Strips of porcine urothelium and lamina propria were isolated from the urinary bladder dome and mounted in isolated tissue baths containing Krebs-bicarbonate solution, perfused with carbogen gas at 37°C. Tissue contractions, as well as changes to the frequency and amplitude of spontaneous activity were recorded after the addition of muscarinic, histamine, 5-hydroxytryptamine, neurokinin-A, prostaglandin E2, and angiotensin II receptor agonists in the absence and presence of 1 µM nifedipine or nominally zero Ca2+ solution. The absence of extracellular Ca2+ influx after immersion into nominally zero Ca2+ solution, or the addition of nifedipine, significantly inhibited the contractile responses  after stimulation with carbachol (1 µM), histamine (100 µM), 5-hydroxytryptamine (100 µM), neurokinin-A (300 nM), prostaglandin E2 (10 µM), and angiotensin II (100 nM). On average, Ca2+ influx from extracellular sources was responsible for between 20–50% of receptor-mediated contractions. This suggests that although the specific requirement of Ca2+ on contractile responses varies depending on the receptor, extracellular Ca2+ plays a key role in mediating G protein-coupled receptor contractions of the urothelium and lamina propria.With many common bladder diseases arising due to abnormal contractions, a greater understanding of the receptor systems involved may aid the development of future treatments. The aim of this study was to identify any difference in the involvement of extracellular calcium (Ca With many common bladder diseases arising as a result of abnormal contractions, an understanding of the potential receptor systems involved in this response is vital for the development of new and upcoming treatments for those affiliated with bladder dysfunctions.While strong and sustained bladder contractions are vital for urinary voiding, during the filling stage abnormal and spontaneous contractions can result in bladder dysfunction. One common presentation is underactive bladder, characterized by a slow urinary stream, hesitancy and straining to void, with or without a feeling of incomplete bladder emptying, and sometimes with storage symptoms . NormallA continuing interest has developed into the potential role of histamine , prostag2+) and membrane potential transients commence in the lamina propria and spread towards the detrusor. In addition, the U&LP can release mediators, such as acetylcholine during stretching , neurokinin-A (NKA), prostaglandin E2 (PGE2), and angiotensin II (ATII) receptors for mediating contractile activity of the urinary bladder U&LP.With stimulation of the G protein-coupled receptors (GPCRs) resulting in contractions of the urinary bladder U&LP, identifying any common mechanisms of action between them is important. One primary function of the GPCRs in the urinary bladder may be the modulation of Caactivity . This st3 24.9 mM, D-glucose 11.7 mM, KCl 4.6 mM, MgSO4 2.41 mM, CaCl2 1.9 mM, and KH2PO4 1.18 mM) maintained at 4 °C to the University research facilities and used within 3 hours of the animal’s slaughter.Urinary bladders from Large White-Landrace-Duroc pigs  were used as the tissue in this study. All bladders were obtained from the local abattoir after slaughter for the routine commercial provision of food with no animals bred, harmed, culled, interfered, or interacted with as part of this research project. As such, animal ethics approval was not required . Only fen values quoted are from paired tissue strips, and as such, the number of animals (N) used can be calculated using n = 208).A single 4 cm strip was taken longitudinally from the bladder wall at the midpoint between the trigone and the bladder apex. Tissue strips were continually washed with a cold Krebs-Henseleit bicarbonate solution during the preparation and dissection stage. The white-coloured detrusor smooth muscle was dissected away from the pink-coloured urothelium and lamina propria using fine scissors. Histological assessment is known to effectively separate the two layers . After dKrebs-Henseleit bicarbonate solution ingredients were obtained from Sigma-Aldrich . Carbamylcholine chloride (carbachol) and histamine dihydrochloride (histamine) were obtained from Sigma-Aldrich , nifedipine and NKA were from Tocris Bioscience , 5-HT was from Toronto Research Centre , and ATII and PGE2 were obtained from Cayman Chemicals . Nifedipine and PGE2 were dissolved in 100% ethanol, while all other pharmaceutical agents were soluble in distilled water. Concentrations chosen for the agonists and antagonists were selected based on their selectivity at each receptor and consistent with concentrations used in previous studies. In each case, the agonist concentration used induced a submaximal contraction (with an aim to achieve around 80% of peak receptor-induced contraction).2+-free studies, control tissues were washed with Krebs solution as normal, whereas the experimental tissues were washed three times with a nominally Ca2+-free Krebs solution. This also ensured that any excess Ca2+ on the tissue, or Ca2+ leaking out from intracellular sources, was cleared from the bath. Tissues were then left to equilibrate for 3 min in nominally zero Ca2+ solution before agonists were added. A single dose of a select GPCR agonist was added to both the control and experimental tissues after equilibration. Tension, frequency, and amplitude of spontaneous contractions were measured with an isometric force transducer  and recorded on a Powerlab system using Labchart v7 software (ADInstruments). Although a threshold was not applied, in all cases each spontaneous contraction exceeded 0.4 g.In the nifedipine studies, a vehicle control of 100% ethanol was added to control tissues or nifedipine (1 µM) was added to the experimental tissues and left to equilibrate for 30 min. Nifedipine was kept in darkness until the final application in the organ bath and experiments were concluded within 30 min to ensure no adverse light impacts. In the Cat-test was used to analyse tissue responses before and after the addition of agonist. A paired Student’s two-tailed t-test was used to analyse the significance of results when comparing tissues with direct controls, as per previous studies , where amplitude was measured from the lowest point of spontaneous phasic contraction to peak. Frequency was expressed as the number of spontaneous phasic contractions per minute (cpm). All data was analysed using GraphPad Prism version 9 , and results were presented as mean ± standard error of the mean (SEM). A paired  studies . A one-w2+ from an isolated tissue bath. Firstly, this may involve the omission of calcium chloride (CaCl) from Krebs-Henseleit bicarbonate solution  . To assess this feasibility of using Ca2+ chelators, we observed that compared to the simple omission of CaCl, the addition of 1 mM EDTA or 0.5 mM EGTA had no effect on contractile responses to our agonists . Thirdly, in some studies, the removal of Ca2+ appeared to result in a hypovolemic solution, and in order to rectify this, the concentration of other ions was altered. In our pilot studies, when incorporating additional Mg2+ to substitute for omitted Ca2+, there was no significant effect on contractions (n = 8). As such, no additional magnesium was added to replace the omission of the Ca2+ divalent cation. Fourthly, there is the potential for Ca2+ to enter the Ca2+-free bath solution from the cytoplasm, or via the activation of other cellular pumps/exchangers. To minimise the impact of this, after mounting in the bath, tissues were washed three times over 5 min in warmed Ca2+-free Krebs. As a final check, before and after contractions, the extracellular buffer was collected and checked with spectrophotometric assessments , with no Ca2+ detected on this assay. This provided confidence in a nominally zero Ca2+ solution. As there were no observable or significant impacts to any contractions from adjusting for the methods listed above, the decision was made to henceforth solely remove Ca2+ from the Krebs solution and not alter anything else, as this was the path of least manipulation.There is variation in the literature surrounding methods to remove extracellular Casolution . Secondl 1–5 mM)  or glyco 1–5 mM)  have bee 1–5 mM) , as welln = 104). When receptor agonists carbachol (1 µM), histamine (100 µM), 5-HT (100 µM), NKA (300 nM), PGE2 (10 µM), and ATII (100 nM) were added to the tissues, U&LP baseline tension increased significantly for all activated receptors , histamine (p < 0.05), 5-HT (p < 0.01), and ATII (p < 0.05), but not NKA or PGE2. Amplitude was reduced by all the agonists, but the changes were statistically significant for only carbachol (p < 0.05), 5-HT (p < 0.001), and ATII (p < 0.01).In the absence of stimulation from any agonist, U&LP tissue strips developed spontaneous phasic contractions at a frequency of 3.65 ± 0.08 cpm with an amplitude of 0.73 ± 0.04 g (When nifedipine (1 µM) or vehicle control  were added to tissues at the start of the 30 min equilibration period, there were no immediate changes to baseline tension, frequency, or amplitude for any of the agonists.t-tests): carbachol by 54% ; histamine by 45% ; 5-HT by 28% ; NKA by 49% ; PGE2 by 29% ; and ATII by 47% . The impact of nifedipine was relatively consistent after the activation of each agonist, with no significant differences found between any of the responses .After activation of the muscarinic, histamine, 5-HT, NKA, PGE2 and ATII receptors, the baseline tension of the U&LP tissue increased, and the increases were similar for all the agonists . In the n = 8, p < 0.01) and ATII  exhibited a reduction in the amplitude of spontaneous phasic contractions in the presence of nifedipine, and when compared to the response in the absence of nifedipine, was a statistically significant difference.The frequencies and amplitudes of spontaneous phasic contractions produced by the U&LP tissues were investigated in the absence and presence of nifedipine for each of the receptor agonists . No sign2+ solution were significantly inhibited compared to the control group in normal Krebs solution. In the absence of any extracellular Ca2+ sources, the contractions of the U&LP were impaired ; histamine by 46% ; 5-HT by 28% ; NKA by 22% ; PGE2 by 32% ; and ATII by 43% . Across all receptors, there were no significant differences between the averaged responses .U&LP contractions for all receptor agonists in the presence of nominally zero Ca2+-free extracellular environment had no alterations to their frequency or amplitude of spontaneous phasic contractions during responses to receptor agonists carbachol (1 µM), histamine (100 µM), 5-HT (100 µM), NKA (300 nM) or PGE2 (10 µM). Although the activation of all receptors induced increases to baseline tensions, only ATII (100 nM) also resulted in the significant decrease in the frequency of spontaneous phasic contractions in the presence of nominally zero Ca2+ solution . Both methods significantly inhibited contractile activity changes in response to the assessed agonists. Overall, when looking at the impact of either nifedipine or nominally zero Ca2+ solution, there were no significant differences (Student’s two-tailed unpaired t-tests for each) between the effectiveness of inhibitions of tension, frequency, or amplitude after the addition of carbachol (1 µM), histamine (100 µM), 5-HT (100 µM), NKA (300 nM), PGE2 (10 µM), and ATII .In this study, two different methods were applied to assess the impact of extracellular Ca2+ has on smooth muscle contraction varies throughout the body, promoting a specific interest towards identifying the prominent sources of Ca2+ influx across different organs. Ca2+ has a clear role in mediating the contractile activity of the bladder  in GPCR-mediated contractions of the U&LP by nifedipine may indicate additional sources of Ca bladder . In addi2+, warranting further investigation into their clinical use for the management of bladder dysfunction.Antimuscarinics and parasympathomimetics have shown success for the management of bladder contractile disorders, and currently sit as first-line pharmaceutical treatments for overactive bladder and underactive bladder, respectively. However, most patients cease treatment regimens due to lower than expected benefits and adverse side effects. Recent success has been found with combination therapies and there is increasing interest in the identification of alternative receptors systems that may be involved in contraction, and hence future targets for therapeutic treatments. This study’s identification of similarities between receptors mediating and modulating contraction in the urinary bladder may present future therapeutic targets or provide insights into mechanisms that may be dysfunctional in overactive or underactive bladder. Of particular interest was this study’s finding that histamine, neurokinin-A and angiotensin II are highly dependent on extracellular Ca2+ to a greater extent than pig tissue (2+ on their responses.A limitation of this study was the use of single-dose applications of receptor agonists to examine changes in frequencies and amplitudes of phasic contractions over 30-min time periods. It should be noted that in detrusor studies of carbachol-induced bladder contractions, human tissue responded to inhibited extracellular Cag tissue , howeverg tissue , as well2+ for urinary bladder contractile activity. The responses obtained from muscarinic, histamine, 5-HT, neurokinin-A, prostaglandin E2 and angiotensin II receptor activation are highly sensitive to extracellular Ca2+, presenting a potential mechanism underlying bladder dysfunction.This study identified a prominent role of extracellular Ca


Ca2+ sparks underlie normal excitation–contraction coupling. However, under pathological conditions, Ca2+ sparks can combine to form Ca2+ waves. These propagating elevations of (Ca2+)i can activate an inward Na+–Ca2+ exchanger current (INCX) that contributes to early after-depolarization (EADs) and delayed after-depolarizations (DADs). However, how cellular currents lead to full depolarization of the myocardium and how they initiate extra systoles is still not fully understood. This study explores how many myocytes must be entrained to initiate arrhythmogenic depolarizations in biophysically detailed computational models. The model presented here suggests that only a small number of myocytes must activate in order to trigger an arrhythmogenic propagating action potential. These conditions were examined in 1-D, 2-D, and 3-D considering heart geometry. The depolarization of only a few hundred ventricular myocytes is required to trigger an ectopic depolarization. The number decreases under disease conditions such as heart failure. Furthermore, in geometrically restricted parts of the heart such as the thin muscle strands found in the trabeculae and papillary muscle, the number of cells needed to trigger a propagating depolarization falls even further to less than ten myocytes.Cardiovascular disease is the leading cause of death worldwide due in a large part to arrhythmia. In order to understand how calcium dynamics play a role in arrhythmogenesis, normal and dysfunctional Ca I. I2+ leay the AP C. The diperiment D due to dynamics . Ca2+ re2+ into the dyadic cleft in 50% of the Ca2+ release units in HF phenotype myocytes. The intial Ca2+ release from the 10 activated channels in these HF myocytes will activate Ca2+ release in the remaining channels within the same RyR cluster. 2+ levels. This large NCX current facilitates ectopic depolarization caused by spontaneous Ca2+ release for failing myocytes. The AP produced in these failing myocytes propagates to the normal myocytes by the gap junction conductance Ggap as shown in 2+ release increased from 7 in 1-D to 70 in 2-D and 512 in 3-D tissue. 2+ release. Increasing the fraction of Ca2+ release units from 50% to 80% in simulated 1-D, 2-D, and 3-D tissues with the HF phenotype decreases the number of myocytes required to depolarize the tissue from 512 to 100 in 3-D tissue, from 70 to 25 myocytes in 2-D tissue, and from 7 to 4 in 1-D tissue, as shown in In the simulations shown in The heart has many fine structures such as Purkinje fibers and trabeculae (lining the chambers of the heart) that are in effect a 1-D structure as shown in 2+ release was simulated. 2+ release in a failing heart, the number of myocytes is reduced to only 32 in the 1-D trabecula compared to 512 in the 3-D ventricular wall. The number is significantly smaller because the small size of the trabecular tissues reduced the electrotonic load that must be overcome to activate adjacent tissue.The depolarization of this 3-D structure by current injection and spontaneous CaThe final set of simulations tested the effect of current injection in a non-contiguous mass of myocytes rather than an adjacent cell mass. The first current was injected into alternate myocytes. In this simulation successful depolarization of these myocytes was not possible even if current was injected into many myocytes because of the continuous leak to the unstimulated myocytes. If a non-contiguous mass of cell as shown in In 2-D tissue the number of myocytes increased almost three-fold to produce successful depolarization of the tissue B. The reCardiac arrhythmias require the aberrant behavior of a sufficient number of myocytes so that they can disrupt the normal depolarization pattern of the heart. This is true whether the arrhythmia is initiated by an ectopic focus or by the excitability/action potential dynamics of the cardiac myocyte. When an ectopic focus elicits a propagating action potential in front of the normal propagating depolarization wave front it can break the wave front, which can lead to spiral waves that are responsible for ventricular tachycardia, polymorphic ventricular tachycardia, and fibrillation . In orde+–Ca2+ exchange to Ca2+ removal varies across species. In rat ventricular myocytes, 92% of the Ca2+ transient is removed by SERCA2a and 7% by the NCX. In humans and rabbits, 70–75% of the Ca2+ transient is removed by SERCA2a, respectively, and 20–25% by the NCX [2+ sparks in paced rat and human ventricular myocytes are similar. Ca2+ sparks in human ventricular myocytes have been observed to have a F/F0 of 1.3 in normal heart and 1.19 in a failing heart [2+ sparks in rat ventricular myocytes have been reported to have a F/F0 of 1.2–1.6 in paced ventricular myocytes [+–Ca2+ exchange in human and rabbit ventricular myocytes and hence be easier to generate an arrhythmia.The contribution of the Na the NCX ,51. Ca2+ng heart . Ca2+ spmyocytes ,54. Howe2+ release is larger, a smaller amount of the NCX overexpression is needed to generate a propagating arrhythmia. The changes to the NCX expression during heart failure varies between species . In huma2+ dynamics will also vary across species and with the extent of progression of the heart failure. In compensated heart failure, beta adrenergic stimulation is able to rescue the myoplasmic Ca2+ transient to be close to normal. In uncompensated heart failure the Ca2+ transient is decreased in amplitude and of a prolonged duration. In the case of decompensated heart failure, the AP can also be very prolonged. In the model presented here, the Ca2+ transient amplitude is only slightly decreased from the control because the beta-adrenergic stimulation has increased the RyR opening. The diastolic myoplasmic Ca2+ level can also vary being elevated, decrease, or unchanged depending upon the species, pacing rate, and extent of heart failure in human patients even with depleted SR Ca2+ [2+. In cases where there is upregulation of the NCX the diastolic myoplasmic Ca2+ will be lower [Similarly, the changes to Ca SR Ca2+ . The modbe lower ,59.2+ release and waves [2+ wave propagation and the increased amount of RyR2 phosphorylation can enhance wave propagation. Our earlier studies have reported the experimentally observed transverse tubule changes during HF [2+ calcium channel and RyR2 during an AP.Previous studies have shown that changes to the T-tubular network also influence the propensity for spontaneous Cand waves ,61. Facturing HF . In thisuring HF . This orThe model simulated the major changes associated with heart failure. There are additional changes that could be studied in the future. For example, the scientific literature has reports that phospholamban changes during heart failure and other reports where it does not . A more 2+ release events do not happen synchronously, it becomes more difficult to generate a propagating depolarization as the temporal spread of myocyte depolarizations gets larger. However, as long as the depolarizations overlap such as would occur if the depolarization spread from cell to cell, a small number of cells is still needed. Further studies are needed to explore how a Ca2+ wave can spread between cells to form the mass of cells needed to trigger an arrhythmia.A simulation series was performed to understand the importance of synchrony of excitation in the excited cell mass. When the release in different myocytes in a cluster are temporally distributed, that is, the triggering CaThe simulations presented here indicate that in 1-D-like structures, the excitation of as little as four myocytes is required to initiate a propagating action potential to the rest of the structure in agreement with experimental findings . In factThe ventricular wall has different cell types in the epicardium, midmyocardium, and endocardium. There is also a transmural gradient moving across the wall transmurally and from apex to base. In the 3-D simulations of a tissue, there was no transmural gradient included in the simulation because in the block of ventricular tissue the number of cells in the simulations were small compared to the wall thickness. For simplicity a single ventricular cell type was used. This is justified because during the activation of the action potential they all have a fast upstroke velocity varying by less than 10% . Further2+ waves and the mechanisms that contribute to their initiation and propagation [2+ wave can be accompanied by the depolarization of the myocyte [2+ release units. Hence, activation of 50% of the release units was used to simulate spontaneous Ca2+ release in the ventricular myocytes. Because depolarization of a single ventricular myocyte is uniform across the cell, compartmental models of the ventricular myocyte are sufficient for this study. The computationally expensive stochastic local control model used has advantages over the computationally fast common pool model. The local control model produces graded release and captures the important interactions between SR Ca2+ release and the Ca2+-dependent inactivation of the L-type channel, which the common pool model fails to accomplish [In this study, compartmental models of the rat ventricular myocyte were used instead of a spatially resolved model. The simulation of spatially resolved ventricular myocytes can simulate spontaneous Capagation . Experim myocyte . The depcomplish ,20,38,682+ dynamics can also be a cause of these arrhythmia [The origination of ectopic foci in both atrial and ventricular tissue has been studied previously. Atrial arrhythmia can originate at the pulmonary veins . Other vrhythmia . The Purrhythmia . The simrhythmia ,75. The rhythmia ,76. In trhythmia . The comExperimental studies have considered the remuscularization of the heart as a means of repairing damaged heart muscle ,79. Mult2+ waves do not contribute to synchronization [2+ wave can synchronize over millions of myocytes and lead to dramatic action potential fluctuations. The modeling in this paper does not spatially resolve individual myocytes and instead treats the myoplasm as one compartment as described previously [2+ waves can be studied using our stochastic spatial rat ventricular myocyte model and is left for future work [One important question not addressed by the current modeling study is how a mass of myocytes synchronize to serve at the ectopic focus for initiating an arrhythmia. This has been studied in other modeling studies. Previous modeling studies have considered the coupling of an SA node cell using a 7 × 7 grid of myocytes . These snization . Howevereviously . The conure work .+ channel dynamics can change model behaviors during arrhythmia [The model predictions of the critical number of cells needed to initiate an ectopic propagating action potential suggest that a small number of cells is needed. These results might vary with other computational models for rat or for other species. For example, Xie and co-workers found very different numbers using a different model with different assumptions about the system being modelled . For exarhythmia ,87. Thisrhythmia .2+ sparks trigger spontaneous Ca2+ release in the rat ventricular myocyte. The detailed 1-D, 2-D, and 3-D modeling of myocytes enables the investigation of how Ca2+ instability arises, and how it may lead to novel electrical activity and arrhythmia. The model presented here supports the idea that spontaneous calcium release during conditions such as heart failure or in an EAD can result in an action potential through the activation of NCX. This is a Ca2+-activated electrogenic transport protein and is one of the critical components of the heart cell that links (Ca2+)i changes with membrane current. Thus, when there is a large increase in (Ca2+)i, as there is with every heartbeat, there is a parallel inward (depolarizing) current. Abnormal Ca2+ release events activate the NCX current (INCX), which may trigger an extrasystole and hence produce an arrhythmia. The subcellular and nanoscopic characterization of (Ca2+)i, which activates INCX, has also been investigated. This requires high temporal and spatial resolution modeling of local Ca2+ signals originating from the junctional sarcoplasmic reticulum (JSR). Both aspects of this behavior have been investigated.The experimentally constrained computational model developed here permits an understanding of how CaSimulations suggest that in order to generate depolarization in the heart, the myocytes need to be in close proximity, and only then a small number of myocytes will be able generate arrhythmia in heart. If these stimulated myocytes were not close to each other, then number of the myocytes to produce successful depolarization increased a lot. We conclude that this could be the reason that makes arrhythmia a rare event. Finally, the simulations suggest that a relatively small number of myocytes are needed to trigger a propagating action potential with the number increasing as the dimensionality increases from 1-D to 2-D to 3-D. Fine structures in such as Purkinje fibers and trabeculae provide a virtual 1-D media in which an arrhythmia can be initiated. This greatly reduces the number or myocytes needed to initiate an arrhythmia in the 3-D heart.


Shugoshin-like protein 2 (SGOL2) is a centromeric protein that ensures the correct and orderly process of mitosis by protecting and maintaining centripetal adhesions during meiosis and mitosis. Here, we examined the potential role of SGOL2 in cancers, especially in hepatocellular carcinoma (HCC).One hundred ninety-nine normal adjacent tissues and 202 HCC samples were collected in this study. Human HCC cells (SK-HEP-1 and HEP-3B) were employed in the present study. Immunohistochemistry, immunofluorescence, western blot, Co-Immunoprecipitation technique, and bioinformatic analysis were utilized to assess the role of SGOL2 in HCC development process.Overexpression of SGOL2 predicted an unfavorable prognosis in HCC by The Cancer Genome Atlas database (TCGA), which were further validated in our two independent cohorts. Next, 47 differentially expressed genes positively related to both SGOL2 and MAD2 were identified to be associated with the cell cycle. Subsequently, we demonstrated that SGOL2 downregulation suppressed the malignant activities of HCC in vitro and in vivo. Further investigation showed that SGOL2 promoted tumor proliferation by regulating MAD2-induced cell-cycle dysregulation, which could be reversed by the MAD2 inhibitor M2I-1. Consistently, MAD2 upregulation reversed the knockdown effects of SGOL2-shRNA in HCC. Moreover, we demonstrated that SGOL2 regulated MAD2 expression level by forming a SGOL2-MAD2 complex, which led to cell cycle dysreuglation of HCC cells.SGOL2 acts as an oncogene in HCC cells by regulating MAD2 and then dysregulating the cell cycle, providing a potential therapeutic target in HCC.The online version contains supplementary material available at 10.1186/s40364-022-00422-z. Hepatocellular carcinoma, a major type of primary liver cancer, is the third leading cause of cancer-related mortality globally . AlthougThe precise separation of chromosomes is critical for the maintenance of genomic stability and function during mitosis , 5. GeneThis study aimed to explore the biological function of SGOL2 in HCC through bioinformatics analysis and to clarify its probable mechanisms. We evaluated its expression profile by bioinformatics, which was further verified in 2 independent HCC cohorts. We also found that SGOL2 promoted HCC progression in vitro and in vivo. Further investigation demonstrated that SGOL2 can promote the expression of MAD2 by forming a SGOL2-MAD2 complex, which subsequently induces cell cycle dysregulation in HCC cells. Thus, the results of this study further our knowledge of SGOL2 and highlight its potential as a new therapeutic target for hepatocellular carcinoma.This study was approved by the Research Ethics Committee of Zhejiang University. All experiments were performed according to regulations. A total of 199 normal adjacent tissues and 202 HCC samples were collected in this study. Part of the samples was obtained from Shanghai Outdo Biotech Company  . The other part was from the Department of Hepatobiliary Surgery, the First Affiliated Hospital of Zhejiang University  . The clinicopathological characteristics of all patients are shown in Table The mRNA level of SGOL2 in tumor versus normal tissues and liver cancer versus normal liver tissues was analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and Oncomine database. The protein level of SGOL2 in HCC was analyzed in the Human Protein Atlas (HPA) database and verified in cohort 2 by Western blot and cohort 1/2 by immunohistochemistry (IHC) staining. Next, we performed a subgroup analysis of SGOL2 mRNA expression using the liver hepatocellular carcinoma (LIHC) dataset from The Cancer Genome Atlas (TCGA) and the UALCAN database. The mRNA expression levels of SGOL2 were evaluated in various cell lines, including seventeen liver cell lines, in the CCLE database.We performed Kaplan–Meier analysis for overall survival (OS), relapse-free survival (RFS), progression-free survival (PFS), and disease-specific survival (DSS) in the Kaplan–Meier Plotter database. In addition, patients in cohort 1 were separated into low-expression and high-expression groups based on the median expression value of SGOL2. Then, we performed overall survival analysis and compared survival curves by the log-rank test. Moreover, multivariate Cox proportional hazards regression analyses were performed to test whether SGOL2 was an independent prognostic factor in R software 3.6.0.Please refer to the The HCC cell lines SK-HEP-1 and HEP3B were purchased from Procell . All cells were cultured in DMEM (Gibco) supplemented with 10% fetal bovine serum (Gibco), 1% streptomycin, and penicillin. Cells were transfected with lentivirus or plasmid purchased from Shanghai Genomeditech  and verified by DNA sequencing. Lentiviruses containing shNC  and shSGOL2 were constructed using the vector pGMLV-SC5. The shRNA sequence used to target SGOL2 was as follows: 5′-GGTCAGAATTCCCTAACTTGT-3′. The pGMLV-SGOL2 plasmid contained the SGOL2 coding sequence, and the pGMLV-MAD2 plasmid contained the MAD2 coding sequence. For plasmid transfection, SK-HEP-1 and HEP3B cells were seeded in 12-well plates and then transfected with plasmids (4 mg per well) using Lipofectamine 2000 Reagent (Invitrogen) according to the protocols. Cells were harvested for analysis after 48 h.Here, cells were seeded in 96-well plates at 2000 cells/well and incubated. Cell proliferation was evaluated by Cell Counting Kit-8 assays  according to the protocol.For cell cycle analyses, cells were fixed with 70% ethanol at 4 °C overnight and stained with RNase A containing propidium iodide . Cell cycle distribution was determined using flow cytometry. For apoptosis analysis, cells were stained with an Annexin V-FITC/PI kit (BD Biosciences) and analyzed in a FACSAria II flow cytometer .The tumor sphere assay, assay, colony formation and migration, and invasion assays of HCC cells were performed as previously described .Western blots were performed as described previously . CoIP waWe conducted immunohistochemistry as previously described . For qRThuman SGOL2, 5′-TAAAGCACAACAACAGGGCAT-3′ (forward) and.5′-AGGCGAAGAAATGTGTTCTCAAA-3′ (reverse);human MAD2, 5′-GGACTCACCTTGCTTGTAACTAC-3′ (forward) and.5′-GATCACTGAACGGATTTCATCCT-3′ (reverse);human SGOL1, 5′-AACTCAGCAGTCACCTCATCT-3′ (forward) and.5′- TGCACCTACGTTTAGGCAGAG-3′ (reverse);We conducted immunohistochemistry as previously described . For detCells were seeded on coverslips. The cells were fixed, permeabilized with 0.1% Triton X-100 for 1 min, blocked with 1% bovine serum albumin for 1 h, and treated with primary antibodies [SGOL2 antibody  and MAD2 antibody ] at 4 °C overnight. The cells were then treated with secondary antibodies and incubated for 1 h and DAPI for 10 min at room temperature. The images were generated from Confocal microscopy .7 cells/200 μl serum-free DMEM). For the lung metastasis model, 7 mice were intravenously injected with SK-HEP-1 shNC or SK-HEP-1 shSGOL2 cells (3 × 106 cells/200 μl serum-free DMEM) through the tail vein. The lung and liver metastatic nodules of HCC were analyzed by HE staining. Tumor volume was calculated by the following formula: tumor size = ab2/2. Three weeks after inoculation, animals were euthanized, and tumors were collected and fixed for immunohistochemical analysis. This animal study was approved by the Research Ethics Committee of Zhejiang University. All experiments were performed according to regulations.Male BALB/c nude mice at 6 weeks of age were purchased from the Zhejiang Academy of Medical Sciences . Three mice were subcutaneously inoculated with SK-HEP-1 shNC or SK-HEP-1 shSGOL2 cells  and SPSS 20.0 software  to perform the statistical analysis.One-way ANOVA and two-tailed Student’s t-tests were employed to analyze the data. All data are presented as the mean ± standard deviation (SD). We described statistical significance as follows: NS, not significant; *P < 0.05), whereas there were no significant differences among the cirrhosis, dysplasia, and normal liver tissue groups. Moreover, SGOL2 protein levels were analyzed using the HPA database. As shown in Fig. p = 0.001).We found that SGOL2 mRNA expression was upregulated in different tumors, including hepatocellular carcinoma, colorectal cancer, and breast cancer, in the Oncomine database Fig. SA. In addN = 371 vs 50) , relapse-free survival (HR = 1.96 (1.38–2.78), P = 0.00013), progression-free survival (HR = 2.1 (1.55–2.84), P = 9.2e-07) and disease-specific survival (HR = 2.84 (1.81–4.47), P = 2.3e-06) in HCC patients  in cohort 1 into a low expression group (n = 44) and a high expression group (n = 53), and then performed survival analysis. As shown in Fig. p = 0.001). Subsequently, we conducted a SGOL2-based prognostic model. All HCC patients in cohort 1 were randomly divided into two groups: the training group (n = 67) and the validation group (n = 30). Next, we identified four variables that were closely related to survival: expression, grade, AJCC TNM, and AFP based on the lasso regression model , .6–3.28,  1.55–2.8, P = 9.2 1.55–2.8, P = 9.2 1.55–2.8, P = 9.2SK-HEP-1 and HEP3B cells were chosen to evaluate the function of SGOL2 according to the mRNA levels of SGOL2 in different HCC cell lines based on the data from CCLE  staining showed that SGOL2 colocalized with MAD2 in both SK-HEP-1 and HEP3B cell lines by Confocal microscopy Fig. A, and thTo further verify the role of SGOL2 in HCC in vivo, we constructed xenograft tumor models by SK-HEP-1 shNC and SK-HEP-1 shSGOL2. The mice were sacrificed on Day 21 after inoculation, and the formed tumors, lung, and liver were statistically analyzed Fig. . Both thWe further analyzed angiogenic markers (CD34), proliferative markers (Ki-67 and proliferating cell nuclear antigen [PCNA]), and EMT-related markers  by IHC in the formed tumors. Downregulation of SGOL2 expression resulted in the suppression of both proliferation and metastasis  , encoded by an 11.5 kbp gene on chromosome 4q27 , 39. In The regulation of MAD2 by SGOL2 has been investigated, and we are further interested in clarifying the molecular mechanism. As reported, SGOL2-MAD2 functions by manipulating the separation of eukaryotic sister chromatids during the cell cycle . Intrigumus musculus, not human, not HCC. In our study, we first found the SGOL2 maybe a novel diagnostic marker in HCC, then we predicted that SGOL2 may exert its function through MAD2, which was demonstrated by vitro and vivo experiments. Furthermore, from our bioinformatic analysis part, we can also observe that the hub genes related with both SGOL2 and MAD2 are not only enriched in cell cycle related pathway, but also other pathways such as classic p53 pathway, FOXO pathway. In this study, SGOL2 mainly exerted its tumor-promoting effects by regulating MAD2 and then dysregulating the cell cycle in HCC. We first proved that SGOL2 regulates MAD2 in HCC cells, which indicates that it may serve as a potential target for molecular-based therapy. Nevertheless, there are several limitations to this study. First, further assays in vivo are required to validate the results in vitro. Second, we should also pay attention to the alterations of other pathways regulated by SGOL2 in addition to manipulating the cell cycle. Third, SK-HEP-1 cell line has been identified as a human cell line of endothelial origin [There are fewer reports of how SGOL2, a member of the shugoshin family, functions in cancer than SGOL1, which has been repeatedly proved to be related to the tumorigenesis and development of cancers , 48–55. l origin , it is tDysregulated transcription factors play a key role in various types of cancers . To dateTaken together, these results suggest that SGOL2 is a novel functional oncogene in HCC and that it accelerates tumor growth via the regulation of MAD2. Moreover, SGOL2 is a potential target and clinical marker for HCC therapy. The role of SGOL2 in promoting tumorigenesis in HCC is reported here for the first time, indicating that a novel therapeutic strategy for HCC involving SGOL2 is worthy of further investigation.Additional file 1. Supplementary methods.Additional file 2: Fig. S1. High expression of SGOL2 in HCC in public database. Fig. S2. The mRNA and protein levels of SGOL1 after the knockdown of SGOL2 in HCC cell lines. Fig. S3. Subgroup expression analysis of SGOL2 in HCC. Fig. S4. Elevated expression of SGOL2 indicated a poor prognosis in HCC patients. Fig. S5. SGOL2 mutations and the associations between SGOL2 and immune cells in HCC. Fig. S6. Genes related to SGOL2 or MAD2 in HCC. Fig. S7. Hub gene analysis. Fig. S8. The predicted transcription factors binding to SGOL2 or MAD2.


To model how each of these processes may affect the processing of information in neural circuits, and how their dysfunction may lead to disease states, requires a computationally efficient modelling framework, capable of generating accurate phenomenology without incurring a heavy computational cost per synapse. Constructing a phenomenologically realistic model requires the precise characterization of the timing and probability of neurotransmitter release. Difficulties arise in that functional forms of instantaneous release rate can be difficult to extract from noisy data without running many thousands of trials, and in biophysical synapses, facilitation of per-vesicle release probability is confounded by depletion. To overcome this, we obtained traces of free Ca2+ concentration in response to various action potential stimulus trains from a molecular MCell model of a hippocampal Schaffer collateral axon. Ca2+ sensors were placed at varying distance from a voltage-dependent calcium channel (VDCC) cluster, and Ca2+ was buffered by calbindin. Then, using the calcium traces to drive deterministic state vector models of synaptotagmin 1 and 7 (Syt-1/7), which respectively mediate synchronous and asynchronous release in excitatory hippocampal synapses, we obtained high-resolution profiles of instantaneous release rate, to which we applied functional fits. Synchronous vesicle release occurred predominantly within half a micron of the source of spike-evoked Ca2+ influx, while asynchronous release occurred more consistently at all distances. Both fast and slow mechanisms exhibited multi-exponential release rate curves, whose magnitudes decayed exponentially with distance from the Ca2+ source. Profile parameters facilitate on different time scales according to a single, general facilitation function. These functional descriptions lay the groundwork for efficient mesoscale modelling of vesicular release dynamics.Chemical synapses exhibit a diverse array of internal mechanisms that affect the dynamics of transmission efficacy. Many of these processes, such as release of neurotransmitter and vesicle recycling, depend strongly on activity-dependent influx and accumulation of Ca Most information transmission between neurons in the brain occurs via release of neurotransmitter from synaptic vesicles. In response to a presynaptic spike, calcium influx at the active zone of a synapse can trigger the release of neurotransmitter with a certain probability. These stochastic release events may occur immediately after a spike or with some delay. As calcium accumulates from one spike to the next, the probability of release may increase (facilitate) for subsequent spikes. This process, known as short-term plasticity, transforms the spiking code to a release code, underlying much of the brain’s information processing. In this paper, we use an accurate, detailed model of presynaptic molecular physiology to characterize these processes at high precision in response to various spike trains. We then apply model reduction to the results to obtain a phenomenological model of release timing, probability, and facilitation, which can perform as accurately as the molecular model but with far less computational cost. This mesoscale model of spike-evoked release and facilitation helps to bridge the gap between microscale molecular dynamics and macroscale information processing in neural circuits. It can thus benefit large scale modelling of neural circuits, biologically inspired machine learning models, and the design of neuromorphic chips. When the action potential of the presynaptic neuron reaches the axon terminal, it triggers a sudden influx of Ca2+ through voltage-dependent Ca2+ channels (VDCCs), and when a sufficient number Ca2+ ions binds to the C2 domains of synaptotagmin, it undergoes a conformational change that triggers the associated SNAREpin to zipper completely, causing the vesicle to fuse with the membrane and to release its neurotransmitter through the newly opened fusion pore i waveform obtained from MCell, one can see that each release mechanism induces vesicle fusion with a histogram that essentially follows a multi-exponential form (r(t), where r∈{S, A} may refer to synchronous or asynchronous release rate) rise quickly from baseline after the spike and decay with several exponential components, approximated asr0 is the spontaneous release rate , t is the time since the last spike, u(t) is the Heaviside step function (so that release occurs only for t≥0), N is the number of exponential decay components, τc are the time constants of exponential decay, and Pc are the expected number of releases from each component for a single vesicle. Note that because the release rate profile is not a probability distribution, but rather it represents the instantaneous rate of release conditioned on having not released yet . The probability that the exponential component causes release at any point in time, ignoring the other components, is prc = 1−exp(−Pc) (and the integrated release probability for the spontaneous component is pr0 = 1−exp(−∞) = 1). The existence of multiple exponential components is apparent from the linear segments visible in log-linear space for synchronous and asynchronous release rates in τ(t) is the instantaneous time constant and r*(t) is the observed instantaneous release rate. We used the well-mixed model for the derivative calculation because it had no noise in the release rate profiles.By driving a deterministic simulation of SNARE dynamics with the i available to the release mechanism. Therefore, most of the spike-evoked release occurs in response to this narrow window of influx. To test how each release mechanism responds to transient Ca2+ spikes, we supplied an instantaneous burst of Ca2+ to a single time step of the deterministic model, allowing us to measure the impulse-response function. These simulations were repeated for various resting Ca2+ levels ([Ca2+]i0), ranging from 0 to 10 μM, to see how the presence of Ca2+ at equilibrium affects the response to spike-evoked transients. As 2+]i, the rate of release for both synchronous and asynchronous mechanisms rises quickly in response to a sudden influx before dropping exponentially with a single exponential component (black). However, when [Ca2+]i0 settles at some level greater than zero, an extra exponential component emerges for both mechanisms (blue and red lines). The exponential decay time constants seem to be mostly independent of resting [Ca2+]i0 at low levels, but they drop off more quickly as spontaneous release rates begin to overtake the spike-evoked rates at high concentrations. The extra component emerges as a result of the back-and-forth Ca2+-binding and unbinding processes, where finite baseline [Ca2+]i0 likely provides a “floor” to “bounce off of” in terms of the number of Ca2+ ions bound to the release mechanism. Note, however, that even though it depends on equilibrium [Ca2+]i0, this secondary release component is still purely spike-evoked and arises due to the nonlinearity of the system. The baseline rate r*(0) was subtracted off to ensure that the function approached zero prior to taking the logarithm (dotted lines). Time constants in Most of the Ca2+]i0, a fast exponential component that acts in response to an impulse of spike-evoked Ca2+, and a slower spike-evoked component that results from a “rebound” interaction with the Ca2+ floor. However, the profiles of the release rate histograms display more complexity than this, which will be discussed in more detail below. Significantly, [Ca2+]i does not drop instantly to baseline after the initial influx, but some leftover Ca2+ continues to have a small effect over a long time window as it slowly unbinds from the calbindin buffer . Because of this, the release process cannot begin until the spike-evoked Ca2+ arrives, which time may vary randomly relative to the timing of the spike. Thus, the process of buffered diffusion acts as a temporal filter on the release dynamics, transforming the equation of release toa(·) is the temporal filter and kc, μc, and σc are parameters to be discussed below. The convolution operation effectively smears the start time of the average release profile in a way that accounts for random temporal jitter across trials.Of course, neurotransmitter release cannot begin at exactly the moment of the spike, both because the action potential itself is not an instantaneous process and because it takes finite time for Caa(·) must integrate to one over all real numbers. That way, it does not affect the probability of release, only its timing. The temporal filter chosen is an ex-Gaussian distribution, resulting from the convolution of an exponential distribution of rate k with a normal distribution of mean μ and standard deviation σ:σ→0, this CDF simply becomes the shifted Heaviside step function u(t−μ), and μ. The values of μ and σ result from the sum of the delays caused by numerous random processes, including the timing of Ca2+ entry relative to the spike, the accumulation of collision events during Brownian motion, and the binding/unbinding events with the calbindin buffer and SNARE complex. Assuming that the individual events of the buffered diffusion process are numerous and similar enough for a given spike, the central limit theorem states that the sum of their delays should approximate a normal distribution  at the Ca2+ sensor. With the mathematical description of the release rate profiles in mind, we ran a fitting algorithm see  to deternd 0.04) . The synμ values are somewhat arbitrary in that they depend on exactly when during the action potential that the spike time is taken to occur. Action potential waveforms last a couple of milliseconds . After the first spike, the release mechanism will respond to a Ca2+ influx after some delay with the release profile of Eq  and starts generating release times in response to the second spike (with a release profile shifted by tCa2), according to2+ arrival times, each resulting from the combination of a spike time with an ex-Gaussian-distributed delay. When the second spike arrives at the release site, the VDCCs produce another influx of Cat−tCa2),where rDc(t) that the Ca2+ sensor has begun responding to the Ca2+ from the latest spike for release component c, allowing for a gradual switch from one spike-evoked release profile to the next, taking into account the variation of delay among all possible trials. For two spike times, ts1<ts2, the net release profile for each component of Eq  to give way to the new release response. And every time another spike arrives, it decreases the probability of release relative to the first spike multiplicatively, such that each component of Eq  of Eq (c(t–tsj). Assuming the latest spike arrives at t = 0, Dc(t) is simply the cumulative distribution of the temporal delay filter:Because this formulation now uses spike times rather than delayed Caj) of Eq  and 7)  2+ arrivj) of Eq  to becomσc→0, the Gaussian component becomes a delta function, and the first-release distribution function above becomes much more simplyMore intuitively, by letting σc is small relative to the median interspike interval, third spikes have an almost imperceptible effect at cutting the first profile short relative to the second spike’s effect. Thus, after the second spike, the histogram of releases from the first spike drops off exponentially, while those due to the second spike rise and fall as for the first spike. When 2+ in the presynaptic space i in neurons obtained from fluorescent reporters in physiological experiments may provide accurate estimates of slow (tens of milliseconds) Ca2+ transients i in the vicinity of the SNARE complex was averaged over 2000 trials at a resolution of 0.1 msec. These calcium transient profiles were then used to drive deterministic simulations of SNARE state probabilities, as described below.Calcium concentration as a function of time is measured at various spatial locations within the presynaptic space  and 0 through 2 for Syt-7 ). Each mechanism X∈{S, A} has a state probability vector sX(t) associated with it that tracks the probability of being in each possible state, that is, the probability of having n Ca2+ ions currently bound for n∈{0…NX}, where NS = 5 and NA = 2. State probabilities add to unity, and they update on each time step according to a [Ca2+]-dependent state transition rate matrix TX, whose superdiagonal terms are the unbinding rates, moving from a higher to a lower-bound state, and whose subdiagonal terms are the binding rates, moving from a lower to a higher-bound state. Specifically, for mechanism X∈{S, A} with NX∈{5,2} calcium ions needed for release to occur, the binding rate isn∈{0…NX} ions currently bound, where b = 0.25 acts as a binding cooperativity factor (see TX (as well as the dimensions of sX) starting from 0 rather than 1 for convenience in representing the number of calcium ions bound in the current state (column) and in the next state (row). To enforce conservation of mass, the diagonal terms must equal the combined rate of leaving the current state through both binding and unbinding:This method tracked the probabilities of a particular release mechanism being in each possible state at every time step. That is, each state represents the number of CaX induces vesicle release at a certain rate γX from its releasable state . Therefore, in addition to the unbinding rate, the diagonal term for the fully bound state of each mechanism also includes the rate of transition to the release state:γX is the mechanism-specific release rate defined in The above formulation does not yet take into account vesicle fusion. Recall that each mechanism 2+ concentration . These TX represents transition rates rather than transition probabilities, it acts as the infinitesimal generator for a continuous-time finite state Markov process  can be regarded as constant . However, in order to avoid too many matrix multiplications and sums, we chose a time step of 0.005 msec, which is small enough to use the linear approximation to Eq . These Ca2+ traces then drove deterministic simulations of synchronous and asynchronous release rate, as described in the previous section. This permitted us to determine a functional form to describe PPF  and on the state of some internal facilitation parameter from the previous spike. The spike trains generally consist of two phases: a spiking ramp and a probe spike. The ramp phase explores how facilitation develops with multiple spikes at fixed ISIs and having anywhere from one to five spikes with an ISI of 2, 5, 10, or 20 ms (time prevented the exploration of ramps with more spikes). The probe phase explores how facilitation wears off with increasing delay between spikes and consists of a single spike at 2, 5, 10, 20, 50, 100, or 200 ms after the end of the ramp, as in the PPF protocol above. All these combinations of ramps and probes add up to 5×4×(7+1) = 160 cases (including those cases without a probe spike) or 136 unique spike trains (discounting the repeats with one spike in the ramp at different ISIs). Future work can explore the steady-state behavior of the facilitation factors by testing longer spike ramps of each ISI.2+ impulse and the Ca2+ traces derived from MCell. Other parameters were initialized through trial and error. For the second step, we applied the Nelder-Mead simplex method of function optimization using the fminsearch function in MATLAB 2016 i from collision events. (B) Chemical kinetics of calcium channels, buffers, and pumps. (C) Effects of buffer and spatial modeling on release dynamics. (D) Applying release start time filter to release rate profiles. (E) Facilitation nonlinearities. (F) Intuitive exploration of facilitation function behavior. (G) Goodness of fit of facilitation models. Fig A. State Diagrams for VDCC, Calbindin, and PMCA. All diagrams reproduced with permission from Nadkarni et al. [ij and βji depend on membrane potential v. (B) State transitions for calbindin (CB) at high-affinity (H) and medium-affinity (M) Ca2+-binding sites. On rates (kh+ and km+) are proportional to [Ca2+]i. (C) PMCA pump state diagram with Ca2+ interactions depicted on the relative side of the membrane. Ca2+ leakage occurs only in state PMCA0. Association rate kpm1 is proportional to [Ca2+]i. Table A. Parameter Values for VDCC, Calbindin, and PMCA. Table adapted from i0. For small [Ca2+]i0, S0 = kS·([Ca2+]i0)5 and A0 = kA·([Ca2+]i0)2, where kS≈6×10−4 ms–1·μM–5 and kA≈2×10−3 ms–1·μM–2. As [Ca2+]i0⟶∞, S0⟶γS and A0⟶γA. Values for S0 and A0 at [Ca2+]i0 = 100 nM, which is used throughout most of this paper, are pointed out for reference. Fig D. Spatial Modeling Important for Capturing Fine-Grain Features of Ca2+Transients. Color scheme identical to that used in 2+ sensors. (A) [Ca2+]i measured at increasing distance from VDCC source (yellow to blue), with well-mixed approximation overlaid for comparison (maroon). Inset focuses on shorter time scale. (B) Profiles with calbindin removed from MCell (yellow to blue) and well-mixed model (maroon). Note that peak [Ca2+]i for the most proximal case extends up to 81 μM, but is cut off for clarity. Fig E. Effect of Calbindin Buffer on Spike-Evoked Ca2+Profile and Release Rates. Action-potential-like stimulus delivered to model axon starting at 0 ms. Diffusion is assumed to be instantaneous, and molecular state probabilities are tracked deterministically over time. (A) Free [Ca2+]i with no calbindin buffer decays linearly with time due to saturation of PMCA pumps. (B) Syt-1/7-mediated release rates are large but short-lived in response to unbuffered Ca2+. (C) Free [Ca2+]i with calbindin added to the axon has much smaller magnitude and much narrower peak but has much longer tail. (D) Vesicle release in response to buffered Ca2+ is much less pronounced. The calbindin buffer reduces the rate of synchronous transmission but extends the window for pronounced asynchronous transmission. Fig F. Synchronous and Asynchronous Release Rates Decrease with Distance from the Ca2+Source. Color scheme identical to that used in 2+ sensors. (A) Synchronous release rate. (B) Integrated probability of synchronous release falls off nearly exponentially with distance to a baseline level. (C) Asynchronous release rates. (D) Integrated probability of asynchronous release also decays with distance to some baseline, but not exponentially. Fig G. Parametric Fits to Release Histogram Profiles at Increasing Distance from the Ca2+Source.  Fitted release profiles (black) imposed over the true histograms for synchronous  and asynchronous . (C) Parameter values as a function of distance for synchronous release. (D) The same for asynchronous release. Fig H. Change in the Balance of Binding Kinetics and Internal State Distribution of Ca2+Sensor with Spike History. State diagrams the same as shown in 2+]i (first red dot), unbinding kinetics (left arrows) overpower binding (right arrows), biasing Syt-1 toward unbound state , with almost no probability of having any Ca2+ ions bound before an action potential (left pie chart). During peak Ca2+ influx (second red dot), binding rates (thicker right arrows) overpower unbinding, biasing Syt-1 toward its fully-bound releasable state , with much greater probability of having at least some Ca2+ bound (right pie chart). (B) The same for asynchronous release with Syt-7, whose releasable state requires two Ca2+ ions bound (A2). Slower kinetics lead to only slight bias in favor of binding during an action potential (slightly thicker right arrows in lower diagram), leading to miniscule increase in probability of being in the releasable state on later spikes (right pie chart). Release becomes more probable on subsequent spikes because previous activity has pushed synaptotagmin into higher-bound states, making reaching the releasable state easier. Fig I. Empirical Facilitation in Release Probability is a Nonlinear Function of Spike History and Ca2+Buildup. (A) [Ca2+]i and (B) release rate in response to a 5-spike ramp stimulus with a 10-ms ISI (black and dark green), followed by a single probe spike at increasing delay from the end of the ramp . Release rate grows much faster than Ca2+ buildup can account for. Fig J. Empirical Facilitation in Release Probability is a Nonlinear Function of Spike History. Integrated release fidelity (P(n)) relative to baseline (P(0)) for the various stimulus cases explored. Ramp # indicates the number of spikes in the ramp preceding the probe spike, and Δt represents the ISI between the last ramp spike and the probe spike. Spike history noticeably affects the growth of facilitation, as seen for ramps with 2-ms ISIs (A), 5-ms ISIs (B), 10-ms ISIs (C), and 20-ms ISIs (D). Different colors distinguish facilitation functions with different spike histories. Dark lines follow relative release fidelity for spikes along spike ramps, and dotted lines follow relative release fidelity for probe spikes. Fig K. Saturation of Facilitation Parameters. (A) Facilitation parameter f(·) increases almost linearly from one spike (f(n-1)) to the next (f(n)), until it approaches some limit N≥1. (B) Curves represent the unseen change in f(·) between spikes. Dots represent actual values observed at spike times, values determined by the Ca2+-triggered increment in release fidelity at each spike. Steady-state value for facilitation parameter limited by stimulus frequency and by value of N. No facilitation above baseline occurs for N = 1. Fig L. Release Rate Parameters and Facilitation Metaparameters Fitted to Empirical Histogram Profiles. Errors across all cases in linear and logarithmic space for the predictive model.(A) Estimating  A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Joanna Jędrzejewska-Szmek, Ph.D.Associate EditorPLOS Computational BiologyKim BlackwellDeputy EditorPLOS Computational Biology***********************ploscompbiol@plos.org immediately:A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact [LINK]Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The study by Garcia et al. is an important contribution that makes it possible to scale-up the implication of synaptic designs to networks of neurons. They have chosen an important synapse, the mossy fiber synapse in the trisynaptic circuit of the hippocampus for their modeling investigation. There exists thousands of morphologically distinct synapses that play a diverse roles in information transfer across neurons. The suggests a strong causal link between synaptic morphology and function. Yet, there is often a disconnect between understanding at the level of synapses and neuronal circuit dynamics in both experimental studies and modeling studies. On the other hand it is unrealistic to simulate in great spatial and physiological detail activity at the synapses when computing activity of hundreds of neurons. This manuscript attempts to do fill this gap.I think, over-all results of the phenomenological are a good quantitative fit to detailed spatiotemporal calculations and therefore useful for scaling up the numbers of synapses in simulations and computation with neuronal networks. I do have a few concerns regarding the specifics of the model that are listed below.• I see that various train stimuli have been explored, however it be useful to show comparison of the phenomeological model with realistic model for more realistic trains of activity (variable frequency and noisy stimulus). Since the parameters of the phenomenological models are retro-fit to specific data, how do the authors envision incorporating it in a network (since thats the point of this study) which sees time varying stimulus?• Figure 9. Description of this figure in the results and and figure both are hard to understand and need to be expanded.• I did not see a description of the mossy fiber synapse design at all. This is a surprising omission given that the model is of that of MF. For that matter, how is the model different from a schaffer collateral? What specifics of the MF have been included  MFs show distinct short-term plasticity profiles as described in detail by several papers from Peter Jonas’s lab, Katalin Toth lab and Joachim Lu?bke lab. This needs to be includedMinor comments• References list seems really long, is a more concise list possible?• It will be useful if the model is available on a public repository like a modelDB. Also final parameters (taus of decay and facilitation etc.) of the model that fit the detailed MCell simulations for various stimuli could be presented in tabular format for ease of use.Reviewer #2: Evaluation of the manuscript by J.W. Garcia, T.M. Bartol and T.J. Sejnowski entitled: Multiscale modeling of presynaptic dynamics from molecular to mesoscaleCommentsThis study is an attempt to develop a presynaptic model of processes determining the dynamics of transmission efficacy. First, the traces of free Ca2+ concentration were obtained that occur in response to various action potential stimulus trains from a Monte Carlo model of a hippocampal mossy fiber axon. Ca2+ was estimated as it diffuses from the voltage dependent Ca channel (VDCC) source, through the buffer, to the Ca2+ sensor in the SNARE complex and its time course determined at varying distance from a VDCC cluster with Ca2+ being buffered by calbindin. The calcium traces then drive deterministic state vector models of synaptotagmin 1 and 7 (Syt-1/7), which respectively mediate synchronous and asynchronous release in excitatory hippocampal synapses. This paper extends their earlier Monte Carlo studies of synaptic transmission, and it comes from a prominent group that worked on these and related problems for a long time. The methodology is well described and discussed. Moreover, there is a definitive need for simulations that combine Ca dynamics and release processes.Authors stress the advantages of this approach over biological experiments in terms of both flexibility and precision when it comes to model validation are discussed. They also discuss disadvantages – a) Does Markov chain Monte Carlo framework accurately replicate the dynamics of diffusion and molecular interactions of these mechanisms? b) Is the molecular kinetics of the included species their true kinetics? and c) Are the molecular species and processes included the only relevant ones? That is needed and appropriate.CriticismsI have two basic issues with this paper:First issue relates to Ca diffusion and how it is addressed. As authors state diffusion plays a key role in presynaptic processes. However, the question of how micro or nanostructures like cytoskeleton affect diffusion and ultimately release is just briefly mentioned. Why? Is it so time consuming to do some additional simulations with idealized geometry to answer the question how such intracellular micro-structures affect the diffusion and release? The importance of vesicles themselves (or of vesicular clusters) as diffusional barriers is not addressed at all. It should be. It is well documented that they play an important role in restricting diffusion. See work of Cooper et al., (1996) J Neurophysiol 75:2451; Glavinovic & Rabie, Pflügers Arch (2001) 443:132; Shahrezaei et al., (2006) J. Neurosci.26:13240. In central synapses individual vesicles play a less important role as diffusion barriers than in adrenal secretory cells, but they cluster. Doing additional simulations with vesicles included would be ideal, but even in the absence of new simulations it should be discussed, earlier work referred to and commented upon. Authors discuss local saturation of both the calbindin buffer and the PMCA pumps in the nanodomains near the VDCC cluster, where free Ca2+ reaches high levels and how it affects proximal Ca curve. Please expand on this in the context of slower diffusion caused by vesicular presence.Second, this paper presents a model for hippocampal mossy fiber axon synapse with a given set of Ca binding-unbinding parameters. However, these parameters which determine the total binding ratio (the ratio of bound to free Ca2+) varies greatly in different secretory systems (Stuenkel (1994) J Physiol 481:251; Tank et al. (1995) J Neurosci 15:7940). Moreover, the binding ratio changes with cell development because the expression of Ca2+ buffers is developmentally regulated (Friauf (1993) J Comp Neurol 334:59; Lohman and Friauf (1996) J Comp Neurol 367:90. How would different Ca binding-unbinding parameters change Ca dynamics and release processes? Could authors comment on these issues, and if possible, do some simulations to answer them more completely. These changes would make this work significantly more interesting to a wider audience.**********Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.The No: Reviewer #1: Reviewer #2: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: Reproducibility:https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocolsTo enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at References:Review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.AttachmentGarcia-Bartol-Sejnowski.docxSubmitted filename: Click here for additional data file. 7 Mar 2022AttachmentReviewer Feedback.docxSubmitted filename: Click here for additional data file. 29 Mar 2022Dear Dr. Garcia,We are pleased to inform you that your manuscript 'Multiscale modeling of presynaptic dynamics from molecular to mesoscale' has been provisionally accepted for publication in PLOS Computational Biology.Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. Best regards,Joanna Jędrzejewska-Szmek, Ph.D.Associate EditorPLOS Computational BiologyKim BlackwellDeputy EditorPLOS Computational Biology***********************************************************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: My concerns have been addressed in this version of the manuscript. I think the manuscript is ready to be published at this point.Reviewer #2: This is an excellent paper by a very competent group. It was a pleasure to read it. Congratulations.**********Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********what does this mean?). 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