Eph receptor tyrosine kinases play a key role in cell-cell communication. Lack of structural information on the entire multi-domain intracellular region of any Eph receptor has hindered understanding of their signaling mechanisms. Here, we use integrative structural biology to investigate the structure and dynamics of the EphA2 intracellular region. EphA2 promotes cancer malignancy through a poorly understood non-canonical form of signaling involving serine/threonine phosphorylation of the linker connecting its kinase and SAM domains. We show that accumulation of multiple linker negative charges, mimicking phosphorylation, induces cooperative changes in the EphA2 intracellular region from more closed to more extended conformations and perturbs the EphA2 juxtamembrane segment and kinase domain. In cells, linker negative charges promote EphA2 oligomerization. We also identify multiple kinases catalyzing linker phosphorylation. Our findings suggest multiple effects of linker phosphorylation on EphA2 signaling and imply that coordination of different kinases is necessary to promote EphA2 non-canonical signaling. Eph receptor tyrosine kinases and their ephrin ligands mediate cell-cell communication. Here, the authors assess the structure and dynamics of the EphA2 intracellular region and uncover complex effects of phosphorylation within the linker region between EphA2 kinase and SAM domains. The fourteen Eph receptors have a conserved domain structure, with an extracellular portion including the N-terminal ligand-binding domain and several other domains6. The intracellular portion of the Eph receptors includes a juxtamembrane segment, the tyrosine kinase domain, a linker, a sterile alpha motif (SAM) domain, and a short C-terminal tail containing a PDZ domain-binding motif . EphA2 non-canonical signaling is often elevated in cancer cells and promotes their oncogenic properties, including epithelial-mesenchymal transition, invasiveness and metastasis, mechanotransduction, stem cell-like properties, and drug resistance25. Despite its profound significance in cancer, the mechanism of non-canonical signaling is not well understood26.EphA2 has been implicated in many physiological and pathological processes, including epithelial homeostasis, immune system function, angiogenesis, inflammation, atherosclerosis, parasitic infections, and cancer malignancy28 (phosphosite.org), but it remains unclear whether their phosphorylation plays a role in EphA2 non-canonical signaling. The significance of other serine/threonine phosphorylation sites identified in the EphA2 juxtamembrane segment and kinase domain (phosphosite.org) is even less clear.It is unknown how phosphorylation of S897 in the linker connecting the EphA2 kinase and SAM domains regulates EphA2 signaling. The kinase-SAM linker contains a cluster of five serine/threonine residues that besides S897 include S892, T898, S899 and S901 Fig. . These rIn this work, we investigate the interplay of the different domains and linkers in the EphA2 intracellular region and mechanistically elucidate how phosphorylation of the kinase-SAM linker affects EphA2 function. Recombinant expression of most of the EphA2 intracellular region enables us to solve its crystal structure and investigate its dynamic behavior in solution. Our findings support a model in which multiple negative charges introduced by linker phosphorylation cooperate to promote an ensemble of open conformations of the EphA2 intracellular region that mediate non-canonical signaling. We find that multiple EphA2 kinase-SAM linker residues can be simultaneously phosphorylated by different kinases, consistent with the notion that cumulative linker phosphorylation is important for EphA2 non-canonical signaling.29, we confirmed phosphorylation on two other residues in the linker, S892 and S901  and the beginning of the SAM domain (starting at F906) adopt the same conformations in both molecules  to obtain information on the EphA2 intracellular region in solution.9. We compared EphA2 WT and four mutants that mimic different kinase-SAM linker phosphorylation states: S892E, S897E, 3E (T898E/S899E/S901E) and 5E   of 104 Å  and to a lesser extent EphA2 S892E (Dmax = 116 Å) feature a broader distance distribution with a more prominent tail skewed towards longer distances, indicating that negative charges in the kinase-SAM linker induce a conformational change leading to a more elongated EphA2 intracellular region.We determined the low-resolution solution structure of the entire unphosphorylated EphA2 intracellular region, except for the first 10 residues (S570-I976). This portion of EphA2 comprises 20 additional juxtamembrane residues compared to the portion analyzed by X-ray crystallography and includes both conserved Y588 and Y594 residues, which interact with the kinase domain in unphosphorylated Eph receptors15) Fig. , inset. Ab-initio model building from the EphA2 WT and 5E SAXS data yields elongated envelopes with a bulky top and a narrower bottom Fig. . The Eph41. We found that the core of the kinase domain, including the C-terminal half of the αC-helix, the hydrophobic spines and the αF-helix, undergo limited exchange within the 5 min time course, indicating that these regions are shielded from the solvent, as expected  to analyze the same unphosphorylated EphA2 WT intracellular portion used for SAXS. We measured HDX at different time points up to 5 min Fig. . This tited Fig. . Conversted Fig. , indicatted Fig. , in agreIn general, the extent of exchange is similar at the different time points examined (0.5 to 5 min). However, in two regions we observed intermediate exchange that gradually increases from 0.5 to 5 min Fig. . One of Considering our SAXS data, we hypothesized that linker phosphorylation may lead to effects whose magnitude depends on the number of residues simultaneously phosphorylated. We therefore analyzed the EphA2 5E mutant, in which all five linker phosphosites are replaced by glutamic acid, to maximize our ability to detect potential effects of linker phosphorylation. The most drastic effect in this mutant, compared to EphA2 WT, is increased HDX in the C-lobe of the kinase domain and most of the SAM domain Fig. , suggestOf all the mutants, EphA2 5E shows the most accelerated exchange compared to EphA2 WT, especially in the C-lobe of the kinase domain and the SAM domain Fig. . SurprisWe also examined the E820K/E825K mutant, in which we replaced two glutamic acid residues in the negatively charged surface of the kinase domain C-lobe with positively-charged lysine residues Fig. , which wInterestingly, in the EphA2 5E, E820K/E825K and S892E mutants, and to a smaller extent in the S897E mutant, we observed a strong correlation in the stabilization of kinase-SAM linker, juxtamembrane segment and parts of the activation loop (L760-A770), all of which undergo lower HDX Fig. . This ma43 to examine how EphA2 mutations mimicking or preventing phosphorylation of kinase-SAM linker residues affect EphA2 dimerization in HEK293 cells in the absence of ligand stimulation. To obtain dimerization curves, FRET efficiencies (which report on the interaction strength between receptor molecules) were measured along with the concentration of transiently transfected EphA2 mutants labeled with mTurquoise or EYFP (a FRET pair) , while mutations to the non-phosphorylatable alanine appear to have a small destabilizing effect  and a positively-charged surface on the opposite side of the kinase domain  or 5E mutant tagged with EYFP, with or without ephrinA1-Fc stimulation  technique, which calculates molecular brightness in small sections of the plasma membrane and creates a histogram of the molecular brightness values46. This revealed that in the absence of ligand treatment the brightness distribution curves for the EphA2 5A and 5E mutants are between the curves for the monomer control (LAT) and the dimer control (E-cadherin)47, suggesting the presence of a mixture of monomers and dimers for both EphA2 mutants  of the kinases tested accounting for ~70% of the total radioactivity incorporated  kinases were chosen for testing Fig.  because We also used a peptide phosphorylatable only on S897 to test kinases predicted by PhosphoNET to phosphorylate the S897 motif. This revealed new candidate S897 kinases, and most notably members of the CAMK2 family Fig. . Some ofThe model emerging from our data is that several kinases can phosphorylate the five serine/threonine residues in the EphA2 kinase-SAM linker, leading to concomitant phosphorylation of multiple residues that likely coordinately affect EphA2 signaling function by regulating the conformation of the EphA2 intracellular region.53. A key question is how phosphorylation of the EphA2 linker regulates non-canonical signaling in the cell. Linkers are known to play a crucial role in the propagation of information between protein modules by communicating conformational and/or dynamic alterations57. Due to their flexible nature, they can serve as hinges, enabling reorientation of protein domains with respect to each other to regulate protein function. Length, amino acid composition and post-translational modifications, such as phosphorylation, can profoundly impact the conformation and dynamics of linkers, affecting their behavior.How the different parts of the Eph receptor intracellular region—including the juxtamembrane segment, kinase domain, linker and SAM domain—are arranged and function together has remained an open question, in part due to lack of structural information that extends beyond single domains and adjacent regions58. Our HDX data in combination with interactions observed in our crystal structures suggest that in the absence of linker phosphorylation the intracellular portion of EphA2 favors more closed conformations that at least in part depend on interactions between the electronegative surface of the kinase domain and the electropositive surface of the SAM domain  has been previously proposed14. Furthermore, in vitro kinase reactions with the recombinant EphA3 intracellular region lacking the SAM domain suggest that the same allosteric network is involved in the regulation of receptor autophosphorylation on tyrosine residues14, implying that canonical and non-canonical signaling may influence each other. Similarly, in EphA2 changes in E820 and the αFG loop may be relayed to the activation loop  were used at a dilution of 1:3,000.Antibodies recognizing EphA2 (rabbit mAb #6997) and the phosphorylated S897 motif (rabbit mAb #6347) were purchased from Cell Signaling Technology and used at a dilution of 1:1,000 for immunoblotting. Affinity purified rabbit antibodies recognizing the phosphorylated S892 or S901 motifs were generated and used at 1 μg/ml for immunoblotting. The EphA2 antibody was confirmed to label transiently transfected EphA2 and not other Eph receptors. The phosphospecific antibodies were validated by showing that they recognize EphA2 WT by immunoblotting, with a stronger signal under conditions promoting kinase-SAM linker phosphorylation, but do not recognize EphA2 in which the Ser in the relevant phosphorylated motif is mutated to Ala 27.The following cell lines were purchased from the American Type Culture Collection : PC3 androgen-independent prostate cancer (CRL-1435), A549, Calu-3 and H1648 lung adenocarcinoma , HCC1937 triple negative breast cancer (CRL-2336), SW626 colon adenocarcinoma (HTB-78), SKOV3 ovarian serous cystadenocarcinoma (HTB-77) and HEK293T human embryonic kidney (CRL-3216). The Mel-Juso melanoma cell line was purchased from the DSMZ (ACC 74); the HOP62 lung adenocarcinoma cell line from the tumor/cell line repository of the Developmental Therapeutics Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute. The BxPC3 pancreatic cancer cell line was kindly provided by P. Itkin-Ansari , the Panc1 cell line by F. Levine ; the U87, T98G and U251-MG glioblastoma cell lines by W. Stallcup ; the BT549 breast cancer cell line by R. Maki ; the MDA-MB-468 breast cancer cell line by K. Vuori ; and the MDA-MB-231 breast cancer cell line by J. Smith . The Mel-Juso cell line was stably infected with the pLVX-IRES-Neo lentivirus encoding EphA2 wild-type with an N-terminal FLAG tag28. Other cell lines were used after few passages to expand cells obtained from the vendor and thus not authenticated. BxPC3 and MDA-MB-468 were not authenticated.The PC3, MDA-MB-231, BT549, T98G, U87 and U251-MG cell lines were authenticated after completion of the experiments and Panc-1 several passages before use in the experiments by performing short tandem repeat analysis on isolated genomic DNA with the GenePrint® 10 System (Promega), and peaks were analyzed using GeneMarker HID (Softgenetics). Allele calls were searched against short tandem repeat databases−1 D-glucose, and 1.5 g l−1 sodium bicarbonate.A549, BxPC3, H1648, HCC1937, HOP62, PC3 and Mel-Juso cells were cultured in RPMI 1640 medium (Gibco); BT549, Calu-3, T98G, MDA-MB-231, MDA-MB-468, Panc1, SW626, U251-MG and U87 cells were cultured in Dulbecco’s Modified Eagle Medium  and SKOV3 cells were cultured in McCoy’s containing 10% FBS. All culture media contained 10% fetal bovine serum, antimycotics and antibiotics. HEK293T cells used in FRET experiments were cultured in DMEM supplemented with 10% fetal bovine serum, 3.5 g lThe peptide substrates were synthesized by Kinexus  at >95% or > 98% purity. The recombinant protein kinases were cloned, expressed and purified using Kinexus proprietary methods and underwent routine quality control testing. The assay conditions were optimized to yield acceptable enzymatic activity. In addition, the assays were optimized to give high signal-to-noise ratio.33P]ATP . The assay was initiated by the addition of [γ-33P]ATP and the reaction mixture incubated at room temperature for 30 min. The assay was terminated by spotting 10 μl of the reaction mixture onto a multiscreen phosphocellulose P81 plate, which was then washed 3 ×15 min in a 1% phosphoric acid solution. The radioactivity on the P81 plate was measured in the presence of scintillation fluid in a Trilux scintillation counter.Protein kinase assays were performed by Kinexus, in most cases using a radioisotope assay format. These assays were performed in a final volume of 25 μl, including 5 μl diluted active kinase , 5 μl stock peptide substrate solution , 10 μl kinase assay buffer, 5 μl ) and 7KJC (EphA2 S901E residues 590-976 [10.2210/pdb7KJC/pdb]). Data collection and refinement statistics are reported in Supplementary Table For crystallization, EphA2 WT or S901E  and at the Australian synchrotron SAXS/WAXS beamline, using an inline size-exclusion chromatography set-up  in 10 mM HEPES pH 7.9, 150 mM NaCl, 5% glycerol. Two-dimensional scattering data were radially averaged, normalized to sample transmission, and scatter patterns from the apex of the size-exclusion chromatography elution peak were averaged. Background scatter (scatter patterns from early in the SEC run) was subtracted using the Scatterbrain software package . The ATSAS suite of software72 was used to perform Guinier analysis (PRIMUS) to calculate the pairwise distance distribution function P(r) and the maximum particle dimension Dmax (GNOM) and to generate the bead models . Bead models and crystal structures were superimposed using the ATSAS subcomb script. The molecular mass of each sample was estimated using the SAXS-MoW2 package73. All structural models were illustrated using PyMOL 2.3.4 (Schrödinger). All data collection and processing statistics are summarized in Supplementary Table EphA2 proteins were dialyzed into 150 mM NaCl, 10 mM HEPES pH 7.9 and their concentration was adjusted to 5 mg mL74. Briefly, deuterium exchange reactions were performed using a Leap HDX PAL autosampler . D2O buffer was prepared by lyophilizing 10 mM HEPES pH 7.9, 100 mM NaCl initially dissolved in ultrapure water (H2O buffer) and redissolving the powder in the same volume of 99.96% D2O  immediately before use. Deuterium exchange was measured in triplicate at each time point . For each deuteration time point, 4 μl of protein (5 µM in H2O buffer) were held at 25 °C for 5 min before being mixed with 56 μl of D2O buffer. The deuterium exchange was quenched for 1 min at 1 °C by combining 50 μl of the deuteration reaction with 50 μl of 250 mM tris(2-carboxyethyl)phosphine (TCEP) pH 2.5. The quenched sample (90 µl) was then injected in a 100 μl sample loop, followed by digestion on an in-line pepsin column  at 15 °C. The resulting peptides were captured on a BEH C18 Vanguard precolumn, separated by analytical chromatography  using a 7–85% acetonitrile gradient in 0.1% formic acid over 7.5 min, and electrosprayed into the Waters Synapt G2Si quadrupole time-of-flight mass spectrometer. The mass spectrometer was set to collect data in the Mobility, ESI + mode; mass acquisition range of 200–2000 (m/z); scan time 0.4 s. Continuous lock mass correction was accomplished with infusion of leu-enkephalin (m/z = 556.277) every 30 s .HDX-MS was performed at the Biomolecular and Proteomics Mass Spectrometry Facility (BPMSF) of the University California San Diego, using a Synapt G2Si system with HDX technology (Waters Corporation) according to methods previously describedE), mobility ESI + mode. Peptide masses were identified from triplicate analyses and data were analyzed using the ProteinLynx global server (PLGS) version 2.5 (Waters Corporation). Peptide masses were identified using a minimum number of 250 ion counts for low energy peptides and 50 ion counts for their fragment ions; the peptides also had to be larger than 1,500 Da. The following cutoffs were used to filter peptide sequence matches: minimum products per amino acid of 0.2, minimum score of 7, maximum MH + error of 5 ppm, and a retention time RSD of 5%. In addition, the peptides had to be present in two of the three ID runs collected. The peptides identified in PLGS were then analyzed using DynamX 3.0 data analysis software (Waters Corporation). Peptides containing mutated residues were manually assigned. The relative deuterium uptake for each peptide was calculated by comparing the centroids of the mass envelopes of the deuterated samples with the undeuterated controls following previously published methods75. The peptides reported on the coverage maps are those from which data were obtained. For all HDX-MS data, 3 technical replicates were analyzed. The deuterium uptake was corrected for back-exchange using a global back exchange correction factor  determined from the average percent exchange measured in disordered termini of various proteins76. Deuterium uptake plots in Supplementary Fig. 77. Relative fractional deuterium uptake plots were plotted using Prism (GraphPad).For peptide identification, the mass spectrometer was set to collect data in mobility-enhanced data-independent acquisition (MS280nm = 0.5 (~9 µM) in 10 mM HEPES pH 7.9, 100 mM NaCl was loaded into 2-channel cells and spun in an An-50 Ti 8-place rotor at 40,000 rpm  and 20 °C for 15 h. Data were analyzed using Sedfit software 78.Sedimentation velocity experiments were performed using a ProteomeLab XL-I (Beckman Coulter) analytical ultracentrifuge. EphA2 590-976 sample with ODSEC-MALS experiments were performed using a Superdex 200 increase 10/300 SEC column (Cytiva), equilibrated in 20 mM HEPES pH 7.0, 150 mM NaCl connected to an HPLC system (Shimadzu Corporation) and DAWN HELEOS-II (Wyatt Technology) light scattering detector and Optilab T-rEX (Wyatt Technology) refractive index detector. 100 µl EphA2 570-976 at a concentration of 3 mg/ml was injected. Data were analyzed in ASTRA software .5 cells/dish in 35 mm glass bottom collagen-coated dishes (MatTek Corporation) and cultured for 24 h at 37 °C in 5% CO2. Dimerization experiments were performed in cells transiently transfected with EphA2 labeled at the C-terminus with fluorescent proteins  attached via a flexible linker (GGS)5. The EphA2 5E, 3E, S897E, S892E, 5A (S892A/S897A/T898A/S899A/S901A), ASAAA (S892A/T898A/S899A/S901A), S897A, or S892A mutants in pcDNA3 were transfected using the Lipofectamine 3000 reagent (ThermoFisher Scientific/Invitrogen). Cells were transfected with 0.5-2 µg total DNA at a 1:2 EphA2-mTurquoise:EphA2-EYFP ratio. Twelve hours later, the cells were washed twice with starvation medium to remove traces of phenol red, and serum-starved overnight to ensure no soluble ligands were present.HEK293T cells were seeded at a density of 2×1043. HEK293T cells expressing full-length EphA2 WT and mutants were imaged under reversible hypo-osmotic conditions. For this, the medium was replaced with hypotonic swelling medium (25 mM HEPES in serum free medium) about 5 min prior to imaging. The cells were then imaged for approximately 1 h. Cell osmotic swelling is necessary to eliminate the numerous wrinkles present in the plasma membrane of live cells, which hinder measurement of receptor concentration in the plasma membrane. FRET efficiencies were measured in the plasma membrane of cells not in contact with other cells.FRET experiments were performed following published protocols79. Two images were acquired for each cell: one upon excitation at 840 nm to mainly excite the donor, and a second one at 960 nm to primarily excite the acceptor43. The FSI-FRET method was used to measure the FRET efficiency and the concentrations of donor (EphA2-mTurquoise) and acceptor (EphA2-EYFP) in small cell plasma membrane areas. Calibration solutions of purified soluble EYFP and mTurquoise, produced using a published protocol, were used to convert pixel-level intensities of the images into EphA2 concentrations as previously described43.A two-photon microscope equipped with the OptiMis True Line Spectral Imaging system  was used to image the cells, and a MaiTai laser (Spectra-Physics) was used to generate femtosecond mode-locked pulsesfD, and on the acceptor fraction, xA, according toThe FRET efficiency due to EphA2 dimerization depends on the fraction of receptors that are dimeric, The Ẽ in Eq.  is a conFRET, xA, total EphA2 concentration [T] (including the concentrations of both EphA2-mTurquoise and EphA2-EYFP), and the two unknowns, Kdiss and Ẽ, was used to fit the data with MATLAB43.The following equation, which provides a link between the experimentally derived parameters 2O) and the cells were treated with 5.5 nM ephrinA1-Fc. Images of the plasma membrane in contact with the plate surface were collected with a confocal microscope (Leica TCS SP8) using the photon counting capabilities of the HyD hybrid detector. Measurements were performed with a 488 nm excitation diode laser and the emission spectra of EYFP were collected from 520 to 580 nm at a scanning speed of 20 Hz, pixel depth of 12-bits, image size of 1024×1024 pixels, and a zoom factor of 2.HEK293T cells were cultured and seeded as for FRET. The cells were transiently transfected with EphA2 5A-EYFP or EphA2 5E-EYFP plasmids and serum starved overnight. About 5 min before imaging, the cell starvation medium was replaced with hypotonic swelling medium (25 mM HEPES in 25% serum free medium, 75% H46. The cells were outlined by hand, and the software segmented the outlined region into 15×15 pixel regions of interest. After segmentation, the data were analyzed using the brightness and concentration calculator in the FIF software, which determines the molecular brightness and receptor concentration in each segment46. For molecular brightness calculations, the following equation was used:80:Images were analyzed using previously published FIF softwareThe brightness values were calculated for thousands of regions of interest. The distributions of the brightness values are shown in Fig. Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Experimental studies provided numerous evidence that caloric/dietary restriction may improve health and increase the lifespan of laboratory animals, and that the interplay among molecules that sense cellular stress signals and those regulating cell survival can play a crucial role in cell response to nutritional stressors. However, it is unclear whether the interplay among corresponding genes also plays a role in human health and lifespan.GCN2/EIF2AK4 and CHOP/DDIT3) that are closely involved in the cellular stress response to amino acid starvation, have been selected using information from experimental studies. Associations of these SNPs and their interactions with human survival in the Health and Retirement Study data have been estimated. The impact of collective associations of multiple interacting SNP pairs on survival has been evaluated, using a recently developed composite index: the SNP-specific Interaction Polygenic Risk Score (SIPRS).Literature about roles of cellular stressors have been reviewed, such as amino acid deprivation, and the integrated stress response (ISR) pathway in health and aging. Single nucleotide polymorphisms (SNPs) in two candidate genes  and in females only. This may reflect sex differences in genetic regulation of the human lifespan. Highly statistically significant associations of SIPRS [constructed for the rs16970024 (GCN2/EIF2AK4) and rs697221 (CHOP/DDIT3)] with survival in both sexes also been found in this study.Significant interactions have been found between SNPs from Identifying associations of the genetic interactions with human survival is an important step in translating the knowledge from experimental to human aging research. Significant associations of multiple SNPxSNP interactions in ISR genes with survival to the oldest old age that have been found in this study, can help uncover mechanisms of multifactorial regulation of human lifespan and its heterogeneity. It is generally acknowledged that human lifespan, aging, and age-associated health disorders are multifactorial traits resulting from the complex interplay among numerous genetic and non-genetic factors. Observed correlations between biomarkers of biological aging and age-associated diseases indicate a possibility of improving health and increasing lifespan through deceleration of the aging-related processes in the body. A better understanding of the mechanisms of multifactorial regulation of respective traits could substantially facilitate the realization of this idea.C. elegans could substantially increase the lifespan of laboratory animals (reviewed in). E.g., Johnson et al.,3 identified the long-lived mutant of C. elegans called age-1. Later, other mutants with substantially longer survival compared to wild animals were detected,4,5. Note that the effects of such mutations on lifespan in other species are much less pronounced.Numerous experiments using animal models were performed to improve such understanding. Surprisingly, a number of studies revealed that mutations in just one gene in –9). Further research showed that separate components of the diet, including carbohydrates,11, lipids, proteins–16, vitamins, minerals, fiber, and water, can influence aging and lifespan through different albeit often interacting genetic regulatory mechanisms. Experiments with different mutants of C. elegans and different strains of mice overall suggested that the effects of CR/DR on lifespan can have a strong genetic component. The genetic mechanisms involved in lifespan regulation in response to amino acid deprivation were linked to genes from the mTOR signaling pathway,23. It has also been shown that amino acid deprivation may influence lifespan by activating interplay among genes from the integrated stress response (ISR) pathway with the GCN2/EIF2AK4 gene serving as a sensor of such stress signals and the CHOP/DDIT3 gene serving as a regulator of cell’s fate deciding between autophagy, cell cycle arrest, or apoptosis–26. Apoptosis is a cellular choice when stress response cannot restore the normal cell’s functioning.Many other experimental studies discovered that better health and longevity could be achieved in animals from different species exposed to caloric/dietary restriction (CR/DR)  interactions have been proposed during the last decades.–30. Most recent reviews describe various extensions of the Multifactor Dimensionality Reduction (MDR) approach–34, using entropy in genetic interaction analyses,36, implementation of machine learning techniques to study epistasis,37,38, as well as many other approaches that differ in definitions of genetic interaction, the accuracy of calculations, and in computation time–45.Critical reviews of the methods, related software packages used to detect the interactions between genetic loci that contribute to human genetic diseases and the difficulties in determining the biological relevance of statistical interactions are provided in the papers that implements a logistic regression framework. This approach allows for evaluating associations of genetic interactions with complex traits in the presence of observed covariates. It has been successfully used in our earlier genetic analyses of genetic interactions. Useful information about other methods can also be found in the review paper.This paper used the INTERSNP software packageInformation about genes involved in multifactorial regulation of aging, health, and lifespan-related traits in laboratory animals serves as a source of useful insights concerning genetic mechanisms that might regulate these traits in humans. This information is used for selecting candidate medications appropriate for testing in clinical trials. The failure of many expensive clinical trials to identify a proper medication  indicates the need to find more reliable and less expensive ways of testing whether genetic connections detected in experimental studies exist in humans. It is proposed that such testing can be done by applying genetic epidemiological methods to available human data on genotyped individuals collected in human longitudinal and cross-sectional studies. The efficient analysis of such data requires a convenient conceptual framework that would allow the researchers to perform comprehensive analyses of biological mechanisms and effciently integrate research findings.Stress-related conceptual framework allows for linking together stressors, sensors of stress signals, and genes from stress response pathways as key players in mechanisms of multifactorial regulation of complex traits.GCN2/EIF2AK4 gene that serves as one of the sensors of cellular stress signals in the ISR pathway and the gene CHOP/DDIT3 involved in the regulation of autophagy and apoptosis may play a crucial role in the regulation of aging, health, and life span/survival traits in laboratory animals,49. This observation allows us to hypothesize that interplay between these genes may influence these traits in humans. Testing this hypothesis using human data would be an important step forward in the translation of knowledge from experimental studies to humans. This is because the interplay between the GCN2/EIF2AK4 and CHOP/DDIT3 genes influences aging, health, and lifespan/survival traits in laboratory animals,50,51 does not mean that these genes play the same roles in humans.The use of a stress-related conceptual framework might be beneficial for studying multifactorial regulation of complex traits because it allows for selecting and linking together non-genetic factors  and genes involved in cellular stress response . It has been recently shown that interplay between the GCN2/EIF2AK4 gene, becomes activated by several cellular stressors, including amino acid starvation (deprivation). The transformed signal sent from this gene  activates other genes in the ISR pathway, including the CHOP/DDIT3 gene, which is the second gene selected for analysis. The product of this gene, among other things, influences the cells’ fate: when stress is mild, and the duration of the stress response is relatively short, the cell has high chances to survive; alternatively, under strong or persistent stress, the CHOP/DDIT3 gene is more likely to activate the process of apoptosis for this cell. The strength and the duration of cellular stress response to a large extent might be determined by the GCN2/EIF2AK4 gene polymorphisms. The cells’ fate has important consequences for the organism’s health and survival outcomes. Because of the importance of stress-related conceptual framework used in our analysis, a brief description of ISR is given below.This paper reviewed experimental evidence about genetic mechanisms that regulate the effects of cellular stress on aging, health, and lifespan/survival traits. These effects are manifested at the cellular level and involve genes from the ISR pathway. To illustrate our approach to the analysis of the effects of genetic interplay on these traits, two genes have been selected from the ISR pathway. One, the . Depending on the strength and duration of the stress response, ISR determines the fate of the cell. Cellular stressors may be intrinsic  or external . Experimental and clinical studies provide evidence about involvement of ISR in lifespan regulation,58, as well as in the development of aging-related diseases including cognitive and neurodegenerative disorders,55, cancer–61, pulmonary disease, atherosclerosis,64, diabetes and other metabolic disorders.In response to various stressors disturbing normal cellular functioning, eukaryotic cells activate an evolutionary conserved adaptive machinery - the ISR,52. This process starts with the phosphorylation of eukaryotic translation initiation factor 2 alpha (EIF2A) by one of the four members of the EIF2A kinase family, which sense cellular stress signals. These include a heme-regulated inhibitor kinase (HRI/EIF2AK1), an interferon-induced, double-stranded RNA (dsRNA)-activated protein kinase (PKR/EIF2AK2), a protein kinase R (PKR)-like endoplasmic reticulum (ER) kinase (PERK/EIF2AK3), and a general control nonderepressible 2 kinase (GCN2/EIF2AK4). Additional details about these kinases are described below.ISR responds to cellular stressors by changing the process of protein synthesisEIF2AK1 gene. Heme is an iron-containing compound that forms the non-protein part of hemoglobin, the substance inside red blood cells that binds to oxygen in the lungs and carries it to the tissues. HRI/EIF2AK1 is an intracellular heme sensor that coordinates heme and globin synthesis in erythropoiesis by inhibiting protein synthesis of globin and heme biosynthetic enzymes during heme deficiency. HRI is also activated by arsenite-induced oxidative stress, heat shock, nitric oxide, 26S proteasome inhibition, and osmotic stress. These types of stressors activate HRI independently of heme but require the presence of heat shock proteins HSP90 and HSP70. Denatured proteins and oxidative stress also activate HRI,67,68.Heme regulating inhibitor kinase (HRI/EIF2AK1) is an enzyme that in humans is encoded by the EIF2AK2 gene. In addition to dsRNA that can be introduced to the cell by a viral infection, PKR is also activated by oxidative and ER stress, growth factor deprivation, cytokines, bacterial infections, ribotoxic stress, caspase activity in the early stages of apoptosis. It can also be activated by the protein PACT (that in humans is encoded by the PRKRA gene) or by heparin and other cellular stress signals–73.Protein kinase R (PKR/EIF2AK2) is an enzyme that in humans is encoded by the EIF2AK3 gene. PERK is activated by accumulation of misfolded (unfolded) proteins in the ER, perturbations in calcium homeostasis, cellular energy, mitochondrial stress (including uncoupling), or redox status,75. It has also been reported to respond to ATP depletion and subsequent sarcoplasmic/ER Ca2+-ATPase pump inhibition in the context of glucose deprivation in neuronal cells and pancreatic β cells–78. It initiates the unfolded protein response,80. PERK plays an important role in Alzheimer’s and other neurodegenerative diseases–83.Protein kinase R-like endoplasmic reticulum kinase (PERK/EF2AK3) is an enzyme that in humans is encoded by the EIF2AK4 gene. GCN2 is evolutionarily conserved from yeasts to humans and plays a key role in modulating amino acid metabolism. It is activated in response to amino acid deprivation when it binds to deacylated transfer RNAs (tRNAs) via histidyl-tRNA synthetase-related domain,84–87. GCN2 can also be activated by other stressors, including ultraviolet irradiation, viral infection, serum starvation, glucose deprivation, and oxidative stress. Recent work shows that GCN2 strongly activates by binding to ribosomal protein, suggesting that GCN2 actively monitors mRNA translation,84–87. Recently, a pivotal role for GCN2 in response to membrane damage has been uncovered,89. Finally, GCN2, a crucial regulator of amino acid metabolism, is necessary for the metabolic homeostasis of tumor cells. Tumors lacking GCN2 or ATF4 grow more slowly. Thus, in cancers where amino acids are scarce, targeting the GCN2 branch of the ISR may be beneficial. Indeed, combination treatment with L-asparaginase and GCN2 inhibitors causes apoptosis in several cancer cell types. GCN2 upregulates a coordinately expressed set of genes involved in amino acid biosynthesis and metabolism.GCN2/EIF2AK4 is an enzyme that in humans is encoded by the ,92. ATF4 mediates the induction of ATF3 and GADD34/PPP1R15A, which dephosphorylates EIF2A-P and leads to the termination of ISR,94. Chronic ISR activates CHOP/DDIT3 leading to apoptosis,95. EIF2A phosphorylation also blocks the action of EIF2B, resulting in a general reduction in protein synthesis and the upregulation of selected genes. One of such genes is the transcription factor ATF4. The product of this gene plays a critical role in the regulation of obesity, glucose homeostasis, energy expenditure, and neural plasticity,97.The EIF2A phosphorylation by one of four kinases results in a decrease in global protein synthesis and the enhancing translation of the activating transcription factor ATF4 and several other genes acting together to restore cellular homeostasis. The ability of ATF4 to interact with multiple other transcription factors makes its target genes highly dependent on stress intensity and cellular context,99–101. For example, when acting in combination with ATF3, ATF4 contributes to re-establishing cellular homeostasis and survival promotion. Conversely, when interacting with CHOP, ATF4 promotes cell death. In addition to the interacting partners that cooperate with ATF4 to promote transcription of target genes, another set of interacting partners prevent ATF4 transcriptional activity, as is the case for PHD3 during hypoxia,97 and TRIB3 during amino acid starvation.Under stress conditions, increased ATF4 expression can activate several transcriptional programs that will ultimately determine the cell fate-from re-establishment of homeostasis to cell deathThis paper aims to show how information from experimental data can be used for testing the connection between genetic factors and survival traits in humans. For this, a set of data is needed on genotyped individuals with large sample size. The Health and Retirement Study (HRS) data on white individuals satisfies this requirement. The information on white HRS study subjects is shown in The genetic data - 2.5 million single nucleotide polymorphisms (SNPs) - were produced on the Illumina platform using Illumina’s Human Omni2.5-Quad (Omni2.5) BeadChip methodology on 15,620 individuals ., resulted in: 129 individuals dropped because of failure in the check of duplicates, missingness rate (5%), heterozygosity outlier (± 3 SD), sex mismatch, and divergent ancestry outlier (± 8 SD); 538,451 variants were removed due to minor allele frequency < 1%, 69,947 variants were removed due to genotyping missing rate higher than 5%; and 411,945 variants were removed based on Hardy-Weinberg test . The second procedure used the protocol proposed in. Individuals and SNP which passed either the first QC step or the second step remained in the final dataset. This procedure allowed us to increase the number of study subjects used in genetic analysis. It led to 15,492 individuals and 1,267,439 variants cleaned and mapped to the human reference genome GRCh38 for further analysis.Quality control (QC) was performed before running the analysis using two procedures. The first, based on the protocol proposed incase: LS ≥ 85; control: 75 ≤ LS < 85 (or age at last follow-up) because we were especially interested in the effects of genetic factors on survival at ages 85+ compared with that around age 80 (± 5 years). Our earlier studies,107 suggested that the age around 80 might be a “switching point” in the course of aging, characterized by declining or leveling-off (after a prior increase) risks of some major diseases . Such behavior of risk trajectories could be due to selection, under-diagnosis, or the aging itself, so that some aging-related changes in the body would negatively affect health and survival chances before age 80 but become protective afterward. To estimate the association of interacting SNP pairs with ST, the logistic regression model as implemented in INTERSNP software was used (with the interaction term being the quantity of interest). Education, smoking status, sex, and first five principal components (PC1-PC5) were included as observed covariates.The following dichotomous survival trait (ST) was used in genetic interaction analysis: GCN2/EIF2AK4 or CHOP/DDIT3 genes with SNPs from many other genes may also contribute to these traits. The interacting SNP pairs associated with these traits can be detected in the genome-wide-like association studies of interacting SNP pairs in which one SNP from the GCN2/EIF2AK4 or CHOP/DDIT3 genes is fixed, and others are all SNPs available for a given dataset that passed the quality control procedure. The results of this analysis can be used for constructing a SNP-specific composite index that can measure the association of many interacting SNP pairs with health and lifespan-related traits. This measure extends the notion of polygenic risk score (PRS), widely used in the genetic epidemiological studies of age-related diseases and longevity–116. A simple measure of such capacity for a given SNP* might be the number of detected associations of interacting SNP pairs (with a fixed SNP*) whose P-values did not exceed a given P-value threshold (similar to the genetic dose index described in ref). This paper introduced a measure of interactive capacity of a given SNP* involved in the lifespan regulation called “SNP-specific Interaction Polygenic Risk Score” (SIPRS). The construction of such indices and some of their properties related to multifactorial regulation of AD are described in Yashin et al..Because of the multifactorial nature of age-related health and lifespan-related traits, the interactions of SNPs from the GCN2/EIF2AK4 and the CHOP/DDIT3 genes have 63 and 8 SNPs, respectively. The analysis of associations of these SNPs with the survival trait (ST) (defined in the Data and methods section) showed that the associations of each of the 71 SNPs with survival trait did not reach the nominal level (P ≤ 0.05) of statistical significance. This allows for the hypothesis that the contribution of these genes to survival might be realized through their interaction effects.To evaluate the significance of associations of the SNPxSNP interactions with the survival trait, the number of tested SNP pairs is needed to know when deciding about the presence or absence of the association of the interacting SNP pair with the trait. This number depends on the population under study and the genotyping platform. In the HRS data, after the quality control procedure, the GCN2/EIF2AK4 and CHOP/DDIT3 genes with survival traits, a set of 504 potentially interacting SNP pairs has been used to estimate the set of probabilities of the type I error, which would happen if the decision about the presence of association of each SNP pair with survival trait would be made. Note that this procedure did not involve the “decision making” about the presence or absence of association of the SNPxSNP interaction with the trait. Probabilities of type I error have been calculated for each SNP pair and, hence, no correction for multiple testing is needed at this stage. Note that the Bonferroni correction for multiple testing, in this case, would be 9.92E-05.To evaluate associations of interaction between the R2 = 0.1 LD threshold and resulted in 8 independent SNP pairs: 8 independent SNPs from the GCN2/EIF2AK4 gene and 1 independent SNP from the CHOP/DDIT3 gene. It gives us a set of 8 SNP pairs that can now be used for testing the null hypothesis about the absence of association of the interacting SNP pairs with survival traits. The smallest P-value resulted from the analysis of the association of SNPxSNP interaction with survival trait is 3.80E-03 for SNP rs16970024 from the GCN2/EIF2AK4 gene interacting with SNP rs697221 related to the CHOP/DDIT3 gene [P-values exceeding 1.00E-03. With eight SNP pairs tested in this analysis, the Bonferroni correction provides us with the P-value threshold 6.25E-03 for testing the null hypothesis, which is larger than 3.80E-03. These results are summarized in The presence of linkage disequilibrium (LD) between SNPs related to each of two genes allows for reducing the number of potentially interacting SNP pairs tested for their associations with lifespan and for increasing the Bonferroni correction threshold. For this, the LD regions in each gene and selected one SNP pair as a representative for each such region have been identified. In the analysis, the SNP pair representatives are those who have the smallest value of the type I error among SNP pairs in this region. This clumping procedure used IT3 gene . All othP-value of 3.80E-03. No significant associations of the interacting SNPs from these two genes with survival traits were detected in males.Separate analysis of males and females revealed a significant association of interactions between the SNPs rs16970024 and rs697221 with female survival, with a GCN2/EIF2AK4 and CHOP/DDIT3 genes may contribute to survival in humans.These results support our hypothesis induced by the results of experimental studies that interactions between It is important to note that regulation of aging, health, and lifespan-related  traits in humans may include genes and connections which are not detected in experimental studies with laboratory animals. Therefore testing associations of interactions between each of two detected SNPs and all other SNPs using available human data might result in the detection of new features of genetic human mechanism of lifespan regulation. It is because each of the detected SNPs may influence lifespan through its interactions with many other SNPs.The number of significant associations with other SNPs and the strength of such associations may differ for each SNP. Measuring the “interacting ability” of a SNP might help better understand the contribution of a given SNP (and corresponding gene) to multifactorial regulation of lifespan. The convenient measure of such ability could be SNP-specific interaction polygenic risk score (SIPRS).Such measures have been calculated for each of the two detected SNPs. For this, genome-wide association study (GWAS)-like analysis of associations of the rs16970024 and then rs697221 SNPs with all other SNPs available in the HRS data were performed. The HRS data on white males and females combined was used in the analysis. The logistic regression model with the interaction term has been used for evaluating associations of interacting pairs of SNPs with survival traits. Education, smoking, sex, first five principal components PC1-PC5, and rs16970024 (rs697221) were used as observed covariates in the regression model. Then by using summary statistics resulted from these analyses, the rs16970024 (the rs697221) composite SIPRSs indices have been constructed using the procedure described inP-values of their associations with survival trait . Both positive and negative associations with survival traits were used in the construction of SIPRS indices. Then the properties of constructed SIPRS indices were investigated. The results of these analyses are shown in The construction of rs16970024 (rs697221) related indices included a set of 67,741  interacting pairs of SNPs ranked with respect to P-value of its association with survival trait P = 5.2E-50. This index contains 220 SNP pairs and explains 13.5% of phenotypic variance of the survival trait. The second most significant rs16970024 related index shown in this diagram corresponds to the threshold 0.001 with the P-value 1.7E-46 of its association with survival trait. This index contains 415 SNP pairs and explains 15.7% of phenotypic variance of the survival trait.The diagram on the left of P-value of its association with survival trait P = 3.8E-92. This index contains 1619 SNP pairs and explains 33.4% of phenotypic variance of survival traits. The second most significant rs697221 SIPRS index corresponds to the threshold 0.001  with the P-value 2.7E-86 of its association with survival trait. This index contains 412 SNP pairs and explains 25.6% of phenotypic variance of the survival trait.The diagram on the right of this figure shows that the rs697221 SIPRS index corresponding to the threshold 0.005  has the smallest The R package pROC was used to calculate the areas under the receiver operating characteristics curves (AUC) for selected rs16970024 (rs697221) SIPRS indices with a corresponding threshold of 0.001. The AUC characterizes the fit of the logistic regression model describing the association of the rs16970024 (rs697221) specific SIPRS constructed from 415 (412) SNP pairs . The area under the curve (AUC) is 0.64 with 95% of confidence interval (0.63-0.66) for rs16970024 SIPRS, and 0.71 with 95% of confidence interval (0.70-0.72) for rs697221 SIPRS, respectively.The properties of interacting SNP pairs most significantly associated with survival trait in which the rs16970024 SNP interacts with other SNPs are presented in P-value smaller than 5.0E-03,  as promising” may make the interpretation of the results of such analysis difficult. At the same time, useful insights about the potential role of interplay between specific pairs of genes in the trait of interest might be obtained from experimental studies. Testing the presence of such connection in humans can be done by estimating the association of SNPxSNP interactions with the trait using SNPs taken only from given two genes. It is important to note that, even in this case, the number of testing SNP pairs can be large enough to create problems with deciding on true-positive association. It turns out that the number of testing SNP pairs can be further reduced using the fact that many SNPs from these two genes are in LD. Making such steps increases the chances of finding true-positive associations without making assumptions which compromise statistical evidence. This paper showed how such analysis could be done. Two candidate genes have been selected, which play important roles in the ISR pathway. One, the GCN2/EIF2AK4 gene, is a sensor of amino acid starvation. Other, the CHOP/DDIT3 gene is involved in the regulation of apoptosis and autophagy. The hypothesis has been tested that the interplay between these genes may contribute to variability in human lifespan. Using data on HRS study participants, it has been found that interaction between SNP rs16970024 from the GCN2/EIF2AK4 gene and SNP rs697221 from the CHOP/DDIT3 gene is significantly associated with human survival traits for females and for males and females combined.The hypothesis-free GWAS of SNPxSNP interactions, including SNPs from all selected candidate genes, is possible but involves testing many SNPxSNP interactions. The too-conservative Bonferroni correction for multiple testing often results in unjustified decisions like “we consider all associations having . The idea to understand and control the individual aging process motivated many researchers to study biological mechanisms of aging and search for possible interventions that could slow down this process. One class of the interventions affecting aging has been discovered in the first half of the last century by McCay et al. in experiments studying the effects of dietary restriction on lifespan in rodents. Subsequent experiments showed that a CR/DR diet is able to improve health and increase lifespan in different animal models,120.The geroscience hypothesis is that slowing down the aging process will postpone the occurrence of many age-associated health disorders, which results in increased healthspan and improved survival. A part of such regulation in laboratory animals is realized through the ISR pathway, in which interplay between the GCN2/EIF2AK4 and CHOP/DDIT3 genes dealing with the response to nutritional stress may play an essential role. Nutritional stress was an important part of life in human ancestors. This explains a major role of the sensor of the amino acid starvation (the GCN2/EIF2AK4 gene) in the ISR regulation of the processes affecting aging, health, and lifespan-related traits in response to changes in nutritional status. Experimental studies demonstrated a high potential of genes from the ISR pathway as targets for pharmacological intervention–128. These studies indicated that evaluating the role of ISR pathway in human aging and lifespan may substantially improve our understanding of the factors and mechanisms initiating the development of major human age-associated health disorders.More recent studies identified genetic pathways that sense disturbances in nutrients supply, regulate metabolic functions in CR/DR conditions, and influence aging, health, and lifespan. The GCN2/EIF2AK4 and mTORC1 genes are both involved in such regulation. The key role of mTOR complex 1 (mTORC1) signaling pathway regulation of aging and lifespan has been established and widely discussed in the literature,130–133. Decreased activation of mTORC1 leads to lifespan extension in yeast, worms, flies, and mice.It was found that several mechanisms involved in sensing and regulation of response to amino acid (AA) deprivation can improve health and increase lifespan,135–137. The activation of the ISR in response to nutrient starvation engages adaptive changes mediated by the induction of genes necessary to produce all the amino acids. Amino acids are needed to maintain various cellular functions, including the Krebs cycle activity for ATP generation. They also provide necessary components for maintaining redox homeostasis. These properties of AA regulation can be used to deal with the consequences of metabolic stress.It turns out that the mechanism is driven by the AA deficiency sensor GCN2/EIF2AK4 also influences aging, health, and lifespan/survival traits GCN2/EIF2AK4 and CHOP/DDI3T genes (the members of the ISR pathway) influences lifespan/survival of laboratory animals does not mean that the same connection holds in humans. Genetic epidemiological analysis of available data on genotyped human individuals has been performed to test whether interplay between these genes is associated with human lifespan/survival. It has been found that one interacting SNP pairs taken from the GCN2/EIF2AK4 and CHOP/DDI3T genes showed a statistically significant association with human survival trait in the analyses of the HRS population of males and females combined. Sex-specific analysis revealed that a statistically significant association of interacting SNPs with survival is confirmed only in females. This result may indicate that survival in males and females are regulated using different biological mechanisms. It can also result from the fact that the population of males used in the analysis was smaller than females. This finding is an important step in the process of translation of the results of experimental studies to human applications.The majority of information about how the interplay of genes from the ISR pathway may influence aging, health, and lifespan/survival traits is obtained in experimental studies of these traits. Even though many genetic stress response pathways are evolutionary conserved, the biological processes that involve groups of such genes in humans may differ from those developing in laboratory animals or cellular cultures. This is because, in different species, such pathways may experience species-specific modifications, acquire some, and lose other functions when adjusting to a specific biological background, nutritional differences, and external conditions. Therefore, the fact that an interplay between the The association of the interaction between GCN2/EIF2AK4 and CHOP/DDIT3 with the human survival trait detected in statistical analysis of data does not necessarily mean that this trait is affected by the result of biochemical interaction between corresponding genetic products. Statistical analysis may capture genetic connections between two genes that could be mediated by a chain of biochemical reactions that involve products of many other genes from ISR and other signaling and metabolic pathways involved in regulating a given survival trait in humans. Identifying such mediators and evaluating their roles in the regulation of human aging, health, and survival traits could shed light on the mechanism of multifactorial regulation of these traits in humans.GCN2/EIF2AK4 and CHOP/DDIT3 along with other genes may influence the development of age-associated diseases, including cancer,139,140, neurodegeneration, diabetes, other, and through them, lifespan and survival traits. Experimental data also show that the GCN2/EIF2AK4 and CHOP/DDIT3 genes can be involved in the regulation of autophagy and apoptosis–146, which play a fundamental role in cancer,148, bacterial infections, other health disorders–156, aging–159. The trade-off regulation between autophagy and apoptosis at the cellular level might be responsible for variability in lifespan[GCN2/EIF2AK4 and CHOP/DDIT3 genes and the survival trait.The ISR pathway that activates the  lifespan,160. Gen. Experiments confirming the positive effects of CR/DR on aging, health, and lifespan illustrate this property–169. The mechanism responsible for the positive effect on survival might be related to the fact that both genes analyzed in this paper are involved in the regulation of autophagy,152. The negative correlation between basal (resting) metabolic rate and human lifespan was also detected,171. All these effects were likely to be manifested, because in most of cells exposed to stress the ISR ended up by restoration of normal cellular functioning. The deleterious effects of the cellular stress response on these traits are likely to be manifested when ISR contributes to the survival of malignant cells or produces destruction of post-mitotic cells by apoptosis.Experimental studies provide evidence about both the positive and negative influence of the ISR initiation on health and survival traits,58,62. Tet al. suggested a centenarian-only approach for assessing such connections. Using data from Italian centenarians, the authors detected an association of interaction between the REN gene and the mitochondrial H haplotype with longevity. The case-control study of Han Chinese centenarians found that the interactions of SNPs from the FOXO1A and FOXO3A genes are associated with survival. This study also found that the interaction of FOXO1A and regular exercise is associated with survival traits. The role of interaction between SNPs from the FOXO1A and FOXO3A genes with longevity has also been studied by implementing a novel permutation test to the data from the Danish 1905 birth cohort. The analysis confirmed the association of interaction of SNPs from these genes with longevity; however, interacting SNPs detected in this study differed from those found in the study of Chinese centenarians. Dato et al. analyzed associations of interacting SNPs from candidate genes on longevity using data from the Danish 1905 cohort. Curk et al. used an information-theoretic approach implemented in the SNPsyn software and the MDR method to select synergistic pairs of SNPs. The best combinations detected in both approaches included SNPs from IGFR and PTPN1, TP53, and ERCC2, TXNRD1 and TP53. The authors also found interacting partners: PAPPA, PTPN1, MRE11A, and PARK7 for the GHSR gene previously identified in a single-SNP association study. Ukraintseva et al. (forthcoming) investigated associations of interactions of SNPs from the group of candidate genes from aging-related pathways  with survival traits. For this, the INTERSNIP software for epistasis analysis was used in the analysis of data from the Atherosclerosis Risk in Communities study. The results of this analysis suggest that the interactions between SNPs from the IGF1R and TGFBR2 genes, as well as SNPs from the BLC2 gene, may influence human lifespan. These results were validated using data from the Cardiovascular Health Study. The results of these analyses showed that interactions between SNPs in genes from aging pathways have higher associations with survival traits than individual SNPs for the same genes.The association of GxG interaction with human longevity has been investigated in several earlier studies. Tan P-value threshold for making the decision about the presence of true-positive association resulting from the Bonferroni correction for multiple testing often becomes responsible for statistically unjustified decisions like “we consider all associations having P-value smaller than 5.0E-03,  as promising” may make the interpretation of the results of such analysis doubtful. At the same time, useful insights about the potential role of interplay between specific pairs of genes in the trait of interest might be obtained from the results of experimental studies. Testing the presence of such connection in humans can be done by estimating the association of SNPxSNP interactions with the trait using SNPs taken only from selected two genes. It is important to note that even in this case, the number of testing SNP pairs can be large enough to create problems with deciding on true-positive association. It turns out that the number of testing SNP pairs can be further reduced using the fact that many SNPs from these two genes are in LD. Making such steps increases the chances of rejecting the null hypothesis about the absence of associations without making assumptions that compromise statistical evidence.The hypothesis-free genome-wide association study (GWAS) of SNPxSNP interactions, including SNPs from all selected candidate genes from the ISR pathway, is possible but involves testing of many SNPxSNP interactions. The too-small GCN2/EIF2AK4 gene, is a sensor of amino acid starvation. Other, the CHOP/DDIT3 gene is involved in the regulation of apoptosis and autophagy. All SNPs in each gene have been divided into subsets of SNPs that are in LD with each other and whose LD measure R2 was larger or equal to a given threshold . This procedure divided all available SNP pairs into corresponding subsets. Note that the smaller is the LD threshold, the larger number of correlated SNPs that could be included in the subsets, and the smaller number of such subsets will be constructed. One representative SNP pair was selected for each subset of SNPs in LD to reduce the number of comparing SNP pairs. In our analysis, the SNP pair representatives were those who have the smallest value of the type I error among SNP pairs in this subset. This procedure resulted in 8 independent SNP pairs constructed from 8 independent SNPs from the GCN2/EIF2AK4 gene and 1 independent SNP from the CHOP/DDIT3 gene. Then this set of 8 SNP pairs was used for testing the null-hypothesis about the absence of association of the interacting SNP pairs with survival trait. Using data on HRS study participants on males and females combined, and then separately for males and females, it has been found that the null-hypothesis about the absence of association of the interaction between SNP rs16970024 from the GCN2/EIF2AK4 gene and SNP rs697221 from the CHOP/DDIT3 gene with survival trait can be rejected for females and for males and females combined, which means that the interaction of these SNPs is significantly associated with human survival trait.This paper showed how such analysis could be done. Two candidate genes have been selected, which play important roles in the ISR pathway. One, the The mechanism of multifactorial regulation of human lifespan is largely unknown. Therefore, each SNP from detected SNP pair may interact with SNPs from many other genes outside ISR pathway, and these interactions may also contribute to lifespan and survival traits. GWAS-like procedure has been used in which one of the detected SNPs is fixed, and others include all available SNPs that passed the quality control procedure. The results of this analysis were used for constructing a SNP-specific measure of collective association of many interacting SNP pairs, called the SIPRS, with survival trait. Using such an index, one gets an opportunity to evaluate collective associations with the trait of interest of different numbers of interacting SNP pairs in which a given SNP interacts with other SNPs. The approach allows for the construction of many ordered indices for a given SNP that consist of different numbers of interacting SNP pairs .P = 5.2E-50. Index constructed from 57 most significant interacting SNP pairs is less significant with P = 2.7E-20 but contains a smaller number of SNP pairs and, hence, might be more convenient for starting a further analysis.The properties of these indices can be compared, and the most appropriate can be selected for further analysis. It has been found that the rs16970024 SIPRS index constructed from 220 most significant interacting SNP pairs has the most significant association with survival trait with P = 3.8E-92. Index constructed from 55 SNP pairs is less significant with P = 2.E-26 [It has also been found that the rs697221 SIPRS constructed from 1619 most significant SNP pairs has the most significant association with survival trait with = 2.E-26  but contP-value not exceeding 9.69E-05. These interactions were used in the construction of the SIPRS for the rs16970024 SNP. P-value not exceeding 9.97E-05. These interactions were used in the construction of the SIPRS for the rs697221 SNP..Results of this study suggest that the interplay between the two key genes from the ISR pathway involved in aging, health, and lifespan traits in laboratory animals can also be involved in the regulation of human survival. This observation does not explain the entire mechanism of the multifactorial regulation of human lifespan, which can involve many other interacting genes. However, the use of only two genes of the many, allowed us to reduce the number of statistical tests and provide a proof of concept that the connection between genes and lifespan discovered in laboratory animals can also take place in humans, in the form of genetic interactions. Experimental aging studies found many other genes whose interplay may also contribute to aging, health, and survival traits. However, detecting the effects of multiple interacting genes on lifespan remains a challenging problem. Its solution requires very large sample sizes of study participants and/or advanced methods of analysis. The latter can be partly addressed by constructing and investigating the SIPRSs–182, allostatic adaptation and allostatic load,184 with health and survival outcomes.Individual lifespan and health status are outcomes of dynamic processes in which the interacting genetic and non-genetic factors, the strength of their interactions, and other influential variables can change with age, time, and other conditions. Genetic interaction analyses that have been performed so far, including in this study, are initial steps in addressing the multifactorial nature of complex traits and do not yet include the dynamic properties of these traits in the analysis. More efficient methods of a comprehensive statistical analysis of the dynamic polygenic regulation of the aging, health, and survival traits are urgently needed to improve our understanding of these traits substantially. Merging biodemographic methods of statistical modeling with genetic epidemiology and systems biology of aging may be a promising way to address this problem. These methods can be developed within the stress-related conceptual framework linking components of individual resistance to stresses, including robustness (vulnerability) and resilience (ability to recover) and Mendelian randomization. These methods have their own limitations.The associations of interacting SNP pairs with survival traits evaluated in this paper do not necessarily describe causal connections. This limitation is common for statistical methods used in genetic analyses of observational data. Information about causal connections is needed for developing intervention therapies, testing candidate medications. Testing causality of connections detected in the analysis of observational data can be performed using methods of mediation analysisThe use of the stress-related conceptual framework in studies of multifactorial mechanisms of lifespan regulation would benefit from information about biomarkers characterizing cellular stressors, as well as about factors and conditions capable of affecting variability  of the cellular stress response. Since many human data on aging, health, and lifespan related traits have limited information about such stressors, factors, and conditions, some useful insights and ideas about possible stressors can be obtained indirectly from the estimates of the roles of specific genetic sensors of cellular stress signals in lifespan regulation obtained in data analyses. This is because each such sensor recognizes and responds to specific (sometimes overlapping) groups of stress signals.,188–191. One should, however, distinguish between biological and statistical epistasis. In biological experiments, epistasis may be detected as a result of proteins’ interactions, in which the effect of one protein can mask the effect of another protein on the phenotype of interest. In genetic epidemiology, the effects of genetic interactions on the trait of interest are evaluated using regression models with the interaction terms. Since these genetic epidemiology methods differ from those used in detecting the effects of epistasis in biological experiments, the results of such analyses may also have different interpretations,192. Associations of genetic interactions with survival traits detected in epidemiological studies may reflect synergistic or antagonistic effects of genes whose products do not directly influence each other and, therefore, will not necessarily be detected in studies of biological epistasis. In contrast to biological epistasis, statistical interactions can capture connections between genes mediated by the chain of intermediate genetic products. Identification of such intermediate genes and evaluation of their roles in aging, health, and lifespan may require new experimental studies. The detected statistical association does not exclude the presence of the effect of biological epistasis between two genetic products on human lifespan. However, the association of statistical genetic interaction with survival may be detected even in the absence of direct biological interaction between genetic products. This might be the case when such interaction and connection with the trait are mediated by other genes.Genetic interactions may produce paradoxical results in the genetic association analyses of complex traits.The results of this paper do not provide us with all the details about mechanisms of multifactorial regulation of human lifespan. They, rather, describe a way to become more informative about it. An important question that was not addressed in this study is how the confirmed (or newly detected) information about multifactorial regulation of aging, health, and survival in humans should be integrated to explain health and survival outcomes? To be seful in practice, mechanisms of such integration should have quantitative description, e.g., in the form of a computer model capable of describing the response of the body  to specific challenges. A promising tool for addressing this problem could be further development of dynamic stochastic modeling of human mortality and aging, which successfully used in the analysis of longitudinal dataGCN2/EIF2AK4  and CHOP/DDIT3 (involved in apoptosis and autophagy), as well as other genes from the ISR pathway, in aging, health, and lifespan of laboratory animals,193, which encourages clarifying the role of interactions among these genes in respective human traits.Experimental studies pointed to a fundamental role of the interplay between the GCN2/EIF2AK4 and CHOP/DDIT3 genes involved in the ISR pathway may influence lifespan in humans. This is a “proof of concept” research and a step forward to translating the evidence about mechanisms of lifespan regulation found in laboratory animals to humans.Results of this study support our hypothesis that the interplay between Individual differences in the exposure to stressful conditions, in the access to health care, the polymorphisms in genes that sense cellular stress signals, and in other genes involved in cellular stress response, together with aging-related changes in robustness and resilience, are likely to be important sources of disparity and heterogeneity in health, lifespan and survival outcomes. Being evaluated, these characteristics can be used in predicting the future burden of aging-related diseases under different scenarios/strategies of reducing disparities, improving access to health care facilities and medical advances for the groups of individuals having different genetic backgrounds and exposed to distinct environmental and living conditions.Supplementary Table 1 and Table 2