Objective. The aim of this study was to describe and analyze the correlations between mentoring functions and political skill development among nurses who have earned or are candidates for a Ph.D. or doctorate of nursing practice (DNP) degree. Background. The healthcare system is in flux; future generations of Ph.D. and DNP nurse leaders will be required to demonstrate political acumen. Political skill to navigate organizational politics has had limited research within nursing. Methods. A cross-sectional research design using a web-based survey of 222 nurses who have earned or are candidates for a Ph.D. or DNP. This study utilized two validated tools to measure mentoring functions and political skill. Results. The response rate was 52% (n = 115) of which 86 were Ph.D. and 29 were DNPs. An informal mentoring relationship was described by 62% of the respondents and formal mentoring by 35% of the protégés; only 25% (n = 74) established a mentoring contract. Mentoring score showed significance for total political skill and moderate effect on the networking ability. The mentoring functions of advocacy, career development facilitation, learning facilitation, and friendship were found to correlate significantly with total political skill scores. Conclusions. This study established a benefit for nurses who have earned or are candidates for a Ph.D. or DNP from mentoring to support political skill development. The evolving healthcare system is poised to benefit from having highly educated nurses leading improvements in healthcare quality, evidence-based practice (EBP), and research. Nurses must possess sufficient political skill to successfully navigate organizational structures and relationships and produce results in these complex systems.Political skill is distinct from policy making ability in that the skill is related to a purposeful strategy to enhance job performance within the context of the organizational environment –5 and noNurses with either an earned degree or candidacy for doctorate in philosophy (Ph.D.) or doctorate of nursing practice (DNP) represent highly educated nurses who are trained to approach issues including political savvy through scholarship. These nurses will be expected to fill leadership roles in both academics and practice, requiring high level political skills to navigate complex environments and organizations. The Ph.D. prepared nurse helps educate future generations of nurses and conducts research to improve nursing practice and health . The Ph.This paper reports results of a study that describes and analyzes the correlations between mentoring functions and political skill development among nurses who have earned or are candidates for a Ph.D. or DNP. Such nurses may excel without a mentor, but having a mentor can assist the protégé in gaining access, momentum, exposure, and visibility within the organizational setting to accomplish their goals , 7. It iThe acquisition of leadership and political skill is refined by experiences preferably in the context of a mentor/protégé relationship. A protégé is defined as a less proficient or uninitiated individual selected as a beneficiary to receive counsel and affirmation by the mentor . The proThe clarification of roles and expectations in the mentor-protégé relationship is important. A mentoring contract acts as a tool to help define the learning objectives of the protégé's professional and career goals . ProtégéThe IOM Future of Nursing Report made several strategic recommendations to help advance healthcare for all Americans including expanding the leadership roles of nurses . Future In this study, contemporary mentoring functions  were utihttp://www.jonascenter.org/), a foundation which provides institutional awards to schools of nursing to support doctoral education for Ph.D. and DNP nurses. Participants for this study were Jonas Nurse Scholars (JNS) in Cohorts 1, 2, and 3 (n = 249) between the years of 2008 and 2014 who either have earned or are candidates for a doctoral degree. Exclusion criteria were newly selected JNS as of September 2014, non-JNS, or withdrawal from doctoral program. The Columbia University Institutional Review Board approved this study. Columbia University's Qualtrics Survey Research Suite, an online secure survey software, was utilized to administer the following surveys: Mentoring Functions Survey (MFS) [A cross-sectional research design was selected using a web-based survey. The mentoring frameworks utilized were informed by Yoder's  concept ey (MFS) , 31 and ey (MFS) .The sample size needed to measure the strength of correlation for a moderate effect of .30 with an alpha of 0.05 and power of 0.80 is 84 . The parmentor provided strategic advice on how to handle people or situations.” The PSI is an 18-item instrument with extensive research use in organizational psychology but with minimal use in nursing [I am good at building relationships with influential people.” The PSI consists of 4 subscales: social astuteness, interpersonal influence, networking ability, and apparent sincerity [Structure, content, scoring, and internal consistency can be described for the two validated instruments: Mentoring Functions Survey (MFS) , 31 and  nursing , 34–38.  nursing . In thisincerity , 34–38. incerity .N = 222 participants of which n = 115 (52%) completed the survey. To assess for missing data, Qualtrics reports were generated indicating 100% completion of survey.Of the 249 scholars, 27 emails bounced back or were coded as spam by mail servers. Survey link successfully reached The respondents' age groups, years of work experience, career stage, and mentor scores were variables with highly skewed distributions . TherefoThis study examined the mentorship experiences of doctoral nurses and students to (a) describe the mentoring functions provided and experienced by the protégé; (b) characterize the associations between the mentoring elements and development of political skills for the protégé; and (c) compare and contrast mentored to nonmentored doctoral nurses with mentoring functions that influence political skill development.n = 19) established a mentoring contract. The length of time of the mentoring relationship ranged from “<2 years (40.5%)” to “6+ years (10.8%)” with the most often being “3–5 years” (48.6%). Protégés reported the most common mentoring phases were “cultivating (51.4%)”  and “redefinition (23.0%)” . The most frequent forms of communication were weekly email (56%), followed by monthly phone call (24%) or formal 1 : 1 meeting (28%), and finally social networking on a quarterly basis (41%) outside of work/school. The similarity ratings between mentor and protégé were rated using a 5-point metric: 1 = strongly disagree to 5 = strongly agree. Based on results of Spearman's Rho, the strongest correlations indicating moderate effect at the significance level of p < 0.01 were intellectual/innovative thinking (.41), ambition (.35), approach to work (.36), social capital (.35), problem solving (.37), and values about life in general (.30) . The interpersonal relationship and mutual selection of mentoring seem important with 62% having an informal mentor, along with the reported high similarities between protégé and mentor. Mentoring contracts to establish clear development goals for protégés were low at 25% and the cultivating phase is the most active phase of the mentoring relationship as reported by protégés.Of the 115 respondents, 74 (64%) reported having a mentor and 41 (35.6%) reported not having a mentor . The proM = 5.53) while the lowest score was friendship (M = 3.60) . In addition, there were no significant differences by type of degree (Ph.D. or DNP) for total political skill score as demonstrated by Mann-Whitney test .The highest mentoring subscale score was role modeling ( = 3.60) . There wThe self-perceived levels of political skill were as follows: 64% gave themselves ratings in the “average” political skill range and another 35.7% considered themselves to have “high” political skills .p = 0.06), age group (p = 0.83), and years of work experience (p = 0.09). The odds ratio found no modifying effect for graduation status (p = 0.12) or participation in a leadership or fellowship program (p = 0.22) to political skill scores. The Mann-Whitney test to assess between mentored and nonmentored nurses for political skill scores was significant . The Spearman Rho subscale correlations between mentoring functions and political skill are displayed in p < 0.05 to total political skill and moderate effect on the networking ability subscale at p < 0.01. The mentoring functions of advocacy, career development facilitation, learning facilitation, and friendship were found to correlate significantly with total political skill scores at p < 0.05. The mentoring function of learning facilitation correlated with the political skill subscales of social astuteness and networking ability. The mentoring functions personal and emotional guidance, coaching, advocacy, career development, strategies and systems advice, and friendship correlated with the networking ability subscale. There were no significant correlations with the apparent sincerity or interpersonal influence subscale.The Kruskal-Wallis rank-based test was utilized to assess for differences for variables identified as potentially modifying political skill scores but was nonsignificant for career stage (n = 115) of which 86 were Ph.D. and 29 were DNPs, 87% female (n = 100), with 64% (n = 74) mentored. For both participants who either have earned or are candidates for a doctoral degree, protégés reported having more than 10 years of work experience and self-identified at the proficient or expert career level. The cadre of Ph.D. and DNP Jonas Nurse Scholars were highly experienced with 59% (n = 68) participating in a leadership or fellowship program, yet benefits from mentoring were found to support political skill development and impact of prior (nonmentored) programs was negligible. There is limited research to assess the mentoring experience of doctoral nurses for psychosocial and career support utilizing a validated mentoring survey. The career mentoring functions most often provided were coaching and advocacy closely followed by career development facilitation and strategies and systems advice. While friendship was the least often reported mentoring function, it was found to correlate to political skill. Friendship may have been fostered by the high rate of similarities between mentors and protégés, allowing for an open and safe learning environment leading to a positive interpersonal exchange. Advice without friendship feels cold, knowledge without support is sterile, and candor without care is harsh [This research study describes the mentoring elements provided to Ph.D. and DNP nurses in the protégé-mentor relationship and their correlations to political skill development. To recap, the response rate was 52% (is harsh  indicatin = 56) so consideration could be given to diversifying settings for mentoring relationships. The contextual learning that occurs in hospital, federal agency, private/nonprofit organization, or community health setting differs as a result of organizational culture. The nuances of interpersonal influence, social astuteness, and networking ability might be tied to mentoring functions if these were more specifically contracted for and experienced in diverse settings.Consistent with the literature, mentored individuals benefit from the mentoring relationship and in this study this was evidenced by higher levels of political skill. While nurses were all highly capable and experienced, mentoring enhanced the protégé's networking ability to gain access to social networks and increase their visibility , 7. PoliPerceived similiarity between mentors-proteges is a better predictor of protégé's satisfaction with and support received from their mentors than is demographic similiarity. In fact deep-level similarity in attitudes, beliefs, and values as well as shared experiences like educational background are likely to influence the mentoring relationship.


Determination of the crystal structure of an archaeal NCX (NCX_Mj) paved the way for a thorough and systematic investigation of ion transport mechanisms in NCX proteins. Here, we review the data gathered from the X-ray crystallography, molecular dynamics simulations, hydrogen–deuterium exchange mass-spectrometry (HDX-MS), and ion-flux analyses of mutants. Strikingly, the apo NCX_Mj protein exhibits characteristic patterns in the local backbone dynamics at particular helix segments, thereby possessing characteristic HDX profiles, suggesting structure-dynamic preorganization  of conserved α1 and α2 repeats at ion-coordinating residues involved in transport activities. Moreover, dynamic preorganization of local structural entities in the apo protein predefines the status of ion-occlusion and transition states, even though Na+ or Ca2+ binding modifies the preceding backbone dynamics nearby functionally important residues. Future challenges include resolving the structural-dynamic determinants governing the ion selectivity, functional asymmetry and ion-induced alternating access. Taking into account the structural similarities of NCX_Mj with the other proteins belonging to the Ca2+/cation exchanger superfamily, the recent findings can significantly improve our understanding of ion transport mechanisms in NCX and similar proteins.The membrane-bound sodium–calcium exchanger (NCX) proteins shape Ca Phy protein ,5,6. How protein ,2,3,7,8.The alternating access mechanism in secondary-active transport systems refers to an alternative exposure of ligand binding domains at opposite sides of the membrane, while adopting at least two major conformational states, assigned as the inward-facing and outward-facing states ,10,11. CAlthough the structure-dynamic determinants of ion transport mechanisms were relatively well studied in ion channels ,2,3, recOver the last few years, major breakthroughs in our understanding of ion transport and selectivity by NCX have been made using diverse structural approaches , hydrogen–deuterium exchanger mass-spectrometry (HDX-MS), and functional (ion-flux) assays). The aim of this review is to summarize current understanding of NCX ion transport and selectivity at the molecular level.2+ is the most important and versatile secondary messenger [2+ [2+ rises in time and space may lead not only to unwanted pathophysiological processes but even to cell death. Thus, cellular Ca2+ concentrations require tight temporal and spatial regulation.Caessenger ,6, partinger [2+  allows i2+ levels involves a multitude of proteins, including various membrane proteins [2+ extrusion from the cytosol is available mainly via the Ca2+-ATPase and the Na+/Ca2+ exchanger (NCX) proteins, and, thereby, these systems play a critical role in maintaining, handling and modifying the dynamic changes in the cellular Ca2+ levels [+ into the cell to mediate the uphill extrusion of 1Ca2+ from the cell [+ and Ca2+ ions are transported in separate steps through the NCX protein [2+ ion with three Na+ ions, where the directionality of the Ca2+-efflux (forward) or Ca2+-influx (reverse) modes depends on ionic concentrations and the membrane potential in a given cell type [Regulation of intracellular Caproteins ,6,16,17.+ levels ,6,16,17.+ levels ,16,17. Uthe cell , where d protein . NCX proell type .2+ homeostasis in many cell types, and, thus, modulate the cardiac rate and contractile force, blood pressure, kidney Ca2+ reabsorption, neurotransmitter and hormones secretion, apoptosis, cellular proliferation, and ATP synthesis rates in mitochondria, among other roles [2+ homeostasis with output affecting numerous Ca2+-dependent events that take place at the cellular or systemic levels. Since the protein expression levels and/or regulation of NCX isoform/variants are disease related in many pathological states , the selective pharmacological targeting of tissue-specific NCX variants could be beneficial, although this remains challenging [The gene family of mammalian NCX proteins consists of three gene isoforms (NCX1-3) which generate at least 17 splice variants and are expressed in a tissue-specific manner ,21. NCX er roles ,17,20,21llenging ,17.+ or Ca2+ binding [+ and 1Ca2+ in sequential steps . However, only recently has a major advancement in understanding the structural basis for this mechanism been achieved by solving the crystal structure of NCX_Mj from the arachaeabacterium Methanococcus jannaschii [+ and 1Ca2+ is thermodynamically forbidden [+ and Ca2+ with a stoichiometry of 3:1, although in contrast with NCX_Mj, the three mammalian genes NCX1-3 contain a large cytosolic regulatory loop (~520 aa) between helices 5 and 6 [It has long been recognized that NCX might operate via an alternating-access mechanism, where the protein undergoes outward-facing (extracellular) and inward-facing (intracellular) conformational transitions upon Na binding ,22,23. Tnnaschii . In comborbidden ,26,27. S 5 and 6 ,29,30. T+ and Ca2+ [+/Ca2+ (CAX) exchangers that were solved after NCX_Mj [+/Ca2+ exchangers have crystallized in the inward-facing conformation. In the eukaryotic vacuolar H+/Ca2+ exchanger, VCX1, TM1 and TM6 are translated diagonally towards the vacuole (matching the extracellular side in NCX_Mj) by ~16 and ~13 Å at either end , forming four binding sites termed Sint, Smid, Sext, and SCa in a diamond-shaped configuration. From the crystal structure, it is reasonable to assume that the Sext and Sint sites are highly selective to Na+, whereas the Smid and SCa sites do not exhibit any preferential ion selectivity. It was originally proposed that 3Na+ bind to Sint, Smid, and Sext, or 1Ca2+ binds to SCa .In NCX_Mj and CAX proteins, TM2-5 and TM6-10 form the tightly packed core domain, where TM2-3 and TM7-8 contain signature αs to SCa C. The pr+ bind to Sint, Smid, and Sext, whereas 1Ca2+ binds to SCa  with features matching known Na+ binding sites. According to the original interpretation, the Smid site is surrounded by only four coordinating oxygens  and is not optimal for the binding of either Ca2+ or Na+ [mid probably binds Na+ only at high concentrations, whereas SCa is surrounded by six coordinating oxygens  consistent with ion-coordinating features of a Ca2+ binding site [As mentioned above, the original interpretation of the crystallographic data was that 3Nas to SCa C. Sint amid, molecular dynamics simulations were performed to systematically assess the different possible ion configurations [+ occupy Sint, SCa, and Sext or that 1Ca2+ occupies SCa, whereas in the Na+-bound state, D240 is protonated and a water molecule occupies Smid [+/Ca2+ and Ca2+/Ca2+ exchange rates [2+ and Na+ translocation steps, whereas, in the ground state, both ions do not bind to Smid. This interpretation is further supported by the lack of an effect of the N81A mutation either on the Na+/Ca2+ or Ca2+/Ca2+ exchange rates  [2+ nor Na+ occupies the Smid site in the ground state [+/Ca2+ exchangers is an asparagine, and not an aspartate. Notably, the aspartate at 240 may account  for the low turnover rates of the ion-exchange cycle of NCX_Mj [2+ in excitable tissues [To resolve the uncertainty regarding the specificity and occupancy of the binding sites, especially Surations . This syies Smid . This inge rates . These das well) . Notablynd state . The prof NCX_Mj , since t tissues ,17,35. + bind to Sint, SCa, and Sext and a water molecule occupies Smid, whereas Ca2+ binds to SCa .Recently, a thorough structural analysis of ion recognition in the outward-facing state was performed by soaking NCX_Mj crystals in solutions with different ionic compositions . These ss to SCa A,B. Intr45Ca2+-uptake assays in Escherichia coli-derived vesicles containing overexpressed NCX_Mj protein and its mutants  of ~0.15. Kint is calculated as the ratio between the cytosolic and extracellular Km [2+ binds to the same site from both sides of the membrane, the difference in Km reflects different rate constants  related to the intrinsic equilibrium of all species involved in alternating access  but had a very minor effect on the intrinsic equilibrium (Kint) [2+-bound transition state. Interestingly, the local backbone dynamics are much more constrained (less flexible) at S51 and E54 (TM2C) than at T209 (TM7B), E213 (TM7C), and D240 (TM8A), thereby suggesting a specific rearrangement (preorganization) of “catalytic side-chains” toward achieving the transition state. In contrast with the “catalytic residues”, mutations of distinct residues located at the entries of the ion-binding pocket or at the short loop between TM5 and TM6 led to dramatic changes in Kint , thereby representing the stabilization of the cytosolic-facing state [Despite continuous debates on specific contributions of physicochemical forces to catalytic preorganization and reorganization in biological catalyzators, the definition of catalytic preorganization remains to be fundamental for investigating the structure-based catalytic mechanisms ,8. In genization ,8. A strm (Kint) ,35. Thesng state ,35. ThesAs a typical transporter, NCX conforms to the alternative excess dogma, according to which ligand binding domains are alternatingly exposed at opposite sides of the membrane, where adopting at least two major conformations, assigned as the inward-facing (IF) and outward-facing (OF) states ,12,13,14+ and Ca2+ in saturating concentrations induced similar changes in deuterium uptake at the ion binding sites  modifies the preexisting backbone dynamics nearby the key ion coordinating residues.The HDX-MS measurements  in conjug pocket . The keyg pocket ,26,27. M2+, Na+ and Sr2+ bound species, thereby providing important information regarding the semi-open and occluded states of ion-bound species at the extracellular side [+ concentrations (<20 mM), only Sint and SCa are occupied, whereas higher concentrations of Na+ (up to 160 mM) are required for occupation of Sout [+-occupied exchanger adopts a conformation that matches the first reported crystal structure [+ to Sext bends the N-terminal halves of TM7 to two short helices, TM7a and TM7b, where TM7b occludes the ion-binding pocket from the extracellular solution. Moreover, at lower Na+ concentrations, the Sext site is empty and TM7a and TM7b form a single straight helix resulting in a partially open state in which Sext and Smid are accessible. This bending of TM7 has an additional important outcome in forming a hydrophobic interface with the C-terminus of TM6, which may “precondition” the ion-coupled alternating access. Importantly, these contacts are absent in the presence of Na+ at Sext  during alternating access, not only in NCX_Mj, but also in the three different crystal structures of the CAX proteins [+ occlusion from the extracellular side, the gaiting bundle does not change its position [Recently, the extracellular-facing state of NCX_Mj was resolved in various occupancies of Calar side . Notably of Sout . The fultructure  and repr at Sext . The Na+proteins ,32,33,34proteins . The strposition . This me2+ concentrations, Ca2+ binds to both Smid and SCa, with a clear preference for SCa. The binding to Smid probably involves deprotonated D240; however, as mentioned above, D240 is protonated during ion transport, and, in most NCX orthologs, this position is occupied by an asparagine residue rather than aspartate. The Ca2+-bound exchanger adopts the partially open outward-facing conformation, similar to the 2Na+-bound state [+ binding, the partially open state is most favorable, but the fully open state is also energetically feasible [+ binding, the occluded state is most favorable, although at a relatively small energetic cost, the partially open state can also be occupied. Thus, sequential Na+ binding progressively reshapes the free-energy landscape of NCX, thereby leading to conformation transitions and presumably underlying alternating access. The Ca2+-bound state can accommodate the open, partially open, and occluded states; this is essential to allow Ca2+ binding/release (open), Ca2+ transport (occluded), and thus also the conformation in between . This is again congruent with the HDX-MS analysis suggesting that different conformations can be accommodated in the presence of either ligand [The structural evidence reveals that, in the presence of high Cand state . In the feasible ,27. Uponr ligand .2+/CA (Ca2+/cation antiporter) superfamily, which play an essential role in maintaining and modifying the cellular Ca2+ homeostasis in many cell types. All members of the Ca2+/CA exchangers superfamily facilitate the coupled transport of Ca2+ against its electrochemical gradient while transporting different cations, such as Na+ (NCX), K+ (NCKX), Li+ (NCLX), or H+ (CAX) along their electrochemical gradient in exchange with Ca2+ [2+/CA superfamily are predicted to share structural similarities with the α-repeats mediating the ion binding/transport events  were minor, whereas the effect on the Km value for Na+ was reduced nearly 10-fold. Currently, it remains unclear whether the stoichiometry of ion-exchange is altered , or, alternatively, the differences in the observed Km values represent changes in the Kd values for 3Na+ binding affinity  modifies the preexisting backbone dynamics nearby the key ion coordinating residues. Future challenges include resolving the structural-dynamic determinants governing the ion selectivity and ion-induced alternating access. Taking into account the structural similarities of NCX_Mj with the other proteins of the Ca2+/CA superfamily, the recent findings can significantly contribute to a better understanding of ion-transport mechanisms in NCX and similar proteins.In recent years, the application of structural biology , computer-aided calculations (molecular dynamics), and biochemical assays  have shed light on the structure–functional relationships underlying the ion-transport mechanisms in NCX proteins. The structural and functional data gained so far have provided a conceptual framework for studying the dynamic mechanisms by using the template model system of the archaeal NCX_Mj protein. Namely, the apo NCX_Mj protein exhibits hallmark patterns in the local backbone dynamics at specific segments of distinct helices and thereby possesses signature profiles for structure-dynamic preorganization of conserved α


The defect on SOCE is mimicked by MCU silencing or mitochondrial depolarization, which prevent mitochondrial calcium uptake. Ca2+ release from de ER and Ca2+ inflow through SOCE in neuroblastoma cells result in a Ca2+-dependent upregulation of respiration which is blunted in GDAP1 silenced cells. Reduced SOCE in cells with CMT recessive missense mutations in the α-loop of GDAP1, but not dominant mutations, was associated with smaller SOCE-stimulated respiration. These cases of GDAP1 deficiency also resulted in a decreased ER-Ca2+ levels which may have pathological implications. The results suggest that CMT neurons may be under energetic constraints upon stimulation by Ca2+ mobilization agonists and point to a potential role of perturbed mitochondria-ER interaction related to energy metabolism in forms of CMT caused by some of the recessive or null mutations of GDAP1.GDAP1 is an outer mitochondrial membrane protein involved in Charcot-Marie-Tooth (CMT) disease. Lack of GDAP1 gives rise to altered mitochondrial networks and endoplasmic reticulum (ER)-mitochondrial interactions resulting in a decreased ER-Ca GDAP1 gene show phenotypic and Mendelian heterogeneity in CMT patients and lead to several forms of CMT including recessive demyelinating (CMT4A)6Charcot-Marie-Tooth (CMT) disease is the most common inherited neuromuscular disorder characterized by wide locus heterogeneity1ating CMTA4, recesGDAP1 is an outer mitochondrial membrane protein containing glutathione-S-transferase type domains9101113GDAP1-knockdown (KD) in the human neuroblastoma SH-SY5Y cells results in a defect in store-operated calcium entry (SOCE)2+ stores1718192+ uptake by mitochondria through mitochondrial calcium uniporter (MCU) regulates both STIM1 activation and SOCE maintenance by preventing its Ca2+-dependent slow inactivation19GDAP1-KD cells and motoneurons from Gdap1-KO miceGDAP1-KD cells was attributed to a Ca2+ induced inactivation of SOCE due to the lack of Ca2+ uptake by nearby mitochondriaGDAP1 gene modifier, plays a role in Ca2+ homeostasis, and is able to restore SOCE activity in GDAP1-KD cells. The presence of mutations in both genes (JPH1 and GDAP1) has been associated with a more severe phenotype2+ refilling232+ levels, which are continuously lost at restGDAP1-KD or Gdap1-KO cells have lower ER-Ca2+ levels21The role of SOCE has been related to ER-Ca2+ transients upon stimulation3 receptors or incubated with antagonists of IP3 receptors in which the lack of matrix Ca2+ stimulates autophagy272+ signaling in mitochondria, as shown in neurons lacking Ca2+-regulated AGC1/Aralar, a Ca2+-dependent component of the malate aspartate shuttle2931GDAP1 pathological missense mutations in SOCE activity and SOCE-induced stimulation of respiration.The aim of the present study is to explore the functional consequences of impaired SOCE activity in mitochondrial function of GDAP1 deficient cells. Recent findings have revealed new roles of calcium and its pathway-specific interaction with mitochondria in bioenergetics. For example, basal respiration decreases in cells lacking MCUR12+ levels, with a decrease in SOCE-stimulated respiration which is reproduced by recessive mutations located in the α-loop region of GDAP1 involved in mitochondrial movement, but not by other mutations. The specificity of these defects for different mutants may aid in understanding the pathogenic mechanisms of CMT.The results show that GDAP1 deficiency results in a defect of SOCE activity and ER- Ca2+ influx through SOCE channels2+ in tuning ATP production to ATP demand in excitable cells has been known for a long time3435362+ has been shown to cooperate in adjusting coupled respiration to ATP demand under the workloads induced by carbachol, high K+ depolarization or veratridine in neurons29302+ signals stimulate mitochondrial respiration in control SH-SY5Y pLKO neuroblastoma cells previously described2+ through activation of IP3 receptors, followed by the addition of 2 mM CaCl2. Ca2+ strongly stimulated respiration (2+ (vehicle), the increase in oxygen consumption rate (OCR) was not observed  or non-target control sequence (scrambled) along with mCherry, to identify transfected cells2+]i signals evoked by ATP were the same in scrambled or Mcu-KD cells (2+ uptake in mitochondria (Camit) in response to these signals, studied with 4mt-D3cpv, a FRET calcium indicator targeted to the mitochondrial matrix2+ uptake in Mcu-KD was much lower than in scrambled mitochondria  and a vector with wild type GDAP1 were used as controls. SOCE was activated by emptying ER-Ca2+ with thapsigargin (TG) prior to activate SOCE with 2 mM CaCl2. As GDAP1 silencing results in an increase in resting cytosolic Ca2+ The effect related to each mutation was tested on SH-SY5Y GDAP1 missense mutations produced a completely different effect  domain (defined as 0–2 μm from plasma membrane) after ER-Caanalyzed .2+ mobilization, mitochondria from GDAP1-silenced cells expressing an empty vector fail to localize at SP, resulting in a similar mitochondrial distribution in basal and SOCE-activation conditions  with about 70% reduction in GDAP1 protein levels and a control cell line expressing the empty vector (pLKO-NT)  . To dete2+ with 2,5-di-tert-butylhydroquinone (BHQ)2+-free medium followed by 2 mM CaCl2 addition which resulted in a substantial SOCE-driven stimulation of mitochondrial respiration  or low Ca2+ selectivity or even nonselective such as ISOC2+ uptake regulates SOCE activity in neuroblastoma cells, presumably by preventing its Ca2+-dependent inactivation, and that a failure to take up Ca2+ in mitochondria next to the sites of SOCE channels causes suppression of SOCE in GDAP1 deficient cells. We find a new function of SOCE, that of stimulation of respiration in neural cells. The upregulation of respiration is blocked in the presence of BAPTA, showing that Ca2+ signaling itself through regulation of mitochondrial respiration is required to couple respiration to SOCE activity. Consistently, upregulation of respiration by SOCE activation is impaired by GDAP1 deficiency. The release of ER Ca2+ also stimulates mitochondrial respiration and this stimulation decreases in GDAP1 deficient cells due to their lower ER-Ca2+ levels associated with the impairment of SOCE. Coupling stimulated respiration to ER-Ca2+ mobilization and SOCE-driven Ca2+ signals may involve Ca2+ actions on the external side of the inner mitochondrial membrane, where the Ca2+ binding domains of Aralar/AGC1 or the ATP-Mg/Pi carrier are located, and/or in the matrix, where Ca2+-regulation of mitochondrial dehydrogenases is known to take place35Store-operated calcium channels are an essential pathway for calcium signaling in excitable or non-excitable cells, and mitochondria play an important role in SOCE modulation51GDAP1 mutations also affect SOCE and phenocopy GDAP1 deficiency. These are the recessive mutations located in the α-loop domain involved in protein-protein interaction. The analysis of mitochondrial distribution in cells carrying these recessive mutations has now shown a failure to relocate mitochondria close to plasma membrane under SOCE-activation conditions, and suggest this failure as a likely cause for SOCE impairment. However, neither dominant mutations nor recessive mutations out of the α-loop affect SOCE in the way GDAP1 deficiency does. Interestingly, we have now observed that cells carrying the dominant mutations have an increase in SOCE amplitude, which might be related with a bias in mitochondrial distribution towards the plasma membrane under basal conditions, particularly the region within 1μm of the membrane. This bias may be due to an increased interaction of the dominant mutations with RAB6B and caytaxinInterestingly, we find that a number of missense clinical-CMT GDAP1 mutations are severe with an early onset and two of four recessive mutations associated with more severe phenotype (p.P153L and p.R161H) are located in the α-loop domain2+-dependent bioenergetics failure along with abnormal mitochondrial distribution.It is relevant to point out that the recessive forms of CMT involving GDAP1-KD cells previously described2 and 2 μg/ml puromycin to maintain the selection. HEK293T cells were grown in DMEM with 10% FBS, 2 mM L-glutamine, nonessential amino acids and 100 mg/ml penicillin–streptomycin, at 37 °C with 5% CO2 and for stable GDAP1-silenced HEK293T cell clones 2 μg/ml puromycin was added to maintain the selection. The cultures were maintained at 37 °C in a humidified atmosphere of 5% CO2.Neuroblastoma control SH-SY5Y pLKO, GDAP1-silenced HEK293T cell line, cells were transfected using Lipofectamine 2000 with the pLKO.1 vector  containing a hairpin sequence against GDAP1 or a non-target control vector (pLKO-NT), containing 5 bp mismatches within the shRNAGDAP1 silencing by Western Blot. HEK293T clones were cultured in 100 mm Petri dishes and were removed when cells were 90% confluent. Cells were collected with a scrapper into a homogenization buffer ; and adjusted to pH 7, 4) and were homogenized following differential centrifugation steps to separate different organelles. 40 μg of proteins of mitochondria-enriched fractions were separated by SDS-PAGE and transferred to nitrocellulose membranes. Primary antibodies used were α-GDAP1 (1:1000) mouse polyclonal  and α-SCaMC-1 (1:5000) rabbit polyclonalFor the generation of a stable GDAP1 (wt or mutant form) and GFP. The pCAGIG-GDAP1 WT and p.R120W were previously describedGDAP1 WT or mutant forms in immunofluorescence assays, previously describedWe used the bicistronic pCAGIG expression vector for the simultaneous expression of 4 cells/well onto 12 mm round coverslips. After 24 h, cells were loaded with Fura-2AM by incubation in 15 mM D-glucose Ca2+-free HCSS with 5 μM Fura-2AM and 50 μM pluronic F.127 acid , for 30 min at 37 °C, and rinsed with HCSS 2 mM CaCl2, for 30 min. Fluorescence (emission 510 nm) ratio of Ca2+-free (F380) to Ca2+-bound probe (F340) was analyzed using Aquacosmos 2.5 software (Hamamatsu) and Metafluor for Leica developed by Metamorph . Regions of interest (ROIs) were selected covering single cells. In GDAP1 mutations experiments, SH SY5Y cells were transiently transfected with the bicistronic pCAGIG plasmids using Lipofectamine 2000  24 hours before the experiment. Cells transfected with empty vectors were used as controls. In MCU silencing experiments, N2a cells were transfected 72 hours before with scrambled (shScr) or Mcu-directed (shMCU) containing vectors, kindly provided by Hilmar Bading and Giles E. Hardingham, which co-expressed mCherry protein2+ was depleted using 5 μM of Thapsigargin  or 250 μM carbachol  in Ca2+-free HCSS media and SOCE was induced with 2 mM of CaCl2. ER-Ca2+ content analysis was performed after adding 5 μM of ionophores Br-A23187 or ionomycin  in HCSS Ca2+-free media with 1 mM EGTA.Cytosolic calcium imaging with Fura-2 was performed as described by ref.  probe F3 was anal2+ levels, N2a cells were plated at a density of 4 × 104 cells/well onto 4-wells Lab-Tek chamber slides  and co-transfected using Lipofectamine 2000  72 h prior to the experiment with pcDNA-lynD3cpv  and 535/30 nm (YFP)). Images were collected every 5 s using a filter wheel  and recorded by a Hamamatsu C9100–02 camera mounted on an Axiovert 200M inverted microscope equipped with a 63X/1.4 oil Plan-Apochromat Ph3 objective. ROIs were selected on somas and neurites areas respectively and single-cell fluorescence recordings were analyzed using MetaMorph  and ImageJ (NIH).To image plasma membrane and mitochondrial Cav Addgene or pcDNAv Addgene encoding5 cells/well and incubated for 24 h in a 37 °C, 5% CO2 incubator in culture medium. Cells were equilibrated with bicarbonate-free low-buffered DMEM medium  supplemented with 15 mM glucose and 2 mM CaCl2 or 100 μM EGTA in conditions of ±Ca2+, for 1 h immediately before extracellular flux assay. Drugs were prepared in the same medium and were injected from the reagent ports automatically to the wells at the times indicated. Bioenergetics characterization of culture cells was determined through sequential addition of 6 μM oligomycin, 0.25 mM 2,4- dinitrophenol (DNP), and 1 μM antimycin/1 μM rotenone . This allowed determination of basal oxygen consumption, oxygen consumption linked to ATP synthesis (ATP), non-ATP linked oxygen consumption (leak), maximal uncoupled respiration (MUR), and non-mitochondrial oxygen consumption (for review see ref. 2+− ATPase (preincubated 1 h before the experiment). Then, SOCE activity was triggered by adding 2 mM CaCl2. Finally, oligomycin and antimycin/rotenone were used to calibrate the respiration. When required, BAPTA-AM  loading was performed in Ca2+-free DMEM during 30 min before the experiment. For expressing GDAP1 mutant forms, GDAP1-KD HEK293T cells were cultured in 100 mm Petri dishes and were transfected using the calcium phosphate method when cells were 70–75% confluent. 24 h after transfection, cells were seeded in a XF24 V7 cell culture plate and incubated for 24 h in a 37 °C, 5% CO2 incubator.Cellular oxygen consumption rate (OCR) was measured using a Seahorse XF24 Extracellular Flux Analyzer (Seahorse Bioscience)Neuroblastoma SH-SY5Y cells were fixed with 2% paraformaldehyde (PFA) for 10 min and 4% PFA for 10 min and immunodetection was performed as describedHow to cite this article: González-Sánchez, P. et al. CMT-linked loss-of-function mutations in GDAP1 impair store-operated Ca2+ entry-stimulated respiration. Sci. Rep.7, 42993; doi: 10.1038/srep42993 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Our data suggest that the plant CaMs have evolved to bind certain targets with comparable affinities, respond similarly to a particular Ca2+ signature, but achieve different structural states, only one of which can activate the enzyme. Understanding the basis for differential enzyme regulation by the plant CaMs is the first step to engineering a vertebrate CaM that will selectively alter the CaM signaling network.Plants commonly respond to stressors by modulating the expression of a large family of calcium binding proteins including isoforms of the ubiquitous signaling protein calmodulin (CaM). The various plant CaM isoforms are thought to differentially regulate the activity of specific target proteins to modulate cellular stress responses. The mechanism(s) behind differential target activation by the plant CaMs is unknown. In this study, we used steady-state and stopped-flow fluorescence spectroscopy to investigate the strategy by which two soybean CaMs (sCaM1 and sCaM4) have evolved to differentially regulate NAD kinase (NADK), which is activated by sCaM1 but inhibited by sCaM4. Although the isolated proteins have different cation binding properties, in the presence of Mg This smin (CaM) . Humans in (CaM) . CaM’s C partner . In the  partner . We are  partner . Clues o partner .in plantae, the vast majority differ by only a few amino acids. Some of these CaM isoforms are only expressed during times of stress   . Howeverproteins . Thus, tproteins . In orde/enzymes .2+ and Mg2+ binding affinities are the reason for their differential target activation . Quin-2 was purchased from Calbiochem . Bis-ANS and IAANS were purchased from Invitrogen . All other chemicals were of analytical grade.Arabidopsis thaliana NADK2 (IYVHSKEGVWRTSAMVSRWK)  synthesized the CaM binding domain of AMVSRWK) . CelTek AMVSRWK) .T5C, CaMF19W, sCaM1F19W, and sCaM4F19W were generated from the respective wild type (WT) constructs in the pET17b vector ; soybean CaM isoform 1 (sCaM1); soybean CaM isoform 4 (sCaM4). The DNA constructs for CaMb vector . Primer-escribed .2+ and Mg2+ binding and exchange with the different CaM constructs. Intrinsic tyrosine (Tyr) fluorescence can be used to follow the structural changes that occur within the C-domain of the WT CaMs  . In orde program . Each reescribed .2+ or Mg2+, fluorescence emission intensity was recorded as microliter amounts of CaCl2 or MgCl2 were added to 2 mL of each CaM (1 μM) in titration buffer with constant stirring  (2+ titrations in the presence of NADK or MLCK peptide were performed with 4 μM peptide in the 1 μM CaM solution.To determine the affinity of each CaM for Castirring . The Ca2pCa = 4) . The Ca2The kinetic data were collected using an Applied Photophysics Ltd.  model SX.18 MV stopped-flow instrument with a dead time of 1.4 ms at 20°C. The samples were excited using a 150W xenon arc source , 2007. T2+ dissociation via structural change, stopped-flow buffer with 200 μM Ca2+ and either 1 μM CaMF19W (Trp) or 3 μM CaM (Tyr) was rapidly mixed with 10 mM EGTA in stopped-flow buffer. To measure the Ca2+-dependent rate of hydrophobic pocket closure, stopped-flow buffer with 200 μM Ca2+, 1 μM CaM and 0.5 μM bis-ANS was rapidly mixed with 10 mM EGTA in stopped-flow buffer. For the actual Ca2+ dissociation rate measurements, stopped-flow buffer with 30 μM Ca2+ and 6 μM CaM was rapidly mixed with 150 μM quin-2 in stopped-flow buffer. To directly determine the rate of Ca2+ dissociation in the presence of peptide, stopped-flow buffer with 30 μM Ca2+, 6 μM CaM and 18 μM peptide was rapidly mixed with 150 μM quin-2 in stopped-flow buffer. To determine the rate of Ca2+ dissociation via structural change in the presence of peptide, stopped-flow buffer with 1 μM IAANS-labeled CaM, 200 μM Ca2+, and 3 μM NADK or MLCK peptide was rapidly mixed with 10 mM EGTA in stopped-flow buffer. Labeling of CaMT5C, WT sCaM1, and WT sCaM4 was performed as previously described, taking advantage of the native C26 in the soybean CaMs  was rapidly mixed with 30 mM EDTA in stopped-flow buffer. The kinetics of Ca2+/Mg2+ competition were determined using F19W fluorescence. Stopped-flow buffer with 1 μM CaMF19W, 3 mM Mg2+, and 15 μM EGTA was rapidly mixed with solution containing 2 mM Ca2+ in stopped-flow buffer. The competition experiments with 2,6-ANS rapidly mixed solution containing stopped-flow buffer, 2 μM CaM, 3 mM Mg2+, 15 μM EGTA and 1 μM 2,6-ANS with solution containing 2 mM Ca2+ in stopped-flow buffer.For the Mg2+ transients were generated by mixing a solution containing stopped-flow buffer, 2 μM of each sCaMF19W mutant and 2 mM EGTA with solution containing stopped-flow buffer, 0, 100 μM, 250 μM or 2 mM Ca2+ in both the presence and absence of 3 mM Mg2+. We followed the binding and subsequent dissociation of Ca2+ at the N-domain of CaM or its isoforms using fluorescence from the engineered Trp residue. The visible percent occupancy was calculated using 0 mM Ca2+ as the baseline and 2 mM Ca2+ as the maximum.Rapid Ca2+ transients were generated by mixing solution containing stopped-flow buffer, 3 μM of each sCaM isoform, 9 μM NADK, and 500 μM EDTA with solution containing stopped-flow buffer, 0, 10, 30, 60, 120, and 250 μM, or 2 mM Ca2+ in the presence of 1 mM Mg2+. As for the rapid transients, the visible percent occupancy was calculated using 0 mM Ca2+ as the baseline and 2 mM Ca2+ as the maximum. We followed the binding and subsequent dissociation of Ca2+ using Trp from the NADK peptide.Slow Ca2+ dissociates from the N-domain of sCaM1 or sCaM4. The amplitudes from the Ca2+ dissociation without peptide were set at 100% and the amplitudes in the presence of increasing amounts of NADK  were then expressed as a percentage of this peptide-free amplitude.Bis-ANS fluorescence was used as described above to follow the structural change as Cakoff value represents an average of at least three separate experiments, each averaging at least five traces fit with either a single exponential equation  or a double exponential equation   that uses the non-linear Levenberg–Marquardt algorithm . Each ko quin-2) .p < 0.05) was determined by one-way ANOVA followed by post hoc analysis with Tukey’s test using the statistical analysis software MiniTab16 . Statistical significance (p < 0.05) for the rapid and slow transient occupancy experiments was determined by two-way ANOVA.All values are expressed as mean ± SEM. Statistical significance  and from the N-domain of F19W CaM . Figure 2B shows the Ca2+ dissociation rates from the C-domain of the WT CaM isoforms using Tyr fluorescence. Ca2+ dissociated from the C-domain of sCaM4 approximately fivefold more slowly than from sCaM1. Thus, Ca2+ dissociates from the N-domain faster than from the C-domain for both plant CaM isoforms, again in agreement with vertebrate CaM behavior and consistent with the higher Ca2+ affinity of the C-domain. Figure 2C shows the actual rate of Ca2+ dissociation from both domains of each F19W CaM using the high-affinity fluorescent Ca2+ chelator quin-2  that sCaM1 behaves more similarly to CaM, kinetically this appears to be true only for the N-terminal domain, whereas kinetically the C-terminal domain of sCaM4 appears more similar to CaM . Overall, sCaM4 had a higher Ca2+ affinity and slower Ca2+ dissociation rates than sCaM1, suggesting that differences in Ca2+ binding might be one strategy the plants have used to tune CaM function . The Mg2+ affinity of the N-domain of sCaM4F19W was approximately threefold higher compared to sCaM1F19W, but not significantly different at the C-domain. Based on these data, Mg2+should drastically decrease the apparent Ca2+ sensitivity of the N-domain while only slightly affecting the C-domain. Figure 3B shows the effect of 3 mM Mg2+ on Ca2+ binding to the vertebrate and soybean CaMs. As expected, the apparent Ca2+ sensitivity of the N-domain of all the CaMs decreased approximately sixfold in the presence of Mg2+. On the other hand, the apparent C-domain Ca2+ sensitivity for all the CaMs only marginally decreased by ∼1.5-fold. Thus, in a cell the high level of Mg2+ would be expected to exert a greater effect of competing for Ca2+ binding to the N-domain of CaM, with little effect on the C-terminal domain Ca2+ binding.In addition to Cathe cell . In agre2+ to bind to the N-domain of CaM in the presence of Mg2+, Mg2+ must first dissociate  artificial Ca2+ transients (2+ during the rapid transients was significantly lower than that of sCaM1 (Table 2). Figures 5C,D show the response of the N-domain of the plant CaMs to rapid artificial transients in the presence of Mg2+. Consistent with their similar N-domain Mg2+ dissociation rates, in the presence of Mg2+ the two plant CaMs responded similarly to the rapid artificial Ca2+ transients. Thus, the addition of Mg2+ appears to minimize the differences in the Ca2+ sensing ability between the plant CaMs. Thus, in a cell the rate of Ca2+-binding to the N-domain of CaM will be limited by Mg2+ dissociation.ansients . The perFigure 6 shows the NADK peptide affinity for the Ca2+-saturated CaMs using the intrinsic Trp fluorescence of the peptide. Similar to MLCK (2+ (data not shown)  and NAD kinase (NADK) . Current to MLCK , both plt shown) .Figures 7A,B show the apparent Ca2+ sensitivity of the CaMs in the presence of MLCK and NADK peptides using the intrinsic Trp fluorescence of the peptides. In the presence of the MLCK peptide, there was no significant difference in apparent Ca2+ affinity between the CaMs. In the presence of the NADK peptide, the apparent Ca2+ affinity of sCaM4 was only ∼1.5-fold higher than that of sCaM1, but ∼1.5-fold lower than that of CaM. In general, the NADK peptide had a weaker affinity for the Ca2+-saturated CaMs and also had a substantially weaker effect at sensitizing the CaMs to Ca2+ compared to MLCK.Figures 8A,B show the actual Ca2+ dissociation rates from both domains of the CaMs bound to either MLCK or NADK peptides using quin-2 fluorescence. Surprisingly, unlike the MLCK peptide, NADK only marginally slowed the Ca2+ dissociation from the C-terminal domain of sCaM4 and vertebrate CaM (Table 1). This effect was also observed as Ca2+ dissociated from the CaM-peptide complexes by either following the intrinsic Trp fluorescence of the peptides  or the change in fluorescence from IAANS-labeled CaMs . This data suggests that the C-domains of CaM and SCaM4 bind NADK differently than the C-domain of SCaM1. Although there was minimal influence on the C-domain Ca2+ dissociation rate of sCaM4 with NADK, the Ca2+ dissociation rates and structural changes observed were similar to those of sCaM1. Thus, the differences in Ca2+ binding observed in the absence of the peptide are minimized in the presence of a CaM-binding peptide, and cannot explain the differential regulation of the enzyme. Therefore, we expect that the soybean CaMs should respond similarly to a Ca2+ transient in the presence of Mg2+ and NADK.Figures 9A,B show the response of the plant CaMs to artificial Ca2+ transients  in the presence of physiological Mg2+ and NADK peptide following the change in intrinsic Trp fluorescence from NADK. As we predicted, there was no significant difference in the response of either plant CaM to the Ca2+ transients (Table 2). These data suggest that the plant CaMs may have evolved their differential enzyme regulation without changing their ability to respond to Ca2+ signals.A. thaliana, rice, tobacco, and soybean (2+ and/or Mg2+); and (3) perturbation of the bound CaM structure , it has been proposed that sCaM1 and sCaM4 are tuned to respond to unique Ca2+ signals in order to differentially regulate targets (2+ dissociation rates, both proteins responded similarly to rapid Ca2+ transients since Ca2+ cannot bind until Mg2+ dissociates. Furthermore, in cells there is little free CaM in the cytoplasm since CaM is mostly pre-bound to its targets (2+ and a target peptide, sCaM1 and sCaM4 responded nearly identically to slow Ca2+ transients. Thus, for NADK, the only plant enzyme known to be differentially regulated by the sCaMs, the differential regulation does not appear to be due to an altered response to the Ca2+ signal.Based on the differences in Ca targets . However targets . In the Our current data with the NADK peptide, as well as previous work, suggest the competitive antagonism of sCaM1 and sCaM4 occurs through an altered mode of CaM binding to the enzymes . In the 2+ and the presence of a target peptide, the plant CaMs responded indistinguishably to Ca2+ transients. This suggests that the plant CaMs have conserved their response to Ca2+ signals throughout their evolution. Thus, the CaMs respond to the same Ca2+ signals while only activating certain enzymes through altered modes of binding. This would allow the plant during times of stress to maintain essential CaM activities, activate additional signaling nodes that were not necessary under normal circumstances, and turn off subsets of signaling nodes that might exacerbate the stress. By comparing how naturally occurring CaM isoforms from other species diverge from vertebrate CaM, we can learn more about how changes in the CaM sequence alter downstream target function and cellular responses. In the future, we may be able to smartly engineer CaMs with specific properties that could be used as therapeutics for human disease (In summary, although the sCaM1 and sCaM4 isoforms had unique cation binding properties in isolation, in the more physiological situation with competition from Mg disease  or bette disease .T5C constructs.JD and SW designed the experiments and along with ST wrote the manuscript. SW, HC, and VS performed the stopped-flow experiments. HC, AO, and JS performed the steady-state experiments. BJ developed the CaMThe 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.


We previously reported that both the Na+/Ca2+ exchanger (NCX) and mitochondria play a role in regulating intracellular Ca2+ homeostasis in the rat SCN neurons. Here we present evidence to show differential regulation by NCX and mitochondria of nimodipine-sensitive and -insensitive Ca2+ influx.Transmembrane Ca2+ imaging was used to measure change in [Ca2+]i and patch clamp recordings to study spontaneous firing, membrane potential, and voltage-dependent Ca2+ channels in neurons from reduced SCN slice preparations. Immunofluorescent staining was used to determine the distribution pattern of CaV1.2 and CaV1.3 and their colocalization with NCX1.Ratiometric Ca2+ imaging indicates that nimodipine (2 μM) blocked most of 20 (mM) K+-induced, but less so of 50 K+-induced, Ca2+ rise. The nimodipine-sensitive 50 K+-induced Ca2+ transient rose more rapidly but decayed similarly with the nimodipine-insensitive component, suggesting both components were extruded by NCX. Immunofluorescent stains showed the expression of both CaV1.2 and CaV1.3 and their colocalization with NCX1, whereas functional studies suggest that CaV1.2 mediated most of the nimodipine-sensitive Ca2+ rise but had insignificant effect on spontaneous firing. After normalization relative to the Ca2+-free solution, nimodipine reduced ~ 65% of basal Ca2+ influx, and TTX lowered it by ~ 35%, leaving ~ 25% basal Ca2+ influx in the combined presence of TTX and nimodipine. With the mitochondrial uncoupler carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP) to inhibit mitochondrial Ca2+ uptake, 20 K+-induced Ca2+ transients became larger and slower, both in the absence and presence of nimodipine. FCCP markedly enhanced nimodipine-insensitive, but not nimodipine-sensitive, Ca2+ transients, suggesting that mitochondria preferentially buffer nimodipine-insensitive Ca2+ influx. Results from using CaV2 channel blockers further indicate that FCCP enhanced Ca2+ transients mediated by N-, P/Q-, and the blocker cocktail-insensitive Ca2+ channels.Ratiometric Ca2+ influx by NCX and mitochondria suggests that Ca2+ entry via different sources may be regulated differently to play different roles in SCN physiology.The differential regulation of transmembrane Ca The SCN2+]i –8, and m2+]i , 10, cyt2+]i , and Na+activity . Importathmicity  (for revthmicity ), the inthmicity , 15, andthmicity , 17. Forthmicity  and reduthmicity , 17. Blopression , 19, 20.2+ homeostasis relies on the functional interaction of various Ca2+ handling systems, such as those involved in mediating Ca2+ entry (voltage- and receptor-operated Ca2+ channels), extrusion  and Ca2+-ATPase (PMCA)), and buffering . In a previous study, we demonstrated an important role of NCX and mitochondria in clearing depolarization-induced Ca2+ rise, with NCX mediating fast Ca2+ decay following high K+-induced Ca2+ transients i and reduced the peak amplitude of Ca2+ transients to various extents. As these drugs are known to block L-type Ca2+ channels [2+ channels to basal [Ca2+]i and depolarization-induced Ca2+ rise.Nevertheless, intracellular Caansients . We founchannels , 23, the2+ channels, CaV1.2 and CaV1.3, play important roles in synaptic neurotransmission, neuronal firing, and regulation of gene expression i, and metabolic activity are higher compared to the night. The immunostaining results showed the expression of CaV1.2 and CaV1.3 and their colocalization with NCX1, whereas functional data revealed a major contribution of CaV1.2 to transmembrane Ca2+ influx but not to spontaneous firing. Interestingly, there remained a portion of basal Ca2+ influx that is insensitive to the combined presence of TTX and nimodipine. Our results indicate that while NCX extrudes depolarization-induced Ca2+ influx via both nimodipine-sensitive and -insensitive Ca2+ channels, mitochondria regulate [Ca2+]i by preferentially buffering nimodipine-insensitive Ca2+ influx.While both CaV1.2 and CaV1.3 are found in the mouse SCN , 31, it 2-5% CO2. The ACSF contained (in mM): 125 NaCl, 3.5 KCl, 2 CaCl2, 1.5 MgCl2, 26 NaHCO3, 1.2 NaH2PO4, 10 glucose. A coronal slice (200–300 μm) containing the SCN and the optic chiasm was cut with a DSK microslicer DTK-1000 , and was then incubated at room temperature (22–25 °C) in the incubation solution, which contained (in mM): 140 NaCl, 3.5 KCl, 2 CaCl2, 1.5 MgCl2, 10 glucose, 10 HEPES, pH 7.4, bubbled with 100% O2.All experiments were carried out according to precedures approved by the Institutional Animal Care and Use Committee of Chang Gung University. Sprague-Dawley rats (18–24 days old) were kept in a temperature-controlled room under a 12:12 light:dark cycle (light on 0700–1900 h). Lights-on was designated Zeitgeber time (ZT) 0. All experiments were performed at day and the animal was killed at ZT 2. An animal of either sex was carefully restrained by hand to reduce stress and killed by decapitation using a small rodent guillotine without anaesthesia, and the brain was put in an ice-cold artificial cerebrospinal fluid (ACSF) prebubbled with 95% O2+ imaging, a reduced SCN preparation was obtained by excising a small piece of tissue (circa one-ninth the size of SCN) from the medial SCN using a fine needle , followed by further trimming down to 4–10 smaller pieces with a short strip of razor blade. The reduced preparation  at room temperature (22–25 °C). The spontaneous firing rate was recorded in the cell-attached configuration. The patch electrode was filled with the bath solution or with the patch solution containing (in mM): 20 NaCl, 1 CaCl2, 2 MgCl2, 110 K-gluconate, 11 EGTA, 10 HEPES, 3 Na-ATP, 0.3 Na-GTP, pH adjusted to 7.3 with KOH. The spike counts, in 6-s epochs, always began only after stable recordings were made. At least one or 2 min of spontaneous firing rate were counted before the application of drugs. Membrane potentials were recorded in the perforated patch current-clamp configuration. For perforated patch recordings, the patch pipette also included nystatin  at a final concentration of 250 μg/ml prepared from a stock solution (25 mg/ml DMSO). Pipette resistance was 4–6 MΩ. The measured liquid junction potential was − 12 mV and was corrected for in the presentation of data. Membrane currents were recorded in the whole-cell voltage-clamp configuration. For recording calcium currents in the whole-cell mode, the bath solution contained (in mM): 150 TEACl, 2 CaCl2, 10 HEPES, pH adjusted to 7.4 with TEAOH. The patch electrode contained (in mM): 90 N-methyl-D-glucamine (NMG), 45 NMGCl, 7.5 EGTA, 9 HEPES, 1.8 MgCl2, 4 Mg-ATP, 0.3 GTP, 14 Creatinine phosphate, pH adjusted to 7.4 with H3PO4. Pipette resistance was 5–8 MΩ. The measured liquid junction potential was − 7 mV and was corrected for in the measurements of voltages. The signal was low-pass filtered at 1–5 KHz (2–5 KHz for recording Ca2+ currents) and digitized on-line at 2–10 KHz (5–20 KHz for Ca2+ current recordings) via a 12-bit A/D digitising board  with a custom-made program written in the C Language.The reduced SCN preparation was perfused with bath solution containing (in mM): 140 NaCl, 3.5 KCl, 2 CaCl2+ imaging was performed by pre-loading the SCN neurons with the Ca2+-sensitive fluorescent indicator Fura-2 acetoxymethyl ester (Fura-2 AM) i using ratiometric fluorescence recordings. All experiments began with the application of K+-free solution  solutions were applied for 10 or 20 s to produce depolarization-induced Ca2+ transients in the absence and then presence of 2 μM nimodipine  increased with higher concentrations of K+ to drive membrane potential to more depolarized levels , 55 ± 2% (n = 263), and 45 ± 1% (n = 263), respectively  to detesee ref. ). For thine Fig. . As expeely Fig. .Fig. 2Ni2+ transients, the Ca2+ decay time courses also became more prolonged with increasing K+ concentrations. To illustrate this point, we replot the nimodipine-sensitive 20 K+- and 50 K+-induced Ca2+ transients for curve fitting in Fig. +-induced Ca2+ transient was calculated with a single time constant of 7.8 s. In contrast, the curve (black) for the 50 K+-induced Ca2+ transient was calculated with two exponential decay phases, with a fast time constant of 6 s (80% amplitude) and a slow time constant of 51 s (20% amplitude), in general agreement with our previous observation of fast and slow [Ca2+]i decay for 50 K+-induced Ca2+ transients i of Ca2+-free solution with increasing concentrations of nimodipine , 57 ± 1% (n = 344), and 69 ± 1% (n = 344), respectively , 83 ± 1% (n = 344), and 100 ± 0% (n = 344), respectively, after normalizing with respect to 20 μM nimodipine. The result again suggests a major contribution of CaV1.2 to the nimodipine-sensitive basal Ca2+ influx.To determine the contribution of nimodipine-sensitive Caine Fig. . A repreely Fig. . The extn = 20), 0 ± 7% (n = 20), and 54 ± 12% (n = 20), respectively .Taken together, our results indicate that nimodipine at 0.2 and 2 μM markedly reduced basal and 20 K2+ entry and the involvement of L-type channels, we investigated the effects on the basal [Ca2+]i of 0.3 μM TTX and then increasing concentrations of nimodipine in the presence of TTX   Fig. , suggest2+ influx by 21 ± 1% (n = 121), 33 ± 2% (n = 121), and 39 ± 2% (n = 121), respectively  suggests that TTX-sensitive basal Ca2+ influx comprised approximately equal shares of nimodipine-sensitive and -insensitive components. Interestingly, in the presence of both TTX and nimodipine  compared to the Ca2+-free solution, suggesting the presence of TTX/nimodipine-insensitive basal Ca2+ influx.Taken together the results suggest that TTX-sensitive basal Caine Fig. , there r2+]i and slow decay of 50 K+-induced Ca2+ transients i was also insignificantly increased by FCCP by 7 ± 9% . Taken together the results suggest that mitochondria preferentially buffer Ca2+ influx mediated via the nimodipine-insensitive Ca2+ channels.As the nimodipine-insensitive Caent Fig. , the resCCP Fig. . Figure tal Fig.  and the ent Fig. , but hadent Fig. , suggestine Fig. . Togethe2+ channels was responsible for FCCP-enhanced 20 K+-induced Ca2+ transients, we investigated the effect of CaV2 channel blockers on the Ca2+ transients , 0.0304 ± 0.0020 (n = 194 cells), and 0.0208 ± 0.0015 (n = 187 cells), respectively, with the remaining Ca2+ transient amounting to a peak amplitude of 0.1023 ± 0.0034 (n = 194 cells).To further determine which type of nimodipine-insensitive Cants Fig. . The exp2+ transients in the absence of FCCP. The result of on such experiment is superimposed in Fig. 2+ transients by 0.0119 ± 0.0008 (n = 154 cells), 0.0080 ± 0.0005 (n = 153 cells), and 0.0062 ± 0.0007 (n = 98 cells), respectively, with the remaining Ca2+ transient having a peak amplitude of 0.0290 ± 0.0011 (n = 156 cells). Note that the addition of CaV2 channel blockers may increase more than 10% of the peak amplitude of Ca2+ transients in some cells and these data were not included for analysis.For comparison, a separate set of experiments was performed to determine the drug effects on the nimodipine-insensitive Ca2+ transient that was blocked by each drug and resistant to the cocktail of blockers in the absence (dark grey bars) and presence (black bars) of FCCP. The results indicate that FCCP enhances Ca2+ transients sensitive to ω-agatoxin IVA  and ω-conotoxin GVIA , but not SNX-482 , suggesting that mitochondria buffer Ca2+ entering via P/Q- and N-, but not R-, type Ca2+ channels. FCCP also enhanced the remaining Ca2+ transient in the cocktail of blockers , suggesting that mitochondria also buffer Ca2+ entering via the blocker cocktail-insensitive Ca2+ channels. Interestingly, the fold increase by FCCP (the number on top of each black bar) appeared to be similar for Ca2+ entering via ω-agatoxin IVA- and ω-conotoxin GVIA-sensitive and blocker cocktail-insensitive Ca2+ channels.Figure +-induced Ca2+ transients increased with more depolarized potentials in response to increasing K+ concentrations , with its extent of block by 2 μM nimodipine decreasing from 75, 55, to 45%. The result indicates that 20 K+-induced Ca2+ rise is mediated mostly by the nimodipine-sensitive L-type Ca2+ channels. However, the inhibitory effect of nimodipine on the peak amplitude of high K+-induced Ca2+ transients became smaller with larger depolarizations. The result can not be accounted for by its action as an open channel blocker for the L-type Ca2+ channel, which would produce a larger inhibition with larger depolarizations. Rather the result indicates that larger depolarizations increase the contribution of nimodipine-insensitive Ca2+ rise, suggesting activation of the L-type Ca2+ channels by smaller depolarizations than the non-L-type Ca2+ channels. Indeed, in the rat SCN neurons, the L-type Ca2+ channels, albeit occupying only a small proportion of the total voltage-activated Ca2+ channels, are activated at lower voltages than other high-voltage-activated (HVA) Ca2+ channels i is also not much affected by the presence of FCCP  in the cell processes suggests that L-type channels, along with NCX1, may also regulate Ca2+ levels in these compartments, most likely playing a role in the intercellular communication, for example, by regulating the release of neuropeptides. In this context, it is worth mentioning that TTX- or nimodipine-dependent long-range interactions between the ventrolateral and dorsomedial SCN confers the resistance of SCN to temperature entrainment, with the inter-regional communication mediated by a signaling pathway that may involve VIP and AVP [The contribution of CaV1.2 in mediating most of the basal Caillation . Since N and AVP .2+]i, being increased by high K+ solutions and reduced by a cocktail of Ca2+ channel blockers in Ca2+-free solution i and depolarization-induced Ca2+ influx. Interestingly, we recently found that glucose shortage compromises mitochondrial respiration to activate ATP-sensitive K+ channels in the AVP-SCN neurons [2+ homeostasis by inhibiting respiration and thus reducing mitochondrial Ca2+ uptake. As reduced glucose availability is known to attenuate circadian responses to light in mice [Last, mitochondria preferentially buffer entering Carylation , our res the SCN . Our res neurons . Taken t in mice , it woul2+ rise and contributes to most of the basal Ca2+ influx. Furthermore, whereas NCX1 mediates rapid Ca2+ clearance of both nimodipine-sensitive and -insensitive Ca2+ transients, mitochondria preferentially buffer Ca2+ entry via nimodipine-insensitive N-, P/Q-, and most likely also T-type Ca2+ channels. The differential regulation of transmembrane Ca2+ influx by NCX and mitochondria suggests that Ca2+ entering via different Ca2+ sources may be regulated differently to play different roles in SCN physiology.While both nimodipine-sensitive CaV1.2 and CaV1.3 are expressed and colocalize with NCX1 in the rat SCN, CaV1.2 appears to mediate most of the nimodipine-sensitive Ca


It has been downloaded nearly 6,000 times since 2014.We describe algorithms for discovering immunophenotypes from large collections of flow cytometry samples and using them to organize the samples into a hierarchy based on phenotypic similarity. The hierarchical organization is helpful for effective and robust cytometry data mining, including the creation of collections of cell populations’ characteristic of different classes of samples, robust classification, and anomaly detection. We summarize a set of samples belonging to a biological class or category with a statistically derived template for the class. Whereas individual samples are represented in terms of their cell populations (clusters), a template consists of generic meta-populations (a group of homogeneous cell populations obtained from the samples in a class) that describe key phenotypes shared among all those samples. We organize an FC data collection in a hierarchical data structure that supports the identification of immunophenotypes relevant to clinical diagnosis. A robust template-based classification scheme is also developed, but our primary focus is in the discovery of phenotypic signatures and inter-sample relationships in an FC data collection. This collective analysis approach is more efficient and robust since templates describe phenotypic signatures common to cell populations in several samples while ignoring noise and small sample-specific variations. We have applied the template-based scheme to analyze several datasets, including one representing a healthy immune system and one of acute myeloid leukemia (AML) samples. The last task is challenging due to the phenotypic heterogeneity of the several subtypes of AML. However, we identified thirteen immunophenotypes corresponding to subtypes of AML and were able to distinguish acute promyelocytic leukemia (APL) samples with the markers provided. Clinically, this is helpful since APL has a different treatment regimen from other subtypes of AML. Core algorithms used in our data analysis are available in the flowMatch package at  In this paper, we address a feature selection problem in flow cytometry (FC) data, with a view toward identifying features that would be used by clinicians in characterizing the presence of a disease or response to a drug or vaccine or some other stimulus. In FC data, such features correspond to cell populations in the immune system that express certain subsets of proteins while not expressing others. Hence, we propose a method to identify these immunophenotypes in FC data and to use them for organizing a collection of samples into a hierarchy for classification purposes.Flow cytometry (FC) is a mature technology for measuring the morphology (from scattering) and the expression of multiple biomarkers (from fluorescence) at the single-cell level . Each FCUnlike most prior work that analyzes one sample at a time, here we process heterogeneous FC samples collectively by summarizing a group of similar samples with representative templates. We organize an FC data collection in a hierarchical data structure that supports the identification of immunophenotypes relevant to clinical diagnosis. A robust template-based classification scheme is also developed, but the primary focus of the paper lies in the discovery of phenotypic signatures and inter-sample relationships in FC data collection.flowCore, flowViz, flowClust, flowTrans, flowStats, and flowType packages in Bioconductor  a seven-dimensional healthy dataset consisting of 65 samples from five healthy individuals and (2) an acute myeloid leukemia (AML) dataset consisting 2,872 samples from 43 AML-positive patients and 316 healthy donors. In the first analysis, we show that hierarchical organization of samples efficiently captures different sources of within- and between-subject variations present in healthy immune systems. The second analysis employs templates and meta-clusters to discover immunophenotypes of AML and design a highly accurate classification scheme for AML. For this purpose, we have developed a scoring function that accounts for the diversity of the myeloid cell populations in the various subtypes of AML. In our analysis, we identified thirteen immunophenotypes corresponding to subtypes of AML and separated acute promyelocytic leukemia (APL) samples (APL has a different treatment regimen from other subtypes of AML). Earlier work on the same AML dataset has classified AML samples using methods such as nearest-neighbor classification, logistic regression, matrix relevance learning vector quantization, etc., but they have not identified these immunophenotypes; e.g., Ref. , 27.We organize the rest of the paper as follows. Section 2Aside from some experiment-specific preprocessing, we logically divide FC data analysis into six distinct steps as shown in Figure 2.1In the preprocessing phase, various unintended events such as doublets, dead cells, debris, etc. are removed from the FC data. A “doublet” is a pair of attached cells, which has a larger area but smaller height in the forward scatter (FS) channel relative to a single intact cell. Cell viability dyes, e.g., the amine reactive viability dyes ViViD and Aqua Blue, are often used to separate dead cells from live cells . Figure 2.2spectral unmixing or compensation , etc.] to project cell populations with normally distributed clusters – a choice that usually simplifies subsequent visual analysis –14. Recez across a collection of N samples. After transforming z by asinh(z/c), where c is a normalization cofactor, flowVS identifies one-dimensional clusters (density peaks) in the transformed channel. Assume that a total of m 1-D clusters are identified from N samples with the i-th cluster having variance z/c) transformation works as a variance stabilizer if the variances of the 1-D clusters are approximately equal, i.e., We address the need for explicit VS in FC with a maximum likelihood (ML)-based method, called flowVS, which is built on top of a commonly used inverse hyperbolic since (asinh) transformation. The choice of asinh function is motivated by its success as a variance stabilizer for microarray data . flowVS tio test . From a tio test .2.4population or cell cluster is a homogeneous subset of cells in a sample with similar physical and fluorescence characteristics and thus biologically similar to other cells within the subset but distinct from those outside the subset. Traditionally, cell populations are identified by a manual process known as “gating,” where cell clusters are recognized in a collection of two-dimensional scatter plots  and merges two closest clusters in each iteration. The algorithm then plots the distances between the merged clusters at each iteration and selects the optimum number of clusters where a sharp change in the segmented regression lines is observed. Figure Different algorithms perform better for different FC datasets as was observed in a set of challenges organized by the FlowCAP consortiumk to be used with the algorithm by simultaneously optimizing five cluster validation methods: (1) average silhouette width, (2) Calinski–Harabasz , as well as choosing the best algorithm for a dataset. To this end, we select an algorithm and the number of clusters Harabasz , (3) DunHarabasz , and (5)Harabasz . If diff2.5Population registration is a process of mapping phenotypically or functionally similar cell clusters across FC samples. When performed manually, cell populations are registered by visually mapping 2-D projections of clusters. However, manual registration is challenged by high dimensionality of FC data and large number of samples in a cohort because the number of manual comparisons grows quadratically with the number of clusters and samples. To expedite the registration process and to increase its accuracy, automated algorithms have been proposed recently , 15, 44.c1 and c2 be two normally distributed clusters consisting of n1 and n2 cells, respectively. The Mahalanobis distance d between the clusters is computed as follows:The MEC algorithm matches clusters with high similarity (low dissimilarity) while optimizing a global objective function. For this purpose, we calculate the dissimilarity between a pair of cell populations by the Mahalanobis distance between their distributions. Let Other dissimilarity measures such as Kullback-Leibler (KL) divergence , 46, ear2.5.1A and B consisting of ak and bk cell populations such that ia is the i-th cluster from sample A, and jb is the j-th cluster from B. The mixed edge cover computes a mapping mec, of clusters across A and B such that mec(ia) ∈ P(B) and mec(jb) ∈ P(A), where P(A) (P(B)) is the power set of A(B). When a cluster ia (or jb) remains unmatched under mec, i.e., mec(ia) = ∅, we set d = λ, where the fixed cost λ is a penalty for leaving a vertex unmatched. We set λ to mec is the sum of the dissimilarities of all pairs of matched clusters and the penalties due to the unmatched clusters. A minimum cost mixed edge cover is a mixed edge cover with the minimum cost. We use this minimum cost as the dissimilarity D between a pair of samples A and B:d is computed from equation (O(k3 logk) time, where k is the maximum number of clusters in a sample , and populations from a pair of samples can be registered in less than a second on a desktop computer. An implementation of the MEC algorithm is available in flowMatch package in Bioconductor.Consider two FC samples a sample . The numThe optimum cost of the MEC solution can be used as the dissimilarity measure between a pair of samples. This is similar in spirit with the R-metric, transfer distance or partition distance that computes the minimum number of augmentations and removals of cells needed to transform one partition into another . However2.6When a collection of samples belongs to few representative classes and studying the overall features of these classes is the primary objective, we can summarize each class of samples with a statistically derived template , 15, 19.template tree data structure. A node in the tree represents either a sample (leaf node) or a template . In both cases, a node is characterized by a finite mixture of multivariate normal distributions each component of which is a cluster or meta-cluster. Figure S1, S2, S3, and S4. The algorithm repeatedly merges the least dissimilar (most similar) pair of samples not already included in a template. The dissimilarity between a pair of samples is computed by the cost of an optimal mixed edge cover solution discussed earlier.Here, we describe an algorithm, called the hierarchical matching-and-merging (HM&M) algorithm, which organizes samples in a hierarchy and builds templates at the roots of trees representing the hierarchical organization of samples. The HM&M algorithm arranges a set of similar samples into a binary 2.6.1iv (representing either a sample or a template) in the template tree consist of ik clusters or meta-clusters iv is called an “orphan” if it does not have a parent in the template tree. Consider N flow cytometry samples S1, S2, …, NS belonging to a class. Then, the HM&M algorithm for creating a template tree from these samples can be described by the following three steps.Initialization: create a node iv for each of the N samples iS. Initialize all these nodes to the set of orphan nodes. Repeat the matching and merging steps until a single orphan node remains.Matching: compute the dissimilarity D between every pair of nodes iv and jv in the current orphan set with the mixed edge cover algorithm.Merging: find a pair of orphan nodes  with minimum dissimilarity D and merge them to create a new node iv. Let mec be a function denoting the mapping of clusters from iv to jv. That is, if x ≤ ik and 1 ≤ y ≤ jk. Create a new meta-cluster lk be the number of the new meta-clusters created above. Then, the new node lv is created as a collection of these newly created meta-clusters, i.e., lv is set to D. The node lv becomes the parent of iv and jv, and the set of orphan nodes is updated by including lv and deleting iv and jv from it. If there are orphan nodes remaining, we return to the matching step, and otherwise, we terminate.Let a node a priori, the roots of well-separated branches of tree give different class templates. However, if samples belong to the same class – as is the case for the AML dataset studied in this paper, the root of the template tree gives the class template. The HM&M algorithm requires O(N2) dissimilarity computations and O(N) merge operations for creating a template from a collection of N samples. Let k be the maximum number of clusters or meta-clusters in any of the nodes of the template tree. Then a dissimilarity computation takes O(k3 log k) time whereas the merge operation takes O(k) time when distribution parameters of the meta-clusters are computed by maximum likelihood estimation. Hence, the time complexity of the algorithm is O(N2k3 log k), which is O(N2) for bounded k. The complexity of the algorithm can be reduced to O(N log N) by avoiding the computation of all pairwise dissimilarities between the samples, for larger numbers of samples N, but we did not need to do this here.When the class labels of samples are not known 2.6.2unsupervised manner. Our approach also allows multiple templates to represent substantially different sub-classes within a single class, and therefore it is more flexible in covering sample diversity. Second, instead of clustering population centers, we optimally match populations across samples and then merge the matched clusters into meta-clusters  between S and every template iT. The new sample is predicted to belong to the class whose template it is most similar (least dissimilar) to. If a sample’s dissimilarity with the closest template is above a threshold, then it is not similar to any of the class templates, and we need to create a new class for this sample. The hierarchical organization of samples becomes handy in this situation when all samples are not available in the beginning. Then, samples are inserted in the already existing hierarchical data structure and update templates dynamically when new samples are classified  dissimilarity computations instead of the O(N) dissimilarities that nearest-neighbor classification requires.When the dataset consists of samples belonging to assified . This ap2.8Acute myeloid leukemia is a heterogeneous disease as it perturbs different cell populations differently depending on the subtype of AML; the progression of the disease and the immune profile of the affected individual also affect the cell populations. Hence, we develop a sophisticated scoring function to classify AML and other similar disease in order to improve the classification accuracy. The primary reason for the special scoring function is that an AML sample contains both normal (AML-unrelated) and AML-specific cell populations. The number of AML-specific cells can be smaller than the number of normal cells. Hence, AML-specific cell clusters are given higher weights than AML-unrelated cells. The scoring scheme developed for AML can also be applied to other datasets where the disease affects only a subset of cells.X consisting of k cell populations S = {c1, c2, …, kc}, with the i-th cluster ic containing |ic| cells. Let T− and T+ be the templates created from AML-negative  and AML-positive training samples, respectively. We now describe how to compute a score f(X) in order to classify the sample X to either the healthy class or the AML class.Consider a sample The intuition behind the score is as follows. An AML sample contains two kinds of cell populations: (1) AML-specific myeloblasts and myeloid cells, and (2) AML-unrelated cell populations, such as lymphocytes. The former cell populations correspond to the immunophenotypes of AML-specific meta-clusters in the AML template, and hence, when we compute a mixed edge cover between the AML template and an AML sample, these clusters get matched to each other.  Hence we assign a positive score to a cluster in sample when it satisfies this condition, signifying that it is indicative of AML. AML-unrelated cell populations in a sample could match to meta-clusters in the healthy template, and also to AML-unrelated meta-clusters in the AML template. When either of these conditions is satisfied, a cluster gets a negative score, signifying that it is not indicative of AML. Since AML affects only the myeloid cell line and its progenitors, it affects only a small number of AML-specific cell populations in an AML sample. Furthermore, different subtypes of AML affect different cell types in the myeloid cell line. Hence, there are many more clusters common to healthy samples than there are AML-specific clusters common to AML samples.  denote the set of meta-clusters in T− mapped to a cluster ic in the sample X. Similarly, compute a minimum cost mixed edge cover between X and the AML template T+, and let mec+(ic) denote the set of meta-clusters in T+ mapped to a cluster ic. These sets could be empty if ic is unmatched in the mixed edge cover. We compute the average Mahalanobis distance between ic and the meta-clusters matched to it in the template T−, and define this as the dissimilarity d). From the formulation of the mixed edge cover in ) ≤ 2λ. Hence, we define the similarity between ic and mec−(ic) as s) = 2λ − d). By analogous reasoning, the similarity between ic and mec+(ic) is defined as s) = 2λ − d).Now, we can proceed to compare a sample against the template for healthy samples and the template for AML. We compute a minimum cost mixed edge cover between a sample cover in , we havei, fc(ic), as the sum of two functions f+(ic) and f−(ic) multiplied with suitable weights. A positive score indicates that the sample belongs to AML, and a negative score indicates that it is healthy.The score of a sample is the sum of the scores of its clusters. We define the score of a cluster f+(ic) contributes a positive score to the sum if ic is matched to an AML-specific meta-cluster in the mixed edge cover between the sample X and the AML template T+, and a non-positive score otherwise. For the latter case, there are two subcases: If ic is unmatched in the mixed edge cover, it corresponds to none of the meta-clusters in the template T+, and we assign it a zero score. If ic is matched only to non-AML-specific meta-clusters in the AML template T+, then we assign it a small negative score to indicate that it likely belongs to the healthy class . Hence,The function f−(ic) contributes a negative score to a cluster ic in the sample X if it is matched with some meta-cluster in the healthy template T−, indicating that it likely belongs to the healthy class. If it is not matched to any meta-cluster in T−, then we assign it a positive score λ. This latter subcase accounts for AML-specific clusters in the sample, or a cluster that is in neither template. In this last case, we acknowledge the diversity of cell populations in AML samples. Hence, we haveThe function Finally, we defineX| is the number of cells in the sample X. The score of a cluster ic is weighted by the fractional abundance of cells in it.Here, |33.1healthyFlowData package in Bioconductor. The HD dataset includes three sources of variations: (1) technical or instrumental variation among replicates of the same sample, (2) within-subject temporal (day-to-day) variation, and (3) between-subject natural or biological variation. Multiple sources of variations make the HD dataset an ideal use case to demonstrate the effectiveness of automated tools and software discussed earlier.In our first application, we analyze a healthy donor (HD) dataset measuring partial immune profiles of several healthy individuals in the presence of different sources of variations. In this experiment, peripheral blood mononuclear cells (PBMCs) were collected from five healthy individuals  on up to four different days. Each sample was divided into five parts and analyzed through a flow cytometer at Purdue’s Bindley Bioscience Center. Thus, we have five technical replicates for each sample from a subject, totaling 65 FC samples. Each sample was stained using labeled antibodies against CD45, CD3, CD4, CD8, and CD19 protein markers. A selected 20 samples of this dataset is publicly available in 3.2norm2filter function from the flowCore package in R  vs. side scatter (SS) scatter plot as shown in Figures age in R . From th+ and CD8+ cells, are clearly identifiable after compensation.Next, we compensate for the spectral overlap among fluorescence channels. Figure 3.3+ clusters (T lymphocytes) have much higher variance than CD3− clusters , (b) blue: B cells (CD45+CD3−CD19+), (c) black: helper T cells (CD45+CD3+CD4+), and (d) green: cytotoxic T cells (CD45+CD3+CD8+). Here, every cluster is CD45+ because CD45 is a common leukocyte marker, and we pre-selected lymphocytes (a subtype of leukocytes) on the forward and side scatter channels.The four clusters chosen by the k-means algorithm represent four subtypes of lymphocyte cells. These subtypes of cells are identified in the five-dimensional marker space. For visualization purposes, we show the cell populations by a collection of 2-D scatter plots in Figure 3.5AT represents a template created from 15 samples from subject A). Here, the variations within a subject-specific template come from the effect of environment on individual immune systems on different days and technical variation in flow cytometry sample preparation and measurement. In contrast, the between-subject variations come from the natural biological variations in the healthy subjects. In this dataset, we observe more natural between-subject variations than the temporal and instrumental variations. Hence, samples from the five subjects create concise and well-separated templates representing immune profiles of different healthy individuals.To build templates, we organize the samples in a “template tree” shown in Figure The hierarchical organization of samples has the ability of creating multi-layer templates. For the HD dataset, we observe three levels in the sample hierarchy in Figure + T cells (CD45+CD3+CD8+), (2) black – CD4+ T cells (CD45+CD3+CD4+), (3) blue – B cells (CD45+CD3−CD19+), and (4) red – natural killer cells (CD45+CD3−CD19−). Figure The combined template created at the root of the template tree in Figure 3.6When constructing a hierarchical organization of samples, we have many choices to make at different stages of the algorithm. For example, we can choose other dissimilarity measures between clusters than the Mahalanobis distance when we compute the mixed edge cover, and we can use another hierarchical clustering algorithm instead of the HM&M algorithm to organize samples. Here, we discuss how the other approaches perform on the HD dataset.Beside the Mahalanobis distance, we have tested the Kullback–Leibler divergence and the Euclidean distance between cluster/meta-cluster centers as the dissimilarity between a pair of clusters or meta-clusters (these three options are available in the flowMatch package). We observe that the Kullback–Leibler divergence and Mahalanobis distance perform similarly for the HD dataset. However, when we use the Euclidean distance in the mixed edge cover computation, the template construction algorithm could not group samples from the same subjects, as shown in Figure Next, we use the unweighted pair group method with arithmetic mean (UPGMA) hierarchical clustering algorithm to organize the samples in a hierarchy as shown in Figure 44.1Acute myeloid leukemia is a disease of myeloid stem cells that differentiate to form several types of cells in the blood and marrow. It is characterized by the profusion of immature myeloid cells, which are usually prevented from maturing due to the disease. The myeloid stem cell differentiates in several steps to form myeloblasts and other cell types in a hierarchical process. This hierarchical differentiation process could be blocked at different cell types, leading to the multiple subtypes of AML. Eight different subtypes of AML based on cell lineage are included in the French–American–British Cooperative Group (FAB) classification scheme .  , 59. In 4.6As discussed in the methods section, we build six independent classifiers based on the healthy and AML templates created from Tubes 2–7 of the AML dataset. A sample is classified as an AML sample if the classification score is positive, and as a healthy sample otherwise. Let true positives (TP) be the number of AML samples correctly classified, true negatives (TN) be the number of healthy samples correctly classified, false positives (FP) be the number of healthy samples incorrectly classified as AML, and false negatives (FN) be the number of AML samples incorrectly classified as healthy. Then, we evaluate the performance of each template-based classifier with the well-known four statistical measures: Precision, Recall (Sensitivity), Specificity, and F-value, defined as X, the classification score is computed by comparing it with the healthy and AML templates created from the training set after removing X. The predicted status of X is then compared against true status to evaluate the classification accuracy. Table First, we evaluate the impact of each tube in the classification of the training samples. For a training sample In Figure 4.7Now we turn to the test samples. For each tube, we compute the classification score for each sample in the test set using templates created from the training set and applying equation . Since tX, we assume the null hypothesis: X is healthy (non-leukemic). The sample X receives a positive score if it contains AML-specific immunophenotypes, and the higher the score, the stronger the evidence against the null hypothesis. Since Tube 1 (isotype control) does not include any AML-specific markers, it can provide a background distribution for the classification scores. In Tube 1, 174 out of 179 training samples have negative classification scores, but five samples have positive scores, with values less than 0.2. In the best classifier designed from Tubes 4 to 6, we observe that two AML-positive samples in the training set and three AML-positive samples in the test set have scores between 0 and 0.2. The classifier is relatively less confident about these samples; nevertheless, the p-values of these five samples (computed from the distribution in Tube 1) are still small (<0.05), so that they can be classified as AML-positive. The rest of the AML samples in the training and test sets have scores greater than 0.2, and the classifier is quite confident about their status .When classifying a sample Four AML samples in the test set  were subclassified as APL by comparing against distinct template trees for APL and the other AML samples in the training set  to find the optimal value of the number of clusters. Creating the healthy template from 156 samples in the training set required 10 s on one core, and the AML template for 23 AML samples took 0.5 s on one core. Cross validation (leave one out) of the training set took 30 min, and computing the classification score for the 180 test samples took 15 s, both on four cores. We could have reduced the running time by executing the code in parallel on more cores. We have made the dominant step, the k-means clustering of all the samples with an optimal number of clusters, faster using a GPU, reducing the total time to a few minutes.Finally, we state the computational times required on an iMac with four 2.7-GHz cores and 8-GB memory. Our code is in R. Consider a single tube with 359 samples in it. The 5We have described a set of algorithms for feature selection in a collection of flow cytometry samples by identifying immunophenotypes (cell populations characterizing subsets of the samples that express certain markers and not others). The immunophenotypes are obtained from statistical summaries of similar cell populations in all of the samples. We have used these immunophenotypes to hierarchically organize the samples via a template tree, to identify variations in the samples at multiple levels, and to use them for robust and efficient classification.We report results from two sets of FC data. We show that a collection of healthy samples from different individuals over a number of days are classified by our approach to identify technical replicates, temporal replicates, and individual replicates. We also show that this identification is not obtained using a different method that uses the earth mover’s distance and UPGMA for constructing a template tree. The second set of data represents phenotypically heterogeneous subtypes of AML samples, where we identified thirteen phenotypes corresponding to the different subtypes. Here we were able to distinguish the APL subtype from other AML subtypes, but other markers not included in the study would be necessary to distinguish other subtypes of AML.flowMatch and made it available as an open-source R package in Bioconductor.flowMatch package has been downloaded more than 2,000 times since February 2015. We have also employed various components of this package to analyze other FC datasets and have published the results in peer-reviewed journals and conferences (We have assembled our algorithms for the several steps of FC data analysis into a package called ferences , 20, 44.flowMatch could be used to analyze images from these applications.Stabilizing variance, clustering, matching clusters, and creating templates are general concepts with applications to other areas of biotechnology. Therefore, the algorithms in the flowMatch package can be applied – with simple modifications – to problems outside of flow cytometry. We have already applied the variance stabilization framework to microarray data and compared the results with a state-of-the-art software developed for microarrays . LikewisAA, BR, and AP participated in the discussions to conceive the study, designed the algorithms and experiments, and were involved in writing the manuscript. Additionally, AA implemented the algorithms and created the flowMatch and flowVS software. BR created the Purdue healthy subject dataset. All the three authors read and approved the final 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.