WO2025199015A1 - Transcriptome-based methods for diagnosing alzheimer's disease - Google Patents

Abstract

This invention provides skin cell fibroblast- and blood-based methods for determining whether a human subject has a gene expression profile characteristic of AD. This invention also provides related methods for determining whether a demented human subject is afflicted with AD or non-ADD, and for determining whether a non-demented human subject has an increased likelihood of becoming afflicted with AD.

Description

Dkt. CR-13PCT TRANSCRIPTOME-BASED METHODS FOR DIAGNOSING ALZHEIMER’S DISEASE This application claims the benefit of U.S. Provisional Application No.63/672,306, filed July 17, 2024, and U.S. Provisional Application No.63/566,498, filed March 18, 2024, the contents of both of which are incorporated herein by reference. Throughout this application, various publications are cited. The disclosure of these publications is hereby incorporated by reference into this application to describe more fully the state of the art to which this invention pertains. Background of the Invention Alzheimer's disease (AD) is a progressive neurodegenerative disease and one of the major healthcare challenges world-wide, being the most prevalent form of dementia and the third leading cause of death in the elderly (2). Current AD diagnostic methods, with only preliminary autopsy-validation, are notoriously complex, expensive, slow, invasive and often involving mental status examination, spinal fluid tests, magnetic resonance imaging (MRI) or positron emission tomography (PET) scans (3-6). Beside the emergence of new treatments showing modest promise to reduce the rate of progression when administered in the initial disease stages (7, 8), early diagnosis is rare and more than half of cases remain undiagnosed (9). About 12-23% of patients diagnosed with AD are misdiagnosed, showing no sufficient AD pathology at autopsy (10). Timely and secure AD diagnosis, therefore, represents a huge unmet medical need for successful treatment and drug discovery (11). Accurate, rapid, and early diagnosis would greatly affect clinical trials for new AD therapies, because potential study patients could then be more accurately identified and treated. Furthermore, ascertaining the presence or absence of ongoing AD pathology may monitor therapeutic efficacy, separating responders from non- responders, thus avoiding the prescription of costly and ineffective drugs with potentially harmful side-effects. Gene expression profiles allow investigators to study the simultaneous, integrated expression of thousands of genes in biological systems. In principle, AD gene expression profiles might serve as rapidly implemented molecular fingerprints that may allow for the economic, accurate, and objective diagnosis of AD. To this end, several transcriptomic analyses have been conducted to assess AD status (12-42). While most of these studies were limited to post-mortem brain samples, others identified differentially expressed genes in blood samples of possible or probable AD patients with potential as diagnostic classifiers (12-14, 16, 17, 32-39, 41, 42). None of these studies, however, was performed on skin and/or blood samples of brain autopsy- confirmed patients, in which the definitive presence of AD pathology and/or other comorbidities can only be definitively assessed. It was, therefore, unknown if these classifiers were dataset- or tissue-specific and if they can differentiate the presence or absence of ongoing AD pathology and thereby allow one to monitor effective treatments. Summary of the Invention This invention provides a first method for determining whether a human subject has a gene expression profile characteristic of Alzheimer’s disease (“AD”) comprising the following step: in a skin cell fibroblast population derived from the subject, measuring the expression levels of a plurality of genes in the set of genes set forth in Table 2, whereby the subject has a gene expression profile characteristic of AD if the measured expression levels are consistent with those genes’ expression levels in skin cell fibroblasts derived from AD patients. This invention also provides a second method for determining whether a human subject has a gene expression profile characteristic of Alzheimer’s disease (“AD”) comprising the following step: in a peripheral whole blood sample derived from the subject, measuring the expression levels of a plurality of genes in the set of genes comprising RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17, whereby the subject has a gene expression profile characteristic of AD if the measured expression levels are consistent with those genes’ expression levels in peripheral whole blood derived from AD patients. This invention provides a first method for determining whether a demented human subject is afflicted with Alzheimer’s disease (“AD”) or non-Alzheimer’s disease dementia (“non-ADD”), comprising the following step: in a skin cell fibroblast population derived from the subject, measuring the expression levels of a plurality of genes in the set of genes set forth in Table 2, whereby (i) the subject is afflicted with AD if the measured expression levels are consistent with those genes’ expression levels in skin cell fibroblasts derived from AD patients, and (ii) the subject is afflicted with non-ADD if the measured expression levels are consistent with those genes’ expression levels in skin cell fibroblasts derived from non-ADD patients. This invention also provides a second method for determining whether a demented human subject is afflicted with Alzheimer’s disease (“AD”) or non-Alzheimer’s disease dementia (“non-ADD”), comprising the following step: in a peripheral whole blood sample derived from the subject, measuring the expression levels of a plurality of genes in the set of genes comprising RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17, whereby (i) the subject is afflicted with AD if the measured expression levels are consistent with those genes’ expression levels in peripheral whole blood derived from AD patients, and (ii) the subject is afflicted with non-ADD if the measured expression levels are consistent with those genes’ expression levels in peripheral whole blood derived from non-ADD patients. This invention provides a first method for determining whether a non-demented human subject has an increased likelihood of becoming afflicted with Alzheimer’s disease (“AD”), comprising the following step: in a skin cell fibroblast population derived from the subject, measuring the expression levels of a plurality of genes in the set of genes set forth in Table 2, whereby the subject has an increased likelihood of becoming afflicted with AD if the measured expression levels are consistent with those genes’ expression levels in skin cell fibroblasts derived from AD patients. Finally, this invention also provides a second method for determining whether a non- demented human subject has an increased likelihood of becoming afflicted with Alzheimer’s disease (“AD”), comprising the following step: in a peripheral whole blood sample derived from the subject, measuring the expression levels of a plurality of genes in the set of genes comprising RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17, whereby the subject has an increased likelihood of becoming afflicted with AD if the measured expression levels are consistent with those genes’ expression levels in peripheral whole blood derived from AD patients. Brief Description of the Figures Figures 1A-1D Transcriptome analysis of AD fibroblasts revealed a distinct disease molecular signature. 1A. Unsupervised hierarchical clustering analysis (similarity measure: Euclidean; linkage rule: Wards) of 472 DEGs among AD and control subjects. The heatmap shows the median-normalized expression of individual genes across all samples, where genes and patients were clustered on the basis of expression similarities. In this two-dimensional presentation, each row represents a fibroblast sample from a control or AD patient, while each column represents a single gene. In the dendrograms shown (top and left), the length and subdivision of the branches display the relatedness of the expression of the genes (top) and the fibroblast samples (left). Heatmap colors represent relative mRNA expression as indicated in the color key: red indicates up-regulation, green indicates down-regulation, and black indicates no change. 1B. Venn diagram showing overlap of the inventors’ list of 472 DEGs with genes statistically deregulated (Moderated T-Test FDR corrected p value <0.05) in blood and brain samples of AD patients. 1C. Pie chart showing the top 15 BP-GO terms enriched in the 472 DEGs in AD fibroblasts. Numbers show the percentage of genes in each category. 1D. Representation of the top 15 most significantly enriched (FDR, P value <0.05) canonical pathway maps associated with DEGs in AD fibroblasts vs. control group. A histogram of statistical significance (-log10 FDR P value) is shown: the list is arranged in descending order with the most significant pathways at the top. Figures 2A and 2B PPI network construction of DEGs in AD fibroblasts and hub clustering modules. 2A. PPI network was constructed using the STRING Online Database and visualized by Cytoscape. Each node represents one gene/protein (n = 368), and the interaction between two nodes is defined as the edge (n = 8974). The node size was proportional to the degree of interaction. Node color is associated with the fold change: genes down-regulated in AD fibroblasts vs. CTRL are colored in green, while red nodes correspond to genes up-regulated. 2B. Three significant interacting and functional clusters from the general PPI network were obtained by the MCODE plugin. The node size was proportional to the degree of interaction. Node color is associated with the fold change: genes down-regulated in AD fibroblasts vs. CTRL are colored in green, while red nodes correspond to genes up-regulated. Figures 3A and 3B Prediction performance of the disease classifier in an AD mouse model following treatment with aducanumab. (3A) Histogram and table showing the PLS performance measuring of the AD classifier in the SRP465463 dataset that includes RNA-seq data from hemi-forebrain of APP/PS1-transgenic mice treated from 10 months of age with 4 doses of aducanumab, or IgG controls (1). Performance measurements are reported in terms of overall accuracy, sensitivity and specificity (%). Accuracy indicates the ratio of correctly predicted observations to the total number of observations; sensitivity represents the ability of the model to correctly identify positive cases; specificity is the ability of the model to correctly identify negative cases. (3B) Hierarchical clustering analysis (similarity measure: Euclidean; linkage rule: Wards) of AD signature genes in the hemi-forebrain of APP/PS1-transgenic mice following treatment with aducanumab or IgG controls. Genes were arranged in a dendrogram in which the pattern and length of the branches reflect the relatedness of the expression levels under three different experimental conditions. Green, black and red cells, respectively, are transcript levels below, equal or above the median abundance across all conditions. Color intensity reflects the magnitude of the deviation from the median. Detailed Description of the Invention Definitions As used herein, a subject is afflicted with “AD pathology” if the subject’s brain has amyloid plaques and/or neurofibrillary tangles. In one embodiment, a subject is afflicted with AD pathology if the subject’s brain has amyloid plaques or neurofibrillary tangles. In another embodiment, a subject is afflicted with AD pathology if the subject’s brain has both amyloid plaques and neurofibrillary tangles. Non-human subjects afflicted with AD pathology include, for example, AD transgenic mouse models 3xTg, 5xFAD, Tg2576, and APP/PS1. As used herein, “administer”, with respect to an agent, means to deliver the agent to a subject’s body via any known method. Specific modes of administration include, without limitation, intravenous, oral, sublingual, intramuscular, transdermal, subcutaneous, intraperitoneal, and intrathecal administration. In addition, in this invention, the various agents can be formulated using one or more routinely used pharmaceutically acceptable carriers. Such carriers are well known to those skilled in the art. For example, oral delivery systems include tablets and capsules. These can contain excipients such as binders (e.g., hydroxypropylmethylcellulose, polyvinyl pyrilodone, other cellulosic materials and starch), diluents (e.g., lactose and other sugars, starch, dicalcium phosphate and cellulosic materials), disintegrating agents (e.g., starch polymers and cellulosic materials) and lubricating agents (e.g., stearates and talc). Injectable drug delivery systems include, for example, solutions, suspensions, gels, microspheres and polymeric injectables, and can comprise excipients such as solubility-altering agents (e.g., ethanol, propylene glycol and sucrose) and polymers (e.g., polycaprylactones and PLGA's). Implantable systems include rods and discs and can contain excipients such as PLGA and polycaprylactone. As used herein, “Alzheimer’s disease” (also referred to as “AD”) means a concurrent affliction with the following three symptoms: (i) dementia; (ii) amyloid plaques; and (iii) neurofibrillary tangles. Dementia can be diagnosed during life. Cerebral amyloid plaques and neurofibrillary tangles can, for example, be diagnosed during autopsy. This definition of Alzheimer’s disease is the one provided by the National Institute of Neurological Disorders and Stroke (NINDS) of the National Institutes of Health (NIH) and is known as the “gold standard.” As used herein, a “cognitively impaired” non-demented human subject includes, without limitation, a human subject found to be cognitively impaired using a medically accepted method for making such a determination. Medically accepted methods for determining whether a human subject is cognitively impaired include, for example, the MMSE and the MoCA. Similarly, a human subject afflicted with mild cognitive impairment (MCI) includes, without limitation, a human subject found to be afflicted with MCI using a medically accepted method for making such a determination (e.g., the MMSE and the MoCA). As used herein, a gene’s expression level is “consistent” with that gene’s expression level in a corresponding sample (e.g., a cell sample) derived from AD patients if it is the same as, or close to, that expression level. For example, assume that gene X’s TPM measure in cells derived from AD patients is 10 and its TPM measure is 100 in the same type of cells derived from non-ADD (or NDC) patients. A subject’s gene X expression level would be consistent with gene X’s AD expression level if it were, for example, below 50, below 40, below 30, below 20 or, ideally, 10 or lower. As used herein, a “demented” human subject includes, without limitation, a human subject found to be demented using a medically accepted method for making such a determination. Medically accepted methods for determining whether a human subject is demented include, for example, the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Each of the MMSE and the MoCA takes only minutes to administer and is scored out of 30, with a score below 24 indicating dementia. Accordingly, in the preferred embodiment, a demented human subject is one who scored below 24 on the MMSE and/or MoCA. As used herein, cells “derived” from a subject are cells that arise through (i) removal from the subject, and/or (ii) culturing and/or other physical manipulation performed on cells removed from the subject. For example, cultured skin fibroblasts derived from a subject are those skin fibroblasts that arise through culturing a sample of skin cells (e.g., contained in a punch biopsy) directly removed from the subject. Similarly, peripheral whole blood “derived” from a subject is peripheral whole blood that is removed from the subject (e.g., by being drawn directly from the subject into a tube having anticoagulant in it). As used herein, “diagnosing Alzheimer’s disease”, with respect to a symptomatic or asymptomatic human subject, means determining that there is greater than 50% likelihood that the subject is afflicted with Alzheimer’s disease. Preferably, “diagnosing Alzheimer’s disease” means determining that there is greater than 60%, 70%, 80%, or 90% likelihood that the subject is afflicted with Alzheimer’s disease. As used herein, the phrase “determining whether the subject is afflicted with Alzheimer’s disease” is synonymous with the phrase “diagnosing Alzheimer’s disease.” As used herein, a gene is “differentially expressed” between corresponding cells derived from AD patients and those derived from other subjects (e.g., non-ADD subjects or NDSs) if, for example, the gene’s TPM measure in cells derived from AD patients is different than (e.g., upregulated or downregulated) in the same type of cells derived from other subjects. Similarly, a gene is “differentially expressed” between peripheral whole blood derived from AD patients and that derived from other subjects (e.g., non-ADD subjects or NDSs) if, for example, the gene’s TPM measure in blood derived from AD patients is different than (e.g., upregulated or downregulated) in blood derived from other subjects. For example, gene X would be differentially expressed between corresponding cells derived from AD patients and those derived from other subjects if its TPM measure in cells derived from AD patients were 10 and its TPM measure were 100 in the same type of cells derived from other subjects. As used herein, “expression level”, with respect to a gene, includes, without limitation, any of the following: (i) the rate and/or degree of transcription of the gene (i.e., the rate at which, and/or degree to which, the gene is transcribed into RNA); (ii) the rate and/or degree of processing of the RNA encoded by the gene; (iii) the rate and/or degree of maturation of non-protein-coding RNA encoded by the gene; (iv) the rate at which, and/or degree to which, the RNA encoded by the gene is exported; (v) the rate at which, and/or degree to which, the RNA encoded by the gene is translated (i.e., the rate at which, and/or degree to which, the RNA is translated into protein); (vi) the rate at which, and/or degree to which, the protein encoded by the gene folds; (vii) the rate at which, and/or degree to which, the protein encoded by the gene is translocated; and (viii) the level of function (e.g., enzymatic activity or binding affinity) of the protein encoded by the gene. As used herein, an agent “favorably” affects the expression level of a gene whose expression level correlates with AD if it either decreases or increases that expression toward a level correlative with a non-AD (e.g., disease-free) state. For example, if the expression level of gene X is lower in an AD patient than in a non-afflicted patient, an agent favorably affecting the expression level of that gene would increase its expression level. Similarly, if the expression level of gene X is higher in an AD patient than in a non-afflicted patient, an agent favorably affecting the expression level of that gene would decrease its expression level. As used herein, a human subject has an “increased likelihood” of becoming afflicted with AD if, by way of example, the subject’s likelihood of becoming afflicted with AD is greater than the likelihood of an age-matched control subject becoming afflicted with AD. For instance, a human subject has an increased likelihood of becoming afflicted with AD if that subject has a 20% chance of becoming afflicted with AD and an age- matched control subject has a 10% chance of becoming afflicted with AD. In one embodiment, a human subject has an increased likelihood of becoming afflicted with AD if the subject’s likelihood of becoming afflicted with AD is greater than the likelihood of an age-matched control subject becoming afflicted with AD by at least 10%, 20%, 50%, 100%, 150%, 200%, 250%, 300%, 400%, or 500%. In another embodiment, a human subject has an increased likelihood of becoming afflicted with AD if the subject’s likelihood of becoming afflicted with AD is greater than the likelihood of an age-matched control subject becoming afflicted with AD by a factor of at least two, three, four, five, 10, 20, 50, or 100. As used herein, “measuring” the expression level of a gene means quantitatively determining the expression level via any means for doing so (e.g., Total RNA Sequencing (20 million reads, 2x75bp PE)). Preferably, measuring the expression level of a gene is accomplished by measuring the number of RNA transcripts for that gene per million total RNA transcripts (i.e., “TPM” via FastQ data, and FPKM estimation per sample) present in the RNA population being studied. For example, measuring the expression level of gene X in a cell population might yield a result of 50 TPM. In another embodiment, measuring a gene’s expression level is done via protein quantification (e.g., via the known method of Western blotting). In a further embodiment, measuring a gene’s expression level is done via a quantitative assay for protein function (e.g., via known methods for measuring enzymatic activity and/or protein binding strength). As used herein, a subject afflicted with “non-Alzheimer’s disease dementia” (non-ADD) means a subject not afflicted with AD but nevertheless showing dementia and afflicted with, for example, frontotemporal dementia (FTD), vascular dementia (VD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), Huntington's disease (HD), or schizophrenia (SZ). As used herein, a “non-demented” human subject includes, without limitation, a human subject who has not scored below 24 on the MMSE or MoCA. The term non-demented subject is also referred to as NDS, NDS patient, NDS subject, non-demented control, NDC, NDC patient, and NDC subject. As used herein, a “plurality” of genes can be of any suitable size. By way of example, a plurality of genes in a set of 472 genes can be at least two genes, at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least 10 genes, at least 15 genes, at least 20 genes, at least 30 genes, at least 40 genes, at least 50 genes, at least 60 genes, at least 70 genes, at least 80 genes, at least 90 genes, at least 100 genes, at least 150 genes, at least 200 genes, at least 250 genes, at least 300 genes, at least 350 genes, at least 400 genes, or at least 450 genes. As another example, a plurality of genes in a set of 85 genes can be at least two genes, at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least 10 genes, at least 15 genes, at least 20 genes, at least 30 genes, at least 40 genes, at least 50 genes, at least 60 genes, at least 70 genes, or at least 80 genes. As a further example, a plurality of genes in a set of 73 genes can be at least two genes, at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least 10 genes, at least 15 genes, at least 20 genes, at least 30 genes, at least 40 genes, at least 50 genes, at least 60 genes, or at least 70 genes. As yet a further example, a plurality of genes in a set of 12 genes can be at least two genes, at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, or at least 10 genes. As used herein, a “population” of cells (e.g., a skin cell fibroblast population) includes any number of cells permitting the manipulation and study required to assess gene expression. In one embodiment, the population of cells includes at least 1,000,000 cells. In another embodiment, the population of cells includes between 100,000 cells and 1,000,000 cells, between 10,000 cells and 100,000 cells, between 1,000 cells and 10,000 cells, between 100 cells and 1,000 cells, between 10 cells and 100 cells, and fewer than 10 cells (e.g., one cell or two cells). As used herein, the term “subject” includes, without limitation, a mammal such as a human, a non-human primate, a dog, a cat, a horse, a sheep, a goat, a cow, a rabbit, a pig, a rat, and a mouse. Where the subject is human, the subject can be of any age. For example, the subject can be 50 years or older, 55 years or older, 60 years or older, 65 years or older, 70 years or older, 75 years or older, 80 years or older, 85 years or older, or 90 years or older. The instant methods are envisioned for all subjects, preferably humans (and preferably symptomatic). As used herein, a human subject who is “suspected of being afflicted with AD or non- ADD” is a subject displaying at least one symptom (e.g., dementia) consistent with both AD and non-ADD. Doses, i.e., “therapeutically effective amounts”, used in connection with this invention include, for example, a single administration, and two or more administrations (i.e., fractions). In one embodiment, the therapeutically effective amount of a drug approved for a non-Alzheimer’s indication is the dose and dosing regimen approved for that non- Alzheimer’s indication. As used herein, “treating” a subject afflicted with a disorder shall include, without limitation, (i) slowing, stopping, or reversing the disorder's progression, (ii) slowing, stopping, or reversing the progression of the disorder’s symptoms, (iii) reducing the likelihood of the disorder’s recurrence, and/or (iv) reducing the likelihood that the disorder’s symptoms will recur. In the preferred embodiment, treating a subject afflicted with a disorder means (i) reversing the disorder's progression, ideally to the point of eliminating the disorder, and/or (ii) reversing the progression of the disorder’s symptoms, ideally to the point of eliminating the symptoms. The treatment of AD can be measured according to various clinical endpoints. These include, without limitation, (a) lowering, stabilizing, or slowing progression of (i) dementia, (ii) synaptic loss, (iii) amyloid plaques, and/or (iv) neurofibrillary tangles, and/or (b) favorably affecting the expression levels of one or more genes whose expression levels correlate with AD. Embodiments of the Invention This invention provides accurate gene-based methods for determining whether a human subject has a gene expression profile characteristic of AD and/or is afflicted with AD. These methods are envisioned, for example, for differentiating between subjects afflicted with AD and with non-ADD, and determining whether a non-demented subject has an increased likelihood of becoming afflicted with AD. The subject methods are based, at least in part, on the surprising discovery that 472 particular genes are differentially expressed in human skin cell fibroblasts, and on the added surprising discovery that 85 particular genes are differentially expressed in each of human skin cell fibroblasts, human peripheral whole blood, and human brain. Specifically, this invention provides a first method for determining whether a human subject has a gene expression profile characteristic of Alzheimer’s disease (“AD”) comprising the following step: in a skin cell fibroblast population derived from the subject, measuring the expression levels of a plurality of genes in the set of genes set forth in Table 2, whereby the subject has a gene expression profile characteristic of AD if the measured expression levels are consistent with those genes’ expression levels in skin cell fibroblasts derived from AD patients. In one embodiment of the first method, the set of genes consists of RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. These genes are differentially expressed in each of skin cell fibroblasts, peripheral whole blood, and brain. In another embodiment of the first method, the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. These genes are upregulated in each of skin cell fibroblasts, peripheral whole blood, and brain. In another embodiment of the first method, the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1. These genes are downregulated in each of skin cell fibroblasts, peripheral whole blood, and brain. Methods for obtaining skin cell fibroblasts from a subject’s blood are known, and include, for example, skin punch biopsy, and growing cells out of explants. Methods for culturing skin cell fibroblasts are also known. Typically, during cell culturing, when cell confluence reaches 100%, cells are passaged. Also, typically, after two passages, fibroblasts are purified in a proportion greater than 95%. This invention also provides a second method for determining whether a human subject has a gene expression profile characteristic of Alzheimer’s disease (“AD”) comprising the following step: in a peripheral whole blood sample derived from the subject, measuring the expression levels of a plurality of genes in the set of genes comprising RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17, whereby the subject has a gene expression profile characteristic of AD if the measured expression levels are consistent with those genes’ expression levels in peripheral whole blood derived from AD patients. In one embodiment of the second method, the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. In another embodiment of the second method, the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1. The various embodiments of the methods above for determining whether a human subject has a gene expression profile characteristic of AD as measured in skin cell fibroblasts and in peripheral whole blood apply, mutatis mutandis, to these methods when they are performed by measuring differential gene expression in brain or cerebrospinal fluid. This invention provides a first method for determining whether a demented human subject is afflicted with Alzheimer’s disease (“AD”) or non-Alzheimer’s disease dementia (“non-ADD”), comprising the following step: in a skin cell fibroblast population derived from the subject, measuring the expression levels of a plurality of genes in the set of genes set forth in Table 2, whereby (i) the subject is afflicted with AD if the measured expression levels are consistent with those genes’ expression levels in skin cell fibroblasts derived from AD patients, and (ii) the subject is afflicted with non-ADD if the measured expression levels are consistent with those genes’ expression levels in skin cell fibroblasts derived from non-ADD patients. In one embodiment of the first method, the set of genes consists of RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. In another embodiment of the first method, the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. In another embodiment of the first method, the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1. This invention also provides a second method for determining whether a demented human subject is afflicted with Alzheimer’s disease (“AD”) or non-Alzheimer’s disease dementia (“non-ADD”), comprising the following step: in a peripheral whole blood sample derived from the subject, measuring the expression levels of a plurality of genes in the set of genes comprising RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17, whereby (i) the subject is afflicted with AD if the measured expression levels are consistent with those genes’ expression levels in peripheral whole blood derived from AD patients, and (ii) the subject is afflicted with non-ADD if the measured expression levels are consistent with those genes’ expression levels in peripheral whole blood derived from non-ADD patients. In one embodiment of the second method, the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. In another embodiment of the second method, the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1. This invention provides a first method for determining whether a non-demented human subject has an increased likelihood of becoming afflicted with Alzheimer’s disease (“AD”), comprising the following step: in a skin cell fibroblast population derived from the subject, measuring the expression levels of a plurality of genes in the set of genes set forth in Table 2, whereby the subject has an increased likelihood of becoming afflicted with AD if the measured expression levels are consistent with those genes’ expression levels in skin cell fibroblasts derived from AD patients. In one embodiment of the first method, the set of genes consists of RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. In another embodiment of the first method, the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. In another embodiment of the first method, the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1. In one embodiment of the first method, the non-demented subject is cognitively impaired. For example, the cognitively impaired subject is afflicted with mild cognitive impairment (MCI). In another embodiment, the subject is known to be afflicted with AD pathology. Finally, this invention also provides a second method for determining whether a non- demented human subject has an increased likelihood of becoming afflicted with Alzheimer’s disease (“AD”), comprising the following step: in a peripheral whole blood sample derived from the subject, measuring the expression levels of a plurality of genes in the set of genes comprising RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17, whereby the subject has an increased likelihood of becoming afflicted with AD if the measured expression levels are consistent with those genes’ expression levels in peripheral whole blood derived from AD patients. In one embodiment of the second method, the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. In another embodiment of the second method, the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1. In one embodiment of the second method, the non-demented subject is cognitively impaired. For example, the cognitively impaired subject is afflicted with mild cognitive impairment (MCI). In another embodiment, the subject is known to be afflicted with AD pathology. The following exemplary gene subsets (i.e., pluralities) are envisioned as embodiments of the present methods. First, the following exemplary gene subsets (i.e., pluralities) are envisioned for the 85 genes that are differentially expressed in each of human skin cell fibroblasts, human peripheral whole blood, and human brain. The first group of exemplary subsets is as follows: RECQL, RALA, FYN, RPL26L1, and FAM160A2; THOC3, ITIH4, TNK2, GOLGA5, and DNAJA2; RPL31, TM9SF3, HSP90AA1, MRPL22, and C14orf166; ERH, PSMA6, PSMD10, PBDC1, and TCEB2; RAB11A, RAB2A, RPL18A, BLVRA, and TMEM97; PPP2CA, RPL24, MRPL3, EEF1B2, and ICMT; RPL22, SET, ARAP3, RPL5, and NECAB3; COX7C, ANAPC13, ZNF426, SERINC3, and TPT1; SWAP70, AKIRIN2, MICAL1, BZW2, and MRPL47; PSMB7, RPS6, GTF2B, FBLN5, and TOB1; ATHL1, EFHD2, SH3BGRL3, RPL11, and TMCO1; ARL6IP5, POLE3, EIF3M, PLBD2, and FBXO4; TCEB1, MRPL39, PLCL2, ZDHHC5, and ANKZF1; ATP5C1, PCBP1, TRMT10C, RPL4, and MRPS22; TALDO1, KIAA0195, FAM219B, EXT1, and COL18A1; RPL35A, UBALD2, L3MBTL1, LAMP1, and KPNA4; and NBR1, TPK1, CARD16, ATP5O, and RPL17. The second group of exemplary subsets is as follows: RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, and DNAJA2; RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, and TCEB2; RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, and ICMT; RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, and TPT1; SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, and TOB1; ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, and FBXO4; TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, and MRPS22; TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, and KPNA4; and NBR1, TPK1, CARD16, ATP5O, and RPL17. The third group of exemplary subsets is as follows: RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, and C14orf166; ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, and ICMT; RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, and MRPL47; PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, and FBXO4; TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, and COL18A1; and RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. The fourth group of exemplary subsets is as follows: RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, and TCEB2; RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, and TPT1; SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, and FBXO4; and TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. Second, the following exemplary gene subsets (i.e., pluralities) are envisioned for the genes that are upregulated in each of human skin cell fibroblasts, human peripheral whole blood, and human brain. The first group of exemplary subsets is as follows: RECQL, RALA, FYN, RPL26L1, and THOC3; GOLGA5, DNAJA2, RPL31, TM9SF3, and HSP90AA1; MRPL22, C14orf166, ERH, PSMA6, and PSMD10; PBDC1, TCEB2, RAB11A, RAB2A, and RPL18A; BLVRA, TMEM97, PPP2CA, RPL24, and MRPL3; EEF1B2, ICMT, RPL22, SET, and RPL5; COX7C, ANAPC13, ZNF426, SERINC3, and TPT1; SWAP70, AKIRIN2, BZW2, MRPL47, and PSMB7; RPS6, GTF2B, FBLN5, TOB1, and EFHD2; SH3BGRL3, RPL11, TMCO1, ARL6IP5, and POLE3; EIF3M, PLBD2, FBXO4, TCEB1, and MRPL39; PLCL2, ZDHHC5, ATP5C1, PCBP1, and TRMT10C; RPL4, MRPS22, TALDO1, EXT1, and RPL35A; and UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. The second group of exemplary subsets is as follows: RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, and HSP90AA1; MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, and RPL18A; BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, and RPL5; COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, and PSMB7; RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, and POLE3; EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, and TRMT10C; and RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. The third group of exemplary subsets is as follows: RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, and PSMD10; PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, and RPL5; COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, and EFHD2; SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, and TRMT10C; and RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. The fourth group of exemplary subsets is as follows: RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, and RPL18A; BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, and PSMB7; RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, and TRMT10C; and RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. Third, the following exemplary gene subsets (i.e., pluralities) are envisioned for the genes that are downregulated in each of human skin cell fibroblasts, human peripheral whole blood, and human brain. The first group of exemplary subsets is as follows: FAM160A2, ITIH4, and TNK2; ARAP3, NECAB3, and MICAL1; ATHL1, ANKZF1, and KIAA0195; and FAM219B, COL18A1, and L3MBTL1. The second group of exemplary subsets is as follows: FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, and MICAL1; and ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1. In a preferred embodiment of each of the present methods, measuring the expression level of a gene comprises measuring the number of that gene’s RNA transcripts per number of total transcripts. Methods for measuring the expression level of a gene can be accomplished by any suitable method known in the art (e.g., TaqMan^TM gene expression assays). In the preferred embodiment, measuring the expression level of a gene comprises measuring the number of that gene’s RNA transcripts per number of total transcripts. Gene expression measurements can readily be caried out on cells (e.g., skin cell fibroblasts) and peripheral whole blood (e.g., where RNA is extracted from the blood once it is drawn into a tube containing anticoagulant). Methods for measuring the expression levels of many genes are also known and can be accomplished, for example, using microarray technology (e.g., chip technology and bead technology). In an additional embodiment of the present peripheral whole blood- based methods, the methods are performed on cultured B lymphocytes (preferably immortalized B lymphocytes) generated from the subject’s peripheral whole blood. Methods for culturing B lymphocytes, and generating immortalized B lymphocytes, are known in the art. In the present methods, the skin cell fibroblasts need not be synchronized, and no cells in the peripheral whole blood (e.g., B lymphocytes) need be synchronized either. Therefore, in a preferred embodiment, these cells are not synchronized. In another embodiment, however, these cells are synchronized. Methods for synchronizing cell populations are known in the art (see, e.g., Chirila, et al., U.S. Publication No. US 2019/0323083). This invention further provides an article of manufacture comprising (i) a solid surface (e.g., a chip or a plurality of beads) suitable for use as a microarray and (ii) having affixed thereto, at suitable loci, nucleic acid molecules (e.g., DNA or RNA) that specifically hybridize with their counterpart mRNA molecules, wherein the nucleic acid molecules hybridize to mRNA encoded by one or more of the groups of exemplary gene sets set forth above. Finally, this invention provides a method for treating a human subject afflicted with AD comprising administering to the subject a therapeutically effective amount of an agent (e.g., aducanumab) known to favorably affect the expression levels of a plurality of genes in the set of genes set forth in Table 2. Preferably, the set of genes consists of RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. In one embodiment, the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. In another embodiment, the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1. The present methods above for determining, diagnosing, and treating with respect to human subjects apply, mutatis mutandis, to non-human subjects. These non-human subjects include, by way of example, APP/PS1 transgenic mouse models of AD. In one embodiment, this invention provides a method for treating a non-human subject afflicted with AD pathology (e.g., an APP/PS1 AD transgenic mouse model) comprising administering to the subject a therapeutically effective amount of an agent (e.g., aducanumab) known to favorably affect the expression levels of a plurality of genes in the set of genes set forth in Table 2. Preferably, the set of genes consists of RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. In one embodiment, the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17. In another embodiment, the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1. In another embodiment, this invention provides a method for determining whether an agent favorably affects the expression levels of a plurality of genes in the set of genes set forth in Table 2; the 85-gene subset thereof differentially expressed in skin cell fibroblasts, blood, and brain; the subset of the 85-gene subset wherein the genes are upregulated; or the subset of the 85-gene subset wherein the genes are downregulated. This method comprises (i) measuring the expression levels of a plurality of genes in one of these gene sets in treated brain or brain cells, and (ii) measuring the expression levels of a plurality of genes in one of these gene sets in untreated brain or brain cells and, based on these measurements, determining whether the agent favorably affects the expression levels of the plurality of genes measured. This method can be used to screen AD drug candidates. It can also be used to monitor AD treatment progression, whereby the method is repeated after a suitable interval (e.g., several months) to determine whether the treatment has favorably affected a subject’s gene expression profile. This invention will be better understood by reference to the experimental section which follows, but those skilled in the art will readily appreciate that the specific examples detailed are only illustrative of the invention as described more fully in the claims which follow thereafter. Experimental Section Synopsis In this study, the inventors first developed a machine learning (ML)-based classifier using gene expression profiles of skin fibroblasts from control and autopsy-confirmed AD patients. This group of skin fibroblasts represented a limited subset of a much larger cohort of autopsy-validated skin fibroblasts that demonstrated high diagnostic sensitivity and specificity even in the presence of co-morbid brain pathologies (43). The inventors then validated this classifier against four published datasets currently available, which included a total of 727 patient samples: blood (n=466) and brain (n=261) samples from healthy controls (n=377) and AD patients (n=350). Finally, they demonstrated the utility of this molecular signature in predicting AD pathology and treatment outcome in APP/PS1 transgenic mice treated with aducanumab. Abstract Many potential Alzheimer’s disease (AD) biomarkers, although promising, have, to date, not approximated the unequivocally certain specificity of brain autopsy pathology from AD patients. Therefore, a highly accurate AD diagnostic test is urgently needed to rapidly identify patients early in the disease, to discover and to implement effective therapeutics. To meet this huge unmet medical need, the inventors used transcriptome profiling of patient-derived cells to identify an unbiased molecular diagnostic signature. Using skin fibroblasts of control and autopsy-confirmed AD patients, they identified 472 differentially expressed transcripts predominantly involved in Tau dysregulation, synaptic formation, apoptosis and other metabolic processes. These transcripts mirrored transcriptomic changes in blood and brain tissues of AD patients in publicly available datasets. Based on a subset of 228 transcripts common to these datasets, the inventors developed a machine learning AD classifier that showed highly significant predictive power in both a discovery dataset and in four independent publicly available gene expression datasets obtained from blood (n=466) and brain (n=261) samples of healthy controls (n=377) and AD patients (n=350). The inventors’ model showed a highly significant predictive power in discriminating AD from healthy population, with an average accuracy, sensitivity and specificity of 81.5%, 85%, 79.5% in blood and 69%, 61%, 79% in brain, respectively. The AD classifier was also able to recognize AD pathology in brain samples of APP/PS1 transgenic mice, showing a reversal following aducanumab treatment. These remarkably reproducible, rapidly obtained, transcriptomic AD signatures in diverse tissues and species suggest that this classifier has etiological relevance to AD and support its utility for assessing disease status and therapeutic benefits. Materials and Methods Patients and samples Primary human fibroblast cultures were obtained from skin biopsies of 5 normal subjects and 8 autopsy-confirmed sporadic AD patients. Clinical diagnoses were made according to criteria developed by the National Institute of Neurologic and Communicative Disorders and Stroke and the AD and related Disorders Association (NINCDS-ADRDA) and were confirmed for all patients who were then further confirmed by autopsy criteria (amyloid plaques and neurofibrillary tangles, together with dementia in life). A summary of the disease characteristics and demographics of all subjects enrolled in this study is shown in Table 3. All methods were performed according to the relevant guidelines and regulations of the Declaration of Helsinki of Medical Research Involving Human Subjects. Ethical approval was obtained from the appropriate authority. Primary fibroblast isolation and culture Skin biopsies (3 mm) from the backside of the upper arm were obtained by skin punch at the clinical sites. Biopsies were placed in a previously sent transport medium, packed into a specialized package, and stored at 2–4 °C. Details of the cell cultures were described elsewhere (44, 45). The inventors tested the effect of the number of passages in their previous study (43, 46). They found the assay was consistently accurate with passages between 5 and 15. They restricted their assay, therefore, to have a total number of passages to be between 5 and 15. RNA-seq analysis pipeline Total RNA was isolated using the Norgen Total RNA Purification Kit (Norgen Biotek Corporation, ON, Canada) following manufacturer’s recommendations. PolyA enrichment and RNA-seq were performed by Applied Biological Materials Inc. through their Total RNA Sequencing service. Quality check for the RNA sample was assessed by Qubit RNA Assay Kit (Invitrogen, CA, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Library construction was carried out using Illumina Tru-Seq Stranded mRNA Library Prep Kit (Illumina, San Diego, CA, USA) following the manufacture’s recommendations. The quality of the libraries was assessed using Qubit DNA assay (Invitrogen, CA, USA), Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) and qPCR. All libraries passed internal QC. Sequencing was performed on the Illumina Next-Seq 500 platform Illumina system (Illumina, San Diego, CA, USA). Transcript abundance (in terms of raw read counts) was calculated by mapping reads to the human genome GRCh38 using HISAT2 (47), and genome-guided assembly was performed with String-Tie (48). Transcriptomic data analysis The expected number of fragments per kilobase of transcript sequence per millions base pairs (FPKM) was calculated for estimating gene expression levels and RPKM levels were imported into Gene-Spring GX14.9.1 suite (Agilent Technologies, Palo Alto, CA, USA) for testing relative differential expression between the AD and control samples. In particular, a moderate t-test followed by Benjamini and Hochberg’s False Discovery Rate (FDR) was applied to detect differential expression across AD and control patients. Transcripts were defined as differentially expressed if they differed between groups with a fold change (FC) of >1.5 fold and an FDR-corrected p-value of <0.05. Unsupervised hierarchical clustering of differentially expressed genes (DEGs) was performed using a Euclidean distance measure and Ward’s linkage rule by GeneSpring GX14.9.1 suite (Agilent Technologies, Palo Alto, CA, USA). Functional enrichment and network analysis of differentially expressed protein- coding genes Statistically significant DEGs were further analyzed in Metacore software (Clarivate Analytics, London, United Kingdom) to perform Gene ontology (GO) and pathway enrichment analyses. Each gene identifier was mapped to its corresponding gene object in the MetaCore database. The genes were then compared with both GO processes and MetaCore maps to determine processes and pathways which were significantly overrepresented in the differentially expressed gene list. The p values for maps and processes were calculated using a hypergeometric distribution. FDR < 0.05 and gene counts >2 were set as thresholds for significance. Next, to assess the relationships between the DEGs, the inventors constructed a protein–protein interaction (PPI) network by using Search Tool for the Retrieval of Interacting Genes (STRING v12.0) (49, 50). The resulting PPI network was visualized with the Cytoscape software (v.3.10.1), an open-source software for visualization, modelling and integration of biomolecular interaction networks (51). To avoid an inaccurate PPI network, the inventors used a cutoff ≥ 0.9 (high-confidence interaction score) to obtain the significant PPIs. In the PPI network, the protein is defined as the node, the interaction between two nodes is defined as the edge and nodes having large number of interacting partners represent hubs in the network. Hubs were detected by calculating the node degree distribution using the Network Analyzer plugin of Cytoscape (52) and the top ten genes scoring the highest in the PPI network were identified as hub genes in the present study. The global network was then subjected to cluster analysis to identify densely connected regions in the network using the Molecular Complex Detection (MCODE) plugin of Cytoscape (53), with the following cut-off criteria: Degree cutoff = 2, node score cutoff = 0.2, k-core = 2, and max. depth = 100. This approach detects dense and connected regions by weighting nodes based on their local neighborhood density. The top clusters from MCODE were subjected to Clue-GO v2.5.9/Clue-Pedia v1.5.9 analysis, with the human genome as a background, to obtain comprehensive GO and pathway results from the PPI network. Clue-GO combines GO and pathway analyses from KEGG and Bio-Carta and provides a fundamentally structured GO or pathway network from the PPI network (54). Functionally grouped networks with terms as nodes were connected based on their kappa score level (≥ 0.4). Pathways showing a p-value < 0.05 (2-tailed) were regarded as remarkably enriched for both GO and KEGG /BioCarta pathways and a functionally organized GO/pathway term network was created. Class prediction analysis With the aim of determining the reliability of the inventors’ fibroblast-derived molecular signature as a classification model to discriminate AD patients from healthy controls, they subjected the list of statistically significant DEGs to the Partial least squares discrimination (PLS) Class Prediction algorithm in the Gene-Spring GX14.9.1 suite (Agilent Technologies, Palo Alto, CA, USA). By using a subset of genes common to various datasets, they built a model with their study cohort (training test) using sample classifiers ‘AD’ and ‘CTRL’ and the PLS-prediction procedure with a k-fold cross- validation analysis (k = 3-fold and ten repetitions). The classification model was then validated in 4 independent human AD datasets (test sets) to prevent over-fitting the predictive signature. Test/validation datasets were obtained from GEO (https://www.ncbi.nlm.nih. gov/geo/; accessed on 19 October 2023) and Array-Express database (https://www.ebi.ac.uk/biostudies/arrayexpress; accessed on 19 October 2023). In particular, the inventors used two publicly available datasets that included gene expression data of peripheral whole-blood from possible or probable AD patients (GSE97760 and GSE140831) and two datasets reporting gene expression data of post- mortem brain areas (GSE5281 and GSE122063). Detailed information for each dataset is described in the original articles (18, 19, 25-28, 31, 41, 55-57). The final validation comprised a total human sample size of n = 466 peripheral whole blood samples (AD n = 207, healthy control n =259) and n = 261 brain tissue samples (AD n = 143, healthy control n =118). To assess the accuracy of their AD model in predicting AD pathology and treatment outcome in mice, the inventors used gene expression data from hemi-forebrain of APP/PS1-transgenic mice (N=3-4/group) treated from 10 months of age with aducanumab or IgG control via IP injection (40 mg antibody/kg mouse) once a week for 5 weeks (4 doses) (Sequence Read Archive repository SRP465463; https://www.ncbi.nlm.nih.gov/sra; accessed on 12 March 2024) (1). All validation datasets were loaded into Gene Spring GX14.9.1 suite (Agilent Technologies, Palo Alto, CA, USA) for pre-processing and normalization steps (quantile normalization and median centering). Results Transcriptome profiling reveals an AD-associated molecular signature Differential gene expression analysis between fibroblast lines of autopsy confirmed AD patients and control subjects revealed a total of 472 DEGs (FDR-adjusted p value <0.05) (Table 5). The majority of these (350; 74%) were upregulated, while the rest (122; 26%) were downregulated. Among DEGs, at the time of writing, only COX7C showed genome-wide significant evidence of affecting AD risk (Alzheimer’s Disease Sequencing Project dataset, https://adsp.niagads.org/gvc-top-hits-list/) (58). Unsupervised hierarchical cluster analysis using DEGs clearly discriminated AD patients from controls and a substantial overlap was found between the list of DEGs identified in this study and those previously found in brain areas and peripheral blood samples of AD patients (Figures 1A and 1B, Table 5). The biological roles of the DEGs in AD fibroblasts were investigated by performing a GO enrichment analysis, whose results showed the enrichment of these dysregulated genes in metabolic and biosynthetic processes, response to oxidative stress, gene expression, cell cycle and synaptic plasticity (Figure 1C, Table 6). Moreover, pathway enrichment analysis revealed that these genes were enriched in pathways previously implicated in the overall decline of neurons and synaptic connections associated with AD, including those related to tau dysregulation in Alzheimer’s disease, apoptosis and survival, immune response and axonal transport (Figure 1D, Table 7). Next, to reveal functional interactions among proteins encoded by the DEGs, a PPI network was constructed on the basis of the STRING database (Figure 2A). The resulting PPI network consisted of 368 nodes and 8974 edges, with the most interconnected (hub) genes associated with the regulation of gene expression and RNA metabolic process (RPL11, RPL19, RPL24, RPL30, RPL34, RPS3A, RPL35, RPS14, RPS18, SNRPG) (Figure 2A). The high degree of these hub genes indicated that these proteins may serve crucial roles in maintaining the whole protein interaction network. Subsequently, the general PPI network was divided into closely connected subnetworks to detect the main interacting and functional modules. A total of 13 modules were found, among which three modules were detected with a node score >7.0 (Figure 2B). DEGs in these modules are predominantly implicated in RNA metabolism, regulation of gene expression and DNA repair. In particular, Cluster 1 and Cluster 3 included a large number of up-regulated DEGs significantly enriched in RNA processing and RNA binding, implicating the potential role of altered RNA dynamics in AD (Figure 2B) (59). Discovery and validation of a molecular signature for the diagnosis of AD To evaluate the reliability of their transcriptomic signature as a multigene classifier capable of discriminating patients with AD from controls, the inventors subjected the DEGs to class prediction modelling (PLS model). In particular, given the subsequent external validation of the predictive model, a subset of 228 out of 472 DEGs presents in all validation datasets were used to build the PLS model. The predictive performance of this PLS-based classifier was assessed in the inventors’ AD fibroblast dataset (training test) showing an ability to discriminate AD patients from controls with 100% accuracy, sensitivity and specificity (Table 1). Then, the established classifier was validated in 4 independent AD datasets (test sets), including gene expression data of peripheral whole blood (GSE97760, GSE140831) and post-mortem brain tissues (GSE5281, GSE122063) from AD patients and healthy controls. The average prediction accuracy, sensitivity and specificity achieved by the classifier were 81.5%, 85%, 79.5% in blood and 69%, 61%, 79% in brain test sets, respectively (Table 1). Taken together, these results indicate that the constructed classification signature showed a significant predictive power in distinguishing individuals with AD from controls, using both blood and brain samples. Performance of the disease classifier in an AD mouse model Due to its potential clinical utility, the inventors also evaluated the performance of the AD classifier in brain samples of APP/PS1 transgenic mice to investigate if this expression signature was able to predict AD pathology and to discriminate the effects of a beneficial therapy. The inventors’ AD classifier was able to recognize AD pathology in IgG treated control APP/PS1 transgenic mice (accuracy 86%, sensitivity 100%, specificity 75%), while treatment with aducanumab partly reversed the expression patterns of most AD classifier genes. Discussion Treatments to prevent and/or halt the progression of AD have remained elusive in hundreds of clinical trials over the past 20 years, making accurate and timely diagnosis of this disease a priority. Current diagnostic methods lack specificity and are notoriously complex, expensive and invasive, involving mental status examination, spinal fluid tests and magnetic resonance imaging (MRI) or positron emission tomography (PET) scans. Therefore, large efforts are being made in the search for minimally invasive and cost-effective tests of AD in peripheral cells and fluids, such as blood and fibroblasts (60, 61). Compared with DNA genotyping or sequencing, transcriptomics appears to potentially be more informative for developing diagnostic tools for complex disorders, including sporadic AD, since it informs not only about inherited but also, albeit indirectly, about non-inherited genomic information, allowing the capture of both genetic and environmental consequences as well as complex biological regulatory processes. Different studies have identified transcriptional signatures in AD blood samples (12-14, 16, 17, 32-38), but the inclusion of possible or probable AD patients – in the absence of autopsy validation - render these gene expression profiles less effective in discriminating AD from other neurodegenerative diseases. In addition, none of these signatures has been mirrored in other tissues and it is still unknown if they are useful in monitoring responses to treatment. In the present study, the inventors first identified a set of 472 DEGs in skin fibroblasts from control and autopsy-confirmed AD patients, where the definitive presence of AD pathology or other comorbidities was assessed. By investigating the expression of these genes in other available datasets, they found their changes were also mirrored in blood and brain areas of AD patients, sustaining a conserved multi-tissue transcriptome signature of AD pathology. The overlapping deregulated expression of a set of genes across different cells or tissues (fibroblasts, whole blood and brain areas) of AD patients is consistent with the presence of systemic abnormalities in AD (62). Deregulated genes in AD fibroblasts have been associated with decline of neurons and synaptic connections, including dysregulation of Tau metabolism and deficits in RNA metabolism (Figure 2D). Notably, the PPI network analysis revealed many protein- coding ribosomal genes as the most influential genes in the network, supporting the view that intense ribosomal changes and the consequent abnormalities in protein synthesis and DNA transcription can affect the maintaining of synaptic integrity and play a pivotal role in the pathogenesis of AD (Figures 3A and 3B). Consistently, diverse ribosome genes and proteins (i.e., RPL11, RPL19, RPL24, RPL30, RPL35, RPS3A, RPS14, RPS18) have been found to be significantly dysregulated in brain and/or blood samples of AD patients and animal models (63-66). To evaluate the power of DEGs in predicting AD in different tissues, the inventors developed a PLS-based classifier and tested the predictive power of a subset of 228 DEGs to distinguish AD patients from controls in their training dataset and 4 independent test datasets obtained from blood and brain samples of an extensive number of AD patients (n=350) and healthy controls (n=377). Their transcriptome- based classifier showed consistent performance in discriminating AD patients, with high rates of accuracy, specificity, and sensitivity in both blood and brain (Table 1). In addition, the unprecedented value of their classifier is its potential for the accurate prediction of treatment response. Such prediction analysis has not been performed for previously characterized AD classifiers since, in humans, effective treatments emerged only recently (7, 8). When the inventors applied their AD predictive model to transcriptomic data from APP/PS1 mice treated with aducanumab, the first anti-amyloid beta-directed antibody FDA approved, or IgG controls, their classifier recognized AD pathology in IgG treated controls (accuracy 86%, sensitivity 100%, specificity 75%), while treatment with aducanumab partly reversed the expression patterns of the majority of AD classifier genes (Figure 3)(1). In conclusion, the inventors have found a molecular transcriptomic signature that accurately identifies Alzheimer’s disease in different tissues and allows one to monitor responses to treatment in AD mice. Different technologies are already available to translate them into non-invasive and low-cost diagnostic tests. The use of these markers constitutes a novel approach in the management of AD patients where early diagnostic tests will not only enhance a sense of conviction but help tailor personalized therapies, separating responders from non-responders, thus avoiding the prescription of costly and ineffective drugs with potentially harmful side-effects. The following publicly available datasets were analyzed: Gene Expression Omnibus Series (GSE97760, GSE140831, GSE5281, GSE26927, GSE122063) and the raw sequencing SRP465463 dataset in SRA. Table 1. Summary of the prediction results for the AD transcriptome signature in training and test/validation sets True Positive (TP): the number of cases correctly identified as patients. True Negative (TN): the number of cases correctly identified as healthy. False Positive (FP): the number of cases incorrectly identified as patients. False negative (FN): the number of cases incorrectly identified as healthy cases. * Accuracy = TP + TN/TP + TN + FP + FN; ** Sensitivity (%) = TP/(TP + FN); *** Specificity (%) = TN/(TN + FN). Table 2. List of 472 genes that are differentially expressed in skin cell fibroblasts, whole blood, and brain. AC246787.1 CAPRIN1 DSEL GOLGA5 LGI4 NIPBL AC004057.1 CARD16 DUSP14 GOLPH3 LINC00342 NSDHL LINC02094 CARD6 EAF1-AS1 GPX8 LINC01133 NTF3 AC004987.2 CASC4 EEF1A1P11 AC004552.1 LINC01184 NUBP1 AC009245.1 CCDC12 EEF1A1P9 GSK3B LIPA NUCKS1 LINC01943 CCDC146 EEF1B2 GTF2B LRRC1 NUDT15 ACSL4 CCDC183-AS1 EFCAB11 GTPBP10 LRRC37B OAF ACTG1P17 CCDC86 EFHD2 GUCD1 LRRC46 OAS2 ADAMTS14 CD63 EHD2 H3F3BP1 LRRC56 OGFRL1 ADSSL1 CDK14 EI24 HBP1 LSM1 PA2G4 AGER CDKN3 EIF1P3 HCN3 LSM12P1 PAK2 AKAP2 CEBPZ EIF2AK2 MFSD14B MAPKAP1 PATL1 AKIRIN2 CENPF EIF3K HLA-DOB MATN1-AS1 PBDC1 AKT3 CES4A EIF3M HMGN2P15 MCFD2 PCBP1 ANAPC13 CGB7 EIF4EBP2 HNRNPA1P10 MGMT PCDHGC3 ANKZF1 CHMP1B EIF5A2 HNRNPUL2 MICAL1 PCNA ANP32E UTP4 EME2 HSD17B12 CARMN PET117 ANXA5 CKS2 EML2 HSP90AA1 MLF2 PFDN2 AP001471.1 CLIC2 ERH ICMT MPHOSPH10 PFN1P1 AP003419.1 CLIC4 ERICH2 IDI1 MRPL22 PGAP2 APPL1 CLTA ETS1 IFI16 MRPL3 PHAX ARAP3 COA6 EXT1 IFIT5 MRPL39 PHF5A ARF3 COL18A1 F8A2 IFITM1 MRPL47 PI4K2A ARHGAP18 COL7A1 FAM118B IFITM2 MRPS22 PIAS3 ARIH2 COQ10B FAM160A2 IFITM3 MRVI1 PLBD2 ARL6IP5 COX7C FAM166A IL13RA2 MSH3 PLCG1 ARPC3P1 CRACR2A FAM219B IL6ST MSL1 PLCL2 ATF5 CREG1 FAM96A IRX5 MSN PLXNB3 PGGHG AC025449.1 FBL ITIH4 MUM1 PNO1 ATL3 AC034236.1 FBLN5 JHDM1D-AS1 MX1 POLE3 ATMIN AC084125.2 FBXO4 KCNK2 COPS9 POLR2H ATP5F1C AC015922.4 FDX1 KCTD1 MYL5 PPM1N ATP5PO AC100793.2 FMOD PCLAF MYO1D PPP2CA AURKC CTNNBL1 FOXL1 TMEM94 NAA15 PPP3R1 BANF1P3 CWC22 FOXN2 KIAA0753 NBR1 PRDX5 BCAS2 CYP20A1 FSD1 CCDC191 NCOA4 PRIMPOL BCL10 DBF4B FTO KIFC2 NDFIP1 PRNP BICC1 DDX6 FYN KLF11 NDN PROCR BLVRA DMKN GCSHP5 KLF9 NDUFA6 PRPS2 BMPR2 DMPK GHRLOS KPNA4 NDUFS6 PSMA6 BTG3 DNAJA2 GM2A L3MBTL1 NECAB3 PSMB3 BZW2 DPY19L1 GMPS LAMP1 NFS1 PSMB7 RTRAF DPY19L2 GNAI3 LAT NINJ2 PSMD10 C3orf14 DR1 RACK1 LENG1 NINL PSMF1 PTP4A2 RP2 RPS2P46 SRFBP1 TXN2 AC009118.3 PTP4A2P1 AL009174.1 RPS3A SRP72 TXNP6 AC098487.1 PTPRF CCDC18-AS1 RPS3AP26 SRP9P1 U73166.1 AL138724.1 RAB11A RPL11 RPS6 ST13P5 UBALD2 AC025165.4 RAB22A RPL12P12 RPS7P1 STAG2 UNG AC104563.1 RAB2A RPL14P3 RPS8 STRAP UTP3 AC064799.1 RAB3IL1 RPL17 RPSAP54 STRN UXT AL445363.3 RAB7A RPL17P43 RRAD STS VCPIP1 AC087343.1 RAD52 RPL18A RUSC1-AS1 SUPT7L VPS26A AL121603.2 RALA RPL18AP3 S100A10 SWAP70 VTN AC012085.1 RAPGEF3 RPL19 S1PR1 TAF3 WDR59 AC010969.2 RBM27 RPL21P28 SAV1 TALDO1 WDR70 AC015961.2 RECQL RPL22 SBSN TBC1D16 AC245297.1 RPL7AP30 RFPL3S RPL24 SCAMP5 TBC1D9 IER3-AS1 RPL7P23 RGS12 RPL26 SCART1 TBX19 ZBTB10 RPLP2 RNF113A RPL26L1 SCRN1 ELOC ZBTB2 RPS10 RNF168 RPL26P19 SDE2 ELOB ZC3HAV1 RPS13 ROR1 RPL30 SEMA4A TGM1 ZDHHC5 RPS14 AL137784.2 RPL31 SEPT5 THOC3 ZFAND2A RPS18 AC139795.2 RPL34 SERINC3 TIMM10 ZFAS1 RPS18P12 AC078909.2 RPL34P18 SERPING1 TIMM23 ZFP90 RPS25 AC097641.2 RPL35 SET TM9SF3 ZMYM1 RPS26P11 RPL7AP50 RPL35A SETD4 TMCO1 ZNF135 RPS26P6 AL590560.2 RPL36 SGK2 TMED6 ZNF211 RPS26P8 AC005726.3 RPL37A SH3BGRL3 TMEM140 ZNF251 TONSL-AS1 AC005899.6 RPL38 SH3BP5 TMEM19 ZNF267 TP53RK AC060780.1 RPL3P4 SH3BP5-AS1 TMEM50A ZNF426 TPK1 AL591845.1 RPL4 SHOC2 TMEM54 ZNF43 TPT1 AL591895.1 AC090498.1 SLC43A3 TMEM97 ZNF440 TRIL AL354714.2 RPL4P5 SLC9A5 TNK2 ZNF547 TRIM74 AC092902.1 RPL5 SLFN5 TOB1 ZNF607 TRIM8 AC024293.1 RPL6P27 SMIM15 TOMM6 ZNF76 TRMT10C ^{SNHG14 TRPV1 SNORA11 TSNAXIP1} SNORA64 TUBA3FP SNRPD1 TUBG2 S^{NRPE SNX9 SNRPG SPEG} SNRPGP15 SPRED2 S^{NRPGP2 SPTY2D1} Table 3. Clinical and demographic data for study subjects. Table 4. Characteristics of human AD datasets used as test sets in class prediction analysis. [AD= Alzheimer's Disease; CTRL= Control subjects] Table 5. List of differentially expressed genes in AD fibroblasts vs control individuals EnsemblID Gene Symbol FDR corr. p value p value FC Regulation ENSG00000225200 AB019441.29 4.46E-02 1.18E-04 3.33 up ENSG00000196656 AC004057.1 1.69E-02 2.05E-06 7.38 up ENSG00000227078 AC004448.2 4.58E-02 1.85E-04 -3.15 down ENSG00000233225 AC004987.9 4.46E-02 1.72E-04 3.17 up ENSG00000230383 AC009245.3 3.38E-02 7.64E-06 9.42 up ENSG00000280721 AC133644.2 3.65E-02 4.31E-05 2.96 up ENSG00000068366 ACSL4 3.55E-02 3.34E-05 4.69 up ENSG00000259315 ACTG1P17 8.08E-03 7.01E-07 -2.53 down ENSG00000138316 ADAMTS14 3.38E-02 2.49E-05 -7.05 down ENSG00000185100 ADSSL1 4.32E-02 9.96E-05 -3.86 down ENSG00000204305 AGER 4.58E-02 1.86E-04 -3.04 down ENSG00000241978 AKAP2 3.38E-02 1.88E-05 5.02 up ENSG00000135334 AKIRIN2 4.94E-02 3.30E-04 2.87 up ENSG00000117020 AKT3 4.71E-02 1.97E-04 2.04 up ENSG00000129055 ANAPC13 4.71E-02 2.16E-04 2.80 up ENSG00000163516 ANKZF1 4.46E-02 1.51E-04 -2.21 down ENSG00000143401 ANP32E 4.46E-02 1.74E-04 2.61 up ENSG00000164111 ANXA5 4.71E-02 2.15E-04 3.11 up ENSG00000227438 AP001471.1 3.58E-02 3.67E-05 -2.90 down ENSG00000256514 AP003419.11 3.67E-02 4.43E-05 14.09 up ENSG00000157500 APPL1 4.71E-02 2.22E-04 2.31 up ENSG00000120318 ARAP3 4.64E-02 1.92E-04 -4.08 down ENSG00000134287 ARF3 4.10E-02 7.89E-05 2.36 up ENSG00000146376 ARHGAP18 4.86E-02 2.57E-04 2.33 up ENSG00000177479 ARIH2 4.46E-02 1.31E-04 -1.77 down ENSG00000144746 ARL6IP5 4.96E-02 3.76E-04 4.20 up ENSG00000226284 ARPC3P1 4.46E-02 1.68E-04 3.25 up ENSG00000169136 ATF5 3.38E-02 2.91E-05 4.94 up ENSG00000142102 ATHL1 4.88E-02 2.88E-04 -2.62 down ENSG00000184743 ATL3 4.71E-02 1.98E-04 3.32 up ENSG00000166454 ATMIN 4.86E-02 2.63E-04 2.11 up ENSG00000165629 ATP5C1 4.94E-02 3.57E-04 3.09 up ENSG00000241837 ATP5O 4.46E-02 1.71E-04 2.51 up ENSG00000105146 AURKC 3.83E-02 5.16E-05 -2.93 down ENSG00000237758 BANF1P3 4.46E-02 1.36E-04 5.65 up ENSG00000116752 BCAS2 4.96E-02 3.73E-04 4.11 up ENSG00000142867 BCL10 4.94E-02 3.21E-04 2.02 up ENSG00000122870 BICC1 4.94E-02 3.54E-04 1.80 up ENSG00000106605 BLVRA 4.46E-02 1.44E-04 2.60 up ENSG00000204217 BMPR2 4.87E-02 2.71E-04 3.10 up ENSG00000154640 BTG3 4.97E-02 3.94E-04 2.11 up ENSG00000136261 BZW2 4.97E-02 3.94E-04 1.78 up ENSG00000087302 C14orf166 4.94E-02 3.31E-04 3.18 up ENSG00000114405 C3orf14 4.32E-02 1.02E-04 8.64 up ENSG00000135387 CAPRIN1 4.94E-02 3.26E-04 2.19 up ENSG00000204397 CARD16 4.88E-02 2.82E-04 7.34 up ENSG00000132357 CARD6 4.94E-02 3.55E-04 3.58 up ENSG00000166734 CASC4 4.94E-02 3.38E-04 2.99 up ENSG00000160799 CCDC12 4.94E-02 3.61E-04 2.51 up ENSG00000135205 CCDC146 1.69E-02 1.95E-06 -2.27 down ENSG00000228544 CCDC183-AS1 4.98E-02 4.00E-04 -1.25 down ENSG00000110104 CCDC86 4.46E-02 1.27E-04 3.40 up ENSG00000135404 CD63 4.94E-02 3.49E-04 2.06 up ENSG00000058091 CDK14 3.38E-02 9.56E-06 2.68 up ENSG00000100526 CDKN3 4.94E-02 3.09E-04 5.59 up ENSG00000115816 CEBPZ 4.71E-02 2.00E-04 2.52 up ENSG00000117724 CENPF 3.07E-02 4.79E-06 3.53 up ENSG00000172824 CES4A 4.95E-02 3.69E-04 -3.41 down ENSG00000196337 CGB7 3.38E-02 1.19E-05 -4.22 down ENSG00000255112 CHMP1B 3.38E-02 3.00E-05 2.90 up ENSG00000141076 CIRH1A 4.88E-02 2.89E-04 2.30 up ENSG00000123975 CKS2 4.71E-02 2.18E-04 2.98 up ENSG00000155962 CLIC2 4.98E-02 4.02E-04 6.37 up ENSG00000169504 CLIC4 4.86E-02 2.66E-04 3.79 up ENSG00000122705 CLTA 4.94E-02 3.46E-04 2.73 up ENSG00000168275 COA6 4.74E-02 2.38E-04 4.09 up ENSG00000182871 COL18A1 4.15E-02 8.40E-05 -12.44 down ENSG00000114270 COL7A1 4.87E-02 2.74E-04 -6.41 down ENSG00000115520 COQ10B 4.46E-02 1.60E-04 3.18 up ENSG00000127184 COX7C 4.94E-02 3.33E-04 2.32 up ENSG00000130038 CRACR2A 4.71E-02 2.30E-04 2.58 up ENSG00000143162 CREG1 3.90E-02 5.85E-05 6.20 up ENSG00000270558 CTD-2124B8.2 3.56E-02 3.52E-05 3.28 up ENSG00000185641 CTD-2287O16.1 4.46E-02 1.33E-04 3.15 up ENSG00000255182 CTD-2517M22.14 4.87E-02 2.73E-04 -5.93 down ENSG00000276855 CTD-3157E16.2 4.88E-02 2.88E-04 4.37 up ENSG00000267042 CTD-3193K9.4 4.18E-02 8.92E-05 -2.03 down ENSG00000132792 CTNNBL1 4.46E-02 1.66E-04 2.43 up ENSG00000163510 CWC22 5.00E-02 4.09E-04 2.21 up ENSG00000119004 CYP20A1 4.71E-02 2.03E-04 1.81 up ENSG00000161692 DBF4B 4.96E-02 3.76E-04 -3.35 down ENSG00000110367 DDX6 4.97E-02 3.95E-04 1.68 up ENSG00000161249 DMKN 3.38E-02 2.38E-05 20.14 up ENSG00000104936 DMPK 4.91E-02 2.95E-04 -3.70 down ENSG00000069345 DNAJA2 4.92E-02 2.96E-04 2.22 up ENSG00000173852 DPY19L1 3.41E-02 3.13E-05 2.48 up ENSG00000177990 DPY19L2 3.38E-02 7.02E-06 -6.12 down ENSG00000117505 DR1 4.94E-02 3.10E-04 2.85 up ENSG00000171451 DSEL 4.15E-02 8.60E-05 2.50 up ENSG00000276023 DUSP14 4.88E-02 2.88E-04 5.39 up ENSG00000249786 EAF1-AS1 3.38E-02 8.59E-06 -4.79 down ENSG00000228502 EEF1A1P11 3.38E-02 7.82E-06 35.09 up ENSG00000249264 EEF1A1P9 3.76E-03 2.61E-07 7.05 up ENSG00000114942 EEF1B2 4.86E-02 2.64E-04 2.51 up ENSG00000140025 EFCAB11 4.74E-02 2.43E-04 2.11 up ENSG00000142634 EFHD2 4.94E-02 3.40E-04 2.00 up ENSG00000024422 EHD2 3.65E-02 3.89E-05 2.49 up ENSG00000149547 EI24 4.94E-02 3.48E-04 2.74 up ENSG00000231684 EIF1P3 4.94E-02 3.35E-04 6.50 up ENSG00000055332 EIF2AK2 4.87E-02 2.67E-04 2.06 up ENSG00000178982 EIF3K 4.91E-02 2.93E-04 2.34 up ENSG00000149100 EIF3M 4.71E-02 2.11E-04 2.75 up ENSG00000148730 EIF4EBP2 4.96E-02 3.77E-04 2.39 up ENSG00000163577 EIF5A2 4.71E-02 2.18E-04 3.13 up ENSG00000197774 EME2 4.97E-02 3.89E-04 -2.66 down ENSG00000125746 EML2 3.90E-02 5.83E-05 -2.22 down ENSG00000100632 ERH 4.46E-02 1.29E-04 2.83 up ENSG00000204334 ERICH2 4.71E-02 2.27E-04 5.87 up ENSG00000134954 ETS1 4.96E-02 3.74E-04 2.50 up ENSG00000182197 EXT1 4.32E-02 1.02E-04 3.09 up ENSG00000274791 F8A2 4.96E-02 3.72E-04 2.56 up ENSG00000197798 FAM118B 4.84E-02 2.52E-04 2.33 up ENSG00000051009 FAM160A2 4.71E-02 2.26E-04 -3.09 down ENSG00000188163 FAM166A 4.94E-02 3.61E-04 -1.88 down ENSG00000178761 FAM219B 4.46E-02 1.27E-04 -5.34 down ENSG00000166797 FAM96A 4.71E-02 2.24E-04 3.88 up ENSG00000105202 FBL 4.53E-02 1.80E-04 2.04 up ENSG00000140092 FBLN5 4.94E-02 3.14E-04 3.32 up ENSG00000151876 FBXO4 4.88E-02 2.80E-04 1.79 up ENSG00000137714 FDX1 4.46E-02 1.50E-04 2.53 up ENSG00000122176 FMOD 4.94E-02 3.61E-04 3.16 up ENSG00000176678 FOXL1 4.57E-02 1.82E-04 1.85 up ENSG00000170802 FOXN2 4.94E-02 3.44E-04 1.89 up ENSG00000105255 FSD1 5.00E-02 4.07E-04 -3.18 down ENSG00000140718 FTO 4.71E-02 2.04E-04 2.30 up ENSG00000010810 FYN 3.58E-02 3.73E-05 1.94 up ENSG00000224837 GCSHP5 4.92E-02 2.98E-04 2.87 up ENSG00000240288 GHRLOS 4.46E-02 1.50E-04 -2.58 down ENSG00000196743 GM2A 4.18E-02 9.04E-05 4.05 up ENSG00000163655 GMPS 4.86E-02 2.57E-04 2.26 up ENSG00000065135 GNAI3 4.97E-02 3.84E-04 2.06 up ENSG00000204628 GNB2L1 4.10E-02 7.71E-05 2.56 up ENSG00000066455 GOLGA5 4.46E-02 1.60E-04 3.19 up ENSG00000113384 GOLPH3 4.88E-02 2.90E-04 2.31 up ENSG00000164294 GPX8 4.10E-02 7.96E-05 2.85 up ENSG00000225071 GS1-184P14.2 3.38E-02 1.76E-05 6.50 up ENSG00000082701 GSK3B 3.38E-02 2.89E-05 2.40 up ENSG00000137947 GTF2B 4.97E-02 3.88E-04 2.78 up ENSG00000105793 GTPBP10 4.46E-02 1.17E-04 1.60 up ENSG00000138867 GUCD1 4.95E-02 3.68E-04 1.85 up ENSG00000236534 H3F3BP1 4.46E-02 1.28E-04 7.47 up ENSG00000105856 HBP1 4.88E-02 2.88E-04 2.12 up ENSG00000143630 HCN3 4.86E-02 2.63E-04 -2.72 down ENSG00000148110 HIATL1 4.94E-02 3.47E-04 2.75 up ENSG00000241106 HLA-DOB 4.46E-02 1.32E-04 -2.25 down ENSG00000214578 HMGN2P15 3.67E-02 4.54E-05 -3.62 down ENSG00000214223 HNRNPA1P10 3.38E-02 1.25E-05 6.12 up ENSG00000214753 HNRNPUL2 4.87E-02 2.69E-04 2.22 up ENSG00000149084 HSD17B12 4.86E-02 2.63E-04 5.06 up ENSG00000080824 HSP90AA1 4.94E-02 3.54E-04 2.68 up ENSG00000116237 ICMT 4.46E-02 1.22E-04 2.38 up ENSG00000067064 IDI1 5.00E-02 4.10E-04 5.34 up ENSG00000163565 IFI16 4.46E-02 1.28E-04 4.74 up ENSG00000152778 IFIT5 4.92E-02 3.00E-04 3.60 up ENSG00000185885 IFITM1 3.38E-02 2.49E-05 3.33 up ENSG00000185201 IFITM2 4.32E-02 1.00E-04 3.34 up ENSG00000142089 IFITM3 4.94E-02 3.44E-04 3.04 up ENSG00000123496 IL13RA2 4.32E-02 1.00E-04 7.64 up ENSG00000134352 IL6ST 4.71E-02 2.26E-04 2.74 up ENSG00000176842 IRX5 4.94E-02 3.48E-04 2.07 up ENSG00000055955 ITIH4 3.38E-02 2.42E-05 -3.04 down ENSG00000260231 JHDM1D-AS1 3.67E-02 4.78E-05 2.61 up ENSG00000082482 KCNK2 3.38E-02 2.65E-05 4.72 up ENSG00000134504 KCTD1 4.46E-02 1.66E-04 2.79 up ENSG00000166803 KIAA0101 4.46E-02 1.60E-04 4.37 up ENSG00000177728 KIAA0195 4.46E-02 1.14E-04 -2.11 down ENSG00000198920 KIAA0753 4.10E-02 7.92E-05 -1.95 down ENSG00000163617 KIAA1407 4.97E-02 3.78E-04 -2.51 down ENSG00000167702 KIFC2 3.67E-02 4.76E-05 -3.25 down ENSG00000172059 KLF11 4.94E-02 3.46E-04 2.06 up ENSG00000119138 KLF9 4.86E-02 2.65E-04 3.19 up ENSG00000186432 KPNA4 4.64E-02 1.91E-04 3.04 up ENSG00000185513 L3MBTL1 4.94E-02 3.57E-04 -4.33 down ENSG00000185896 LAMP1 4.46E-02 1.51E-04 2.39 up ENSG00000213658 LAT 4.46E-02 1.39E-04 -3.48 down ENSG00000105617 LENG1 4.97E-02 3.97E-04 2.65 up ENSG00000153902 LGI4 3.38E-02 1.47E-05 -4.87 down ENSG00000232931 LINC00342 4.64E-02 1.91E-04 -2.78 down ENSG00000224259 LINC01133 4.10E-02 7.72E-05 4.80 up ENSG00000245937 LINC01184 4.94E-02 3.22E-04 3.14 up ENSG00000107798 LIPA 4.94E-02 3.52E-04 4.99 up ENSG00000137269 LRRC1 4.71E-02 1.95E-04 -2.38 down ENSG00000185158 LRRC37B 3.38E-02 1.31E-05 -1.69 down ENSG00000141294 LRRC46 3.38E-02 1.39E-05 -2.17 down ENSG00000161328 LRRC56 4.86E-02 2.66E-04 -4.47 down ENSG00000175324 LSM1 4.97E-02 3.96E-04 3.57 up ENSG00000232024 LSM12P1 4.94E-02 3.60E-04 3.76 up ENSG00000119487 MAPKAP1 4.88E-02 2.87E-04 2.19 up ENSG00000186056 MATN1-AS1 4.96E-02 3.73E-04 -2.41 down ENSG00000180398 MCFD2 4.86E-02 2.56E-04 2.58 up ENSG00000170430 MGMT 3.38E-02 1.85E-05 2.54 up ENSG00000135596 MICAL1 4.46E-02 1.23E-04 -2.45 down ENSG00000249669 MIR143HG 4.94E-02 3.28E-04 -10.45 down ENSG00000089693 MLF2 4.97E-02 3.90E-04 1.59 up ENSG00000124383 MPHOSPH10 4.71E-02 2.04E-04 2.63 up ENSG00000082515 MRPL22 4.74E-02 2.37E-04 2.13 up ENSG00000114686 MRPL3 4.92E-02 3.00E-04 3.29 up ENSG00000154719 MRPL39 4.71E-02 2.15E-04 3.17 up ENSG00000136522 MRPL47 4.95E-02 3.67E-04 3.28 up ENSG00000175110 MRPS22 4.97E-02 3.87E-04 2.20 up ENSG00000072952 MRVI1 4.94E-02 3.24E-04 -8.64 down ENSG00000113318 MSH3 3.83E-02 5.37E-05 2.43 up ENSG00000188895 MSL1 4.46E-02 1.70E-04 -1.77 down ENSG00000147065 MSN 4.86E-02 2.62E-04 2.51 up ENSG00000160953 MUM1 4.71E-02 1.97E-04 -2.63 down ENSG00000157601 MX1 3.83E-02 5.31E-05 3.28 up ENSG00000172428 MYEOV2 4.38E-02 1.07E-04 2.61 up ENSG00000215375 MYL5 3.96E-02 6.46E-05 -2.32 down ENSG00000176658 MYO1D 4.94E-02 3.53E-04 3.16 up ENSG00000164134 NAA15 4.74E-02 2.39E-04 1.97 up ENSG00000188554 NBR1 4.46E-02 1.21E-04 2.25 up ENSG00000266412 NCOA4 3.58E-02 3.68E-05 3.23 up ENSG00000131507 NDFIP1 4.52E-02 1.78E-04 3.30 up ENSG00000182636 NDN 3.67E-02 4.83E-05 3.88 up ENSG00000184983 NDUFA6 4.94E-02 3.55E-04 3.06 up ENSG00000145494 NDUFS6 4.96E-02 3.75E-04 2.56 up ENSG00000125967 NECAB3 4.46E-02 1.29E-04 -4.20 down ENSG00000244005 NFS1 4.46E-02 1.50E-04 -1.92 down ENSG00000171840 NINJ2 4.97E-02 3.96E-04 4.63 up ENSG00000101004 NINL 4.46E-02 1.35E-04 -2.00 down ENSG00000164190 NIPBL 4.94E-02 3.25E-04 2.21 up ENSG00000147383 NSDHL 4.71E-02 2.12E-04 4.70 up ENSG00000185652 NTF3 4.71E-02 2.14E-04 -5.61 down ENSG00000103274 NUBP1 4.97E-02 3.87E-04 2.43 up ENSG00000069275 NUCKS1 4.10E-02 7.53E-05 2.47 up ENSG00000136159 NUDT15 4.71E-02 2.03E-04 3.57 up ENSG00000184232 OAF 4.46E-02 1.43E-04 6.01 up ENSG00000111335 OAS2 4.10E-02 7.32E-05 3.14 up ENSG00000119900 OGFRL1 3.38E-02 1.81E-05 4.13 up ENSG00000170515 PA2G4 4.94E-02 3.10E-04 2.62 up ENSG00000180370 PAK2 4.52E-02 1.79E-04 2.07 up ENSG00000166889 PATL1 4.95E-02 3.68E-04 2.23 up ENSG00000102390 PBDC1 4.15E-02 8.64E-05 2.40 up ENSG00000169564 PCBP1 4.94E-02 3.41E-04 4.25 up ENSG00000240184 PCDHGC3 4.94E-02 3.15E-04 1.83 up ENSG00000132646 PCNA 4.71E-02 2.26E-04 2.53 up ENSG00000232838 PET117 4.46E-02 1.23E-04 2.30 up ENSG00000143256 PFDN2 3.96E-02 6.66E-05 3.77 up ENSG00000233328 PFN1P1 4.46E-02 1.63E-04 2.95 up ENSG00000148985 PGAP2 3.67E-02 4.76E-05 -3.26 down ENSG00000164902 PHAX 4.88E-02 2.88E-04 2.40 up ENSG00000100410 PHF5A 4.46E-02 1.68E-04 2.73 up ENSG00000155252 PI4K2A 3.90E-02 5.87E-05 2.30 up ENSG00000131788 PIAS3 4.94E-02 3.54E-04 -1.76 down ENSG00000151176 PLBD2 4.74E-02 2.41E-04 2.46 up ENSG00000124181 PLCG1 4.74E-02 2.40E-04 -1.82 down ENSG00000154822 PLCL2 4.46E-02 1.16E-04 1.95 up ENSG00000198753 PLXNB3 3.38E-02 1.68E-05 -7.45 down ENSG00000115946 PNO1 4.15E-02 8.62E-05 3.00 up ENSG00000148229 POLE3 4.87E-02 2.70E-04 1.76 up ENSG00000163882 POLR2H 4.97E-02 3.88E-04 -2.04 down ENSG00000213889 PPM1N 3.83E-02 5.23E-05 -2.52 down ENSG00000113575 PPP2CA 4.46E-02 1.61E-04 2.72 up ENSG00000221823 PPP3R1 4.87E-02 2.74E-04 2.68 up ENSG00000126432 PRDX5 4.71E-02 2.32E-04 2.52 up ENSG00000164306 PRIMPOL 4.46E-02 1.62E-04 -2.27 down ENSG00000171867 PRNP 4.18E-02 8.95E-05 4.18 up ENSG00000101000 PROCR 4.46E-02 1.59E-04 2.77 up ENSG00000101911 PRPS2 4.46E-02 1.64E-04 3.78 up ENSG00000100902 PSMA6 4.46E-02 1.28E-04 2.50 up ENSG00000277791 PSMB3 4.46E-02 1.44E-04 2.29 up ENSG00000136930 PSMB7 4.46E-02 1.55E-04 3.05 up ENSG00000101843 PSMD10 4.74E-02 2.42E-04 2.94 up ENSG00000125818 PSMF1 4.46E-02 1.43E-04 3.34 up ENSG00000184007 PTP4A2 3.38E-02 2.92E-05 2.55 up ENSG00000267185 PTP4A2P1 3.65E-02 4.29E-05 2.94 up ENSG00000142949 PTPRF 3.38E-02 1.10E-05 -4.62 down ENSG00000103769 RAB11A 4.94E-02 3.51E-04 3.15 up ENSG00000124209 RAB22A 3.65E-02 4.24E-05 2.92 up ENSG00000104388 RAB2A 4.94E-02 3.31E-04 3.01 up ENSG00000167994 RAB3IL1 4.18E-02 8.77E-05 2.60 up ENSG00000075785 RAB7A 4.94E-02 3.17E-04 3.50 up ENSG00000002016 RAD52 4.87E-02 2.72E-04 -2.40 down ENSG00000006451 RALA 4.64E-02 1.91E-04 4.61 up ENSG00000079337 RAPGEF3 4.87E-02 2.69E-04 -8.44 down ENSG00000091009 RBM27 4.71E-02 2.32E-04 2.19 up ENSG00000004700 RECQL 4.94E-02 3.07E-04 1.67 up ENSG00000205853 RFPL3S 4.71E-02 2.31E-04 -2.63 down ENSG00000159788 RGS12 3.38E-02 2.21E-05 -2.02 down ENSG00000125352 RNF113A 4.94E-02 3.04E-04 2.74 up ENSG00000163961 RNF168 4.46E-02 1.59E-04 2.31 up ENSG00000185483 ROR1 4.88E-02 2.85E-04 2.41 up ENSG00000272017 RP1-199J3.7 3.76E-03 2.33E-07 -3.46 down ENSG00000247679 RP11-1277A3.1 4.71E-02 2.10E-04 -2.37 down ENSG00000274444 RP11-128A17.2 3.38E-02 6.15E-06 -3.22 down ENSG00000277728 RP11-143K11.7 4.03E-02 7.03E-05 -2.22 down ENSG00000213609 RP11-170M17.2 5.00E-02 4.05E-04 2.52 up ENSG00000272668 RP11-190A12.8 4.95E-02 3.66E-04 -4.65 down ENSG00000264608 RP11-192H23.8 4.46E-02 1.30E-04 -2.58 down ENSG00000274341 RP11-227G15.10 3.90E-02 5.97E-05 -2.94 down ENSG00000267002 RP11-242D8.1 4.46E-02 1.12E-04 -1.70 down ENSG00000116883 RP11-268J15.5 4.94E-02 3.18E-04 -3.34 down ENSG00000242861 RP11-285F7.2 4.46E-02 1.18E-04 -1.99 down ENSG00000213080 RP11-312J18.5 4.86E-02 2.60E-04 2.89 up ENSG00000239804 RP11-379B18.1 4.10E-02 7.92E-05 -2.38 down ENSG00000244313 RP11-425L10.1 4.46E-02 1.53E-04 2.14 up ENSG00000276259 RP11-481J2.4 3.38E-02 2.84E-05 -2.25 down ENSG00000248161 RP11-499E18.1 3.96E-02 6.51E-05 4.08 up ENSG00000272269 RP11-500C11.3 4.46E-02 1.72E-04 2.31 up ENSG00000269903 RP11-571M6.18 4.15E-02 8.60E-05 -2.42 down ENSG00000240036 RP11-587D21.1 4.94E-02 3.33E-04 3.22 up ENSG00000212664 RP11-592N21.1 4.46E-02 1.34E-04 13.70 up ENSG00000279423 RP11-671J11.5 4.46E-02 1.74E-04 2.12 up ENSG00000243181 RP11-734J24.1 4.71E-02 2.33E-04 5.35 up ENSG00000258738 RP11-73E17.2 4.78E-02 2.47E-04 2.87 up ENSG00000186076 RP11-887P2.3 4.97E-02 3.81E-04 8.95 up ENSG00000269973 RP11-95D17.1 4.97E-02 3.90E-04 2.27 up ENSG00000267707 RP11-95O2.5 4.71E-02 2.27E-04 -1.98 down ENSG00000102218 RP2 4.94E-02 3.42E-04 2.85 up ENSG00000227008 RP3-417G15.1 3.96E-02 6.56E-05 4.55 up ENSG00000223745 RP4-717I23.3 3.76E-03 2.52E-07 -4.26 down ENSG00000142676 RPL11 4.46E-02 1.42E-04 4.09 up ENSG00000236992 RPL12L3 3.38E-02 2.81E-05 3.16 up ENSG00000241923 RPL14P3 4.32E-02 1.04E-04 3.17 up ENSG00000265681 RPL17 3.38E-02 2.20E-05 2.04 up ENSG00000228331 RPL17P43 4.46E-02 1.70E-04 6.39 up ENSG00000105640 RPL18A 3.90E-02 5.90E-05 3.26 up ENSG00000213442 RPL18AP3 4.71E-02 2.21E-04 3.51 up ENSG00000108298 RPL19 4.94E-02 3.09E-04 2.85 up ENSG00000220749 RPL21P28 4.46E-02 1.70E-04 6.36 up ENSG00000116251 RPL22 4.88E-02 2.84E-04 3.77 up ENSG00000114391 RPL24 4.94E-02 3.48E-04 3.55 up ENSG00000161970 RPL26 4.71E-02 2.27E-04 3.56 up ENSG00000037241 RPL26L1 4.94E-02 3.37E-04 2.94 up ENSG00000226221 RPL26P19 4.94E-02 3.33E-04 4.53 up ENSG00000156482 RPL30 4.46E-02 1.50E-04 2.16 up ENSG00000071082 RPL31 4.46E-02 1.50E-04 2.29 up ENSG00000109475 RPL34 4.46E-02 1.53E-04 3.34 up ENSG00000240509 RPL34P18 3.67E-02 4.54E-05 2.81 up ENSG00000136942 RPL35 4.84E-02 2.51E-04 3.48 up ENSG00000182899 RPL35A 4.32E-02 1.02E-04 2.25 up ENSG00000130255 RPL36 3.56E-02 3.49E-05 2.93 up ENSG00000197756 RPL37A 3.38E-02 3.00E-05 2.59 up ENSG00000172809 RPL38 4.23E-02 9.53E-05 4.35 up ENSG00000232573 RPL3P4 4.46E-02 1.58E-04 2.89 up ENSG00000174444 RPL4 4.98E-02 4.01E-04 2.69 up ENSG00000279483 RPL41 4.74E-02 2.39E-04 8.25 up ENSG00000230207 RPL4P5 4.03E-02 6.92E-05 4.81 up ENSG00000122406 RPL5 4.71E-02 2.32E-04 3.53 up ENSG00000235552 RPL6P27 4.91E-02 2.93E-04 6.79 up ENSG00000241741 RPL7AP30 4.46E-02 1.69E-04 2.71 up ENSG00000244363 RPL7P23 4.88E-02 2.81E-04 4.54 up ENSG00000177600 RPLP2 4.98E-02 4.01E-04 2.45 up ENSG00000124614 RPS10 4.86E-02 2.58E-04 2.69 up ENSG00000110700 RPS13 4.71E-02 2.19E-04 3.04 up ENSG00000164587 RPS14 4.46E-02 1.31E-04 2.61 up ENSG00000231500 RPS18 3.55E-02 3.38E-05 3.03 up ENSG00000230897 RPS18P12 4.94E-02 3.50E-04 2.15 up ENSG00000118181 RPS25 4.94E-02 3.11E-04 4.05 up ENSG00000196933 RPS26P11 4.46E-02 1.63E-04 6.63 up ENSG00000212994 RPS26P6 3.38E-02 2.72E-05 6.19 up ENSG00000204652 RPS26P8 4.46E-02 1.18E-04 8.54 up ENSG00000189343 RPS2P46 4.10E-02 7.52E-05 3.89 up ENSG00000145425 RPS3A 4.46E-02 1.51E-04 3.28 up ENSG00000214389 RPS3AP26 3.65E-02 4.08E-05 2.79 up ENSG00000137154 RPS6 3.65E-02 4.22E-05 3.50 up ENSG00000263266 RPS7P1 4.97E-02 3.88E-04 2.69 up ENSG00000142937 RPS8 3.83E-02 5.38E-05 2.74 up ENSG00000213621 RPSAP54 4.94E-02 3.50E-04 6.49 up ENSG00000166592 RRAD 4.71E-02 2.13E-04 -4.86 down ENSG00000225855 RUSC1-AS1 3.38E-02 1.58E-05 -3.05 down ENSG00000197747 S100A10 3.67E-02 4.82E-05 4.14 up ENSG00000170989 S1PR1 4.74E-02 2.39E-04 3.16 up ENSG00000151748 SAV1 4.46E-02 1.36E-04 3.61 up ENSG00000189001 SBSN 3.65E-02 4.00E-05 6.54 up ENSG00000198794 SCAMP5 4.95E-02 3.63E-04 -2.03 down ENSG00000214279 SCART1 4.97E-02 3.93E-04 -4.46 down ENSG00000136193 SCRN1 4.88E-02 2.88E-04 2.48 up ENSG00000143751 SDE2 4.71E-02 2.16E-04 2.73 up ENSG00000196189 SEMA4A 4.95E-02 3.65E-04 -4.66 down ENSG00000184702 SEPT5 5.00E-02 4.09E-04 -3.71 down ENSG00000132824 SERINC3 3.38E-02 2.01E-05 2.82 up ENSG00000149131 SERPING1 4.94E-02 3.41E-04 5.32 up ENSG00000119335 SET 4.94E-02 3.29E-04 2.39 up ENSG00000185917 SETD4 4.46E-02 1.72E-04 -2.21 down ENSG00000101049 SGK2 4.32E-02 1.04E-04 -3.36 down ENSG00000142669 SH3BGRL3 5.00E-02 4.09E-04 3.77 up ENSG00000131370 SH3BP5 4.57E-02 1.83E-04 3.13 up ENSG00000224660 SH3BP5-AS1 3.96E-02 6.65E-05 -2.95 down ENSG00000108061 SHOC2 4.98E-02 4.02E-04 2.66 up ENSG00000134802 SLC43A3 4.46E-02 1.27E-04 2.06 up ENSG00000135740 SLC9A5 4.46E-02 1.45E-04 -4.20 down ENSG00000166750 SLFN5 4.71E-02 2.00E-04 2.70 up ENSG00000188725 SMIM15 4.94E-02 3.26E-04 3.58 up ENSG00000224078 SNHG14 3.65E-02 4.03E-05 -1.52 down ENSG00000221716 SNORA11 4.46E-02 1.52E-04 -3.87 down ENSG00000207405 SNORA64 3.38E-02 2.93E-05 -9.88 down ENSG00000167088 SNRPD1 4.97E-02 3.85E-04 4.00 up ENSG00000182004 SNRPE 4.94E-02 3.55E-04 2.48 up ENSG00000143977 SNRPG 4.71E-02 2.26E-04 3.71 up ENSG00000224543 SNRPGP15 4.22E-02 9.44E-05 1.78 up ENSG00000264350 SNRPGP2 4.97E-02 3.85E-04 2.08 up ENSG00000130340 SNX9 4.71E-02 2.24E-04 3.40 up ENSG00000072195 SPEG 3.90E-02 6.00E-05 -4.73 down ENSG00000198369 SPRED2 4.88E-02 2.83E-04 2.61 up ENSG00000179119 SPTY2D1 4.88E-02 2.81E-04 2.54 up ENSG00000151304 SRFBP1 4.10E-02 7.85E-05 3.37 up ENSG00000174780 SRP72 4.97E-02 3.88E-04 2.31 up ENSG00000180581 SRP9P1 3.96E-02 6.49E-05 7.16 up ENSG00000212789 ST13P5 4.46E-02 1.59E-04 3.43 up ENSG00000101972 STAG2 4.18E-02 9.06E-05 1.99 up ENSG00000023734 STRAP 4.86E-02 2.54E-04 2.79 up ENSG00000115808 STRN 4.78E-02 2.46E-04 2.15 up ENSG00000101846 STS 4.94E-02 3.60E-04 2.04 up ENSG00000119760 SUPT7L 3.90E-02 5.87E-05 -1.92 down ENSG00000133789 SWAP70 4.94E-02 3.06E-04 2.42 up ENSG00000165632 TAF3 4.15E-02 8.20E-05 2.17 up ENSG00000177156 TALDO1 4.71E-02 2.23E-04 2.42 up ENSG00000167291 TBC1D16 4.88E-02 2.82E-04 1.64 up ENSG00000109436 TBC1D9 4.71E-02 2.13E-04 1.64 up ENSG00000143178 TBX19 4.41E-02 1.09E-04 -1.60 down ENSG00000154582 TCEB1 4.38E-02 1.07E-04 2.60 up ENSG00000103363 TCEB2 4.22E-02 9.37E-05 1.75 up ENSG00000092295 TGM1 2.73E-02 3.78E-06 -2.60 down ENSG00000051596 THOC3 4.92E-02 3.01E-04 2.13 up ENSG00000134809 TIMM10 3.38E-02 3.04E-05 2.94 up ENSG00000265354 TIMM23 4.94E-02 3.04E-04 2.74 up ENSG00000077147 TM9SF3 4.94E-02 3.20E-04 3.38 up ENSG00000143183 TMCO1 4.46E-02 1.51E-04 4.82 up ENSG00000157315 TMED6 4.86E-02 2.62E-04 -1.35 down ENSG00000146859 TMEM140 3.38E-02 9.81E-06 5.36 up ENSG00000139291 TMEM19 4.47E-02 1.75E-04 3.09 up ENSG00000183726 TMEM50A 4.88E-02 2.85E-04 3.09 up ENSG00000121900 TMEM54 3.90E-02 6.03E-05 2.34 up ENSG00000109084 TMEM97 4.22E-02 9.26E-05 10.13 up ENSG00000061938 TNK2 4.94E-02 3.12E-04 -3.54 down ENSG00000141232 TOB1 3.96E-02 6.25E-05 3.26 up ENSG00000214736 TOMM6 4.46E-02 1.18E-04 48.31 up ENSG00000232600 TONSL-AS1 3.93E-02 6.13E-05 -4.79 down ENSG00000172315 TP53RK 4.94E-02 3.38E-04 2.42 up ENSG00000196511 TPK1 4.46E-02 1.55E-04 2.26 up ENSG00000133112 TPT1 4.46E-02 1.60E-04 4.63 up ENSG00000255690 TRIL 4.71E-02 2.08E-04 3.70 up ENSG00000155428 TRIM74 4.22E-02 9.32E-05 -3.68 down ENSG00000171206 TRIM8 4.94E-02 3.20E-04 2.05 up ENSG00000174173 TRMT10C 4.97E-02 3.84E-04 3.70 up ENSG00000196689 TRPV1 3.38E-02 2.19E-05 -2.75 down ENSG00000102904 TSNAXIP1 4.95E-02 3.69E-04 -3.02 down ENSG00000161149 TUBA3FP 3.38E-02 1.13E-05 -2.18 down ENSG00000037042 TUBG2 4.46E-02 1.74E-04 -2.13 down ENSG00000100348 TXN2 4.92E-02 3.01E-04 1.76 up ENSG00000234036 TXNP6 4.38E-02 1.08E-04 2.93 up ENSG00000230454 U73166.2 3.38E-02 1.05E-05 -1.68 down ENSG00000185262 UBALD2 5.00E-02 4.07E-04 2.25 up ENSG00000076248 UNG 4.15E-02 8.61E-05 2.24 up ENSG00000132467 UTP3 4.94E-02 3.30E-04 3.06 up ENSG00000126756 UXT 4.94E-02 3.20E-04 2.17 up ENSG00000175073 VCPIP1 4.95E-02 3.66E-04 2.61 up ENSG00000122958 VPS26A 4.71E-02 2.03E-04 3.67 up ENSG00000109072 VTN 4.74E-02 2.36E-04 -2.56 down ENSG00000103091 WDR59 4.71E-02 2.15E-04 -1.91 down ENSG00000082068 WDR70 4.46E-02 1.62E-04 1.75 up ENSG00000215861 WI2-1896O14.1 4.46E-02 1.52E-04 4.08 up ENSG00000272273 XXbac-BPG252P9.10 4.71E-02 2.32E-04 2.95 up ENSG00000205189 ZBTB10 4.32E-02 1.04E-04 2.51 up ENSG00000181472 ZBTB2 3.38E-02 2.43E-05 3.44 up ENSG00000105939 ZC3HAV1 4.94E-02 3.14E-04 3.12 up ENSG00000156599 ZDHHC5 3.38E-02 2.15E-05 2.83 up ENSG00000178381 ZFAND2A 4.91E-02 2.94E-04 1.98 up ENSG00000177410 ZFAS1 4.71E-02 2.19E-04 3.13 up ENSG00000184939 ZFP90 4.84E-02 2.52E-04 -1.99 down ENSG00000197056 ZMYM1 4.58E-02 1.85E-04 -2.32 down ENSG00000176293 ZNF135 3.38E-02 3.05E-05 -2.60 down ENSG00000121417 ZNF211 4.46E-02 1.35E-04 -2.81 down ENSG00000198169 ZNF251 4.98E-02 4.01E-04 -1.95 down ENSG00000185947 ZNF267 4.15E-02 8.52E-05 2.24 up ENSG00000130818 ZNF426 4.10E-02 7.38E-05 1.93 up ENSG00000198521 ZNF43 4.46E-02 1.32E-04 -1.46 down ENSG00000171295 ZNF440 4.03E-02 7.05E-05 -2.76 down ENSG00000152433 ZNF547 4.03E-02 6.98E-05 -2.03 down ENSG00000198182 ZNF607 4.71E-02 2.05E-04 -2.31 down ENSG00000065029 ZNF76 4.92E-02 3.01E-04 -2.54 down Differentially expressed Differentially expressed in the peripheral blood Gene Symbol in brain samples of AD patients of AD patients (GSE5281) (GSE63060) AB019441.29 A_C004057.1 AC004448.2 AC004987.9 A_C009245.3 AC133644.2 ACSL4 * A_{CTG1P17 ADAMTS14} ADSSL1 A_{GER * AKAP2 *} AKIRIN2 * * AKT3 * ANAPC13 * * ANKZF1 * * ANP32E * A_{NXA5 *} AP001471.1 AP003419.11 A_{PPL1 *} ARAP3 * * ARF3 * A_{RHGAP18 * ARIH2 *} ARL6IP5 * * ARPC3P1 A_TF5 ATHL1 * * ATL3 A_{TMIN *} ATP5C1 * * ATP5O * * A_{URKC * BANF1P3} BCAS2 * BCL10 * B_ICC1 BLVRA * * BMPR2 * B_{TG3 *} BZW2 * * C14orf166 * * C_{3orf14 *} CAPRIN1 * CARD16 * * C_{ARD6 *} CASC4 * CCDC12 * C_{CDC146 * CCDC183-AS1} CCDC86 CD63 C_{DK14 *} CDKN3 * CEBPZ * C_{ENPF *} CES4A CGB7 C_{HMP1B *} CIRH1A * CKS2 * C_{LIC2 CLIC4 *} CLTA * COA6 * COL18A1 * * COL7A1 * COQ10B * COX7C * * CRACR2A CREG1 * C_{TD-2124B8.2 CTD-2287O16.1} CTD-2517M22.14 CTD-3157E16.2 C_TD-3193K9.4 CTNNBL1 * CWC22 C_{YP20A1 *} DBF4B * DDX6 * D_MKN DMPK DNAJA2 * * D_PY19L1 DPY19L2 * DR1 * D_{SEL DUSP14 *} EAF1-AS1 EEF1A1P11 E_EF1A1P9 EEF1B2 * * EFCAB11 EFHD2 * * EHD2 * EI24 E_IF1P3 EIF2AK2 * EIF3K * EIF3M * * _{EIF4EBP2 *} EIF5A2 * EME2 * E_{ML2 *} ERH * * ERICH2 E_{TS1 *} EXT1 * * F8A2 * F_{AM118B *} FAM160A2 * * FAM166A * FAM219B * * F_{AM96A *} FBL * FBLN5 * * FBXO4 * * FDX1 * FMOD F_OXL1 FOXN2 FSD1 * F_{TO *} FYN * * GCSHP5 G_{HRLOS GM2A *} GMPS * GNAI3 G_{NB2L1 *} GOLGA5 * * GOLPH3 G_PX8 GS1-184P14.2 GSK3B * GTF2B * * GTPBP10 GUCD1 * H_{3F3BP1 HBP1 *} HCN3 HIATL1 H_{LA-DOB *} HMGN2P15 HNRNPA1P10 H_{NRNPUL2 *} HSD17B12 * HSP90AA1 * * ICMT * * I_{DI1 *} IFI16 * IFIT5 I_{FITM1 *} IFITM2 * IFITM3 * I_{L13RA2 *} IL6ST * IRX5 ITIH4 * * JHDM1D-AS1 KCNK2 K_{CTD1 *} KIAA0101 * KIAA0195 * * K_{IAA0753 * KIAA1407 *} KIFC2 * KLF11 * K_{LF9 *} KPNA4 * * L3MBTL1 * * LAMP1 * * LAT * LENG1 L_{GI4 *} LINC00342 LINC01133 L_{INC01184 LIPA *} LRRC1 * LRRC37B L_{RRC46 *} LRRC56 * LSM1 * L_SM12P1 MAPKAP1 * MATN1-AS1 M_{CFD2 * MGMT *} MICAL1 * * MIR143HG M_{LF2 *} MPHOSPH10 * MRPL22 * * MRPL3 * * MRPL39 * * MRPL47 * * MRPS22 * * MRVI1 * MSH3 * M_{SL1 *} MSN * MUM1 * M_{X1 * MYEOV2 *} MYL5 * MYO1D * N_{AA15 *} NBR1 * * NCOA4 * N_{DFIP1 *} NDN * NDUFA6 * N_{DUFS6 *} NECAB3 * * NFS1 * N_{INJ2 * NINL *} NIPBL * NSDHL * N_TF3 NUBP1 NUCKS1 _NUDT15 OAF * OAS2 * O_{GFRL1 * PA2G4 *} PAK2 * PATL1 PBDC1 * * PCBP1 * * PCDHGC3 * P_{CNA *} PET117 * PFDN2 * P_FN1P1 PGAP2 PHAX * P_{HF5A *} PI4K2A PIAS3 PLBD2 * * P_{LCG1 *} PLCL2 * * PLXNB3 P_NO1 POLE3 * * POLR2H P_PM1N PPP2CA * * PPP3R1 * P_{RDX5 *} PRIMPOL PRNP * P_{ROCR PRPS2 *} PSMA6 * * PSMB3 * PSMB7 * * PSMD10 * * PSMF1 * P_{TP4A2 *} PTP4A2P1 PTPRF * RAB11A * * R_{AB22A *} RAB2A * * RAB3IL1 * R_{AB7A *} RAD52 RALA * * R_{APGEF3 *} RBM27 * RECQL * * R_FPL3S RGS12 * RNF113A * R_{NF168 *} ROR1 * RP1-199J3.7 _{RP11-1277A3.1 RP11-128A17.2} RP11-143K11.7 RP11-170M17.2 _{RP11-190A12.8} RP11-192H23.8 RP11-227G15.10 R_P11-242D8.1 RP11-268J15.5 RP11-285F7.2 R_P11-312J18.5 RP11-379B18.1 RP11-425L10.1 R_{P11-481J2.4 RP11-499E18.1} RP11-500C11.3 RP11-571M6.18 _{RP11-587D21.1} RP11-592N21.1 RP11-671J11.5 _{RP11-734J24.1} RP11-73E17.2 RP11-887P2.3 R_{P11-95D17.1 RP11-95O2.5} RP2 RP3-417G15.1 R_P4-717I23.3 RPL11 * * RPL12L3 R_PL14P3 RPL17 * * RPL17P43 RPL18A * * RPL18AP3 RPL19 * R_PL21P28 RPL22 * * RPL24 * * R_{PL26 *} RPL26L1 * * RPL26P19 RPL30 * RPL31 * * RPL34 * RPL34P18 R_{PL35 *} RPL35A * * RPL36 R_PL37A RPL38 RPL3P4 RPL4 * * _RPL41 RPL4P5 RPL5 * * R_PL6P27 RPL7AP30 RPL7P23 R_{PLP2 *} RPS10 * RPS13 * R_{PS14 * RPS18 *} RPS18P12 RPS25 * _RPS26P11 RPS26P6 RPS26P8 R_PS2P46 RPS3A * RPS3AP26 RPS6 * * RPS7P1 RPS8 * R_PSAP54 RRAD RUSC1-AS1 S_{100A10 * S1PR1 *} SAV1 * SBSN S_{CAMP5 *} SCART1 SCRN1 S_DE2 SEMA4A * SEPT5 SERINC3 * * SERPING1 * SET * * S_{ETD4 * SGK2} SH3BGRL3 * * SH3BP5 * _SH3BP5-AS1 SHOC2 * SLC43A3 * S_{LC9A5 *} SLFN5 * SMIM15 * S_{NHG14 SNORA11} SNORA64 SNRPD1 * S_{NRPE *} SNRPG * SNRPGP15 S_NRPGP2 SNX9 * SPEG S_{PRED2 *} SPTY2D1 SRFBP1 S_{RP72 *} SRP9P1 ST13P5 S_{TAG2 * STRAP *} STRN * STS * S_{UPT7L *} SWAP70 * * TAF3 * TALDO1 * * TBC1D16 * TBC1D9 * T_{BX19 *} TCEB1 * * TCEB2 * * T_GM1 THOC3 * * TIMM10 * TIMM23 * TM9SF3 * * TMCO1 * * TMED6 * T_MEM140 TMEM19 * TMEM50A * T_MEM54 TMEM97 * * TNK2 * * TOB1 * * T_{OMM6 *} TONSL-AS1 TP53RK * TPK1 * * TPT1 * * TRIL * T_{RIM74 *} TRIM8 * TRMT10C * * T_{RPV1 *} TSNAXIP1 TUBA3FP T_{UBG2 * TXN2} TXNP6 U73166.2 UBALD2 * * UNG * UTP3 U_{XT *} VCPIP1 * VPS26A * V_{TN *} WDR59 WDR70 * _{WI2-1896O14.1} XXbac- BPG252P9.10 ZBTB10 * Z_BTB2 ZC3HAV1 * ZDHHC5 * * Z_{FAND2A *} ZFAS1 ZFP90 * Z_{MYM1 * ZNF135 *} ZNF211 * ZNF251 * Z_{NF267 *} ZNF426 * * ZNF43 Z_{NF440 *} ZNF547 ZNF607 * Z_{NF76 *} Table 6. List of Gene Ontology-Biological Process terms enriched in 472 genes DE in AD fibroblasts vs Ctrl # GO Biological Process 1 cytoplasmic translation 2 translation at presynapse 3 cellular metabolic process 4 cellular component biogenesis 5 gene expression 6 nucleic acid metabolic process 7 cellular component assembly 8 protein-RNA complex assembly 9 response to oxidative stress 10 ribosome biogenesis 11 protein localization 12 response to reactive oxygen species 13 regulation of biosynthetic process 14 RNA metabolic process cell cycle RNA processing regulation of gene expression positive regulation of programmed cell death response to L-glutamate cell redox homeostasis protein transport post-transcriptional regulation of gene expression regulation of apoptotic process regulation of DNA-templated transcription regulation of translation RNA splicing regulation of proteolysis regulation of cell cycle regulation of molecular function ribosome assembly endothelial cell differentiation regulation of intracellular signal transduction response to interferon-alpha regulation of catabolic process leukocyte activation involved in immune response cell activation involved in immune response cellular response to stress regulation of binding regulation of cell growth regulation of DNA metabolic process transport cellular response to type I interferon positive regulation of positive chemotaxis metabolic process regulation of calcineurin-NFAT signaling cascade establishment of protein localization to mitochondrion regulation of calcineurin-mediated signaling innate immune response activating cell surface receptor signaling pathway regulation of catalytic activity regulation of calcium ion transport spliceosomal snRNP assembly regulation of glutamate receptor signaling pathway intracellular signal transduction Fc receptor signaling pathway dendritic spine maintenance 6 regulation of cell population proliferation 7 response to stress 8 TOR signaling 9 response to lectin 0 cellular response to lectin 1 regulation of growth 2 regulation of developmental process 3 regulation of cell motility 4 regulation of neuron apoptotic process 5 regulation of defense response 6 nuclear transport 7 interleukin-27-mediated signaling pathway 8 response to insulin-like growth factor stimulus 9 positive regulation of protein phosphorylation 0 regulation of mitochondrial membrane potential 1 vesicle-mediated transport 2 positive regulation of gene expression 3 positive regulation of protein transport 4 cellular response to oxidative stress 5 DNA biosynthetic process 6 regulation of protein ubiquitination 7 regulation of protein targeting 8 DNA damage response 9 protein-RNA complex assembly 0 ribonucleoprotein complex biogenesis 1 7-methylguanosine cap hypermethylation 2 regulation of viral genome replication 3 G protein-coupled receptor signaling pathway 4 nucleobase-containing compound metabolic process5 nucleobase-containing compound metabolic process Background # p-value FDR AD DEGs genes 1 1.193E-27 6.602E-24 32 142 2 1.705E-20 2.703E-17 19 57 3 2.931E-20 2.703E-17 214 7828 4 2.848E-14 7.505E-12 119 3745 5 2.74E-12 7.10E-09 78 1823 6 5.063E-11 8.004E-09 99 3210 7 1.005E-10 1.545E-08 103 3440 3.043E-10 4.318E-08 22 263 8.055E-10 1.087E-07 36 699 2.542E-09 3.057E-07 26 407 6.315E-09 7.108E-07 82 2691 9.309E-09 9.538E-07 25 403 9.802E-09 9.861E-07 139 5607 1.921E-08 1.898E-06 74 2384 3.511E-08 3.349E-06 60 1786 8.425E-08 7.769E-06 42 1070 1.169E-07 9.949E-06 155 6714 3.599E-07 2.845E-05 38 968 5.008E-07 3.745E-05 8 45 6.733E-07 4.838E-05 9 63 7.028E-07 4.985E-05 53 1633 7.508E-07 5.258E-05 32 762 7.732E-07 5.348E-05 69 2377 9.027E-07 6.166E-05 112 4564 1.461E-06 9.187E-05 27 598 1.523E-06 9.262E-05 23 458 1.624E-06 9.766E-05 41 1155 1.793E-06 1.066E-04 53 1685 2.568E-06 1.465E-04 99 3974 2.873E-06 1.615E-04 10 95 3.070E-06 1.682E-04 11 118 7.150E-06 3.501E-04 69 2534 7.777E-06 3.742E-04 7 46 8.628E-06 4.115E-04 46 1459 1.031E-05 4.715E-04 18 340 1.208E-05 5.303E-04 18 344 1.395E-05 5.938E-04 65 2388 1.446E-05 6.108E-04 26 639 1.711E-05 7.011E-04 28 724 1.842E-05 7.441E-04 28 727 3.174E-05 1.133E-03 116 5143 3.836E-05 1.310E-03 6 40 3.836E-05 1.310E-03 6 40 2.48E-06 1.48E-03 193 8056 4.633E-05 1.535E-03 7 60 4.746E-05 1.563E-03 9 105 5.741E-05 1.795E-03 7 62 5.896E-05 1.833E-03 8 84 6.222E-05 1.920E-03 73 2915 6.248E-05 1.920E-03 20 463 6.685E-05 1.997E-03 6 44 9.943E-05 2.737E-03 4 16 1.196E-04 3.163E-03 59 2262 1.255E-04 3.244E-03 7 70 1.284E-04 3.289E-03 4 17 1.379E-04 3.355E-03 67 2680 1.386E-04 3.355E-03 115 5264 1.553E-04 3.672E-03 6 51 1.649E-04 3.759E-03 5 33 1.649E-04 3.759E-03 5 33 1.652E-04 3.759E-03 33 1045 1.658E-04 3.759E-03 86 3696 1.679E-04 3.792E-03 42 1460 1.690E-04 3.794E-03 19 460 1.694E-04 3.794E-03 36 1182 1.948E-04 4.193E-03 16 353 2.039E-04 4.322E-03 4 19 2.121E-04 4.428E-03 3 8 2.168E-04 4.510E-03 34 1106 2.185E-04 4.528E-03 9 128 2.331E-04 4.742E-03 49 1821 2.703E-04 5.204E-03 50 1882 2.711E-04 5.204E-03 21 557 2.724E-04 5.204E-03 17 401 2.750E-04 5.230E-03 9 132 2.785E-04 5.277E-03 15 328 3.076E-04 5.708E-03 9 134 3.214E-04 5.907E-03 32 1039 2.94E-05 1.35E-02 14 197 1.08E-04 1.84E-02 14 224 4.49E-05 1.89E-02 4 8 5.27E-05 2.15E-02 9 86 8.18E-05 3.18E-02 6 1220 3.26E-04 4.49E-02 38 1112 3.26E-04 4.49E-02 38 1112 Table 7. List of biological pathways enriched in 472 genes DE in AD fibroblasts vs Ctrl. Pathway Maps # 1 Tau dysregulation in Alzheimer disease 2 Apoptosis and survival_NGF/ TrkA PI3K-mediated signaling 3 Signal transduction_AKT signaling 4 Development_WNT/Beta-catenin signaling. Signalosome 5 Neurophysiological process_Netrin-1 signaling 6 IgE- and MGF-induced Lyn-mediated prod. of cytokines and arachidonic acid metabs. (lung) 7 Neurophysiological process_Dynein-dynactin motor complex in axonal transport in neurons 8 Signal transduction_Angiotensin II signaling via Beta-arrestin 9 Signal transduction_FGFR4 signaling 10 Immune response_B cell antigen receptor (BCR) pathway 11 Immune response_Regulation of T cell function by CTLA-4 12 Possible regulation of HSF-1/ chaperone pathway in Huntington's disease 13 Immune response_CD28 signaling 14 Signal transduction_mTORC2 upstream signaling 15 Cell adhesion_PLAU signaling 16 Neurophysiological process_ACM2 and ACM4 signaling in the brain 17 Development_VEGF signaling via VEGFR2 - generic cascades 18 Immune response_IL-11 signaling via MEK/ERK and PI3K/AKT cascades 19 Immune response_TCR alpha/beta signaling 20 Neurophysiological process_Dopamine D2 receptor transactivation of PDGFR in CNS 21 Immune response_IL-6 signaling via MEK/ERK and PI3K/AKT cascades 22 Signal transduction_Ephrin reverse signaling 23 Development_Role of CNTF and LIF in regulation of oligodendrocyte development 24 G-protein signaling_Proinsulin C-peptide signaling 25 Apoptosis and survival_nAChR in apoptosis inhibition and cell cycle progression 26 Signal transduction_IGF-1 receptor signaling 27 Role of CNTF and LIF in regulation of oligodendrocyte development in multiple sclerosis 28 Chemotaxis_SDF-1/ CXCR4-induced chemotaxis of immune cells 29 Cell cycle_Influence of Ras and Rho proteins on G1/S Transition 30 Signal transduction_Non-neuronal ACM2 and ACM4 signaling 31 Immune response_IFN-gamma signaling via PI3K and NF-kB Cytoskeleton remodeling Regulation of actin cytoskeleton organization by kinase effectors of Rho 32 GTPases 33 Schizophrenia: neurodevelopmental hypothesis 34 Regulation of CFTR activity (normal and cystic fibrosis) Development_ErbB3 signaling Insulin-like growth factor family signaling in melanoma Immune response_IFN-alpha/beta signaling via PI3K and NF-kB pathways Muscle contraction_Regulation of eNOS activity in endothelial cells NF-AT signaling in cardiac hypertrophy Autophagy_Autophagy AKT signaling in Prostate Cancer Stem cells_Pancreatic cancer stem cells in tumor metastasis Cytoskeleton remodeling_Simona Mitochondrial pathway_De Pinto Cytoskeleton remodeling_Simona Development_Growth factors in regulation of oligodendrocyte progenitor cell proliferation Immune response De Pinto_Pathway1_Dpor1vsWT Immune response_Fc epsilon RI pathway: calcium-dependent signaling Stem cells_Putative pathways of telomerase regulation in glioblastoma stem cells Signal transduction_Relaxin family peptides signaling via RXFP3 and RXFP4 receptors Transcription_Negative regulation of HIF1A function Background # p-value FDR AD DEGs genes 1 2.397E-08 2.541E-05 11 85 2 1.096E-05 5.811E-03 8 77 3 2.610E-05 6.918E-03 6 43 4 2.610E-05 6.918E-03 6 43 5 4.604E-05 9.761E-03 7 69 6 6.278E-05 1.109E-02 6 50 7 9.745E-05 1.304E-02 6 54 8 1.323E-04 1.304E-02 6 57 9 1.394E-04 1.304E-02 7 82 0 1.446E-04 1.304E-02 8 110 1 1.476E-04 1.304E-02 5 37 2 1.779E-04 1.450E-02 4 21 3 2.022E-04 1.531E-02 7 87 4 2.747E-04 1.800E-02 6 65 5 2.747E-04 1.800E-02 6 65 6 3.047E-04 1.800E-02 5 43 7 3.057E-04 1.800E-02 7 93 3.244E-04 1.810E-02 6 67 3.956E-04 2.097E-02 7 97 4.208E-04 2.124E-02 4 26 5.558E-04 2.491E-02 6 74 5.640E-04 2.491E-02 4 28 5.640E-04 2.491E-02 4 28 6.411E-04 2.639E-02 6 76 6.472E-04 2.639E-02 4 29 6.761E-04 2.654E-02 7 106 7.388E-04 2.797E-02 4 30 7.876E-04 2.870E-02 6 79 8.123E-04 2.870E-02 5 53 8.852E-04 3.027E-02 5 54 1.046E-03 3.464E-02 5 56 1.227E-03 3.940E-02 5 58 1.325E-03 4.132E-02 5 59 1.657E-03 5.017E-02 5 62 1.780E-03 5.216E-02 5 63 1.825E-03 5.216E-02 4 38 1.945E-03 5.216E-02 6 94 2.045E-03 5.216E-02 5 65 2.045E-03 5.216E-02 5 65 2.188E-03 5.216E-02 5 66 2.212E-03 5.216E-02 4 40 2.212E-03 5.216E-02 4 40 2.281E-03 5.216E-02 6 97 2.281E-03 5.216E-02 6 97 2.281E-03 5.216E-02 6 97 2.339E-03 5.216E-02 5 67 2.425E-03 5.216E-02 4 41 2.483E-03 5.216E-02 9 208 2.496E-03 5.216E-02 5 68 2.509E-03 5.216E-02 3 20 2.651E-03 5.323E-02 4 42 2.662E-03 5.323E-02 5 69 References 1. 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Claims

What is claimed is: 1. A method for determining whether a human subject has a gene expression profile characteristic of Alzheimer’s disease (“AD”) comprising the following step: in a skin cell fibroblast population derived from the subject, measuring the expression levels of a plurality of genes in the set of genes set forth in Table 2, whereby the subject has a gene expression profile characteristic of AD if the measured expression levels are consistent with those genes’ expression levels in skin cell fibroblasts derived from AD patients.

2. The method of claim 1, wherein the set of genes consists of RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17.

3. The method of claim 1, wherein the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17.

4. The method of claim 1, wherein the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1.

5. A method for determining whether a human subject has a gene expression profile characteristic of Alzheimer’s disease (“AD”) comprising the following step: in a peripheral whole blood sample derived from the subject, measuring the expression levels of a plurality of genes in the set of genes comprising RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17, whereby the subject has a gene expression profile characteristic of AD if the measured expression levels are consistent with those genes’ expression levels in peripheral whole blood derived from AD patients.

6. The method of claim 5, wherein the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17.

7. The method of claim 5, wherein the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1.

8. A method for determining whether a demented human subject is afflicted with Alzheimer’s disease (“AD”) or non-Alzheimer’s disease dementia (“non-ADD”), comprising the following step: in a skin cell fibroblast population derived from the subject, measuring the expression levels of a plurality of genes in the set of genes set forth in Table 2, whereby (i) the subject is afflicted with AD if the measured expression levels are consistent with those genes’ expression levels in skin cell fibroblasts derived from AD patients, and (ii) the subject is afflicted with non-ADD if the measured expression levels are consistent with those genes’ expression levels in skin cell fibroblasts derived from non-ADD patients.

9. The method of claim 8, wherein the set of genes consists of RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17.

10. The method of claim 8, wherein the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17.

11. The method of claim 8, wherein the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1.

12. A method for determining whether a demented human subject is afflicted with Alzheimer’s disease (“AD”) or non-Alzheimer’s disease dementia (“non-ADD”), comprising the following step: in a peripheral whole blood sample derived from the subject, measuring the expression levels of a plurality of genes in the set of genes comprising RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17, whereby (i) the subject is afflicted with AD if the measured expression levels are consistent with those genes’ expression levels in peripheral whole blood derived from AD patients, and (ii) the subject is afflicted with non-ADD if the measured expression levels are consistent with those genes’ expression levels in peripheral whole blood derived from non-ADD patients.

13. The method of claim 12, wherein the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17.

14. The method of claim 12, wherein the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1.

15. A method for determining whether a non-demented human subject has an increased likelihood of becoming afflicted with Alzheimer’s disease (“AD”), comprising the following step: in a skin cell fibroblast population derived from the subject, measuring the expression levels of a plurality of genes in the set of genes set forth in Table 2, whereby the subject has an increased likelihood of becoming afflicted with AD if the measured expression levels are consistent with those genes’ expression levels in skin cell fibroblasts derived from AD patients.

16. The method of claim 15, wherein the set of genes consists of RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17.

17. The method of claim 15, wherein the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17.

18. The method of claim 15, wherein the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1.

19. The method of any of claims 15-18, wherein the subject is cognitively impaired.

20. The method of claim 19, wherein the subject is afflicted with mild cognitive impairment (MCI).

21. The method of any of claims 15-20, wherein the subject is known to be afflicted with AD pathology.

22. A method for determining whether a non-demented human subject has an increased likelihood of becoming afflicted with Alzheimer’s disease (“AD”), comprising the following step: in a peripheral whole blood sample derived from the subject, measuring the expression levels of a plurality of genes in the set of genes comprising RECQL, RALA, FYN, RPL26L1, FAM160A2, THOC3, ITIH4, TNK2, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, ARAP3, RPL5, NECAB3, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, MICAL1, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, ATHL1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ANKZF1, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, KIAA0195, FAM219B, EXT1, COL18A1, RPL35A, UBALD2, L3MBTL1, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17, whereby the subject has an increased likelihood of becoming afflicted with AD if the measured expression levels are consistent with those genes’ expression levels in peripheral whole blood derived from AD patients.

23. The method of claim 22, wherein the set of genes consists of RECQL, RALA, FYN, RPL26L1, THOC3, GOLGA5, DNAJA2, RPL31, TM9SF3, HSP90AA1, MRPL22, C14orf166, ERH, PSMA6, PSMD10, PBDC1, TCEB2, RAB11A, RAB2A, RPL18A, BLVRA, TMEM97, PPP2CA, RPL24, MRPL3, EEF1B2, ICMT, RPL22, SET, RPL5, COX7C, ANAPC13, ZNF426, SERINC3, TPT1, SWAP70, AKIRIN2, BZW2, MRPL47, PSMB7, RPS6, GTF2B, FBLN5, TOB1, EFHD2, SH3BGRL3, RPL11, TMCO1, ARL6IP5, POLE3, EIF3M, PLBD2, FBXO4, TCEB1, MRPL39, PLCL2, ZDHHC5, ATP5C1, PCBP1, TRMT10C, RPL4, MRPS22, TALDO1, EXT1, RPL35A, UBALD2, LAMP1, KPNA4, NBR1, TPK1, CARD16, ATP5O, and RPL17.

24. The method of claim 22, wherein the set of genes consists of FAM160A2, ITIH4, TNK2, ARAP3, NECAB3, MICAL1, ATHL1, ANKZF1, KIAA0195, FAM219B, COL18A1, and L3MBTL1.

25. The method of any of claims 22-24, wherein the subject is cognitively impaired.

26. The method of claim 25, wherein the subject is afflicted with mild cognitive impairment (MCI).

27. The method of any of claims 22-26, wherein the subject is known to be afflicted with AD pathology.

28. The method of any of claims 1-27, wherein measuring the expression level of a gene comprises measuring the number of that gene’s RNA transcripts per number of total transcripts.