belimumab (Benlysta), a monoclonal antibody against soluble B-lymphocyte survival factor, has been approved in recent years for this category of patients.

[0071] As used herein, "microRNA" (also referred to as "miRNA") broadly refers to a type of non-coding RNA (ncRNA) that is not thought to code for proteins. MicroRNAs are processed from their precursors to mature forms. Mature microRNAs are known to have lengths on the order of 20-25 bases. Human microRNAs are named hsa. Precursors are labeled mir and mature forms are labeled miR. Identified sequences are numbered in the order of identification, with similar sequences being numbered with lowercase alphabets. In cases where there is a 5'-end derived precursor and a 3'-end derived precursor, the 5'-end derived microRNA is labeled with 5p and the 3'-end derived microRNA is labeled with 3p. These symbols and numbers are joined by a hyphen. Mature microRNAs may be double-stranded. miRNAs may be important regulators of cell proliferation, differentiation, and apoptosis and therefore may be important for normal development and physiology.

[0072] "Ridge regression," as used herein, refers broadly to a method for performing linear regression with constraints on the L2 norm of the vector of regression coefficients.

[0073] "Severe SLE" as used herein broadly refers to severe flares of life- or organ-threatening disease, such as significant kidney disease, brain disease, very low platelet or red blood cell counts, and vasculitis. In such severe manifestations of SLE, treatment typically begins with pulsed solumedrol (1 gram / day IV for 3 days), followed by high-dose prednisone at 1-2 mg / kg per day. More potent immunosuppressants such as IV cyclophosphamide (Cytoxan), mycophenolate mofetil (Cellcept), azathioprine (Imuran), or more recently developed biologic therapies such as Benlysta and rituximab (RTX) (trade names Rituxan) may be added.

[0074] As used herein, "SLE comorbidities" refers broadly to comorbidities associated with SLE. SLE may be associated with a higher risk of cancer, cardiovascular disease, renal disease, liver disease, rheumatic and neurological diseases, as well as hypothyroidism, psychiatric disease, and anemia. The onset of comorbidities may be most frequent in the first two years after SLE diagnosis. Vascular disease may be one of the most common of the many comorbidities associated with SLE. In addition to cardiovascular disease, patients with SLE may have several other comorbidities, including osteoporosis, Sjögren's syndrome, antiphospholipid syndrome, autoimmune thyroid disease, malignant lesions, rheumatoid arthritis, systemic sclerosis, myositis, vasculitis, autoimmune hepatitis, and infectious diseases.

[0075] As used herein, "subject" broadly refers to any animal susceptible to SLE. Such subjects are generally mammalian subjects, including but not limited to humans, primates, dogs, cats, pigs, rabbits, guinea pigs, goats, dairy cows, cattle, horses, and the like. Thus, in some embodiments, the subject can be any livestock, commercially or clinically valuable animal, including animal models of SLE. The subject can be male or female, and of any age, including newborn, juvenile, juvenile, adolescent, adult, and geriatric objects. In certain embodiments, the subject is a human. The terms "subject" and "patient" are used interchangeably.

[0076] As used herein, "Standard of Deviation (SD)" is the spread of individual data points (i.e., across replicates) that reflects the uncertainty of a single measurement.

[0077] As used herein, a "subset" broadly refers to a proper subset, and a "superset" is a proper superset.

[0078] "Subject in need thereof" as used herein refers broadly to subjects who are known or suspected to have SLE or are at high risk of developing SLE.Subjects of the present invention may also include subjects who have not been previously known or suspected to have SLE or have not required treatment for SLE.Subjects of the present disclosure may also be subjects who are known to have SLE or are considered to be at risk of developing SLE.Subjects described herein as at risk of developing SLE are identified by family history, genetic analysis, environmental exposure and / or the onset of early symptoms related to the diseases or disorders described herein.

[0079] "Separation" and "enrichment" as used herein broadly refer to methods for the separation of EVs from cell culture media or bodily fluids at high purity and quality. Separation may refer to the purification or isolation of EVs from other non-EV components of the material (conditioned media, biological fluids, tissues) and of different types of EVs from each other. Enrichment may be a means to increase the number of EVs per unit volume with or without separation. Separation and enrichment of EVs may be achieved by multiple techniques based on the size or expression of surface markers of EVs. These techniques may include differential ultracentrifugation, density gradient centrifugation, immunoaffinity, ultrafiltration, polymer-based precipitation, and size exclusion chromatography.

[0080] As used herein, "substantially free" broadly refers to the presence of a particular component in an amount of less than 1%, preferably less than 0.1% or 0.01%. More preferably, the term "substantially free" broadly refers to the presence of a particular component in an amount of less than 0.001%. Amounts may be expressed as w / w or w / v depending on the composition.

[0081] As used herein, "solid support," "support," and "substrate" refer broadly to any material that provides a solid or semi-solid structure to which another material can be attached, including, but not limited to, smooth supports (e.g., metal, glass, plastic, silicon, and ceramic surfaces) as well as rough and porous materials. Substrate materials include acrylic, carbon (e.g., graphite, carbon fiber, nanotubes), ceramic, controlled pore glass, cross-linked polysaccharides (e.g., agarose or SEPHAROSE®), gels, glasses (e.g., modified or functionalized glasses), graphite, inorganic glasses, inorganic polymers, metal oxides (e.g., SiO2, TiO2, stainless steel), nanomaterials (e.g., highly oriented pyrolitic graphite (HOPG) nanosheets), organic polymers, plastics, polyacryloylmorpholide, poly(4-methylbutene), poly(ethylene terephthalate), poly(vinyl butyrate), polybutylene, polydimethylsiloxane (PDMS), polyethylene, polyformaldehyde, polymethacrylate, polypropylene, polystyrene, polyurethane, polyvinylidene fluoride (PVDF), and the like. difluoride), resin, silica, silicon (e.g., silicon surface oxide), or combinations thereof.

[0082] As used herein, "surface" refers broadly to a portion of a support structure (e.g., substrate) that is accessible for contact with a reagent, bead, or analyte. A surface can be substantially flat or planar. Alternatively, a surface can be rounded or contoured. Exemplary contours that can be included on a surface are wells, depressions, pillars, ridges, channels. The terms "surface" and "substrate" are used interchangeably herein.

[0083] As used herein, a "training set" is a set of samples used to train and develop a machine learning system, such as the algorithms used in the methods and systems described herein.

[0084] As used herein, "treatment" refers broadly to alleviating the signs and / or symptoms of a disease or injury condition. Treatment may also include preventative measures in which a therapeutic composition is administered prior to the onset of signs and / or symptoms or exposure to a disease or injury condition to reduce the onset of the signs and / or symptoms of a disease or injury condition.

[0085] As used herein, "True Negative (TN)" refers to the test results of an algorithm indicating that a miRNA is not associated with SLE when in fact the miRNA is associated with SLE.

[0086] As used herein, "True Positive (TP)" is when the test result of the algorithm indicates that a miRNA is associated with SLE when in fact SLE is associated with SLE.

[0087] "Truncation," as used herein, refers broadly to sequences that are shortened at the 5' and / or 3' ends, in the case of polynucleotides, and at the N- and / or C- ends, in the case of polypeptides.

[0088] As used herein, "urine extract" refers broadly to a product extracted from urine that has a higher concentration of certain components, particularly microRNAs, than the urine prior to extraction.

[0089] As used herein, a "validation set" refers broadly to a set of samples that is blinded and used to validate the functionality of the algorithms used in the methods and systems described herein. This is also known as a blinded set. Systemic lupus erythematosus (SLE)

[0090] Systemic lupus erythematosus (SLE) is a prototypic chronic autoimmune disease that affects multiple organs with unknown causes. Despite considerable research on SLE, there are few effective targeted therapies in SLE. Existing treatment options to alleviate symptoms and control disease progression include drugs that provide nonspecific immunosuppression to keep the disease under control, such as nonsteroidal anti-inflammatory drugs (NSAIDs) and immunosuppressants such as hydroxychloroquine, corticosteroids, methotrexate, azathioprine, cyclophosphamide, and mycophenolate mofetil. Belimumab is the first-ever targeted biologic for the treatment of SLE patients with active autoantibody-positive disease who are already receiving standard of care. Belimumab is a fully human IgG1λ recombinant monoclonal antibody against B-lymphocyte stimulatory factor (BLyS). Specific binding of belimumab to soluble BLyS prevents the interaction of BLys with its three receptors, indirectly reducing B-cell survival and autoantibody production.

[0091] Symptoms of SLE include, but are not limited to, joint pain / arthralgia, fever over 100°F / 38°C, arthritis / swollen joints, prolonged or extreme fatigue, skin rash, anemia, kidney lesions, chest pain / pleurisy when breathing deeply, butterfly rash on the cheeks and nose, sun or light sensitivity / photosensitivity, hair loss, blood clotting problems, Raynaud's phenomenon / fingers turning white and / or blue with cold, seizures, mouth or nose ulcers, and any combination of these.

[0092] The SLE condition may be mild SLE, where patients suffer from mild to moderate flares that commonly present as rashes, oral ulcers, and arthritis. These flares may be mostly confined to the skin and joints and may also be accompanied by fever and fatigue. Treatment options for mild flares (e.g. malar rash, fatigue, and joint pain) may include antimalarials (such as hydroxychloroquine 200-400 mg), nonsteroidal anti-inflammatory drugs (NSAIDs), and low-dose steroids.

[0093] The SLE condition may be moderate SLE where the patient suffers from moderate flares (e.g. more severe skin, rash, alopecia) and moderate doses of steroids may be used. Immunosuppressants such as methotrexate or azathioprine may be added for a “steroid sparing” effect in patients who required prednisone >10 mg / day to control symptoms. Antimalarial adjustment options for moderate flares may include maximizing hydroxychloroquine, adding or substituting quinacrine, or switching to chloroquine. These drugs can help reduce symptoms, improve disease signs, and sometimes induce remission, but they can also have significant negative side effects. Steroids in particular commonly cause insomnia, osteoporosis, muscle weakness, and many others. A monoclonal antibody against soluble B-lymphocyte survival factor, belimumab (Benlysta), for example, has been approved in recent years for this category of patients.

[0094] The SLE condition may be severe SLE, where patients suffer from severe flares that refer to life- or organ-threatening diseases such as significant kidney disease, brain disease, very low platelet or red blood cell counts, vasculitis, etc. In such severe manifestations of SLE, treatment usually begins with pulsed solumedrol (1 gram / day IV for 3 days), followed by high-dose prednisone at 1-2 mg / kg per day. More powerful immunosuppressants such as IV cyclophosphamide (Cytoxan), mycophenolate mofetil (Cellcept), azathioprine (Imuran), or more recently developed biologic therapies such as Benlysta and rituximab (RTX) (trade names Rituxan) may be added.

[0095] SLE may be associated with other conditions, referred to as "SLE comorbidities." SLE may be associated with cancer, a higher risk of cancer, cardiovascular disease, kidney disease, liver disease, rheumatic and neurological diseases, as well as hypothyroidism, psychiatric disease, and anemia. The onset of comorbidities may be most frequent in the first two years after SLE diagnosis. Vascular disease may be one of the most common of the many comorbidities associated with SLE. In addition to cardiovascular disease, patients with SLE may have several other comorbidities, including osteoporosis, Sjögren's syndrome, antiphospholipid syndrome, autoimmune thyroid disease, malignant lesions, rheumatoid arthritis, systemic sclerosis, myositis, vasculitis, autoimmune hepatitis, and infections.

[0096] As described herein, approximately 22,000 protein-coding transcripts mRNA (and a subset thereof) may be used to distinguish SLE patients from healthy controls. MicroRNAs represent a pure regulatory process, as opposed to a structural process, that fine-tunes mRNA expression. The combinatorial nature of nucleotide complementarity allows individual miRNAs to regulate the expression of hundreds of genes through post-transcriptional modification of their cognate messenger RNA.

[0097] Mature microRNAs may be double-stranded. miRNAs may be important regulators of cell proliferation, differentiation, and apoptosis and therefore may be important for normal development and physiology. As a result, dysregulation of miRNA function may lead to human diseases such as cancer, immune disorders, and viral infections. Differential expression of miRNAs may be useful in the diagnosis / treatment of SLE.

[0098] miRNA expression may be a richer source of information about disease pathogenesis than messenger RNA profiling and therefore holds promise for practical application as mechanism-based molecular biomarkers for preventive, predictive, personalized and participatory medicine ("P4 medicine"). See, e.g., Flores et al., "P4 medicine: how systems medicine will transform the healthcare sector and society," Per Med. (2013) 10(6):565-576.

[0099] The embodiments of the present disclosure include the identification of SLE patients using biomarkers, and the treatment of SLE patients based on such identification.For example, the methods described herein may utilize a classifier to identify miRNAs from a dataset of miRNAs and expression levels, for example, to identify miRNAs and / or their expression levels associated with SLE.In one embodiment, the miRNA data obtained from the method of detecting the expression levels of miRNAs described herein or in the art is collected into a database and processed by a classifier to classify miRNAs and their expression levels as indicative or non-indicative of SLE.See, for example, US Patent Application Publication No. 2020 / 0255906. miRNA Detection Methods

[0100] The methods described herein may include obtaining a sample and analyzing the miRNA content in the sample.

[0101] The sample may be a bodily fluid. The bodily fluid may be blood, urine, plasma, saliva, ascites, bronchoalveolar lavage fluid, cerebrospinal fluid, or a combination thereof. Samples containing bodily fluids may be collected by any means known in the art. An extractor such as a syringe may be used to extract, collect, and collect a solution from a subject.

[0102] The sample containing the bodily fluid may be taken from a subject, including a subject with a particular disease, or may be a bodily fluid from a subject suspected of having or to be tested for having a particular disease, in some embodiments, the disease may be an immune disease, such as SLE.

[0103] The sample may be a urine extract, an aqueous solution (solution or suspension), or a solid obtained by drying a urine sample. In the urine extract, an extract from which components other than extracellular vesicles and nucleic acids in urine are substantially removed may be called a urine purified product. The urine extract may contain a surfactant, preferably a non-ionic surfactant. The urine extract may contain detergents and extracellular vesicle fragments (e.g., exosomes and / or microvesicles). The urine extract may be free or substantially free of one or more selected from the group consisting of detergents and extracellular vesicle fragments (e.g., exosomes and / or microvesicles). The urine extract may further contain a stabilizer (e.g., a nucleic acid stabilizer) and / or a pH adjuster (e.g., a buffer). The urine extract may contain a salt. The urine extract may include one or more urine components selected from the group consisting of urea, creatinine, uric acid, ammonia, urobilin, riboflavin, urinary protein, sugar, and urinary hormones (e.g., chorionic gonadotropin). The pH of the urine extract may be equal to or greater than a value such as 2, 3, 4, or 5. The pH of the urine extract may be equal to or less than a value such as 10, 9, 8, 7, 6, or 5. The urine extract includes microRNA. In the present disclosure, the urine extract may include enriched / concentrated microRNA or a group thereof. In the present disclosure, the urine extract may include microRNA extracted by the extraction method described herein.

[0104] The method described herein comprises: (a) obtaining a body fluid sample from a patient suspected of having SLE; (b) analyzing miRNA expression in the obtained samples; (c) Patients (i) as having a marker correlating with SLE comorbidity if an increase in the expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in a patient sample compared to a body fluid sample obtained from a healthy individual; or (ii) Assuming that the subject does not have a marker correlating with SLE comorbidity when an increase in the expression of at least one miRNA selected from SEQ ID NOs: 1 to 160 and 243 to 402 and / or a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161 to 242 and 403 to 484 compared to a body fluid sample obtained from a healthy individual is not detectable. and a step of identifying may also include

[0105] The method described herein comprises: (a) introducing the obtained bodily fluid sample into a fluidic device containing nanowires; (b) capturing extracellular vesicles in a body fluid sample on the nanowire; (c) disrupting the captured extracellular vesicles; (d) extracting at least one miRNA from the disrupted extracellular vesicles; (e) hybridizing the extracted miRNA to an miRNA array; (f) measuring miRNA hybridization to the array; The method may include a step of analyzing, including generating an miRNA profile from the sample.

[0106] Extracellular vesicles may be broadly classified into exosomes, microvesicles (MVs), and apoptotic bodies depending on the size and surface markers. Exosomes usually have a diameter of 40 to 120 nanometers and may be capable of expressing one or more or all molecules selected from the group consisting of Alix, Tsg101, CD9, CD63, CD81, and flotillin. Exosomes may contain proteins and nucleic acids such as mRNA, miRNA, and ncRNA. Microvesicles usually have a diameter of 50 to 1,000 nanometers and may be capable of expressing one or more or all molecules selected from the group consisting of integrins, selectins, and CD40. Microvesicles may contain proteins and nucleic acids such as mRNA, miRNA, and ncRNA. Apoptotic bodies usually have a diameter of 500 to 2,000 nm and may be capable of expressing one or more molecules selected from the group consisting of annexin V and phosphatidylserine. Apoptotic bodies may contain fragmented nuclei and organelles.

[0107] Isolation and enrichment of extracellular vesicles (EVs) may be achieved by multiple techniques based on the size of EVs or the expression of surface markers. These techniques may include differential ultracentrifugation, density gradient centrifugation, immunoaffinity, ultrafiltration, polymer-based precipitation, and size-exclusion chromatography. Differential centrifugation may be a common approach for EV isolation. Briefly, samples may be first centrifuged at low speed to remove cells (500 x g). Cellular debris may then be removed after centrifugation at 2500 x g. The supernatant may be collected and then centrifuged at 10,000 x g to pellet larger EVs such as microvesicles. The final supernatant may then be ultracentrifuged at 100,000×g to pellet small EVs that may correspond to exosomes. The final pellet may then be washed with a large volume of phosphate buffered solution (PBS) to remove contaminating proteins and then centrifuged one final time at 100,000×g. One or more additional techniques may be used to achieve better specificity of EV or EV subtype separation. The purity of EVs may be further improved by density gradient centrifugation (velocity or flotation). Exosomes may be purified at buoyant density using a discontinuous gradient of sucrose solution or iodixanol cushion. Further purification may also be achieved by immunoaffinity. Antibodies (CD63, CD81, CD9) may be conjugated to magnetic beads and incubated with the EV-containing sample. EVs may be separated based on their size by ultrafiltration. Common filter pore sizes may be 0.8 μm and 0.22 μm. Some commercial products may also use polyethylene glycol (PEG) for precipitation to separate EVs. Size exclusion chromatography may separate EV particles by their size. To confirm the purity of the isolated EVs, electron microscopy, nanoparticle tracing analysis (NTA), and Western blotting may be performed to characterize the shape, size, and biomarker expression of EVs. At least three positive protein markers (such as CD63, CD9, CD81, TSG101) and a negative protein marker (such as calnexin) may be required to define an EV. Single EVs may be characterized by two different complementary techniques: microscopy (such as scanning probe microscopy, atomic force microscopy, or super-resolution microscopy) or single particle analyzer (NTA, high-precision flow cytometry, and dynamic light scattering). Microfluidic chips for EV isolation and analysis

[0108] To increase the efficiency of EV capture on microfluidic devices, nanostructures, such as nanowires, may be designed on the chip to provide a larger surface area that may allow for direct incorporation of capture antibodies. Nanowires may be structures whose section maximum, minimum, average, or other characteristic size may be at the nanometer, subnanometer, 10 nanometer, 100 nanometer, or submicrometer level, unless a diameter or characteristic size is defined.

[0109] The length of the nanowire may be a defined size in the longitudinal direction and may be on the nanometer level to the 10 nanometer level, the 100 nanometer level, or the submicrometer level. In one embodiment, the length of the nanowires described herein may be from about 0.1 nanometers to about 500 nanometers, from about 1 nanometer to about 250 nanometers, from about 1 nanometer to about 100 nanometers, or from about 5 nanometers to about 50 nanometers. The length of the nanowires is about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 6, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 4, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 1 74, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322,323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 46 4, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, or 500 nanometers (nm). The length of the nanowires may be between about 1-500 nm, 100-500 nm, 200-400 nm, 250-500 nm, 50-250 nm, 10-100 nm, 2-200 nm, 300-500 nm, 400-500 nm, 150-450 nm, 250-300 nm, 10-50 nm, 100-350 nm, 350-500 nm, or 200-300 nm.

[0110] The length of the nanowires can be, for example, but not limited to, greater than 500 nm, 1 μm, 1.5 μm, 2 μm, 3 μm, 4 μm, 5 μm, 6 μm, 7 μm, 8 μm, 9 μm, 10 μm, 11 μm, 12 μm, 13 μm, 14 μm, 15 μm, 17 μm, 20 μm, etc. The length of the nanowires can be, for example, but not limited to, less than or equal to 1 μm, 1.5 μm, 2 μm, 3 μm, 4 μm, 5 μm, 6 μm, 7 μm, 8 μm, 9 μm, 10 μm, 11 μm, 12 μm, 13 μm, 14 μm, 15 μm, 17 μm, 20 μm, 50 μm, 100 μm, or 200 μm.

[0111] The length of the nanowires is about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 , 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169 4, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 1 59, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, or 200 μm. The length of the nanowires may be between about 1-100 μm, 100-200 μm, 120-140 μm, 150-175 μm, 5-25 μm, 10-10 μm, 2-20 μm, 30-100 μm, 15-125 μm, 10-45 μm, 25-180 μm, 60-75 μm, 1-150 μm, 35-200 μm, or 2-180 μm.

[0112] The diameter (or size along the thickness) of the nanowires may be, for example, 5 nm, 10 nm, 15 nm, 20 nm, 25 nm, 30 nm, 40 nm, 50 nm, 60 nm, 70 nm, 80 nm, 90 nm, 100 nm, 150 nm, 200 nm, 250 nm, 300 nm, 400 nm, 500 nm, etc. or more. The diameter (or size along the thickness) of the nanowires may be, for example, 10 nm, 15 nm, 20 nm, 25 nm, 30 nm, 40 nm, 50 nm, 60 nm, 70 nm, 80 nm, 90 nm, 100 nm, 150 nm, 200 nm, 250 nm, 300 nm, 400 nm, 500 nm, 1 μm or less.

[0113] The diameter of the nanowire is about , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 99, 100, 101, 102, 1 3, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118 , 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 1 81, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 310, 31 1, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 440, 441, 442 , 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473,It may be 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, or 500 nanometers (nm). The length of the nanowires may be between about 1-500 nm, 100-500 nm, 200-400 nm, 250-500 nm, 50-250 nm, 10-100 nm, 2-200 nm, 300-500 nm, 400-500 nm, 150-450 nm, 250-300 nm, 10-50 nm, 100-350 nm, 350-500 nm, or 200-300 nm.

[0114] The cross section of the nanowire may be substantially circular, elliptical, regular polygonal, polygonal, or hollow. The outer shape of the nanowire may be substantially cylindrical, elliptical, or polygonal. The nanowire may be hollow or hollow, or may be a substantially solid structure. The nanowire may be made of one material or multiple materials. The nanowire may be coated on its surface with a coating material.

[0115] The material of the nanowire may be an inorganic material or an organic material. The nanowire may be or include a metal, a nonmetal, a semiconductor, a mixture or alloy thereof, or an oxide or nitride thereof. The material of the nanowire may be or include a polymeric material. The nanowire may be a wire, a whisker, a fiber, a mixture or a composite thereof. Metals used in the material of the nanowire include main group metals (alkali metals: Li, Na, K, Rb, Cs, alkaline earth metals: Ca, Sr, Ba, Ra), magnesium group elements: Be, Mg, Zn, Cd, Hg, aluminum group elements: Al, Ga, In, rare earth elements: Y, La, Ce, Pr, Nd, Sm, Eu, tin group elements: Ti, Zr, Sn, Hf, Pb, Th, iron group elements: Fe, Co, Ni, earth elements: V The metals may include, but are not limited to, Nb, Ta, chromium group elements: Cr, Mo, W, Au, Cu, copper group elements, Rh, Pd, Os, Ir, Pt, naturally occurring radioactive elements: radioactive decay products of U and Th: U, Th, Ra, Rn, actinides, transuranium elements: Np, Pu, Am, Cm, Bk, Cf, Es, Fm, Md, No, and other elements beyond uranium, or alloys thereof. The nanowires may be or include an oxide of any one of the above metals or alloys, or an alloy or mixture. The material of the nanowires, or at least the surface of the nanowires, e.g., cladding, may be, for example, but not limited to, ZnO, SiO2, Li2O, MgO, Al2O3, CaO, TiO2, Mn2O3, Fe2O3, CoO, NiO, CuO, Ga2O3, SrO, In2O3, SnO2, Sm2O3, and EuO. The nanowires may be charged. The nanowires may have an opposite charge to that of the material to be collected or extracted. This may allow for efficient attraction and adsorption of charged biomolecules, such as, by way of non-limiting example, extracellular vesicles, nucleic acids, etc.

[0116] The substrate may include, but is not limited to, a semiconductor, a metal, an insulator, an organic material, a polymeric material, and the like, by way of example. In one aspect, the substrate may have any shape structure, such as a planar structure whose major surfaces may be parallel to each other, a curved structure whose major surfaces may not be parallel to each other, or a combination thereof. The substrate may have a three-dimensional structure. The substrate may be formed of a material on which the catalyst layer may be laminated, such as a semiconductor material such as silicon, a glass material such as quartz glass, Pyrex® glass, ceramics, a polymeric material including plastic, and the like, and the like may be used. In some embodiments, the substrate may be substantially flexible and may be stretchable. In some embodiments, the substrate may be substantially non-flexible. The material of the substrate may be a material selected from, but is not limited to, polyethylene, polypropylene, polyvinyl chloride, polyvinylidene chloride, polystyrene, polyvinyl acetate, polytetrafluoroethylene, ABS (acrylonitrile butadiene styrene) resin, AS (acrylonitrile styrene) resin, thermoplastic resin such as acrylic resin (PMMA), phenolic resin, epoxy resin, melamine resin, urea resin, unsaturated polyester resin, alkyd resin, polyurethane, polyimide, silicone rubber, polymethylmethacrylate (PMMA), and polycarbonate (PC).

[0117] The nanowires may be disposed on a substrate (also called a nanowire substrate), and a "cover" or "cover member" may be used to mean a substrate, different from the substrate on which the nanowires are disposed, that is bonded to the nanowire substrate and used to form a fluid chamber or flow path.

[0118] The nanowires may be attached to a substrate. The nanowires may be located within a chamber or well.

[0119] Substrates for arrays used in the systems and methods described herein can be any material that provides a solid or semi-solid structure to which another material can be attached, including, but not limited to, smooth supports (e.g., metal, glass, plastic, silicon, and ceramic surfaces) as well as rough and porous materials. Substrate materials include, but are not limited to, acrylic, carbon (e.g., graphite, carbon fiber, nanotubes), ceramic, controlled pore glass, cross-linked polysaccharides (e.g., agarose or SEPHAROSE®), gel, glass (e.g., modified or functionalized glass), graphite, inorganic glass, inorganic polymer, metal oxide (e.g., SiO2, TiO2, stainless steel), nanomaterials (e.g., highly oriented pyrolytic graphite (HOPG) nanosheets), organic polymer, plastic, polyacryloylmorpholide, poly(4-methylbutene), poly(ethylene terephthalate), poly(vinyl butyrate), polybutylene, polydimethylsiloxane (PDMS), polyethylene, polyformaldehyde, polymethacrylate, polypropylene, polystyrene, polyurethane, polyvinylidene fluoride (PVDF), resin, silica, silicon (e.g., surface oxidized silicon).

[0120] The substrate need not be flat and can include any type of shape, including spherical (e.g., beads) or cylindrical (e.g., fibers). The nanowires attached to the solid support can be attached to any portion of the solid support (e.g., an interior portion of a porous solid support).

[0121] The substrate can be patterned such that the nanowires attached to the substrate are provided in a pattern. A pattern, such as stripes, swirls, lines, triangles, rectangles, circles, arcs, checks, lattice stripes, diagonal lines, arrows, squares, or crosshatches, can be etched, printed, treated, sketched, cut, engraved, engraved, imprinted, fixed, stamped, coated, embossed, embedded, or layered onto the substrate such that the nanowires can be provided in a pattern on the substrate.

[0122] The surface of the nanowire may be positively charged. Thus, for example, negatively charged extracellular vesicles may be efficiently collected. For example, the nanowire may be formed of a positively charged material such as ZnO, nickel oxide, etc., or the nanowire may be coated with such a material. device

[0123] The device can be used to separate extracellular vesicles from a sample, such as blood, plasma, or urine.

[0124] The devices described herein that may be used with the methods described herein include: A sample input in fluid communication with (a) and (b). (b) a separation means in fluid communication with (c) or (d), optionally a membrane, a filter, at least one nanowire, or a combination thereof; (c) a waste chamber; or (d) Waste Output Section The microfluidic device may include

[0125] The devices described herein that may be used with the methods described herein may be a solid substrate comprising a plurality of wells, each well comprising at least one nanowire, optionally an array comprising nanowires.

[0126] The devices described herein that may be used with the methods described herein may be solid substrates comprising a plurality of chambers, optionally in fluid communication with one another, each chamber comprising at least one nanowire, optionally an array comprising nanowires.

[0127] The devices described herein may include a cover over the well or chamber, which may optionally be removable.

[0128] The sample may be introduced into the sample input, for example, by a syringe, a syringe pump. The sample input is fluidly connected to a separation means, including but not limited to a membrane, a filter, at least a nanowire, or a combination thereof, that allows for the capture of extracellular vesicles. After passing the sample through the separation means, the extracellular vesicles come into contact with the membrane, filter, nanowire, or a combination thereof, and the extracellular vesicles are captured on the membrane, filter, nanowire, or a combination thereof. The captured extracellular vesicles may be examined, for example, by microscopy and / or imaging means.

[0129] After the sample is introduced, the nanowires may be washed with a buffer to remove unreacted extracellular vesicles and other materials, and the extracellular vesicles adsorbed to the nanowires may be analyzed.

[0130] When observing the sample adsorbed on the nanowire of the device by optical or electron microscopy, the cover may be peeled off from the substrate. When the substrate and the cover member are in close contact with an adhesive, the cover member may be removed, for example, by cutting with a blade. By observing by microscope, for example, the size and number of the captured sample may be determined. Also, by binding an optical label, for example a fluorescent label, to the sample, a quantitative analysis of the surface proteins of the captured sample may be performed.

[0131] For example, a urine extract may be obtained by contacting urine with nanowires having a positively charged surface (e.g., nanowires having at least one surface selected from the group consisting of ZnO, SiO2, Li2O, MgO, Al2O3, CaO, TiO2, Mn2O3, Fe2O3, CoO, NiO, CuO, Ga2O3, SrO, In2O3, SnO2, Sm2O3, EuO, or combinations thereof) in a urine pH environment, then washing (optionally) with a buffer containing a non-ionic surfactant, and extracting the urine extract to yield a urine extract. The urine may be pH adjusted such that the surface charge of the nanowires is positive at, before, after, or during contact of the nanowires with urine. Detection of extracellular vesicles (EVs)

[0132] After introducing EVs into the nanowire-containing device, the nanowire containing the extracellular vesicles may be washed with a buffer to remove the extracellular vesicles and any other foreign body(s) that are not captured by the nanowire.

[0133] The buffer may be any isotonic solution, such as normal saline, buffered saline, lactated Ringer's solution, 5% dextrose in water (D5W), Ringer's solution, or 0.9% saline. Buffers include mineral buffers, balanced saline solutions (BSS), TRIS buffer solutions (TBS), phosphate buffered saline (PBS), organic buffers, borate buffer solutions, carbonate buffer solutions, citrate buffer solutions, glycine buffer solutions, Tris buffered saline solutions, Dulbecco's Phosphate Saline Buffer (DPBS), Dulbecco's Eagle Media (DMEM), Hank's Balanced Salts and Saline Solution (HBSS), Tyrode's Balanced Salts and Saline Solutions (TBSS), Minimum Essential Medium, Eagle Basal Medium (EBM), Earle's Balanced Salts and The buffer may be selected from the group consisting of glycerol, glycerol, tert-butyl ether ...

[0134] After washing with buffer, a buffer containing a blocking agent (including those described herein) may be introduced and allowed to incubate for about 1-60 minutes. The blocking agent may be bovine serum albumin (BSA), non-fat dry milk (NFDM), fish gelatin, whole serum, or a polymer including, but not limited to, polyethylene glycol (PEG), polyvinyl alcohol (PVA), and polyvinylpyrrolidone (PVP). The blocking agent may be used at a concentration of about 0.1%-10%, for example 1% or 4%.

[0135] For example, a blocking solution containing a buffer containing 1% bovine serum albumin (BSA) may be introduced and incubated for about 15 minutes. The device may be incubated with the buffer containing the blocking agent at a temperature between about -20°C and 25°C. Following incubation with the buffer containing the blocking agent, the device may be washed with the buffer and incubated with an antibody that binds to the extracellular vesicles. The primary antibody may be visualized with a secondary antibody using methods known in the art.

[0136] For example, the device may be washed with PBS, and Alexa Fluor 488-labeled mouse anti-human CD63 monoclonal antibody (10 μmg / ml) or mouse anti-human CD81 monoclonal antibody (10 μmg / ml) may be introduced into the device and left for 15 minutes. To detect CD81, the device may be washed, and then Alexa Fluor 488-labeled goat anti-mouse IgG polyclonal antibody may be introduced into the device as a secondary antibody and left for 15 minutes. Finally, the device may be washed with PBS, and the fluorescence intensity may be observed under a fluorescent microscope. PBS may be used instead of the EV sample to obtain a background value. For detection using a 96-well plate, the EV sample may be injected into the wells of the plate and left for 6 hours, and then the wells may be washed with PBS. A 1% BSA solution may be introduced into the wells of the plate and left for 90 minutes. The wells may then be washed with PBS, and Alexa Fluor488-labeled mouse anti-human CD63 monoclonal antibody (10 μg / ml) or mouse anti-human CD81 antibody (10 μg / ml) may be introduced into the wells of the plate and left for 45 minutes. For CD81 detection, in addition to this, the wells of the plate may be washed with PBS, and then Alexa Fluor488-labeled goat anti-mouse IgG polyclonal antibody (5 μg / ml) may be introduced into the wells of the plate as a secondary antibody and left for 45 minutes. Finally, the wells of the plate may be washed with PBS, and the fluorescence intensity may be observed using a plate reader. PBS may be used instead of the EV sample to obtain background values. miRNA detection

[0137] The detection of microRNAs can be performed using miRNA detection methods known to those skilled in the art, such as quantitative polymerase chain reaction (PCR), microarrays for miRNA detection, RNA-Seq (e.g., next generation sequencing (NGS)), and multiplex miRNA profiling. A sample containing urine or a urine extract may contain, for example, 500 or more miRNAs. Therefore, to confirm the expression of all these miRNAs, for example, microarrays for miRNA detection, RNA-Seq, and multiplex miRNA profiling methods can be used. Quantitative PCR-based methods and multiplex miRNA profiling methods can also be used to detect one or more specific miRNAs in urine or urine extracts.

[0138] In the methods described herein, the detection and quantification of the miRNA markers of the present disclosure in a subject can be performed according to methods well known in the art.For example, RNA can be obtained from any suitable sample from a subject that can contain RNA, and then RNA can be prepared and analyzed according to well-established protocols for the presence and / or identification of miRNA(s) according to the methods of the present disclosure.

[0139] The purified miRNA may be labeled using methods known in the art. Thus, for example, labeling can be performed using the mirVana™ miRNA labeling kit (Ambion) and amine-reactive dyes as recommended by the manufacturer. The amine-modified miRNAs can be cleaned up and conjugated to NHS-ester modified Cy5 or Cy3 dyes (Amersham Bioscience). SLE samples can be labeled with Cy5 and healthy controls with Cy3. Unincorporated dye can be removed and samples can be hybridized in duplicate according to methods known to those skilled in the art. Thus, for example, the mirVana™ miRNA Bioarray (Ambion) kit can be used according to the manufacturer's instructions.

[0140] A nucleotide sequence that hybridizes to a nucleotide sequence complementary to that encoding one of the miRNA sequences disclosed herein (SEQ ID NOs: 1-484) under stringent conditions, such as hybridization to filter-bound DNA in 6× sodium chloride / sodium citrate (SSC) at about 45° C., followed by one or more washes in 0.2×SSC / 0.1% SDS at about 50-65° C., such as hybridization to filter-bound nucleic acid in 6×SSC at about 45° C., followed by one or more washes in 0.1×SSC / 0.2% SDS at about 68° C., or other stringent hybridization conditions known to one of skill in the art. See, e.g., Ausubel FM et al., eds., 1989, Current Protocols in Molecular Biology, Vol. I, Green Publishing Associates, Inc. and John Wiley & Sons, Inc., New York, pages 6.3.1-6.3.6 and 2.10.3.

[0141] Detection and analysis of miRNA by microarray may include labeling miRNA (e.g., using a fluorescent label as a label), preparing a solution for hybridization, hybridizing miRNA in a sample with a miRNA detection reagent such as a nucleic acid on a microarray, washing the microarray, and then measuring the amount of label (e.g., the amount of fluorescence). The quality of the extracted RNA sample may be confirmed, for example, by using methods well known to those skilled in the art or commercially available instruments and kits (e.g., Agilent 2100 Bioanalyzer and RNALabChip from Agilent Technologies, Inc.) as an indicator of the appearance of a peak with a size between 20 and 30 nucleotides. Labeling of miRNA can be performed, for example, using methods well known to those skilled in the art and commercially available kits (e.g., 3D-Gene™ miRNA labeling kit (Toray Corporation). Also, for example, miRNA analysis by microarray can be performed using 3D-Gene™ Human / Mouse / Rat / 4animal miRNA Olico chip-4plex manufactured by Toray Corporation according to the product manufacturer's instructions.

[0142] The microarray for detecting microRNAs may be a microarray that includes probes for one or more selected from a group of microRNAs that exhibit higher expression in one, two, or three patients suspected of having SLE than in any of one, two, or three healthy individuals. The microarray may comprise probes for one or more of a group of microRNAs that exhibit higher expression in SLE patients than in healthy individuals (e.g., 1.01 fold or more, 1.02 fold or more, 1.03 fold or more, 1.04 fold or more, 1.05 fold or more, 1.06 fold or more, 1.07 fold or more, 1.08 fold or more, 1.09 fold or more, 1.1 fold or more, 1.2 fold or more, 1.3 fold or more, 1.4 fold or more, 1.5 fold or more, 1.6 fold or more, 1.7 fold or more, 1.8 fold or more, 1.9 fold or more, 2 fold or more, 3 fold or more, 4 fold or more, 5 fold or more, 6 fold or more, 7 fold or more, 8 fold or more, 9 fold or more, or 10 fold or more). The microarray may comprise probes for one or more of a group of microRNAs that exhibit lower expression in SLE patients than in healthy individuals (e.g., 0.99 fold or less, 0.98 fold or less, 0.97 fold or less, 0.96 fold or less, 0.95 fold or less, 0.94 fold or less, 0.93 fold or less, 0.92 fold or less, 0.91 fold or less, 0.9 fold or less, 0.8 fold or less, 0.7 fold or less, 0.6 fold or less, 0.5 fold or less, 0.4 fold or less, 0.3 fold or less, 0.2 fold or less, 0.1 fold or less, 0.09 fold or less, 0.08 fold or less, 0.07 fold or less, 0.06 fold or less, 0.05 fold or less, 0.04 fold or less, 0.03 fold or less, 0.02 fold or less, or 0.01 fold or less).

[0143] In one embodiment, the species of microRNA detected (i.e., the types of probes carried on the microarray) can be, for example, 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 200 or more, 300 or more, 400 or more, 500 or more, 600 or more, 700 or more, 800 or more, 900 or more, 1000 or more, 1500 or more, 2000 or more, 2500 or more, or 3000 or more.

[0144] In another embodiment, the species of microRNA to be detected (i.e., the types of probes carried on the microarray) can be, for example, 3000 or less, 2500 or less, 2000 or less, 1900 or less, 1800 or less, 1700 or less, 1600 or less, 1500 or less, 1400 or less, 1300 or less, 1200 or less, 1100 or less, 1000 or less, 900 or less, 800 or less, 700 or less, 600 or less, 500 or less, 400 or less, 300 or less, 200 or less, 100 or less, 90 or less, 80 or less, 70 or less, 60 or less, 50 or less, 40 or less, 30 or less, 20 or less, or 10 or less.

[0145] The probe for the microRNA in the microarray can be a nucleic acid or its derivative that can hybridize to the microRNA, and can be appropriately designed by those skilled in the art. For example, the probe for the miRNA that shows SLE may include a ribonucleotide sequence that hybridizes to the miRNA of the ribonucleotide sequence of SEQ ID NO: 1 to 484, and a combination thereof.

[0146] Prior to detection of miRNA contained in the extracellular vesicles, the extracellular vesicles may be disrupted by incubation in a cell lysis buffer, pretreatment with an alkali / detergent, storage at about -25°C for 1 to 10 days, preferably about 7 days, or a combination thereof. Further, the extracellular vesicles may be disrupted by incubation in a cell lysis buffer, pretreatment with an alkali / detergent, storage at about -25°C for 1 to 10 days, preferably about 7 days, or a combination thereof. Methods in Molecular Biology (2017) 1660:367-376.

[0147] For example, alkaline / detergent pretreatment may include treating the sample with 0.4 N NaOH with 0.5% Triton X-305 for approximately 20 minutes, followed by incubation with 0.01% sodium dodecyl sulfate (SDS) for 10 minutes to disrupt EV membranes. Classification system

[0148] Exemplary classification systems for use in diagnosing and predicting the occurrence of medical conditions may include those described in U.S. Pat. Nos. 7,321,881; 7,467,119; 7,505,948; 7,617,163; 7,676,442; 7,702,598; 7,707,134; 7,747,547; and 9,952,220, each of which is incorporated by reference herein in its entirety.

[0149] The present invention relates inter alia to characterization of miRNAs based on data comprising experimental miRNA expression datasets from healthy individuals and SLE patients, including those with various severities of SLE. The miRNA expression datasets may be proprietary or may be accessed from publicly available databases.

[0150] As used herein, a classification system may include computer executable software, firmware, hardware, or a combination thereof. For example, a classification system may include reference to a processor and supporting data storage. Additionally, a classification system may be implemented across multiple devices or other components, local or remote to each other. A classification system may be implemented in a centralized system or as a distributed system for additional scalability. Additionally, reference to software may include a non-transitory computer readable medium that, when executed on a computer, causes the computer to perform a sequence of steps.

[0151] The classification systems described herein may include data storage such as network accessible storage, local storage, remote storage, or combinations thereof. Data storage may utilize redundant array of inexpensive disks ("RAID"), tape, disk, storage area network ("SAN"), internet small computer systems interface ("iSCSI") SAN, Fibre Channel SAN, common Internet File System ("CIFS"), network attached storage ("NAS"), network file system ("NFS"), or other computer accessible storage. Data storage may be a database such as an Oracle database, a Microsoft SQL Server database, a DB2 database, a MySQL database, a Sybase database, an object-oriented database, a hierarchical database, a cloud-based database, a public database, or other database. Data storage may utilize a flat file structure for storage of data. An exemplary embodiment used two Tesla K80 NVIDIA GPUs, each with 4992 CUDA cores and a large number of GB of memory (e.g., over 11 GB), to train the deep learning algorithms.

[0152] In the first step, a classifier is used to describe a given set of data: this is the "learning step" and is performed on the "training" data.

[0153] The training database is a computer-implemented data store reflecting a plurality of miRNA expression data for a plurality of miRNAs, each miRNA classified with respect to SLE and / or SLE severity. The miRNA expression data may include miRNA expression data, predicted miRNA expression data, or a combination thereof. The format of the stored data may be a flat file, a database, a table, or any other searchable data storage format known in the art. The test data may be stored as a plurality of vectors, each vector corresponding to an individual miRNA, each vector including a plurality of miRNA expression data measures for the plurality of miRNA expression data together with a classification of the miRNA with respect to SLE and / or SLE severity characterization. The vector may further include a miRNA expression data measure for the plurality of experimental miRNA expression data together with a classification of the miRNA with respect to SLE and / or SLE severity characterization. Typically, each vector includes an entry for each miRNA expression data measure in the plurality of miRNA expression data measures. The entry may further include the presence or absence of the miRNA in different body fluid data. The training database may be linked to a network such as the Internet so that its contents can be remotely searched by an authorized entity (e.g., a human user or a computer program). Alternatively, the training database may be located on a network-isolated computer. Furthermore, the training database may be cloud-based, including proprietary and public databases that contain miRNA expression data (e.g., experimental, predicted, and combinations thereof) for miRNAs that are useful in diagnosing SLE.

[0154] In an optional second step, the classifier is applied in a "validation" database, and various accuracy measures, including sensitivity and specificity, are observed. In an exemplary embodiment, only a portion of the training database is used for the learning step, and the remaining portion of the training database is used as a validation database. In a third step, the miRNA expression data measures from the subject are sent to the classification system, which outputs a calculated classification for the subject (e.g., characterization of the miRNA as being associated with SLE and / or SLE severity). In addition, the presence or absence of the miRNA in different body fluid data may also be used.

[0155] There are many possible classifiers that can be used on the data. Machine and deep learning classifiers can be used to classify the data, including but not limited to AdaBoost, Artificial Neural Network (ANN) learning algorithms, Bayesian belief networks, Bayesian classifiers, Bayesian neural networks, boosted trees, case-based reasoning, classification trees, convolutional neural networks, decision trees, deep learning, elastic nets, Fully Convolutional Networks (FCN), genetic algorithms, gradient boosting trees, k-nearest neighbor classifiers, LASSO, linear classifiers, naive Bayes classifiers, neural nets, penalized logistic regression, logistic regression models, random forests, ridge regression, support vector machines, or ensembles thereof. See for example, Chapter 6 of Han & Kamber (2006), Data Mining, Concepts and Techniques, 2nd ed., Elsevier, Amsterdam. As described herein, any classifier or combination (e.g., ensemble) of classifiers may be used in the classification system. As discussed herein, data may be used to train the classifier. Other classifiers and machine learning systems known in the art may also be used. For example, scikit-leam, a machine learning system in the Python computer language, may be used.

[0156] Scikit-learn (also known as sklearn) is a machine learning library for the Python programming language.

[0157] Scikit-learn uses classification, regression, and clustering algorithms including support vector machines, random forests, gradient boosting, k-means, and density-based spatial clustering of applications with noise (DBSCAN), and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

[0158] The preferred classifier is a logistic regression model using the following formula:

[0159] (True positive + true negative) / true positive + true negative + false positive + false negative).

[0160] The classifier described herein may be constructed using a logistic regression model classifier as follows: JPEG2024516504000001.jpg22169Y was the prediction objective variable, x was the fluorescence intensity of each miRNA species, b was the weight coefficient of each miRNA species, and a was the intercept. In this model, b and a were estimated from the fluorescence intensity of each urinary miRNA species extracted with nanowires by supervised machine learning. The value of Y was defined as less than 0.5 as non-cancer subjects and Y value of 0.5 or more as cancer subjects. The classifier simultaneously solved the optimization problem of the least square error term and the L1 regularization term in fitting the logistic regression classifier, with l acting as an adjuster between the two terms. Higher AUC, sensitivity, and specificity values ​​were shown when l=1 was used. Training Data

[0161] In another embodiment, the method described herein includes training about 75%, about 80%, about 85%, about 90%, or about 95% of the data, totaling 100% of the data in the library or database, and testing the remaining percentage. In one embodiment, about 70% to about 90% of the data is trained and about 10% to about 30% of the remaining data is tested, about 80% to about 95% of the data is trained and about 5% to about 20% of the remaining data is tested, or about 90% to about 10% of the data is trained and the remaining data is tested. In one embodiment, the database or library includes data from the analysis of more than about 20, about 50, about 100, about 150, about 200, or about 300 miRNAs. In further embodiments, the library or database includes only validated experimental data, for example from miRNA expression methods. In yet another embodiment, the library or database does not include miRNA expression data that has been theoretically prepared without determining the presence or frequency of miRNAs by analyzing patient samples. The training data may include miRNA expression levels, the presence or absence of miRNAs in bodily fluids, or a combination thereof. How the data is classified using the classification system or systems

[0162] The present invention provides methods for classifying data (test data, e.g., miRNA expression levels, the presence or absence of miRNA in bodily fluids, or a combination thereof) obtained from an individual. These methods include preparing or obtaining training data, and evaluating the test data obtained from the individual (compared to the training data) using one of the classification systems that includes at least one classifier described herein. Preferred classification systems use classifiers such as, but not limited to, support vector machines (SVM), AdaBoost, penalized logistic regression, logistic regression, naive Bayes classifier, classification tree, k-nearest neighbor classifier, deep learning classifier, neural net, random forest, full layer convolutional network (FCN), convolutional neural network (CNN), and / or ensembles thereof. Scikit-learn is a preferred machine learning library that includes an ensemble of classification, regression, and cluster algorithms including support vector machines, random forest, gradient boosting, k-means, and density-based spatial clustering with noise (DBSCAN). The classification system outputs a classification of the miRNA based on the test data, such as the miRNA expression level, presence in a body fluid, or a combination thereof.

[0163] Particularly preferred in the present invention is the ensemble method used for the classification system, combining multiple classifiers.For example, the ensemble method can include SVM, AdaBoost, penalized logistic regression, logistic regression, naive Bayes classifier, classification tree, k-nearest neighbor classifier, neural net, full convolution network (FCN), convolutional neural network (CNN), random forest, deep learning, or any ensemble thereof, to make predictions about the correlation of miRNA expression with SLE and / or SLE severity.The ensemble method has been developed to take advantage of the advantages provided by each classifier and the replicate measurement of each miRNA expression data.

[0164] 1. A method for classifying test data, the test data comprising miRNA expression data, (a) accessing a set of electronically stored training data vectors, each training data vector or k-tuple representing an individual miRNA and including miRNA expression data for each miRNA in each replicate, the training data vector further including a classification for a miRNA characterization of each miRNA; (b) training an electronic representation of a classifier or ensemble of classifiers described herein using a set of electronically stored training data vectors; (c) receiving test data comprising a plurality of miRNA expression data; (d) evaluating the test data using the electronic representation of the classifier and / or ensemble of classifiers described herein; (e) outputting a classification of miRNAs based on the evaluating step. The test data may further include data regarding the presence or absence of miRNA in the bodily fluid.

[0165] In another embodiment, the present invention provides a method for classifying test data, the test data comprising miRNA expression data, (a) accessing a set of electronically stored training data vectors, each training data vector or k-tuple representing an individual human and including miRNA expression data for each human in each replicate, the training data further including a classification for correlation of each miRNA with SLE; (b) constructing a classifier and / or an ensemble of classifiers using the set of electronically stored training data vectors; (c) receiving test data comprising a plurality of miRNA expression data for a human test subject; (d) evaluating the test data using the classifier(s); (e) outputting a classification of the human test subject based on the evaluating step. Alternatively, all replicates (or any combination) may be averaged to generate a single value for each miRNA expression data for each subject. Outputting according to the present invention includes displaying the information regarding the classification of the human test subject in a human readable format on an electronic display. The miRNA data may include miRNA expression data, the presence or absence of miRNA in the bodily fluid, or a combination thereof.

[0166] The set of training vectors may include at least 20, 25, 30, 35, 50, 75, 100, 125, 150, or more vectors.

[0167] The test data may be any informative measure, such as the presence or absence of miRNA in a bodily fluid, miRNA expression data, or a combination thereof.

[0168] The data used to train the machine learning system may include data from SLE patients comprising at least 5, 10, 15, 20, or 25 different symptoms, data from normal tissue comprising at least about 5, 10, 15, 20, 25, 30, 35, 40, or 45 normal tissues, or a combination thereof. In addition, the data used to train the machine learning system may include data from SLE patients comprising at least about 5, 10, 15, 20, 25, 30, 35, 40, or 45 normal tissues, or a combination thereof.

[0169] It will be understood that the method of classifying data may be used in any of the methods described herein.In particular, the method of classifying data described herein may be used in a method for identifying miRNAs associated with SLE and / or SLE severity for use in diagnostic and therapeutic methods.

[0170] Particularly preferred in the present invention is the ensemble method used for the classification system, combining multiple classifiers.For example, the ensemble method may include support vector machine (SVM), AdaBoost, penalized logistic regression, logistic regression, naive Bayes classifier, classification tree, k-nearest neighbor classifier, neural net, deep learning system, random forest, or any combination thereof, to make predictions about the association of miRNA with SLE and / or the severity of SLE.In addition, the ensemble may be used to make predictions about the association of miRNA with SLE type.The ensemble approach takes advantage of the advantages provided by each classifier and the replicate measurements of each miRNA.

[0171] In one aspect, the present disclosure provides a method for classifying test data, the test data comprising: (a) receiving, at at least one processor, test data including miRNA expression data; (b) using at least one processor, evaluating the test data using classifiers that are electronic representations of a classification system, each said classifier being trained using a set of electronically stored training data vectors, each training data vector representing an individual miRNA and including miRNA expression data for the miRNA, each training data vector further including a classification as to whether the miRNA is indicative of SLE; and (c) using the at least one processor, outputting a classification of the sample from the miRNA expression data as to the likelihood that the miRNA is indicative of SLE or not based on the evaluating step.

[0172] In another aspect, the present disclosure provides a method of classifying test data, the test data comprising: (a) using at least one processor to access a set of electronically stored training data vectors, each training data vector representing an individual patient and including miRNA expression data for a respective patient, each training data vector further including a classification as to whether miRNA expression is associated with SLE; (b) training an electronic representation of the classification system using the set of electronically stored training data vectors; (c) receiving, at the at least one processor, test data including miRNA expression data; (d) using at least one processor, evaluating the test data using the electronic representation of the classification system; and (e) outputting a classification of the test data as to whether or not miRNA expression is associated with SLE based on the evaluating step.

[0173] In another aspect, the present disclosure provides a method of classifying test data, the test data comprising: (a) using at least one processor, accessing a set of electronically stored training data vectors, each training data vector representing a severity of SLE and including miRNA expression data for each severity of SLE, each training data vector further including a classification as to whether the miRNA is associated with the severity of SLE; (b) training an electronic representation of the classification system using the set of electronically stored training data vectors; (c) receiving, at the at least one processor, test data including miRNA expression data; (d) using at least one processor, evaluating the test data using the electronic representation of the classification system; and (e) outputting a classification of the test data as to whether the miRNA is associated with severity of SLE based on the evaluating step.

[0174] In another aspect, the present disclosure provides a method for classifying test data, comprising: (a) obtaining a sample from an individual; (b) obtaining expression data of miRNAs in the sample; (c) comparing the experimental miRNA expression data with miRNA expression data located in a database; (d) creating a match between the experimental miRNA expression data and the miRNA expression data located in the database; (e) generating a dataset of matched miRNAs based on steps (a), (b), (c), (d), or a combination thereof; (g) evaluating the dataset of miRNAs using the classification system to generate a miRNA expression profile indicative of SLE.

[0175] In another aspect, the present disclosure provides a method for classifying test data, comprising: (a) obtaining at least one sample from a patient and a corresponding sample from a healthy individual; (b) identifying at least one miRNA in the sample; (c) generating experimental miRNA expression data from the samples; (e) comparing the experimental miRNA expression data with miRNA expression data in a database; (f) creating a match between the experimental miRNA expression data and the miRNA expression data located in the database; (g) generating a spectral library of miRNA expression data; (h) evaluating the spectral library of miRNA expression using the classification system to generate a miRNA expression predictive model; (i) using the predictive model to generate a predictive miRNA expression pattern associated with SLE.

[0176] In another aspect, the present disclosure provides a method of classifying test data to identify miRNAs associated with SLE, comprising: (a) obtaining at least one sample from a patient and a corresponding sample from a healthy individual; (b) identifying at least one miRNA in the sample to generate experimental miRNA expression data; (c) comparing the experimental miRNA expression data with miRNA expression data in a database; (d) Estimation of the false discovery rate (FDR), (e) generating matches between the experimental miRNA expression data and the miRNA expression data in the database; and (f) inputting the data generated by the comparison into a classification system to train a miRNA expression prediction model; (g) developing a predicted miRNA expression pattern; and (h) identifying the miRNA expression pattern as indicative of SLE.

[0177] In another embodiment, the database may be a public database, a private database, or a combination thereof. In another embodiment, the miRNA expression data may be experimental miRNA expression data, predicted miRNA expression data, or a combination thereof. In another embodiment, the miRNA expression data is experimental miRNA expression data. In another embodiment, the test data may further include data regarding the presence or absence of miRNA in bodily fluids. In another embodiment, the miRNA expression may be identified using microarray analysis, or a combination thereof.

[0178] In another aspect, the classification system may be AdaBoost, an artificial neural network (ANN) learning algorithm, a Bayesian belief network, a Bayesian classifier, a Bayesian neural network, boosted trees, case-based reasoning, classification trees, convolutional neural networks, decision trees, logistic regression models, deep learning, elastic nets, fully convolutional networks (FCNs), genetic algorithms, gradient boosting trees, k-nearest neighbor classifiers, LASSO, linear classifiers, naive Bayes, neural nets, penalized logistic regression, random forests, ridge regression, support vector machines, or ensembles thereof. In another aspect, the classification system may be an ensemble of classification systems.

[0179] In another embodiment, the library or database may contain more than about 70%, more than about 80%, more than about 85%, more than about 90%, more than about 95%, or 100% of the miRNA expression data. In another embodiment, the miRNAs may be identified by predicted miRNA expression data that have an identification correlation within about 2% to about 15% to the actual technical variation of the experimentally determined miRNA expression data. In another embodiment, the method may further include comparing miRNA expression in a sample obtained from a patient suspected of having SLE to miRNA expression in a body fluid sample obtained from a healthy individual. Diagnosis of SLE

[0180] A subject may be identified as having SLE according to diagnostic parameters known in the art, and may have a good or bad prognosis according to diagnostic and / or clinical parameters also known in the art. Prognosis may include prediction of overall survival, improvement or maintenance of scores (SLEDAI, SLAM, BILAG, etc.), reduction of medications such as immunosuppressants, reduction or improvement of comorbidities such as osteoarthritis, and / or improvement of quality of life. For example, a subject with SLE that may be identified as having a good prognosis may be a subject with mild or moderate symptoms and / or that responds (i.e., shows improvement) to a standard treatment protocol. A subject with SLE that may be identified as having a bad prognosis may be a subject with severe symptoms and / or that responds poorly or is unresponsive (i.e., shows little or no improvement) to a standard treatment protocol. According to the method of the present disclosure, a correlation between good and poor prognosis and a subject's miRNA markers may be made, which allows a clinician to determine the most effective treatment plan for the subject. Thus, a poor or good prognosis for SLE may be identified by one of ordinary skill in the art.

[0181] Thus, an association may be made between the likelihood of a poor prognosis and an increase or decrease in the amount of one or more miRNAs by detecting an increase or decrease in the amount of one or more miRNAs in a population of subjects who have SLE and a poor prognosis, i.e., subjects who have severe symptoms and / or who are poorly or unresponsive to a standard treatment protocol (i.e., who show little or no improvement), and correlating the detected increase or decrease in the amount of one or more miRNAs with a poor prognosis in the population of subjects who have SLE and a poor prognosis.

[0182] Similarly, an association may be made between the likelihood of a poor prognosis and a particular miRNA profile by detecting an increase or decrease in the amount of one or more miRNAs in a population of subjects who have SLE and a poor prognosis, i.e., subjects who are severely symptomatic and / or who are poorly or unresponsive to standard treatment protocols (i.e., who show little or no improvement), generating a miRNA profile from the detection of the increase or decrease in the amount of one or more miRNAs, and correlating the miRNA profile with a poor prognosis in a population of subjects who have SLE and a poor prognosis.

[0183] Alternatively, an association may be made between the likelihood of a good prognosis and an increase or decrease in the amount of one or more miRNAs by detecting an increase or decrease in the amount of one or more miRNAs in a population of patients with SLE and a good prognosis, i.e., subjects who have mild or moderate symptoms and / or who respond (i.e., show improvement) to a standard treatment protocol, and correlating the detected increase or decrease in the amount of one or more miRNAs with a good prognosis in a population of subjects with SLE and a good prognosis.

[0184] Further, an association may be made between the likelihood of a good prognosis and a particular miRNA profile by detecting an increase or decrease in the amount of one or more miRNAs in a population of subjects with SLE and a good prognosis, i.e., subjects who have mild or moderate symptoms and / or who respond (i.e., show improvement) to a standard treatment protocol, creating a miRNA profile from the detection of the increase or decrease in the amount of one or more miRNAs, and correlating the miRNA profile with a good prognosis in a population of subjects with SLE and a good prognosis.

[0185] One aspect of the present disclosure includes a step of identifying a patient as having a marker that correlates with SLE upon detecting increased expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or decreased expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 in a patient sample compared to a body fluid sample obtained from a healthy individual; or Provided is a method for diagnosing SLE using a body fluid sample obtained from a subject, comprising a step of identifying a patient as not having a marker correlated with SLE if an increase in expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 cannot be detected compared to a body fluid sample obtained from a healthy individual.

[0186] In another aspect, a method for diagnosing SLE using a body fluid sample obtained from a subject is provided, comprising the steps of: identifying the patient as having a marker correlated with SLE if an increase in the expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in the patient sample compared to a body fluid sample obtained from a healthy individual; or identifying the patient as not having a marker correlated with SLE if an increase in the expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is not detectable in the patient sample compared to a body fluid sample obtained from a healthy individual.

[0187] In another aspect, a method for diagnosing SLE using a body fluid sample obtained from a subject is provided, comprising the steps of: identifying the patient as having a marker correlated with moderate SLE if a decrease in expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in the patient sample compared to a body fluid sample obtained from a healthy individual; or identifying the patient as not having a marker correlated with SLE if a decrease in expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is not detectable compared to a body fluid sample obtained from a healthy individual.

[0188] In another aspect, a method for diagnosing SLE using a body fluid sample obtained from a subject is provided, comprising the steps of: identifying a patient as having a marker correlated with SLE comorbidity A if an increase in expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in a patient sample compared to a body fluid sample obtained from a healthy individual; or identifying a patient as not having a marker correlated with SLE comorbidity A if an increase in expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is not detectable in a body fluid sample obtained from a healthy individual.

[0189] In another aspect, a method for diagnosing SLE using a body fluid sample obtained from a subject is provided, comprising the steps of: identifying the patient as having a marker correlated with SLE comorbidity B if an increase in expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in the patient sample compared to a body fluid sample obtained from a healthy individual; or identifying the patient as not having a marker correlated with SLE comorbidity B if an increase in expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is not detectable in the patient sample compared to a body fluid sample obtained from a healthy individual.

[0190] In another aspect, a method for diagnosing SLE using a body fluid sample obtained from a subject is provided, comprising the steps of: identifying the patient as having a marker correlated with SLE comorbidity C if increased expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or decreased expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in the patient sample compared to a body fluid sample obtained from a healthy individual; or identifying the patient as not having a marker correlated with SLE comorbidity C if increased expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or decreased expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is not detectable in the patient sample compared to a body fluid sample obtained from a healthy individual.

[0191] In another aspect, a method for diagnosing SLE using a body fluid sample obtained from a subject is provided, comprising the steps of: identifying a patient as having a marker correlated with SLE comorbidity D if an increase in expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in a patient sample compared to a body fluid sample obtained from a healthy individual; or identifying a patient as not having a marker correlated with SLE comorbidity D if an increase in expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is not detectable in a body fluid sample obtained from a healthy individual.

[0192] In other embodiments, the 5' and / or 3' ends of the miRNA may be truncated, for example, about 1 to about 10 ribonucleotides may be deleted from either the 5' and / or 3' end of the miRNA. Pharmaceutical Compositions

[0193] The miRNAs and / or their agonists or antagonists of the present disclosure may be used directly or in combination with other agents to treat diseases, such as SLE. The present disclosure may also provide pharmaceutical compositions that may contain a safe and effective amount of the miRNAs and / or their agonists or antagonists of the present disclosure and a pharma- ceutically acceptable carrier or excipient. Such carriers may include (but are not limited to) salt solutions, buffered salt solutions, dextrose, water, glycerol, ethanol, and combinations thereof. The pharmaceutical formulation may be tailored to the mode of administration. The pharmaceutical compositions of the present disclosure may be produced as injectable forms, such as physiological salt solutions or aqueous solutions containing glucose and other supplements prepared by conventional methods. Pharmaceutical compositions, such as injectable compositions and solutions, may be manufactured under sterile conditions. A therapeutically effective amount of a pharmaceutical composition may be an effective amount of the active ingredient of the pharmaceutical composition to be administered, for example, from about 0.1 μg to about 10 mg per kg of body weight.

[0194] The miRNAs and / or agonists or antagonists thereof of the present disclosure in pharmaceutical compositions may be administered to a subject, e.g., an SLE patient, in a safe and effective amount of at least about 0.1 μg / kg body weight, in most cases up to about 10 mg / kg body weight, and preferably from about 0.1 μg / kg to about 100 μg / kg body weight. The particular dosage may be determined based on the route of administration and the condition of the patient, all of which are within the skill of a physician. treatment

[0195] Examples of treatment regimens for SLE are known in the art and may include, but are not limited to, administration of nonsteroidal anti-inflammatory drugs (NSAIDs), immunosuppressants such as hydroxychloroquine, corticosteroids, azathioprine, methotrexate, cyclosporine, mycophenolate mofetil, cyclophosphamide, and tacrolimus, and biologic agents such as belimumab, rituximab, TNF-alpha inhibitors, and interferon inhibitors.

[0196] In accordance with the methods provided herein, patients who respond well to a particular treatment protocol can be analyzed for a unique miRNA profile (e.g., an increase or decrease in the amount of one or more miRNAs associated with SLE) and a correlation can be established. Alternatively, patients who respond poorly to a particular treatment plan can be analyzed for a unique miRNA profile (e.g., an increase or decrease in the amount of one or more miRNAs associated with SLE) and correlated with the poor response. Subjects who are candidates for treatment of SLE can then be evaluated for the presence of the appropriate miRNA profile and the most appropriate treatment plan can be provided.

[0197] Thus, by detecting an increase or decrease in the amount of one or more miRNAs in a population of subjects having SLE and for which an effective treatment regimen for SLE has been identified, and correlating the detected increase or decrease in the amount of the one or more miRNAs with an effective treatment regimen for SLE, an association is made between the effective treatment regimen and the increase or decrease in the amount of the one or more miRNAs.

[0198] Similarly, an association may be made between an effective treatment regimen and a particular miRNA profile by detecting an increase or decrease in the amount of one or more miRNAs in a population of subjects having SLE and for which an effective treatment regimen for SLE has been identified, creating an miRNA profile from the detection of the increase or decrease in the amount of the one or more miRNAs, and associating the created miRNA profile with the effective treatment regimen for SLE.

[0199] In some embodiments, the method of correlating miRNA profiles with treatment plans can be performed using a computer database. Thus, the present disclosure may provide a computer-assisted method of identifying suggested treatments for SLE. The method includes: (a) storing a database of biological data for a plurality of patients, the stored biological data including, for each of the plurality of patients, (i) a treatment type, (ii) at least one miRNA whose increased or decreased amount is associated with SLE, and (iii) at least one disease progression measure for SLE that can determine treatment efficacy; and (b) querying the database to determine a dependence of the effectiveness of a type of treatment in treating SLE on said increase or decrease in the amount of at least one miRNA, thereby identifying a treatment that is proposed as an effective treatment for a subject having a miRNA profile that correlates with SLE.

[0200] In one embodiment, a patient's treatment information may be entered into the database (through any suitable means, such as a window or text interface), the patient's miRNA information (e.g., miRNA profile) is entered into the database, and disease progression information is entered into the database. These steps may then be repeated until a desired number of patients have been entered into the database. The database may then be queried to determine whether a particular treatment is effective for patients with a particular miRNA profile, is ineffective for patients with a particular miRNA profile, etc. Such queries may be performed prospectively or retrospectively on the database by any suitable means, but are typically performed by statistical analysis according to known techniques as described herein.

[0201] Small ribonucleic acids, such as miRNAs, or agonists or antagonists thereof, can be formulated in a non-toxic, inert, pharma- ceutically acceptable aqueous carrier medium, which may have a pH of about 5 to 8, preferably about 6 to 8, although the pH may vary depending on the properties of the small ribonucleic acid, such as miRNAs, or agonists or antagonists thereof, and also with changes in the disease state being treated. Pharmaceutical compositions may be administered via conventional routes, including (but not limited to) intramuscular, intravenous, or subcutaneous administration.

[0202] Administration of the miRNAs disclosed herein and / or their agonists or antagonists to a subject, e.g., an SLE patient, may result in increased amount and / or expression of the miRNA, thereby preventing or treating diseases associated with decreased amount and / or expression of such miRNAs, which may play an important role in the pathogenesis of SLE, e.g., diseases related to aberrant activation of the interferon pathway.

[0203] In one aspect, the disclosure provides a method of treating SLE comprising the step of administering to an SLE patient a composition comprising an antagonist of one or more miRNAs consisting of a nucleotide sequence selected from the group consisting of SEQ ID NOs: 1-160 and 243-402, e.g., an antisense molecule of one or more of SEQ ID NOs: 1-160 and 243-402, or administering to an SLE patient a composition comprising an agonist of one or more miRNAs consisting of a nucleotide sequence selected from the group consisting of SEQ ID NOs: 161-242 and 403-484, e.g., a molecule of one or more of SEQ ID NOs: 161-242 and 403-484.

[0204] In another aspect, the present disclosure provides a method for treating moderate SLE, comprising the step of administering to an SLE patient a composition comprising an antisense molecule of one or more miRNAs consisting of a nucleotide sequence selected from the group consisting of SEQ ID NOs: 1-160 and 243-402 and / or a composition comprising one or more miRNAs consisting of a nucleotide sequence selected from the group consisting of SEQ ID NOs: 161-242 and 403-484. kit

[0205] It is further contemplated that the present disclosure may provide a kit for use in screening, diagnosing and identifying subjects with SLE. The kit may include a pharmaceutical composition of the present disclosure, such as miRNA (SEQ ID NO: 1-484). As is well known in the art, it will be appreciated by those of ordinary skill in the art that the kit of the present disclosure may include one or more containers and / or receptacles for holding the reagents of the kit (e.g., nucleic acids, etc.), along with suitable buffers and / or diluents and / or other solutions, and instructions for using the kit. Such kits may further include adjuvants and / or other immunostimulating or immunomodulating agents, as are well known in the art. Working Example Example 1: In Situ Extraction of miRNAs from Urinary EVs Using a Nanowire-Integrated Microfluidic Device

[0206] Using the miRNA microarray analysis described herein, it was demonstrated that specific miRNAs are differentially expressed in SLE peripheral blood mononuclear cells (PBMCs) compared to age- and sex-matched healthy normal controls. Using a stringent criterion of 3-fold differential miRNA expression levels between SLE and healthy samples, unique patterns of altered miRNA expression were identified. Such patterns provide a complex fingerprint that may serve as molecular biomarkers for the diagnosis, prognosis, and / or prediction of treatment response in SLE.

[0207] Table 1 JPEG2024516504000002.jpg131170

[0208] Urine samples obtained from SLE patients and healthy individuals listed in Table 1 were centrifuged (15 mm, 4 °C, 3000 g) to remove apoptotic bodies before use. 1 ml of urine sample was then introduced into the nanowire-integrated device using a syringe pump (KDS-200, KD Scientific Inc.) at a flow rate of 50 μl / min. Extraction of miRNA from EVs collected on the nanowire was performed by introducing cell lysis buffer M [20 mM Tris-HCl (pH 7.4), 200 mM sodium chloride, 2.5 mM magnesium chloride, and 0.05 w / v% NP-40; (Wako Pure Chemical Industries Ltd.)] into the nanowire-integrated device using a syringe pump at a flow rate of 50 μl / min (Figure 1). Microarray analysis of miRNA expression

[0209] miRNA expression profiles were obtained using Toray 3D-Gene human miRNA chips (Toray Industries). The miRNAs extracted with lysis buffer were purified using SeraMir Exosome RNA Purification Column Kit (System Biosciences Inc.) according to the manufacturer's instructions. 15 μl of purified miRNAs were analyzed for 2,632 miRNA profiling using 3D-Gene Human miRNA Oligo chip ver.21 (Toray Industries). In the microarray analysis of miRNA expression, each signal intensity corresponds to one miRNA. The expression level of each miRNA is expressed as the signal intensity of all miRNAs in each microarray minus background. A scatter plot was made of the overall normalized intensity and is shown for intensities 10 and above. Thus, each point on the scatter plot is a normalized intensity. Signal intensities were log2 transformed. For comparison of miRNAs between urine samples of SLE patients and healthy donors, the normalized intensities across samples were log2 transformed. (Figure 1) Identification of urinary miRNAs as biomarkers for SLE

[0210] According to the Z score of 1.96 (95% confidence level and 5% significance level) and the relationship of the coefficient of variation (CV) to log2(intensity) provided by Toray (no specific value), the 95% confidence interval was calculated using (mean) ±1.96 × (mean × CV / 100). Using X% for the CV in the relationship of log2(intensity) = 3, the upper limit of the confidence interval was 8 + 0.16X. The CV values ​​at log2(intensity) = 5 or 6 were 0.7X% and 0.5X% according to the relationship. Considering the 5% significance level, the CV in each case was less than 40 and 71%.

[0211] Figure 2 is a volcano plot showing that 242 miRNAs were differentially expressed in SLE patients compared to healthy individuals, with 160 miRNAs (Table 2) significantly upregulated and 82 miRNAs (Table 3) significantly downregulated (p<0.05 by t-test). The 82 downregulated miRNAs across cohorts appear to have a greater fold change than the 160 upregulated miRNAs. These 242 miRNAs represent potential biomarkers for SLE.

[0212] Table 2. 160 upregulated miRNAs associated with SLE JPEG2024516504000003.jpg255169JPEG2024516504000004.jpg255167JPEG20245165040 00005.jpg255168JPEG2024516504000006.jpg255165JPEG2024516504000007.jpg255167 JPEG2024516504000008.jpg255167JPEG2024516504000009.jpg255169JPEG20245165040 00010.jpg255165JPEG2024516504000011.jpg255169JPEG2024516504000012.jpg166170

[0213] Table 3. 82 downregulated miRNAs associated with SLE JPEG2024516504000013.jpg236170JPEG2024516504000014.jpg237170JPEG2024516504000015.jpg237170 JPEG2024516504000016.jpg237170JPEG2024516504000017.jpg237170JPEG2024516504000018.jpg160170

[0214] Figure 3A shows the expression levels of the top 10 upregulated miRNAs (out of 160 miRNAs shown in Table 2) in SLE patients (dark boxes) compared to those in healthy donors (light boxes). These results suggest that upregulation of these 10 miRNAs may correlate with SLE and therefore may serve as biomarkers for SLE.

[0215] Figure 3B shows the expression levels of the top 10 downregulated miRNAs (out of 82 miRNAs listed in Table 3) in SLE patients (dark boxes) compared to those in healthy donors (light boxes). These results suggest that downregulation of these 10 miRNAs may correlate with SLE and therefore may also serve as biomarkers for SLE. Identification of urinary miRNAs as biomarkers of SLE severity

[0216] Figure 4 shows the correlation between the expression level of each miRNA and SLE severity, e.g., moderate SLE vs. mild SLE. The results show that the top downregulated miRNAs (shown as circles) seem to correlate with moderate SLE (Q4). In contrast, the top upregulated miRNAs (shown as circles) seem to be unrelated to SLE severity, as they appear to be associated with both moderate SLE (Q1) and mild SLE (Q2).

[0217] Figure 5A shows the expression levels of the top 10 upregulated miRNAs associated with moderate SLE patients (dark box), mild SLE patients (light dark box) and healthy donors (light box). The expression levels of the top 10 upregulated miRNAs appear to be significantly higher in moderate SLE patients than those in mild SLE patients. Therefore, the upregulation of these miRNAs may serve as a biomarker of SLE severity, especially in moderate SLE.

[0218] Figure 5B shows the expression levels of the top 10 down-regulated miRNAs in moderate SLE patients (dark box), mild SLE patients (light dark box) and healthy donors (light box).The expression levels of the top 10 down-regulated miRNAs appear to be significantly lower in moderate SLE patients than those in mild SLE patients.Therefore, the down-regulation of these miRNAs may serve as a biomarker of SLE severity, especially in moderate SLE. Identification of urinary miRNAs as biomarkers for SLE comorbidities

[0219] Figure 6 shows that upregulation of 4 miRNAs and downregulation of 3 miRNAs correlates in SLE patients with comorbidity A (red boxes) compared to SLE patients without comorbidity A (pink boxes). (n=6)

[0220] FIG. 7 shows that upregulation of 10 miRNAs and downregulation of 10 miRNAs correlates in SLE patients with comorbidity B (red boxes) compared to SLE patients without comorbidity B (pink boxes). (n=4)

[0221] FIG. 8 shows that upregulation of 10 miRNAs and downregulation of 10 miRNAs correlates in SLE patients with comorbidity C (red boxes) compared to SLE patients without comorbidity C (pink boxes). (n=8)

[0222] FIG. 9 shows that upregulation of 6 miRNAs and downregulation of 10 miRNAs correlates in SLE patients with comorbidity D (red boxes) compared to SLE patients without comorbidity D (pink boxes). (n=4)

[0223] The advantages of the present disclosure may include the collection of non-invasive samples, such as bodily fluids, for the isolation of miRNAs that are analyzed for the diagnosis of SLE, SLE severity, and SLE comorbidities.In individuals suspected of having SLE, the presence or absence of SLE may be determined based on the miRNA expression profile of the individual.In individuals who are SLE positive, the miRNA expression profile of the individual may confirm the SLE severity and SLE-related comorbidities.To the surprise of the inventors, the use of bodily fluids and the detection of miRNAs for the diagnosis of SLE is found to be unusual compared to methods known in the art.Then, based on these analyses, a treatment plan may be personalized. Example 2: Classifier development

[0224] The inventors developed a classifier to classify samples as indicative of SLE or free of SLE by comparing the values, e.g., expression levels, of individual miRNAs. The inventors identified 484 miRNA sequences, SEQ ID NOs: 1-484. The inventors used the median miRNA expression levels of 60 samples as a "cutoff" and classified patients as having or not having SLE if the value was higher / lower than the cutoff. Accuracy, sensitivity, specificity, and AUC (area under the curve) were adopted from common indices to evaluate classifiers based on simple cutoffs.

[0225] 242 miRNAs were significantly differentially expressed (160 upregulated, 82 downregulated) [p<0.05 T-test]. Downregulated miRNAs tended to have a larger fold change between cohorts. See Figure 2. Significantly differentially expressed miRNAs were found for 160 out of 242 miRNAs. Differential expression analysis was performed by comparing each miRNA signal from the two groups. Fold change between cohorts for each miRNA was plotted against p-value of t-test, and statistically significant miRNAs (p-value<0.05) were selected as biomarker candidates.

[0226] The expression level of each miRNA was compared with SLE disease severity. In Figure 4, the expression level of each miRNA was compared with SLE severity. Scatter plot of fold change of each miRNA (x-axis: SLE vs. non-SLE, y-axis: moderate SLE vs. mild). Figure 5A shows the top 10 up-regulated miRNAs, and Figure 5B shows the top 10 down-regulated miRNAs, comparing by no disease, mild SLE, and moderate SLE.

[0227] Expression levels were compared between SLE patients with and without comorbidities, and miRNAs with p<0.05 by t-test were selected as biomarkers. SLE manifestations

[0228] Accuracy was calculated based on the classification of the logistic regression model by the following formula (true positive + true negative) / (true positive + true negative + false positive + false negative). Logistic regression model to predict whether a sample is from SLE or not. Models were developed independently for each miRNA, and the expression level of the miRNA in each sample was used as a feature. Models were developed based on python sklearn (l1 regularization, c = 1, leave-one-out cross-validation). To develop the classifier, we selected 5 to 20 random miRNAs from a set of 242 miRNAs, developed the classifier multiple times, and evaluated the score. miRNAs are randomly selected from the 242 miRNAs in expression. The expression level of the selected miRNAs was used to develop a logistic regression model. For each miRNA selection, the classification was repeated 20 times. Accuracy, sensitivity and specificity, AUC are the respective results achieved by developing a logistic regression model using the selected 5 to 20 miRNAs. Receiver Operating Characteristic (ROC) curves were plotted based on the raw values ​​of miRNA expression levels, which represents the performance of the classifier.

[0229] All references cited herein are incorporated by reference as if each reference was specifically and individually indicated to be incorporated by reference. The citation of any reference is for its disclosure prior to the filing date and should not be construed as an admission that the present disclosure is not entitled to antedate such reference by virtue of prior invention.

[0230] It will be understood that each of the above elements, or two or more taken together, may find useful application in other types of methods different from those described above. Without further analysis, the above will make the gist of the present disclosure so completely clear that others can easily adapt it to various applications by applying their current knowledge, without omitting features that fairly constitute essential characteristics of the general or specific aspects of the present disclosure in view of the prior art, as set forth in the appended claims. The foregoing embodiments are presented by way of example only, and the scope of the present disclosure is intended to be limited only by the scope of the following claims.

Claims

1. 1. A method for identifying a patient as having a marker that correlates with systemic lupus erythematosus (SLE), comprising: (a) obtaining a sample from a patient suspected of having SLE; (b) analyzing miRNA expression in said sample; (c) (i) as having said marker correlated with SLE if an increased expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decreased expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in said sample compared to a sample obtained from a healthy individual; or (ii) as not having the marker correlated with SLE if an increase in the expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 compared to a sample obtained from a healthy individual is not detectable; identifying the patient; A method for providing the above.

2. 1. A method for identifying a patient as having a marker that correlates with SLE severity, comprising: (a) obtaining a sample from a patient suspected of having SLE; (b) analyzing miRNA expression in said sample; (c) (i) as having said marker correlated with moderate SLE if a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in said sample compared to a sample obtained from a healthy individual; or (ii) the subject does not have the marker correlated with moderate SLE if a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 cannot be detected compared to a sample obtained from a healthy individual; identifying the patient; A method for providing the above.

3. 1. A method for identifying a patient as having a marker that correlates with a comorbidity of SLE, comprising: (a) obtaining a sample from a patient suspected of having SLE; (b) analyzing miRNA expression in said sample; (c) (i) as having said markers correlating with SLE comorbidity if increased expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or decreased expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 is detected in said sample compared to a sample obtained from a healthy individual; or (ii) as not having said markers correlated with SLE comorbidity if there is no detectable increase in the expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in the expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 compared to a sample obtained from a healthy individual; identifying the patient; A method for providing the above.

4. 4. The method according to claim 1, wherein the analyzing step comprises: (a) introducing the sample into a fluidic device containing nanowires; (b) capturing extracellular vesicles in the sample on the nanowire; (c) disrupting the captured extracellular vesicles; (d) extracting at least one miRNA from the disrupted extracellular vesicles; (e) detecting the extracted miRNA; (f) analyzing the detected miRNA; generating an miRNA profile from the sample, comprising:

5. The method according to any one of claims 1 to 4, wherein the sample is a body fluid.

6. 6. The method of claim 5, wherein the bodily fluid is blood, urine, plasma, saliva, peritoneal fluid, bronchoalveolar lavage fluid, cerebrospinal fluid, or a combination thereof.

7. The method according to any one of claims 1 to 6, further comprising isolating extracellular vesicles from the sample.

8. 8. The method of claim 7, wherein the extracellular vesicles are isolated by differential ultracentrifugation, density gradient centrifugation, immunoaffinity, ultrafiltration, polymer-based precipitation, size exclusion chromatography, or a combination thereof.

9. The method according to any one of claims 1 to 8, wherein detection of increased expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or decreased expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 in a sample obtained from the patient compared to a sample obtained from a healthy individual is indicative that the patient has systemic lupus erythematosus (SLE).

10. The method according to any one of claims 1 to 9, wherein the subject does not have the marker correlated with SLE if an increase in expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or a decrease in expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484 compared to a body fluid sample obtained from a healthy individual is not detectable.

11. The method according to any one of claims 4 to 10, wherein the nanowires are made of ZnO, SiO ２ , Li ２ O, MgO, Al ２ O ３ , CaO, TiO ２ , Mn ２ O ３ , Fe ２ O ３ , CoO, NiO, CuO, Ga ２ O ３ , SrO, In ２ O ３ , SnO ２ , Sm ２ O ３ , EuO, and combinations thereof.

12. The method of any one of claims 4 to 11, wherein the nanowires are porous, magnetic, or both porous and magnetic.

13. 13. The method of any one of claims 4 to 12, wherein the length of the nanowires is about 6, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 2 The method may be 40, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, or 500 nanometers (nm).

14. 14. The method of any one of claims 4 to 13, wherein the length of the nanowires is between about 1-500 nm, 100-500 nm, 200-400 nm, 250-500 nm, 50-250 nm, 10-100 nm, 2-200 nm, 300-500 nm, 400-500 nm, 150-450 nm, 250-300 nm, 10-50 nm, 100-350 nm, 350-500 nm, or 200-300 nm.

15. The method according to any one of claims 4 to 14, wherein the cross section of the nanowire is substantially circular, elliptical, regular polygonal, polygonal or hollow.

16. The method according to any one of claims 4 to 15, wherein the nanowires may have a substantially cylindrical, elliptical or polygonal outer shape.

17. The method according to any one of claims 4 to 16, wherein the nanowires may be hollow or hollow bodies or may be substantially material-filled structures.

18. The method according to any one of claims 4 to 17, wherein the nanowires are made of one material or of multiple materials.

19. The method according to any one of claims 4 to 18, wherein the nanowires are coated on their surfaces with a coating material.

20. The method according to any one of claims 4 to 19, wherein the extracellular vesicles are disrupted by a cell lysis buffer.

21. The method according to any one of claims 4 to 20, wherein the step of extracting miRNA is performed in situ.

22. The method according to any one of claims 4 to 21, wherein the extracellular vesicles are exosomes, microvesicles, apoptotic bodies, or a combination thereof.

23. The method according to any one of claims 1 to 22, wherein the sample comprises: A sample input in fluid communication with (a) and (b). (b) a separation means in fluid communication with (c) or (d), optionally a membrane, a filter, at least one nanowire, or a combination thereof; (c) a waste chamber or (d) Waste output section The method of claim 1, further comprising introducing into a device comprising the steps of:

24. 24. The method of any one of claims 1 to 23, wherein the sample is introduced into a device comprising a solid substrate, the solid substrate comprising a plurality of wells, each well comprising at least one nanowire.

25. 25. The method of any one of claims 1 to 24, wherein the sample is introduced into a device comprising a solid substrate comprising a plurality of chambers, optionally in fluid communication with one another, each chamber comprising at least one nanowire.

26. 26. The method of claim 24 or 25, wherein the device is provided with a cover, optionally a removable cover.

27. 27. The method of any one of claims 1-26, wherein the SLE is associated with a comorbidity selected from the group consisting of cancer, higher risk of cancer, cardiovascular disease, renal disease, liver disease, rheumatic disease, neurological disease, hypothyroidism, psychiatric disease, anemia, and combinations thereof.

28. 28. The method of claim 27, wherein when there is increased expression of at least one miRNA selected from SEQ ID NOs: 1-160 and 243-402 and / or decreased expression of at least one miRNA selected from SEQ ID NOs: 161-242 and 403-484, the comorbidity is selected from the group consisting of cancer, higher risk of cancer, cardiovascular disease, renal disease, liver disease, rheumatic disease, neurological disease, hypothyroidism, psychiatric disease, anemia, and combinations thereof.

29. 1. A method of treating SLE, comprising: Identifying a patient according to any one of claims 1 to 28 as having a marker that correlates with SLE; administering to the patient an effective amount of a compound selected from the group consisting of nonsteroidal anti-inflammatory drugs (NSAIDs), immunosuppressants, and anti-BLyS antibodies; method.

30. An isolated miRNA sequence comprising a ribonucleotide sequence selected from the group consisting of SEQ ID NOs: 1-484 or a combination thereof.

31. A composition comprising an isolated miRNA sequence comprising a ribonucleotide sequence of SEQ ID NO: 1-484 or a combination thereof.

32. An array comprising a plurality of nucleotides that hybridize to miRNA ribonucleotide sequences selected from the group consisting of SEQ ID NOs: 1-484, or a combination thereof.

33. 29. The method of any one of claims 1 to 28, wherein the analyzing step comprises detection of miRNAs by quantitative polymerase chain reaction (PCR), miRNA microarray, next generation RNA sequencing (NGS), and / or multiplex miRNA profiling.

Images