Patent Application
20230270783
The present disclosure generally relates to the field of restoring and/or adjusting an immune system of a subject using a cell-based therapy, in particular to immune system restoration and/or adjustment based on subject etiology and/or immune system status evaluation.
A significant change observed in aging relates to the composition and functionally of CD4 T cells, the main orchestrators of adaptive immune responses. With aging, this naïve subset shrinks along with the accumulation of highly differentiated memory cells which often shows dysregulated properties. These changes are assumed to result from age-related thymus involution, repeated antigen encounters and intrinsic cellular senescence processes. In addition, systemic low-grade chronic inflammation that develops with age, also appears to impact the phenotype and function of CD4 T cells.
Unfortunately, the changes in the immune system have a tendency to result in dysregulation and/or impaired function of the immune system, rendering the elderly more prone to so called “inflammaging”—infections, chronic inflammatory disorders, and vaccination failure on the one hand, and to increased occurrence of cancer on the other.
There remains an unmet need to test immune dysfunction in an individual long before or during disease process and to restore and/or adjust the balance of the immune system based on the identified alteration and/or patient etiology.
The following embodiments and aspects thereof are described and illustrated in conjunction with compositions and methods which are meant to be exemplary and illustrative, not limiting in scope. In various embodiments, one or more of the above-described problems have been reduced or eliminated, while other embodiments are directed to other advantages or improvements.
According to some embodiments, there is provided compositions and method for treating a dysregulated immune system. According to some embodiments, there is provided compositions and method for treating senescence-associated diseases.
Advantageously, the herein disclosed composition and method enable providing personalized treatment to a subject in need thereof, in particular elderly, according to the status of their immune system, e.g., whether the immune system is identified as balanced, imbalanced making the subject more vulnerable to inflammatory and neurodegenerative diseases or imbalanced making the subject more prone to cancer.
According to some embodiments, there is provided a method or a pharmaceutical composition for use in the treating a senescence-associated disease in a subject in need thereof, the method comprising: obtaining information of a disease etiology of the subject; wherein the disease etiology comprises a senescence-associated disease; and administering to the subject cytotoxic CD4 T-cells (CD4-CTLs) and/or an agent capable of inducing CD4-CTLs differentiation and/or proliferation, thereby treating the senescence-associated disease; or administering an agent capable of aTreg depletion and/or inhibition.
According to some embodiments, the cytotoxic CD4 T-cells are autologous to the subject.
According to some embodiments, the method further comprises a step of isolating effector memory CD4 T-cells (EMs) from the subject and cause their differentiation into CD4-CTLs. According to some embodiments, causing the differentiation comprises cultivating the EMs in the presence of one or more marker selected from IL-27, IL-6, IL1, TNF. According to some embodiments, the isolating of the EMs comprises sorting EMs from the subject using CD44, CD62L, CD45, Itgb7 and/or IL-18R1 as biomarkers. Each possibility is a separate embodiment.
According to some embodiments, the senescence-associated disease is selected from frailty, cancer, chronic infection, chronic inflammation, Alzheimer's disease, dementia, Parkinson's disease, tissue senescence or any combination thereof. Each possibility is a separate embodiment.
According to some embodiments, there is provided a method or a pharmaceutical composition for use in the treating an immune-associated disease of a subject in need thereof, the method comprising: obtaining information of a disease etiology of the subject; wherein the disease etiology comprises immune-inflammatory condition or an immune-insufficient condition; and providing cell-based therapy to the subject based on the disease etiology; wherein the cell therapy comprises administering to the subject aTregs CD4 T cells and/or an agent capable of inducing CD4 aTregs differentiation and/or proliferation or administering to the subject cytotoxic CD4 T-cells (CD4-CTLs) and/or an agent capable of inducing CD4-CTL differentiation and/or proliferation.
According to some embodiments, the cell therapy is autologous cell therapy.
According to some embodiments, the therapy comprises administering to the subject aTregs CD4 T cells and/or an agent capable of inducing CD4 aTregs differentiation. According to some embodiments, the method further comprises a step of isolating and proliferating aTregs CD4 T-cells isolated from the subject. According to some embodiments, the isolating comprises sorting aTregs from the subject using one or more biomarkers selected from CD137, CD134, FOXP3+, GITR+, Helios+, CD74, HLA-DR CD81, TIGIT, PD1. Each possibility is a separate embodiment.
According to some embodiments, the therapy comprises administering to the subject cytotoxic CD4 T-cells (CD4-CTLs) and/or an agent capable of inducing CD4-CTL differentiation and/or proliferation. According to some embodiments, the method further comprises a step of isolating effector memory CD4 T-cells (EMs) from the subject and cause their differentiation into CD4-CTLs. According to some embodiments, causing CD4 CTL differentiation comprises cultivating the EMs in the presence of one or more marker selected from IL-27, IL-6, IL1, TNF. According to some embodiments, the isolating of the EMs comprises sorting EMs from the subject using CD44, CD62L, CD45, Itgb7 and/or IL-18R1 as biomarkers.
According to some embodiments, the immune-inflammatory associated disease etiology is an autoinflammatory and/or autoimmune disease and wherein the cell-based therapy comprises administering to the subject aTregs CD4 T cells and/or an agent capable of inducing CD4 aTregs differentiation and/or proliferation.
According to some embodiments, the immune-insufficient disease etiology is selected from frailty, cancer, chronic infection, chronic inflammation, Alzheimer's disease, dementia, Parkinson's disease, tissue senescence. Each possibility is a separate embodiment. According to some embodiments, the cell-based therapy comprises administering to the subject cytotoxic CD4 T-cells (CD4-CTLs) and/or an agent capable of inducing CD4-CTLs differentiation and/or proliferation.
According to some embodiments, there is provided a method or a pharmaceutical composition for use in the restoring and/or adjusting an immune system of a subject, the method comprising: obtaining a biological sample from a subject, the biological sample comprising one or more subsets of CD4 T-cells; identifying the presence, frequency and/or ratio of cytotoxic CD4 T-cells (CD4-CTLs), exhausted CD4 T-cells and/or aTreg cells, and providing cell-based therapy to the subject based on the identification, thereby restoring and/or adjusting the immune system of the subject.
According to some embodiments, the identifying of the presence of one or more subsets of CD4 T-cells comprises determining the amount of each identified subset of CD4 T-cells relative to a control value.
According to some embodiments, the method further comprises identifying the presence, frequency and/or ratio of one or more additional subsets of CD4 T cells in the immune system, selected from activated regulatory CD4 T cells (aTregs), effector memory CD4 T-cells (EMs); naïve CD4 T-cells, naïve_Isg15 CD4 T-cells, rTregs CD4 T-cells or any combination thereof. Each possibility is a separate embodiment.
According to some embodiments, the evaluating is based on the level of one or more biomarkers associated with the CD4-CTLs. According to some embodiments, the biomarker is EOMES.
According to some embodiments, the evaluating is based on a plurality of biomarkers selected from Nkg7, Runx3, EOMES, Gzmk, IFN-b, IFN-g, IL-27, IL21, IL 17A, Ccl3, Ccl4 and Ccl5. Each possibility is a separate embodiment.
According to some embodiments, the therapy comprises administering to the subject CD4 aTregs and/or an agent capable of inducing CD4 aTregs differentiation and/or proliferation. According to some embodiments, the aTregs CD4 T cells are autologous to the subject. According to some embodiments, the method further comprises a step of isolating and optionally also proliferating aTregs CD4 T cells of the subject. According to some embodiments, the isolating comprises sorting aTregs from the subject using one or more biomarkers selected from CD137, CD134, FOXP3+, GITR+, Helios+, CD74, HLA-DR, CD81, TIGIT, PDJ. Each possibility is a separate embodiment.
According to some embodiments, the therapy comprises administering to the subject an agent targeting the CD4-CTLs. According to some embodiments, the agent is selected from the group consisting of an antibody, a siRNA, a microRNA, a small molecule or any combination thereof. Each possibility is a separate embodiment. According to some embodiments, the antibody is an NKG2D antibody, a CD7 antibody, a CD134 antibody, a CD137 antibody, a GITR antibody, a CCL5 antibody, an IL-27 antibody or any combination thereof. Each possibility is a separate embodiment.
According to some embodiments, the therapy comprises administering to the subject CD4-CTLs and/or an agent capable of inducing CD4-CTL differentiation and/or proliferation. According to some embodiments, the CD4-CTLs are autologous to the subject. According to some embodiments, the method further comprises a step of isolating effector memory CD4 T-cells (EMs) from the subject and cause their differentiation into CD4-CTLs. According to some embodiments, the isolating of the EMs comprises sorting EMs from the subject using CD44, CD62L, CD45, Itgb7 and/or IL-18R1 as biomarkers. Each possibility is a separate embodiment.
According to some embodiments, the method further comprises evaluating a grade of tissue senescence, based on the presence, frequency and/or ratio of cytotoxic CD4 T-cells (CD4-CTLs), exhausted CD4 T-cells, aTreg cells or combinations thereof.
According to some embodiments, there is provided a method or a pharmaceutical composition for use in the evaluating tissue senescence in a subject in need thereof, the method comprising evaluating obtaining data relating to the subjects age, medical history and/or genetic background, measuring the presence, frequency and/or ratio of cytotoxic CD4 T-cells (CD4-CTLs), exhausted CD4 T-cells, aTreg cells or combinations thereof, and assessing the degree of tissue senescence in the subject based on the data relating to the subjects age, medical history and/or genetic background and the identified presence, frequency and/or ratio of cytotoxic CD4 T-cells (CD4-CTLs), exhausted CD4 T-cells, aTreg cells or combinations thereof.
According to some embodiments, the data obtained from the subject include at least the subject's age and his/her medical history.
According to some embodiments, at least the presence, frequency and/or ratio of cytotoxic CD4 T-cells (CD4-CTLs) is measured.
According to some embodiments, the method further comprises determining a likely location of the senescent tissue based on the medical history of the subject.
According to some embodiments, there is provided a method or a pharmaceutical composition for use in the treatment of cancer of a subject, the method comprising administering to the subject CD4-CTLs and/or an agent capable of inducing CD4-CTL differentiation and/or proliferation.
According to some embodiments, the CD4-CTLs are autologous to the subject. According to some embodiments, the method further comprises a step of isolating effector memory CD4 T-cells (EMs) from the subject and cause their differentiation into CD4-CTLs. According to some embodiments, the isolating of the EMs comprises sorting EMs from the subject using CD44, CD62L, CD45, Itgb7 and/or IL-18R1 as biomarkers. Each possibility is a separate embodiment.
According to some embodiments, there is provided a pharmaceutical composition for treating an immune system imbalance, the composition comprising isolated CD4 T-cells and one or more excipients.
According to some embodiments, immune system imbalance is related to a senescence-associated disease and the CD4 T-cells are CD4 CTLs. According to some embodiments, the senescence-associated disease is selected from frailty, cancer, chronic infection, chronic inflammation, Alzheimer's disease, dementia, Parkinson's disease, tissue senescence or any combination thereof.
According to some embodiments, the immune system imbalance is related to an autoinflammatory and/or autoimmune disease and the CD4 T-cells are CD4 aTregs.
Certain embodiments of the present disclosure may include some, all, or none of the above advantages. One or more technical advantages may be readily apparent to those skilled in the art from the figures, descriptions and claims included herein. Moreover, while specific advantages have been enumerated above, various embodiments may include all, some or none of the enumerated advantages.
In addition to the exemplary aspects and embodiments described above, further aspects and embodiments will become apparent by reference to the figures and by study of the following detailed descriptions.
The invention will now be described in relation to certain examples and embodiments with reference to the following illustrative figures.
In the following description, various aspects of the disclosure will be described. For the purpose of explanation, specific configurations and details are set forth in order to provide a thorough understanding of the different aspects of the disclosure. However, it will also be apparent to one skilled in the art that the disclosure may be practiced without specific details being presented herein. Furthermore, well-known features may be omitted or simplified in order not to obscure the disclosure.
The complexity of the immune system, and, particularly, the large variety of the cells comprising it, renders its investigation challenging. Until recently, the various cell lineages of the immune system were explored and traced either by using known cell markers or by analyzing bulk populations; however, these analyses cannot detect small populations of cells or novel subtypes of cells whose markers are yet unknown. These difficulties may be addressed by the single-cell RNA-sequencing (scRNA-seq) technology—a technology that provides RNA-sequencing profiles for hundreds and even thousands of single cells, which are then characterized and clustered in an unbiased manner. Each cluster can be associated with a potentially new marker gene, and the population structure can be assessed at a larger scale. For example, applying this technology, lymphopoiesis was shown to be decreased with age and CD4 T cells were shown to demonstrate a higher cell-to-cell variability in the expression of core activation programs in older ages.
As used herein, the term “biomarker” refers to a nucleic acid sequence of a gene or a fragment thereof the expression of which is indicative of one or more subsets of CD4 T cells. The biomarker may be a serum biomarker released into circulation. Alternatively, the biomarker may be expressed at the cell surface of CD4 T cell. The biomarker may be DNA, mRNA or the cDNA corresponding thereto, which represent the gene or a fragment thereof. A polynucleotide may comprise modified nucleotides, such as methylated nucleotides and nucleotide analogs. The sequence of the biomarker may be interrupted by non-nucleotide components. A biomarker may be further modified after polymerization, such as by conjugation with a labeling component. The term also includes both double- and single-stranded molecules.
As used herein, the term “biomarker associated with the one or more subsets of CD4 T cells” may refers to any measurable indicator of the one or more subsets of CD4 T cells, such as expression levels (including single cell expression levels) of RNA and/or proteins associated with certain CD4 T cell phonotypes, such as but not limited to, CD4 cytotoxic cells and/or activated regulatory (aTreg) CD4 T cells. According to some embodiments, the markers (or some thereof) may be indicative of the subset of CD4 T-cells regardless of the activation status (whether activated or not). Alternatively, the markers (or some thereof) may be indicative of the subset of CD4 T-cells and their activation status (e.g. activated CD4 cytotoxic cell).
As used herein, the term “biomarker identifier” may refer to any molecule capable of identifying a biomarker. Non-limiting examples of biomarker identifiers include, RNA/DNA probes, primers, antibodies etc.
As used herein the term “CD4 cytotoxic cell” refers to a subset of CD4+ T cells with cytotoxic activity (CD4-CTL). These cells are characterized by their ability to secrete granzyme B and perforin and to kill the target cells in an MHC class II-restricted fashion.
As used herein the term “regulatory CD4 T cells” or “rTreg” refer to a subpopulation of CD4+ T cells that modulate the immune system, maintain tolerance to self-antigens, and prevent autoimmune disease. As used herein the term “activated regulatory CD4 T cells” or “aTreg” refer to a subpopulation of Treg cells with an activated phenotype and a very strong inhibitory function on T cell proliferation.
As used herein, the term “exhausted CD4 T cells” refer to a subpopulation of CD4+ T cells characterized by poor effector functions and high expression of multiple inhibitory receptors.
As used herein the term “effector-memory T cells” or “TEM” refer to a subpopulation of antigen-experienced and long-surviving cells CD4+ T cells characterized by distinct homing capacity and effector function.
As used herein, the term “cell-based therapy” may refer to a therapy configured to boost, activate, inhibit, enlarge the population of or otherwise change the functionality and/or activity and/or distribution of a particular CD4 cell population. According to some embodiments, the cell-based therapy may include administration cells of a CD4 cell subset (also referred to herein as “cell-therapy”. As a non-limiting example, the cell therapy may include administration of CD4-CTLs or of aTreg. According to some embodiments, the cell-based therapy may include administration of an agent capable of inhibiting a particular CD4 cell subset. As a non-limiting example, the agent may be an siRNA targeting Eomes, thereby inhibiting CD4-CTLs. As another non-limiting example, the agent may be an antibody (e.g. an NKG2D antibody). According to some embodiments, the cell-based therapy may include administration of an agent capable of inducing/inhibiting proliferation and/or differentiation of a CD4 T-cell subset, such as but not limited to IL-27, IL-6, IL1, TNF or combinations thereof.
As used herein, the term “biological sample” may refer a sample obtained from a subject which is a body fluid or excretion sample including, but not limited to, seminal plasma, blood, peripheral blood, serum, urine, prostatic fluid, seminal fluid, semen, the external secretions of the skin, respiratory, intestinal, and genitourinary tracts, tears, cerebrospinal fluid, sputum, saliva, milk, peritoneal fluid, pleural fluid, peritoneal fluid, cyst fluid, lavage of body cavities, broncho alveolar lavage, lavage of the reproductive system and/or lavage of any other organ of the body or system in the body and stool. Each possibility is a separate embodiment of the present invention.
In some embodiments, the biological sample, also termed hereinafter ‘the sample’, obtained from the subject comprises blood. In some embodiments, the sample obtained from the subject is peripheral blood. In some embodiments, the sample obtained from the subject comprises serum. In some embodiments, the sample obtained from the subject is a sample of serum.
In some embodiments, the term “peripheral blood”, as used herein, refers to blood comprising of red blood cells, white blood cells and platelets. Typically, the sample is a pool of circulating blood. According to some embodiments, the sample is a peripheral blood sample not sequestered within the lymphatic system, spleen, liver, or bone marrow.
In some embodiments, the sample is a plasma sample. In some embodiments, the sample is a plasma sample derived from peripheral blood.
As used herein, the term “isolate” of a biological sample refer to a subset, derivative or extract derived from the sample. A non-limiting example of an isolate of a biological sample are white blood cells derived from a blood sample. Another non-limiting example includes a T-cell population or a CD4 T-cell population derived from a blood sample.
As used herein the term “functionality” when referring to CD4 T-cells, e.g., CD4 cytotoxic T-cells or Treg cells refers to the “behavior” of the cells after their activation. According to some embodiments, the functionality of the CD4 T-cells may refer to the profile and/or level of cytokines and/or chemokines secreted by the cells (e.g., anti-CD3/anti-CD28, PMA, ConA). According to some embodiments, the profile and/or level of cytokines and/or chemokines secreted may provide an additional layer of validation regarding the status of the immune system (e.g., that the cells are dysregulated).
The term “control value” and “predetermined threshold” (with referral to presence, frequency and/or ratio of CD4 T cells or CD4 T-cell subset), as used herein refers to a standard or reference value which represents the average, standard or normal number of CD4 T cells. This value can be a single value obtained from a single measurement or a mean value obtain from multiple measurements and/or multiple CD4 cell populations and/or CD4 cell populations derived from multiple biological samples. In some embodiments, the control value is a mean value obtained from a plurality of biological sample derived from human subjects. In some embodiments, the control value is an age-matched control. In some embodiments, the terms ‘control value’ and ‘age-matched control” are exchangeable. In some embodiments, the control value comprises young threshold value, also termed hereinafter regulated, efficient and/or naïve threshold value and old threshold value also termed hereinafter dysregulated, aged, mature and/or exhausted threshold value, the former is calculated from a plurality of biological sample derived from young human subjects and the latter is calculated from a plurality of biological sample derived from old human subjects.
In some embodiments, the predetermined threshold with regards to presence, frequency and/or ratio of cytotoxic CD4 T-cells (CD4-CTLs) is about 1%, about 2%, about 5%, about 10%, about 20%, about 30% or about 40% of the total CD4 T-cell population. Each possibility is a separate embodiment.
In some embodiments, when the presence of CD4-CTLs is above about 2%, or above about 5%, above about 10%, above about 20%, above about 30% or above about 40% of the total CD4 T-cell population, the immune system is evaluated as being pro-inflammatory, as tissue undergoing senescence. Each possibility is a separate embodiment.
In some embodiments, when the presence of CD4-CTLs is below 0.5%, below about 1%, below about 2%, below about 5%, below about 10% or below about 20% of the total CD4 T-cell population, the immune system is evaluated as being immune-insufficient. Each possibility is a separate embodiment.
According to some embodiments, when tissue is evaluated as undergoing senescence and/or when a senolytic treatment is desired, the therapy may include administering CD4-CTLs and/or an agent capable of inducing differentiation and/or proliferation of CD4-CTLs. According to some embodiments, evaluation of senescence comprises evaluating the level of one or more senescence markers (e.g., p21 and/or p16) and/or the level of CD4-CTLs and/or identifying low grade systemic inflammation.
According to some embodiments, there is provided a method for evaluating tissue senescence grade, based on the level of CD4-CT1s measured in blood and/or in the tissue. According to some embodiments, the evaluation further comprises taking into account the age of the subject. According to some embodiments, the evaluation further comprises taking into account the medical history and/or genetic background of the subject.
According to some embodiments, when the level of CD-4 CTLs increases above 1%, 2%, 5%, 8% or 10% of the total CD4 T-cell population, the subject has tissue senescence. Each possibility is a separate embodiment. According to some embodiments, the region of tissue senescence may be estimated based on the subject's medical history and/or genetic background.
According to some embodiments, the CT4 T-cells and/or the agent capable of inducing differentiation and/or proliferation of CD4-CTLs is administered systemically, e.g., by IV-injection. According to some embodiments, the CT4 T-cells and/or the agent capable of inducing differentiation and/or proliferation of CD4-CTLs is administered locally e.g., by injection into senescent tissue.
According to some embodiments, the threshold with regards to presence, frequency and/or ratio of cytotoxic CD4 T-cells (CD4-CTLs) for evaluating the immune system as proinflammatory may be the same as the threshold with regards to presence, frequency and/or ratio of cytotoxic CD4 T-cells (CD4-CTLs) for evaluating the immune system as immune-insufficient. For example, the immune system may be evaluated as proinflammatory if the presence, frequency and/or ratio of CD4-CTLs is above 10% and as immune-insufficient if below 10% of the total CD4 T-cell population.
According to some embodiments, the threshold with regards to presence, frequency and/or ratio of cytotoxic CD4 T-cells (CD4-CTLs) for evaluating the immune system as proinflammatory may be different than the threshold with regards to presence, frequency and/or ratio of cytotoxic CD4 T-cells (CD4-CTLs) for evaluating the immune system as immune-insufficient. For example, the immune system may be evaluated as proinflammatory if the presence, frequency and/or ratio of CD4-CTLs is above 20% and as immune-insufficient if below 10% of the total CD4 T-cell population.
According to some embodiments, the threshold may be age related. As a non-limiting example, the threshold with regards to presence, frequency and/or ratio of cytotoxic CD4 T-cells (CD4-CTLs) for evaluating the immune system as proinflammatory may be lower in young adults as compared to elders.
In some embodiments, the term “a plurality”, as used herein, refers to at least two. According to some embodiments, the term “a plurality” refers to at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 and 17. Each possibility is a separate embodiment.
In some embodiments, “detecting a level of a biomarker” comprises assessing the presence, absence, quantity or relative amount (which can be an “effective amount”) of each biomarker in the plurality of biomarkers, within a clinical or subject-derived sample, including qualitative or quantitative concentration levels of such biomarker.
In some embodiments, “detecting a level of a biomarker” comprises determining the expression level of each biomarker of said plurality of biomarkers or determining the amount, or relative amount, of DNA or cDNA corresponding to the expression level of mRNA biomarker(s).
In some embodiments, the plurality of biomarkers are selected from the group consisting of: EOMES, CCL3, CCL4, CCL5, CCR7, CD7, CD8, CD74, CD137, CD134, CD25, CD44, CD62L, CD81, CD200, Cst7, Ms4a4b, NKG2D, Nfatc1, Runx2, Runx3, Tbx21, GzmB, GzmK, perforin, FOXP3, GITR, Helios, Lgals1, IGFbp4, LAG3, IL-1a, IL-1b, IL1R2, IL2RA, IL-6, IL-10, IL-17A, IL-21, IL-18R1, IL-27, IFN-b, IFN-g, Isg15, PD1, Lef1, Lfit3, MCP1, Satb1, Ccr7, Aw112010, S100a10, S100a11, S100a4, Sell, Pdcd1, Izumo1r, Ikzf2, Igfbp4, Itgb1, Itgb7, GM-CSF, Sostdc1, Tbcld4, TNF, TNFRSF4, TNFSF8, TNFRSF8, Ct1a2a, Ct1a4 and TNF-RSF9/4. Each possibility is a separate embodiment.
In some embodiments, the plurality of biomarkers comprises at least two biomarkers. In some embodiments, the plurality of biomarkers comprises at least three biomarkers. In some embodiments, the plurality of biomarkers comprises at least four biomarkers.
According to some embodiments, obtaining a biological sample comprising tissue or fluid is carried out by any one or more of the following collection methods blood sampling, urine sampling, stool sampling, sputum sampling, aspiration of pleural or peritoneal fluids, fine needle biopsy, needle biopsy, core needle biopsy and surgical biopsy, and lavage. Each possibility is a separate embodiment of the present invention. Regardless of the procedure employed, once a biopsy/sample is obtained the level of the plurality of biomarkers can be determined and evaluation can thus be made.
The proportion and identity of a pharmaceutically acceptable excipients used in the pharmaceutical composition may be determined by the chosen route of administration, compatibility with live cells, and standard pharmaceutical practice. Generally, a pharmaceutical composition is formulated with components that do not destroy or significantly impair the biological properties of the active ingredients.
In some embodiments, the pharmaceutical composition is administered locally, e.g., in a tumor, or systemically.
The term “treating” as used herein refers to an approach for obtaining beneficial or desired results, including clinical results. Beneficial or desired clinical results can include, but are not limited to, alleviation or amelioration of one or more symptoms or conditions, diminishment of extent of disease, stabilization of the state of disease, prevention of spread or development of the disease or condition, delay or slowing of disease progression, amelioration or palliation of the disease state, and remission (whether partial or total). “Treating” can also mean prolonging survival of a patient beyond that expected in the absence of treatment. “Treating” can also mean inhibiting the progression of disease, slowing the progression of disease temporarily, although more preferably, it involves halting the progression of the disease permanently.
As used herein, the term “inflammaging” refers to age associated infections, chronic inflammatory disorders, such as but not limited to arthritis.
In some embodiments, the subject in need thereof is a subject in need of treatment or prevention, is human. In some embodiments, the human subject is over the age of 60, over the age of 70 or over the age of 80. In some embodiments, the human subject is is suffering from a immune associated disorder (optionally regardless of age).
The term “about” when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass variations of ±20% or in some instances ±10%, or in some instances ±5%, or in some instances ±1%, or in some instances ±0.1% from the specified value, as such variations are appropriate to perform the disclosed methods.
The terms “subject”, “patient” or “individual” generally refer to a human, although the methods of the invention are not necessarily limited to humans and should be useful in other mammals.
Materials and Methods
Mice
WT C57BL/6 and CD45.1 (B6.SJL-Ptprca Pepcb/BoyJ) mice were purchased from the Jackson Laboratory (Bar Harbor, Me.) and were housed under specific pathogen-free conditions at the animal facility of Ben-Gurion University. WT C57BL/6 Mice were kept in different age batches from 2 to 24 months. All mice were checked for any macroscopic abnormalities (according to the Jackson guide—“AGED C57BL/6J MICE FOR RESEARCH STUDIES”). Animals with skin lesions, organ specific problems, or behavioral issues were discarded from the study. All surgical and experimental procedures were approved by the Institutional Animal Care and Use committee (IACUC) of Ben-Gurion University of the Negev, Israel.
Tissue Processing for Flow Cytometry and In Vitro Assays
Spleen: see Materials and methods, section “Samples processing for single-cell RNA sequencing (scRNA-seq)”.
Lymph nodes: Mice were killed with overdose of isoflurane and lymph nodes were harvested from inguinal, mesenteric, cervical and axillar areas. Then, lymph nodes were mashed into 70 m cell strainer and cells were washed and counted.
Blood: Blood was collected into EDTA-coated tubes (MiniCollect, Greiner Bio-One) from euthanized mice using cardiac puncture. Red blood cells were then lysed using blood lysis buffer (BD bioscience) and the remaining leukocytes were washed twice and counted.
Bone marrow: Mice were killed with overdose of isoflurane. Femurs and tibias were collected. Cells from the bone marrow were obtained by flushing the bones with injected sterile PBS. Red blood cells were removed using 500 μl ACK lysis buffer for 1.5 minutes.
Flow Cytometry
For extracellular staining, cells were washed with FACS staining buffer (PBS supplemented with 2% FBS and 1 mM EDTA) and incubated with Fc receptor blocker (TrueStain fcX; BioLegend) for 5 minutes at 4° c. To differentiate between live and dead cells, a viability staining step was done using an eFluor780-Fixable Viability dye (eBioscience) following manufacture instructions. Cells were then incubated with primary antibodies for 25 minutes at 4° c. and were washed twice with a FACS staining buffer. The following antibodies were used for membranal staining: PE-conjugated anti-CTLA4 (4C10-4B9; BioLegend), PE/cy7-conjugated anti-CD25 (3C7; BioLegend), AF700-conjugated anti-CD62L (Mel-14; BioLegend), BV605 or BV785-conjugated anti-PD1 (29F.1A12; BioLegend), APC-conjugated anti-CD81 (Eat-2; BioLegend), FITC-conjugated anti-CD8 (53-6.7; BioLegend), PerCP/cy5.5-conjugated anti-CD44 (IM7; BioLegend), AF700 or BV785-conjugated anti-CD4 (RM4-5; BioLegend), PE-conjugated anti-CD121b (4E2; BD Biosciences), BV421-conjugated anti-CD25 (PC61; BioLegend), BV605-conjugated anti-CD195 (C34-3448; BD Biosciences), BV785-conjugated anti-LAG3 (C9B7W; BioLegend), BV421-conjugated anti-CD4 (GK1.5; BioLegend), PerCP/cy5.5-conjugated anti-CD8 (53-6.7; BioLegend), PE-conjugated anti-CD137 (17B5; BioLegend), PE/cy7-conjugated anti-CD134 (OX-86; BioLegend) and PE-conjugated anti-CD178 (MFL3; eBioscience). After staining for membranal markers, intracellular labeling was performed: Cells were fixed and permeabilized using the FOXP3/Transcription factor staining kit (eBioscience), blocked with Rat serum (1 μl per 100p of staining buffer) and stained with the following antibodies: BV605-conjugated anti-TNF (MP6-XT22; BioLegend), BV605-conjugated anti-IL17a (TC11-18H10.1; BioLegend), FITC or BV510-conjugated anti-IL2 (JES6-5H4; BioLegend), BV421 or BV786-conjugated anti-IFN-γ (XMG1.2; BioLegend), BV421-conjugated anti-IL10 (JES5-16E3; BioLegend), APC-conjugated anti-Granzyme B (QA16A02; BioLegend), PE-conjugated anti-CCL5 (2E9/CCL5; BioLegend), PE/cy7-conjugated anti-EOMES (Dan11mag; eBioscience), AF488-conjugated anti-FOXP3 (150D; BioLegend), APC-conjugated anti-IL21 (#149204; R&D systems), PE/dazzle-conjugated anti-Helios 22F6; Biolegend) and APC-conjugated anti-Perforin (B-D48; BioLegend). All flow cytometry experiments were performed with the CytoFLEX instrument (Beckman Coulter). Data were analyzed with the FlowJo (v-10.5.3) software. Gating strategies were set based on fluorescence minus one (FMO), unstained samples and unstimulated samples (when needed). All the samples in the experiment excluded dead cells, clumps and debris.
Clustering Analysis of Flow Cytometry Data
Clustering analysis of flow cytometry data was done using FlowJo (v-10.5.3). First dead cells, doublets and non-lymphocyte cells were excluded (based on viability staining and FSC/SSC channels). CD4+ cells were used for further analysis. Data were sampled using “down sampling” function to get 40,000 representative cells from each sample. Then, a t-SNE algorithm was applied with the following parameters: Iteration=1000, Perplexity=40, Learning rate (eta)=2800. Mean fluorescence intensity (MFI) projected on the t-SNE plots for each protein to infer the cluster identity.
Suppression Assay
For in vitro suppression assay, naïve CD4+ T cells were isolated from spleens of young (2 months) CD45.1 mice using naïve isolation kit (EasySep™ Mouse Naïve CD4+ T Cell Isolation Kit, StemCell Technologies), labelled with CFSE (CellTrace™ proliferation kit, Invitrogen) and used as responder cells (2×104 cells per well). Then, cells were cultured in 96-well plates with irradiated 2×104 APCs (as feeder cells) in the presence of sorted CD25highCD81− or CD25highCD81+ Tregs at 1:1, 1:2 and 1:4 responders:Tregs ratios. Cells were stimulated with anti-CD3 (1 μg/ml) for 72 hours. Proliferation (defined as all cells with CFSE dilution) of responder cells was analyzed to assess the suppression of Tregs cells. The percentage of suppression was determined as (100—(% of proliferating cells with Tregs)/(% of proliferating cells without Tregs)).
Serum Cytokines Measurement
Mouse peripheral blood was extracted after right atrial puncture into a 2 ml Eppendorf. Then, blood tubes were incubated at room temperature for coagulation (15 minutes). After incubation, tubes underwent centrifugation step (450 g), and serum was collected. For cytokines measurement, LEGENDplex mouse inflammation kit (BioLegend) was used following manufacture instruction. Data were acquired on CytoFLEX instrument (Beckman Coulter), and analyzed using LEGENDplex analysis software.
Statistical Analysis for Flow Cytometry Experiments
Spearman correlation between the age of mice and the proportions of RECs and naïve cells in spleen was computed in R v3.4.2 using stats package v3.4.1. For statistical analysis GraphPad Prism (version 7.0a) was used. Paired T test was used for comparisons between two groups from the same biological samples. For analysis of more than two group, one-way ANOVA was used and corrected by Bonferroni correction for multiple comparisons.
In order to classify CD4 T-cell subsets in an unbiased manner, cells were clustered by their transcriptomic profiles and the robustness of the clusters' identity assessed. Seven distinct and robust clusters were identified.
Of the seven distinct clusters, four were matching established subsets: two populations of naïve T cells overexpressing Sell, Lef1 and Igfbp4 genes, which differ by the expression of Isg15 gene (denoted naïve and naïve_Isg15); a population of resting regulatory T cells (rTregs), labeled based on their classical expression of Foxp3 and II2ra genes, together with the expression of naïve-associated genes Lef1 and Sell; and effector-memory T cells (TEM) expressing the S100a4, Igals1 and Itgb1 genes. The transcriptional signatures of the three remaining subsets have not been previously defined in the context of aging, and include: activated regulatory T cells (aTregs) overexpressing Foxp3, Cd81, Cd74 and Cst7 genes, together with aTregs-associated genes such as Tnfrsf4, Tnfrsf9, Tnfrsf18 and Ikzf2 genes; cells with an exhaustion signature (denoted exhausted) overexpressing the Lag3, Tbcld4, Sostdc1 and Tnfsf8 genes; and cells overexpressing genes such as EOMES, Gzmk and Ct1a2a, which are commonly associated with CD8 T cells (denoted cytotoxic), and were previously described in the context of viral infection and cancer as CD4 cytotoxic T cells.
Next, the proportion of each subset in old versus young mice was compared and is presented in Table 1, below and further illustrated in
As seen, naïve subsets were enriched in young mice (Naïve: log(median)=−0.27, p=0.03, and Naïve_Isg15: log(median)=−0.23, p=0.03). rTregs subset was equally distributed (log(median)=0.02, p=0.89). Four subsets were enriched in every old mouse: TEM (log(median)=0.51, p=0.03), aTregs (log(median)=1, p=0.03), exhausted (log(median)=1.32, p=0.03) and cytotoxic (log(median)=1.46, p=0.03) subsets. Whereas the two naïve subsets were significantly enriched in young mice, the rTregs subset had a similar abundance in both age groups, while the TEM subset was dominant in old mice. Notably, the aTregs, exhausted, and cytotoxic subsets (collectively denoted RECs to represent these Regulatory, Exhausted and Cytotoxic subsets) were highly enriched in all aged mice, accounting for ˜30% of the CD4 T cells and were negligible in young mice (˜1%).
Overall, the results demonstrate that aging is marked by a complex landscape of CD4 T cells, with expansion of subsets with effector (including TEM, exhausted and cytotoxic cells) and regulatory (aTregs) signatures, associated with serum markers of chronic inflammation.
To assess the dynamics of RECs over time, their relative abundance in spleens of healthy mice at 2, 6, 12, 16 and 24 months of age, was measured using flow cytometry. Exhausted cells (defined as CD4+PD1+CD62L-FOXP3-EOMES-CCL5-, steadily accumulated from 6 months of age (r=0.94, p=1.5 ×10-12, Spearman correlation), and coincided with continuous decreased proportions of naïve cells (defined as CD4+CD62L+PD1-CD81−EOMES-CCL5— r=−0.96, p=1.7×10-14, Spearman correlation;
To assess suppressive function of Tregs aTregs (CD25highCD81+;
A preliminary clinical study in human subjects revealed that CD4-CTLs accumulate also in elderly, but not in adult, healthy human individuals, as shown in
Blood PBMCs was obtained from healthy young (age: 25-35 years, n=7) and old (age: >70 years, n=5) human individuals and analyzed by flow cytometry. As shown in
In addition, the frequency of CD4-CTLs is higher in elderly than in young human individuals, where in young individual these cells are barely detected (
EM cells were live sorted by FACS using CD4+CD62L-CD44+CCL5+PD1low as markers. The sorted cells are subsequently expanded in the presence of circulating inflammatory cytokines such as but not limited to IL-27, IL-6, IL1, TNF etc. and evaluated for cytotoxic activity by FACS for the presence of EOMES, GrzK IFNg and/or other CD4-CTL markers.
The re-organization of the CD4 T-cell compartment with aging—and, specifically, at the stage where the CD4-CTL subset sharply increases to 30-40% of the CD4 T-cell compartment-may provide protection from tumors and chronic viral infections; however, it can also facilitate chronic inflammation, declined immunity, and killing functions, which can result in significant tissue damage and severe defects in overall immunity and tissue repair.
Since EOMES is expressed in CD4-CTLs and is essential for their inflammatory and cytotoxic function, mice incapable of producing CD4-CTLs were generated.
To this end, CD4CreER mice were crossed with EOMESlox/lox mice and administered IP with TMX at 12, 13, and 14 months of age, i.e., when the CD4-CTLs accumulate.
CD4-CreER, EOMESlox/lox, ROSAmT/mG, OTII+ TCR and C57BL/6 CD45.1+ mice were purchased from the Jackson Laboratory (Bar Harbor, Me.) and housed under specific pathogen-free conditions at the animal facility of Ben-Gurion University of the Negev, Israel (BGU). Mice are kept in different age batches, from 2 to 24 months old, and routinely monitored for pathogens and health issues. All surgical and experimental procedures will be approved by the Institutional Animal Care and Use committee (IACUC) of BGU.
EOMESlox/lox genotype was confirmed by PCR as shown in
In order to evaluate the impact of CD4-CTLs on chronic inflammation, CD4CreER-EOMESlox/lox mice are administered IP with TMX at 12, 13, and 14 months of age, i.e., when the CD4-CTLs usually accumulate. Two weeks after the final TMX administration, the mice are analyzed for aging biomarkers and subsequently injected with the influenza vaccine or with an adjuvant alone and analyzed, 14 d later, for presence of naïve, CM, EM, exhausted, Treg, and CD4-CTL subset composition in the blood, BM, and spleen, as compared with littermate controls. Splenocytes are stimulated with an influenza lysate and response of the CD4 T-cell subset is analyzed with ELISA and flow cytometry.
Serum samples are analyzed for influenza-specific antibodies and for an array of cytokines and chemokines, including, but not limited to, IL-27, GM-CSF, IL-1b, IL-6, TNF-a, IFNb, IFN-g, IL-17A and CCL2.
Rotarod test: CD4CreER-EOMESlox/lox mice and littermate controls are trained on the RotaRod for 3 days at speeds of 4, 6, and 8 rounds per minute (RPM) for 200 seconds. On the test day, mice will be placed onto the RotaRod, starting at 4 RPM and accelerating to 40 RPM over 5 min trials. The speed is recorded when the mouse drops off the RotaRod. Results are averaged from 3 or 4 trials and normalized to the baseline speed of young mice. The data is compared to those obtained for wt mice, as shown in
Hanging Test: For the hanging test CD4CreER-EOMESlox/lox mice and littermate controls are placed onto a 2-mm-thick metal wire placed 35 cm above a padded surface. The mice are allowed to grab the wire with their forelimbs only. Hanging time is normalized to body weight as hanging duration (sec)×body weight (g). Results are averaged from 2-3 trials for each mouse. The data is compared to those obtained for wt mice, as shown in
Grip Test: Forelimb grip strength is performed using a Grip Strength Meter (Columbus Instruments, Columbus, Ohio) for CD4CreER-EOMESlox/lox mice and littermate controls. Results are averaged over 3 or 4 trails. The data is compared to those obtained for wt mice, as shown in
Metabolic Tests: A comprehensive metabolic and physical monitoring is performed using the PROMETHION system (Sable systems, NV, USA). Daily activity, wheel usage, sleeping, food intake, water intake and gas exchange will be recorded over a 48h period. The data is extracted using expeData software (Sable systems, NV, USA), and analyzed using prism 8.2.1 (GraphPad). The data is compared to those obtained for wt mice, as shown in
The correlation between the frequency of CD4 T cell subsets (naïve, exhausted, memory, and CD4-CTLs) and the physical and metabolic phenotypes of CD4CreER-EOMESlox/lox mice is further compared to the correlation observed for wt mice (
Wildtype C57BL6 mice aged 18-20 months are treated IP once a week with anti-IL27 (25 microgram/mouse). After 4 injections, the mice undergo analysis for frailty and metabolic parameters (as described in
In order to evaluate the correlation between CD4-CTLs and ageing, old mice (18-24 months) underwent a physical and metabolic assessment using metabolic cages. The mice were monitored for 48 h using the PROMETHION system (Sable systems, NV). Subsequently, the mice were sacrificed and analyzed for their level of inflammatory cytokines and chemokines in serum samples using Multiplex ELISA, Immunohistochemistry (IHC) analysis for senescent cell in liver tissues, and CD4 T-cell subsets analysis using flow cytometry (see
A heat-map showing correlations between the frequency of CD4 T cell subsets (naïve, exhausted, memory, CD4-CTL's) and CD8 cells, and the physical and metabolic tests (including food and water intake in g, wheel activity overall activity in m/48h and energy expenditure (EE) in Kcal/hr (n=12)) was generated. All correlations were calculated assuming the data exhibit a Gaussian distribution (Pearson correlation). As seen from
Moreover, when a per mice correlation between was calculated between wheel activity (m), wheels speed (m/s), overall activity (m) and the percentage of CD4-CTLs a high variability between mice was observed, but a tight correlation between low activity and high percentage of CD4-CTLs was observed (red dots in
As mice are known to be particularly active during the night the correlation was between CD4-CTL level and the activity(m) and wheel activity(m) was further evaluated while splitting the activity into day and night cycles. As seen from
In order to evaluate the correlation between ageing and certain easily detectable biomarkers, a heat-map was generated, assuming the data exhibit a Gaussian distribution (Pearson correlation). As seen from
A similar correlation was found between abundance of p16 and p21 (senescence markers) around the central vein in liver tissue and high CD4-CTL levels, as seen from the representative images of immunohistochemistry stainings (
In order to evaluate the impact of ageing on T-cell population distribution, splenocytes obtained from young (1-month-old) CD45.1 mice were injected into two groups of mice: (a) young B6 WT mice (2 months old) and (b) old B6 WT mice (26 months old). Thirty days after the injection, the spleens were harvested and analyzed via flow cytometry for T-cell distribution, as outlined in
As seen from
However, as seen from the representative flow cytometry plots of
Importantly, a comparison between the percentage of endogenous CD4 T cells (CD45.2) and exogenous CD4 T cells (cd45.1) in the old mice showed no difference in the abundance of cytotoxic CD4 T cells and exhausted effector cells (CD44+PD1+) (
Carbon tetrachloride treatment induces liver senescence and liver fibrosis in mice. Accordingly, using this model, liver, spleen and blood may be analyzed for their T-cell distribution, as essentially outlined in
As seen from
Notably, as seen from the histograms of
Furthermore, as seen from the histograms of
In order to further evaluate the role of CD4-CTLs in senescence, Eomes KO mice (CreER+) and control mice (CreER−) were injected with Tamoxifen prior to and during treatment with carbon tetrachloride. After 48 days, liver, spleen and blood were analyzed, as outlined in
As seen from
As seen from
Moreover, as seen from
In order to evaluate the role of CD4 T-cells in cancer, young (2 month) and old (12+ months) C57BL6 mice were injected with either cells3*106 cells/injection obtained from an aggressive or less aggressive and more immunogenic head and neck squamous cell carcinoma tumors, as outlined in table 2.
Tumor size was evaluated over time. As seen from
As seen from
When specifically evaluating the level of CD4 CTLs, an increase in the percentage of CD4 CTLs in the spleen was observed in all old mice as compared to young mice (see
Notably, a similar pattern was observed for the naïve CD-4 T-cells, which were found at high-levels in the low-grade tumor, but not in the high-grade tumor of the young mice (
The role of CD4-CTLs in tumors is assessed in CD4CreER− EOMESlox/lox mice and littermate control mice induced to form a tumor (e.g. by orthotopic injection of B16 melanoma cells). Tumor size and/or tumor progression is evaluated e.g., by imagining.
The role of CD4-CTLs in Alzheimer's disease is assessed in a mouse model of Alzheimer's disease (AD)-like pathology (Eremenko, 2019 Mittal, 2019; Strominger, 2018). Bone marrow chimera mice are generated by transplanting the bone marrow of CD4CreER-EOMESlox/lox mice into the 5XFAD mouse model of AD at 6-8 months of age. Two months later, the amyloid deposition and the associated pathology in the brain, are analyzed as essentially described (Eremenko E. et al., EBioMedicine. 2019 May; 43:424-434and Mittal K. et al., iScience. 2019 Jun. 28; 16:298-311).
aTreg cells are injected IP or IV into 5XFAD chimera mice at age 10-12 mo. Two months later, the amyloid deposition and the associated pathology in the brain, are analyzed as essentially described (Eremenko, ibid. and Mittal, ibid.).
Bone marrow of CD4CreER-EOMESlox/lox is transplanted into 5XFAD mice. Two months later, the amyloid deposition and the associated pathology in the brain, are analyzed as essentially described (Eremenko, ibid. and Mittal, ibid.).
Alternatively, CD4-CTLs are depleted by weekly IP injections of anti-IL27 (25 microgram/mouse) into wt C57BL6 mice aged 18-20 months. 1-2 months later, the amyloid deposition and the associated pathology in the brain, are analyzed as essentially described (Eremenko, ibid. and Mittal, ibid.).
CD4-CTLs are injected IV or IP into control and/or CD4CreER-EOMESlox/lox mice induced for tumor formation. Tumor size and/or tumor progression is evaluated, e.g. by imagining.
Fibroblasts are induced by gamma-radiation to become senescent. Subsequently the fibroblasts are incubated with effector memory cells with or without inflammatory cytokines such as IL-1, TNF, IL-6, TGFb, IFN-g alone or in combination for 1-6 days. CD4-CTL differentiation and proliferation is inspected by flow cytometry. Cytokines are validated by neutralizing ab's to specific cytokines. In order to evaluate whether the differentiation depends on antigen presentation the cells are co-cultured with MHCII blocking ab's.
Alternatively, CD4-CTLs are generated by retroviral transduction to overexpress the key transcription factors of CD4-CTLs, specifically EOMRS, Runx3, Tbet, RORa. Effector memory CD4 T cells are isolated and undergo activation while being transduced with retroviral vectors to over express one or more of the transcription factors.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/IL2021/051047 | 8/25/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63073183 | Sep 2020 | US |
