Whereas ORAI1 activation by Ca2+-sensing STIM proteins is known, still obscure is how ORAI1 is turned off through Ca2+-dependent inactivation (CDI), protecting against Ca2+ toxicity. Here we identify a spatially-restricted Ca2+/cAMP signaling crosstalk critical for mediating CDI. Binding of Ca2+-activated adenylyl cyclase 8 (AC8) to the N-terminus of ORAI1 positions AC8 near the mouth of ORAI1 for sensing Ca2+. Ca2+ permeating ORAI1 activates AC8 to generate cAMP and activate PKA. PKA, positioned by AKAP79 near ORAI1, phosphorylates serine-34 in ORAI1 pore extension to induce CDI whereas recruitment of the phosphatase calcineurin antagonizes the effect of PKA. Notably, CDI shapes ORAI1 cytosolic Ca2+ signature to determine the isoform and degree of NFAT activation. Thus, we uncover a mechanism of ORAI1 inactivation, and reveal a hitherto unappreciated role for inactivation in shaping cellular Ca2+ signals and NFAT activation.ORAI1 constitutes the store-operated Ca 2+ release-activated Ca2+ (CRAC) channel, but how this channel is turned off through Ca2+-dependent inactivation (CDI) remained unclear. Here the authors identify a spatially-restricted Ca2+/cAMP signaling crosstalk critical for mediating CDI which in turn regulates cellular Ca2+ signals and NFAT activation.ORAI1 constitutes the store-operated Ca The biophysical manifestation of SOCE is the highly Ca2+-selective Ca2+ release-activated Ca2+ (CRAC) current4. ORAI1-mediated Ca2+ entry is triggered by activation of phospholipase C (PLC)-coupled receptors, generation of inositol-1,4,5-trisphosphate (IP3), and subsequent depletion of endoplasmic reticulum (ER) Ca2+ stores5. The cellular and physiological outcomes controlled by ORAI1 are ultimately determined by net CRAC channel activity, which is shaped by the critical balance between its store-dependent activation and its Ca2+-dependent inactivation (CDI). Mutations in ORAI1 that disrupt CRAC channel function yield loss or gain of function channels and cause a wide spectrum of disease states, including immunodeficiency and disorders of muscle, skin, and secretory tissues8.Store-operated Ca2+ store depletion to activation of ORAI1 channels are now well characterized9. Depletion of Ca2+ from the ER lumen causes the ER Ca2+ sensor protein, stromal interaction molecule 1 (STIM1), to lose Ca2+ binding from a low-affinity luminal EF hand, and gain an extended conformation that interacts with PM lipids within ER-PM junctions where it physically binds and opens ORAI1 to mediate CRAC current9. In contrast, the molecular mechanisms of CRAC channel CDI are less clear. CRAC channel CDI develops within milliseconds with kinetics that can be fitted to a bi-exponential curve (time constants of ∼5 and 50 ms)11. CDI is driven by an unknown Ca2+-dependent mechanism located either on the CRAC channel complex or situated a few nanometers from the mouth of the channel, reflecting a role of a highly localized Ca2+ microdomain at the close vicinity of the CRAC channel pore2.The mechanisms linking ER CaOrai1 mRNA at methionine-64 producing a protein lacking the first 63 N-terminal amino acids13 14, which is constitutively bound to an ORAI1 N-terminal site containing three arginines , is crucial for CDI. Generation of cyclic adenosine monophosphate (cAMP) by AC8 activates PKA to induce CDI through direct phosphorylation of serine-34 in ORAI1 in an A-kinase-anchoring protein 79 (AKAP79)-dependent manner. Recruitment of the phosphatase calcineurin antagonizes the effect of PKA and reduces CDI. Importantly, we show that CDI shapes the frequency of Ca2+ oscillations triggered by physiological concentrations of agonizts. When reconstituted to physiological levels in ORAI1-knockout cells, ORAI1 shows significantly lower frequency of Ca2+ oscillations compared with ORAI1β. Consequently, NFAT4, an NFAT isoform sensitive to small increases in cytosolic Ca2+ 15 , is translocated to the nucleus slower and less efficiently in ORAI1-expressing cells compared with ORAI1β-expressing cells. Our findings identify a molecular mechanism for ORAI1 CDI and provide evidence that CDI, driven by spatially-restricted Ca2+-cAMP crosstalk, plays a crucial role in shaping cellular signaling and NFAT activation.Recently, we discovered a naturally-occurring short translational variant of ORAI1, which we termed ORAI1β. ORAI1β is generated from an alternative translation-initiation site on s13 Fig. . Here weOrai1 mRNA produces ORAI1 protein from methionine-1 and a shorter ORAI1β protein starting at methionine-6413  blocker, thapsigargin at 2 μM . The expression of ORAI1-CFP and ORAI1β-CFP were driven by either the strong cytomegalovirus (CMV) promoter  phosphorylation sites (Serine-27 and Serine-30)19, an AC8-binding domain (residues 26–34)14 that overlaps with a putative phosphatidylinositol-4,5-bisphosphate (PIP2)-binding sequence (residues 28–33)20, and a caveolin (cav) binding domain (residues 52–60)21  an ORAI1-CFP construct deficient in AC8 binding in which arginines 31–33 were mutated to alanines (R31–33A) and (2) because adenylyl cyclase enzymes are typically located within cav-rich regions of the plasma membrane22, we generated an ORAI1-CFP construct deficient in cav binding in which residues tyrosine-52 and tryptophane-55 were mutated to alanines . Co-expression of either construct with eYFP-STIM1 in ORAI1-KO cells revealed that both mutants have significantly reduced CDI compared with wild-type ORAI1 , was similar to wild-type ORAI1-CFP  and the exchange factor directly activated by cAMP (EPAC)24. Therefore, we set out to determine whether the AKAP isoform widely expressed in HEK293 cells, AKAP79 is preferentially associated with ORAI1 and required for ORAI1 CDI. We used two independent conditions where we expressed either full-length AKAP79 cDNA or a truncated version of AKAP79 lacking the first 107 residues (Δ1-107AKAP79), corresponding to the membrane-targeting domain25. We expected that only full-length AKAP79 would mediate enhancement of ORAI1 CDI. However, ORAI1 CDI was unaffected by expression of either AKAP79 or Δ1-107AKAP79  in the bath, expression of AKAP79 further enhanced ORAI1 CDI while Δ1-107AKAP79 was without effect  with disrupted structure by substitution of two isoleucine residues with proline (DLIEEAASRPVDAVPEQVKAAGAY). ORAI1 CDI was inhibited by the st-Ht-31 peptide whereas the control st-Ht-31P peptide that is unable to inhibit PKA-AKAP79 interactions, had no effect , which inhibits the interaction between the RII regulatory subunit of PKA and AKAPect Fig. .Fig. 4PK2+ (ARC) channels formed by heteromeric associations between ORAI1 and ORAI328. Wild-type STIM1-pVenus, and two STIM1 mutants in which threonine-389 was mutated to either alanine  or glutamate  were co-expressed with ORAI1-CFP in STIM1/STIM2 double knockout HEK293 cells that we generated using CRISPR/Cas9 technology and characterized for completely lacking both STIM1 and STIM2 proteins and SOCE  predicted serine-34 on ORAI1 (a residue missing in ORAI1β) and threonine-389 on STIM1 as strong candidates for PKA phosphorylation. PKA-mediated phosphorylation of threonine-389 on STIM1 was recently shown to mediate the activation of store-independent arachidonate-regulated Ca2+-mediated production of cAMP, because the difference in CDI between wild-type ORAI1 and S34A ORAI1 observed in Ca2+-containing bath solutions disappeared when recordings were performed in Ba2+-containing bath solutions  mutant, a mutation of serine-34 to a positively charged residue, arginine (S34R) and two phosphomimetic mutants with either glutamate (S34E) or aspartate (S34D). The CDI of the phosphomimetic mutants (S34E and S34D) was indistinguishable from that of wild-type ORAI1 Fig. . Interes2+, which occurs within tens of seconds to minutes of activation  are indistinguishable  to stimulate cAMP production and ORAI1 phosphorylation was detected by western blot with an anti-phosphoserine specific antibody on immunoprecipitated ORAI1-eGFP proteins Fig. . These a30. Comparison of S34A-ORAI1 CDI measured with either 10 mM EGTA or 20 mM BAPTA in the pipette showed that CDI of S34A-ORAI1 is significantly reduced in BAPTA compared with EGTA  to serve as the actual trigger for ORAI1 CDI2+ concentrations next to individual ORAI1 channels at sites inaccessible to BAPTA . Due to their extremely low single channel activity32, direct measurements of CRAC single channel conductance is not possible. As an alternative, we compared patch clamp recordings performed with 10 mM EGTA in the pipette and 20 mM Ca2+ in bath to recordings performed with a combination of weaker intracellular buffer  and enhanced driving force (50 mM Ca2+ in bath). We show that differences in CDI between ORAI1, S34A-ORAI1, and ORAI1β persist when a combination of low cytosolic buffer and enhanced Ca2+ driving force is used  showed reduced fast CDI36. Subsequent studies used truncated constructs and identified a negatively charged region of STIM1 (residues 470–491) required for fast CDI38. Although both ORAI1 and ORAI1β are gated by STIM1 and both generate robust STIM1-dependent currents, weaker STIM1 binding to ORAI1β versus ORAI1 might account for reduced ORAI1β CDI. We measured ORAI1 and ORAI1β interactions with STIM1 using FRET microscopy under basal conditions, after store depletion with 2 µM thapsigargin and in presence of the PKA inhibitor H89 (10 µM). Unexpectedly, we discovered that on store depletion ORAI1/STIM1 interactions were significantly less than those of ORAI1β/STIM1 and PKA inhibition had no effect on those interactions  or cyclosporine A (100 nM) caused a significant enhancement of ORAI1 CDI  promoter42 , Calls Fig. , while clls Fig. .Fig. 5CD2+ signatures generated by ORAI1 and ORAI1β differentially impact the activation of two critical isoforms of NFAT with distinct Ca2+ sensitivities, namely NFAT1 and NFAT4. With each ORAI1 isoform, we co-expressed one construct encoding either NFAT1-GFP or NFAT4-GFP fusion proteins. We used live-cell imaging to monitor NFAT-GFP isoform nuclear translocation upon stimulation with either thapsigargin or carbachol. Complete NFAT1 nuclear translocation in ORAI1-expressing cells required robust Ca2+ entry such as that induced by thapsigargin . However, while both ORAI1 and ORAI1β supported NFAT4 nuclear translocation with similar magnitude and kinetics in response to maximal CRAC channel activation with 2 µM thapsigargin , ORAI1β supported faster and more robust NFAT4 nuclear translocation compared with ORAI1 under physiological concentrations of agonist (10 µM Cch) stimulation  and exclusive (without STIM1) expression of either ORAI1-CFP or ORAI1β-CFP in HEK293 ORAI1-KO cells, to test whether different agonist-triggered oscillatory Ca2+-dependent binding of calmodulin (CaM) to an isolated ORAI1 N-terminal peptide  facing the pore lumen and not the cytosolic side where they could interact with CaM45. Several mutagenesis studies have identified critical regions in STIM1 and ORAI1 important for CDI. These include C-terminal residues 470–491 of STIM138, and the critical interplay between the three major cytosolic domains of ORAI1  as well as inner pore residues47, highlighting the complexity of the mechanisms of CDI. Some residues in STIM1 and ORAI1 described in previous studies might represent Ca2+ binding sites required for CDI. Furthermore, phosphorylation of serine-34 in ORAI1 might affect CDI by altering STIM1 interaction with pore-lining residues. Clearly, incorporation of previous findings with ours into a unifying model of ORAI1 CDI requires additional studies.Previous studies reported in vitro Ca2+-activated AC8 and cAMP signaling through a sequence (residues 26–34) in its N-terminal 63 amino acids, which are absent from its shorter translational variant, ORAI1β. We show strong associations between AC8 and ORAI1, by comparison with AC8/ORAI1β interactions. Disruption of AC8/ORAI1 interactions by mutating the AC8-binding site or the caveolin-binding site on ORAI1 and knockdown of AC8, each inhibit ORAI1 CDI without effect on ORAI1β. Maneuvers that enhance cytosolic cAMP levels including AC8 overexpression, activation of adenylyl cyclase with forskolin or introduction of cAMP directly into the cytosol through the patch pipette, enhance ORAI1 CDI without affecting CDI of ORAI1β. We show that cAMP mediates its effects on ORAI1 CDI through PKA catalytic activity and requires PKA interaction with the anchoring protein AKAP79 at the plasma membrane. PKA directly phosphorylates ORAI1 on serine-34 to induce CDI and this effect of PKA is opposed by the signaling action of the Ca2+-activated phosphatase calcineurin. Our findings contribute to understanding of ORAI1 inactivation by uncovering a mechanism of ORAI1 fast CDI and show that alternative translation-initiation of any channel regulates intracellular signaling.Our results  forward signaling from ORAI1 to cAMP can control specific gene transcription, most notably through activation of cAMP-responsive element binding (CREB) transcription factors, which are involved in transcription of metabolic genes48 and (2) reverse signaling from cAMP to ORAI1 can enhance ORAI1 CDI to limit Ca2+ entry and fine-tune ORAI1 Ca2+ signature and NFAT activation. Our results show that enhanced CDI of ORAI1 generates low frequency Ca2+ oscillations driven by low physiological concentrations of a G protein-coupled receptor (GPCR) agonist. While Ca2+ oscillations result from Ca2+ release through IP3 receptors embedded in the ER membrane, Ca2+ entry through CRAC channels is required to replenish the ER reserves and sustain these oscillations49. Therefore, the low frequency of ORAI1-mediated Ca2+ oscillations (compared with ORAI1β) is the direct result of CDI.ORAI1 and ORAI1β do not appear to heteromerize and instead form distinct homomeric channels2+ originating from CRAC channels are NFAT isoforms (NFAT1-4)50. NFAT1 and NFAT4 isoforms possess distinct activation kinetics; whereas NFAT4 shows fast nuclear localization, NFAT1 nuclear translocation is slow17. Here we show that NFAT1 requires a robust CRAC-mediated Ca2+ signal for nuclear translocation. Even when supramaximal concentrations of GPCR agonist are used, only slight NFAT1 movement to the nucleus is detected and this translocation is robust only when maximal activation of CRAC channels is achieved through pharmacological depletion of ER stores with thapsigargin. However, NFAT4 migration to the nucleus is fast and is readily observed when low physiological concentrations of GPCR agonist are used to stimulate cells. Previous studies showed that different frequencies of oscillatory Ca2+ signals activate different families of Ca2+-sensitive transcription factors51. Here we show that an isoform of the same family of transcription factors is differentially activated depending on the extent of ORAI1 CDI. NFAT1 nuclear translocation, which requires robust Ca2+ signals is supported equally by both ORAI1 and ORAI1β. However, NFAT4, which is activated by low physiological concentrations of GPCR agonizts, is preferentially activated by ORAI1β. Previous studies showed that unlike NFAT2 and NFAT4 isoforms, which regulate physiological functions such as cell proliferation and secretion, NFAT1 is pro-apototic and its activation causes cell cycle arrest and death52. Therefore, the implication from our data is that CDI is not merely a means to curtail pathophysiological Ca2+ overload-mediated cellular toxicity under conditions of supramaximal stimulation, but rather CDI acts as a modulator of signaling and transcription factor activation that enhances the diversity of Ca2+ signaling pathways under physiological conditions. Thus we show that CDI differentially regulate transcription factor activation.The major transcription factors activated by CaAC8 expression is elevated in cells of neuro-epithelial lineage such as HEK293 cells. Whether ORAI1 CDI is more prominent in these primary cell types to mediate specific physiological functions requires future clarifications. Based on our work, it is reasonable to propose that the relative proportions of ORAI1 versus ORAI1β and the abundance of AC8 in a given cell type alters cellular signaling to fine-tune signaling and transcription. The distinction between ORAI1 and ORAI1β and their relative proportions compared with each other and to AC8 in primary cell types and their impact on cell signaling, transcription and physiological function awaits investigation.2 at 37 °C. Six days after puromycin selection, cells were collected and seeded at one cell per well into 96-well plates. Disruption of the ORAI1 gene in individual colonies was detected using the Guide-it Mutation Detection Kit  and confirmed by sequencing, as well as western blotting and functional Ca2+ imaging assays. The ORAI1 knockout HEK293 cell line generated was named ORAI1-KO HEK293. The ORAI1 guide RNA used in this study was: 5′-GTTGCTCACCGCCTCGATGT-3′.ORAI1 sequence-specific guide RNAs were inserted into the lentiCRISPR v2 vector  with the BsmBI restriction site to create a gRNA-Cas9-encoding plasmid. The complete list of primers used is included in Supplementary Table The procedures of generating STIM1 knockout, STIM2 knockout, and STIM1/2 double knockout HEK293 cell lines are similar to those for ORAI1-KO HEK293 cells, except for guide RNAs. Guide RNA for STIM1: 5′-TGATGAGCTTATCCTCACCA-3′. Guide RNA for STIM2: 5′-AGATGGTGGAATTGAAGTAG-3′.Orai1 Kozak sequence upstream of the first ATG start site (TGCTCCATG) with a stronger Kozak sequence (GCCACCATG), thus forcing the translational machinery to exclusively start at the first ATG. For a vector exclusively expressing ORAI1β, the first ATG start site of ORAI1-CFP cDNA was mutated to GCG , leaving only the start site at methionine-6413  or Nucleofector II Device (Amaxa Biosystems) and placed onto 30-mm round glass coverslips (#1.5 thickness) in 6-well plates. For patch clamp recordings, 4.0 µg of a plasmid encoding eYFP-STIM1 and 1.0 µg of a plasmid encoding either ORAI1-CFP or ORAI1β-CFP were used. For siRNA against AC8 experiments, siRNA oligonucleotides, and non-targeting control siRNA were transfected at a final concentration of 300 nM. For siRNAs against PPP3R1 and PPP3R2 experiments, siRNAs oligonucleotides, and non-targeting control siRNA were transfected at a final concentration of 250 nM. Cells were used 24–48 h post transfection. The siRNA used throughout the study are reported in Supplementary Table To generate a vector exclusively expressing long ORAI1, we used a wild-type ORAI1-CFP cDNA clone in which we exchanged the relatively weak native 36. To reliably obtain large CRAC currents, cDNA plasmids encoding STIM1, ORAI1, and ORAI1β were driven by the robust cytomegalovirus (CMV) promoter and CRAC channel recordings were performed 24–48 h post transfection. HEK293 cells were seeded onto glass coverslips in the 6-well plates 24 h before electrophysiological recordings. We used fluorescence microscope to identify transfected cells with sufficient intensity of fluorescence of tagged protein. Traditional patch clamp electrophysiological recordings were carried out using an Axopatch 200B and Digidata 1440A (Molecular Devices) as previously published54. Pipettes were pulled from borosilicate glass capillaries (World Precision Instruments) with a P-1000 Flaming/Brown micropipette puller (Sutter Instrument Company) and polished using DMF1000 (World Precision Instruments). Resistances of filled pipettes were 1.5–4 МΩ. Under whole-cell configuration, only cells with <8 МΩ series resistances and tight seals (> 8 GΩ) were chosen to perform recordings. Clampfit 10.3 software was used for data analysis. Bath solution: 115 mM Na-methanesulfonate, 10 mM CsCl, 1.2 mM MgSO4, 10 mM Hepes, 20 mM CaCl2, and 10 mM glucose (pH 7.4 with NaOH). Pipette solution: 135 mM Cs-methanesulfonate, 10 mM EGTA, 8 mM MgCl2, and 10 mM Hepes (pH 7.2 with CsOH). Immediately after break-in and before CRAC currents have developed, we ran a stimulus protocol consisting of families of 150 ms voltage steps . We allowed 2 s interval between steps. At the beginning of each pulse, a 2.5 ms voltage step to 0 mV was used to eliminate residual capacitative artifacts from cell capacitance ; I is current, I0 is steady-state current, A1 and A2 are amplitudes, and τ1 (fast) and τ2 (slow) are time constants of inactivation. Supplementary Table We used ORAI1-KO cells transfected with 4.0 µg of a plasmid encoding eYFP-STIM1 and 1.0 µg of a plasmid encoding either ORAI1-CFP or ORAI1β-CFP. This 4:1 STIM1/ORAI1 transfection ratio was used throughout the study to ensure that STIM1 proteins are not limiting and maximal CDI is attained2+ oscillations, we used the lower affinity indicator Fura-5F [Kd = 400 nM] to better resolve oscillations and to ensure that the data are well below dye saturation; most of the ratios were < 2.0, well below the average observed Rmax of 5.6. Glass coverslips with attached HEK293 cells were mounted in a Teflon chamber and incubated in DMEM with 4 µM Fura-5F-AM at 37 °C incubator for 40 min. After incubation, cells were immediately rinsed three times with a Hepes-buffered salt solution  and incubated at room temperature for 10 min prior to imaging. Then glass coverslips with attached HEK293 cells were mounted onto a Nikon TS100 inverted microscope equipped with a ×20 Fluor objective , and fluorescence images were recorded and analyzed with a digital fluorescence imaging system  equipped with a light-sensitive charge-coupled device camera . Fura-5F fluorescence was monitored by alternately exciting the dye at 340 and 380 nm and collecting the emission at 510 nm. Changes in intracellular calcium are represented as the ratio of Fura-5F fluorescence due to excitation at 340 nm to that due to excitation at 380 nm . The ratio changes in fields of Fura-5F-loaded cells were collected from multiple regions of interest (ROIs), with each ROI representing an individual cell. Typically, 10–30 ROIs were monitored per experiment.For Ca2+ measurements were performed as above with few minor changes. Coverslips with attached cells were mounted in a Teflon chamber and incubated at 37 °C for 1 h in culture medium (DMEM with 10% FBS) containing 2 μM Fura2-AM . Cells were then washed and bathed in Hepes-buffered saline solution (in mM): 140 NaCl, 1.13 MgCl2, 4.7 KCl, 2 CaCl2, 10 D-glucose, and 10 HEPES, with pH adjusted to 7.4 with NaOH for 10 min before Ca2+ was measured. Fluorescence images of several cells were recorded and analyzed with a digital fluorescence imaging system . Fura2 fluorescence at an emission wavelength of 510 nm was induced by excitation of Fura 2 alternately at 340 and 380 nm. The ratio of fluorescence at 340 nm to that at 380 nm was obtained on a pixel-by-pixel basis. All experiments were performed at room temperature.CaFRET signals were measured between transiently expressed N-terminally tagged YFP-ORAI1/β and CFP-AC8/AKAP, or between C-terminally tagged ORAI1/β-CFP and STIM1-YFP using a Leica DMI 6000B inverted automated fluorescence microscope equipped with CFP (438Ex/483Em), YFP (500Ex/542Em), and FRET (438Ex/542Em) filter cubes. For ORAI1/β and STIM1 constructs, which show high levels of expression and relatively fast interactions on store depletion, FRET images were captured every 20 s for a total of 10 min. For experiments measuring FRET interactions between ORAI1/β and AC8/AKAP, AC8, and AKAP constructs show low expression levels and only constitutive interactions with ORAI1/β. For assessing potential long-term interactions between ORAI1/β and AC8/AKAP induced by agonist stimulation, experiments were performed for a total of 40 min. As such, images were captured every 10 min to minimize potential photobleaching.55 using the formula: Eapp = Fc/(FC + G × IDD), where G is the instrument specific constant, which was determined by measuring the CFP fluorescence increase after YFP acceptor photo-bleaching using HEK293 cells transiently transfected with eYFP-eCFP calibrator construct. The value of G was calculated and is 1.9 ± 0.1. For all E-FRET summary data, the regions of interest were close to the plasma membrane, and only cells with similar YFP/CFP ratios were used for E-FRET analysis. For quantification of co-localization between AC8 (or AC8M1 mutant) and ORAI1, ROIs were drawn on the perimeter of cells and the Pearson’s correlation coefficient was calculated using Image J software and the Coloc 2 plugin.Three sets of images  were collected at room temperature using a ×40 oil objective  at each time point and processed using SlideBook 6.0 software (Intelligent Imaging Innovations). Exposure times for the CFP, YFP, and FRET channels were 1000 ms. Three-channel corrected FRET was calculated using the formula: FC = IDA − Fd/Dd × IDD − Fa/Da × IAA. IDD, IAA, and IDA represent the intensity of the background-subtracted CFP, YFP, and FRET images, respectively. FC is equal to the corrected energy transfer. Fd/Dd is the experimentally measured bleed-through of CFP through the FRET filter (0.50), and Fa/Da is the experimentally measured bleed-through of YFP through the FRET filter (0.025). The E-FRET method was used to analyze 3-cube FRET images as described by ref. Cells were washed with chilled PBS and lysed in 400 µl RIPA buffer (Sigma-R0278) containing cocktail of protease inhibitors (Invitrogen-A32975) and phosphatase inhibitors (Thermo-88667). To probe for PKA-mediated ORAI1 phosphorylation, cells were incubated with or without 10 µM Forskolin (Sigma-F6886) for 15 min at room temperature before being scraped and lysed in chilled RIPA buffer. Co-immunoprecipitation was performed using Dynabeads co-immunoprecipitation kit (Invitrogen-14321D). According to the manufacturer protocol, the beads were coated with mouse anti-GFP antibody (R and D Systems-MAB42401) to immunoprecipitate eGFP-ORAI1. Equal amount of protein was added to the antibody-coated beads and manufacturer protocol was followed to elute the protein complex. Each step was performed at 4 °C. The eluted protein was resolved in 4–12% gel  and analyzed using western blotting with anti-ORAI1  or anti-phosphoserine . A similar protocol was used for pull-down assays between untagged AC8 and either ORAI1-CFP or ORAIβ-CFP. Beads were coated with mouse anti-GFP antibody to immunoprecipitate eGFP-ORAI1, followed by elution of the protein complex and western blotting with anti-ORAI1 or anti-AC8 antibodies.Cells were washed with chilled PBS and lysed in 100 µl RIPA buffer (Sigma-R0278) containing cocktail of protease inhibitors (Invitrogen-A32975). 20 µg of protein extract was loaded on a 4–12% gel  and transferred to a polyvinylidene difluoride membrane. The membrane was incubated in 0.1% TBST with 5% non-fat dry milk (Bio-Rad-1706404) for 2 h at room temperature. For probing with anti-phosphoserine antibody, the membrane was incubated for 2 h at room temperature in 0.05% TBST with 5% BSA (VWR-97061416). Then the blot was incubated overnight at 4 °C with primary anti-phosphoserine antibody. Primary antibodies were used at the following dilutions: rabbit-anti-ORAI1 (Sigma-O8264) 1:4000; rabbit-anti-Phosphoserine (Millipore-AB1603) 1:1000, rabbit-anti-ADCY8  1:4000, and mouse anti-GFP (R and D Systems-MAB42401). The membrane was washed with 0.1% TBST for 10 min (three times) at room temperature and followed by incubation in secondary antibody for 2 h at room temperature. Secondary antibodies: HRP-conjugated anti-Mouse IgG (Invitrogen A27025), dilution 1:4000; and HRP-conjugated anti-rabbit (Jackson Immuno Research-323005021), dilution 1:5000. The membrane was washed with 0.1% TBST for 10 min (three times) at room temperature. Then the peroxidase activity was detected by ECL prime western blotting detection regent . Densitometry analysis of the bands on membranes was performed using ImageJ.6 cells were transfected with 1.0 µg of NFAT-GFP plasmids and 0.5 µg of TK-ORAI1/β plasmids using Lipofectamine 2000. Fluorescence imaging was performed on a Leica DiM8 confocal microscope with a ×40 objective. After baseline recordings, carbachol (Cch) or thapsigargin (TG) were added at 1 min. A ratio of nuclear NFAT-GFP to total NFAT-GFP was calculated on a cell by cell basis.For NFAT-GFP nuclear translocation experiments, 1.5 × 10t method. Experimental samples were normalized to the reference gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH). All data were generated in technical and biological triplicates to ensure reproducibility.RT-qPCR was performed following established methods. Cells were first homogenized in TRIzol (Invitrogen) and total RNA isolated according to the manufacturer protocol. Total RNA was then quantified using a nanodrop 2000 spectrophotometer (Thermo Scientific). A volume corresponding to 1 µg of total RNA was reverse transcribed into cDNA using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Total cDNA (6 µl) was then added to SYBR Green qPCR Master Mix (Applied Biosystems), and the corresponding primers added resulting in a 20 µl reaction. Samples were then loaded into a 96-well plate and data from the resulting reaction was collected and quantified using the QuantStudio 3 real-time PCR system (Applied Biosystems). The PCR protocol for all reactions began with an initial activation of 2 min at 50 °C step followed by a 95 °C 2-min melt step. The initial steps were followed by 40 cycles that began at 95 °C for 15 s followed by 54.3 °C for 15 s and 72 °C for 30 s. After completion of 40 cycles, a standard melt curve was generated to ensure primer specificity. All primer sequences used are provided within supplementary table 2+ imaging assays. SlideBook 6.0 software was used for quantifying FRET fluorescence signals. LAS X Life Science Microscope Software was used for quantifying NFAT1-GFP and NFAT4-GFP nuclear translocation.ImageJ software was used for quantifying protein levels from western blots. InCytim2 V5.35 software was used for quantifying fluorescence signals from Cat-test with Origin 9.0 software. Statistical analyses comparing more than two experimental groups were performed using one-way ANOVA with Origin 9.0 software. See Supplementary Table p-values of < 0.05, < 0.01, and < 0.001, respectively. Differences were considered significant when p < 0.05.All data are expressed as mean ± SEM. Statistical analyses comparing two experimental groups were performed using two-sample Further information on experimental design is available in the Supplementary InformationReporting SummaryPeer Review FileSource Data


Increases of [Ca2+]i in GBM cells often result from Ca2+ release from the endoplasmic reticulum (ER), promoted by a variety of agents present in the tumor microenvironment and able to activate the phospholipase C/inositol 1,4,5-trisphosphate PLC/IP3 pathway. The Ca2+ signaling is further strengthened by the Ca2+ influx from the extracellular space through Ca2+ release-activated Ca2+ (CRAC) currents sustained by Orai/STIM channels, meant to replenish the partially depleted ER. Notably, the elevated cytosolic [Ca2+]i activates the intermediate conductance Ca2+-activated K (KCa3.1) channels highly expressed in the plasma membrane of GBM cells, and the resulting K+ efflux hyperpolarizes the cell membrane. This translates to an enhancement of Ca2+ entry through Orai/STIM channels as a result of the increased electromotive (driving) force on Ca2+ influx, ending with the establishment of a recurrent cycle reinforcing the Ca2+ signal. Ca2+ signaling in migrating GBM cells often emerges in the form of intracellular Ca2+ oscillations, instrumental to promote key processes in the migratory cycle. This has suggested that KCa3.1 channels may promote GBM cell migration by inducing or modulating the shape of Ca2+ oscillations. In accordance, we recently built a theoretical model of Ca2+ oscillations incorporating the KCa3.1 channel-dependent dynamics of the membrane potential, and found that the KCa3.1 channel activity could significantly affect the IP3 driven Ca2+ oscillations. Here we review our new theoretical model of Ca2+ oscillations in GBM, upgraded in the light of better knowledge of the KCa3.1 channel kinetics and Ca2+ sensitivity, the dynamics of the Orai/STIM channel modulation, the migration and invasion mechanisms of GBM cells, and their regulation by Ca2+ signals.Cell migration and invasion in glioblastoma (GBM), the most lethal form of primary brain tumors, are critically dependent on Ca The large majority (more than 90%) of cancer deaths are due not to the primary tumor per se, but to relapses arising from new foci established in distant organs via metastasis . GlioblaIDH1/2) mutations as one of the most critical biomarkers. Accordingly, IDH1/2 wildtype gliomas are categorized as glioblastoma (formerly primary glioblastoma) and IDH1/2-mutated gliomas (including formerly classified secondary glioblastoma) as astrocytic glioma and oligodendroglioma. GBMs are further subdivided into four groups , mainly based on the abnormally high levels of mutated genes , which is most frequently mutated in Proneural GBM (50–60% of patients) is rarely mutated in Classical GBM). In spite of the intensive basic and clinical studies carried out over the past decades, and modern diagnostics and treatments, the average life expectancy for GBM patients is still only around 15 months. The major obstacle with GBM remains its high migratory and invasive potential into healthy brain parenchyma, which prevents complete surgical removal of tumor cells. Even with full clinical treatment (temozolomide-based chemotherapy and radiation therapy), tumors normally recur at some distance from the site of resection, establishing new tumor lesions that are by far the primary cause of mortality in GBM patients. Arguably, at the time of surgery, large numbers of cells have already detached from the original tumor mass and invaded normal brain tissue. Although GBM cell migration and invasion have been deeply investigated, many aspects of these processes are still poorly understood.The 2016 World Health Organization classification of the various types of brain tumors regards the presence of isocitrate dehydrogenase gene  disconnection, and acquiring a highly migratory phenotype, which is a critical feature of the invasive process. The basic mechanisms underlying migration of GBM cells are common to most types of migratory cells. Migration is a property of many non-tumor cells, although it is often restricted to specific developmental stages or environmental conditions; the migration of tumor cells could be viewed as the result of mutation-induced dysregulation of specific biochemical pathways that in healthy tissue keep cell migration dormant.+/K+/2Cl− cotransport accompanied by isoosmotically obliged water, and actin polymerization, with formation of pseudopods; (ii) retraction of the rear cell body in the direction of motion, due to forces produced by actomyosin contraction, accompanied by loss of cell volume generated by passive ion (mainly K+ and Cl−) fluxes and osmotic water  in the cytosol via IP3 receptor causes a positive feedback activation of the IP3 receptor that results in a higher Ca2+ released from internal stores  Ca2+ oscillations, external Ca2+ pumped out of the cell by PMCAs must be allowed to re-enter the cell. This is most commonly accomplished through the mechanism of store-operated Ca2+ entry (SOCE).The second phase occurs when the cytosolic i, while the other group includes both the intermediate conductance KCa3.1 and small-conductance KCa2.1-3 channels, gated solely by cytosolic Ca2+ increase.The intermediate conductance Ca+ selectivity, moderate inward rectification, and single-channel conductance of 20–60 pS in symmetrical 150 mM K+. The KCa3.1 channel is voltage-insensitive, but highly sensitive to [Ca2+]i showing an EC50 of <200 nM. Ca2+ ions activate the KCa3.1 channel via the Ca2+-binding protein CaM, which is constitutively bound to the membrane-proximal region of the intracellular C terminus of the channel. Binding of Ca2+ to CaM results in conformational changes of the channel, and its opening. In addition to serving as a Ca2+ sensor for channel opening, CaM also regulates the assembly and trafficking of the channel protein to the plasma membrane i, consisting of a fast peak due to IP3-dependent Ca2+ release from intracellular stores followed by a sustained phase of Ca2+ influx across the plasma membrane, supposedly activated by intracellular stores’ depletion, was found upon prolonged application of histamine on GL-15 GBM cells [2+ was shown by the marked reduction of the sustained histamine-induced [Ca2+]i increase following application of TRAM-34. This observation could be significant with regard to the KCa3.1 channels’ contribution to GBM cell migration exerted through modulation of Ca2+ signals.Evidence of cross-talk between the KCa3.1 channel and store depletion-activated CaBM cells . The enh2+ oscillations, and these oscillations cyclically activate the KCa3.1 channels during cell migration i is relatively low  protrusion of the cell front, associated to asymmetric polymerization of the actin-based cytoskeleton; (ii) adhesion of the protruded cell front to the substratum, mediated by the binding of focal adhesions to the extracellular polymers in contact with the cell; (iii) unbinding of the focal adhesions at the cell rear; (iv) retraction of the rear cell body in the direction of motion cf. . Two of + ions . Following the increase in [Ca2+]i towards the peak of the Ca2+ oscillation (Cl and EK, a condition that would allow a significant efflux of KCl with consequent loss of water and cell volume decrease (By contrast, the control of osmolarity during cell rear retraction critically depends on the activation of KCa3.1 channels and ensuing efflux of Killation c,d, KCa3decrease C,D. This2+ oscillations may have both direct and indirect modulatory effects on GBM cell migration. As has long been recognized, the K+ efflux through KCa3.1 channels directly participates in the cell volume changes during cell rear retraction. The same oscillatory K+ efflux, however, will also determine the membrane potential oscillations that alter the driving force for Ca2+ influx and indirectly modulate the properties and even the presence of Ca2+ oscillations, and thus the timing of the migratory machinery. Both modulatory roles make KCa3.1 channels pivotal in GBM cell migration, and thus potential pharmacological targets for this deadly tumor. Although this review concentrates on the role of KCa3.1 and STIM/Orai channels in modulating Ca2+ oscillations and cell migration, it needs to be said that other channels including TRPM8 and KCa1.1, also abundantly expressed in glioblastoma cells, may also be important in these processes. Accordingly, TRPM8 [2+ oscillations.Substantial evidence suggests that the oscillatory activity of the KCa3.1 channel during Cay, TRPM8  and KCa1y, TRPM8  have bee


Chronic urticaria is a disease characterized by the appearance of weals, angioedema or both longer than 6 weeks. Degranulation of cutaneous or submucosal mast cells leads to release of mediators including histamine resulting in redness, swelling and itch. Because mast cells are widely distributed throughout the body, the question is why they are not activated systemically or does systemic activation occur without overt end organ dysfunction? We have conducted an exploratory literature search for reports that have evidence of organ-specific dysfunction in chronic urticaria that might justify prospective observational studies. This search revealed some evidence of systemic effects of chronic urticaria in cardiac, respiratory, gastrointestinal, central nervous and musculo-skeletal systems. The relevance of these findings needs to be further determined. However, they justify prospective studies in larger numbers of patients and at different stages of disease activity. Chronic urticaria (CU) is a disease characterized by the appearance of weals, angioedema or both in which mast cells have a central role . DegranuSome chronic spontaneous urticaria (CSU) patients have gastrointestinal symptoms, flushing, joint pain or swelling, palpitations, headache/fatigue and wheezing during active urticarial flares suggesting systemic as well as cutaneous mast cell activation . In an eWe have conducted an exploratory literature search using PubMed and Google Scholar before May 2019 for reports that have evidence of organ-specific dysfunction in CU (spontaneous and inducible) that might justify prospective observational studies. Because the terminology of CU has historically been inconsistent we have included the specific terminology used in each report, accepting that chronic idiopathic urticaria (CIU), chronic spontaneous urticaria (CSU) and even CU itself relate to the same patient population unless specific subtypes of inducible urticaria have been specified. We performed a literature search strategy for evidence including every organ system of the body  and pancreolauryl tests [Histamine is found in high concentrations in the healthy heart and in very high concentrations in the coronary arteries of patients known to have died from coronary heart disease , 27. GinThe concurrence of acute coronary syndromes with conditions associated with mast cell degranulation has been termed Kounis syndrome (KS) after his description in 1991 . Three tChang et al. reported that hypertension was associated with CIU. Their patient cohort had a 1.37-fold greater risk of developing subsequent hypertension than controls. They attributed this association to alterations in the blood coagulation and fibrinolysis pathways as well as systemic inflammation associated with CIU . In anotThe search for “urticaria and brain” revealed a case of cholinergic urticaria with seizures. Harada et al. reported a 10-year-old boy with cholinergic urticaria associated with epileptic seizures and abnormalities on electroencephalogram. They proposed that sweat-promoting stimuli, such as heat, exercise and tension stimulate the autonomic centre in the diencephalon or brain stem, and that excitation of the autonomic centre is transmitted to the efferent sympathetic nerves, causing cholinergic urticaria. When the intensity of stimulation is high, the autonomic centre exhibits abnormal activities and causes epileptic seizures . Fumal eWang et al. investigated the functional and structural alterations of the striatum in chronic spontaneous urticaria (CSU). They performed amplitude of low frequency fluctuations (ALFF), voxel-based morphometry (VBM), and seed-based resting-state functional connectivity (rs-FC) analysis on 40 CSU patients and 40 healthy controls to assess brain activity and related plasticity. They found that CSU patients had higher ALFF values in the right ventral striatum/putamen, which were positively associated with clinical symptoms as measured by UAS7; a higher gray matter volume in the right ventral striatum and putamen; and decreased rs-FC between the right ventral striatum and the right occipital cortex and between the right putamen and the left precentral gyrus. In summary, by using multiple-modality brain imaging tools, they demonstrated dysfunction of the striatum in CSU. The authors concluded that CSU may be associated with disrupted reward, motivation, and motor processing . These il-thyroxine. Both urticaria and arthralgia responded to treatment [A paper from 1989 reported four HLA-B51 positive patients with simultaneous urticarial and articular manifestations but without evidence of immune complex disease. The authors suggested that a common genetic background might be present in some cases of urticaria with articular complaints . McNeil reatment .Recently, an association between CU and osteoporosis was reported . In thisWe found no reports of nephritis, proteinuria or haematuria associated with chronic urticarial activity. However, interference with micturition due to delayed pressure urticaria swelling after sexual intercourse has been reported .Systemic consequences of urticaria could result from increased circulation of cutaneous mast cell mediators, generalized enhancement of releasability of mast cells, a reactive increase in mast cell numbers , increase in plasma histamine level or all of these. It might be expected that any systemic effect would be most obvious when the disease is most active. The natural history of CU is one of natural improvement followed by resolution so looking for evidence of dysfunction would be logical during the acute stage of the illness or during acute urticaria. Patients presenting acutely unwell with severe urticaria are often treated with short courses of systemic steroids that are likely to mask subclinical effects of the illness.Searching for reports of organ-based dysfunction in patients with urticaria revealed some evidence in cardiac, respiratory, gastrointestinal, central nervous and musculo-skeletal systems Fig. . The relThe possibility that relevant reports were missed in the literature search by using incomplete search terms.The anecdotal reports may have been co-incidental rather than directly related to urticaria.It was not possible to assess the effects of urticaria treatment on potential improvement of any organ-based dysfunction without it.Limitations of this review include:


Chromosomal abnormalities are diagnostic and prognostic key factors in acute myeloid leukemia (AML) patients, as they play a central role for risk stratification algorithms. High hyperdiploidy (HH), a rare cytogenetic abnormality seen commonly in elder male AML patients, is normally categorized under AML with complex karyotype (CK). Accordingly, patients with HH generally are associated with low remission rates and a short overall survival.EVI1/PRDM16 gene fusion, and monoallelic loss of tumor suppressor gene TP53. After successful chemotherapeutic treatment the patient experienced a relapse to AML-M1, and she developed secondary AML-M6 with tetraploidy and HH. Unfortunately, the young woman died 8.5 months after initial diagnosis.Here we report a case of 21-year-old female, diagnosed with a de novo AML-M1 according to WHO classification and a CK at diagnosis. Cytogenetic, molecular cytogenetic approaches (standard fluorescence in situ hybridization (FISH), array-proven multicolor banding (aMCB)) and high resolution array comparative genomic hybridization (aCGH) analyses revealed a unique complex but still near diploid karyotype involving eleven chromosomes was identified. It included pentasomy 4, three yet unreported chromosomal aberrations t, t, and t, and a combination of two cytogenetic events, yet unreported to appear in together, i.e. a reciprocal translocation t leading to TP53 at diagnosis, and HH in secondary AML were not previously reported. Thus, the combination of the here seen chromosomal aberrations in adult primary AML seems to indicate for an adverse prognosis.To the best of our knowledge, a comparable adult AML associated with such a CK, coexistence of 3q rearrangements with loss of  It is well established, that acquired chromosomal rearrangements play a central role in risk stratification of the disease –4. AccorApproximately 10 to 15% of AML patients had a CK , 4, 6, 7MLL translocations) can confidently be assigned to the adverse risk group, whereas those with numerical changes only, should be classified into the intermediate risk group , anemia (Hgb 9.6 g/dl) and thrombocytopenia (Plt 17 × 109/l). Now the patient stopped the treatment on her own request for 1 month. Afterwards she suffered from fever (more than 40 °C for more than 3 days), menorrhagia and blurred vision in the right eye. Approximately 8.5 months after initial diagnosis she died in her house and no autopsy was performed. Her husband agreed with scientific evaluation of her case and the study was approved by the ethical committee of the Atomic Energy Commission, Damascus, Syria.Ten days later, the patient relapsed; her PB shows cytopenia [WBC 1.5 × 10Conventional cytogenetics analysis on unstimulated BM sample according to standard procedures was performed  prior anETV6 break apart probe and a specific probe for 17p13 (TP53)  were applied according to manufacturer’s instructions. Array-proven multicolor banding (aMCB) probes sets for chromosomes 1, 2, 3, 8, 10, 11, 12 and 17 were used [48–50,X,- X,der(1)t,der(1)t1;3),+ 4,+ 4,+ 4,+ 6,t,t10;12),dic× 2  ,+ 4,dic48–50,X,-X,der(1)t,der(1)t,der(2)(:1p36.21- > 1p35::2p22- > 2qter),+ 4,+ 4,+ 4,+ 6,der(8)t,der(10)t,del(10)(q21q21),dic,del(15)q14q14),del(15)(q21.1q21.1),del(15)(q22.32q24)del(17)(q12q12) [q14,del; CNAs involving variable numbers of genes (e.g. deletion on 17q21.3); CNAs involving large parts of chromosomal p or q arms (e.g. duplication of 3q26.1q29) and CNAs of whole chromosomes  was positive for CD45To the best of our knowledge we report here the first case of a patient with an AML-M1 relapsing with a secondary AML-M6. In AML-M1 the patient presented a CK involving eleven chromosomes and yet unreported acquired chromosomal aberrations, while in AML-M6 a completely different, two-clonal karyotype with tetraploidy and HH was observed.According to the literature, HH (≥49 chromosomes) and tetraploidy (4n = 92 chromosomes) has been reported to date in 15 and 99, respectively, of 18,334 AML cases listed in Mitelman database [Gains of chromosomes, in particular tetrasomies 4, 8, 13, 14, 20 and 21, as well as pentasomies 13, 21 and 22, have been observed in AML rarely. However, there was no influence on survival observed according to the number or types of trisomies or tetrasomies . Also, tIn general, HH and tetraploidy appears infrequently in AML; it seen primarily in de novo disease in older male patients > 60 years) with low remission rates and short overall survival (OS) , 10. Unf0 years wTP53, ETV6, BRCA1 genes and or gain of copy numbers for EVI1  gene. TP53 gene mutation is observed in approximately 5–10% of all AML cases, occurring frequently in elderly subjects and cases with FAB classification M6, as well as in cases with CK; it is associated with unfavorable prognosis [EVI1 gene occurs in approximately 6–8% of AML cases and has been associated with poor treatment outcome [EVI1 gene maps to chromosomal band 3q26.2 and was first identified to be aberrantly upregulated in almost all AML cases with t [EVI1 locus at 3q26 is translocated to PRDM16  at 1p36, being highly homologous to EVI1 (PRDM3) [Concerning aberrations observed in the present case there was specifically in AML-M1 monoallelic losses for rognosis . Aberran outcome , 23. The1;q26.2)  or inv. , 25. In TP53 deletion and translocation t involving EVI1 gene, but also by HH. ICE therapy might have been helpful here, however, due to interrupted treatment this cannot be finally assessed.According to the literature the here observed, we report the first AML-M1 case relapsing to a completely independent biclonal secondary AML-M6 case. Adverse outcome of the case may be partially caused by adverse mutations in AML-M1 like


In the context of precision medicine, assessment of minimal residual disease (MRD) has been used in acute myeloid leukemia (AML) to direct individual treatment programs, including allogeneic stem cell transplantation in patients at high-risk of relapse. One of the limits of this approach has been in the past the paucity of AML markers suitable for MRD assessment. Recently, the number of biomarkers has increased, due to the identification of highly specific leukemia-associated immunophenotypes by multicolor flow-cytometry, and of rare mutated gene sequences by digital droplet PCR, or next-generation sequencing (NGS). In addition, NGS allowed unraveling of clonal heterogeneity, present in AML at initial diagnosis or developing during treatment, which influences reliability of specific biomarkers, that may be unstable during the disease course. The technological advances have increased the application of MRD-based strategies to a significantly higher number of AML patients, and the information deriving from MRD assessment has been used to design individual post-remission protocols and pre-emptive treatments in patients with sub-clinical relapse. This led to the definition of MRD-negative complete remission as outcome definition in the recently published European Leukemianet MRD guidelines. In this review, we summarized the principles of modern technologies and their clinical applications for MRD detection in AML patients, according to the specific leukemic markers. Acute myeloid leukemia (AML) is a heterogeneous group of diseases with variable response to therapy, due to intrinsic genetic complexity, present at diagnosis, and/or developing during disease evolution , 2. For FLT3-ITD allelic burden, defined by the ITD allelic ratio (AR), which has been associated to the risk of relapse (NPM1-mutated AML with FLT3-ITD AR < 0.5 (FLT3-ITDlow) are classified as prognostically favorable, similar to NPM1-mutated/FLT3 wild-type AML (FLT3-ITD mutated AML are classified as “favorable” (NPM1 wild-type AML with FLT3-ITD AR >0.5 (FLT3-ITDhigh) are classified as “adverse-risk,” while AML NPM1-mutated/FLT3-ITDhigh are considered “intermediate-risk”  in first remission as part of frontline therapy , 11, whiRecent technological advances have contributed to increase the list of AML mutations associated to adverse prognosis. In particular, in the past decade, the advent of Next Generation Sequencing (NGS) technologies led to the identification of novel molecular abnormalities that shed new light into the pathogenesis and prognosis of the disease, and have been therefore, incorporated in the revised ELN guidelines , 15, 16.10 to 1012 may still persist in a normal adult , some studies have demonstrated the prognostic value of MRD detection and quantification after induction therapy  is becoming an important tool for the molecular dissection of AML at the time of initial diagnosis, especially in cytogenetically normal AML, which is characterized by high clonal heterogeneity . Indeed,DNMT3A, ASXL1, and TET2  and OS  and OS . RUNX1-Mdary AML , 61, 62.Conventionally, although NGS-based MRD measurements could be applicable in all AML patients, its current error rates set the sensitivity level at about 1% . MoreoveIn the clinical practice and research, MRD evaluation requires the ability to identify disease related mutations present at very low frequencies. Digital droplet PCR (ddPCR) is a recently introduced molecular assay with great potential for MRD monitoring, due to its high sensitivity and specificity. It is a high-throughput technology that, unlike conventional RT-qPCR, produces an absolute quantification, by amplifying target genes without a reference standard curve , 67. IndNPM1 are suitable for sensitive ddPCR testing. Actually, NPM1-mutated monitoring is sometimes troublesome due to the presence of several frameshift insertions and lack of information on the mutated sequence at diagnosis. In fact, MRD analysis in NPM1-mutated patients requires identification of the specific insertion by DNA sequencing to design the appropriate allele-specific RT-qPCR test , the rapidity, and the ability to distinguish viable cells from debris and dead cells –80. FurtThe detection of residual leukemic cells by MFC relies on the detection of antigens and/or physical abnormalities that are absent or infrequent in normal BM. Such aberrancies are mainly represented by cross-lineage expression, overexpression, reduced, absent, and asynchronous antigens expression and are defined as leukemia associated immunophenotypes or LAIPs. A complementary technique of analysis is represented by the “different from normal” (DfN) approach. In this approach, a standardized combination of antibodies is applied to all MRD analyses, regardless of LAIP, allowing the identification of events outside the maturation patterns of normal BM. The harmonization of these analytical strategies has been endorsed by the ELN group  received autologous-SCT or allo-SCT depending on the level of FCM-MRD, considering a threshold of 0.035%. OS and disease-free survival (DFS) at 24 months were superimposable (78.6 and 61.4% in MRD-positive and 69.8 and 66.6% in MRD-negative patients). These results suggest that whereas in intermediate risk patients, allo-SCT can be avoided if MRD is not detectable, in MRD positive ones allo-SCT can actually prolong OS and DFS up to those of the MRD-negative category.The value of MRD by MFC has been retrospectively validated as a biomarker of mCR quality with a remarkable impact on DFS, OS, and cumulative incidence of relapse (CIR) . Few pubA further explanation may be the presence of minority populations undetectable with current phenotypical approaches  that may survive chemotherapy and eventually lead to relapse. Both normal stem cells and LSC reside in the CD34+/CD38– cells and MFC can distinguish LSC by applying a multicolor analysis including a specific set of markers. When the presence of LSC, at diagnosis or during the treatment course, was determined and compared with MRD the presence of LSC represented a further negative prognosticator both in MRD-positive and, remarkably, in the MRD-negative populations of patients. This observation was confirmed both in a retrospective study, and prospectively in the context of HOVON/SAKK 102 study. Accordingly, ELN guidelines suggest the inclusion of MFC determination of residual LSC during treatment of AML, suggesting that a dedicated test tube should be developed to this purpose.The occurrence of relapse in a proportion of patients achieving a MRD-negative status, roughly ranging between 20 and 25%, still represents a major drawback of all MRD studies . This evPossible future developments in MRD detection by MFC will be accomplished by the use of high-throughput methods, e.g., next-generation flow cytometry (NGF). The potential advantage of these techniques is that they can process huge amounts of immunophenotypically stained cell (>107cells) in parallel, substantially increasing the level of sensitivity of the conventional 8–10-color MFC assays. Due to their technical features, these techniques may be prospectively repeated along the treatment course, providing an estimate of clonal evolution of the disease. This may prove useful in AML, whose over time intra-clonal variability may hamper the possibility of a real-time estimation of disease plasticity. NGF has been reported to show significant utility in the monitoring of tumor burden in the setting of multiple myelomaand acute lymphoblastic leukemia, suggesting that similar approaches may be exploited in AML.Another promising technique in leukemia diagnostics is mass-cytometry time-of-flight (CyTOF). In CyTOF, antibodies are labeled with transition elements instead of fluorochromes, allowing up to 130 different parameters to be combined in the evaluation of a single cell. Furthermore, since rare elements are absent in biological systems, this approach virtually eliminates the background noise, which commonly affects MFC assays . NowadayMinimal Residual Disease (MRD) detection has strong clinical relevance in AML and new technological advances have increased its sensitivity and specificity, making it an essential tool in the overall strategy adopted to treat AML. However, different methods have specific indications and require highly specialized expertises . WhateveDedicated to our dear mentor and friend Prof. Francesco Lo-Coco.MV, TO, and FB planned and wrote the manuscript. AV, SL, LM, and FL-C reviewed and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Mentored research apprenticeships are a common feature of academic outreach programs that aim to promote diversity in science fields. The current study tests for links between three forms of mentoring  and the degree to which undergraduates psychologically identify with science. Participants were 66 undergraduate-mentor dyads who worked together in a research apprenticeship. The undergraduate sample was predominantly composed of women, first-generation college students, and members of ethnic groups that are historically underrepresented in science.Findings illustrated that undergraduates who reported receiving more instrumental and socioemotional mentoring were higher in scientist identity. Further, mentors who reported engaging in higher levels of negative mentoring had undergraduates with lower scientist identity. Qualitative data from undergraduates’ mentors provided deeper insight into their motivation to become mentors and how they reason about conflict in their mentoring relationships.Discussion highlights theoretical implications and details several methodological recommendations. Mentoring relationships occur when someone more experienced  takes responsibility for guiding and supporting someone less experienced . It is arguably a truism to state that mentoring relationships can have important implications for protégés. Literature reviews and meta-analyses have linked mentoring to a variety of protégé outcomes that range from self-reported wellbeing to objective performance metrics  examining data from undergraduates and their research mentors and (2) focusing on both positive and negative aspects of mentoring relationships. This approach aligns with recommendations from Eby et al. , who empGiven the novelty of our research design, we advanced a combination of exploratory research questions and hypotheses. First, we examined whether undergraduates and their mentors are “on the same page” about the amount of mentoring that mentors provided. Specifically, our first research question is as follows:RQ1: Do undergraduates and their research mentors significantly differ in their perceptions of the amount of instrumental, socioemotional, and negative mentoring provided by the mentor?Next, we examined whether undergraduates’ scientist identity was associated with undergraduates’ and their mentors’ reports of instrumental, socioemotional, and negative mentoring. As noted earlier, mentor reports appear to contribute important information above and beyond student data . Most participants were juniors (29%) or seniors (58%); however, first-years (1%) and sophomores (12%) also participated. All participants reported that they were majoring in fields related to science. Consistent with the outreach organization’s mission of diversifying science fields, nearly all participants were members of groups that are historically underrepresented in science fields. Most participants (75%) identified as women, and over half (51%) were first-generation college students. Participants identified as members of the following ethnic groups: Mexican American, Chicana/o, Latina/o, or Puerto Rican (32%); European American (20%); American Indian, Native Hawaiian, or First Nations (6%); Asian American (9%); African American (3%); and Multiracial (30%). In total, 70% of the undergraduate sample identified with at least one ethnic group that is historically underrepresented in science fields; the remaining 30% identified as Asian American and/or European American.Undergraduates participated through an online survey. As detailed below, the current research focuses on their responses to scales assessing their scientist identity and the mentoring they received. The survey they completed also assessed constructs that were not the focus of the present study such as ethnic identity, cultural values, self-efficacy, and career aspirations.α = .92).We measured scientist identity with a scale that has been used in prior research with science-focused undergraduates .Participants used a scale ranging from 1 (definitely no) to 5 (definitely yes) to rate the extent to which their mentors engaged in various mentoring behaviors. We assured undergraduates that their responses would not be shared with their mentors. The After securing permission from undergraduates to contact their mentors, the research team sent each mentor a personalized email. The email provided a general overview of the current study. It also provided the name of the focal student who had listed her or him as an important science mentor. The email concluded by requesting that the mentor complete a brief survey that focuses on her or his relationship with the focal student. Mentors who were interested in participating used a link to access the informed consent form and online survey. As with the undergraduate participants, we assured mentors that their responses would not be shared with their students.A total of 66 mentors participated. Most of the mentors (80%) reported that they were faculty members; the remainder described themselves as program staff (14%) and graduate/postdoctoral students (6%). Mentors’ mean age was 45.51 years . Over half of the mentors (56%) identified as women. With respect to ethnic background, mentors identified as members of the following groups: European American (64%); Mexican American, Chicana/o, Latina/o, or Puerto Rican (17%); Asian American (4%); American Indian, Native Hawaiian, or First Nations (2%); African American (2%); and Multiracial (11%). In total, 37% of the mentor sample identified with at least one ethnic group that is historically underrepresented in science fields, whereas 63% identified as either Asian American or European American.Mentors participated through an online survey. As detailed below, the current research focuses on their responses to scales assessing their mentoring behaviors as well as open-ended questions pertaining to (a) their motivation to become a mentor and (b) their views of difficulties in the mentoring relationship. The survey that mentors completed included several closed- and open-ended questions that were not the focus of the present study. These questions focused on costs and rewards of mentoring as well as strategies that mentors use to bolster their students’ self-efficacy.α = .75; socioemotional: α = .85; negative: α = .73).We measured instrumental, socioemotional, and negative mentoring with the same scales that the undergraduate participants completed. More specifically, the survey prompt encouraged mentors to envision their relationship with the undergraduate who nominated them as an important mentor. With this student in mind, mentors used a scale ranging from 1 (definitely no) to 5 (definitely yes) to rate the extent to which they engaged in various mentoring behaviors. The wording for each item paralleled the items the undergraduate participants rated (see above). For instance, when rating their own instrumental and socioemotional mentoring, mentors respectively responded to items such as, “I helped this student to improve their writing skills” and “I showed this student that I cared.” Alternatively, when rating their own negative mentoring, mentors responded to items such as, “I sometimes ignored this student.” All three mentoring scales had adequate internal reliability  were resolved through discussion and consensus.We used thematic analysis  general support, in which mentors referenced the importance of providing research resources, science opportunities, or relational support to students broadly. The motivation coding category achieved high interrater reliability .We coded mentors’ motivation to become a mentor in two ways: (1) research underperformance, in which the protégé exercised poor preparation, organization, or time management with respect to research tasks; (2) communication problems, in which mentors explicitly named ineffective communication as a problem; and (3) personal challenges experienced by the protégé outside of the research domain that hindered doing research. In addition, some mentors reported (4) other idiosyncratic difficulties or (5) no difficulties. Second, we coded responses for difficulty responsibility. Mentors’ attributions of why difficulties occurred, and how they should be resolved, took two forms: (1) student centered, in which mentors blamed students for difficulties in the relationship, and (2) non-student centered, in which mentors blamed themselves, larger circumstantial issues, or normalized difficulties as common to the research process. The two difficulty coding categories achieved high interrater reliability .With respect to mentors’ perceptions of difficulty in the relationship, we first coded mentors’ responses for difficulty type. Mentors reported three specific types of difficulties with their students: (1) After describing preliminary analyses, we present our findings in the following three sections. First, we compare undergraduates’ and mentors’ prevalence ratings for instrumental, socioemotional, and negative mentoring. Second, we examine the degree to which undergraduates’ and mentors’ evaluations of the three forms of mentoring predict undergraduates’ scientist identity. Third, we draw from qualitative data to provide deeper insight into mentors’ motivations to become mentors and how mentors characterized difficult moments in the mentoring relationship.We began by examining bivariate correlations among scientist identity and ratings of instrumental, socioemotional, and negative mentoring. The correlation matrix and corresponding descriptive statistics are presented in Table underrepresented ethnic group  with undergraduates who solely identified as members of overrepresented ethnic groups . A main effects analysis of variance showed that scientist identity did not significantly differ on the basis of undergraduates’ ethnicity (p = .80) or gender (p = .64). Similarly, a main effects multivariate analysis of variance showed that ratings on the three mentoring scales did not significantly differ according to undergraduates’ ethnicity (p = .84) or gender (p = .88).We also tested for ethnic and gender differences in the mentoring scales and scientist identity. In testing for ethnic differences, we compared undergraduates who identified with at least one n = 11; 17% of dyads) were overrepresented in the sample, χ2  = 5.28, p = .02, V = .28. Relatedly, there was a tendency for undergraduates and their mentors to report the same gender identity: 69% of male undergraduates worked with a male mentor, and 64% of female undergraduates worked with a female mentor. In contrast, the chi-square assessing the ethnic composition of the dyads was nonsignificant (p = .94): undergraduates and mentors from under- and overrepresented ethnic groups worked together in proportion to their numbers in the overall sample. However, from a descriptive standpoint, it merits noting that the majority of undergraduates from underrepresented groups (62%) worked with a mentor from an overrepresented ethnic group.Last, we conducted chi-square tests of independence to examine whether the gender and ethnic composition of the mentoring dyads differed from expected counts. The chi-square assessing the gender composition of the dyads was significant. Investigation of the standardized residuals illustrated that mentoring dyads composed of a male undergraduate and a male mentor  and socioemotional mentoring (p < .001). In both instances, median ratings were higher among undergraduates than they were among mentors. In contrast, undergraduates and their mentors did not significantly differ in their ratings of negative mentoring (p = .26).Our first research question asked whether undergraduates and their mentors perceived different levels of instrumental, socioemotional, and negative mentoring in the relationship. We assessed this question through a related-samples Wilcoxon signed-rank test. Similar in principle to a paired samples Hypotheses 1a, 1b, and 1c focus on undergraduates’ and their mentors’ ratings of instrumental, socioemotional, and negative mentoring. Specifically, we predicted that undergraduates’ scientist identity would be positively associated with instrumental mentoring (H1a) and socioemotional mentoring (H1b), and negatively associated with negative mentoring (H1c). We tested our hypotheses through three multiple regressions. Regression diagnostics indicated that our data were suitable for multiple regression. Further, all variable inflation factors were less than 1.5, which indicates that multicollinearity was not a concern . The model was significant, F = 6.71, p = .002, and accounted for 18% of the variance in scientist identity. As detailed in Table In the first model, we examined whether undergraduates’ and mentors’ ratings of ficant, F, 63 = 6.socioemotional mentoring independently predicted undergraduates’ scientist identity (H1b). The model was significant, F = 3.17, p = .04, and accounted for 9% of the variance in scientist identity. As detailed in Table In the second model, we examined whether undergraduates’ and mentors’ ratings of ficant, F, 63 = 3.negative mentoring predicted undergraduates’ scientist identity (H1c). Although the overall model was nonsignificant, F = 2.42, p = .10  (R2 = 7%), we did find tentative support for one of our predictions. As detailed in Table In the third model, we examined whether undergraduates’ and mentors’ ratings of ficant, F, 63 = 2.In sum, our hypotheses received partial support. As expected, undergraduates tended to have higher scientist identity when they reported receiving more instrumental and socioemotional mentoring; also, they tended to have lower scientist identity when their mentors reported engaging in more negative mentoring. Yet, in none of the three models did undergraduates’ and mentors’ ratings independently predict scientist identity.We conclude with exploratory qualitative analyses focusing on open-ended data provided by the mentors. These findings are presented in two main sections. First, we examined mentors’ reports of why they wanted to mentor undergraduates in science (RQ2). Next, we analyzed the types of difficult experiences reported by mentors, how mentors appraised responsibility for the difficulty, and how these appraisals mapped onto their undergraduates’ scientist identity (RQ3). It is important to note that we generally did not find differences in the qualitative responses based on the gender and ethnicity of the mentors.n = 23) explained why they chose to be a mentor. To preserve a realistic account of how often each motivation occurred, the remaining percentages are reported for the whole sample . Nearly one fifth  of mentors referenced enhancing gender or ethnicity equity in science as the reason they became a research mentor. One male mentor from an underrepresented ethnic group explained his motivation in a way that referenced his personal experience: “It is extremely important that I facilitate the success of Native American students, as this is a way of giving back to my People, as well as repaying the debts I owe to those Native Peoples who mentored me along the way.” A more general sentiment came from a male mentor from an overrepresented ethnic group, who reported, “I wanted to help Hispanic students become scientists.” A slightly smaller group of mentors  gave general support accounts when explaining their motivation to become a mentor. These accounts emphasized the value of providing students with opportunities to experience and learn about science, but they did not explicitly cite equity as a motivation. For example, a female mentor from an underrepresented ethnic group expressed her desire to “make sure that our strong students are aware of the wide variety of opportunities out there, including research, study abroad, more challenging classes, so that they will be in a much stronger position when they apply for grad school and/or look for jobs.”Our second research question was as follows: What motivating factors lead mentors to engage in research mentoring? We were especially interested in whether promoting educational equity was a salient motivation. Approximately one third of mentors  and a strong scientist identity group if they measured more than 4 . As seen below, we limited these categorical analyses to comparative descriptive statistics due to small cell sizes.Our third research question focused on mentors’ accounts of difficulty in the mentoring relationship and how these accounts mapped onto their students’ scientist identity. For analyses concerning students’ scientist identity, we were especially interested in students with relatively low scientist identity compared to the overall sample mean of 4.32. Of note, students who were lower in scientist identity than their peers were not objectively low in scientist identity. Indeed, all students in the sample had sought out research mentoring, which implies at least some psychological identification with science. Accordingly, we grouped students into a n = 38) reported having no difficulties in their relationship. The remaining percentages are reported for the whole sample . Among mentors who did report difficulties, research underperformance was the most commonly occurring problem . Responses focusing on research underperformance emphasized students failing to prepare, organize, or manage their research time appropriately. For example, one male mentor from an underrepresented ethnic group complained that “Garrypersonal challenges . For this theme, mentors described events outside of the research domain  that hindered the student’s ability to conduct research. Some mentors also described communication problems as a source of difficulty in the relationship . For instance, one female mentor from an underrepresented ethnic group noted, “I think the hardest part is my schedule is quite busy, so at certain times of the year, it can take me several days to respond to an email.” Last, some mentors  described idiosyncratic difficulties that were coded as “Other” due to their specificity.The first part of our third research question was as follows: According to mentors, what are the most common sources of difficulty and conflict in the relationship with their students? Do the types of difficulties that mentors report vary depending on students’ scientist identity? Over half of the mentors  and students with strong scientist identity (57%). This finding suggests that difficulty in the relationship from the mentor’s perspective does not necessarily undermine scientist identity. To understand whether conflict management could help explain mean differences in students’ scientist identity, we next coded the mentors’ appraisals of responsibility for the difficulty.n = 8) or non-student centered . Student-centered responsibility occurred when mentors blamed difficulties in the relationship on the student. Oftentimes, mentors targeted the student’s personality traits. For instance, in one dyad where both the mentor and student were women from overrepresented ethnic groups, the mentor reported, “Carol tended to be ‘anal’ on some things and didn’t really want to explore. She wanted to keep using those techniques she knew well and didn’t want to learn new ones.” In addition to blaming the student for the difficulty, this appraisal style typically required the student to initiate resolving the difficulty. This was the case in one female dyad where the mentor was from an overrepresented ethnic group, and the student was from an underrepresented ethnic group. Specifically, the mentor reported, “Most problematic was when Sandra was afraid to discuss problems with me. Once she was able to talk, we were able to find good solutions.”The second part of our third research question was as follows: When conflict is present, how do mentors attribute blame? Does their tendency to shoulder blame vary depending on students’ scientist identity? The following percentages are once again reported for the whole sample . We coded mentors’ accounts of responsibility for difficulty as either student centered  and a non-student-centered manner . In contrast, when students were strong in scientist identity, mentors were three times more likely to assign responsibility for difficulty in a non-student-centered manner  versus a student-centered manner . These exploratory findings suggest that mentors might preserve their students’ scientist identity by taking a mutual approach to understanding the cause and resolution of difficulty.When students were moderate in scientist identity, a similar number of mentors assigned responsibility for difficulty in a student-centered manner  their own mentoring ability relative to protégé ratings  This interpretation is consistent with social exchange theory  could make students feel unrecognized as scientists, and may even serve to exclude them from science communities of practice (Carlone and Johnson Our analysis of mentors’ motivation and perceptions of difficulty in the mentoring relationship was exploratory, and small sample sizes precluded rigorous hypothesis testing. Nonetheless, the qualitative findings align with prior research and reveal important suggestions for future research. Over half of the mentors who responded to the question about their motivation to become a research mentor expressed a desire to enhance gender or ethnic equity in the sciences, whereas the remaining accounts focused on more generally supporting students’ access to scientific training. Across the qualitative data, we found few differences based on the gender and ethnicity of the participants. For example, mentors from underrepresented backgrounds did not appear to endorse equity as a reason for being a mentor in higher numbers than mentors who were from overrepresented groups. Nevertheless, some individual accounts suggested that mentors from underrepresented groups pursued equity from a more personal standpoint, whereas mentors from overrepresented groups pursued equity as a more abstract, ideological goal.Mentors’ accounts of difficulty in the mentoring relationship most commonly referenced problems with research underperformance, followed by personal challenges that were external to doing research. These findings, which came from mentors reflecting on a single mentoring relationship, mirror findings that Eby and McManus  obtainedMentoring challenges were present regardless of whether undergraduates were moderate or strong in scientist identity. However, when undergraduates were strong in scientist identity, their mentors appeared to be more likely to appraise and resolve the conflict with a non-student centered approach. That is, mentors took personal responsibility for the negativity, took a collaborative approach to resolving conflict, or located the difficulty in some circumstance external to the mentoring dyad. A non-student-centered approach conceivably builds trust and closeness with students, which may help to preserve students’ scientist identity in the presence of conflict.This possibility aligns with work showing that conflict is not necessarily a problem in itself De Dreu . Indeed,undergraduates’ ratings of instrumental and socioemotional mentoring, but mentors’ ratings of negative mentoring. Moreover, mentors’ open-ended accounts provided meaningful contextual information that could not have been obtained from undergraduates. Therefore, collecting data from just one person in the mentoring relationship would have yielded some, but not all, of the findings we obtained in the current study. Given that collecting data from student-mentor dyads is labor-intensive, we encourage researchers to be especially strategic about the questions they ask students and their mentors to ensure that they utilize both sources of data to their full potential.On the basis of the current study’s findings, we provide several methodological recommendations. First, prior research indicates that there are advantages to collecting data from both protégés and mentors (Eby et al. Our second set of recommendations pertains to negative mentoring. We echo others in noting that negative aspects of mentoring relationships merit additional attention in future research (Eby et al. Findings from the current study need to be considered alongside several key limitations. First, our sample size was fairly small, which reduced statistical power. Further, the small sample size precluded analyses that assess whether scientist identity and mentoring behaviors vary as a function of the sociodemographic breakdown within the mentoring relationship (for discussion, see Blake-Beard et al. Second, it is important to be cautious about generalizability. We recruited undergraduates from a research conference, and most of our participants presented at this conference. This suggests that our participants were not only more motivated than the average undergraduate, but also more successful researchers than many students who work in research labs. Relatedly, undergraduates tended to rate their mentors very favorably. It is plausible that undergraduates who had less positive mentoring experiences either self-selected out of the study or did not feel comfortable allowing the research team to contact their mentors. The low base rate for negative mentoring may help to explain why undergraduates’ ratings of negative mentoring were not significantly associated with their scientist identity. In future research, it would be worthwhile to collect data from students with less favorable mentoring experiences. We suspect that findings would indicate that negative experiences with mentors can seriously undermine undergraduates’ burgeoning scientist identity.open triads, graduate students serve as an intermediary between faculty and undergraduates. Conversely, in closed triads, undergraduates interact with graduate students and faculty. Closed triads appear to have especially positive implications for scientist identity. Future research should examine whether different types of mentoring have different implications depending on who in the triad provides them.Another limitation pertains to our focus on undergraduate-mentor dyads. In a recent study, Aikens et al.  observedA final limitation is our reliance on a self-report scale to measure scientist identity. Although this is a common approach (e.g., Chemers et al. The current study suggests that instrumental, socioemotional, and negative mentoring behaviors may have implications for scientist identity among undergraduates who are historically underrepresented in science. Of the three forms of mentoring, instrumental mentoring explained the most variance in undergraduates’ scientist identity, which suggests that it is especially important for undergraduates to receive skill-based guidance from their mentors. The associations between mentoring and scientist identity varied depending on whether we considered mentoring evaluations from undergraduates versus their faculty mentors. Moreover, qualitative data from mentors revealed challenges in mentoring relationships that were not captured through the quantitative data from undergraduates. Thus, from a methodological standpoint, future research may benefit from taking an approach that can provide insight into the dynamic complexity of mentoring relationships.