Patent Grant
11618922
The present invention relates to diagnostic biomarkers of immune suppression/dysfunction. The diagnostic biomarkers may be used to evaluate the capability of immune cells in subjects, and screen subjects for immune suppression/dysfunction in response to stress and/or pathogen exposure.
The present invention further relates to diagnostic biomarkers suitable for diagnosing Staphylococcus Enterotoxin B (SEB) exposure in a subject, and methods of using the same. These diagnostic biomarkers are suitable for diagnosing SEB exposure in the presence of comprised immune response or stress.
Diagnostic biomarkers for diagnosing immune suppression/dysfunction. The diagnostic biomarkers are transcripts that are up or down regulated compared to normal expression when a subject has been stressed either mentally and/or physically. The invention also relates to a method of detecting comprised or suppressed immune response in a subject by comparing certain diagnostic biomarkers in the subject to a control set of diagnostic biomarkers.
Previous studies suggest that excessive or prolonged stress impairs protective immunity towards infection leading to increase susceptibility to illness. Comprehensive molecular explanations of the host's physiological stress response and the results of failed adaptation over time offer the potential to identify the debilitating pathophysiologic consequence of severe stress on health. More importantly, molecular approaches offer the opportunity to implement clinical strategies to differentiate immune impaired individuals from their normal counterparts.
Applicants examined the effects of long-term battlefield-like stressors of U.S. Army Ranger Training on genome wide expression profiles for biomarker identification of prolonged severe, stress-induced, compromised immune response. Applicants identified 59 differentially regulated transcripts using comparative Welch's T-test along with Bonferroni correction (q<0.01) followed by 3-fold change. These 59 differentially regulated transcripts are identified at Table 3 herein. Among the 59 differentially regulated transcripts identified, 48 were down regulated and 11 were up regulated. Most of the down-regulated transcripts were directly involved in protective immunity.
Differentially regulated transcripts identified and their cognate pathways were confirmed using quantitative real-time PCR arrays. Antigen preparation and presentation, chemotaxis, inflammation, and activation of leukocytes were among overrepresented immune response processes that were significantly associated with suppressed transcripts. Differentially regulated transcripts identified or genes from their corresponding pathway can serve as diagnostic biomarkers to differentiate/identify individuals with stress-induced immune suppression. cDNAs of some of these transcripts can be electrochemically tethered in the wells of micro- or nano-chips for quick diagnosis purpose.
Diagnostic biomarkers within the scope of the present invention for use in identifying or screening individuals for immune suppression/dysfunction include five (5) or more, seven (7) or more, or ten (10) or more of the 59 differentially regulated transcripts identified herein or genes from their corresponding pathway. For example purposes, Applicants provide herein a subset of 14 of the 59 transcripts that can be used as a single batch of biomarkers (see Table 3A and 3B). The five (5) or more, seven (7) or more, ten (10) or more or twenty (20) or more of the differentially regulated transcripts or genes from their corresponding pathway may, for example, be selected from these. It is understood to one of ordinary skill in the art that there may be additional biomarkers, not yet identified, that can be used to screen individuals for immune suppression/dysfunction. This invention is not limited to the 59 biomarkers listed in Table 3.
These diagnostic biomarkers would be useful to diagnose immune suppression/dysfunction in a subject due to stress. The present invention further relates to diagnostic kits for use in screening immune function of a subject, where the kit employs the diagnostic biomarkers identified herein.
Applicants further conducted studies on the effect of stress on a patient's ability to respond to other pathogens. More specifically, Applicants studied the effect of Staphylococcus Enterotoxin B (SEB) on host response gene expression profiles, and identified genes that showed consistent differential expression towards SEB whether or not the host had been exposed to stress. These transcripts or genes from their corresponding pathway were SEB-specific (independent of the physiologic and pathologic status of the host), and may serve as diagnostic markers of SEB exposure.
Therefore, this invention proposes a simple test to identify the capability of immune cells to respond to pathogenic agents in military personnel. This biomarker profile would allow for a semi-quantitative method to evaluate the immune system in terms of gene expression.
Transcriptomic Characterization of Immune Suppression from Battlefield-Like Stress
This invention identifies changes in transcriptome of human due to battlefield-like stress. Thorough understanding of stress reactions is likely to produce better strategies to manage stress, and improve health1. Stress modulates gene expression, behavior, metabolism and immune function2-5. Chronic physiological and psychological stresses are major contributors of stress-induced suppression of protective immunity. For example, chronic stress impairs lymphocyte proliferation, vaccination efficacy6-9, NK cell activity, resistance to bacterial and viral infection10, and increases risk of cancer11.
Yet, comprehensive descriptions of molecular responses to stress are needed to fully understand modulated networks and pathways, and hence to reduce and prevent pathophysiologic effects of intense and prolonged stresses.
Here we report gene expression changes occurring in leukocytes collected from Army Ranger Cadets before and after eight-week Ranger Training. Ranger cadets are exposed to different and extreme physical and psychological stressors of Ranger Training Course, which is designed to emulate extreme battlefield scenarios: sleep deprivation, calorie restriction, strenuous physical activity, and survival emotional stresses—pushing cadets to their physical and psychological limits. The Ranger population provides a rare opportunity to study intense chronic battlefield-like stress, and to contribute to the understanding of intense chronic stress in general. Ranger Training has been shown to impair cognitive function, cause significant declines in 3,5,3′-triiodothyroxine and testosterone, and increase cortisol and cholestero12; 13.
Transcriptomic alterations, in this study, were assayed using cDNA microarrays. Results were corroborated with oligonucleotide, microRNAs, and real-time QPCR arrays, and were confirmed using Quantitative RT-PCR and ELISA. Analyses of functional and regulatory pathways of differentially altered transcripts revealed suppression of immune processes due to battlefield-like stress. Some of stress induced microRNAs, and a number of stress inhibited transcription factors were found to regulate or be modulated by many compromised immune response transcripts. Suppressed immune response genes remained suppressed even after exposure of post-stress leukocytes to mitogenic toxin, SEB. This impaired activation is a clear indicator of anergy, and compromised protective immunity.
Results
Ranger Trainees experience an average daily calorie deficit of 1000-1200 kcal, restricted and random sleep of less than 4 hours per day, strenuous and exhaustive physical toiling and emotional survival stressors. Five of the initial fifteen Trainees enrolled in our study were replaced with five others due to attrition (to maintain 15 study subjects at both time points). All study subjects had complete and differential blood counts performed, and were observed for infections and injuries. By the end of training, Trainees showed significant average weight loss, decreased body mass index and diastolic blood pressure, and significant increase in average body temperature and systolic blood pressure (
Differential and complete blood counts showed small but significant differences between pre- and post-Training cells, yet all were within normal ranges (
As shown in
Transcriptome Profiling of Pre- and Post-Training Leucocytes
We used three transcriptome profiling techniques to cross-validate our findings: cDNA and oligonucleotide microarrays, and quantitative real time PCR arrays. Expression profiles were done on total RNAs isolated using two different methods: Trizol (Invitrogen. Inc) and PAXgene, (Qiagen.Inc).
cDNA Microarrays Analyses
To analyze gene expression profiles of leukocytes of Ranger Cadets collected before and after eight-week Training, we used custom cDNA microarrays that contained ˜10 000 well-characterized cDNA probes of 500 to 700 base pairs representing ˜9 000 unique human gene targets. Welch's (unpaired unequal variance) t-test along with false discovery rate (FDR) correction was used on normalized expression data to identify 1 983 transcripts that were significantly changed (q≤0.05), with 1 396 showing ≥1.5 fold change in expression level between pre- and post-Training samples (Table 4). Among 1 396 differentially regulated genes, 288 genes
Functional enrichments of significantly regulated genes using both hypergeometric test (FDR correction, q≤0.05), and Fishers exact test identified the immune system as the most affected biological process. Apoptosis, stress response, response to wounding, metabolism, hormone receptor signaling (peptide and steroid), cell cycle and unfolded protein response signaling were also significantly associated with altered transcripts. Yet, immune system process was most significantly over-represented (q<1.7E-16), and was associated with 177 differentially regulated genes. Of the 177 genes, 151 were down-regulated, and 26 were up-regulated. Further functional enrichment of the 151 genes indicated that these genes were significantly associated with microbial recognition, inflammation, chemotaxis, antigen presentation, and activation of lymphocytes, mast cells and macrophages (Tables 1). The 26 Up-regulated immune response genes were associated with response to steroid hormone stimulus, regulation of leukocyte activation, complement activation, negative regulation gene expression, and negative regulation of phosphorylation (Table 1).
Oligonucleotide Microarrays
Gene expression alterations in leukocytes of Rangers before and after Training were also analyzed using PAXgene RNA isolation and oligonucleotide microarrays representing 24 650 human gene probes. This different RNA isolation procedure and microarray assay again showed that the immune system was most significantly affected process. Normalized expression levels were analyzed using Welch's t-test (p<0.05, without multiple correction), and fold change filter (>=1.5 fold). Among 1570 genes (that passed these filters), 104 genes were associated with the immune response processes including microbial recognition, chemotaxis, inflammation, antigen presentation, and T-cell, B-cell and NK-cell activations (
Real Time Quantitative PCR Array
We used real time quantitative PCR (QPCR) arrays to confirm differential expression of genes identified by cDNA and oligonucleotide microarrays, and to survey additional immune related genes. Assay results of PCR arrays that contained more than 160 genes in antigen presentation and NFkB signaling pathways (RT2 Profiler™ PCR Arrays, SABioscience, MD) verified down-regulation of 116 immune response genes, consistent with microarray data (Tables 3A, 3B and 4). The vast majority of the genes important for microbial pattern recognition, inflammation, antigen presentation, T-cell activation and transcription factors related to immune response were suppressed across cDNA, oligonucleotide and PCR arrays (
Referring to
Real Time Quantitative PCR
Additional quantitative real-time PCR assays were carried out using specific primer pairs to confirm 10 representative genes among 1396 significantly altered genes shows number of genes that passed Welch's t-test at different q-values (FDR corrected p-values) and Fold Change cut-offs) (
Genes Associated with Microbial Recognition
Genes associated with microbial pattern recognition were significantly suppressed in post-Training leukocytes (Table 5, & Tables 1 &
CD 14, along with TLR4/TLR4 and TLR2/TLR6, recognize lipopolysaccharides and peptideoglycans, respectively. TLR3, CLP1 and DICER1 bind to double stranded viral RNAs. TLR9 and CD93 recognize unmethylated CpG dinucleotides of bacterial DNA, and patterns of apoptotic cells, respectively. FPR1 and FPRL1 bind bacterial N-terminal formyl-methionine peptides. CHIT1 recognizes fungal and pathogens with chitin patterns. PF4 and PF4V1 recognize patterns of plasmodium and tumor cells. TICAM1 and MYD88 are important cytosolic adaptor molecules of microbial pattern recognitions. Transcripts of these genes were down-regulated suggesting a compromised innate immune response with regard to microbial recognition.
Genes Associated with Chemotaxis and Inflammation
Stress suppressed transcripts associated with chemotaxis and inflammation included interleukins (IL 1A, IL1B, IL4, IL8), interleukin receptors (IL1R1, IL1RN, IL2RB, IL10RA), chemokine (C-X-C motif) ligands (CXCL 1), chemokine (C-C motif) ligands (CCL13, CCL18, CCL20), tumor necrosis factor alpha (TNFα), TNF receptor super-family members 1B, 10B and 10C (TNFRSF1B, TNFRSF10B and TNFRSF10C), TNF superfamily members 3, 8, (LTB, TNFSF8), complement component 8 gamma (C8G), cytochrome b-245 beta (CYBB), CD97 and interferon gamma receptor (IFNGR2) (Tables 1 & 5).
Genes Associated with Activation of Myeloid Leukocytes
Tables 1 & 5 show suppressed transcripts associated with activation of mast cells and macrophages. These included toll-like receptors (TLR4), TNF, LAT, lymphocyte cytosolic protein 2 (LCP2), SYK, CD93, and IL4 RELB. Suppressed genes associated with inflammatory responses (ILL CD14, INFGR1) were also significantly associated with activation of myeloid cells. Differentiations of myeloid leukocytes were significantly associated with interferon gamma inducible proteins 16 and 30 (IFI16), myosin heavy chain 9 (MYH9), IL4, Spi-B transcription factor (SPIB), NFkB3, MYST histone acetyltransferases (MYST1 and 3), TNF, PF4, hematopoitic cell-specific lyn substrate 1 (HCLS1), V-yes-1 Yamaguchi sarcoma viral related oncogene homolog (LYN) and V-maf (musculoaponeurotic fibrosarcoma) oncogene homolog b (MAFB). Down-regulation of hemopoietic transcription factors (MAFB and HCLS1) and CSF1R may indicate less viability of myeloid cells to expand or to replenish. Suppression of mRNAs of these genes suggests poor activation, differentiation and proliferation of myeloid leukocytes in response to infection, and hence poor innate and adaptive immune responses.
Genes Associated with Antigen Presentation
Genes associated with antigen preparation encompass MHC classes (I & II), CD1s, B-cell co-receptors and integrins (Tables 1 and 5). Transcripts of MHC class I (HLA-B, HLA-C, HLA-G, beta-2-microglobulin (B2M)), MHC class II (HLA-DRB1, HLA-DRA, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, CD74, HLA-DOB), B-cell co-receptors (CD79A, CD79B), Ig heavy constant gamma 1 (IGHG1), Ig heavy constant alpha 1 (IGHA1), MHC class I polypeptide related sequence A (MICA), adaptor-related protein complex 3 beta1 (AP3B1), intercellular adhesion molecules 1, 2 and 3 (ICAM1, ICAM2, ICAM3) were down-regulated implying poor antigen preparation and presentation, and hence impaired adaptive immune response.
Genes Associated with Activation of Lymphocytes
Suppressed transcripts associated with T-cell activation, differentiation and proliferation included TCR co-receptors (CD4, CD8α, CD8β, CD3ϵ, CD3δ, CD247), linker for activation of T cells (LAT), TCR signaling molecules [protein kinase c theta (PRKCQ), protein tyrosine phosphatase receptor type C (PTPRC), C-SRC tyrosine kinase (CSK), spleen tyrosine kinase (SYK) lymphocyte specific protein tyrosine kinase (LCK)], integrins CD2, CD44, integrin alpha L, M and X (ITGAL, ITGAM, ITGAX), and cyclin D3 (CCND3) (Tables 1 & 5).
Interleukin 4, SYK, PRKCD, CD40, PTPRC, cyclin-dependent kinase inhibitor 1A (CDKN1A), Kruppel-like factor 6 (KLF6), SLAM family member 7 (SLAMF7), and killer cell Ig-like receptor three domains long cytoplasmic tail1 (KIR3DL1) were significantly associated with activation, differentiation and proliferation of B-cells, and NK-cells (Tables 1 & 5).
Transcription Factors Associated with Immune Responses
Transcription factors that are important regulators of immune response genes were down-regulated. Suppressed factors included nuclear factor kappa B family (NFkB1, NFkB2, RELA, RELB), interferon regulatory factors 1, 5, 7, 8 (IRF1, IRF5, IRF7 and IRF8), signal transducer and activator of transcription (STAT2, STAT6), and SP transcription factors (SP1, SP140) (Tables 1 & 5). In addition, transcription factors GA binding protein alpha (GABPA), POU class 2 homeobox 2 (POU2F2), p53 (TP53), p53 binding protein 1 (TP53BP1), early growth response 2 (EGR2), splicing factor 1 (SF1), and hypoxia inducible factor 3 and alpha subunit (HIF3A) were down-regulated. Up-regulated transcription factors included hepatocyte nuclear factor 4 alpha (HNF4A) hepatic leukemia factor (HLF), sterol regulatory element binding transcription factor 2 (SREBF2) transcription factor AP-2 alpha (TFAP2A), transcription factor 7-like 2 (TCF7L2) and NF-kappa-B inhibitor-like 2 (NFKBIL2) (Tables 1 & 5).
ELISA Assays of Plasma Proteins
Plasma concentrations of insulin-like growth hormones 1 and 2 (IGF1 and IGF2), prolactin (PRL), tumor necrosis factor alpha (TNF), and enzymatic-activity of superoxide dismutase 1 (SOD 1) were determined by ELISA to examine gene expression alterations at the protein level. Relative quantities of proteins, and levels of transcripts profiled by cDNA and oligonucleotide microarrays were compared (
Response of Leukocytes to Ex Vivo Treatment of Staphylococcal Enterotoxin B
Staphylococcus enterotoxin B (SEB) is a superantigen, and a potent T cell activator known to induce proinflammatory cytokine release in vitro14. Leukocytes of Ranger Trainees collected before and after Training were challenged ex vivo with SEB and immune response transcripts were analysed. In pre-Training leukocytes, SEB toxin induced majority of immune response genes (
In
MicroRNA Arrays
Differentially regulated microRNAs (miRs) in pre- and post-Training samples were assayed using Agilent's human microRNA chip containing ˜15 000 probes representing 961 unique miRs. Comparison of 535 miRs (that passed normalization and flag filters) using Welch's t-test at p<0.1 with a 1.3 fold change cutoff gave 57 miRs (
See also
Expression Data Based Prediction of Transcription Factors and Target Genes
Computational & data analyses tools, and databases (see Materials and Methods) were used for empirical and predictive association of transcription factors (TFs) and their regulatory targets among stress-altered genes. Activated or inhibited TFs, common regulatory sites of target genes, and prediction z-scores of identified TFs were computed based on 1369 differentially regulated genes obtained from cDNA array data (Table 2). TFs at the top of stress-inhibited list (IRF7, RELA, NFkB1, RELB, CREB1, IRF1, HMGB1 & CIITA) and their differentially expressed targets (Table 2) were found to be involved in inflammation, priming of adaptive immune response, and glucocorticoid receptor signaling (
Regulatory sites for a number of transcription factors including SP1, CREB1, ATF6, cEBP, and binding sites for the defense critical—NFkB transcription factors complex, and stress response sites (STRE) were among common regulatory motifs identified for some of stress-suppressed genes, STRE site being predicted to be regulated by MAZ and MZF1. Stress activated factors included GFI1, MYC, FOXM1, GLI2, MAX and HNF1A (Table 2), and these factors induced genes important for hormone biosynthesis and suppressed immune related genes.
Most immune response genes were down-regulated in post-Training leukocytes compared to pre-Training leukocytes. Functional enrichment of these down-regulated genes revealed their involvement in microbial pattern recognition, cytokine production and reception, chemotaxis, intercellular adhesion, immunological synapse formation, regulation of immune response, and activation and proliferation of immune cells (
Our data suggest that stress induced suppression of microbial patterns of innate immunity (
In
In
In
Adaptive cells' antigen receptors, co-receptors, signal transducers, intercellular adhesion molecules, and chemokine receptors were highly suppressed (
Discussion
Suppression of transcripts of critical immune response pathways, and regulatory networks are consistent with impaired innate and adaptive immune responses, including cellular and humoral immunity, as a result of battlefield-like stress.
Down-regulation of transcripts involved in Toll-like receptor, and chemokine and chemokine receptor signaling pathways indicate suppressed inflammatory response, impaired maturation of antigen presenting cells (APCs), impaired affinity maturation of integrins, and impaired migration, extravasation & homing of APCs and T-cells to nearby draining lymph nodes or infection sites.
Antigen preparation and presentation was the most suppressed pathway among immune response processes (
Unlike reports of differential regulations of Th1 and Th2 type responses observed in college students on the day of a stressful examination15, and in caregivers of chronically sick relatives16, our data suggest that battlefield-like stressors impair not only Th1 but also Th2 type responses as shown by suppressed transcripts of TLR2 and 4, and the cytokines IL4, IL4R and IL10RA in post-Training leukocytes. Suppression of inflammatory molecules (e.g., IL1A & 1B, and IL1R1, TNF members and TNF receptors, and NFkB class of factors), and Th2 classes of cytokines show features of battlefield-like stress that are distinct from acute and psychological stresses.
Previously miR-155 is reported to be proinflammatory. MiR-155 (−/−) mice are highly resistant to experimental autoimmune encephalomyelitis17, and show suppressed antigen-specific helper cell, and markedly reduced articular inflammation18. Here, miR-155 transcripts were elevated in post-Training leukocytes (with or without SEB exposure), but its expression was suppressed by SEB in pre-training leukocytes (
It seems that miR-155 is anti-inflammatory in humans exposed to stress and SEB toxin. Regulatory connection of miR-155 to many of stress-suppressed inflammatory cytokines may indicate its involvement in regulation of these cytokines, and glucocorticoid receptor elements, and modulate maturation of antigen presenting cells under battlefield-like stress.
Poor response of post-Training leukocytes to SEB ex vivo challenge is consistent with suppressed expression of MHCs, T-cell receptors, co-receptors and integrins which are important for activations of APCs and T-cells. Overall, our results clearly demonstrated that battlefield-like stressors suppress a broad spectrum of immune system process. This suppression of broad categories of immune response pathways may explain why chronically stressed individuals show poor vaccine responses and susceptibility to infections.
Conclusion
Suppressed expression of genes critical to innate, humoral and cellular immunity is an indicator of compromised protective immunity as confirmed by impaired response of post-Training leukocytes to SEB challenge. Numbers and ratios of different subpopulations of leukocytes being within normal ranges, our observation (of anergic leukocytes of severely stressed individuals) draws some caution on current diagnostic practice of counting immune cells to ascertain integrity of the immune system, and its ability of protection against infection.
On the basis of suppressed inflammatory molecules and pathways, we hypothesized that exposure to battlefield-like and similar stresses may make exposed individuals less susceptible to autoimmune diseases, and sepsis; yet they may easily succumb to toxin or infection since their protective immunity already depleted.
Characterization of molecular signatures of stress pathologies can potentially reveal biomarkers and new pharmacologic targets for improving adaptation to stress and preventing stress-induced pathogenesis. Results such as ours together with proteomic analyses may yield novel preventative, prognostic and therapeutic opportunities to intervene the negative consequences of stress on heath.
Materials and Methods
Blood Sample Collection
Whole blood (from each subject) was drawn in Leucopack tubes (BRT Laboratories Inc., Baltimore, Md.) before and after the eight-week Training, and immediately spun at 200×g for 10 minutes. The concentrated leukocyte layer (buffy coats) was collected and treated with TRIzol™ reagent (Invitrogen, Carlsbad, Calif.) for RNA isolation and then stored at −80° C. Differential and complete blood counts (CBC) were obtained immediately after blood collection using a hemocytometer, and subsequently using an ABX PENTRA C+ 60 flow cytometer (Horiba ABX, Irvine, Calif.). Blood samples were also collected in PAXgene™ Blood RNA Tubes (VWR Scientific, Buffalo Grove, Ill.) for direct RNA isolation.
RNA Isolation
For cDNA microarray analysis, total RNA was isolated using the TRIzol™ reagent according to the manufacturer's instructions. The RNA samples were treated with DNase-1 (Invitrogen, Carlsbad, Calif.) to remove genomic DNA and were re-precipitated by isopropanol. The TRIzol™ isolated RNA was used in cDNA microarrays analysis19. For oligonucleotide microarrays, total RNA was isolated using PAXgene tubes following the manufacturer's protocol. The PAXgene tube contains a proprietary reagent that immediately stabilizes RNA at room temperature (18-25° C.) without freezing. Isolated RNA samples were stored at −80° C. until they were used for microarray and real time PCR analyses. The concentration and integrity of RNA were determined using an Agilent 2000 BioAnalyzer (Palo Alto, Calif.) according to manufacturer's instructions. The ArrayControl RNA Spikes from Ambion (Austin, Tex.) were used to monitor RNA integrity in hybridization, reverse transcription and RNA labeling.
cDNA Synthesis, Labeling, Hybridization and Image Processing
RNA was reverse transcribed and labeled using Micromax Tyramide Signal Amplification (TSA) Labeling and Detection Kit (Perkin Elmer, Inc., Waltham, Mass.) following the manufacturer's protocol. The slides were hybridized at 60° C. for 16 h (for cDNA microarrays and Trizol isolated RNA) and at 55° C. for 16 h (for oligonucleotide microarrays and PAXgen isolated RNA). Hybridized slides were scanned and recorded using a GenePix Pro 4000B (Axon Instruments Inc., Union City, Calif.) optical scanner, and the data were documented using Gene Pix 6.0 (Axon Instruments Inc, Union City, Calif.).
Preparation of cDNA Microarrays
Human cDNA microarrays were prepared using sequence-verified PCR elements produced from ˜10,000 well-characterized human genes of The Easy to Spot Human UniGEM V2.0 cDNA Library (Incyte Genomics Inc., Wilmington, Del.). The PCR products, ranging from 500 to 700 base pairs, were deposited in 3× saline sodium citrate (SSC) at an average concentration of 165 μg/ml on CMT-GAPS™ II (γ-aminopropylsilane) coated slides (Corning Inc., Corning, N.Y.), using a Bio-Rad VersArray MicroArrayer (Hercules, Calif.). The cDNAs were UV-cross-linked at 120 mJ/cm2 using UV Stratalinker® 2400 from Stratagene (La Jolla, Calif.). The microarrays were baked at 80° C. for 4 h. The slides were treated with succinic anhydride and N-methyl-2-pyrrolidinone to remove excess amines.
Oligonucleotide Microarrays
The Human Genome Array Ready Oligo Set Version 3.0 Set from Operon Biotechnologies (Huntsville, Ala.) includes 34,580 oligonucleotide probes representing 24,650 genes and 37,123 RNA transcripts from the human genome. The oligonucleotide targets were deposited in 3× saline sodium citrate (SSC) at an average concentration of 165 μg/ml onto CMT-GAPS II aminopropylsilane-coated slides (Corning, Corning, N.Y.) using a VersArray Microarrayer. Microarrays were UV-crosslinked at 120 mJ/cm2 using UV Stratalinker® 2400. Then slides were baked at 80° C. for 4 hours, and were treated with succinic anhydride and N-methyl-2-pyrrolidinone to remove excess amines on the slide surface. Slides were stored in boxes with slide racks and the boxes were kept in desiccators.
Real Time QPCR
Quantitative real time PCR arrays of one hundred genes associated with inflammation, transcription factors, and antigen preparation and presentation pathways were carried out using Dendritic & Antigen Presenting Cell Pathway (PAHS 406) and NFkB Pathway (PAHS 25) RT2 Profiler™ PCR Arrays (SABiosciences, Frederick, Md.) according to manufacturer's instructions. Four replicates of RNA samples isolated using PAXgene™ from Trainees before and after Training were assayed. The data were analyzed using ABiosciences' web-based software.
Reverse transcriptase reagent (iScript) and real time PCR master mix (QuantiTect™ SYBR® Green PCR Kit) were obtained from BioRad Inc., CA and QIAGEN Inc., Valencia, Calif., respectively. Real time polymerase chain reactions (PCR) were carried out in i-Cycler Real-time PCR apparatus (BioRad Inc, Milpitas, Calif.), using three to five biological replicates for each primer pair (based on sample availability). The custom oligonucleotide primers were designed using Primer3 software, or based on those from UniSTS and Universal Probe Library for Human (Roche Applied Science). Their specificities were verified in the BLAST domain at NCBI. Parallel amplification reaction using 18S rRNA primers was carried out as a control. Threshold cycle (Ct) for every run was recorded and then converted to fold change using the equation: [(1+E)ΔCt]GOI/[(1+E)ΔCt]HKG, where ΔCt stands for the difference between Ct of control and treated samples of a given gene, which is either gene of interest (GOI) or housekeeping genes (HKG), and E stands for primer efficiency, calculated from slope of best fitting standard curve of each primer pair.
ELISA
Plasma concentrations of prolactin (PRL), insulin-like growth factors I and II (IGF-I & II), tumor necrosis factor alpha (TNFα), and enzymatic activity of superoxide dismutase were determined using ELISA kits from Calbiotech, Inc. (Spring Valley, Calif., Catalog #PR063F), Diagnostic Systems Laboratories, Inc. (Webster, Tex., Catalog #s DSL-10-2800 and DSL-10-2600), Quantikine® of R&D Systems, Inc. (Minneapolis, Minn., Catalog #DTA00C) and Dojindo Molecular Technologies, Inc (Gaithersburg, Md., Catalog #S311), respectively, following manufacturers' protocols.
Microarray Data Analyses
Background and foreground pixels of the fluorescence intensity of each spot on the microarrays were segmented using ImaGene (BioDiscovery Inc., El Segundo, Calif.) and the spots with the highest 20% of the background and the lowest 20% of the signal were discarded. Local background correction was applied. Genes that passed this filter in all experiments were selected for further study. Then, sub-grid based Lowes normalization was performed for each chip independently. Additional per spot (dividing by control channel) and per gene (to specific samples) normalization were also performed under the Genespring GX platform (Agilent Technologies Inc, Santa Clara, Calif.). Statistical analysis was computed using Welch's t-test (p<0.05) with Benjamini and Hochberg False Discovery Rate (FDR) Multiple Correction to select the genes with high altered expression (for cDNA microarray data, but oligonucleotide microarray data were analyzed without FDR Correction). Two-dimensional clustering was carried out based on samples and genes for visualization and assessment of reproducibility in the profile of the significant genes across biological replicates.
Interaction Networks and Gene Ontology Enrichment
Bingo 2.3 was used for gene ontology enrichment with hypergeometric distribution with FDR (false discover rate) or Bonferroni corrections (p<0.05). Biological processes, molecular functions, and cellular components of each cluster of genes were compared to the global annotations and over-represented categories after corrections were analyzed and visualized. Functional analysis and pathways associated with stress and pathogen-regulated genes were analyzed using Ingenuity Pathway Analysis (Ingenuity Systems Inc.; Redwood City, Calif.). Cytoscape Version 2.6.1 was used for visualizing and analyzing enriched gene ontologies, and molecular interaction network constructions.
MicroRNA Analysis
Expression profiles of MicroRNAs were assayed using Agilent's human miRNA v3 microarray (Agilent Technologies Inc) consisting of 15 k targets representing 961 microRNAs. Differentially expressed microRNAs were analyzed using Qlucore Omices Explorer 2.2 (Qlucore AB) and GeneSpring GX 11.5 (Agilent Technologies Inc.). Target transcripts of profiled microRNAs were identified using target scan of Genespring, and Ingenuity Pathway Analysis (IPA) (Ingenuity Systems Inc.). Interaction networks of differentially expressed microRNAs and their target mRNAs were constructed using IPA.
Treatment of Leukocytes with Staphylococcal Enterotoxin B (SEB)
Leukocytes isolated from leucopack blood samples were plated in six well tissue culture plates (˜106 cells/ml in RPMI 1640 and 10% human AB serum) and treated with SEB (Toxin Technology Inc., Sarasota, Fla.) at a final concentration of 100 ng/ml SEB. Cells were incubated for 6 h at 37° C. and 5% CO2. At the end of the incubation period, treated leukocytes were collected by centrifugation at 350×g for 15 minutes. Cell pellets were treated with 2 ml TRIzol™ and kept at −80° C. for RNA isolation.
cDNA Microarray (Expression) Data Based Prediction of Transcription Factors, Regulatory Binding Sites and Downstream Target Identification
Potential regulatory sites of differentially regulated genes were identified using HumanGenome9999 (Agilent Technologies Inc., CA) containing partial human genome sequences (9999 bp upstream region for 21787 genes). Statistically significant (p<0.05) common regulatory motifs of 5 to 12 nucleotides long were identified. The searching region was set to range 1 to 500 nucleotides upstream of transcription start sites. Other tools used for this purpose include MATCH and TFSEARCH. Cognate transcription factors of identified (common regulatory) sites were searched from different prediction and repository databases: DBD, JASPAR, TRANSFAC® 7.0—Public using ChipMAPPER20, ConTra, Pscan and Ingenuity Pathway Analysis (IPA, ingenuity inc). Expression databased prediction Z-scores and regulatory targets were analyzed using IPA. Regulator-target interaction networks and pathways were generated using Cytoscape (Cytoscape.org) and IPA.
Yersinia
Pestis
Homo sapiens
The biomarker findings are presented which were identified from gene expression changes in leukocytes collected from (informed and consented) US Army Ranger Cadets who underwent eight-weeks of Army Ranger Training (RASP, Ranger Assessment and Selection Program). Our subjects were exposed to extreme physical and psychological stressors of Ranger Training, which is designed to emulate extreme battlefield scenarios such as strenuous physical activity, sleep deprivation, calorie restriction, and survival emotional stresses—pushing cadets to their physical and psychological limits. Though these men were among the best of the best, many trainees dropped out in the first phase of the three-phased RASP Training. The Army Ranger population provides a rare opportunity to study extreme stress, and to contribute to the understanding of intense chronic stress in general. Particularly, the ability to collect pre-training samples for comparison with post-training samples is rarely practical in any other chronically and extremely stressed patients.
Our studies focus in identifying molecular mediators of compromised protective immunity caused by social and battlefield-like stresses, and in identifying pathogen-induced biomarkers under severe stress background. Social and physiological stresses, particularly, which are frequent or chronic are major contributors of stress-induced immune dysfunction. In this study, we employed experimental and computational approaches to identify molecules and signaling pathways involved in the host's response towards battlefield-like stress, and in assessing protective immunity status of the stressed host towards infection.
In the first approach, we used genome-wide transcriptome, and microRNA profiling and in-vitro pathogen exposure of leukocytes (isolated from Army Ranger Trainees) to identify stress-suppressed transcripts and pathways critical in protective immune response. We have identified a number of stress response biomarkers (transcripts and pathways) that have potential implication in compromising the immune function. The most compromised pathways include antigen preparation and presentation, and T-cell activation pathways. Suppressed immune response genes remained suppressed even after ex-vivo exposure of post-RASP leukocytes to the mitogenic toxin, Staphylococcal enterotoxin B (SEB). On the other hand, complete and differential counts of post-training WBCs were within normal ranges. This impaired activation is an indicator of anergy, and compromised protective immunity.
In the second approach, we used rigorous computational analyses in identifying up-stream regulatory modules (and molecular networks) of stress-suppressed genes. We identified up-stream regulators of differentially altered transcripts, which include immune related and steroid hormone inducible transcription factors, stress response factors, and microRNAs. Some stress induced microRNAs, and a number of stress-inhibited transcription factors were found to regulate or be modulated by many compromised immune response transcripts.
The identification of exceptionally enriched suppression of antigen presentation and lymphocyte activation pathways (in spite of normal blood cell counts) are remarkable since these findings are consistent with prior observations of poor vaccine responses, impaired wound healing and infection susceptibility associated with chronic intense stress.
Some of the transcripts were unique to RASP stressors (severe and chronic stress), even in the presence of other pathogens, to which we briefly refer in this manuscript. These specific transcripts may have potential use as diagnostic markers to distinguish debilitating chronic stress from that of infection.
The subject matter of the present invention (biomarkers) solves the drawbacks of other routinely used assays that check the status of the immune system process. Many clinical laboratories do differential and complete white blood cell counting to ascertain integrity of the immune system. Some advanced clinical laboratories do challenge assays (proliferation assays) to check the viability of immune cells (in addition to cell counting). In our case, even though the cells are within their normal ranges (cell counting would have indicated normal), we still see no measurable response to SEB challenge (and we have the molecular indicators of the why). Our molecular markers can be used to check the protective or compromised nature of the immune system regardless of whether the cells are anergic (within normal range in terms of their numbers but not protective) or otherwise.
Welch's t-test: Statistical comparative analysis whereby the means and variance of compared groups are not assumed to be the equal.
Transcriptome: Genome-wide transcripts of human or any other living thing.
Transcript: Messenger RNA (ribonucleic acid) or any other small RNA molecule.
Pathway: regulatory hierarchy of bio-molecules (proteins, transcripts, or metabolites) forming a specific biological process (function).
Normal Control: A person or sample from a person, or genes or transcripts from a person, or expression profile from a person or persons that has not been subjected to stress.
Diagnostic biomarkers: stress effected genes, transcripts, cDNAs, mRNA, miRNAs, rRNA, tRNA, peptides and proteins.
**Gene names and accession numbers presented herein are standard gene names and accession numbers for genes that are found in the NCBI GenBank®. GenBank® is the NIH genetic sequence database, an annotated collection of all publicly available DNA sequences (Nucleic Acids Research, 2013 January; 41(D1):D36-42). GenBank is part of the International Nucleotide Sequence Database Collaboration, which comprises the DNA DataBank of Japan (DDBJ), the European Molecular Biology Laboratory (EMBL), and GenBank at NCBI. These three organizations exchange data on a daily basis.
This application claims priority and is a continuation application of PCT application no. PCT/US2013/000097 filed Mar. 28, 2013, pending, which claims priority of U.S. provisional application No. 61/687,731 filed Apr. 28, 2012.
The invention described herein may be manufactured, used and licensed by or for the U.S. Government.
| Number | Name | Date | Kind |
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| 20120094859 | Redei | Apr 2012 | A1 |
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| Number | Date | Country | |
|---|---|---|---|
| 20150051094 A1 | Feb 2015 | US |
| Number | Date | Country | |
|---|---|---|---|
| 61687731 | Apr 2012 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US2013/000097 | Mar 2013 | US |
| Child | 14121808 | US |
