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  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
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  • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
  • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/158—Expression markers

Definitions

• the present disclosure relates generally to medicine. More particularly, the present disclosure is directed to methods for identifying a subject having obstructive sleep apnea.
• OSA Obstructive Sleep Apnea
• SDB Sleep Disordered Breathing
• OSA affects 1 .2-5.7% of children, with a peak prevalence occurring at 2-8 years of age, which coincides with the peak age of tonsillar and adenoidal hypertrophy.
• OSA is characterized by recurrent events of upper airway obstruction during sleep leading to intermittent hypoxia, episodic arousals (i.e., sleep fragmentation), as well as episodic hypercapnia and increased intrathoracic pressure swings.
• OSA is a major cause of cardiovascular morbidity and neurocognitive dysfunction in children. Significant associations between sleep and cognitive development, temperament and behavior have been observed in infants during the first year of life and in school aged children, even when only mild SDB is present. Children with parent-reported OSA symptoms are associated with composite and domain-specific problem behaviors and alterations of brain structure, mainly in the frontal lobe. [0005] Evidence supports that OSA promotes a low-grade inflammatory state. IL-8 levels were increased in LPS-induced ex vivo cultures of PBMCs from OSA children compared to control children.
• PBMCs Peripheral Blood Mononuclear Cells
• PSG Polysomnography
• the present disclosure is directed to a method of diagnosing obstructive sleep apnea in a subject having or suspected of having obstructive sleep apnea, the method comprising: providing a biological sample from the subject; determining a gene expression level of CD70, HCST, CRIP1, IL 32, ENCI, GZMB, GZMK, HLA.DQA2, CD300A, LINC02446, DUSP2, ITGB1, LGALS1, IFIT3, CEBPD., TNFRSF4, HBB, NEAT1, LTBP1, ZNF683, SOX4, SMC4, LMS1, VIM, TRGC1, MAF, S100A4.
• LYAR LYAR, TRDC, TRGC2, RGS1, ANXAL and combinations thereof, and diagnosing obstructive sleep apnea in the subject if there is a measurable change in the expression level of the CD70, the HCST, the CRIP1, the IL 32, the ENCI, the GZMB, the GZMK, the HLA.DQA2, the CD300A, the LINC02446, the DUSP2, the ITGB1, the LGALS1, the IFIT3, the CEBPD, the TNFRSF4, the HBB, the NEAT1, the LTBP1, the ZNF683, the SOX4, the SMC4, the LMS 1, the VIM.
• the present disclosure is directed to a biomarker panel for identifying a subject as having obstructive sleep apnea comprising: CD70, HCST, CRIPL IL 32, ENCI, GZMB, GZMK, HLA.DQA2, CD300A, LINC02446, DUSP2, ITGB1, LGALS1, IFIT3, CEBPD., TNFRSF4, HBB, NEAT1, LTBP1, ZNF683, SOX4, SMC4, LMS1, VIM, TRGC1, MAF, S100A4. LYAR, TRDC, TRGC2. RGS1, ANXA1, and combinations thereof.
• the present disclosure is directed to a method of treating a subject having or suspected of having obstructive sleep apnea, the method comprising: providing a biological sample from the subject; determining a gene expression level of CD70, HCST, CRIP1, IL 32, ENCI, GZMB, GZMK, HLA.DQA2, CD300A, LINC02446, DUSP2, ITGBL LGALS 1, IFIT3, CEBPD., TNFRSF4, HBB, NEAT1, LTBP1, ZNF683, SOX4, SMC4, LMS1, VIM, TRGC1, MAF, S100A4, LYAR, TRDC, TRGC2, RGS1, ANXA1, and combinations thereof, and diagnosing obstructive sleep apnea in the subject if there is a measurable change in the expression level of the CD70, the HCST, the CRIP1, the IL 32, the ENCI, the GZMB, the GZMK, the HLA.D
• FIGS. 1A-1D depict building and validation of a molecular signature for diagnosis of pediatric sleep apnea.
• FIG. 1 A depicts a molecular signature consisting of 32 genes selected from single-cell transcriptional profiles corresponding to specific cell type and clinical atributes, and combined, according to sample ID.
• FIG. IB depicts the training and testing datasets created using a 70:30 split to generate the observed ROC curves for each statistical method.
• FIG. 1C depict building and validation of a molecular signature for diagnosis of pediatric sleep apnea.
• FIG. 1 A depicts a molecular signature consisting of 32 genes selected from single-cell transcriptional profiles corresponding to specific cell type and clinical atributes, and combined, according to sample ID.
• FIG. IB depicts the training and testing datasets created using a 70:30 split to generate the observed ROC curves for each statistical method.
• FIG. 1C depict building and validation of a molecular signature for diagnosis of pediatric sleep apnea.
• the molecular signature showed a high performance for discriminating OSA patients from no OSA/no snoring controls, with the ROC-AUC analysis resulting in AUC of 0.93, 0.96, and 0.92 for Empirical, Binormal, and Non-parametric ROCs, respectively, with 93% and 95% PPV and NPV, respectively.
• a subject refers to a subset of individuals who are susceptible to, or at elevated risk of, experiencing obstructive sleep disorder.
• a subject can be susceptible to, or at elevated risk of, experiencing symptoms due to family history, age, environment, and/or lifestyle. Based on the foregoing, because some of the methods embodiments of the present disclosure are directed to specific subsets or subclasses of identified individuals (that is, the subset or subclass of subjects having or suspected of having obstructive sleep apnea), not all individuals will fall within the subset or subclass of individuals as described herein for certain diseases, disorders or conditions.
• the term “subject” includes both human subjects and animal subjects.
• Particularly suitable human subjects include pediatric human subjects, adolescent human subjects and adult human subjects.
• pediatric human subject refers to a human subject ranging in age from about 2 years old to about 9 years old.
• adolescent human subject refers to a human subject having an age of about 10 years old to about 19 years old.
• adult human subject refers to a human subject having an age of 19 and older.
• “susceptible” and “at risk” refer to having little resistance to a certain disease, disorder or condition, including being genetically predisposed, having a family history of, and/or having symptoms of the disease, disorder or condition.
• treating refers to processes involving a slowing, interrupting, arresting, controlling, stopping, reducing, or reversing the progression or severity of an existing symptom, disorder, condition, or disease, but does not necessarily involve a total elimination of all disease-related symptoms, conditions, or disorders associated with administration of the therapy.
• biomarkef refers to any molecule or group of molecules found in a biological sample that can be used to characterize the biological sample or a subject from which the biological sample is obtained.
• a biomarker may be a molecule or group of molecules whose presence, absence, or relative abundance is: characteristic of a particular cell or tissue type or state; and/or characteristic of a particular pathological condition or state; and/or indicative of the severity of a pathological condition, the likelihood of progression or regression of the pathological condition, and/or the likelihood that the pathological condition will respond to a particular treatment.
• the biomarker may be a cell type or a substituent molecule or group of molecules thereof.
• Biomarkers provided herein can be diagnostic biomarkers that can be used to detect and/or confirm the presence of obstructive sleep apnea. Biomarkers provided herein can also be monitoring biomarkers that can be serially analyzed to assess the status of obstructive sleep apnea. Biomarkers provided herein can also be pharmacodynamic biomarkers that can be used to determine a patient's response to treatment of obstructive sleep apnea. Biomarkers provided herein can also be predictive biomarkers that can be used to predict or identify an individual or group of individuals more likely to experience a favorable or unfavorable effect from treatment.
• Biomarkers provided herein can also be safety biomarkers that are measured before and/or after treatment to indicate the likelihood, presence, or extent of a toxicity to treatment. Biomarkers provided herein can also be prognostic biomarkers to identify 7 obstructive sleep apnea progression and/or recurrence. Biomarkers provided herein can also be susceptibility/risk biomarkers that can indicates the potential for an individual to develop obstructive sleep apnea but who has not been diagnosed as having obstructive sleep apnea. Biomarkers provided herein can also be surrogate biomarkers that explain the clinical outcome following treatment.
• a change in the expression can be an increase in the expression in a second (or subsequent) sample as compared to the expression in a first sample.
• a change in the expression can also be a decrease in a second (or subsequent) sample as compared to the expression in a first sample.
• the change in expression level in the sample(s) obtained from the subject administered a treatment can further be compared to one of a gene expression level in a sample(s) obtained from a healthy subject (a subject who is not suspected of having or has obstructive sleep apnea) and a gene expression level in a sample(s) obtained from a subject having or suspected of having obstructive sleep apnea who is not administered a treatment.
• expression level of a biomarker refers to the process by which a gene product is synthesized from a gene encoding the biomarker as known by those skilled in the art.
• the gene product can be, for example, RNA (ribonucleic acid) and protein.
• Expression level can be quantitatively measured by methods known by those skilled in the art such as, for example, northern blotting, amplification, polymerase chain reaction, microarray analysis, tag- based technologies (e.g., serial analysis of gene expression and next generation sequencing such as whole transcriptome shotgun sequencing or RNA-Seq), Western blotting, enzyme linked immunosorbent assay (ELISA), and combinations thereof.
• a reference expression level of a biomarker refers to the expression level of a biomarker established for a subject without obstructive sleep apnea, expression level of a biomarker in a normal/healthy subject without obstructive sleep apnea as determined by a medical professional and/or research professional using established methods as described herein, and/or a known expression level of a biomarker obtained from literature.
• the reference expression level of the biomarker can also refer to the expression level of the biomarker established for any combination of subjects such as a subject without obstructive sleep apnea, expression level of the biomarker in a normal/healthy subject without obstructive sleep apnea, and expression level of the biomarker for a subject without obstructive sleep apnea at the time the sample is obtained from the subject, but who later exhibits obstructive sleep apnea.
• the reference expression level of the biomarker can also refer to the expression level of the biomarker obtained from the subject to which the method is applied.
• a plurality of expression levels of a biomarker can be obtained from a plurality of samples obtained from the same subject and used to identify differences between the pluralities of expression levels in each sample.
• two or more samples obtained from the same subject can provide a gene expression level(s) of a biomarker and a reference expression level(s) of the biomarker.
• Obstructive Sleep Apnea refers to a condition that is characterized by a history of habitual snoring, and which is associated with repeated events of partial or complete obstruction of the upper airway during sleep.
• Obstructive Sleep Apnea (OSA) is a clinically complex syndrome affecting up to 3% of pre-pubertal children and causes subjects to experience both daytime and nighttime symptoms. As noted, the main nocturnal system is habitual snoring.
• OSA is typically ranked at the severe end of the clinical spectrum of sleep disordered breathing.
• the terms '‘correlated” and “correlating,” in reference to the use of diagnostic and prognostic biomarkers, refers to comparing the presence or quantity of the biomarker in a subject to its presence or quantity in subjects known to suffer from a given condition (e.g., OSA); or in subjects known to be free of a given condition, i.e. “normal individuals.”
• the present disclosure is directed to a method of diagnosing obstructive sleep apnea in a subject having or suspected of having obstructive sleep apnea.
• the method includes providing a biological sample from the subject; determining a gene expression level of CD70, Hematopoietic Cell Signal Transducer (HCST), Cysteine Rich Protein 1 (CRIP1), Interleukin 32 (IL 32), Ectodermal -Neural Cortex 1 (ENCI), Granzyme B (GZMB), Granzyme K (GZMK), Major Histocompatibility Complex, Class II, DQ Alpha 2 (HLA.DQA2), CD300A, Long Intergenic Non-Protein Coding RNA 2446 (LINC02446), Dual Specificity Phosphatase 2 (DUSP2), Integrin Subunit Beta 1 (ITGB1), Galectin 1 (LGALS1), Interferon Induced Protein With Tetratricopeptide Repeats 3 (IFIT3), CCA
• Latent Transforming Growth Factor Beta Binding Protein 1 (LTBP1), Zinc Finger Protein 683 (ZNF683). Sex Determining Region Y (SRY)-Box Transcription Factor 4 (SOX4), Structural Maintenance Of Chromosomes 4 (SMC4), LIM-Zinc Finger Domain Containing (LIMSI), Vimentin (VIM), T Cell Receptor Gamma Constant 1 (TRGC1), Musculoaponeurotic Fibrosarcoma (MAF BZIP) Transcription Factor (MAF), S I 00 Calcium Binding Protein A4 (S100A4), Lyl Antibody Reactive (LYAR), T Cell Receptor Delta Constant (TRDC), T Cell Receptor Gamma Constant 2 (TRGC2), Regulator Of G Protein Signaling 1 (RGS1), Annexin Al (ANXA1), and combinations thereof, and diagnosing obstructive sleep apnea in the subject if there is a measurable change in the expression
• Suitable subjects having or suspected of having obstructive sleep apnea include a human subject.
• the human subject can be a pediatric human subject, an adolescent human subject, and an adult human subject.
• Suitable biological samples include whole blood.
• a particularly suitable biological sample includes a peripheral blood sample.
• a particularly suitable biological sample includes peripheral blood mononuclear cells.
• a particularly suitable biological sample includes RNA obtained from the peripheral blood mononuclear cells.
• the method can further include isolating peripheral blood mononuclear cells and subjecting the peripheral blood mononuclear cells to cell sorting.
• the method can further include analyzing the peripheral blood mononuclear cells with single cell RNA sequencing, polymerase chain reaction (e.g., quantitative real-time PCR), targeted RNA sequencing, microarray analysis, and combinations thereof.
• the method can further include determining a cell subtype wherein the cell subtypes include a T-cell subtype, a myeloid subtype, a B-cell subtype, and combinations thereof.
• the myeloid subtype can further be analyzed to identify classical monocytes, non-classical monocytes, platelets, dendritic cells, plasmacy toid dendritic cells, and combinations thereof in the myeloid subtype.
• the B-cell subtype can further be analyzed to identify naive B-cells, memory B-cells, plasma B-cells, atypical B-cells, and combinations thereof in the B-cell subtype.
• Whole blood, peripheral blood, and peripheral blood mononuclear cells can be analyzed for gene expression using amplification (e.g., qPCR), targeted sequencing, and other methods for detecting gene expression.
• the method can further include performing Polysomnography (PSG) on the subject having or suspected of having obstructive sleep apnea.
• PSG Polysomnography
• the method can further include determining Apnea Hypopnea Index for the subject having or suspected of having obstructive sleep apnea.
• the method can further include administering a treatment for obstructive sleep apnea to the subject diagnosed as having obstructive sleep apnea.
• Suitable treatments include a lifestyle change (e.g., weight loss, regular exercise, moderate alcohol consumption, smoking reduction/cessation, a nasal decongestant, an allergy medication, change in sleeping position, avoidance of sedative medications), positive airway pressure (e.g., continuous positive airwaypressure "CPAP"; autotitrating pressure "APAP”; bilevel positive airway pressure "BPAP”), an oral appliance, and surgery.
• CPAP continuous positive airwaypressure
• APAP autotitrating pressure
• BPAP bilevel positive airway pressure
• the present disclosure is directed to a method of treating obstructive sleep apnea in a subject having or suspected of having obstructive sleep apnea.
• the method includes providing a biological sample from the subject; determining a gene expression level of CD70, HCST, CRIP1, IL 32, ENCI, GZMB, GZMK, HLA.DQA2, CD300A, LINC02446, DUSP2, ITGB1, LGALS1, IFIT3, CEBPD., TNFRSF4, HBB, NEAT1, LTBP1, ZNF683, SOX4, SMC4, LMS1.
• TRGCL MAF, S100A4, LYAR, TRDC, TRGC2, RGS 1, ANXA1, and combinations thereof obtained from a subject who does not have obstructive sleep apnea; and treating the subject diagnosed as having obstructive sleep apnea by administering a treatment for obstructive sleep apnea to the subject diagnosed as having obstructive sleep apnea.
• the method can further include determining a gene expression level of CD70, HCST, CRIP1, IL 32, ENCI, GZMB, GZMK, HLA.DQA2, CD300A, LINC02446, DUSP2, ITGB1, LGALS1, IFIT3, CEBPD., TNFRSF4, HBB, NEAT1, LTBP1, ZNF683, SOX4, SMC4, LMS1, VIM, TRGC1, MAF, S100A4, LYAR, TRDC, TRGC2, RGS 1, ANXA1.
• the method can further include providing a peripheral blood mononuclear cell sample from the subject; determining an amount of a cell subtype selected from a T-cell subtype, a myeloid subtype, and a B-cell subtype, and combinations thereof in the peripheral blood mononuclear cell sample.
• the method can further include analyzing RNA obtained from the peripheral blood mononuclear cells with single cell RNA sequencing, polymerase chain reaction (e.g., quantitative real-time PCR), targeted RNA sequencing, microarray analysis, and combinations thereof.
• Suitable treatments include a lifestyle change (e.g., weight loss, regular exercise, moderate alcohol consumption, smoking reduction/cessation. a nasal decongestant, an allergy medication, change in sleeping position, avoidance of sedative medications), positive airway pressure (e.g., continuous positive airway pressure "CPAP”; autotitrating pressure “APAP”; bilevel positive airway pressure “BPAP”), an oral appliance, and surgery.
• a lifestyle change e.g., weight loss, regular exercise, moderate alcohol consumption, smoking reduction/cessation.
• a nasal decongestant e.g., an allergy medication, change in sleeping position, avoidance of sedative medications
• positive airway pressure e.g., continuous positive airway pressure "CPAP”; autotitrating pressure "APAP”; bilevel positive airway pressure "BPAP”
• CPAP continuous positive airway pressure
• BPAP bilevel positive airway pressure
• the methods can further include analyzing clinical metrics including polysomnography and Apnea Hypopnea Index for the subject having or suspected of having obstructive sleep apnea.
• the present disclosure is directed to a biomarker panel for diagnosing obstructive sleep apnea in a subject in need thereof comprising: CD70, HCST, CRIP1, IL 32, ENCI, GZMB, GZMK, HLA.DQA2, CD300A, LINC02446, DUSP2, ITGB1, LGALS1, IFIT3, CEBPD., TNFRSF4, HBB, NEAT1, LTBP1, ZNF683. SOX4, SMC4, LMS1, VIM, TRGC1, MAF. S100A4, LYAR. TRDC. TRGC2, RGS1, ANXA1, and combinations thereof.
• the OSA group consisted of 11 patients who were polysomnographically diagnosed with OSA (Apnea Hypopnea Index (AHI) ⁇ 5 events/hour sleep).
• BMI Body Mass Index
• scRNA-seq libraries were generated from isolated PBMCs using Chromium 3’ v3.1 (10X Genomics, Pleasanton, CA), with a targeted output of 10,000 cells per sample. Libraries were sequenced using an NextSeq (Illumina. San Diego, CA) instrument, targeting over 50,000 reads per cell. The dataset is available at the NCBI Gene Expression Omnibus (GEO) repository (accession number pending). scRNAseq data Processing
• the normalized values for these genes were then scaled using Seurat’s ScaleData implementation, where the percent of mitochondrial genes from each cell was used as a variable to regress out its undesirable contribution to variance.
• the scaled genes were then used for principal component analysis using the Seurat’s RunPCA implementation under default parameters. Feature selection of principal components was performed by taking the top ranked principal components based on the amount of variance they explained in the data. Top principal components were excluded whenever their covariate genes represented signals related to patient sex (i.e., Y-chromosome versus XIST), as well as patient-specific expression patterns irrelevant to cell type classifications (e.g., patient- restricted heat shock protein expression).
• All cell population proportions are presented as a percentage makeup among each cell types respective cell grouping (e.g., T helper cells as a % among T/ILCs). Each value is calculated independently for each patient, patients with zero cells collected from a given cell type are considered missing values, not true zeroes. Significant differences in cell type compositions were assessed by a Wilcoxon rank-sum test performed between disease and control patient’s percent values for a given cell type.
• Correlations between a patient's AHI and gene expression levels for a given cell type were performed by making a ’‘pseudo-bulk” value by averaging the gene expression values of all cells from a given cell type from the same patient.
• a Spearman correlation test was then used to calculate a rho and p-value in the relationship between AHI and the patient’s average gene expression for the cell type being assessed.
• Results collected from scRNAseq analytical pipeline were combined according to variables indicating the genetic symbol, cell type cluster, fold-change average after log2 transformation, and dimensional coordinates Principal Component Analysis (PCA).
• Package stringr version 1.4.0
• R for Statistical Computing version 4.1.0
• Our approach affords for a more robust analysis by including the specific cell ty pes with the metrics of genetic expression by isolating unique values using the cell-specific subtype. It is a technique for mitigating analytical conflicts associated with redundancy, (i. e. , incorrectly removing duplicate values), encountered during computational modeling.
• Threshold for the observed correlations was initially set to 0.700, then increased to 0.875 (i.e., approximated midpoint between median and 3rd quartile).
• each of the gene and cluster variable combinations were separated, once again, into a total of four resulting columns: (a.) clusterOl; (b.) geneOl; and (c.) cluster02; (d.) gene02.
• This strategy leverages cell-type specific clusters maximizing the reach of the genetic coordinate values, (i.e., Euclidean distance) and recombining the variables according to ty pe, or initial category’ (e.g., genes or clusters, Tike-type’). Recombination of these components constrained cell-type clusters according to their paired alignment after this processing step, allowing the identification of the most relevant genetic correlations, consistent with cell type.
• the molecular signature was extracted from each of the Seurat objects corresponding to specific cell type and clinical attributes, and combined, according to sample ID. Variables yvith greater than 70% of missing values yvere removed from the dataset. Remaining variables were imputed using the median.
• Empirical, binormal, and non-parametric statistical methods were the parameters leveraged in the construction of three ROC curves using R-package ROCit (version 2.1.1). Training and testing datasets were created using a 70:30 split to generate the observed ROC curves for each statistical method.
• the molecular signature yvas determined to be the best performer of the potential biomarker panels and comprises a total of 32 genes.
• An additional ROC-AUC analysis was performed to validate the observed results using an independent datasetl 7 composed of Microarray expression values. Implementation of the ROC analysis was conducted using the molecular signature with the same computational parameters, outlined for the following groups listed: (a.) Normal vs. OSA; (b.) Normal vs. Primary snoring; and (c.) Primary snoring vs OSA.
• Single cell transcriptional profiling discriminates cellular populations in PBMC from OSA children and controls
• Single-cell transcriptional profiling enabled the identification of cell populations among the PBMCs.
• Uniform manifold approximation and projection demonstrated that cells clustered according to their expression profiles.
• Cross-referencing the UMAP results with known markers for each cell type enabled the identification of the cellular types represented in each cluster. Variations in the cell type composition were observed between the patients, and when cells were stratified according to OSA status, and OSA severity. Noteworthy, we observed heterogeneity on the single-cell expression profiles across individuals, highlighting the potential for personalization of the marker panels.
• Differentially expressed genes between the different clusters included genes whose expression was associated with known T-cell populations (e.g., CD3. CD4, FOXP3, CTL4, etc.) as well as other genes which may define previously unknown cellular populations that are characteristic among OSA patients.
• T-cell populations e.g., CD3. CD4, FOXP3, CTL4, etc.
• other genes which may define previously unknown cellular populations that are characteristic among OSA patients e.g., CD3. CD4, FOXP3, CTL4, etc.
• the biochemical pathways and molecular networks associated with genes differentially expressed in the cells identified as T-Cell_Xl and T-Cell_X2 were determined (FIGS. 2D and 2E).
• cMonocytes classical monocytes
• ncMonocytes non-classical monocytes
• eDCs dendritic cells
• pDCs plasmacytoid dendritic cells
• cMonocyte_Platelet two clusters sharing gene expression features with platelets
• ncMonocytes intMonocytes
• a cluster (Monocyte_IFN) whose gene expression profiles faithfully corresponded to an IFN-primed phenotype was identified.
• genes highly expressed in the Monocyte_IFN cluster genes were detected corresponding to the dynamin like GTPases (e.g., MX1, MX2), interferon-induced proteins (e.g., IFI44L, IFIT1, etc.), signal transducer and activator of transcription proteins (i.e., STAT1, STAT2), and 2'-5'-oligoadenylate synthetases (e.g., 0AS1, OAS2, etc ).
• GTPases e.g., MX1, MX2
• interferon-induced proteins e.g., IFI44L, IFIT1, etc.
• signal transducer and activator of transcription proteins i.e., STAT1, STAT2
• 2'-5'-oligoadenylate synthetases e.g., 0AS1, OAS2, etc ).
• 895 genes were identified showing differential expression between the cell types, of which 674 were differentially expressed uniquely in one of the cell ty pes: 16/58 in cMonocytes, 3/23 in ncMonocytes, 634/741 in eDC and 25/56 in pDC groups, respectively.
• B_Naive Naive B-Cells
• B Memory Memory B-Cells
• Plasma B-Cells B-Plasma
• B-Atypical B-Cells B-Atypical.
• Subpopulations within the B_Memory cells were identified according to the Class-Switch Recombination (CSR) Status 22 reflecting the replacement of the immunoglobulin heavy chain constant region from IGHD/IGHM (i.e., preCSR) to IGHG/IGHA/IGHE (i.e..
• B-Plasma and B-Plasma_CC Two clusters of Plasma B cells (B-Plasma and B-Plasma_CC) were identified which have undergone CSR and expressed immunoglobulin at higher rates than other B cells populations.
• B- Plasma_CC One of these populations (B- Plasma_CC) expressed high levels of transcripts related to mitosis and DNA synthesis highlighting a high rate of cell proliferation.
• the B Atypical population consisted of a mixed cell population of CD18+/CD20+/CD21-/CD27- B-cells sharing a distinctive gene expression signature. Aty pical B-Cells have been reported in response to vaccination and chronic infections 23-25 and can be also characterized according to their CSR status.
• PC A Principal Component Analysis
• phenoty pic variables i.e., age, BMI and AHI
• Graph of variables revealed the AHI, and subpopulations of mature B-Cells (i.e., B Memory, B Atypical, B-Plasma, and B-Plasma_CC) were the variables dragging the sample distribution towards the OSA samples.
• B_Naive counts dragged sample distribution towards the position of Control samples.
• similar PCA analysis using counts from the T-cells and Myeloid subgroups discriminated the OSA and Control groups to a much lesser extent.
• a machine learning approach was applied to build a molecular signature that discriminates between OSA patients and controls by combining markers of PBMC cellular composition with those associated with the occurrence of OSA.
• a molecular signature consisting of 32 genes (FIG. 1 A) was extracted from each of the Seurat objects corresponding to specific cell type and clinical attributes, and combined, according to sample ID. The performance of the signature was evaluated using three statistical methods for distinguishing between OSA and Control patients in the same sample set and a cross-validation strategy. Training and testing datasets were created using a 70:30 split to generate the observed ROC curves for each statistical method (FIG. IB).
• ROC-AUC analyses was performed to assess the performance of the molecular signature in a bulk RNA expression microarray-based independent PBMC sample set.
• Implementation of the ROC analysis was conducted using the same computational parameters outlined for building the signature.
• the performance of the signature was further evaluated according to positive predictive and negative predictive values (PPV and NPV, respectively).
• the molecular signature showed a high performance for discriminating OSA patients from no OSA/no snoring controls, with the ROC-AUC analysis resulting in AUC of 0.93, 0.96. and 0.92 for Empirical, Binormal, and Nonparametric ROCs, respectively (FIG. 1C). with 93% and 95% PPV and NPV, respectively. Likewise, the molecular signature had a high performance to distinguish between OSA patients from primary 7 snoring individuals (FIG.
• OSA-induced changes in cellular heterogeneity' were detected in the three cell lineages in PBMCs (i.e.. T-cells, Myeloid and B-cells). Besides the classical cell types in each lineage, single-cell transcriptomic profiles defined previously undescnbed populations which are associated with OSA occurrence. Here two previously undescribed T-cell subpopulations (i.e., T- Cell_Xl and T-Cell_X2) were identified, whose expression profiles were mainly related to T-Cell activation, inflammation and intracellular signaling, as well as two major pathways in OSA: Hypoxia-inducible factor 1 alpha (HIF-la) and signaling and oxidative stress.
• HIF-la Hypoxia-inducible factor 1 alpha
• the percentage of cells in T-Cell_X2 significantly correlated with OSA severity, represented as increased AHI values.
• Specific cellular signaling pathways were activated in the T-Cell_X2 population (i.e., Phospholipase-C-, HGF-, and WNT/p-catenin signaling pathways) compared with other T-Cell populations.
• a monocyte subpopulation was identified whose expression profile corresponded to an IFN-primed phenotype (i.e., monocyte IFN). This is a discrete variety of classical monocytes with a stereotypic transcriptional phenotype, whose relative abundance has been linked to inflammation and promoting T cell response in tumors.
• the percentage of classical monocytes was significantly increased in OSA patients compared to controls. Although, the mechanisms for OSA-mediated activation and migration of monocytes are still undetermined, the Examples show the activation of the NF-kB signaling pathway in classical monocytes of OSA patients. NF-KB activity was increased in circulating neutrophils and monocytes in adults, which can be mitigated by therapy. Lastly, an atypical B memory cell subpopulation (i.e.. B Atypical) was detected which was significantly increased in OSA samples compared with the control group.
• B Atypical atypical B memory cell subpopulation
• the Examples further identified specific cell types in subjects having OSA. Additionally, differential gene expression for each cell type can be used to identify 7 a subject having OSA.

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• 2023
  • 2023-09-21 EP EP23868932.7A patent/EP4590859A2/de active Pending
  • 2023-09-21 WO PCT/US2023/033341 patent/WO2024064255A2/en not_active Ceased

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