US20120107825A1

US20120107825A1 - Methods and compositions for assessing patients with reproductive failure using immune cell-derived microrna

Info

Publication number: US20120107825A1
Application number: US13/284,739
Authority: US
Inventors: Edward E. Winger; Jane L. Reed
Original assignee: Winger Edward E; Reed Jane L
Current assignee: Ewinger Inc
Priority date: 2010-11-01
Filing date: 2011-10-28
Publication date: 2012-05-03
Also published as: CN104039983A; US20190249254A1; EP2771490A4; EP2771490A1; WO2013063322A1; US11268147B2; CN109666728B; US20130245135A1; CN109666728A; AU2012328689A1; CA2853377C; US20220325345A1; ES2666282T3; US20160333406A9; NO2822887T3; EP2771490B1; US10323282B2; CA2853377A1; CN104039983B

Abstract

The invention is directed to methods and compositions for collecting immune cells, preferably peripheral blood mononuclear cells (PBMCs), before or after an intervention, extracting microRNA-comprising RNA from said cells, quantifying microRNAs within the extracted RNA, determining one or more microRNAs that display a bimodal response amongst a statistically sufficient number of patient samples. Patients are then preferably segregated into groups according to their response.

Description

RELATED APPLICATIONS

This application relies on provisional application Ser. No. 61/456,063 filed on Nov. 1, 2010.

FIELD OF THE PRESENT INVENTION

This disclosure generally relates to immunology and more specifically to methods and compositions for characterizing individuals or groups of individuals using patterns of expression of one or more microRNA sequences.

BACKGROUND OF THE INVENTION

Infertility and pregnancy failure is a vexing problem faced by couples who want to raise families. Spontaneous abortion occurs in 15 percent of pregnancies. Recurrent spontaneous abortion, defined as the loss of at least three consecutive pregnancies under 24 weeks gestation, occurs in 3-4 percent of women. In addition, very early losses may go unrecognized in couples thought to be infertile where no diagnostic test has confirmed the existence of a transient pregnant state. Still other women suffer from pregnancy complications such as pre-eclampsia, intrauterine growth retardation (IUGR), preterm labor, premature rupture of membranes (PROM) and stillbirth. Childhood conditions like asthma, autism, attention deficit hyperactivity disorder (ADHD), diabetes, schizophrenia and Tourette's syndrome have been associated with pregnancy related disorders and the late complications of pregnancy-related disorders according to recent literature.
A significant body of literature now exists associating a number of these conditions with immunologic dysfunction. Immunologic intervention has been shown to improve reproductive success in women who have immunological conditions such as low levels of T regulatory cells, elevated natural killer cells and high TNF-alpha/11-10 cell T-helper ratios as determined by in vitro assays. Further it has been shown that immunotherapeutic intervention is helpful in specific subgroups of those afflicted. For example, intravenous immunoglobulin (IVIG) has been useful to reduce natural killer cell numbers in a subset of women in whom in vitro measurements of immune function are elevated. This immunotherapeutic intervention has been shown to improve reproductive success in these women. JAMA (1995) 273:1933-36 Hum Reprod (1998) 13(9):2620-3, Am J Reprod Immunol (2002) 38(5):312-18, Ann NY Acad Sci (2005) 1051:743-78. In addition, heparin has been shown to reduce miscarriage rates in those with elevated antiphospholipid antibodies (Salmon J E, Girardi G. Antiphospholipid antibodies and pregnancy loss: an inflammatory disorder. Reprod Immunol. 2008 January; 77(1):51-6. Epub 2007 Apr. 5 Review.) Many reproductive conditions that have previously been considered to be “unexplained” may be immunologically caused.
Amongst immunologic cells including lymphocytes, monocytes (including their derivatives macrophages and dendritic cells) various lymphoid subpopulations, alternatively known as subsets, mediate and regulate immunologic cytotoxicity. These cells may include NK cells, NKT cells, CD8 T cells, CD4 T cells, gamma delta T cells T regulatory cells and Th17 cells. It has been suggested that the activity of such subsets may have particular significance in defining populations of women suffering from or at risk of suffering from reproductive failure who might benefit from immunotherapy. In addition, assays quantifying the numbers and activity of such cells have been helpful in monitoring such immunotherapy. In the clinical setting, the immunologic cells cited above are collected from the peripheral blood compartment and assessed.
For example, one area of clinical interest has been the T helper lymphocytes (CD3+/CD4+). These cells can be classified into subpopulations according to cytokine profiles revealed following in vitro stimulation as either T helper 1 (Th1) or T helper 2 (Th2) cells. T helper cells selectively secrete specific clusters of cytokines. For example, Th2 cells produce interleukins, IL-4, IL-5, IL-6, IL-9, IL-10 and IL-13, that, in turn, are involved in the development of humoral immunity against extracellular pathogens but inhibit several functions of phagocytic cells. Th1 cells, alternatively, produce interferon-gamma (IFN-gamma.), IL-2 and tumor necrosis factor-alpha (TNF-alpha). These cytokines are involved cell-mediated immunity and phagocyte dependent inflammation (Mosmann & Coffman, 1989; Romagnani, 2000).
Another area of clinical interest has been complement-mediated pregnancy loss (Cohen D, Buurma A, Goemaere N N, Girardi G, le Cessie S, Scherjon S, Bloemenkamp K W, de Heer E, Bruijn J A, Bajema I M. Classical complement activation as a footprint for murine and human antiphospholipid antibody-induced fetal loss. (J. Pathol. 2011 Mar. 10.) (Yu G, Sun Y, Foerster K, Manuel J, Molina H, Levy G A, Gorczynski R M, Clark D A. LPS-induced murine abortions require C5 but not C3, and are prevented by upregulating expression of the CD200 tolerance signaling molecule. Am J Reprod Immunol. 2008 August; 60(2):135-40). Timely intervention with anticoagulants prevent many of these losses.
Raghupathy observed significantly higher serum levels of Th2 cytokines, IL-6 and IL-10, in normal pregnancy compared to unexplained recurrent pregnancy losses. Further, significantly higher serum levels of the Th1 cytokine, IFN-gamma, were present in women with recurrent pregnancy losses compared to normal pregnancy (Raghupathy et al., 1999). Together, these observations suggest a Th2 bias in women with normal pregnancies while a Th1 bias exists in many women with a history of recurrent pregnancy loss, unexplained infertility and pregnancy complications as, for example, delineated above.
Alan Beer reasoned that rebalancing the Th1/Th2 ratio of women with a Th1 bias toward a Th2 bias might help them achieve greater reproductive success. He proposed that treatment of such women with anti-TNF alpha agents would result in shifting the Th1/Th2 balance toward a Th2 bias.
To identify candidates for anti-TNF alpha therapy, Beer proposed that in vitro assessment of the proportions of CD4 expressing T cells differentiated toward producing cytokines of either the Th1 or Th2 groupings could be conducted. If a patient with a reproductive disorder, in particular the issues of unexplained infertility and recurrent unexplained abortion, demonstrated an elevation in the Th1/Th2 ratio compared with normal control patients, then the patient is considered a candidate for anti-TNF alpha therapy. Beer proposed that patients under treatment undergo repeated assessments of the Th1/Th2 ratio to assess efficacy of the therapy. Effective therapy, he reasoned, should result in a shift in the Th1/Th2 ratio toward a Th2 bias. Winger, Reed et al. have shown that women with preconception Th1/Th2 ratio elevation do indeed demonstrate improved reproductive success when treated with anti-TNF alpha therapy over similar women who do not undergo such therapy. Moreover, the treatment period was found to be effective even when the treatment period concluded prior to implantation. (Reference 1: Edward E. Winger, Jane L. Reed, Treatment with Tumor Necrosis Factor Inhibitors and Intravenous Immunoglobulin Improves Live Birth Rates in Women with Recurrent Spontaneous Abortion, 60(1), 8-16, Published Online: 28 Jun. 2008. Reference 2: Edward E. Winger, Jane L. Reed, Sherif Ashoush, Sapna Ahuja, Tarek El-Toukhy, Mohamed Taranissi, Treatment with Adalimumab (Humira and Intravenous Immunoglobulin Improves Pregnancy Rates in Women Undergoing IVF, American Journal of Reproductive Immunology 61 (2009) 113-120)).
The method of Beer has received significant criticism. One such criticism is articulated by Chaouat who notes that implantation of the embryo into the uterine lining is an inflammatory event. (Gerard Chaouat, Natalie Ledée-Bataille, Sylvie Dubanchet, Sandrine Zourbas, Olivier Sandra, Jacques Marta, Th1/Th2 Paradigm in Pregnancy: Paradigm Lost?, Cytokines in Pregnancy/Early Abortion: Reexamining the Th1/Th2 Paradigm, Int Arch Allergy Immunol 2004; 134:93-119) Predominance of Th1 cytokines at the time and place of implantation is essential to the process thereby identifying a deficiency in the Th1/Th2 hypothesis. Moreover, anti-TNF alpha therapy, as suggested in the patent application of Kwak Kim et al. (USPTO #20040105858) might result in diminished reproductive efficiency. Moreover, determination of the Th1/Th2 ratio at the site of implantation may not be effectively determined from the analysis of lymphocytes isolated from the peripheral blood. Another criticism that has been leveled at attempts to assay peripheral blood cells has been made by Moffett et al. They challenge the examination of peripheral blood white cells as non-representative of cellular events within the placenta. (Ashley Moffett, Lesley Regan, Peter Braude, Natural killer cells, miscarriage, and infertility, BMJ 2004; 329:1283-5.) They conclude 1) “Uterine NK cells are different from those circulating in peripheral blood”, 2) “Tests to measure NK cells in peripheral blood give no useful information on uterine NK cells” and 3) “Enthusiasm for new treatments aimed at natural killer cells in women with reproductive failure is unfortunately not backed up by the science”.)
Anticoagulant therapy for antiphospholipid antibody related recurrent miscarriage has also received criticism. Antiphospholipid antibody tests have been criticized as unreliable and poorly standardized. (Lakos G, Favaloro E J, Harris E N, Meroni P L, Tincani A, Wong R C, Pierangeli S S. International consensus guidelines on anticardiolipin and anti-â(2) glycoproteinI testing: A report from the APL task force at the 13(th) international congress on antiphospholipid antibodies. Arthritis Rheum. 2011 Sep. 27. doi:10.1002/art.33349. Review.) In addition, it has been suggested that miscarriages associated with Antiphospholipid Antibody Syndrome are actually caused by increased complement activity rather than increased thrombotic activity (Salmon J E, Girardi G. Theodore E. Woodward Award: antiphospholipid syndrome revisited: a disorder initiated by inflammation. Trans Am Clin Climatol Assoc. 2007; 118:99-114.) (Lynch A M, Salmon J E. Dysregulated complement activation as a common pathway of injury in preeclampsia and other pregnancy complications. Placenta. 2010 July; 31(7):561-7. Epub 2010 Apr. 27. Review.) MicroRNA markers may better identify the underlying inflammatory markers that respond well to anticoagulant treatment.
In addition, mechanisms of maternal tolerance of the fetal hemiallograft have invoked the interaction of another group of CD4 T cells known as T regulatory cells. Jasper et al. (Molecular Human Reproduction Vol. 12, No. 5 pp. 301-308, 2006) quantified FoxP3 mRNA, a master regulator of T regulatory cell differentiation, in mid-secretory endometrial tissues from women with unexplained infertility and levels from unaffected women. They found reduced levels of FoxP3 mRNA in affected women when compared with controls. Winger and Reed have found diminished levels of CD4+ CD25+ Foxp3+ T regulatory cells in the peripheral blood lymphocytes of women experiencing recurrent abortion (Edward E. Winger, Jane L. Reed, Low Circulating CD4+ CD25+ Foxp3+ T Regulatory Cell Levels Predict Miscarriage Risk in Newly Pregnant Women with a History of Failure, Am J Reprod Immunol. 2011 October; 66(4):320-8).
Th17 cells are now thought to play a role in the immunology of pregnancy (Shigeru Saito, Akitoshi Nakashima, Tomoko Shima, Mika Ito, Th1/Th2/Th17 and Regulatory T-Cell Paradigm in Pregnancy, American Journal of Reproductive Immunology 63 (2010) 601-610). Together with T regulatory cells, with which they appear to act in concert, they affect the balance in much the same manner as Th1 and Th2 cells have been proposed to affect pregnancy success.
Recently, a variety of conditions affecting infants born wherein the mother demonstrated immunologic abnormalities in or about the time of pregnancy have been described. For example, immunologic abnormalities during the course of pregnancy have been implicated a cause of autism. Autism is thought to be a spectrum of disorders the etiology of which remains unknown. However, immunological factors during early pregnancy have been invoked. Skewed Th1/Th2 cytokine profiles, altered lymphocyte numbers and decreased T cell mitogen responses have been identified in affected children. The authors suggest that immune abnormalities during early pregnancy may be involved (Paul Ashwood, Sharifia Wills, and Judy Van de Water, The immune response in autism: a new frontier for autism research, Journal of Leukocyte Biology. 2006; 80:1-15)
Controversy regarding the Th1/Th2 hypothesis is also related to timing of testing and therapy. Chaouat, as noted above, teaches that the hypothesis is flawed and Th bias may not remain constant through the implantation period. Winger, Reed et al. show that patients with elevation in their Th1/Th2 cell ratio assessed in the pre-conception period enjoy significantly superior pregnancy results when treated with anti-TNF alpha therapy. (Winger E E, Reed J L, Ashoush S, El-Toukhy T, Ahuja S, Taranissi M. Degree of TNF-α/IL-10 cytokine elevation correlates with IVF success rates in women undergoing treatment with Adalimumab (Humira) and IVIG. Am J Reprod Immunol. 2011 June; 65(6):610-8) Winger and Reed have also demonstrated that T regulatory cells assessed during the period of implantation predict pregnancy outcome as well. (Winger E E, Reed J L. Low Circulating CD4(+) CD25(+) Foxp3(+) T Regulatory Cell Levels Predict Miscarriage Risk in Newly Pregnant Women with a History of Failure. Am J Reprod Immunol. 2011 October; 66(4):320-8.) Timing and the nature of the laboratory parameters used appears quite significant.
In addition, Winger and Reed have identified a subset of patients in whom the behavior of the ratio was not as expected. Occasionally Th1/Th2 ratios rise following therapy while efficacy of therapy appears intact. (Winger E E, Reed J L, Ashoush S, El-Toukhy T, Ahuja S, Taranissi M. Degree of TNF-α/IL-10 cytokine elevation correlates with IVF success rates in women undergoing treatment with Adalimumab (Humira) and IVIG. Am J Reprod Immunol. 2011 June; 65(6):610-8.)
Moreover, the technique for determining the Th1/Th2 ratio requires isolation of viable mononuclear cells from peripheral blood (PBMCs), stimulating them in vitro after having blocked cytokine secretion, a step that is somewhat toxic to the cells. Successful induction of intracellular cytokine expression may thus be compromised and test results theoretically less accurate. Further, while Th1 cell quantification appears relatively robust, Th2 quantification is hampered by its low prevalence amongst CD4 positive T cells. Gating of the cells as currently practiced can be somewhat subjective leading to imprecise results. Assay results, therefore may vary because of minor gating variations. For these and other reasons, an improved technique for assessing Th1 and Th2 numbers is needed.
In addition, the predictive power of the different assays currently being employed to assess pregnancy risk factors is not sufficiently sensitive to detect all affected individuals. Additional immune cell testing parameters are sorely needed. For example, currently two tests of natural killer cells are currently performed. The first is a phenotypic assay quantifying NK cells. Lymphocytes expressing CD56 but not expressing CD3 are defined as NK cells and can be enumerated by flow cytometry. A second test assesses NK cell function whereby mononuclear cells are incubated with labeled cells known to be damaged by NK cells or “target cells” are coincubated and subsequently detected and quantified by flow cytometry. Both of these tests can be used to assess a patient's risk of reproductive failure with some degree of success. However, some patients with normal natural killer cell results still continue suffer from immunological—based pregnancy failure.
As noted, T regulatory cells (Treg) are a new and important cell type that may help in diagnosis and assessment in many of these cases. Diminished numbers of these cells in peripheral blood have been associated with pregnancy loss particularly in the immediate post-conceptual period. Jasper et al quantified FoxP3 mRNA, a master regulator of T regulatory cell differentiation, in mid-secretory endometrial tissues from women with unexplained infertility and levels from unaffected women (Molecular Human Reproduction Vol. 12, No. 5 pp. 301-308, 2006). They found reduced levels of FoxP3 mRNA in affected women when compared with controls. Winger and Reed have found diminished levels of T regulatory cells in the peripheral blood lymphocytes defined by their concurrent expression of CD4, CD25 and FoxP3 in pregnant women who go on to early pregnancy loss. Winger E E, Reed J L. Low Circulating CD4(+)/CD25(+)/Foxp3(+) T Regulatory Cell Levels Predict Miscarriage Risk in Newly Pregnant Women with a History of Failure. Am J Reprod Immunol. 2011 October; 66(4):320-8.)
A variety of markers are currently being employed for T regulatory cells quantification, however, no comparable assay is available that permits facile functional assessment of T regulatory activity. Such an assay is sorely needed.
In addition to lack of sensitivity demonstrated by the current reproductive immunology assays, they can be vulnerable to specimen collection transportation conditions. The NK cytotoxicity assay is particularly vulnerable. In the assay, effector cell activity is tested. Any stress upon the effector cells can be expected to diminish the measured cytotoxic activity. Not withstanding these effects, target cells are subject to significant variation in their vulnerability to cytotoxic effect. Thus, the assay system is subject to considerable variability. An assay system that is not subject the variability of a functional assay is also sorely needed.
The current functional assays are further limited in their inability to detect functional cell intermediaries. T regulatory cells exert their regulatory activity through a number of intermediaries. The current assays that enumerate the numbers of T regulatory cells do not identify nor quantify any intermediaries. An assay system that permits recognition and quantification of known intermediaries would provide a significant improvement.
More recently, CD4 expressing T cells have been divided into additional subgroups based on their cytokine secreting profile. In addition to Th1 and Th2 cell types, T regulatory, Th3 and Th17 cells have been defined. Also, Th9 and T follicular helper cells (T_FH) have been described. Jasper et al. have assessed the relative amount of FoxP3 mRNA in luteal phase endometrium and have shown distinct patterns distinguishing patients with a history of recurrent loss from normal patients. Similarly quantification of mRNA for transcriptional regulators and comparing them with control patients can provide information supporting the classification of patients into candidates for immune-based therapies and subsequent monitoring of interventional therapy.
In addition to the aforementioned deficiencies of immune tests of function and phenotype, specificity and sensitivity are known to be limited. An assay system that permits assessment of multiple different parameters that together provide a profile or signature of the PBMC status of a patient might overcome deficiencies seen in single tests. The present testing methods assess individual characteristics of the patient's immune system. A combinatorial approach wherein a number of different parameters are assessed together might improve both sensitivity and specificity as well improve discrimination of different forms of immune dysfunction that individually affect a single immune parameter in the same manner. An approach broader than assessing immune status of PBMCs might provide better information as to the diagnosis and status of pregnancy disorders. Ideally, such an approach would discriminate between different mechanisms resulting in a shared abnormality in an immune or other parameter.
Personalized medicine, as understood, utilizes testing, in particular testing at the level of DNA and RNA, to determine the most appropriate therapeutic intervention for an individual rather than applying a single therapeutic intervention to all patients with a particular complaint. Ideally, a diagnostic strategy would divide patients into categories where patients are identified who would likely respond to a therapeutic intervention. Ideally, patients the testing strategy would identify patients in whom a therapeutic strategy is unlikely to be of benefit thereby saving application of a costly therapy or a therapy with potential risks to those patients who are unlikely to enjoy a positive therapeutic response. Because of aforementioned deficiencies, it would be useful to useful to identify more robust and stable surrogate markers for the immunologic tests currently being performed in clinical practice as well as identifying surrogate markers for inflammation and coagulation markers.
In addition, new tests are needed that can identify patients who will not benefit from therapy despite a positive disease diagnosis using traditional testing.
Also, new tests are needed that can predict which patients will experience negative side effects from immunotherapy.
MicroRNAs are small, endogenous, non-protein encoding RNA sequences of approximately 22 nucleotide bases that predominantly negatively regulate gene expression. Several hundred such sequences have been identified in humans (Lee et al., PLoS Comput Biol 3:e67 (2007); O'Driscoll, Anticancer Res 26:4271 (2006); Kusenda et al., Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 150:205 (2006)). The primary transcript or “pri-microRNA” comprises one or more microRNA precursors each comprised within a hairpin structure. These sequences are most commonly found within the introns of their host genes (Lee et al., PLoS Comput Biol 3:e67 (2007)). They may also be found within exons and across exon-intron boundaries (Kusenda et al., Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 150:205 (2006)). These sequences are known to target at least 30 percent of all human genes, fine-tuning their expression. The final short sequence is generated through a series of cleavages involving two enzymes, Drosha and Dicer, from relatively long RNA primary RNA sequences. The final, cleaved form is incorporated into a complex known as RISC (RNA induced silencing complex) that comprises catalytic proteins such as Argonaut, specifically Ago-2. Jeker and Bluestone (Journal of Clinical Immunology (2010), 30:347-357) hypothesize microRNAs act to stabilize cell phenotype, sharpen gene expression, aid in setting thresholds amongst other regulatory functions. microRNAs appear to be important regulators of cell growth, differentiation, and apoptosis (Lee et al., PLoS Comput Biol 3:e67 (2007)). microRNAs have been extensively studied in cancer pathogenesis because of their known impact on cell dedifferentiation, growth, and apoptosis, each of which individually are important cellular events in the development of cancer (Esau and Monia, Adv Drug Deliv. Rev. 59: 101-114 (2007); Hammond, Nat Genet 39:582 (2007)). microRNA profiles within cancer cells has been an area of intense study. Information gained provides information about the functional state of individual cells.
Investigators have found that global expression of microRNA appears to be more useful than mRNA expression in the classification of cancers (Eis et al. Proc Natl Acad Sci USA 102: 3627 (2005)). More recently, a variety of studies have demonstrated the importance of microRNA in homeostasis and function of the immune system of B lymphocytes, T lymphocytes, macrophages, dendritic cells and the heart (That et al., Science 316:604 (2007); Rodriguez et al., Science 316:608 (2007); O'Connell et al., Proc Natl Acad Sci USA 104:1604 (2007); Care et al., Nat Med 13:613 (2007); Taganov et al., Proc Natl Acad Sci USA I (2006).
MicroRNA 155 (mir-155) is exemplary of microRNA effects upon immune system function as well as immune cell differentiation. The microRNAs disclosed herein are all of human origin, mir-155 acts to stabilize the expression FoxP3 expression in T regulatory cells. (Foxp3-Dependent MicroRNA155 Confers Competitive Fitness to Regulatory T Cells by Targeting SOCS1 Protein Immunity, Volume 30, Issue 1, Pages 80-91 L. Lu, T. That, D. Calado, A. Chaudhry, M. Kubo, K. Tanaka, G. Loeb, H. Lee, A. Yoshimura, K. Rajewsky) T regulatory cells are profoundly important in the prevention of autoimmunity and for the establishment of foreign tissue tolerance. Further, That et al. have shown mir-155 plays a role in regulating both T helper cell differentiation and the germinal center reaction regulating the T cell-dependent antibody response (That et al., Science 316:604 (2007); Rodriguez et al., Science 316:608 (2007). Further, transcriptosome analysis of microRNA-155-deficient CD4+ T cells demonstrate a wide spectrum of mir-155 regulated genes, including cytokines, chemokines, and transcription factors (That et al., Science 316:604 (2007); Rodriguez et al., Science 316:608 (2007).
mir-155 is exemplary of the pleitropism of microRNAs. Evidence to date has shown that mir-155 is involved in numerous biologic processes. These include hematopoiesis, inflammation and immunity. It is also involved in regulation of the angiotensin II receptor. Deregulation of mir-155 has been associated with certain cancers, cardiovascular disease as well as viral infections. T regulatory cell development as well as mediation of FoxP3 effects may both involve mir-155 (Susan Kohlhaas, Oliver A. Garden, Cheryl Scudamore, Martin Turner, Klaus Okkenhaug, Elena Vigorito, Cutting Edge: The Foxp3 Target mir-155 Contributes to the Development of Regulatory T Cells, The Journal of Immunology (2009) 182:2578-2582). Surprisingly, these investigators found that while Treg development could not dispense with bic/mir-155, the pri-microRNA, it was dispensable for Treg proliferation and survival in the periphery. Despite the lower number of Tregs, their suppressor function in vitro remained intact. Such unexpected actions are characteristic of microRNAs. microRNAs can act at different time points in the development of cells and their subsequent functional activation. While they may act individually, they may also act in concerted action of a plurality of microRNAs on a single target mRNA. To add to the complexity of microRNA action, individual microRNAs may also act on many different target mRNAs. Together, these complexities make prediction of their action under differing circumstances tenuous. To date, only empiric methods have defined the role of individual and multiple microRNAs under specific disease conditions.
Other microRNAs are significantly associated with immune responses. For example, TNF alpha and IL-1 beta are regulated by yet another microRNA, mir-146a. (K. D. Taganov, M. P. Boldin, K. J. Chang and D. Baltimore, NF-B-dependent induction of the microRNA mir-146, an inhibitor targeted to signaling proteins of innate immune responses, Proc Natl Acad Sci USA 103 (2006), pp. 12481-12486, M. M. Perry, S. A. Moschos, A. E. Williams, N. J. Shepherd, H. M. Larner-Svensson and M. A. Lindsay, Rapid changes in microRNA-146a expression negatively regulate the IL-1beta-induced inflammatory response in human lung alveolar epithelial cells, J Immunol 180 (2008), pp. 5689-5698.) Quantification of these microRNAs provides insight into regulatory status. Further, studies in patients suffering from rheumatoid arthritis display interesting microRNA profiles in their PBMCs. (Kaleb M Pauley, Minoru Satoh, Annie L Chan, Michael R Bubb, Westley H Reeves and Edward K L Chan, Upregulated mir-146a expression in PBMCs from rheumatoid arthritis patients, Arthritis Research & Therapy 2008, 10:R101 (doi:10.1186/ar2493) This article is online at: http://arthritis-research.com/content/10/4/R101) They observed significant increases in mir-146a, mir-155, mir-132, mir-16, mir-let-7a relative expression over normal controls as well as significant differences between active and inactive clinical states.
Hunter et al. (Hunter M P, Ismail N, Zhang X, Aguda B D, Lee E J, et al. (2008) Detection of microRNA Expression in Human Peripheral Blood Microvesicles. PLoS ONE 3(11): e3694. doi:10.1371/journal.pone.0003694) find microRNAs circulating in peripheral blood in several compartments, plasma microvesicles, platelets and PBMCs. They suggest differing roles for microRNAs residing in the different compartments. microRNAs resident within PBMCs are most closely associated with CD4 expressing T cell subclass specification and stable expression. Interrogation of microRNAs resident within the PBMC population is most likely to provide information regarding CD4 expressing T cells. Moreover, monocytes and cells of the monocyte lineage and of dendritic cells, one of the two peripheral blood mononuclear populations, regulate the functional state and activity of T cells and in particular CD4 expressing T cells. Importantly, monocytes participate in initiation of coagulation expressing Tissue Factor on their surface. Mir-19b and mir-20a appear to modulate tissue factor expression in patients with lupus (Raúl Teruel, Carlos Pérez-Sánchez, Javier Corral, María Teresa Herranz, Virginia Pérez-Andreu, Encarnación Saiz, Nuria García-Barberá, Irene Martínez-Martínez, Vanessa Roldán, Vicente Vicente, López-Pedrera, Constantino Martinez, Identification of microRNAs as potential modulators of tissue factor expression in patients with systemic lupus erythematosus and antiphospholipid syndrome, Journal of Thrombosis and Haemostasis, in press). Therefore, interrogation of microRNAs within PBMCs as a whole provides significant information regarding the immune balance and stability of circulating mononuclear cells.
Selective interrogation of subsets of mononuclear cells can impart additional information. For example, removal of monocytes from isolated PBMCs permits selective microRNA interrogation of lymphocytes. Conversely, monocytes can be interrogated directly for microRNAs. Further, lymphocyte subpopulations, themselves, can be individually interrogated following their selective isolation by such techniques, for example, flow cytometric sorting following interaction with fluorescently labeled monoclonal antibody combinations that are capable of discreetly characterizing the individual subclasses. For example, T regulatory cells may be contacted under selective binding conditions with fluorescently labeled anti-CD3, CD4, CD25 and CD127 and selected by their expression of CD3, CD4, CD25 and absence or low expression of CD127.
MicroRNA provides information that is different than quantification of lymphocyte subsets, their function or markers of these subsets that might be surrogates for these subsets such as FoxP3 mRNA which might be regarded as a surrogate for quantification of T regulatory cells. microRNAs may be found in a variety of different cell types and represent different functions in different cell types or may represent activity states or other features that are not disclosed from quantification of cell types or functional states (such as cytotoxicity). Alterations in microRNA expression varies from one disease to another. The microRNA alterations noted in rheumatoid arthritis differ from those seen in lupus. (Pauley K M, Satoh M, Chan A L, Bubb M R, Reeves W H, Chan E K. Upregulated mir-146a expression in PBMCs from rheumatoid arthritis patients. Arthritis Res Ther. 2008; 10(4):R101, Gastroenterol Hepatol (N Y). 2010 November; 6(11): 714-722. MicroRNA (microRNA) Expression in Ulcerative Colitis (UC) and Crohn's Disease (CD) and Dai Y, Huang Y S, Tang M, Lv T Y, Hu C X, Tan Y H, Xu Z M, Yin Y B. Microarray analysis of microRNA expression in peripheral blood cells of systemic lupus erythematosus patients. Lupus. 2007; 16(12):939-46., Raúl Teruel, Carlos Pérez-Sánchez, Javier Corral, Maria Teresa Herranz, Virginia Pérez-Andreu, Encarnación Saiz, Nuria García-Barberá, Irene Martínez-Martínez, Vanessa Roldán, Vicente Vicente, §hary López-Pedrera, Constantino Martinez, Identification of microRNAs as potential modulators of tissue factor expression in patients with systemic lupus erythematosus and antiphospholipid syndrome, Journal of Thrombosis and Haemostasis, in press).
Aspects of immune disorders have implicated alterations in microRNA expression. microRNAs have significant effects in the regulation of immunological functions and the prevention of autoimmunity (Kaleb M. Pauley, Seunghee Cha, and Edward K. L. Chan, MicroRNA in autoimmunity and autoimmune diseases, J Autoimmun. (2009) 32(3-4): 189-194). Anti-phospholipid antibody syndrome is an example of an autoimmune condition associated with diminished fertility, recurrent unexplained abortion and pregnancy complications as well as increased risk of autoimmune, cardiovascular and thrombotic disease. Diminished expression of certain microRNAs (mir-19b and 20a) may identify patients at increased risk of pregnancy complications treatable by anticoagulation therapy (e.g. aspirin and/or heparins) Teruel, Carlos Pérez-Sánchez, Javier Corral, María Teresa Herranz, Virginia Pérez-Andreu, Encarnación Saiz, Nuria García-Barberá, Irene Martínez-Martínez, Vanessa Roldán, Vicente Vicente, §hary López-Pedrera, Constantino Martinez, Identification of microRNAs as potential modulators of tissue factor expression in patients with systemic lupus erythematosus and antiphospholipid syndrome, Journal of Thrombosis and Haemostasis, in press)).
Identification of surrogate measures of the dysfunctional status of patients suffering reproductive disorders, it is recalled that pregnancy constitutes an immunologic paradox where typical alloimmune responses to tissues such as tissue/organ grafts are rejected, in the healthy pregnancy tolerance to alloantigen prevails preventing the rejection of the hemialloantigenic embryo. Multiple and seemingly redundant mechanisms have been described that appear to maintain allo-tolerance. The existence of these mechanisms differentiate allo-responses to the fetus and autoimmunity. It is expected, therefore, that microRNA patterns identified amongst patients suffering reproductive disorders are unlikely to be similar to those identified in autoimmunity. Moreover, there appears to be no direct surrogacy of microRNAs for currently applied immune, coagulation tests currently being applied because of their broad and varied presence and function in different cell types.
Mishra (US patent application 20100216142 A1, Mishra; Nilamadhab, microRNA Biomarkers in Lupus) identifies a variety of microRNAs that are deregulated in lupus. Comprised amongst immune abnormalities seen in lupus are antibodies directed against various phospholipids and increased levels of inflammatory markers such as tissue factor. A recent study by Ceribelli identifies antibodies directed against Ago2, a component of the RISC moiety required for the genesis of microRNAs (Angela Ceribelli, Angela Tincani, Franco Franceschini, Roberto Cattaneo, Brad A. Pauley, Jason Y. F. Chan, Edward K. L. Chan, Anti-argonaute2 (Ago2/Su) and -Ro antibodies identified by immunoprecipitation in primary anti-phospholipid syndrome (PAPS), Posted online on Aug. 9, 2010 (doi:10.3109/08916934.2010.499886)). Intravenous immunoglobulin (IVIG) therapy is an example an immune therapy used to reduce miscarriage incidence in women with a history of immunologic recurrent spontaneous abortion. Winger and Reed have demonstrated efficacy of IVIG therapy in a group of women with recurrent spontaneous abortion when compared with a group of women not receiving IVIG therapy. (Winger E E, Reed J L, Ashoush S, El-Toukhy T, Ahuja S, Taranissi M. Elevated Preconception CD56(+) 16(+) and/or Th1:Th2 Levels Predict Benefit from IVIG Therapy in Subfertile Women Undergoing IVF. Am J Reprod Immunol. 2011 May 30.) In addition, when patients with anti-phospholipid antibodies were excluded from the patient pool in this study, significant differences could no longer be found between the treated and non-treated groups (unpublished data). Therefore the ability to identify patients with markers associated with antiphospholipid antibodies may be particularly useful when deciding who may benefit from immunologic treatment.
Detection of microRNAs in PBMCs and their constituent selected subsets, provides the clinician with an additional means of characterizing patients with or at risk of reproductive immunologically-related disorder into groups whose disorder is mediated by an imbalance in immune cell activity, inflammation or coagulation. Detection of microRNAs in women with pregnancy disorders is taught by Taylor and Gercel-Taylor (Taylor and Gercel-Taylor in US patent application 20100151480 A1). They teach methods for diagnosis of cancer and adverse pregnancy outcomes in a subject by measuring the amount of one or more RNAs present in exosomes isolated from a biologic sample from the affected individual. The methods they teach can be distinguished from the present subject matter. They teach that the microRNAs they interrogate are derived directly from placental tissues and, thereby represents the pathophysiologic state of that organ. The presently disclosed material teaches methods for defining the systemic and/or local immune status of the affected individual thereby providing an alternative and supplemental means for assessing the immune status of an individual that might adversely affect their reproductive health. The addition of such parameters should increase the sensitivity of a diagnostic panel. For example, mir-155 has been shown to be important in the stabilization of FoxP3 expression and, moreover, for the effector function of T regulatory cells, in part, through the regulation of CTLA-4 expression (Lu et al. Immunity 30, 80-91 (2009)). Numerous and redundant mechanisms are postulated to induce a tolerant state in the mother operative at various time points that include the egg development (oogenesis) period, preconceptual period, insemination, implantation through the remaining course of pregnancy and post-parturition. Egg quality may also be affected by immunologic events. A complex interplay of cytokines, hormones, growth factors are involved in oogenesis which engage immune cell interactions. microRNAs quantification in immune cells may profile both a normal course and be distinguishable from the course in patients with reproductive and other immunologically related disorders.
MicroRNA quantification provides additional benefits. Were patterns of response to immunotherapies identified, they could be used to predict response patterns amongst patient subgroups identified by their microRNA profiles. Were specific microRNAs or groups of microRNAs identified whose responses following therapies could be used to dichotomize or multiperize patient groups, then patients so grouped could be assessed according to the responses to therapies and used to more precisely predict responses of individual patients so grouped.
The ability to separate patients into groups of two or more is important. The use of Herceptin to treat breast cancer could not be shown to be effective when used in otherwise clinical similar breast cancers. Herceptin (trastuzumab) targets the HER2/neu receptor. In early breast cancer, presence or absence of the receptor is not clinically apparent. However, when expressed by a breast cancer, it denotes a class of tumors with more aggressive future behavior. Molecular techniques can be used to assess the HER2/neu status of the tumor. Clinical studies revealed that when use of the drug was restricted to those patients whose tumor was positive for HER2/neu expression, the drug was shown to be effective. The identification of microRNA profiles comprising one or more individual microRNAs could be of similar importance in separating patients into separate groups otherwise indistinguishable that could be assessed to define groups of individuals with similar drug responses.
This invention accomplishes these and other goals.

SUMMARY OF THE INVENTION

In accordance with the above needs and those that will be mentioned and will become apparent below, this disclosure is directed to a method for identifying at least two characteristic groups in a patient population on the basis of microRNA expression including the steps of collecting immune cells, extracting microRNA-comprising RNA from the immune cells, quantifying at least one microRNA within the extracted RNA, and segregating the patient population into the groups on the basis of expression of the at least one microRNA. Preferably, the step of collecting immune cells comprises collecting peripheral blood mononuclear cells.
In one aspect, the step of segregating the patient population includes assigning patients expressing a relatively high level of the at least one microRNA to a first group and assigning patients expressing a relatively low level of the at least one microRNA to a second group. Collecting immune cells may include collecting cells before or after an immunotherapy treatment. A further aspect is directed to collecting the cells before and after an immunotherapy treatment such that segregating the patient population includes determining the change in expression level of the at least one microRNA after the immunotherapy treatment. Preferably, segregating the patient population comprises assigning patients exhibiting a first change in the expression level to a first group and assigning patients exhibiting a second change in the expression level to a second group. The first change may be a relatively large change in expression level and the second change may be a relatively small change in expression level. Alternatively, the first change may be a positive change in expression level and the second change may be a negative change in expression level.
Preferably, the absolute value of the mean of the change in the expression level of the at least one microRNA in the first group divided by the standard deviation is greater than or equal to one. Further, one embodiment is directed to the further step of identifying a subset of microRNAs within the group of known microRNAs that exhibit a change in expression level in the first group such that the absolute value of the mean of the change in expression level divided by the standard deviation is greater than or equal to one. In addition, the method can also include identifying a microRNA within the group of known microRNAs that exhibits the greatest change in expression level in the first group.
Another embodiment of the invention is directed to the additional steps of collecting immune cells from an additional patient, extracting microRNA-comprising RNA from the immune cells of the additional patient, quantifying at least one microRNA within the extracted RNA from the additional patient, and identifying the additional patient as belonging to one of the segregated groups on the basis of expression of the at least one microRNA. Preferably, this also include administering a treatment to the additional patient based on the identification, such as IVIG.
In another aspect, the methods of the invention also include diagnosing a patient as having a condition based on membership in a segregated group. One embodiment is directed to diagnosing a patient having a reproductive disorder.
Yet another aspect of the invention includes the additional step of monitoring treatment of a patient belonging to one of the segregated groups by collecting immune cells, extracting at least one microRNA and quantifying the at least one microRNA at a subsequent time.
Presently preferred embodiments of the invention include the use of at least one microRNA selected from hsa-let-7e, hsa-mir-1181, hsa-miR-1183, hsa-miR-1224-5p, hsa-miR-127-3p, hsa-mir-1296, hsa-mir-132, hsa-mir-136, hsa-miR-139-3p, hsa-mir-141, hsa-miR-142-3p, hsa-mir-142-5p, hsa-mir-144, hsa-mir-153, hsa-mir-1537, hsa-miR-154, hsa-miR-191, hsa-mir-193a-3p, hsa-miR-19a, hsa-mir-219-5p, hsa-mir-29b, hsa-mir-301a, hsa-miR-301b, hsa-miR-30e, hsa-mir-32, hsa-mir-33a, hsa-miR-340, hsa-miR-362-3p, hsa-miR-371-5p, has-377, hsa-miR-423-3p, hsa-miR-432, hsa-mir-513a-5p, hsa-mir-545, hsa-miR-548a-5p, hsa-miR-574-5p, hsa-mir-582-3p, hsa-mir-590-5p, hsa-mir-15a, hsa-mir-548c-5p, hsa-mir-1225-3p, hsa-mir-29b, hsa-mir-21, hsa-mir-1237, hsa-mir-101, hsa-mir-1539, hsa-mir-557, hsa-mir-125a-3p and hsa-mir-423-5p. More preferably, the microRNA is selected from hsa-mir-136, hsa-mir-141, hsa-mir-142-5p, hsa-mir-144, hsa-mir-153, hsa-mir-1537, hsa-mir-193a-3p, hsa-mir-219-5p, hsa-mir-29b, hsa-mir-301a, hsa-mir-32, hsa-mir-33a, hsa-mir-545, hsa-mir-582-3p, hsa-mir-590-5p, hsa-mir-1181, hsa-mir-513a-5p, hsa-mir-132 and hsa-mir-1296.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features and advantages will become apparent from the following and more particular description of the preferred embodiments of the invention, as illustrated in the accompanying drawings, and in which like referenced characters generally refer to the same parts or elements throughout the views, and in which:

FIG. 1 shows the CT levels of the patients having high initial microRNA expression before and after IVIG treatment, and

FIG. 2 shows the CT levels of the patients having low initial microRNA expression before and after IVIG treatment.

DETAILED DESCRIPTION OF THE INVENTION

At the outset, it is to be understood that this disclosure is not limited to particularly exemplified materials, architectures, routines, methods or structures as such may, of course, vary. Thus, although a number of such option, similar or equivalent to those described herein, can be used in the practice of embodiments of this disclosure, the preferred materials and methods are described herein.
It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments of this disclosure only and is not intended to be limiting.
This summary provides a listing of several embodiments of the presently disclosed subject matter. However, it should be understood that variations and permutations of these embodiments exist. This summary is intended to serve as exemplary of potential embodiments.
The term “immune cells” as used herein shall mean lymphocytes, monocytes and granulocytes, their precursors and maturational derivatives. These shall include, for example, plasma cells, dendritic cells, mast cells, granulocytes and macrophages. It is understood that immune cells participate in a broad range of activities. These include immunologic surveillance and intervention (such as elimination of malignant cells and elimination of infectious agents). Moreover, cells of the immune system, in particular monocytes and their derivatives are involved in coagulation. Further, it is understood inflammation is a manifestation of activities of cells of the immune system.
As used herein, the term “immunotherapy”, “immunotherapeutic” or “immune therapy” shall include therapeutic intervention directed to modification of activities of cells of the immune system thereby contemplating actions affecting immune cells wherein said cells affect coagulation and inflammation.
Herein, specific microRNAs are identified by their prefix mir- and their identifier, such as mir-155 in this case. Specific microRNA abbreviations may also include an additional prefix identifying the species of origin, such as hsa for homo sapiens. Although the primary embodiments described herein are directed to humans, one of skill in the art will appreciate that the techniques of this disclosure can be applied to other species.
The term “control individual” as used herein has a special meaning. A “control individual” shall mean individuals of comparable characteristics such as age and sex who do not have a reproductive disorder and are not at known risk of developing a reproductive disorder. The term “control sample” as used herein shall mean a biologic sample from the same source, such a peripheral blood, and collected under the same or comparable conditions as a patient sample comprising immune cells collected from a control individual that is processed and analyzed in the same manner as a patient sample.
It is further understood that the term “control sample” as used herein may represent the mathematical mean of multiple samples from control individuals wherein a number of samples considered sufficient by an individual of ordinary skill in the art are collected. Additional statistical parameters may be derived from said samples such as standard deviation of the mean. Said additional statistical parameters may be used for purposes of comparison of a patient test result with control samples to estimate the probability that the patient's test result represents an abnormal finding and, thereby suggests that the patient is suffering from a reproductive disorder or risk of a reproductive disorder. For purposes of simplicity the term may also be used in another way wherein a plurality of comparable, temporally separate, samples are collected and assayed from a single individual and compared with one another such that a first sample or a particular subsequent sample are compared as though the first is a control for the second, permitting assessment of a change in condition potentially as a function of the clinical state, or stage of pregnancy or as a result of therapeutic intervention.
The term “reproductive immune dysfunction” or “reproductive disorder” as used herein shall comprise those disorders of the reproductive system suspected of having an immune component. These shall include but not limited to the following: infertility and post-conceptive failure such as implantation failure that may be unrecognized and thereby diagnosed as infertility; miscarriage; conditions that do not lead to miscarriage but compromise optimal pregnancy outcome such as intrauterine growth retardation, PROM (Premature Rupture Of Membranes), pre-eclampsia, preterm labor, placental abruption and stillbirth; those conditions known to contribute to infertility, pregnancy complications and early implantation failure such as endometriosis and autoimmune thyroiditis and anti-phospholipid antibody syndrome, those pregnancy disorders that compromise optimal fetal growth, maturation and development in pregnancy and/or compromise potential childhood development after delivery, those reproductive disorders that compromise the long term reproductive potential of the mother over the course of her reproductive lifespan.
The term “Immunologic disorder” as used herein shall comprise disorders caused by abnormal, whether humoral, cell-mediated, or both and/or related to an immune component such as an inflammatory, complement or coagulation-mediated component.
As used herein, the term “differentially expressed” or “differential expression” shall mean a detectable difference by the selected detection means in the quantification of a specific microRNA between the biologic sample of the patient and the corresponding mean value of a control population wherein said difference has been identified between a statistically significant patient population with a reproductive disorder and a corresponding control population without the disorder or risk of the disorder. The terms may also be applied wherein quantification of a plurality of individual microRNAs form a pattern that can be distinguished from a corresponding pattern identified in controls. Differential expression of one or more microRNAs between patients with a reproductive disorder and/or risk of a reproductive disorder and control individuals is preferably determined in a screen of a panel of microRNAs such as that provided by SABiosciences (catalog MAH-104A). Differentially expressed microRNAs between patient and control values may be determined by a variety of means. Each method requires inclusion of a minimum number of samples from each group so that a significant difference in expression between the two groups can be ascertained. A preferred embodiment to define differentially expressed microRNAs between patients and controls utilizes the microRNA human immunopathology-related microRNA array (SuperArray technology, SABiosciences, Frederick, Md. catalogue MAH-104A for Stratagene Mx3005p). Reactions of three or more RNA extracts from patients with a reproductive disorder or at risk of a reproductive disorder and three or more control samples are performed following the manufacturer's instructions. The quantitative PCR is run on a Stratagene 3005p real-time thermocycler following the manufacturer's instructions. For each set of triplicates or greater, the mean value for each microRNA is determined and used to calculate the differences in levels. A microRNA value is determined to be differentially expressed when there is a difference between patient value and control value with a P value of ≦0.05. Other P values may be selected as determined by someone with ordinary skill in the art. It should be understood that a reproductive immunologic disorder or risk of such a disorder can comprise different patterns of differential expression. Further, the time at which a patient is tested may result in a different pattern of microRNA expression and further, patterns may differ with respect to ongoing therapy. For example, T regulatory cells physiologically increase following conception. It would be expected that microRNAs such as mir-155 which is closely involved in regulation of FoxP3, a transcriptional regulator involved in T regulatory function, would be differentially regulated between pre and post conception.
The term “bimodal” or “bimodal distribution” will have the meaning commonly understood by those of ordinary skill in the art of statistics. Histograms of data that comprise two peaks are referred to as “bimodal” while those with a single peak are referred to as “unimodal”. Erhard Reschenhofer of the University of Vienna in the Journal of Statistics Education, 9(1) (2001) http://www.amstat.org/publications/jse/v9nl/reschenhofer.html) downloaded Oct. 24, 2011) formally defines “bimodality” and provides statistical tests for determining bimodality. It is clear that not all distributions with two peaks, particularly overlapping peaks are bimodal. They must be clearly separable. Bimodality can be assessed less formally by inspection of a histogram of the data. Where there is significant overlapping of the ranges as determined from the means and standard deviations of the two peaks, bimodality cannot be claimed.
The embodiments discussed herein are primarily discussed in terms of bimodality such that dichotomous groups exist. However, it should be understood that if multiple patient groups are distinguishable using the techniques of the disclosure, the principles will still apply.
As used herein the term “making a diagnosis” or equivalent term as used herein shall refer to the aggregate of methods used by an individual, preferably a physician skilled in the art of reproductive medicine, shall mean predicting a clinical outcome with or without treatment, selecting treatment and monitoring treatment utilizing measurement of one or more microRNA levels or profiles derived from immune cells comprised in one or more biologic samples of the patient and compared with appropriate controls. It is further understood that said diagnosis may involve concomitant assessment of other clinical findings together with said assessment of microRNA quantification.
Unless otherwise indicated, all numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term “about”. Accordingly, unless indicated to the contrary, the numerical parameters set forth in this specification are approximations that can vary depending upon the desired properties sought to be obtained by the presently disclosed subject matter.
As used herein, the term “about,” when referring to a value or to an amount of mass, weight, time, volume, concentration or percentage is meant to encompass variations of in some embodiments ±20%, in some embodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, in some embodiments ±0.5%, and in some embodiments ±0.1% from the specified amount, as such variations are appropriate to perform the disclosed methods.
All patents and references cited herein are incorporated in entirety by reference. All technical and scientific terms used within this description shall have the same meaning as commonly understood by those of ordinary skill in the art disclosed herein except where otherwise specifically defined. Following long-standing patent law convention, the terms “a”, “an”, and “the” refer to “one or more” when used in this application, including the claims. Thus, for example, reference to “a peptide” includes a plurality of such peptides, and so forth.
The presently disclosed subject matter comprises a method for diagnosis of patients with reproductive immunological disorders. It is understood that these disorders additionally may have inflammatory and coagulation aspects. The method comprises providing a biological specimen wherein said specimen comprises cells of the immune system, separating said cells and isolating microRNA from said cells and quantifying the amounts of one or more microRNAs comprised within said cells and comparing the amount of one or more microRNAs to one or more microRNA control levels. The subject is diagnosed as having a reproductive immunologic disorder if the amount of one or more microRNA is differentially expressed.
Another aspect of the invention is the use of microRNAs to distinguish patients whom are likely to respond to a therapeutic intervention from those who are unlikely to respond. In particular, specific microRNAs may identify patients whom are likely to respond to intravenous immunoglobulin therapy (WIG) from those who are not likely to respond. It is understood that such patient identification extends to patients not suffering reproductive disorders but rather to include all patients in whom IVIG therapy is contemplated. MicroRNA monitoring can be extended to the monitoring of IVIG efficacy in those patient candidates for IVIG therapy for other conditions. Moreover, the invention may be utilized in the same manner to determine the suitability of other immunotherapeutic agents for example a TNFα blocker such as Humira as well as steroids, intralipid, lymphocyte immunization and IL-1 blockers (Anakinra).
Said method constitutes assessment of the quantity of one or more microRNAs wherein quantification of said microRNAs clinically correlates to microRNA present in cells of the immune system within the biologic sample. Thus, wherein a biologic sample is whole blood and microRNA is extracted from whole blood, so long as quantification of individual microRNAs correlates clinically with microRNA present within cells of the immune system, is regarded as quantification of microRNA wherein said microRNA is isolated from cells of the immune system isolated from whole blood. Quantification of microRNAs from cells of the immune system may require normalization to a standard such as a housekeeping gene. Quantification as meant herein contemplates the use of an internal standard.
In an embodiment of the presently-disclosed subject matter, a method for diagnosis of women with reproductive disorders is disclosed wherein a single microRNA known to be differentially expressed in patients affected by a reproductive disorder is interrogated. The method involves isolating microRNA from immune cells isolated from a biologic specimen and identifying a microRNA known to be differentially expressed in patients with a reproductive disorder comprised within said cells and comparing the subject's microRNA to a microRNA control. The subject is diagnosed as having a reproductive disorder if the microRNA is differentially expressed.
In an embodiment of the presently-disclosed subject matter, a method for diagnosis of women with reproductive disorders is disclosed wherein a plurality of microRNAs known to be differentially expressed in patients affected by a reproductive disorder are interrogated. The method comprises providing a particular microRNA profile identified with a reproductive disorder or risk of reproductive disorders. The method involves isolating microRNA from said cells and identifying a microRNA profile comprised within said cells and comparing the subject's microRNA profile to a microRNA control profile. The subject is diagnosed as having a reproductive disorder if said microRNA profile is seen to be present.
In an embodiment of the presently-disclosed subject matter, a method for diagnosis of women with reproductive disorders is disclosed wherein one or more microRNAs markers are selected from the group consisting of mir-155, mir-146a, mir-16-1, mir16-2, let7a-1, let7a-2, let7a-3, let7e, let7g, mir-132, mir-9, mir-142-3b, mir-17-92, mir-223, mir-181a are interrogated. The method comprises providing a particular microRNA profile identified with a reproductive disorder or risk of reproductive disorders. The method involves isolating microRNA from said cells and identifying a microRNA profile comprised within said cells and comparing the subject's microRNA profile to a microRNA control profile. The subject is diagnosed as having a reproductive disorder if said microRNA profile is seen to be present.
In another embodiment of the presently-disclosed subject matter, a method for evaluating treatment efficacy and/or the progression of a reproductive disorder is disclosed. The process comprises provision of a plurality of biological specimens over a period of time wherein said specimen comprises cells of the immune system, separating said cells and isolating microRNA from said cells and quantifying the type and amount of multiple microRNAs comprised within said cells and comparing this microRNA profile to one or more microRNA control profiles for the purpose of determining differential expression of the profiles, thereby permitting assessment of the progress of the condition or the efficacy of therapy.
Subjects may be human or other animal. “Reproductive disorders” comprise one or more disorder selected from a group exemplified by but is not limited to premature rupture of membranes, preeclampsia, preterm birth, intrauterine growth restriction, and recurrent pregnancy loss and anti-phospholipid antibody syndrome.
Immunologic dysfunction includes immunologic disorders caused by abnormal immunologic mechanisms, whether humoral, cell-mediated, or both and/or is related to an immune related mechanism such as an inflammatory, complement or coagulation-related condition.
Immunologic dysfunction may increase the risk of bearing children who later develop conditions, for example, asthma, autism, attention deficit hyperactivity disorder (ADHD), Tourette's syndrome, diabetes and schizophrenia wherein said immunologic dysfunction are included within the concept of reproductive and/or immunologic disorders. The invention also contemplates peri-pregnancy periods comprising about one year preceding or following pregnancy. However, it is within the scope of this invention to include the period of time involved in oogenesis. This period may exceed one year prior to pregnancy. As noted in Winger and Reed, the period prior to pregnancy may constitute an immunologic state adverse to pregnancy outcome (Winger E E, Reed J L, Ashoush S, Sapna A, El-Toukhy T, Taranissi M: “Treatment with adalimumab (Humira) and intravenous immunoglobulin (IVIG) improves pregnancy rates in women undergoing IVF. American Journal of Reproductive Immunology, 2009; 61:113-120). Likewise the period following pregnancy may be affected as in, for example, autoimmune diseases such as rheumatoid arthritis, a disorder known to flare following pregnancy. A biologic specimen comprising cells of the immune system from a subject suspected of a reproductive disorder is processed wherein said cells are isolated by a variety of means known to those skilled in the art. In a preferred embodiment, the biologic sample is whole blood. In a more preferred embodiment, cells from the blood are isolated by Ficoll-hypaque density gradient centrifugation in the method taught by Boyum (Boyum A 1983. Isolation of human blood monocytes with Nycodenz, a new non-ionic iodinated gradient medium. Scand J Immunol 17: 429-436). RNA is extracted utilizing a method suitable for extracting short RNA sequences. Preferably said method utilizes a kit optimized for recovery of microRNA sequences such as mirNeasy Mini Kit Qiagen catalogue 217004 following instructions provided. Quantification of microRNA may be determined a variety of techniques known to those skilled in the art. In a preferred embodiment, individual microRNAs are quantified by real-time polymerase chain reaction (PCR). In a more preferred embodiment, a kit provided by SABiosciences provide reagents and methods for individual microRNA's known to be involved in human immunopathologic conditions for said quantification optimized for specific real-time thermocycling equipment such as the Stratagene Mx3005p (catalog MAH-104A) (www.SABioscies.com). Operating instructions for the Stratagene Mx3005p are provided by the manufacturer. Comprised therein are instructions for spectrophotometric quantification of recovered RNA, recommendations for input quantity of RNA and PCR master mix. Quantification may be performed concurrently with quantification of a “housekeeper gene” (a gene that is expressed with relative constancy in the cells being interrogated thereby permitting relative quantification). Housekeeping genes may be selected from, for example, beta actin, glyceraldehyde-3P-dehydrogenase (GAPDH), annexin A2 (ANXA2), glutathione S-transferase (GST), ornithine decarboxylase (ODC), hypoxanthine phosphoribosyltransferase (HPRT1), ubiquitin (UBQ glyceraldehyde-3P-dehydrogenase (GAPDH), annexin A2 (ANXA2), glutathione S-transferase (GST), ornithine decarboxylase (ODC), hypoxanthine phosphoribosyltransferase (HPRT1), ubiquitin (UBQ), 18s RNA. The resulting ratio comprises a relative quantity that is independent of the quantity of RNA input into the assay system. This permits comparison with a control sample quantified in a similar manner as a ratio of the analyte signal and the selected housekeeping gene.
microRNAs of interest known to participate in immunopathologic conditions comprise but are not limited to mir146a, 1 mir-46b, mir-155, mir-605, mir-623, mir-583, mir-26a, mir-519d, 1 mir-26, 1 mir-6, 3 mir-69-3, Let-7a and 125b mir-126, mir-155, mir-21, Let-7a, let-7c, let-7d, let-7e, let-7g, mir-214, mir-409-3p, mir-451, mir-103, mir-105, mir-106a, mir-125a-5p, mir-125b, mir-126, mir-128, mir-130a, mir-132, mir-134, mir-135a, mir-135b, mir-138, mir-142-3p, mir-142-5p, mir-143, mir-145, mir-147, mir-148a, mir-149, mir-150, mir-15a, mir-15b, mir-16, mir-181a, mir-183, mir-184, mir-185, mir-187, mir-18a, mir-191, mir-195, mir-196a, mir-198, mir-19a, mir-19b, mir-200a, mir-203, mir-205, mir-206, mir-20a, mir-20b, mir-21, mir-214, mir-223, mir-23b, mir-26a, mir-26a, mir-26b, mir-27a, mir-27b, mir-298, mir-299-3p, mir-29b, mir-29c, mir-302a, mir-302c, mir-30b, mir-30c, mir-30e, mir-31, mir-325, mir-335, mir-34a, mir-369-3, mir-370, mir-379, mir-383, mir-409-3p, mir-46b, mir-493, mir-519d, mir-574-3p, mir-577, mir-583, mir-605, mir-623, mir-9, mir-98, mir-99b.
In a preferred embodiment, one or more microRNAs known to be differentially expressed from control sample values are used. Also preferably, a plurality of microRNAs are identified as indicating a pattern or signature of differentially expressed microRNAs. Determination of said single or plurality of microRNAs may be determined by quantifying microRNA levels in both patients suffering from reproductive disorders or at risk of reproductive disorders in panels of microRNAs and comparing microRNA determining from biologic samples of patients levels with those derived from control samples.
The invention may be used to suggest the suitability of subjects with reproductive and/or immunologic disorders for specific immunotherapies that might mitigate such disorders and may also be used to assess a patient's risk for developing such disorders. Further, the invention may be used to monitor the progress of the disease treatment or monitor reduction in disease risk by providing a series of assays and comparing the results. Serial studies may be performed during, prior to and following pregnancy as well as during the course therapy and compared to determine the success and adequacy of the therapy provided.
Quantification of microRNA from PBMCs permits the investigator or clinician to compare the result to the appropriate control wherein a difference in expression is identified. For example, wherein an microRNA such as mir-155 in a patient with a reproductive disorder or risk of such disorder such as recurrent abortion, is differentially expressed from a control comprised of individuals who have not experienced recurrent abortion, the patient may be diagnosed as having a reproductive disorder and that the patient is a candidate for immunologic intervention such as with a TNF alpha blocker. Patients so diagnosed may be treated as described for patients defined as having similar disorders by Winger and Reed (Edward E. Winger, Jane L. Reed, Treatment with Tumor Necrosis Factor Inhibitors and Intravenous Immunoglobulin Improves Live Birth Rates in Women with Recurrent Spontaneous Abortion, 60(1), 8-16, Published Online: 28 Jun. 2008, Edward E. Winger, Jane L. Reed, Sherif Ashoush, Sapna Ahuja, Tarek El-Toukhy, Mohamed Taranissi, Treatment with Adalimumab (Humira) and Intravenous Immunoglobulin Improves Pregnancy Rates in Women Undergoing IVF, American Journal of Reproductive Immunology 61 (2009) 113-120)
In some embodiments, the cells from which the RNA is extracted shall be PBMCs while in others cells shall be immune cells derived from body tissues such as endometrium, decidua, fetal and placental tissues and secondary lymphoid organs such as lymph node. Mononuclear cells may be further selected by a variety of techniques, for example, by flow cytometric sorting following labeling of the cells with markers such as monoclonal antibodies that permit their segregation into immune cell subtypes. For example, T regulatory cells can be selected following their labeling by monoclonal antibodies such as CD4, CD127 and CD25 (with or without the addition of CD3) according to the Becton Dickinson Co. (utilizing reagents and equipment from the company according to http://www.bdbiosciences.com/research/tcell/regulatorytcells/workflow/identifyingtregs.jsp downloaded Apr. 28, 2010. RNA may be extracted from cells isolated cells selected by said means may be prepared by extraction according to instructions from the manufacturer (Qiagen catalogue 763134). microRNA such as for mir-155 may be detected and quantified by PCR (polymerase chain reaction) by the method described by Chen et al. (http://www3.appliedbiosystems.com/cms/groups/mcbmarketing/documents/generaldocuments/cms_—040548.pdf downloaded May 11, 2010).
Primers and reagents may be selected for individual microRNAs from those described in product overview (http://www3.appliedbiosystems.com/cms/groups/mcb_marketing/documents/generaldocuments/cms_—068884.pdf downloaded May 11, 2010). This document provides information teaching the detection and quantification of individual microRNAs.
The method comprises providing a biological sample from a subject with a history of reproductive and/or immunologic disorder or risk of such disorder said sample being derived from immune cells, for example, derived from peripheral blood, and then isolating mononuclear cells as taught by Boyum (Boyum A 1983. Isolation of human blood monocytes with Nycodenz, a new non-ionic iodinated gradient medium. Scand J Immunol 17: 429-436) and then determining the amount of non-coding RNA such as preferably microRNA (microRNAs) and comparing to the amount of the corresponding RNA in the sample to similarly treated biological sample from control individuals. In addition, the method can comprise quantification of a plurality of individual microRNAs from the biological sample and quantifying the individual microRNAs and comparing the amount of microRNAs to corresponding microRNA control levels. The subject is then diagnosed as having a reproductive and/or immunologic disorder or risk of developing such a disorder if there is differential expression in the amount of one or more of the RNAs from the sample as compared to corresponding RNA control levels. In some embodiments, the method further comprises selecting a treatment or modifying a treatment based on the amount of the one or more RNAs determined. This determination may be based upon assessment of specific individual or combinations of the individual microRNAs.
In other embodiments of the presently-disclosed subject matter, a method for evaluating treatment efficacy and/or progression of a patient with a reproductive or immunologic disorder and/or risk of developing a reproductive disorder in a subject is provided. In some embodiments, the method comprises providing a series of biological samples over a time period from a subject, isolating the RNA as described above comprising RNAs from the series of biological samples, determining an amount of one or more of the microRNAs in each of the biological samples from the series and determining any measurable change in the amounts of the one or more microRNAs in each of the biological samples from the series permitting a measure of treatment efficacy and/or progression of the reproductive disorder or risk of reproductive disorder in the subject.
In still other embodiments of the presently-disclosed subject matter, a method for characterizing a reproductive disorder in a subject is provided. In some embodiments, the method comprises providing a biological sample from a subject, isolating the RNA comprising microRNAs from the biological sample, determining an amount of one or more of the RNAs and comparing the amount of one or more microRNAs to corresponding microRNA control levels. The reproductive disorder is then characterized based on differential expression of the amount of the one or more microRNAs from the sample as compared to the one or more microRNA control levels. In some embodiments, the reproductive disorder is characterized when compared with well characterized individuals with known reproductive disorders.
In some of these methods, quantification of microRNAs comprises using a real-time polymerase chain reaction. SABiosciences provide reagents and methods for individual microRNA's known to be involved in human immunopathologic conditions for said quantification optimized for specific real-time thermocycling equipment such as the Stratagene Mx3005p (catalog MAI-I-104A) (www.SABioscies.com). Quantification may be performed concurrently with quantification of a “housekeeper gene” (a gene that is expressed with relative constancy in the cellular material being interrogated permitting standardization). Further, in some embodiments of these methods, the mirRNAs are one or more microRNAs. Amongst microRNAs quantified include but are not limited.
Various polymorphisms are known to exist within a specific microRNA, and also within short sequence comprised within the microRNA known as the “guide sequence” as well as polymorphisms within the hairpin structure in which the microRNA is comprised. Further polymorphisms may be found in the target RNA sequence. These polymorphisms may be result in altered efficiency of microRNA regulation of target mRNAs. (Jazdzewski K, Murray E L, Franssila K, Jarzab B, Schoenberg D R, de la Chapelle A. Common SNP in pre-mir-146a decreases mature mir-expression and predisposes to papillary thyroid carcinoma. Proc Natl Acad Sci USA 2008; 105:7269-74.) Further, Polymorphisms may potentially affect microRNA-mediated regulation of the cell can be present in the 3′-UTR of a microRNA target gene. Additional polymorphisms may also be present in the genes involved in microRNA biogenesis as well as in pri-, pre- and mature-microRNA sequences. The consequences of such polymorphisms in processed microRNAs may have profound effects on the expression of a multiplicity of target genes and have serious consequences, whereas a polymorphism in microRNA target site, in the 3′-UTR of the target mRNA, may be more target and/or pathway specific (Prasun J Mishra and Joseph R Bertino, MicroRNA polymorphisms: the future of pharmacogenomics, molecular epidemiology and individualized medicine, Pharmacogenomics. 2009 March; 10(3): 399-416.doi: 10.2217/14622416.10.3.399).
Detection of such polymorphisms by such techniques as real-time allele discrimination, for example, is also within the scope of this invention. Methods can be found in Mx3000P instruction manual (www.bio.davidson.edu/courses/Bio343/Mx3000P_Manual.pdf downloaded Oct. 24, 2010)).
The invention is particularly well suited for use in personalized medicine. Nucleic acid characterization and quantification are used to assess the probability of success of a particular therapeutic intervention. It is the goal of personalized medicine to identify patients whom are likely, or conversely unlikely to respond to a candidate therapy. Cost, side-effects and improved therapeutic response are accepted reasons for pursuing nucleic acid testing as a means of selecting therapies and for following the course of therapy. Not only might quantification of microRNAs be helpful in identifying patients suffering from reproductive disorders, but such quantifications would be of corresponding assistance in selecting and directing therapeutic choices and monitoring their effects in a virtually unlimited variety of disorders.
The ability to separate patients into groups of two or more by characteristics of their response to an intervention such as for example immunotherapy for example IVIG, permits more specific prediction of therapeutic response to the specific intervention and also may permit prediction of response to other interventions. Another aspect of such separation is better prognostication and vulnerability to other disorders for example autoimmune diseases. If a microRNA were identified whose response pattern amongst the patient cohort was bimodal, then patients could be grouped according to their response into said groups.
The invention comprises collecting immune cells, preferably PBMCs, before and after an intervention, preferably immunotherapeutic, for example IVIG, extracting microRNA-comprising RNA from said cells, quantifying microRNAs within the extracted RNA, determining whether one or more microRNAs quantified display a bimodal response amongst a statistically sufficient number of patient samples. If a bimodal response pattern is demonstrated in one or more microRNAs, then patients may be segregated into groups according to their response.
A “response” as used herein is defined as the difference between a result on a first sample and a second sample wherein there is an intervening intervention or the intervening effect of an intervention previously made. It may be an increase, a decrease or an absence of change. The term “bimodal” or bimodality” as used herein refers to a non-normal distribution of responses wherein two distinct modes characterize the data. The data can be displayed graphically in a histogram and bimodality assessed by inspection. Statistical methods have been developed to discern bimodality. A first step is the recognition of clustering of results into two populations. While recognition of the separation point between the two populations is commonly done by visual inspection of histograms of the data, statistical methods can be applied by one of ordinary skill in the art of statistics. The mean and standard deviation of the two clusters are calculated. In a preferred definition of bimodality data are considered bimodal if the means of the two clusters are equal or further apart than the sum of the standard deviations of the two clusters (Schilling, Watkins and Watkins, “Is Human Height Bimodal?”, The American Statistician (2002), 56(3): 223-229).
Populations are regarded as dichotomous with respect to the microRNA responses where a bimodal distribution amongst responses of the test cohort can be demonstrated for one or more microRNA. It is understood that not all patient cohorts are dichotomous with respect to their response to the individual microRNA responses following any given intervention. When a bimodal response is found in one or more microRNAs, then the patients populations are regarded as dichotomous.
In a preferred embodiment, an individual of ordinary skill in the art using a human microRNA array from Agilant Technologies (for example cat. G4471A-029297) and following the directions of the manufacturer) quantifies all known human microRNAs on specimens RNA extracted from PBMCs according to instructions of the microarray manufacturer. Testing is performed on specimens drawn preferably one to three weeks or less prior to and one to three weeks or less following a therapeutic intervention.
In a preferred embodiment, a statistically significant number of patients are selected who have similar demographic and clinical characteristics such as age, sex, and clinical condition, e.g. recurrent pregnancy loss. It is the goal of analysis of these data to segregate patients into groups of two or more. In a more preferred embodiment, it is the goal to dichotomize patients into two groups each possessing a unique microRNA profile.
A variety of methods are suitable for determining unique microRNA profiles defining each group. In a first step, the differences between each microRNA sampled prior to therapy and subsequent to therapy are calculated. The means and standard deviations of each of differences between the various “before and after” sample subgroups are calculated and microRNAs sorted in order of statistical significance. Subgroups with microRNAs with the most statistically significant difference in mean and SD are selected (Graphpad software t test). By inspection one identifies a microRNA where the results are distributed dichotomously. Patient results are then sorted into two groups as determined by the group into which that selected microRNA falls.
As discussed below, a bimodal hsa-mir-132 response is shown amongst a cohort of seventeen patients. The RNA before and after specimens from patients from each the two cohorts defined by the bimodal distribution of hsa-mir-132 were subsequently assessed in a microarray of all “known” or suspected human microRNAs. As such, hsa-mir-132 is identified as meeting the above stated criterion.
Seventeen female patients (average age 35.8±4.8 years) being seen at the Alan E. Beer Center for recurrent miscarriage and infertility and being treated with intravenous immunoglobulin (IVIG) (average 1.5±1.8 prior miscarriages; 1.6±1.5 prior IVF failures) were selected. Each patient had signed a consent permitting their blood to be used for research purposes. Each patient selected for the study had a blood draw an average of 13.2±6.0 days prior to IVIG therapy and an average of 11.8±5.6 days following IVIG therapy (an average of 25.1±7.9 days between microRNA blood draws). Blood was drawn as a part of routine blood studies performed on patients. PBMCs were isolated from twenty-four to forty-eight hour old heparinized blood that had been maintained at room temperature by Ficoll-Hypaque density gradient centrifugation. Approximately 10×10⁶cells were preserved in one ml Trizol (Invitrogen) and maintained at −20° C. until used. Table 1 summarizes the characteristics of the patients.

	TABLE 1

	Population parameters	Mean ± SD

	Age (yrs)	35.8 ± 4.8
	#prior miscarriages	1.5 ± 1.8
	#prior IVF failures	1.6 ± 1.5
	#days prior to IVIG therapy	13.2 ± 6.0
	(Sample1)
	#days after IVIG therapy	11.8 ± 5.6
	(Sample2)
	#days between Sample 1 and 2	25.1 ± 7.9

The PBMCs from the seventeen patients were then interrogated for mir-16, mir-132, mir-146a, mir-155, mir-181a, mir-196a, mir-223 using RNU48 as a housekeeping gene for purposes of normalization on reversed transcribed RNA by real-time PCR. Each patient had been treated with intravenous immunoglobulin (IVIG) therapy and blood collected prior to and following therapy and the microRNAs quantified in each sample. Candidate microRNAs were selected from review of the literature. The microRNAs were selected based on the studies in autoimmune/inflammatory disorders (Lupus and rheumatoid arthritis on PBMCs. (mir16, mir-132, mir146a, mir155, mir181a, mir196a and mir223 were selected based information from the following articles: (1) Pauley K M, Satoh M, Chan A L, Bubb M R, Reeves W H, Chan E K. Upregulated mir-146a expression in peripheral blood mononuclear cells from rheumatoid arthritis patients. Arthritis Res Ther. 2008; 10(4):R101.; (2) Dai Y, Huang Y S, Tang M, Lv T Y, Hu C X, Tan Y H, Xu Z M, Yin Y B. Microarray analysis of microRNA expression in peripheral blood cells of systemic lupus erythematosus patients. Lupus. 2007; 16(12):939-46.; (3) Fehniger T A, Wylie T, Germino E, Leong J W, Magrini V J, Koul S, Keppel C R, Schneider S E, Koboldt D C, Sullivan R P, Heinz M E, Crosby S D, Nagarajan R, Ramsingh G, Link D C, Ley T J, Mardis E R. Next-generation sequencing identifies the natural killer cell microRNA transcriptome. Genome Res. 2010 November; 20(11):1590-604.)
Blood samples are collected in EDTA-treated tubes and PBMCs are isolated by standard Ficoll density-gradient cenrifugation according to the procedure known to those of ordinary skill in the art. Alternatively, the procedure utilizing Accupsin Tubes from Sigma-Aldrich following the manufacturer's procedure (procedure no AST-1) may be followed. PBMCs are washed once in sterile phosphate-buffered saline (PBS) before RNA isolation. Total RNA is isolated from freshly obtained PBMCs using the mir-Vana microRNA Isolation kit (Ambion, Austin, Tex., USA), in accordance with the manufacturer's protocol. RNA concentrations are determined by absorption spectroscopy due to the peak absorption of DNA and RNA at 260 nm. 10 ng of each RNA sample are used for quantitative real-time RT-PCR (qRT-PCR). microRNA qRT-PCR was performed using the TaqMan MicroRNA Reverse Transcription Kit, TaqMan Universal PCR Master Mix, and TaqMan MicroRNA Assay (Applied Biosystems, Foster City, Calif., USA.) Primers from SABiosciences for these specific human microRNAs were used: mir-16 (MPH00062A), mir-132 (MPH01167A), mir-146a (MPH00047A), mir-155 (MPH00059A), mir-223 (MPH01231A), let-7a (MPH00001A). mRNA qRT-PCR may be performed using the TaqMan High-Capacity cDNA Reverse Transcription Kit, TaqMan Fast PCR Master Mix, and TaqMan mRNA assay primers (Applied Biosystems). See http://www3.appliedbiosystems.com/cms/groups/mcb_support/documents/generaldocuments/cms_—042167.pdf. Reactions may be analyzed using StepOne Real-Time PCR System (Applied Biosystems). The levels of microRNA is normalized to 18S RNA, for example. The cycle threshold (Ct) values, corresponding to the PCR cycle number at which fluorescence emission reaches a threshold above baseline emission, were determined and the relative microRNA or mRNA expression was calculated using the 2-ΔΔCt method (Applied Biosystems User Bulletin No. 2)
Based on the literature, a coordinate change in microRNAs could be expected. Moreover, changes in the range of several fold between samples would also be expected. For example, changes in mir-146a might be expected from the above Pauley reference. Changes in mir-16 could also be anticipated. However, unexpectedly, a single microRNA, mir-132 was suppressed up to approximately 100-fold. IVIG therapy appears to suppress the expression of mir-132 very specifically and very markedly. Further, initial values varied significantly between patients by up to approximately 100 fold. Table 2 shows that amongst 17 patients in whom before and after studies were performed, IVIG treatment resulted in a statistically significant decrease.

	TABLE 2

		Mean mir-132 CT levels
	17 patient sequential cases	(mean ± SD)

	Before IVIG	22.8 ± 4.7*
	After IVIG	26.7 ± 2.1*
	Difference	+3.9 ± 4.0

	*p = 0.004

As discussed herein, the treatment group may be divided into two groups on the basis of the expression pattern one microRNA, mir-132, prior to IVIG therapy. Group A had low initial CTs (threshold crossing) indicating relatively high initial levels of hsa-mir-132. The remaining patients, Group B, had high initial CTs indicating low levels of hsa-mir-132. Following IVIG treatment, both groups converged in their levels of hsa-mir-132 to high CTs indicating significantly diminished levels of mir-132. The changes were substantially greater in Group A than in Group B. IVIG appears to have been most effective in lowering mir-132 in the group that had the highest levels of mir-132 pretreatment.
Although both groups responded statistically to IVIG treatment, Table 3 dichotomizes patients by their initial hsa-mir-132 CT. It can be seen that the initial CTs cluster into those with relatively low CTs (high concentrations of hsa-mir-132) and those with relatively high CTs (low levels of hsa-mir-132). Following IVIG therapy, the two groups converge such that the CT change in the low CT group is significantly greater than that of the high CT group. Accordingly, an embodiment of the invention is the separation of the patients into two distinct groups (dichotomization) on the basis of microRNA expression.

	TABLE 3

	Low mir-132 group: 6	High mir-132
	cases	group: 11 cases
	(Sample1 >21.0)	(Sample 1 <19.0)

Mean initial mir-132 value	17.1 ± 0.8	25.9 ± 2.2
(Sample 1)
Mean post-IVIG mir-132	25.7 ± 1.2	27.4 ± 2.2
value (Sample 2)
Mean microRNA change	+8.5 ± 1.2**	+1.5 ± 2.4**

**p < 0.0001

These results are represented in FIGS. 1 and 2, which show the CT levels of the patients before and after IVIG treatment, for Groups A and B respectively. As shown in FIG. 1, prior to IVIG treatment, Group A exhibited CT levels of 16-18 and exhibited levels of 24-27 after treatment. On the other hand, Group B exhibited levels of 21-29 before treatment and levels of 23-30 after treatment.
Those patients with initially very low levels had modest to very small degrees of suppression. Following IVIG therapy, mir-132 values from both the patients groups appeared to converge at relatively the same low microRNA activity (or high CT) level (CT value between 23-28). It appears that the effect of IVIG on mir-132 expression is largely confined to those patients with initially relatively large quantities of mir-132 (those with low CT levels). This population has an initial mir-132 CT range between 16-18. Thus IVIG therapy is most effective in suppression of mir-132 in the group of patients with the highest pre-treatment levels of mir-132.
Using hsa-mir-132 to identify patients belonging to two discrete groups as described above, four patients from each of Group A and Group B were then assessed by an Agilant microarray comprising all of the known human microRNAs (approximately 900 microRNAs).
Data from each of the two groups were assessed separately and each group sorted by the mean differences between the first and second quantification of the specific microRNA for each group separately and listed in order of decreasing differences. From the said lists, the differences that are most increased in a first group are compared to the most decreased in a second group. Conversely the most decreased in a first group are compared to the most increased in a second group. Those individual microRNAs that display the greatest differences between the means of the differences in each group are considered to be provisionally optimum markers. The power of these optimum microRNAs are further assessed by the sum of the standard deviations of the means of the respective groups to confirm their status as optimum markers wherein the ratio of the differences is divided by the sum of the standard deviations. Optimum markers have the highest ratios. For example, all of the microRNAs listed below in Tables 4-6 exhibit ratios with an absolute value greater than or equal to one. More preferably, microRNAs may be selected that exhibit a ratio with an absolute value greater than or equal to two.
The data in Table 4 identifies useful microRNA markers meeting the above criteria

TABLE 4

	Group A
Selected	Mean		Group B
MicroRNA	difference	Group A	Mean difference	Group B
markers	after IVIG	Mean SD	after IVIG	Mean SD

hsa-mir-	2.52122	1.497334	−5.43651	0.82098
136
hsa-mir-	1.083703	0.288785	−4.77102	0.469596
141
hsa-mir-	1.041446	0.142776	−5.94462	0.44929
142-5p
hsa-mir-	0.335061	1.466308	−5.88934	1.274726
144
hsa-mir-	2.082454	0.849342	−7.84218	0.691729
153
hsa-mir-	1.301357	0.995047	−7.47155	1.143166
1537
hsa-mir-	0.952666	0.441789	−4.08226	0.877131
193a-3p
hsa-mir-	2.083515	1.609553	−6.60915	0.991381
219-5p
hsa-mir-	1.068173	0.318097	−5.26843	0.506796
29b
hsa-mir-	1.140525	0.150147	−4.74399	0.497264
301a
hsa-mir-32	1.757013	0.663302	−6.60811	0.775835
hsa-mir-	2.208455	0.541141	−6.66695	0.528549
33a
hsa-mir-	1.955594	1.558846	−6.95672	0.388828
545
hsa-mir-	0.885343	0.493095	−5.63284	1.148245
582-3p
hsa-mir-	1.195371	0.219509	−5.34069	0.410219
590-5p
hsa-mir-	−1.82126	1.984385	4.492508	1.085892
1181
hsa-mir-	−0.90253	1.698756	8.720988	1.695736
513a-5p

As can be seen from inspection of the listed microRNAs from each of the two groups, two distinct observations can be made. First, the values moved in opposite directions following IVIG therapy in the two groups. For example, hsa-mir-136 increases following IVIG while it decreases in group B. Conversely, hsa-mir-513-5p decreases in group A while it increases in group B. Second, taken as a single group, the standard deviations are quite broad while the standard deviations within each group, A or B, are greatly diminished. These findings confirm the 1 recognition of two distinct groupings of patients. From a single microRNA result for a patient, one can assign the patient to one of the two groups statistically. One of ordinary skill in the art can assign patients to the appropriate group.
Additional sequences have been identified that may be used separately or in combination to define members of group A or B are selected on the basis of demonstrating opposite behavior after IVIG. Table 5 lists common microRNAs selected from the top 100 most increased in Group A and 100 most decreased in Group B while Table 6 lists common microRNAs selected from the top 100 most decreased in Group A and 100 most increased in Group B.

TABLE 5

	Group A		Group B
	Mean	Group A	Mean	Group B
MicroRNA	Difference	SD	Difference	SD

hsa-mir-1	1.270745	1.003145	−3.44195	2.957159
hsa-mir-101	0.698965	0.235172	−4.10335	0.423924
hsa-mir-	0.332102	2.511271	−7.18912	2.240812
1183
hsa-mir-	0.511253	0.559915	−2.90416	0.69329
1249
hsa-mir-136	2.52122	1.497334	−5.43651	0.82098
hsa-mir-	0.419807	0.265255	−2.03009	0.349193
140-5p
hsa-mir-141	1.083703	0.288785	−4.77102	0.469596
hsa-mir-	1.0677	0.202879	−3.59343	0.583092
142-3p
hsa-mir-	1.041446	0.142776	−5.94462	0.44929
142-5p
hsa-mir-144	0.335061	1.466308	−5.88934	1.274726
hsa-mir-153	2.082454	0.849342	−7.84218	0.691729
hsa-mir-	1.301357	0.995047	−7.47155	1.143166
1537
hsa-mir-15a	0.533715	0.100281	−2.8992	0.322076
hsa-mir-18a	0.382843	0.13646	−1.92863	0.464262
hsa-mir-18b	0.405855	0.187818	−2.19576	0.531698
hsa-mir-	0.952666	0.441789	−4.08226	0.877131
193a-3p
hsa-mir-19a	0.872497	0.133295	−4.3393	0.511408
hsa-mir-19b	0.457604	0.158196	−2.57291	0.322324
hsa-mir-21	0.441811	0.278029	−2.1423	0.382552
hsa-mir-	2.083515	1.609553	−6.60915	0.991381
219-5p
hsa-mir-27a	0.53133	0.25451	−2.12834	0.263011
hsa-mir-29b	1.068173	0.318097	−5.26843	0.506796
hsa-mir-29c	0.443375	0.117018	−2.75245	0.365226
hsa-mir-	1.140525	0.150147	−4.74399	0.497264
301a
hsa-mir-	0.976275	0.357315	−3.26383	0.442348
301b
hsa-mir-30e	0.59185	0.144703	−3.24738	0.251854
hsa-mir-32	1.757013	0.663302	−6.60811	0.775835
hsa-mir-	0.698976	0.359357	−1.87021	0.15051
324-5p
hsa-mir-335	0.617394	0.711001	−2.4438	0.722963
hsa-mir-	1.10453	1.617308	−4.14723	2.179625
337-5p
hsa-mir-	0.391677	0.0348	−3.00793	0.498721
338-3p
hsa-mir-33a	2.208455	0.541141	−6.66695	0.528549
hsa-mir-340	0.830669	0.481914	−4.16497	0.300849
hsa-mir-	0.8536	0.294971	−3.28599	0.665745
362-3p
hsa-mir-	0.971021	1.083568	−2.46387	1.359333
371-5p
hsa-mir-	0.46162	1.952296	−6.23567	1.991346
376b
hsa-mir-	0.363715	0.19741	−2.06665	0.272203
374a
hsa-mir-377	0.873289	1.253133	−1.92671	1.06699
hsa-mir-421	0.824237	1.119436	−2.52667	0.979143
hsa-mir-424	0.676429	0.131418	−3.30699	0.509319
hsa-mir-	0.835738	0.202614	−5.38285	0.608003
542-3p
hsa-mir-545	1.955594	1.558846	−6.95672	0.388828
hsa-mir-	0.710325	0.382632	−1.977	0.399167
548c-5p
hsa-mir-	1.132591	0.828951	−2.30701	0.520072
551b
hsa-mir-	0.885343	0.493095	−5.63284	1.148245
582-3p
hsa-mir-	1.195371	0.219509	−5.34069	0.410219
590-5p

TABLE 6

	Group A
	Mean	Group A	Group B Mean	Group B
MicroRNA	difference	SD	difference	SD

hsa-mir-100	−0.75803	1.061571	1.154144	0.766084
hsa-mir-1181	−1.82126	1.984385	4.492508	1.085892
hsa-mir-1227	−0.36135	0.981072	3.887691	0.570974
hsa-mir-1271	−0.49835	0.575051	1.251907	0.418329
hsa-mir-127-3p	−1.07828	1.295253	1.312743	0.450348
hsa-mir-1275	−0.47159	0.576344	0.968348	0.519534
hsa-mir-	−1.27118	1.97	2.768853	0.297402
1300_v13.0
hsa-mir-1307	−0.41137	1.091938	5.269371	0.913262
hsa-mir-139-3p	−0.37258	0.494547	2.047472	0.768133
hsa-mir-181a-2*	−0.39928	0.430931	2.779431	0.677482
hsa-mir-182	−0.53534	0.745866	3.998238	0.721076
hsa-mir-191	−0.60784	0.657613	3.506353	0.577494
hsa-mir-224	−0.5147	1.993012	2.390506	1.670223
hsa-mir-300	−1.48121	1.881716	2.084424	1.659764
hsa-mir-339-5p	−1.7283	1.326469	1.405327	0.459023
hsa-mir-483-3p	−0.58871	2.243166	2.07591	0.843905
hsa-mir-486-5p	−1.20003	1.525774	1.160433	1.15879
hsa-mir-501-5p	−0.48667	1.118063	4.393115	0.758058
hsa-mir-513a-5p	−0.90253	1.698756	8.720988	1.695736
hsa-mir-564	−0.38684	0.709046	1.351019	0.832139
hsa-mir-602	−0.45617	0.664475	1.969851	0.830315
hsa-mir-630	−1.20769	3.331194	0.968519	2.660711
hsa-mir-647	−0.84299	1.117714	4.483333	1.436393
hsa-mir-770-5p	−0.57186	1.473769	1.18536	0.33416
hsa-mir-885-5p	−0.47354	3.442087	3.238278	1.381079
hsa-mir-892b	−0.48425	1.487033	2.127233	0.882215
hsa-mir-92b	−0.5896	0.830008	5.23262	0.53149

Identification of the dichotomized groups can be determined from a single microRNA assay performed. As can be seen from above, hsa-mir-132 discretely separates the A and B groups. Additional sequences tabulated in table 6 provide additional sequences that may be used to define the two groupings. They may be used singly or in combination. The dichotomous nature of a population can be ascertained from selection of microRNAs that display large standard deviation. These microRNAs are likely to comprise a bimodal distribution of microRNA means. Inspection or application of statistical tests by one of ordinary skill in the art of statistics can verify bimodality.
To test the hypothesis that patients can be divided into dichotomous groups wherein individual microRNAs can be identified that respond differently to a therapeutic intervention, a sham grouping of the same patients wherein dichotomous groupings demonstrated differential microRNA response as seen in tables 4-6, were constructed. In the above examples, patients were designated as group A or B according to the response of hsa-mir-132 following IVIG therapy. Each group comprised four patients. In the sham experiment, two patients were arbitrarily selected from Group A and two from Group B to comprise a sham group C while the remaining two patients from group A and the two remaining patients from group B comprise a sham group D. The differences in the means and standard deviations before and after therapy were calculated and displayed as the 30 most increased following therapy and the 30 most decreased as shown in Table 7.

TABLE 7

	Mean Δ			Mean Δ
MicroRNA	Group C	SD	MicroRNA	Group D	SD

hsa-miR-24-1*	1.139664	1.751306	hsa-miR-34b*	−0.495378	0.661462
hsa-miR-513b	1.10647	1.745331	hsa-miR-1207-	−0.499623	0.345578
			5p
hsa-miR-513a-5p	1.081663	2.387287	hsa-miR-1274a	−0.507595	0.863872
hsa-miR-513c	1.006773	1.331825	hsa-miR-1280	−0.516564	0.713269
hsa-miR-345	0.873768	1.69969	hsa-miR-28-3p	−0.525916	0.890524
hsa-miR-892b	0.823898	0.561379	hsa-miR-181a*	−0.526303	0.449876
hsa-miR-	0.753516	0.90899	hsa-miR-1226*	−0.537768	0.995808
1826_v15.0
hsa-miR-501-5p	0.721953	1.183809	hsa-miR-582-5p	−0.538055	0.930185
hsa-miR-760	0.720185	1.313588	hsa-miR-500a	−0.539322	0.799103
hsa-miR-92b	0.719088	0.986911	hsa-miR-133a	−0.553997	1.031349
hsa-miR-23a*	0.711232	0.624211	hsa-miR-720	−0.570416	0.810315
hsa-miR-607	0.694857	0.669177	hsa-miR-1260	−0.575785	0.947614
hsa-miR-338-5p	0.694838	0.66914	hsa-miR-135a*	−0.577329	1.032842
hsa-miR-665	0.657335	0.594854	hsa-miR-92a-1*	−0.595985	0.496119
hsa-miR-576-3p	0.640433	0.562254	hsa-miR-1202	−0.62187	0.826414
hsa-miR-574-3p	0.636028	1.161566	hsa-miR-194*	−0.632359	1.510496
miRNABrightCorner30	0.619267	0.43915	hsa-miR-629*	−0.635601	1.061238
hsa-miR-494	0.574397	0.384558	hsa-miR-26b*	−0.66143	0.511278
hsa-miR-1288	0.572928	0.526309	hsa-miR-192*	−0.672978	1.370112
hsa-miR-378	0.508068	0.394278	Has-miR-630	−0.679997	2.042544
hsa-miR-139-3p	0.463569	0.641463	hsa-miR-183	−0.687276	2.202468
hsa-let-7a*	0.448805	0.297109	hsa-miR-373*	−0.707562	1.093235
hsa-let-7c*	0.448805	0.297109	hsa-miR-1181	−0.727918	2.188832
hsa-let-7e*	0.448805	0.297109	hsa-miR-557	−0.759721	0.679964
hsa-let-7f-2*	0.448805	0.297109	hsa-miR-134	−0.761638	0.814211
hsa-let-7g*	0.448805	0.297109	hsa-miR-892b	−0.78585	0.907683
hsa-miR-100*	0.448805	0.297109	hsa-miR-18b*	−0.803171	1.135087
hsa-miR-103-as	0.448805	0.297109	hsa-miR-188-5p	−0.885158	1.808363
hsa-miR-105	0.448805	0.297109	hsa-miR-760	−0.901819	1.305335
hsa-miR-105*	0.448805	0.297109	hsa-miR-150*	−0.93806	0.866707

Not only were the differences in means between the two sham groups relatively small, but the standard deviations were sufficiently large such that no microRNA was identified that could be considered bimodal. In other words, the absolute value of the differences in means divided by the sum of the standard deviations was less than one for each microRNA using this random grouping. Therefore a random “dichotomization” does not identify separate patient groups as defined in this disclosure.
To predict assignment of patients to a response group, in a sample acquired before or after an intervention, microRNAs are sorted by the maximum difference between microRNA levels between the two groups defined above. The power of these microRNA markers is further assessed by the ratio of the differences in means of the microRNA values divided by the sum of their standard deviations. Tables 8 and 9 comprise a selective list and a more comprehensive list of such microRNAs, respectively, with the means and standard deviations of expression levels before IVIG treatment.

TABLE 8

Ratio from
calculation	microRNA

5.120617	hsa-miR-548d-5p
4.969583	hsa-miR-548a-5p
3.438886	hsa-miR-1537
2.615145	hsa-miR-590-5p
2.558528	hsa-miR-33a
2.547821	hsa-let-7e
2.480264	hsa-miR-32
2.330219	hsa-miR-301a
2.27536	hsa-miR-30e
2.22848	hsa-miR-19a
2.151041	hsa-miR-142-5p
2.144081	hsa-miR-362-3p
2.10237	hsa-miR-301b
2.052236	hsa-miR-1183
2.051132	hsa-miR-142-3p
2.027244	hsa-miR-340
1.977995	hsa-miR-371-5p
−1.98464	hsa-miR-154
−2.11305	hsa-miR-423-3p
−2.1648	hsa-miR-1224-5p
−2.19	hsa-miR-191
−2.19444	hsa-miR-127-3p
−2.24513	hsa-miR-574-5p
−2.4263	hsa-miR-139-3p
−2.7148	hsa-miR-432

TABLE 9

Ratio from
calculation	microRNA

5.120617	hsa-miR-548d-5p
4.969583	hsa-miR-548a-5p
3.438886	hsa-miR-1537
2.615145	hsa-miR-590-5p
2.558528	hsa-miR-33a
2.547821	hsa-let-7e
2.480264	hsa-miR-32
2.330219	hsa-miR-301a
2.27536	hsa-miR-30e
2.22848	hsa-miR-19a
2.151041	hsa-miR-142-5p
2.144081	hsa-miR-362-3p
2.10237	hsa-miR-301b
2.052236	hsa-miR-1183
2.051132	hsa-miR-142-3p
2.027244	hsa-miR-340
1.977995	hsa-miR-371-5p
1.861578	hsa-miR-15a
1.857642	hsa-miR-548c-5p
1.850123	hsa-miR-1225-3p
1.837303	hsa-miR-29b
1.834596	hsa-miR-21
1.820351	hsa-miR-1237
1.813523	hsa-miR-101
1.802173	hsa-miR-1539
−1.38055	hsa-miR-602
−1.38147	hsa-miR-132
−1.44539	hsa-miR-1471
−1.45911	hsa-miR-495
−1.59946	hsa-miR-1181
−1.60944	hsa-miR-339-5p
−1.62878	hsa-miR-134
−1.6329	hsa-miR-183
−1.72047	hsa-miR-557
−1.81989	hsa-miR-125a-3p
−1.83853	hsa-miR-423-5p
−1.94998	hsa-miR-382
−1.98464	hsa-miR-154
−2.11305	hsa-miR-423-3p
−2.1648	hsa-miR-1224-5p
−2.19	hsa-miR-191
−2.19444	hsa-miR-127-3p
−2.24513	hsa-miR-574-5p
−2.4263	hsa-miR-139-3p
−2.7148	hsa-miR-432

Correspondingly, Tables 10 and 11 comprise a selective list and a more comprehensive list of such microRNAs, respectively, representing significant ratios in patients after IVIG treatment.

TABLE 10

Ratio from
calculation	microRNA

2.883848	hsa-miR-125a-5p
2.589854	hsa-miR-92b
2.108284	hsa-let-7e
2.083486	hsa-miR-1307
−2.13615	hsa-miR-376b
−2.25541	hsa-miR-29b
−2.28617	hsa-miR-543
−2.29295	hsa-miR-301a
−2.35023	hsa-miR-15a
−2.37193	hsa-miR-1249
−2.40239	hsa-miR-542-3p
−2.43732	hsa-miR-136
−2.44794	hsa-miR-140-5p
−2.5479	hsa-miR-32
−2.56767	hsa-miR-33a
−2.58454	hsa-miR-545
−2.75021	hsa-miR-340
−2.7781	hsa-miR-590-5p
−2.80882	kshv-miR-K12-10b
−2.82348	hsa-miR-142-5p
−3.0019	hsa-miR-923_v12.0
−3.02147	hsa-miR-101
−3.20361	hsa-miR-19a
−3.88505	hsa-miR-141
−4.08184	hsa-miR-548c-5p
−4.53511	hsa-miR-30e
−5.1227	hsa-miR-153

TABLE 11

Ratio from calculation	microRNA

2.883848	hsa-miR-125a-5p
2.589854	hsa-miR-92b
2.130259	miRNABrightCorner30
2.108284	hsa-let-7e
2.083486	hsa-miR-1307
2.073077	hsa-miR-886-3p_v15.0
1.956005	hsa-miR-222
1.860369	mr_1
1.798634	hsa-miR-338-5p
1.793474	hsa-miR-664
1.781682	hsa-miR-1227
1.717443	hsa-miR-99b
1.676623	hsa-miR-363
−1.6569	hsa-miR-377
−1.68107	hsa-miR-450a
−1.72193	hsa-miR-376c
−1.76059	hsa-miR-382
−1.78329	hsa-miR-1537
−1.82416	hsa-miR-29c
−1.8549	hsa-miR-144
−1.85679	hsa-miR-19b
−1.88001	hsa-miR-106b
−1.88841	hsa-miR-499-5p
−1.90758	hsa-miR-376a
−1.96386	hsa-miR-362-3p
−1.99976	hsa-miR-154
−2.035	hsa-miR-337-5p
−2.04325	hsa-miR-424
−2.06191	hsa-miR-219-5p
−2.13615	hsa-miR-376b
−2.25541	hsa-miR-29b
−2.28617	hsa-miR-543
−2.29295	hsa-miR-301a
−2.35023	hsa-miR-15a
−2.37193	hsa-miR-1249
−2.40239	hsa-miR-542-3p
−2.43732	hsa-miR-136
−2.44794	hsa-miR-140-5p
−2.5479	hsa-miR-32
−2.56767	hsa-miR-33a
−2.58454	hsa-miR-545
−2.75021	hsa-miR-340
−2.7781	hsa-miR-590-5p
−2.80882	kshv-miR-K12-10b
−2.82348	hsa-miR-142-5p
−3.0019	hsa-miR-923_v12.0
−3.02147	hsa-miR-101
−3.20361	hsa-miR-19a
−3.88505	hsa-miR-141
−4.08184	hsa-miR-548c-5p
−4.53511	hsa-miR-30e
−5.1227	hsa-miR-153

To assess to ability of microRNAs to predict microRNA response to therapy, microRNAs that function similarly upon treatment between the dichotomized groups, microRNAs that either increase together or decrease together were collected and listed in Table 12.

TABLE 12

	Mean Δ		Mean Δ		Net Δ
MicroRNA	Group A	SD	Group B	SD	A and B

hsa-mir-1470	down	−0.7673864	1.235473	−4.12664194	1.5502503	down	−3.35926
hsa-mir-1290	down	−0.0147208	1.1018548	−3.154401463	1.1577212	down	−3.13968
hsa-mir-1202	down	−0.3401371	0.8489831	−3.3743004	1.1013233	down	−3.03416
hsa-mir-212	down	−0.694552	0.7209563	−3.7040352	0.89993	down	−3.00948
hsa-mir-26b	down	−0.0189347	0.3277201	−1.486439	0.2695336	down	−1.4675
hsa-mir-623	up	0.5154214	0.3045152	3.2896302	1.1049068	up	2.774209
hsa-mir-	up	0.0226423	0.5811046	2.88962271	1.1343319	up	2.86698
1826_v15.0
hsa-mir-574-	up	0.2439299	1.5461713	3.300805	0.7679533	up	3.056875
3p
hsa-mir-1471	up	0.6108116	2.9157016	4.0440545	1.3350063	up	3.433243
hsa-mir-337-	up	0.2684066	1.4140522	4.8525722	0.9392156	up	4.584166
3p
hsa-mir-513b	up	0.6818242	1.3846447	5.8524707	1.7107123	up	5.170647

Certain patterns of response may indicate resistance or responsiveness of one the two groups. While there may be a significant change in value of one or more microRNAs in one group there may be little response in the other. The group exhibiting lesser change may indicate a lack of response to the therapeutic intervention. This group may be resistant to the intervention or that the intended therapeutic effect is not needed. The response of hsa-mir-132 is exemplary. Group A patients had relatively low CTs by PCR (relatively high concentrations of the microRNA) while group B patients had relatively high CTs. A brisk response was noted following therapy in group A patients substantially converging on the low levels noted in group B patients. The possibility that the therapeutic effect on the levels of the microRNA in group B were already at a level that the therapeutic intervention was capable of effecting. The following tables demonstrate subsets of microRNAs exhibiting relatively significant change in one group and reduced change in the other group. Further, it can be seen that the group exhibiting a significant change may either converge to a level of expression corresponding to the other group or diverge. Specifically, Tables 13 and 14 are selective lists of microRNAs having divergent and convergent behavior, respectively, which exhibit significant change in Group A but relatively little change in Group B.

TABLE 13

Mean Δ
Group A	SD	SystematicName	Direction

2.52122	1.497334	hsa-miR-136	Most increased
2.208455	0.541141	hsa-miR-33a	Most increased
2.083515	1.609553	hsa-miR-219-5p	Most increased
2.082454	0.849342	hsa-miR-153	Most increased
1.955594	1.558846	hsa-miR-545	Most increased
1.757013	0.663302	hsa-miR-32	Most increased
1.301357	0.995047	hsa-miR-1537	Most increased
1.195371	0.219509	hsa-miR-590-5p	Most increased
−1.82126	1.984385	hsa-miR-1181	Most decreased

TABLE 14

Mean Δ
Group A	SD	SystematicName	Direction

1.270745	1.003145	hsa-miR-1	Most increased
0.933754	1.321562	hsa-miR-376a	Most increased
−1.20769	3.331194	hsa-miR-630	Most decreased
−1.26517	0.328726	hsa-miR-886-3p_v15.0	Most decreased
−1.27118	1.97	hsa-miR-1300_v13.0	Most decreased
−1.29486	3.144593	hsa-miR-485-3p	Most decreased
−1.32161	1.728257	hsa-miR-1224-5p	Most decreased
−1.48121	1.881716	hsa-miR-300	Most decreased
−1.50279	0.935894	hsa-miR-132	Most decreased
−1.7283	1.326469	hsa-miR-339-5p	Most decreased
−1.74399	3.594059	kshv-miR-K12-9	Most decreased

Likewise, Tables 15 and 16 are more comprehensive lists of microRNAs having divergent and convergent behavior, respectively, which exhibit significant change in Group A but relatively little change in Group B.

TABLE 15

Mean Δ
Group A	SD	SystematicName	Direction

2.52122	1.497334	hsa-miR-136	Most increased
2.208455	0.541141	hsa-miR-33a	Most increased
2.083515	1.609553	hsa-miR-219-5p	Most increased
2.082454	0.849342	hsa-miR-153	Most increased
1.955594	1.558846	hsa-miR-545	Most increased
1.757013	0.663302	hsa-miR-32	Most increased
1.301357	0.995047	hsa-miR-1537	Most increased
1.195371	0.219509	hsa-miR-590-5p	Most increased
1.140525	0.150147	hsa-miR-301a	Most increased
1.083703	0.288785	hsa-miR-141	Most increased
1.068173	0.318097	hsa-miR-29b	Most increased
1.041446	0.142776	hsa-miR-142-5p	Most increased
0.952666	0.441789	hsa-miR-193a-3p	Most increased
0.885343	0.493095	hsa-miR-582-3p	Most increased
−0.90253	1.698756	hsa-miR-513a-5p	Most decreased
−1.82126	1.984385	hsa-miR-1181	Most decreased

TABLE 16

Mean Δ
Group A	SD	SystematicName	Direction

1.270745	1.003145	hsa-miR-1	Most increased
1.13486	1.316732	hsa-miR-376c	Most increased
1.132591	0.828951	hsa-miR-551b	Most increased
1.10453	1.617308	hsa-miR-337-5p	Most increased
1.0677	0.202879	hsa-miR-142-3p	Most increased
1.049269	1.235692	hsv1-miR-H1_v14.0	Most increased
1.035347	2.190203	hsa-miR-410	Most increased
0.976275	0.357315	hsa-miR-301b	Most increased
0.971021	1.083568	hsa-miR-371-5p	Most increased
0.933754	1.321562	hsa-miR-376a	Most increased
0.88329	0.745268	hsa-miR-133b	Most increased
−0.75803	1.061571	hsa-miR-100	Most decreased
−0.76739	1.235473	hsa-miR-1470	Most decreased
−0.80433	0.948616	hsa-miR-1260	Most decreased
−0.84299	1.117714	hsa-miR-647	Most decreased
−0.8683	2.617779	hsa-miR-595	Most decreased
−0.87714	1.87364	hsa-miR-525-5p	Most decreased
−0.88588	2.114378	hsa-miR-188-5p	Most decreased
−0.94706	0.701991	hsa-miR-28-3p	Most decreased
−0.99436	2.03129	hsa-miR-96	Most decreased
−1.02709	1.633777	hsa-miR-451	Most decreased
−1.07284	0.683653	hsa-miR-134	Most decreased
−1.07828	1.295253	hsa-miR-127-3p	Most decreased
−1.08028	2.11365	hsa-miR-663	Most decreased
−1.20003	1.525774	hsa-miR-486-5p	Most decreased
−1.20769	3.331194	hsa-miR-630	Most decreased
−1.26517	0.328726	hsa-miR-886-3p_v15.0	Most decreased
−1.27118	1.97	hsa-miR-1300_v13.0	Most decreased
−1.29486	3.144593	hsa-miR-485-3p	Most decreased
−1.32161	1.728257	hsa-miR-1224-5p	Most decreased
−1.48121	1.881716	hsa-miR-300	Most decreased
−1.50279	0.935894	hsa-miR-132	Most decreased
−1.7283	1.326469	hsa-miR-339-5p	Most decreased
−1.74399	3.594059	kshv-miR-K12-9	Most decreased

Correspondingly, Tables 17 and 18 are a selective list of microRNAs having divergent and convergent behavior, respectively, which exhibit significant change in Group B and relatively little change in Group A.

TABLE 17

Mean Δ
Group B	SD	SystematicName	Direction

8.720988	1.695736	hsa-miR-513a-5p	Most increased
4.492508	1.085892	hsa-miR-1181	Most increased
−5.88934	1.274726	hsa-miR-144	Most decreased
−5.94462	0.44929	hsa-miR-142-5p	Most decreased
−6.60811	0.775835	hsa-miR-32	Most decreased
−6.60915	0.991381	hsa-miR-219-5p	Most decreased
−6.66695	0.528549	hsa-miR-33a	Most decreased
−6.95672	0.388828	hsa-miR-545	Most decreased
−7.47155	1.143166	hsa-miR-1537	Most decreased
−7.84218	0.691729	hsa-miR-153	Most decreased

TABLE 18

Mean Δ
Group B	SD	SystematicName	Direction

6.045644	1.810638	hsa-miR-1296	Most increased
5.852471	1.710712	hsa-miR-513b	Most increased
5.269371	0.913262	hsa-miR-1307	Most increased
5.23262	0.53149	hsa-miR-92b	Most increased
4.852572	0.939216	hsa-miR-337-3p	Most increased
4.483333	1.436393	hsa-miR-647	Most increased
4.446777	2.354756	hsa-miR-345	Most increased
4.393115	0.758058	hsa-miR-501-5p	Most increased
−6.23567	1.991346	hsa-miR-376b	Most decreased
−7.18912	2.240812	hsa-miR-1183	Most decreased

Finally, Tables 19 and 20 are more comprehensive lists of microRNAs having divergent and convergent behavior, respectively, which exhibit significant change in Group B but relatively little change in Group A

TABLE 19

Mean Δ
Group B	SD	SystematicName	Direction

8.720988	1.695736	hsa-miR-513a-5p	Most increased
4.492508	1.085892	hsa-miR-1181	Most increased
−4.08226	0.877131	hsa-miR-193a-3p	Most decreased
−4.74399	0.497264	hsa-miR-301a	Most decreased
−4.77102	0.469596	hsa-miR-141	Most decreased
−5.26843	0.506796	hsa-miR-29b	Most decreased
−5.34069	0.410219	hsa-miR-590-5p	Most decreased
−5.43651	0.82098	hsa-miR-136	Most decreased
−5.63284	1.148245	hsa-miR-582-3p	Most decreased
−5.88934	1.274726	hsa-miR-144	Most decreased
−5.94462	0.44929	hsa-miR-142-5p	Most decreased
−6.60811	0.775835	hsa-miR-32	Most decreased
−6.60915	0.991381	hsa-miR-219-5p	Most decreased
−6.66695	0.528549	hsa-miR-33a	Most decreased
−6.95672	0.388828	hsa-miR-545	Most decreased
−7.47155	1.143166	hsa-miR-1537	Most decreased
−7.84218	0.691729	hsa-miR-153	Most decreased

TABLE 20

Mean Δ
Group B	SD	SystematicName	Direction

6.045644	1.810638	hsa-miR-1296	Most increased
5.852471	1.710712	hsa-miR-513b	Most increased
5.269371	0.913262	hsa-miR-1307	Most increased
5.23262	0.53149	hsa-miR-92b	Most increased
4.852572	0.939216	hsa-miR-337-3p	Most increased
4.483333	1.436393	hsa-miR-647	Most increased
4.446777	2.354756	hsa-miR-345	Most increased
4.393115	0.758058	hsa-miR-501-5p	Most increased
4.139791	1.284721	hsa-miR-765	Most increased
4.10815	2.241581	hsa-miR-299-5p	Most increased
4.044055	1.335006	hsa-miR-1471	Most increased
3.998238	0.721076	hsa-miR-182	Most increased
3.887691	0.570974	hsa-miR-1227	Most increased
3.841755	0.995814	hsa-miR-34b	Most increased
3.622937	1.26476	hsa-miR-491-3p	Most increased
3.506353	0.577494	hsa-miR-191	Most increased
3.399269	1.692025	hsa-miR-513c	Most increased
3.300805	0.767953	hsa-miR-574-3p	Most increased
3.28963	1.104907	hsa-miR-623	Most increased
3.238278	1.381079	hsa-miR-885-5p	Most increased
3.047651	0.813464	hsa-miR-338-5p	Most increased
2.961104	1.572459	hsa-miR-609	Most increased
2.889623	1.134332	hsa-miR-1826_v15.0	Most increased
−4.09785	0.929085	kshv-miR-K12-10b	Most decreased
−4.10335	0.423924	hsa-miR-101	Most decreased
−4.12664	1.55025	hsa-miR-1470	Most decreased
−4.14723	2.179625	hsa-miR-337-5p	Most decreased
−4.16497	0.300849	hsa-miR-340	Most decreased
−4.3393	0.511408	hsa-miR-19a	Most decreased
−4.3514	0.995718	hsa-miR-487a	Most decreased
−5.38285	0.608003	hsa-miR-542-3p	Most decreased
−6.23567	1.991346	hsa-miR-376b	Most decreased
−7.18912	2.240812	hsa-miR-1183	Most decreased

The tables reproduced above represent a number of suitable techniques for classifying microRNAs that exhibit dichotomous behavior in the groups of patients. In turn, it can be expected that such microRNAs may be used to segregate patient populations and to assign individual patients to appropriate groups for the purpose of diagnosis, treatment or the like. Further, one of skill in the art will recognize that certain microRNAs stand out as being identified in a number of the tables. Accordingly, in a presently preferred embodiment, the techniques of this disclosure can be practiced using one or more microRNAs selected from the following group:
hsa-let-7e, hsa-mir-1181, hsa-miR-1183, hsa-miR-1224-5p, hsa-miR-127-3p, hsa-mir-1296, hsa-mir-132, hsa-mir-136, hsa-miR-139-3p, hsa-mir-141, hsa-miR-142-3p, hsa-mir-142-5p, hsa-mir-144, hsa-mir-153, hsa-mir-1537, hsa-miR-154, hsa-miR-191, hsa-mir-193a-3p, hsa-miR-19a, hsa-mir-219-5p, hsa-mir-29b, hsa-mir-301a, hsa-miR-301b, hsa-miR-30e, hsa-mir-32, hsa-mir-33a, hsa-miR-340, hsa-miR-362-3p, hsa-miR-371-5p, has-377, hsa-miR-423-3p, hsa-miR-432, hsa-mir-513a-5p, hsa-mir-545, hsa-miR-548a-5p, hsa-miR-574-5p, hsa-mir-582-3p, hsa-mir-590-5p, hsa-mir-15a, hsa-mir-548c-5p, hsa-mir-1225-3p, hsa-mir-29b, hsa-mir-21, hsa-mir-1237, hsa-mir-101, hsa-mir-1539, hsa-mir-557, hsa-mir-125a-3p and hsa-mir-423-5p
Even more preferably, Table 21 provides a list of 19 selected microRNAs having been selected on the basis of one or more of the indicated criteria.

TABLE 21

				Known
Top 17	Uni-	Before	After	AB
bidirectional	directional	IVIG	IVIG	divergent/
(A&B)	mover	A or B	A or B	convergent
movers	(A or B)	predictor	predictor	behavior

hsa-mir-136	x	x		x	x
hsa-mir-141	x			x
hsa-mir-142-5p	x		x	x
hsa-mir-144	x
hsa-mir-153	x	x
hsa-mir-1537	x	x	x
hsa-mir-193a-	x
3p
hsa-mir-219-5p	x
hsa-mir-29b	x			x
hsa-mir-301a	x		x
hsa-mir-32	x		x	x
hsa-mir-33a	x	x	x	x
hsa-mir-545	x
hsa-mir-582-3p	x
hsa-mir-590-5p	x		x	x
hsa-mir-1181	x	x
hsa-mir-513a-	x	x
5p
hsa-mir-132				x	x
hsa-mir-1296		x

Several groups of sequences have been discovered by use of the invention wherein the sequences are identified with groups dichotomous with respect to their response to an immunotherapy. Paired sequences, those that represent levels or differences in levels of one or more immune-cell comprised microRNA may be used clinically.
Accordingly, one embodiment includes the method of collecting samples of immune cells, preferably PBMCs, from a statistically sufficient number of patients contemplating an immunotherapy before therapy and following therapy. It is understood that while PBMCs may be used, subsets of PBMCs may be selected by means known in the art, for example flow cytometric sorting. Patients are sorted into dichotomous groups by the differences. Expected response ranges or normal ranges for patients in each group are established in accordance with practices well-known in the clinical laboratory field. Patients may be classified as members of one or the other group wherein their individual results fall within non-overlapping ranges by one or more microRNAs demonstrated to have the power to distinguish a patient population into the groups as described above.
In another embodiment, a panel of microRNA sequences of non-sequential microRNAs quantified on immune-cell samples preferably PBMCs, taken prior to a contemplated immunotherapy may be used clinically to catagorize patients into dichotomous groups. Samples of immune cells, preferably PBMCs, from a statistically sufficient number of patients contemplating an immunotherapy are collected before therapy. Patients are sorted into dichotomous groups by the differences. Expected response ranges or normal ranges for patients in each group are established in accordance with practices well-known in the clinical laboratory field. Patients may be classified as members of one or the other group wherein their individual results fall within non-overlapping ranges by one or more microRNA. Said classification permits determining suitability of the contemplated immunotherapy, for example resistance to the contemplated immunotherapy.
The invention not only teaches a method for the dichotomization or multimerization of groups of clinically similar patients into groups distinguishable by their microRNA profiles, it also discloses important microRNA species that are different within the groups that may be of great clinical significance. For example, in a genome-wide dissection of microRNA functions predicted by a computer algorithm, a number of microRNAs were identified whose response patterns segregate with dichotomization were ascribed in vivo functions (John S. Tsang, Margaret S. Ebert, and Alexander van Oudenaarden, Genome-wide dissection of microRNA functions and co-targeting networks using gene-set signatures, Mol Cell. 2010 Apr. 9; 38(1): 140-153. doi: 10.1016/j.molce1.2010.03.007). Amongst microRNAs that showed significant differences in response between the two groups, hsa-mir-582-3p and 140-5p was identified as differentially expressed in non-functioning pituitary adenomas (Pituitary (2011) 14:112-124). These microRNAs were predicted to target Smad3, a member of the TGFβ signaling cascade. It does not escape our attention that TGFβ is of primary importance in maintenance of pregnancy. Onouchi and Hata identify IVIG resistance in approximately fifteen percent of patients treated for Kawasaki Disease requiring increased dosing or alternative therapy. They associated genetic variations (SNPs) in the genes coding for ITPKC and caspase-3 with IVIG unresponsiveness (Yoshihiro Onouchi and Akira Hata, Responsible Genetic Factors for Vasculitis in Kawasaki Disease, Advances in the Etiology, Pathogenesis and Pathology of Vasculitis, 71-92). Identification of molecular markers that assess drug resistance or responsiveness is very important clinically. Further it does not escape us identification of patients into dichotomous groupings might allow definition of individuals with higher risk of diseases including but not limited to autoimmune disease is an important aspect of this invention.
In a preferred embodiment the method comprises providing a biological sample comprising immune cells from a subject with a history of reproductive disorder or risk of reproductive disorder said sample being derived from immune cells, for example, derived from peripheral blood, and then isolating mononuclear cells as taught by Boyum (Boyum A 1983. Isolation of human blood monocytes with Nycodenz, a new non-ionic iodinated gradient medium. Scand J Immunol 17: 429-436). A kit optimized for recovery of microRNA sequences such as mirNeasy Mini Kit Qiagen catalogue 217004 may be used following instructions provided. Quantification of microRNA may be determined by a variety of techniques known to those skilled in the art. In a preferred embodiment, individual microRNAs are quantified by real-time polymerase chain reaction (PCR). In a more preferred embodiment, instructions provided for real-time, quantitative PCR are followed as provided by SABiosciences, Frederick, Md. for use with primers listed below and reagents optimized for specific real-time thermocycling equipment such as the Stratagene Mx3005p (www.SABioscies.com). Operating instructions for the Stratagene Mx3005p are provided by the manufacturer. Comprised therein are instructions for spectrophotometric quantification of recovered RNA, recommendations for input quantity of RNA and PCR master mix. Quantification may be performed concurrently with quantification of a “housekeeper gene” (a gene that is expressed with relative constancy in the cells being interrogated thereby permitting relative quantification, preferably 18s RNA) and then determining the amount of microRNAs and comparing to the amount of the corresponding microRNA in the sample to similarly treated biological sample from control individuals. The subject is then diagnosed as being a candidate for immunotherapy such as IVIG, lymphocyte immunotherapy or anti-TNF alpha therapy after the methods of Winger et. al. if there is differential expression and/or pattern of expression in the amount of one or more of the microRNAs from the sample as compared to corresponding microRNA control levels and/or control patterns. Differential expression is defined statistically from three or more samples from control individuals wherein the patient value and/or pattern is two standard deviations above the control mean value and/or pattern. Preferably control values/patterns and patient values/patterns are determined on specimens drawn at the same time with respect to pregnancy, for example, during the period of preconception and at the same time during a menstrual cycle or following implantation. In addition, the method can comprise quantification of a plurality of samples wherein the control sample is the first sample value for one or more of the above listed microRNAs. Progress of the condition can be assessed by comparing values and/or patterns subsequent to the control value and/or pattern. Efficacy of therapeutic intervention can be determined by comparing values and/or patterns subsequent to initiation of a therapeutic intervention to a control value and/or pattern on a biologic sample taken prior to initiation of therapy.
Other embodiments are directed to the diagnosis and treatment of immunological reproductive problems. According to the methods described above, PMBC MicroRNA levels and patterns may be used to diagnose immunological reproductive problems. Examples of such reproductive problems include implantation failure, infertility, miscarriage, preterm labor, PROM (premature rupture of membranes), IUGR (intrauterine growth retardation), antiphospholipid antibody syndrome, stillbirth, endometriosis and others. Further, PMBC MicroRNA levels and patterns may also be used to monitor treatment for these immunological reproductive problems.
As will be appreciated, PBMC MicroRNA levels and patterns may also be used to diagnose and monitor treatment for reproductive problems that may not be immunological but may be correlated with immunological reproductive problems. Representative conditions include increased tissue factor levels (microRNA 19b and 20a specifically) in Anti-phospholipid Antibody Syndrome, increased coagulation risk factors, PCOS (polycystic ovary syndrome) and premature ovarian failure (POF.)
In another aspect, PBMC MicroRNA levels and patterns may be used to diagnose and monitor treatment to prevent long term risks to the baby resulting from immunologically compromised pregnancy. These may include risk of the baby developing asthma, autism, ADHD, diabetes, schizophrenia, Tourette' syndrome, bipolar disorder or other conditions. Further, PBMC MicroRNA levels and patterns may be used to diagnose and monitor treatment for problems that may not be reproductive but may be correlated with reproductive immunological-problems, including autoimmune thyroiditis, migraines, lupus (SLE), rheumatoid arthritis flares, estrogen deficiency, osteoporosis, insulin resistance and others.
The methods and compositions of the invention may also be applied to the diagnosis or treatment of non-reproductive immunological diseases. Specifically, PBMC MicroRNA levels and patterns may be used to diagnose patients with non-reproductive immunological diseases that may that may benefit from immunotherapy (such as IVIG), including Kawasaki disease, ITP, Guillain-Barre Syndrome, autism, MS, lupus (SLE) or other conditions not yet identified that respond to immunotherapy treatment.
By identifying representative patient groups and microRNA responses, optimal immunologic and other therapies may be selected. Suitable representative therapies include use of IVIG, intralipid, G-CSF (Neupogen), corticosteroids (Prednisone, prednisolone, dexamethasone, etc), anti-TNFα alpha therapies (Humira, Enbrel, Simponi), fish oil (omega-3 oils), vitamin D, lymphocyte immunization therapy (LIT), levothyroxin, metformin, heparin and others.
Further embodiments include using PBMC MicroRNA levels and patterns to determine optimal doses and treatment choices and/or combinations to control the specific immunologic conditions identified. Doses and combinations may include a single 25g of IVIG dose combined with Clexane 20 mg qd followed by microRNA retesting 2 weeks later, monthly Humira injections followed by no retesting, monthly Intralipid only, 2 mg dexamethasone daily for 3 months then drug tapering, or others.
As will be appreciated, PBMC MicroRNA levels and patterns may be used to identify patients that will not benefit or may experience negative side effects from therapy despite a positive disease diagnosis.
Yet another aspect of this disclosure is directed to identification of characteristic behavioral levels and patterns of a PBMC MicroRNA relative to other MicroRNAs. These levels and patterns may be associated with different disease and treatment phenotypes. These may be defined by ranges at which individual microRNAs operate, including basal levels, fully active levels, mean levels, observed standard deviations, from patterns observed in individual levels, from sequential microRNA samples based on patterns of deltas (differences) between sequential samples, and patterns and pattern changes observed between multiple microRNAs from individual or sequential samples.
PBMC MicroRNA levels and patterns may also be defined in part by polymorphisms. As discussed above, polymorphisms may occur in the 22-24 base of the microRNA itself, in the region around the microRNA in the pre-mircroRNA that affect folding of the hairpin and its transcription, in the target mRNA, or in the primary RNA strand complementary to the guide strand entering the RISC complex, for example.
Some microRNa levels and patterns may be best measured by effectively testing microRNA changes within the mononuclear cell population via the PBMCs. Effectively testing microRNA mononuclear cell population is demonstrated when removal of PBMCs eliminates the mononuclear microRNA measurement.
As discussed above, microRNA patterns and levels may be determined in a number of suitable manners. In a preferred embodiment, bimodal (dichotomous) or multimodal behavior of one or more microRNAa may be verified in before and/or after treatment groups. Further, useful clinical characteristics associated with these separated patient groups (such as degree of clinical improvement with IVIG), may be identified. Similarly, bimodal or multimodal behavior microRNAs may be used as markers to select optimal groups for further microRNA analysis or other diagnostic procedures. Also preferably, suitable methods include selection of microRNAs in common between most increased and most decreased top fractions of separated groups refines the identification of the groups.
Described herein are presently preferred embodiments. However, one skilled in the art that pertains to the present invention will understand that the principles of this disclosure can be extended easily with appropriate modifications to other applications.

Claims

1. A method for identifying at least two characteristic groups in a patient population on the basis of microRNA expression comprising the steps of:

a) collecting immune cells;

b) extracting microRNA-comprising RNA from the immune cells;

c) quantifying at least one microRNA within the extracted RNA; and

d) segregating the patient population into the groups on the basis of expression of the at least one microRNA.

2. The method of claim 1, wherein the step of collecting immune cells comprises collecting peripheral blood mononuclear cells.

3. The method of claim 1, wherein the step of segregating the patient population comprises assigning patients expressing a relatively high level of the at least one microRNA to a first group and assigning patients expressing a relatively low level of the at least one microRNA to a second group.

4. The method of claim 1, wherein the step of collecting immune cells comprises collecting the cells before an immunotherapy treatment.

5. The method of claim 1, wherein the step of collecting immune cells comprises collecting the cells after an immunotherapy treatment.

6. The method of claim 1, wherein the step of collecting immune cells comprises collecting the cells before and after an immunotherapy treatment and wherein the step of segregating the patient population includes determining the change in expression level of the at least one microRNA after the immunotherapy treatment.

7. The method of claim 6, wherein the step of segregating the patient population comprises assigning patients exhibiting a first change in the expression level to a first group and assigning patients exhibiting a second change in the expression level to a second group.

8. The method of claim 7, wherein the first change is a relatively large change in expression level and the second change is a relatively small change in expression level.

9. The method of claim 7, wherein the first change is a positive change in expression level and the second change is a negative change in expression level.

10. The method of claim 7, wherein the absolute value of the mean of the change in the expression level of the at least one microRNA in the first group divided by the standard deviation is greater than or equal to one.

11. The method of claim 7, further comprising the step identifying a subset of microRNAs within the group of known microRNAs that exhibit a change in expression level in the first group such that the absolute value of the mean of the change in expression level divided by the standard deviation is greater than or equal to one.

12. The method of claim 11, further comprising the step of identifying a microRNA within the group of known microRNAs that exhibits the greatest change in expression level in the first group.

13. The method of claim 1, further comprising the steps of:

a) collecting immune cells from an additional patient;

b) extracting microRNA-comprising RNA from the immune cells of the additional patient;

c) quantifying at least one microRNA within the extracted RNA from the additional patient; and

d) identifying the additional patient as belonging to one of the segregated groups on the basis of expression of the at least one microRNA.

14. The method of claim 13, further comprising the step of administering a treatment to the additional patient based on the identification.

15. The method of claim 14, wherein the step of administering a treatment comprises administering IVIG.

16. The method of claim 1, further comprising the step of diagnosing a patient as having a condition based on membership in a segregated group.

17. The method of claim 16, wherein the condition is a reproductive disorder.

18. The method of claim 1, further comprising the step of monitoring treatment of a patient belonging to one of the segregated groups by collecting immune cells, extracting at least one microRNA and quantifying the at least one microRNA at a subsequent time.

19. The method of claim 1, wherein the at least one microRNA is selected from the group consisting of hsa-let-7e, hsa-mir-1181, hsa-miR-1183, hsa-miR-1224-5p, hsa-miR-127-3p, hsa-mir-1296, hsa-mir-132, hsa-mir-136, hsa-miR-139-3p, hsa-mir-141, hsa-miR-142-3p, hsa-mir-142-5p, hsa-mir-144, hsa-mir-153, hsa-mir-1537, hsa-miR-154, hsa-miR-191, hsa-mir-193a-3p, hsa-miR-19a, hsa-mir-219-5p, hsa-mir-29b, hsa-mir-301a, hsa-miR-301b, hsa-miR-30e, hsa-mir-32, hsa-mir-33a, hsa-miR-340, hsa-miR-362-3p, hsa-miR-371-5p, has-377, hsa-miR-423-3p, hsa-miR-432, hsa-mir-513a-5p, hsa-mir-545, hsa-miR-548a-5p, hsa-miR-574-5p, hsa-mir-582-3p, hsa-mir-590-5p, hsa-mir-15a, hsa-mir-548c-5p, hsa-mir-1225-3p, hsa-mir-29b, hsa-mir-21, hsa-mir-1237, hsa-mir-101, hsa-mir-1539, hsa-mir-557, hsa-mir-125a-3p and hsa-mir-423-5p.

20. The method of claim 19, wherein the at least one microRNA is selected from the group consisting of hsa-mir-136, hsa-mir-141, hsa-mir-142-5p, hsa-mir-144, hsa-mir-153, hsa-mir-1537, hsa-mir-193a-3p, hsa-mir-219-5p, hsa-mir-29b, hsa-mir-301a, hsa-mir-32, hsa-mir-33a, hsa-mir-545, hsa-mir-582-3p, hsa-mir-590-5p, hsa-mir-1181, hsa-mir-513a-5p, hsa-mir-132 and hsa-mir-1296.