SLC1A3/2) to regulate glutamate uptake for the benefit of tumor growth. Thus, EAAT1/2 may be an attractive target for therapeutic intervention in oncology. Genetic variation of EAAT1 has been associated with rare cases of episodic ataxia, but the occurrence and functional contribution of EAAT1 mutants in other diseases, such as cancer, is poorly understood. Here, 105 unique somatic EAAT1 mutations were identified in cancer patients from the Genomic Data Commons dataset. Using EAAT1 crystal structures and in silico studies, eight mutations were selected based on their close proximity to the orthosteric or allosteric ligand binding sites and the predicted change in ligand binding affinity. In vitro functional assessment in a live-cell, impedance-based phenotypic assay demonstrated that these mutants differentially affect L-glutamate and L-aspartate transport, as well as the inhibitory potency of an orthosteric (TFB-TBOA) and allosteric (UCPH-101) inhibitor. Moreover, two episodic ataxia-related mutants displayed functional responses that were in line with literature, which confirmed the validity of our assay. Of note, ataxia-related mutant M128R displayed inhibitor-induced functional responses never described before. Finally, molecular dynamics (MD) simulations were performed to gain mechanistic insights into the observed functional effects. Taken together, the results in this work demonstrate 1) the suitability of the label-free phenotypic method to assess functional variation of EAAT1 mutants and 2) the opportunity and challenges of using in silico techniques to rationalize the in vitro phenotype of disease-relevant mutants.Glutamate is an essential excitatory neurotransmitter and an intermediate for energy metabolism. Depending on the tumor site, cancer cells have increased or decreased expression of excitatory amino acid transporter 1 or 2 (EAAT1/2,  These mPh (Tk (+ ions and/or inhibitors. Glutamate transporters assemble in obligate homo-trimers of which the protomers operate independently of each other. Each protomer consists of a rigid trimerization or scaffold domain (scaD) and a dynamic transport domain (tranD) that engages with the substrate and co-transported Na+ ions  dataset M. A.  were cha our lab . MutantsWT) or mutant EAAT1 (see section 2.6–2.9) were kindly provided by the RESOLUTE consortium . L-glutamic acid monosodium salt monohydrate, L-aspartic acid monosodium salt monohydrate, doxycycline hyclate, Dulbecco’s modified Eagle’s medium (DMEM) and Dulbecco’s phosphate-buffered saline (PBS) were purchased from Sigma Aldrich . 2-amino-4-(4-methoxyphenyl)-7--5-oxo-5,6,7,8-tetrahydro-4H-chromene-3-carbonitrile (UCPH-101) was purchased from Santa Cruz Biotechnology . -3-[3-[4-(trifluoromethyl)benzoylamino]benzyloxy] aspartate (TFB-TBOA) was purchased from Axon Medchem . Lipofectamine 3,000, P3000 buffer, Gateway LR Clonase II enzyme mix and Proteinase K solution were purchased from ThermoFischer . QuikChange II kit was purchased from Agilent Technologies . QIAprep Spin Miniprep Kit was purchased from QIAGEN . xCELLigence PET E-plates 96  were purchased from Bioké . All other chemicals were of analytical grade and obtained from standard commercial sources.Modified Jump In T-REx HEK 293 (JumpIn) cells overexpressing human wild-type  in all cancer types. The 105 unique mutations found were mapped onto the 3D structure of EAAT1 [PDB 5LLU, 5MJU  mutations found in natural variance in the 1,000 Genomes dataset  and UCPH-101  caused by point mutations. This analysis was performed in ICM-Pro as follows. The difference in binding energy  is calculated as the difference between the Gibbs binding energy  in the mutant and the WT. ΔGbind is calculated for fixed backbone and Monte Carlo-sampled flexible side chains in the vicinity of the mutated residue as the energy of the protein-ligand complex minus the energy of the protein and ligand separately.To prioritize mutations for bind was calculated for endogenous ligands L-aspartate and L-glutamate (previously docked) in system 5LLU. Moreover, ΔΔGbind was calculated for the competitive inhibitor TFB-TBOA in system 5MJU. For the cancer-related mutations found in the allosteric binding site , ΔΔGbind was calculated for the allosteric inhibitor UCPH-101 in system 5MJU. For V247F, which is at the interface of both ligand binding sites, ΔΔGbind was calculated for L-glutamate, L-aspartate, TFB-TBOA and UCPH-101 as described above.For the cancer-related mutations found in the orthosteric binding site , ΔΔGAll visualizations of EAAT1 structures were generated in PyMOL using PDB 7AWM. Where TFB-TBOA was visualized, PDB 5MJU was superimposed on 7AWM.SLC1A3) in a pDONR221 vector ) together with 10 µM forward and reverse primer, 1 µL dNTP mix, 2.5 µL 10x reaction buffer and 2.5 U DNA polymerase were run in a PCR thermal cycler for 22 cycles . Non-mutated DNA was removed by addition of 5 U DpnI restriction enzyme for 2 h at 37°C. Mutant DNA was transformed into XL1-Blue competent cells in the presence of 50 μg/mL kanamycin for selection. Plasmid was isolated using a QIAprep Spin Miniprep Kit verified by Sanger sequencing .DNA primers for EAAT1 mutants were designed with a single or double base pair substitution for the resultant amino acid using the QuikChange Primer Design Program and synthesized by Integrated DNA Technologies  . Site-diSLC1A3 plasmids were cloned into a pJTI R4 DEST CMV TO pA expression vector with a C-terminal Twin-Strep-tag and a hemagglutinin (HA)-tag using Gateway cloning. The expression vector contains a tet-operon (TO) that allows doxycycline (dox)-inducible expression of the transgene. In brief, 150 ng pDONR221-SLC1A3 plasmid and 150 ng pJTI R4 DEST CMV TO pA in TE buffer  were incubated with Gateway LR Clonase II enzyme mix at 25°C for 1 h. To remove endogenous nucleases, the mixture was incubated with a Proteinase K solution for 10 min at 37°C. The resulting vectors (WT or mutant pJTI-SLC1A3) were transformed into XL1-Blue competent cells in the presence of 100 μg/mL ampicillin for selection. Plasmid was isolated and sequenced as described in the previous section.To allow stable transfection into JumpIn cells, the WT and mutant pDONR221-2. After thawing and recovery, cells were grown for 3–5 days in culture medium with 5 μg/mL blasticidin and 2 mg/mL G418 before switching to culture medium.JumpIn-EAAT1 cells were split twice per week to 10 cm dishes in culture medium  at 37°C and 5% COSLC1A3 plasmid in OptiMEM was added to a mix of 2.1 µL Lipofectamine 3,000 in OptiMEM  and incubated for 5 min at RT. As a control for antibiotic selection, one dish of cells was incubated with sterile water instead of pJTI-SLC1A3. Cells were transfected with 60 µL of the total mix. On the next day the transfection medium was replaced by fresh culture medium. After 24 h cells were trypsinized and seeded onto 6 cm culture dishes at 200,000 cells/well to grow for 3–4 days. When 70% confluence was reached medium was replaced with selection medium (culture medium with 1 mg/mL G418) to select for successfully transfected cells. Selection medium was refreshed every 2–3 days for 2 weeks until non-transfected cells were all dead and colonies had grown in the transfected dishes. Colonies were resuspended in selection medium and grown to confluence before cryofreezing pools of transfected cells. Prior to use in experiments, cells were cultured in regular culture medium for at least 24 h.JumpIn cells were seeded at 90,000 cells/well in culture medium onto a 24-well culture plate and grown within 24 h to 60%–70% confluence. Per mutant or WT, a mix of 1.8 µL P3000 buffer, 450 µg pJTI R4 Integrase plasmid and 450 µg pJTI-2. Cells were washed with PBS and fixed with 4% formaldehyde for 10 min, then washed with Tris-buffered saline (TBS). To allow access of the antibodies to the intracellular HA-tag, cells were incubated with permeabilization buffer (TBS +0.5% Tween-20 (TBST), 2% bovine serum albumin (BSA) and 0.2% saponin) for 60 min at RT. After blocking and permeabilization, cells were incubated with 1:2,500 rabbit anti-HA polyclonal antibody  for 60 min at RT and washed with TBST. Subsequently, cells were incubated for with 1:3,000 goat anti-rabbit horse radish peroxidase (HRP)-conjugated IgG antibody  for 30 min at RT and washed with TBS. Immunoreactivity was visualized by addition of 3,3′,5,5′-tetramethylbenzidine (TMB) for 2.5 min at RT and subsequent quenching with 1 M H3PO4. Absorbance was measured at 450 nm using a Wallac EnVision multimode plate reader .To determine the relative amount of C-terminal HA-tagged protein expressed in doxycycline (dox)-induced JumpIn-EAAT1 WT and mutant cells, an enzyme-linked immunosorbent assay (ELISA) was performed on whole, permeabilized cells. Each condition was tested in quintuplicate per experiment. Cells were seeded in culture medium onto a 96-well culture plate coated with 0.1 mg/mL poly-D-lysine at 60,000 cells/well in the presence or absence of 1 μg/mL dox  and were grown for 22–24 h at 37°C and 5% CO+-dependent substrate influx induces cell swelling, which leads to cell spreading. This results in an increased cellular impedance over time and as such is a readout of transporter function. For the assay, JumpIn-EAAT1 cells are cultured in medium onto gold-plated electrodes of a 96-well E-plate and for each well the impedance is measured on predefined time intervals at 10 kHz. The impedance is converted to the unitless parameter Cell Index (CI), which can be plotted over time:i is the impedance at any given time point and Z0 is the baseline impedance measured at the start of each experiment  or, in inhibitor experiments, 1 nM–10 µM of TFB-TBOA or UCPH-101 or 1 µM ouabain, and impedance was recorded for 60 min. Subsequently, cells were stimulated with 5 µL vehicle (PBS), 10 μM–1 mM L-glutamate  or L-aspartate, 200 nM TFB-TBOA (EC50) or 6.3 µM UCPH-101 (EC50), and impedance was recorded for 120 min. Each condition was tested in duplicate per experiment and levels of DMSO were kept constant at 0.1% for all assays and wells.Assays were performed at 37°C and 5% COWT cells to obtain fold expression over–dox cells. To assess whether total protein expression of dox-induced (+dox) JumpIn-EAAT1 mutant cells was significantly different from +dox JumpIn-EAAT1WT cells, a one-way ANOVA with Dunnett’s post hoc test was done for cells that were tested on the same ELISA plate.In each experiment, the mean absorbance for each condition was divided over the mean absorbance of non-induced (–dox) JumpIn-EAAT1WT) was done using a one-way ANOVA with Dunnett’s post hoc test. Differences were considered statistically significant when p-values were below 0.05.Data was recorded using RTCA Software v2.0 or v2.1.1 (ACEA Biosciences). Depending on the part that was used for analysis, the CI values were normalized to the time of inhibitor pretreatment or substrate stimulation yielding normalized CI (nCI) values for all subsequent data points. The nCI values were exported and analyzed in GraphPad Prism v9 . Vehicle-only conditions were subtracted from all other conditions to correct for vehicle-induced, ligand-independent effects. The remaining nCI curves were quantified by analyzing the net area under the curve (AUC) of the first 120 min after substrate stimulation. The AUC values, which are expressed as the cellular response, were fitted to a sigmoidal concentration-effect curve with a variable slope to determine the potencies of the EAAT1 substrates and inhibitors. Data are shown as the mean ± standard error of the mean (SEM) of at least three separate experiments each performed in duplicate, unless stated otherwise. Comparison of multiple mean values to a control  were sampled. All systems were prepared in four steps: (a) the mutation of interest was introduced; (b) default protein preparation wizard was run; (c) the system was stripped to contain the protein trimer and the ligands and ions of interest as defined for each type of system i-iii; (d) the system was embedded in a POPC lipid bilayer respect to the α-helices, solvated with SPC water molecules, the charge was neutralized with Cl− ions, and NaCl was added in physiological concentration (0.15 M). Subsequently, the systems were relaxed with the default protocol, which includes a restrained minimization followed by an unrestrained minimization and four stages of MD runs with decreasing constraints. The production runs were simulated for 500 ns with a recording interval of 500 ps  in an NPT ensemble with temperature 300 K and pressure 1 bar. Each system was run for ten replicates with velocities randomly initialized with random seeds . Using Desmond analysis scripts, the trajectories’ Root Mean Square Deviation (RMSD) was calculated for the protein α carbon (Cα) atoms and for the ligand (L-Aspartate) with respect to the protein. These RMSD values represent the stability of the protein system and the ligand, respectively, over the time of the simulation. Moreover, Root Mean Square Fluctuation (RMSF) was calculated for the protein Cα atoms. The RMSF values represent the stability/flexibility through the simulation of each of the protein residues. RMSD and RMSF values were calculated independently for each chain in the trimeric system. Protein RMSD was used as an overall measure of the system’s stability. Therefore (chain) systems with protein RMSD reaching 10 Å were excluded from further analysis.et al. for GltPh (Ph. (2) Na+ coordination in Na1 site: distance between Na+ ion originally coordinated in Na1 site and one of the Na1 coordinating atoms. The distance was measured between Na+ with residue number 601 and D487 Cα. (3) Na+ coordination in Na2 site: distance between Na+ ion originally coordinated in Na2 site and one of the Na2 coordinating atoms. The distance was measured between Na+ with residue number 603 and T396 Cα. (4) Na+ coordination in Na3 site: distance between Na+ ion originally coordinated in Na3 site and one of the Na3 coordinating atoms. The distance was measured between Na+ with residue number 602 and D400 Cα. These distances were measured independently for each chain in the trimeric system.In PyMOL, the trajectories were loaded and fitted to the first frame in the simulation to correct for rotations and translations. Subsequently, the distance in each frame was calculated between a pair of atoms to obtain four measures (1–4). (1) HP2 opening: distance between HP1 and HP2 domain tips, as defined by Alleva or GltPh . In EAATSampling density of MD metrics computed per frame  was plotted in Python 3.8 using Matplotlib and Seaborn libraries . The samWT. For EAAT1WT and each simulated mutant, five random frames with the most frequent HP2 opening distances in the distribution across all replicates and frames were selected  and 7AWM (UCPH-101). The characteristics of the different pocket conformations used for ensemble docking were further analyzed by predicting all possible pockets using the pocket finder tool in ICM-Pro and visually selecting the orthosteric and allosteric sites. Pocket volume, hydrophobicity, buriedness, and DLID score were used to confirm pocket conformational variability. ICM-Pro implementation of flexible docking (4D docking) was performed using the five extracted frames to build a receptor map complex per mutant. The rest of the docking setup and parameters followed the general framework described in section System preparation and molecular docking. In 4D docking, the stack of receptor conformations provided are considered as a single receptor object and ten docking poses are generated on the most favorable conformations. The best docking pose in terms of docking score for each mutant was selected for analysis.The orthosteric (TFB-TBOA) and allosteric (UCPH-101) inhibitors were docked in a representative selection of MD frames with the most frequent HP2 opening distances but different binding pocket conformations. Chain A was selected for docking because it showed the highest substrate stability in EAAT1selected . Frames + ions, as well as in the HP2 domain  M. A. , 105 uni2 domain . Moreove methods .in silico to prioritize mutations for in vitro testing. Changes in binding energy ΔΔGbind were calculated for two endogenous substrates (L-aspartate and L-glutamate), one competitive ‘orthosteric’ inhibitor (TFB-TBOA), and one non-competitive ‘allosteric’ inhibitor UCPH-101 , which suggests the relative importance of these residues in protein function  or have unaltered transport (T318A). Neither of these two residues are conserved in other glutamate transporters  and two allosteric mutants (Y127C and V390M) were selected for function . To valisporters . M128 issporters .in vitro, a series of HEK293 JumpIn cell lines were generated and modified to stably express either WT (EAAT1WT) or mutant EAAT1 upon induction with 1 μg/mL doxycycline for 24 h. None of the ten mutants showed either a decreased or increased expression of the HA-tagged EAAT1 compared to EAAT1WT after doxycycline treatment, indicating that the mutations did not affect translation of the transgene  and interpreted as a readout of EAAT1 function expressing EAAT1 are cultured on gold-plated electrodes in a 96-well E-plate. Upon stimulation with high concentrations (10 μM–1 mM) of substrate  the cells started spreading as a result of Nafunction . Growth eriments .WT (pEC50 = 3.5 ± 0.0), which was reflected by a gradual increase of the normalized Cell Index (nCI) in the first 120 min after substrate stimulation (50 = 3.3 ± 0.0) with a slightly increased maximal response (Emax), whereas the L-glutamate response was completely abolished for M128R (50 = 3.8 ± 0.0) and V390M (pEC50 = 3.5 ± 0.0) produced similar L-glutamate potencies compared to EAAT1WT, where V247F has a 62% reduced Emax (50 = 4.1 ± 0.1), but displayed a substantial drop (94%) in Emax (50 = 3.8 ± 0.0) and L448Q (pEC50 = 3.3 ± 0.1) showed no significant change in L-glutamate potency, although the concentration-effect curve for L448Q appeared more linear and shifted rightward and did not appear to reach a maximum within the tested concentration range , whereas no concentration-dependent L-glutamate response was observed for R479W  similar to L-glutamate (50 = 3.5 ± 0.0) with an elevated Emax, whereas in M128R no L-aspartate response was observed at 1 mM (50 = 3.6 ± 0.0) was identical to EAAT1WT (50 = 3.8 ± 0.0), P392L (pEC50 = 3.9 ± 0.0) and L448Q (pEC50 = 3.7 ± 0.1) produced similar L-aspartate potencies, but a substantially lowered Emax (∼60%) compared to EAAT1WT  inhibitor TFB-TBOA and the non-competitive  inhibitor UCPH-101, the cells were pretreated for 1 h with increasing concentrations of inhibitor prior to stimulation with 1 mM L-glutamate. In EAAT1 the nCI . Strikin the nCI . These i the nCI . This suvice versa, indicating that the cellular responses elicited by either inhibitor are additive and are constituted by independent mechanisms (+/K+-ATPase (NKA) inhibitor ouabain prevented any inhibitor- or substrate-induced cellular responses in M128R cells, which indicates that TFB-TBOA and UCPH-101 responses are likely dependent on ion influx  (50 = 6.9 ± 0.1), allosteric site mutant V390M (pIC50 = 6.7 ± 0.0) and orthosteric site mutant P392L (pIC50 = 6.5 ± 0.1) did not affect the inhibitory potency of TFB-TBOA (50 = 6.2 ± 0.0) and V247F (pIC50 = 5.7 ± 0.1) significantly decreased the potency, whereas L448Q (pIC50 = 7.9 ± 0.0) significantly enhanced the inhibitory potency of TFB-TBOA (50 = 7.4 ± 0.2), whereas A446V as well as R479W did not display any sigmoidal concentration-dependent inhibition by TFB-TBOA (For EAAT17 ± 0.1) ; Table 3TFB-TBOA . Both Y1TFB-TBOA . InteresTFB-TBOA .WT, UCPH-101 could inhibit the response of L-glutamate in a concentration-dependent manner (pIC50 = 5.4 ± 0.0) (50 = 5.3 ± 0.0), T318A (pIC50 = 5.4 ± 0.0) and V390M (pIC50 = 5.4 ± 0.0) did not affect L-glutamate response inhibition by UCPH-101 (50 = 5.9 ± 0.1) and A446E (pIC50 = 5.9 ± 0.2) enhanced the inhibitory potency of UCPH-101, although this was not significant for A446E (p = 0.0919) (The effects of EAAT1 mutants on UCPH-101 inhibition were different from TFB-TBOA. In EAAT14 ± 0.0) ; Table 3UCPH-101 . In Y127UCPH-101 . Similar 0.0919) . Taken tin vitro . The simulations started from the endogenous substrate L-Aspartate-bound conformation, with coordinated Na+ ions in sites Na1-3 and closed HP2 domain. This represents the transporter conformation prior to its transition to the inward facing conformation. The stability of this conformation was followed over time in regards to the system overall , the substrate in the binding site , opening of the HP2 domain , and coordination of the Na+ ions . Compared to WT  affected ligand stability, they barely had an impact on Naimulated . In part mutants . On the  mutants . Compare mutants . HoweverTo evaluate whether the conformational changes in the HP2 domain observed upon mutation affect inhibitor binding as they do substrate coordination, molecular docking was performed per mutant in a representative selection of five frames from the MD trajectories . The selCompared to TFB-TBOA, the binding of allosteric inhibitor UCPH-101 was less affected by mutations as represented by the range in docking scores  and poseThe role of glutamate and aspartate in cancer is increasingly appreciated . Indeed,WT, therefore not affecting protein translation  or enhanslation ) surfaceion   . Substita3) (max . In addia3) (max . The doca3) (max . Howevera3)  are located in HP2, which is an important structural element that regulates the access of Nang sites . In our ng sites , which cng sites . Trackinng sites . Such ‘cng sites . Notablyng sites ; Table 3ng sites . The difto A446E . A clearto A446E .+-dependent re-translocation of the tranD during the transport cycle (50 for both TFB-TBOA and UCPH-101 are markedly increased in L448Q. These results are also supported by the favorable poses generated from the docking studies for both inhibitors (50 for both UCPH-101 and TFB-TBOA may be the result of a reduced affinity of L-glutamate in the orthosteric site, which could augment the apparent inhibitory potency.The adjacent HP2 residue L448 is involved in HP2 backbone flexibility, which is essential for Krt cycle . Strikinhibitors . In a prhibitors . The enh+ re-translocation, which sterically hinders closure of HP2 and substrate binding (+-independent and results in drastically reduced glutamate/aspartate affinity (Ph upon mutation of Arg to Cys (bind values for R479W compared to other mutated residues (The Arg at position 479 confers substrate selectivity and is conserved among glutamate/aspartate transporters. The guanidinium group of R479 forms a hydrogen bond with the sidechain carboxylate of the substrate during translocation . Moreove binding . Neutralaffinity , which wg to Cys . As obseg to Cys  and disrg to Cys , which lg to Cys . This loresidues , which iin vitro and in silico experiments likely arise from the fact that the simulations focused only on a small part of the complex elevator transport cycle and cannot therefore provide a complete mechanism for all the analyzed mutants. Adding to the complexity of the system, heterogeneity was observed among the dynamic behavior of the three protomers, which has been described for glutamate transporter analogs to trigger heterogeneous substrate binding (in vitro or in silico experiments that investigate alterations in protein solvation, anion conductivity and substrate transport kinetics (Discrepancies observed between the  binding . These rkinetics , which ckinetics .in vitro effects. Importantly, to allocate these missense variants to a substantial involvement in cancer development and progression translational studies that link genotype to phenotype would be required. Thus, the methods presented in this study may aid in the identification and characterization of pathogenic transporter variants, which may have implications for the development of selective and efficacious therapeutics.Taken together, divergent effects of EAAT1 disease-related variants were observed on substrate-induced cellular responses, as well as orthosteric and allosteric inhibition, in an impedance-based phenotypic assay. Subsequent MD simulations and docking studies aided in the formulation of hypotheses that could substantiate the observed


The primary causes of cervical cancer are human papillomavirus type 16 (HPV16) and/or other high-risk (Hr −) HPV infections. Hr-HPVE5, E6, and E7 have been identified as oncoproteins that play roles in the development of cancer. However, other HPV proteins, especially E1, may also be involved in cancer development. In this study, the role of HPV16E1 in cervical carcinogenesis was examined by siRNA knockdown experiments using SiHa cells as a model. The results showed that HPV16E1 regulated P-FOXO3a and HPV16E7 expression. Various cell functions associated with the hallmarks of cancer, including cell viability, colony formation, invasion, and anchorage-independent cell growth, were altered when HPV16E1 was downregulated. However, no effect on cell migration and apoptosis properties was found. Moreover, HPV16E1 downregulation resulted in an increase in cisplatin susceptibility. In conclusion, this is the first demonstration that HPV16E1 might be regarded as a possible novel oncoprotein involved in several processes related to oncogenesis. HPV belongs to the family Papillomaviridae, currently divided into 16 genera, of which 5 genera contain members that infect humans, i.e., Alpha-, Beta-, Gamma-, Mu-, and Nupapillomavirus1. Over 170 distinct types of HPV have been identified so far2. HPV is divided into two main categories based on the likelihood that cervical cancer will develop: low-risk (Lr-) HPVs and high-risk (Hr-) HPVs. There are at least 15 Hr-HPV types that have been identified, including types 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68, 73, and 822. Among Hr-HPV types, HPV16 is the most prevalent, and is frequently found in more than 50% of cervical carcinoma cases4. In addition, Hr-HPV infection has been linked to cancer in other body parts, including head and neck squamous cell carcinomas (HNSCC)6, oral cancer7, and penile cancer8.HPV is a circular, double-stranded DNA virus. The genetic material of this virus is contained in the naked icosahedral capsid and packaged as a minichromosome with cellular nucleosomal histone9. Early proteins take part in viral DNA replication by interacting with cellular gene products, whereas late proteins are responsible for the viral capsid's structural components10. There are diverse transcriptional patterns in HPV’s genes11. Some genes usually express from major monocistronic transcript, for example, E1, whereas many genes are polycistronic mRNAs, e.g., E6 and E713. To this date, Hr-HPV E5, E6, and E7 proteins have been recognized as significant oncoproteins16. However, there is some evidence that other HPV proteins contribute to oncogenesis, such as E1, the main replicative helicase protein17. Recently, HPV16 E1 role in the emergence of cervical cancer has been investigated18. It was demonstrated that there is a positive correlation between E1 mRNA expression and cervical cancer progression 18. In addition, the expression of the forkhead box O3 (FOXO3) protein, a protein that acts as an apoptosis regulator by upregulating genes necessary for cell death, was one of the genes that were mostly down-regulated in HPV16 E1 overexpressing HEK 293 T cells, according to the results of the microarray and real-time RT-PCR19. Therefore, FOXO3a downregulation in HPV16 E1-expressing cells may promote cell growth and survival. However, no study reveals the association between HPV16 E1 and the FOXO3 protein.Six early proteins—E1, E2, E4, E5, E6, and E7—and two late proteins—L1 and L2—are encoded by the HPV genomeIn the current study, we identified the potential function of HPV16E1 in cervical carcinogenesis through siRNA knockdown experiments. Functions related to carcinogenesis were observed, such as cell growth, proliferation, apoptosis, invasion/migration, and anchorage-independent cell growth. In addition, the association between HPV16E1 and FOXO3a was also demonstrated.20, the expression of other HPV genes, i.e., HPV16E6, E6*I, and E7, was also observed. No effect on the expression of HPV16E6 mRNA was demonstrated  were used as a control. The expression of HPV16E1 was quantified using RT-qPCR at the specified time. The findings showed that after 72 h of 50 nM siRNA transfection, the expression of HPV16E1 declined by about 50%  , rhodamine phalloidin (actin staining), and DAPI (nuclear staining). In E1KD SiHa cells, the results revealed localization of P-FOXO3a (green) in both the nucleus (blue) and cytoplasm (red) . This indicated that E1KD SiHa cells frequently found P-FOXO3a nuclear localization.P-FOXO3a localization plays a critical role in the control of cell growthE1KD and NTC SiHa cells at 24, 48, 72, and 96 h were determined for viability and proliferation. To evaluate cell viability and proliferation, Crystal Violet assay, Trypan Blue staining, and MTS assay were done. The results obtained from the crystal violet assay revealed that cell viability (%) between E1KD and NTC SiHa cells was the same at 24–48 h after transfection. At 72 and 96 h post-transfection, the cell viability of E1KD SiHa cells dropped, while NTC SiHa cells showed no change Fig. A. Simila23, it is interesting to observe whether knocking down E1 affects the susceptibility of the cervical cancer cells. E1KD and NTC SiHa cells were exposed to various doses of cisplatin  for 48 h. The cell survival rate (%) was measured using the MTS assay. The result demonstrated that the sensitivity to cisplatin in both E1KD and NTC SiHa cells was dose-responsive. Interestingly, the survival rate of E1KD SiHa cells was significantly lower than that of NTC at 50 µM of cisplatin treatment   293 T cells, FOXO3a was another host gene that was most downregulated19. This regulatory pathway is regulated by the PI3K/AKT pathway, which promotes cellular functions of cell growth and proliferation28. FOXO3a is inactivated by degradation through phosphorylation by P-AKT29, leading to an increase in cell survival signals30. In our study, only P-FOXO3a (S253) protein expression was altered when HPV16E1 was knocked down, whereas neither non-phosphorylated FOXO3a nor upstream signaling molecules (AKT and P-AKT) were changed  suggested that cell characteristics, particularly cell growth, would be altered. According to this hypothesis, the effect of P-FOXO3a regulated by HPV16E1 on cell growth, apoptosis, and other properties, e.g., metastasis and anchorage-independent properties, were further investigated.HPV disrupted a number of host cellular pathways in order to trigger cervical carcinogenesis. The most prominent examples are the p53 and pRb degradation caused by Hr-HPV E6 and E7, respectively37. FOXO1 reactivation reduced the proliferation rate of SiHa and HeLa cells32. In addition, there was a possibility that the alteration of cell growth in HPV16E1KD SiHa cells might be an indirect effect of HPV16E7 downregulation since E7 was also downregulated in E1KD SiHa cells , which prevented TNF-mediated apoptosis in healthy human fibroblasts45. On the other hand, E7 may cause cell apoptosis in primary human keratinocytes, according to some evidence46. Even though E7 was reduced in the E1KD SiHa cells  and the alteration of cell cycle profiles are essential mechanisms that control the growth of cells. Apoptosis and necrosis are the two main subtypes of programmed cell death. While apoptotic cells quietly die without releasing their contents into the environment, necrotic cells release their contents into the environment, which triggers inflammatory reactionsThe cell cycle decision is one of the most crucial processes in cell growth. A cell spends the majority of its time among the interphase . The cell exits interphase, enters mitosis, and begins to divide when the growth signal is activated. On the other hand, when a dysregulation is detected, such as when DNA damage occurs, the cell cycle is stopped in order to aid in the repair process. There was no difference in the percentage of E1KD SiHa cells in the sub-G1, G0-G1, S, and G2 phases Fig. H–M. Thes48. In addition to cervical cancer, nephroblastoma cells  were found to exhibit reduced cell migration due to overexpression of FOXO3a37. Additionally, there was a significant correlation between FOXO3 and the metastasis of a number of cancers, including breast, pancreatic, and kidney cancers36. Unexpectedly, our findings did not detect any effects of HPV16E1 on cell migration properties , FOXO3a encourages cell invasion through the induction of matrix metalloproteinases (MMPs)50. It has been shown that overexpressing FOXO3a encourages cell invasion in some cancers, such as glioblastoma and gastric cancer52. Taken together, although it has no effect on cell migration, HPV16 E1 may contribute to the invasion of cervical cancer cells. The possibility that HPV16E1 might control key enzymes like MMPs, which are crucial for breaking down the ECM. To confirm this hypothesis, the expression of MMP9, one of the key types of MMPs usually involved in ECM digestion, was observed. Unexpectedly, there was no relationship between MMP9 expression and HPV16E1 downregulation 19. It's interesting to note that activation of SGK1 has been shown to be sufficient to promote anchorage-independent growth in soft agar. As a result, the loss of the anchorage-independent growth property in E1KD SiHa cells may be related to SGK1 expression; however, no research on SGK1 expression in E1KD SiHa cells has been demonstrated. Future research ought to be done.Anchorage-independent growth is the ability of cancer-derived cells and transformed cells to persist and multiply in the absence of anchorage to the ECM and their surrounding cells. This characteristic exhibits a strong correlation with tumorigenicity30. Therefore, the role of HPV16E1 in the FOXO signaling pathway should be explored in future studies. However, this study strongly demonstrated that HPV16E1 affects the properties of cancer cells, whether through direct or indirect effects. Hence, these findings provided a new potential role for HPV16E1 in cervical carcinogenesis.In conclusion, this study showed that HPV16E1 downregulation impacts P-FOXO3a and HPV16E7 expression. E1 downregulation also affected some essential properties of cancer cells, including cell viability, colony formation, cisplatin susceptibility, invasion, and anchorage-independent cell growth. Nevertheless, the expression of an essential oncogene, HPV16E7, was also downregulated in E1KD SiHa cells. Because of the synergistic effect of E7 downregulation along with E1 downregulation, all of the changed properties in E1KD SiHa cells might be affected either indirectly by E7 together with E1 or directly by E1. The suggested role of HPV16E1 in cervical carcinogenesis according to this study is summarized in Fig. 3, 100 units penicillin, 100 µg streptomycin, and grown under 5% CO2 at 37 °C.SiHa cells (EP-CL-0210), an HPV16-positive (1–2 copies per cell) cervical cancer cell line, were obtained from Elabscience Biotechnology (USA). These cells were cultured in Modified Eagle Medium  supplemented with 10% fetal bovine serum (FBS) , 0.01 M HEPES, 0.2% NaHCO2 at 37 °C.C-33 A cells, cervical cancer cells without HPV infection, were cultured in Dulbecco's Modified Eagle Medium  supplementary with 10% FBS , 100 units penicillin, 100 µg streptomycin, and grown under 5% COhttps://horizondiscovery.com/en/ordering-and-calculation-tools/sidesign-center), and synthesized by Dharmacon (UK) with ONTARGETplus modification. The siRNA sequences were shown in Supplemental Table 5 SiHa cells/well (in a 24-well plate) or 7 × 103 SiHa cells/well (in a 96-well plate) were grown overnight at 37 °C in 5% CO2 to reach 60–80% confluency. Prior to transfection, the cells were washed with 1X PBS, and the 10% MEM growth medium (500 µL/well for a 24-well plate or 100 µL/well for 96-well plate) was added. Then, the siRNA mixture was transfected into the cells by Lipofectamine RNAiMAX Reagent , using a standard protocol provided by the manufacturer . E1 knockdown (E1KD) cells were incubated for the specified number of days in each experiment (1–4 days) at 37 °C with 5% CO2. Cell scrapers were used for the RT-qPCR and western blot experiments, while cell trypsinization was used for the rest to harvest the cells for subsequent experiments. ON-TARGETplus Non-targeting Control#1 siRNAs  were used as a negative control (NTC).The siRNA targeting HPV16E1 mRNAs was designed using the web-based online siDESIGN center  , HPV16E7 siRNA , HPV16E6 + E7 siRNA, or NTC to a final concentration of 5 pmol/well by using a similar protocol as described above.RNA from each condition was extracted through the RNeasy mini kit . The purity and yield of the extracted RNA were accessed by the NanoDrop . After that, first-stranded cDNA was synthesized by SuperScriptTM III Reverse Transcriptase  using 200 ng of RNA input. The cDNA construction reaction was performed by following a standard protocol provided by the manufacturer. Then, 2 µL cDNA samples were amplified by 2X SsoAdvanced Univer-sal SYBR Green Supermix , and specific primers (0.25 nM/reaction)  by vortexing for 10 s. The cells were then incubated for another five min at room temperature. After that, the protein sample  from whole cell lysate was 1:1 mixed with SDS loading buffer , and heated at 95 ºC for 5 min using ProFlex™ Thermal Cycler . Then, the protein was separated by SDS-PAGE and western blot using the protocol previously described58. The blots were cut prior to hybridization with antibodies. 1:1000 primary antibody, Phospho-Akt (Ser473) (587F11) Mouse monoclonal antibody (mAb) #4051 , recombinant rabbit mAb anti-FOXO3a antibody ab109629 , mouse anti-MMP9 antibody (E-11) sc-393859 , or mouse anti-GAPDH antibody sc-47724 , diluted in 1% BSA in TTBS were used as primary antibody by incubating at 4 ºC overnight, whereas 1:15,000 secondary antibody, goat polyclonal antibody to mouse IgG conjugated with horseradish peroxidase (HRP) or Goat polyclonal Antibody to rabbit IgG conjugated with horseradish peroxidase (HRP)  diluted in 1% BSA in TTBS were used as secondary antibody by incubating for one hour at room temperature. The target band was developed by SuperSignal® West Femto Maximum Sensitivity Substrate  and observed by Chemidoc XRS + . The band intensity was measured using ImageJ software version 1.53t. Three independent experiments were conducted. For the re-probing protocol, the membrane was washed with StripPRO 1 Min Stripping Buffer  by using the standard protocol from the manufacturer. Then, the membranes were probed with 1:1000 of primary antibody (either rabbit anti-FOXO3a (phospho S253) antibody ab47285 , Akt Rabbit mAb #4685 , or mouse anti-RECK antibody sc-373929 ,). The subsequent procedure was carried out in the same manner.The E1 knockdown (KD) SiHa cells (72 h of 50 nM siRE1.3–6 transfected cells) were harvested using Cell Scrapers and then collected by centrifugation at 300 × 3 cells) were grown overnight at 37 °C with 5% CO2 in the 8-well slide chamber  and transfected with 50 nM siRE1.3–6 using Lipofectamine RNAiMax reagent. After 72 h of transfection, the cells were washed and fixed in cold acetone, and then blocked for an hour with 1% BSA in 1X PBS at room temperature. The air-dry slide was then stained with 1:100 rabbit anti-FOXO3a (phospho S253) antibody and 1:20 rhodamine phalloidin  mixture, at 4 °C overnight in a moisture chamber. After that, the slide was incubated with a 1:1000 goat polyclonal antibody to rabbit IgG conjugated with FITC  for another hour at 37 °C. Then, the slide was stained with 300 nM DAPI solution  for 5 min at room temperature. Then, the slide was treated with anti-fade fluorescence mounting medium before being observed under the 63 × magnification of the LSM800 airy scan confocal microscope . The FOXO-positive cells were counted for five fields in each condition to observe the localization. Prism8.0.1 software was used to calculate the Pearson correlation coefficient (r) from the total number of cells and the cell containing nuclear FOXO3a. Three independent experiments were conducted.SiHa cells (7 × 104 to 7 × 104 cells/well). Three independent experiments were done.siRE1.3–6 transfected SiHa cells in a 96-well plate at 24, 48, 72, and 96 h were stained with 100 µL 0.1% crystal violet solution for 30 min. Then, the plate was washed with distilled water (DW) and air-dried at room temperature. To elute pre-stained crystal violet, 100 µL 10% acetic acid  in DW was added to each well. The absorbance was measured at 590 nm using a VICTORTM X3 microplate reader . The number of live cells was determined by using the viable cell standard curve, which has been constructed from various concentrations of SiHa cells  and cell suspension were combined in an equal volume (1:1) before being loaded onto a hemocytometer . The cells were then examined using a microscope in four areas (16 squares = 1 area).® Aqueous One Solution Cell Proliferation Assay , incubating at 37 ºC for one hour, and measuring at an absorbance of 490 nm using a microplate reader. Three independent experiments were conducted.The proliferation rate of siRE1.3–6 and NTC transfected SiHa and C-33A cells was observed at 72-, and/or 96-h post-transfection by mixing with 10 µL CellTiter 96At 48 h post-transfection, the E1KD and NTC SiHa cells were incubated for an additional 48 h with 100 µL of cisplatin  diluted in 10% MEM growth medium at various concentrations . The MTS assay was applied to measure the survival rate of the cells following cisplatin treatment. ED50 was calculated by Prism8.0.1 software. Three independent experiments were done.6 72-h E1KD and NTC SiHa cells were stained by APC Annexin V and propidium iodide (PI) . The procedures were performed according to the manufacturer’s instructions. For the cell cycle stage, the 72-h E1KD and NTC SiHa cells were treated with 100 µg/mL of RNase A, DNase, and protenase-free  before staining with propidium iodide (PI). The method was followed according to the manufacturer’s instructions. The stained cells were analyzed by BD FACSCalibur flow cytometry . Three independent experiments were done.To observe the population of apoptotic cells, 1 × 105 cells/well of 48-h E1KD and NTC SiHa cells were seeded onto the 24-well plate and cultured for 24 h. A sterile 200 µL pipette tip was used to make the wound on the cell monolayer. The cells were then continued in culture with a new medium at 37 °C and 5% CO2. The wound width was measured at 0, 24, 48, and 72 h under 4 × magnification of the inverted microscope ECLIPSS TS100  6 fields/well using PixitPro A4 software . The percentage of wound closure was calculated. Three independent experiments were conducted.1 × 1059. Briefly, the 48-h E1KD SiHa cells were harvested from the 24-well plate by trypsinization. Then, E1KD SiHa cells (1 × 105 cells/well) in serum-free MEM were seeded onto each cell insert, a transparent PET membrane 24-well with a 8.0 µm pore size . Next, the cells were incubated at 37 °C for 10 min, and complete 10% MEM (growth medium) was added to the lower well. After 24 h of incubation at 37 °C, cell insert was removed, and non-migrated cells were removed by the cotton-tipped applicator. After being fixed with 70% ethanol, the migrated cells were then stained with a 0.2% crystal violet solution and washed by dipping in water. Under an inverted microscope, 5 fields/well of migrated cells were counted. Three separate tests were conducted.The protocol was adapted from Justus C.R. et al. 2014The cell invasion assay was performed by adding extracellular matrix (ECM) from Engelbreth-Holm-Swarm murine sarcoma  to each insert at a final concentration of 250 µg/mL. Then, the plate was incubated at 37 °C for 2 h to allow the gel to solidify. Next, the cells were added to the insert in the same manner of the transwell migration assay.2 in a moisture chamber. The colonies were stained after incubating for 11 days with 0.005% crystal violet. ImageJ software quantified the colony counts. Three independent experiments were conducted.500 cells/3 mL of 48-h E1KD and NTC SiHa cells were seeded onto a 6-well plate, which was then cultured at 37 °C with 5% CO60. In brief, 0.5% agar–agar  in 10% MEM was added to the 6-well plate (1.5 mL/well) to create the bottom layer of agar. Then, the agar was allowed to solidify for 30 min at room temperature. To prepare the upper layer of agar-containing cells, 1.5 mL of a mixture of 1 × 104 cell/well of E1KD/NTC SiHa cells and 0.3% agar–agar in 10% MEM was added. The agar was allowed to solidify at room temperature for another 30 min. The plate was then incubated for 21 days in a moisture chamber at 37 °C, 5% CO2 with continuous adding 10% MEM twice a week. The colonies were stained with 0.05% crystal violet solution. Five fields of colonies per well were counted using an inverted microscope. Three independent experiments were done.The protocol was adapted from Borowicz S. et al. 2014unpaired t-test using GraphPad Prism version 7. p < 0.05 was considered as statistically significance.Data were shown as the mean ± standard error of mean (SEM) and analyzed by This study was approved by the Institutional Review Board of the faculty of Medicine, Chulalongkorn University, a WHO-certified ethics committee . The protocol of study was also approved by the Institutional Biosafety Committee (IBC) of the faculty of Medicine, Chulalongkorn University (MDCU-IBC018/2019 and MDCU-IBC006/2023).Supplementary Information.


Methanosarcina acetivorans, which contains two sufCB gene clusters, was investigated. The CRISPRi-dCas9 and CRISPR-Cas9 systems were utilized to repress or delete sufC1B1 and sufC2B2, respectively. Neither the dual repression of sufC1B1 and sufC2B2 nor the deletion of both sufC1B1 and sufC2B2 affected the growth of M. acetivorans under any conditions tested, including diazotrophy. Interestingly, deletion of only sufC1B1 led to a delayed-growth phenotype under all growth conditions, suggesting that the deletion of sufC2B2 acts as a suppressor mutation in the absence of sufC1B1. In addition, the deletion of sufC1B1 and/or sufC2B2 did not affect the total Fe–S cluster content in M. acetivorans cells. Overall, these results reveal that the minimal SUF system is not required for Fe–S cluster biogenesis in M. acetivorans and challenge the universal role of SufBC in Fe–S cluster biogenesis in methanogens.Iron–sulfur (Fe–S) proteins are essential for the ability of methanogens to carry out methanogenesis and biological nitrogen fixation (diazotrophy). Nonetheless, the factors involved in Fe–S cluster biogenesis in methanogens remain largely unknown. The minimal SUF Fe–S cluster biogenesis system  is postulated to serve as the primary system in methanogens. Here, the role of SufBC in  Decades of research have demonstrated that the formation of Fe–S clusters within cells is not spontaneous but requires dedicated protein machinery to synthesize and transfer clusters to apo-proteins. The NIF (nitrogen fixation) system dedicated to the biogenesis of the simple and complex Fe–S clusters in the components of nitrogenase was the first Fe–S cluster biogenesis system discovered2. Nitrogenase is required for biological nitrogen fixation (diazotrophy) by prokaryotes3. Subsequently, the general Fe–S cluster biogenesis systems were discovered in bacteria—ISC (iron–sulfur cluster) and SUF (sulfur formation) systems. Escherichia coli contains both ISC and SUF, with ISC serving as the housekeeping system and SUF being important during stress conditions4. However, some bacteria only contain SUF  where it serves as the primary system6. Eukaryotes contain homologous ISC and SUF systems housed within Fe–S protein containing organelles, with ISC found in mitochondria and SUF within plastids. An additional system, CIA (cytosolic iron sulfur assembly) supports the biogenesis of cytosolic and nuclear Fe–S cluster proteins in eukaryotes7. The minimal components of the NIF, ISC, and SUF systems are a cysteine desulfurase  that liberates sulfur from L-cysteine and transfers it to a scaffold protein  where the Fe–S cluster is assembled. The Fe–S cluster is then transferred directly to a target apo-protein or to an intermediate carrier protein4.Iron–sulfur (Fe–S) clusters are ancient inorganic cofactors present in cells from all domains of life, where they serve numerous biological functions. Notably, Fe–S clusters serve a critical role in electron transfer in various metabolic enzymes, with [2Fe–2S], [3Fe–4S], and [4Fe–4S] clusters being the most common8. As such, the genomes of obligate anaerobes encode substantially more Fe–S proteins than obligate and facultative aerobes9. In particular, anaerobic acetogenic bacteria (acetogens) and anaerobic methanogenic archaea (methanogens) are predicted to contain the highest percentage of Fe–S proteins per genome10. For example, the number of Fe–S proteins in the methanogen Methanococcus maripaludis is ~ 15-fold higher than facultative E. coli11. Methanogens contain the largest number of Fe–S cluster proteins among archaea and Fe–S proteins are critical to their ability to produce methane and fix dinitrogen. The methanogenic CO2 reducing enzyme formyl-methanofuran dehydrogenase (Fwd) and heterodisulfide reductase (Hdr-Mvh) involved in the first and last steps of methanogenesis respectively, contain multiple Fe–S clusters13. Nitrogenase contains a simple [4Fe-4S] cluster and complex [8Fe-7S] and [7Fe-9S-Mo-C-homocitrate] clusters, referred to as the P- and M-clusters, respectively14.Aerobic organisms have developed metabolic pathways that rely less on Fe–S proteins due to the vulnerability of Fe–S clusters to oxygen. Oxygen can cause the disassembly of Fe–S clusters and generate highly toxic hydroxyl radicalssuf operon contains six genes in E. coli—sufA, sufB, sufC, sufD, sufS, and sufE. SufE acts as a sulfur acceptor from the cysteine desulfurase SufS, thereby enhancing the activity of SufS. SufSE donates sulfur to the scaffold formed by the SufBC2D complex. SufA is suggested to be a Fe–S cluster carrier protein that delivers Fe–S clusters to the target apo-proteins15. SufB2C2 can also function as an Fe–S cluster scaffold16. Virtually all archaea encode homologs of the core components of the SUF system (SufBC) shown to function in Fe–S cluster biosynthesis in bacteria and eukaryotes17. Recently, this minimal Fe–S cluster biogenesis system comprising only of the SUF scaffold proteins (SufB and SufC) was suggested to be renamed to SMS . The minimal SUF system (SMS) is common in archaea, while the complex SUF system (SufABCDSU) is common in bacteria18. Nearly all methanogens encode SufBC and some encode IscSU, the minimal components of the ISC system. Methanogens do not encode components of the NIF system despite many methanogens containing nitrogenase and being capable of fixing dinitrogen19. M. maripaludis contains only SufBC but no cysteine desulfurase (neither IscS nor SufS). Importantly, unlike other characterized organisms, M. maripaludis was shown to use sulfide as the sulfur donor to Fe–S clusters and does not use cysteine as a sulfur source11. Hence, sulfide-dependent Fe–S cluster assembly in M. maripaludis likely involves SufBC and it is hypothesized that SufBC serves as the core Fe–S cluster biogenesis system in methanogens. Results from a transposon mutagenesis screening indicated that SufBC is essential to M. maripaludis20. Thus, bioinformatics analysis and some experimental evidence support that SufBC is essential to Fe–S cluster biogenesis in methanogens21.Components of the SUF system are present in diverse species across all domains of life. The Methanosarcina acetivorans contains two copies of SufBC (SufBC1-2) and three copies of IscSU (IscSU1-3). M. acetivorans can also use either sulfide or cysteine as a sulfur source. IscS2U2 was recently shown to function in Fe–S cluster biogenesis and deletion of iscS2U2 slightly impaired the growth of M. acetivorans when cysteine was the sulfur source19. Thus, it was hypothesized that IscSU may function in cysteine-specific Fe–S cluster biogenesis, whereas SufBC may function in sulfide-specific Fe–S cluster biogenesis in M. acetivorans19. More recently, recombinant M. acetivorans SufB2C2 was shown to be capable of in vitro Fe–S cluster transfer consistent with M. acetivorans containing a functional minimal SUF Fe–S cluster biogenesis system18. M. acetivorans also contains all three types of nitrogenase and is capable of Mo-dependent and Mo-independent diazotrophy23. However, M. acetivorans lacks NifSU, components of the NIF system, specific to nitrogenase Fe–S cluster biogenesis in most bacteria. It was recently suggested that SufBC in methanogens, including M. acetivorans, is required for Fe–S cluster assembly in nitrogenase18. However, the in vivo importance of the minimal SUF system (or SMS) to M. acetivorans, and methanogens in general, is largely unknown. To assess the in vivo importance of SufB1C1 and SufB2C2 to M. acetivorans, we used the recently developed CRISPRi-dCas9 (CRISPRi) repression and CRISPR-Cas9 gene editing systems to repress and delete sufC1B1 and sufC2B2, respectively24. The impact of repression and deletion of sufCB on the growth of M. acetivorans under several conditions was tested.M. acetivorans SufB2 and SufC2 expressed in E. coli were previously shown to form a complex capable of Fe–S cluster assembly and transfer18. The ability of SufB2 and SufC2 to form a stable heterocomplex in M. acetivorans was assessed by first generating M. acetivorans strain DJL66  from a second sufC2B2 gene cluster. Expression of strep-SufC2 in M. acetivorans strain DJL66 was confirmed by Western blot with strep-tag specific antibodies  or both gRNAs (Tables M. acetivorans strain WWM73 to generate repression strains DJL130 (gRNA-sufC1), DJL131 (gRNA-sufC2) and DJL133 (gRNA-sufC1/gRNA-sufC2)  was compared to control strain DJL72  with cysteine, sulfide, and cysteine + sulfide as sulfur sources and methanol as the carbon and energy source  . The strains were grown with cysteine and/or sulfide and with NH4Cl or N2. Methanol was used as the carbon source. As shown in Fig. sufC1B1 showed a delayed-growth phenotype under all conditions compared to the other strains. The impact of the loss of SufBC on methylotrophic methanogenesis with trimethylamine (TMA) was also tested. The growth of each strain with TMA was similar to that observed with methanol, with strain DJL143 again having a delayed-growth phenotype  from ate Fig. . No sign17. As such, the metabolism of methanogens became highly dependent on Fe–S proteins and continues in modern lineages. Indeed, methanogens contain the largest number of Fe–S cluster proteins11. Recently, methanogens have been shown to use pyrite as a source of Fe/S and may directly uptake pyrite for the synthesis of Fe–S clusters into proteins28. However, current evidence supports that all organisms use dedicated protein machinery for the biogenesis of Fe–S clusters in proteins. As such, the presence of SufBC and/or IscSU in methanogens suggests they use similar protein factors as those used by bacteria and eukaryotes. Indeed, we have recently shown that M. acetivorans contains a functional minimal ISC system composed of IscSU219. Results from this study demonstrate that M. acetivorans SufB2C2 form a complex in vivo consistent with previous results with recombinant protein18. However, results from genetic studies clearly reveal that SufBC is not required for Fe–S cluster biogenesis in M. acetivorans, challenging the universal role of SufBC in methanogens.Methanogens are an ancient lineage of anaerobes that arose early on Earth when the atmosphere was likely devoid of oxygen. The absence of oxygen allowed reduced Fe and sulfur to associate to form Fe–S complexes  that drove early energy-conserving reactionsM. acetivorans, a transposon mutagenesis screen indicated SufBC is essential to M. maripaludis20. M. maripaludis is a more deeply rooted methanogen. M. acetivorans is more metabolically versatile and is postulated to have acquired additional genes from anaerobic bacteria29. Thus, it is possible that SufBC is the primary Fe–S cluster biogenesis system in M. maripaludis and other deeply rooted methanogens, but in later-evolving M. acetivorans and Methanosarcinales in general, it may serve an auxiliary role. Similarly, the SUF system is essential in some bacteria, such as Staphylococcus aureus and Mycobacterium tuberculosis, that only contain the SUF system for Fe–S cluster biogenesis30. In contrast, E. coli contains ISC and SUF and the deletion of the suf operon is not lethal under normal growth conditions. However, simultaneous deletion of SUF and ISC is lethal in wild type E. coli. Moreover, the SUF system has the ability to complement for the loss of the ISC system31. M. acetivorans also contains a minimal ISC system, hence, it is possible that SUF is not essential in organisms that contain additional Fe–S cluster biogenesis systems.In contrast to results with M. acetivorans likely expanded the ability to use cysteine, as well as sulfide and pyrite as a direct source of sulfur for Fe–S cluster biogenesis. Indeed, loss of IscS2U2 in M. acetivorans confers a cysteine-specific growth defect19. In contrast, M. maripaludis can only use sulfide as the sulfur donor for Fe–S cluster biogenesis and cannot use cysteine11. The results of these studies led us to hypothesize that IscSU is the primary Fe–S cluster biogenesis system when cysteine is the sulfur source while SufBC might be the major player during growth with sulfide. However, the results presented here do not support the hypothesis, since a sulfide-specific phenotype was not observed with any of the M. acetivorans suf mutants. Interestingly, deletion of sufC1B1 resulted in a delayed growth phenotype under all the conditions tested, and the phenotype is restored back to wild type when both sufC1B1 and sufC2B2 are deleted, indicating deletion of sufC2B2 acts as a suppressor mutation. However, given the lack of a significant alteration of the bulk Fe–S cluster content in any of the sufBC deletion mutants , indicating other factors may be involved.Nitrogenase is composed of two components, dinitrogenase reductase (NifH) and dinitrogenase (NifDK). NifH is a homodimer with a single [4Fe–4S] cluster required for electron transfer to the [8Fe–7S] cluster (P-cluster) in NifDK. Electrons are then transferred to the [7Fe-9S-Mo-C-homocitrate] cluster called the M-cluster or FeMo-co in NifDK. FeMo-co is the site of NM. acetivorans. Overall, the results presented in this study lead to the following possibilities—(1) the Fe–S cluster biogenesis function of SufBC is redundant in M. acetivorans, or (2) SufBC is not involved in Fe–S cluster biosynthesis and serves a different unknown function in this methanogen. Considering SufBC are likely ancient Fe–S cluster biogenesis components present in the last universal common ancestor (LUCA)18, it is possible that the appearance of other Fe–S cluster biosynthesis machineries such as ISC and NIF in bacteria and archaea resulted in the redundancy of SUF. For example, Candidatus Methanoplasma termitum is an example of a methanogen that lacks SufBC38. Clearly, considerable knowledge gaps remain regarding the mechanisms of Fe–S cluster biogenesis in methanogens, and it is highly likely that additional factors are involved that await discovery in these ancient and biotechnologically important anaerobes.While SufBC is universally conserved in archaea, the results presented here do not support its role in Fe–S cluster biogenesis in M. acetivorans strain WWM73 was the parent strain for all experiments. All strains of M. acetivorans are listed in Table 39. HS medium was prepared inside an anaerobic chamber (Coy Laboratories) containing 75% N2, 20% CO2, and 5% H2. The medium was supplemented with 2 µg/mL puromycin when required. Growth experiments were performed in Balch tubes containing 10 ml of HS medium reduced with 1.5 mM DTT. 125 mM methanol or 50 mM TMA or 100 mM acetate, 1 mM sulfide and/or 3 mM cysteine and 18 mM NH4Cl were added from anaerobic sterile stock solutions prior to inoculation where indicated. The optical density was measured at 600 nm using a spectrophotometer.sufC1B1 (ma0937-ma0936) and/or sufC2B2 (ma4406-ma4407) in M. acetivorans parent strain WWM73. All primers . The gBlock was then introduced into digested pDL734 using a Gibson Assembly Ultra Master mix kit (Codex DNA) as per the manufacturer’s instructions. E. coli WM4489 competent cells were transformed with Gibson Assembly reactions. Transformants were screened by PCR using primers P1 and P2. Next, M. acetivorans WWM73 was separately transformed with pDL366 and pDL367 using liposome-mediated transformation method40. Transformants were selected on anaerobic HS agar plates containing 125 mM methanol and 2 µg/mL puromycin. The plates were incubated at 35º C in an anoxic mason jar containing 2.5 mL of 2.5% w/v sulfide in a vial and incubated. Colonies were screened by PCR using two sets of primers P3/P4 and P5/P6 and positive colonies were selected and designated as DJL130 (pDL366) and DJL131 (pDL367). The strains were maintained in HS medium containing 125 mM methanol, 1 mM sulfide and 2 µg/mL puromycin.The CRISPRi system was used to repress rs Table  and gBloC1B1 ma097-ma0936 suf1 was introduced into HpaI-digested pDL367 using Gibson Assembly. Transformants were screened by PCR using primers P7 and P8. Next, M. acetivorans WWM73 was transformed with pDL371 as described above. Colonies were screened by PCR using two sets of primers P3/P4 and P5/P6 and positive colony was selected and designated as DJL133. The strain was maintained in HS medium containing 125 mM methanol, 1 mM sulfide and 2 µg/mL puromycin.For the construction of CRISPRi-dCas9 plasmid with two gRNAs (pDL371), pDL367 was digested with HpaI (NEB) and a gBlock containing gRNA-M. acetivorans cells were harvested at mid-log phase (OD600 of 0.4–0.5) by anaerobic centrifugation. Pellets were resuspended in 1 mL Trizol reagent  and stored at -80º C. RNA was extracted using the Direct-zol RNA MiniPrep kit (Zymo Research) followed by DNase treatment using the DNA-free DNA Removal kit . cDNA was synthesized from 300 ng of RNA using the iScript Select cDNA Synthesis kit (Bio-Rad). Gene expression analysis was done by qPCR using cDNA (300-fold dilution) and SsoAdvanced Universal SYBR Green Supermix (Bio-Rad). Primers used for qPCR were designed using Geneious Prime software and purchased from IDT. sufB1 expression was analyzed using primers P41/42, sufC1 expression using primers P43/44, sufB2 expression using primers P45/46, and sufC2 expression using primers P47/48. Primers P39 and P40 were used to measure the expression of 16 s rRNA . The reactions were carried out in a CFX96 Real-Time PCR Detection system (Bio-Rad). The data were analyzed using ∆∆Cq calculation method.M. acetivorans were generated using a CRISPR-Cas9 system with a few modifications24. All primers and gBlocks were designed using Geneious Prime software and purchased from IDT. Strain DJL63 was generated by introducing sufC2B2-editing DNA (homology repair template + gRNA1-suf2 + gRNA2-suf2) into pUC18 followed by introducing it to pDL238. Briefly, homology regions upstream and downstream of sufC2B2 were amplified by PCR using primers P9/10 and P11/12 respectively. Plasmid pDL248 was constructed by digesting pUC18 with BamHI-HF and introducing sufC2B2-editing DNA into digested pUC18 using Gibson Assembly. E. coli DH5α competent cells were transformed with Gibson Assembly reaction. Transformants were screened by PCR using P9 and P13. The sufC2B2-editing DNA was amplified by PCR using Q5 polymerase, primers P14/P15 and pDL248 as template. The amplified sufC2B2-editing DNA was introduced into AscI-digested pDL238 using Gibson Assembly followed by transformation into WM4489 competent cells. Transformants were screened by PCR to confirm the presence of plasmid pDL249 using primers P9 and P13. pDL249 was retrofitted with pAMG40 using Gateway BP Clonase II enzyme mix (Invitrogen) followed by transformation into WM4489 cells. Transformants were screened by PCR using the primer set P16/P17 and the plasmid was designated pDL250. Next, M. acetivorans WWM73 cells were transformed with pDL250. Transformants were selected on anaerobic HS agar plates containing 125 mM methanol and 2 µg/mL puromycin as described above. Colonies were screened by PCR using primers that anneal outside the homology region (P18 and P19) and gene-specific primers (P20/21 for sufB2 and P22/23 for sufC2). To construct a markerless deletion mutant, cells were plated on HS plates containing 125 mM methanol and 50 µg/mL 8-Aza-2,6-diaminopurine sulfate (8-ADP) as described24. The colonies cured of the plasmid were screened by PCR using primers P24 and P25.All deletion strains of suf1 deletion mutant) and DJL142 (suf1/2 deletion mutant), homology regions upstream and downstream of sufC1B1 were amplified by PCR using primers P26/27 and P28/29 respectively. Plasmid pDL381 was constructed by digesting pUC18 with BamHI-HF and introducing sufB1C1-editing DNA (homology repair template + gRNA1-suf1) into digested pUC18 using Gibson Assembly. E. coli DH5α competent cells were transformed with Gibson Assembly reaction. Transformants were screened by PCR using primers P13 and P26. The sufC1B1-editing DNA was amplified by PCR using Q5 polymerase, primers P15/30 and pDL381 as template. The amplified sufC1B1-editing DNA was introduced into AscI-digested pDL238 using Gibson Assembly followed by transformation into WM4489 competent cells. Transformants were screened by PCR to confirm the presence of plasmid pDL385 using primers P15 and P26. To introduce the second gBlock containing gRNA2-suf2, pDL385 was digested with HpaI and gRNA2-suf2 was introduced into HpaI-digested pDL385 using Gibson Assembly followed by steps mentioned above to construct a retrofitted plasmid (pDL387). M. acetivorans WWM73 and DJL63 were separately transformed with plasmid pDL387 to generate suf1 (DJl143) and suf1/2 (DJL142) deletion mutants. Transformants were selected on anaerobic HS agar plates as described above. Colonies were screened by PCR using primers that anneal outside the homology region (P31 and P32) and gene-specific primers (P33/34 for sufB1 and P35/36 for sufC1). To construct a markerless deletion mutant, cells were plated on HS plates containing 125 mM methanol and 50 µg/mL 8-ADP (Biosynth). The colonies cured of the plasmid were screened by PCR using primers P24 and P25.For the construction of DJL143 (M. acetivorans were verified by DNA sequencing (Plasmidsaurus Sequencing). Additionally, deletions were confirmed by sequencing PCR-amplified upstream and downstream regions around the deleted genes.All plasmids transformed into M. acetivorans strains was determined by measuring total acid-labile sulfur content using the methylene blue method as previously described41. M. acetivorans cells were harvested at mid-log phase (OD600 of 0.4–0.5) by anaerobic centrifugation (8000×g for 15 min at 4 °C). The cell pellet was resuspended in buffer containing 50 mM Tris, 150 mM NaCl, 1 mM benzamidine, and 1 mM phenylmethylsulfonyl fluoride and stored at -80º C using anaerobic vials. Cell pellets were lysed anaerobically by sonication on ice using a sonicator (QSonica) followed by anaerobic centrifugation . The cell lysate was collected, and the total protein concentration was determined using Qubit protein assay kit . The assay was performed in sealed vials containing protein samples. 1% zinc acetate and 12% sodium hydroxide were added to the vials containing samples and incubated at room temperature for 2 h. The reactions were terminated by the addition of 5 mM N,N-Dimethyl-p-phenylenediamine dihydrochloride in 5 M HCl and 0.23 M FeCl3 in 1.2 N HCl followed by incubation at room temperature for 2 h. The vials were quickly vented, solutions were spun down at 16,000 × g and absorbances were read at 670 nm using a spectrophotometer.The total Fe- S cluster within sufC2B2 from M. acetivorans genomic DNA using Q5 high-fidelity DNA polymerase (NEB) as per the manufacturer’s instructions. Primers were designed and purchased from IDT. Restriction site NcoI was added at the 5’ end of sufC2 and restriction site NruI at the 3’ end of sufB2 using primers P37 and P38. The PCR product and plasmid pSK226 were digested with NcoI and NruI using standard digestion protocol (NEB). Digested PCR product and pSK2 were gel purified as per manufacturer’s instructions (Qiagen) followed by ligationusing T4 DNA ligase. E. coli DH5α competent cells (NEB) were transformed with the ligation reaction. Transformants were screened by PCR using primer set P37/38 and confirmed by DNA sequencing (Eurofins). The pSK2 plasmid containing the sufC2B2 gene sequence was designated as pDL365. M. acetivorans strain WWM73 was transformed with pDL365 and pSK2 (control plasmid that expresses strep-UidA) using a liposome-mediated transformation protocol as previously described40. Transformants were selected on HS agar plates as described above. Transformants were screened by PCR for the presence of strep-sufC2B2, and a positive transformant designated as strain DJL66 and maintained in HS medium containing 125 mM methanol, 1 mM sulfide and 2 µg/mL puromycin.PCR was used to amplify M. acetivorans cells were harvested at mid-log phase (OD600 of 0.4–0.5) by aerobic centrifugation (8000×g for 10 min at 4 °C). The cell pellet was resuspended in buffer A containing 50 mM Tris, 150 mM NaCl, 1% Tween, 1 mM Benzamidine, 2 mM EDTA and sonicated 3 times on ice using a sonicator (QSonica) followed by aerobic centrifugation (8000×g for 10 min at 4 °C). The cell lysate was collected, and the total protein concentration was determined using Bio-Rad protein assay42. Protein sample (15 µg) was resolved in a 12% SDS-PAGE gel and transferred to a nitrocellulose blotting membrane . The membrane was blocked for 20 min in blocking buffer containing 50 mM Tris, 150 mM NaCl, 0.1% Tween (TBST), 5% milk and incubated overnight with the α-strep antibody (Qiagen). The membrane was washed three times with goat anti-mouse IgG antibody (GenScript) for one hour followed by washing three times with TBST again. The membrane was developed with Western blotting substrate (Thermo Scientific) and scanned using a FlourChem 8900 imaging system (Alpha Innotech).Western blot analysis was used to detect strep-tagged protein in strain DJL66 expressing strep-SufC2 and in control strain WWM73 (pSK2) expressing strep-UidA. M. acetivorans cells were harvested at an OD600 of 0.8 by anaerobic centrifugation (8000×g for 10 min at 4 °C) and the pellets were stored at -80ºC. For purification of recombinant protein, all the steps were carried out in an anaerobic chamber (Coy Laboratories) containing 95% N2 and 5% H2. The cells were thawed and resuspended in buffer NP  containing 4 μg/mL DNase I and 1 mM Benzamidine. The cells were sonicated 3 times followed by centrifugation (8000×g for 10 min at 4 °C). The cell lysate was loaded on a chromatography column containing 1 mL of Strep-Tactin superflow plus resin (Qiagen) pre-equilibrated with 12 mL of buffer NP. The column was washed with 5 ml buffer NP. Protein was eluted in 3 mL buffer NP containing 2.5 mM desthiobiotin (NPD) and stored at − 80 °C. The protein concentration was determined using the Bio-Rad protein assay42. The purified protein was analyzed by 12% SDS-PAGE gel stained with Coomassie blue solution followed by de-staining. The protein bands were excised, trypsin digested, and sent for mass spectrometry (The University of Arkansas Statewide Mass Spectrometry Facility). UV–visible absorption spectrum was recorded using a Cary 60 spectrophotometer (Agilent Technologies) under anaerobic conditions.Supplementary Information.


Here, we critically examine the biophysical properties of jGCaMP8f and report development by rational design of two novel variants of jGCaMP8f.Genetically encoded biosensors for monitoring intracellular calcium changes have advanced our understanding of cell signaling and neuronal activity patterns in health and disease. Successful application of GCaMP biosensors to a wide range of biological questions requires that sensor properties such as brightness and dynamic range, ligand affinity and response kinetics be tuned to the specific conditions or phenomena to be investigated. Random as well as rational targeted mutations of such sensor molecules have led to a number of important breakthroughs in this field, including the calcium sensors GCaMP6f and GCaMP6fin vitro biophysical properties of jGCaMP8f and selected variants by fluorescence spectroscopies and compared their performance monitoring intracellular Ca2+ transients with previously developed fast and bright GCaMP sensors by live cell imaging.We determined the 2+ not only majorly affects the kinetic responses of GCaMPs but also their brightness and fluorescence dynamic range. We developed novel variants jGCaMP8f L27A which has threefold faster off-kinetics and jGCaMP8f F366H which shows a ∼3-fold greater dynamic range than jGCaMP8f, in vitro as well as in HEK293T cells and endothelial cell line HUVEC in response to ATP stimulation.We demonstrate that the physiologically highly relevant Mgin vivo imaging outcomes with increased signal-to-noise ratio. The L27A variant with faster kinetics than jGCaMP8f has larger cellular responses than previous fast GCaMP variants. The jGCaMP8f generation and novel improved variants presented here will further increase the application potential of GECIs in health and disease.We discuss the importance of optimization of biosensors for studying neurobiology in the context of the novel variants of jGCaMP8f. The jGCaMP8f F366H variant with a large dynamic range has the potential to improve  Fluorescence intensity increases as a result of a conformational change in the CaM-RS20-cpEGFP complex (2+ binding)  firing rate at the presynaptic terminals where GCaMPs were initially introduced  . The restroduced . Faster  peptide . The taroff-kinetics than GCaMP6f. We measured the biophysical properties of jGCaMP8f in order to determine how targeted mutations affect them. We report novel variants L27A and F366H with further increased off-rate and fluorescence dynamic range, respectively, demonstrated in vitro and in cellular environments.jGCaMP8f  is a memE. coli XL10-Gold and BL21 (DE3) Gold cells were purchased from Invitrogen. Subsequently, jGCaMP8f was cloned into a bacterial expression vector (jGCaMP8f pET30b) using DNA assembly .pGP-AAV-syn-FLEX-jGCaMP8f-WPRE was purchased from Addgene (Plasmid #162379). pET30b vectors were obtained from Novagen. ® Site-Directed Mutagenesis Kit (New England Biolabs), a range of mutations of jGCaMP8f pET30b were generated. Primers (5′-3′) used for mutagenesis were:Using Q5A18S: CAAGGAGGTGAGCACCGCCGTGL27A: CATCGCCCGCGCAATGGGCCTGAAGD294A (EF-1): CTCCCTATTTGCCAAGGACGGGGD330A (EF-2): AATGAAGTAGCTGCCGACGGTGF366A: GTTCGGTGTGGCTGATAAGGATGF366H: GTTCGGTGTGCATGATAAGGATGD367A (EF-3): GGTGTGTTTGCTAAGGATGGCD403A (EF-4): AGGGAAGCAGCCATCGATGGGAfter mutagenesis, DNA sequences were sent to Genewiz for checking.E. coli B21 Gold cells using 0.5 mM isopropyl thio-β-D-galactoside (IPTG) then purified on a NiNTA column  at 4 °C as described previously .Protein concentration was calculated from the absorption spectra of the proteins at wavelength 280 nm using the molar extinction coefficient of 26,360 M+, 100 mM KCl and 5 mM EGTA in the presence or absence of 2 mM MgCl2, pH 7.5) at 20°C were recorded using a Fluorolog-3 spectrofluorometer (Horiba©) at 492 nm excitation and 500 – 550 nm emission wavelengths. Subsequently, a saturated amount of CaCl2 was added to obtain the maximum fluorescence emission intensity. Data were normalized and plotted using GraphPad Prism 9 software.Fluorescence emission spectra of 0.5–1 μM jGCaMP8f or variant proteins in assay buffer  and the fluorescence spectra were recorded on a Fluorolog-3 (Horiba©). GCaMP6f quantum yield measured in Ca2+ -saturated buffer was used as a reference (ΦCa+2+ = 0.59) (protein = ΦGCaMP6f × (Sprotein/SGCaMP6f).The concentration of jGCaMP8f proteins was adjusted such that the absorbance at the excitation wavelength (492 nm) was between 0.001 and 0.1. A series of dilutions was prepared in a buffered solution  . Data weKa for each jGCaMP8f protein, a series of buffers were prepared. Depending on their respective pH buffering range, an appropriate buffer was used for the measurements . The pH titrations were performed by recording fluorescence spectra in Ca2+ -free  or Ca2+-saturated  measuring 1 μM protein at 0.5 pH unit intervals at 20°C . Excitation wavelength was set at 492 nm and emission in the 500–600 nm range was recorded. BAPTA was chosen as Ca2+ chelator because of its stable affinity for Ca2+ over the pH range.To determine the apparent p2+ association and dissociation rates were measured using a TgK Scientific KinetAsyst™ double-mixing stopped-flow apparatus in single mixing mode at 20°C. The fluorescence was excited at 492 nm and the emission was collected using a 530 nm cut-off filter. For Ca2+ association, 500 nM jGCaMP8f or variant proteins in 50 mM HEPES-K+, 100 mM KCl, 2 mM MgCl2, 5 mM EGTA, pH 7.5 at 20°C were rapidly mixed with increasing concentrations of Ca2+ , calculated free [Ca2+] ranged from 108 nM to 252 μM in the mixing chamber). For Ca2+ dissociation, 500 nM jGCaMP8f or variant proteins in 50 mM HEPES-K+, 100 mM KCl, 2 mM MgCl2, 200 μM CaCl2, pH 7.5 at 20°C were rapidly mixed with 50 mM HEPES-K+, 100 mM KCl, 2 mM MgCl2, 10 mM EGTA, pH 7.5 at 20°C. Experiments were repeated at least three times. Kinetic records shown are the average of at least four traces. Averaged data were fitted to a single or double exponential using KinetAsyst software (TgK Scientific) to obtain association or dissociation rates. The error quoted is the standard error of the fit. For the rate plots, the mean values obtained from at least three experiments are shown, the error bars represent S.E.M.Ca2+ concentration [Ca2+] was calculated using the two-chelators Maxchelator program  was used to verify the calculated [Ca2+].Free Ca program . Fluo-3 ©) at specific excitation and emission wavelengths, 20°C. When bound to Ca2+, the Fluo-3 excitation peak is at 506 nm and the emission peak is at 526 nm; the respective wavelengths for jGCaMP8f and all variant proteins are 492 nm and 512 nm. One μM jGCaMP8f or variant proteins in assay buffer  were titrated by stepwise addition of a solution of 500 mM CaCl2 until no further changes in fluorescence emission intensity were observed. Experiments were done in triplicates. Data was corrected for dilution, normalized and expressed as mean ± S.E.M. Kd and Hill coefficient for indication of cooperativity (n) were obtained by fitting the data to the Hill equation using GraphPad Prism 9 software.Fluorescence emission spectra were recorded using a Fluorolog-3 spectrofluorometer  at 300,000 cells per dish for 24 h. Cryopreserved pooled primary human umbilical vein endothelial cells (HUVEC) were purchased from PromoCell GmbH (Heidelberg Germany). HUVEC were grown in a human growth medium (HGM) which comprised media M199 supplemented with 20% FCS. A total of 50 μg/mL of the antibiotic gentamicin along with ECGS  and heparin (10 U/mL) were added to the media. HUVEC were grown on 24 well trays or 9 mm glass coverslips that had been pre-treated with porcine skin gelatine (1% w/v) for 30 min at 37°C. Cells were cultured for 4 days in a 5% CO2 incubator at 37 °C until fully confluent as assessed by visual examination by light microscopy.HEK293T cells were maintained in Dulbecco’s Modified Eagle’s Media  supplemented with 10% fetal bovine serum and 4 mM L-glutamine, in 75 cmTransfection of HEK293T was accomplished using Lipofectamine™ 2000 (Invitrogen): 2.5 μg of plasmid DNA was combined with 5 μL Lipofectamine™ 2000 reagent in Opti-MEM, this was then added to the 2 mL cell culture medium per dish. Transfection of HUVEC was by Nucleofection using an Amaxa Nucleofector 2b device and the HUVEC old transfection reagent  according to manufactures. Experiments were performed 24 h after transfection.2, 1 mM MgCl2, 1 mM NaH2PO4, 10 mM glucose, 20 mM Na+-HEPES pH 7.4) and placed on the stage of an inverted Olympus IX71 fluorescence microscope. The microscope was equipped with an Olympus UPLSAPO × 100 1.40 NA objective and an OptoLED light source  for EGFP (470 ± 20 nm) fluorescence. Excitation light was directed to the specimen via a 500DCXR dichroic mirror  and emitted light collected through a 535 ± 30 nm emission filter onto an Ixon3 EMCCD camera  operated in frame transfer mode at full gain and cooled to −68 °C. Images were acquired at 20 frames/s using Winfluor software.1 Intracellular Ca2+ release in HEK293T cells was induced by ATP. ATP was administered as a bolus of 50 μL diluted in imaging buffer to give a final concentration of 50 μM in HEK293T cells and 2 μM for HUVEC. The higher sensitivity of HUVEC to ATP is reflected in the different ATP concentrations used. Full dynamic range was measured following the addition of 10 μM ionomycin. The fluorescence changes during ATP stimulation were analyzed using Winflour software. A square region of interest (ROI) was placed over a peripheral cytoplasmic region of each cell. The mean background fluorescence was measured by placing an ROI with the same dimensions at a point at which no cell was present. GraphPad Prism 9 was used to plot and fit data. Time-to-peak was determined by the time in which the rise amplitude increased from 10 to 90%. Decay t1/2 was determined from an exponential function fitted for individual records when possible, or by determining the time required for the signal dropping to half the amplitude. Data are expressed as mean ± S.E.M. For further statistical analysis one-way ANOVA tests were carried out with post-hoc Dunnett’s multiple comparison test. Different number of data points could be extracted for the different parameters due to the variability of individual cell responses. For example, following a clear rising phase, the decay phase may include oscillations or plateaus that complicate fitting and analysis. In some cells when ionomycin was added, no clear response was seen.Transfected HEK293T cells or HUVEC were transferred to a physiological saline solution   . The relD: 7ST4) .We generated variants by introducing mutations in the CaM EF-hands and selected sites in the eNOS peptide . Mutatio2+ binding sites of CaM and/or in the eNOS peptide of jGCaMP8f will weaken the binding affinity resulting in faster calcium off-kinetics  ] . In desi binding , was exp2+/CaM – eNOS peptide complex may increase the brightness and fluorescence dynamic range . The F366 residue in CaM is adjacent to EF-3 and is involved in hydrophobic interactions with the peptide. We designed two mutations, F366H and F366A, with the view of reducing hydrophobicity and size to increase dynamic range.In turn, mutations that stabilize the Ca2+ affinity and kinetic responses of the variants in comparison with jGCaMP8f. We furthermore tested the response of jGCaMP8f, its novel variants and previously generated fast GCaMP variants to ATP stimulation in HEK293T cells and HUVEC.We were interested to see how these mutations affected the brightness and dynamic range, apparent Ca2+ binding and an off-rate of 37 s–1 at 20°C (2+ and at pH 7.2 as opposed to our buffer that contains 2 mM Mg2+ and is adjusted to pH 7.5. Remeasuring the dynamic range of jGCaMP8f in the absence of Mg2+ (pH 7.5), we got a remarkably different result: the fluorescence of jGCaMP8f increased 31-fold upon Ca2+ binding. We then compared fluorescence intensities with or without added Mg2+ in the presence of EGTA and in the presence of Ca2+ , we obtained a 42-fold and a 37-fold increase upon Ca2+ binding, respectively, compared to 31-fold for jGCaMP8f ] in saturating Ca2+ of jGCaMP8f, 54,226 M–1cm–1 and a quantum yield of 0.52 gave a brightness value of 28.3 mM–1cm–1. This compares well with 29.7 mM–1cm–1 measured for GCaMP6f in the absence of Mg2+  48,107 M–1cm–1 (quantum yield 0.54) and F366H, εo(492 nm) 21,203 M–1s–1 (quantum yield 0.53) suggest that lower brightness of the apo-form rather than increased brightness of the Ca2+-bound form of the F366H variant underlies its increased fluorescence dynamic range compared to jGCaMP8f. In contrast, for the L27A variant, the Mg2+ induced large increase in fluorescence intensity in the apo form accounts for the loss in dynamic range. Neither fluorescence intensity nor εo(492 nm) is affected by Mg2+ in saturating Ca2+. The only available data for comparison of the apo form with and without Mg2+ is for jGCaMP8f (2+ εo(492 nm) is 8,413 M–1cm–1, in the absence of Mg2+it is reduced to 1,930 M–1cm–1, consistent with the 4-fold increase in fluorescence intensity by Mg2+ in the absence of Ca2+  present revealed that for jGCaMP8f and the L27A and F366H variant, the intensities increased for both the Ca2+-bound and apo-forms, the dynamic ranges decreased with increasing pH, peaking at pH 7 , R1 , R1 . With anas 22 ms .–1) initial on-rate (R1) (off-rate of the L27A variant was 84.4 s–1 (decay t1/2 of 8.2 ms)  . The off 8.2 ms)  threefol–1 initial on-rates (R1) measurable in the μM [Ca2+] range  and D403A (EF-4) mutations had very little effect on either the kinetic or fluorescence responses of jGCaMP8f (off-rate (25 s–1) than jGCaMP8f , D330A (EF-2), and F366H variants were studied.Measurement of the Ca2+ dependence of the four phases of the on-fluorescence response of jGCaMP8f and its L27A, D294A (EF-1), D330A (EF-2), and F366H variants, revealing that rates (R1 ↑) of the fast, rising phase  are highest at around 1–2.5 μM [Ca2+] for all five proteins  of 5 s–1 which increases to ∼30 s–1 in the 1–2.5 μM [Ca2+] range for the F366H variant, while only at high [Ca2+] for the other four proteins (kobs for the fourth phase (R4 ↑) is slow ∼1 s–1  shows that for the jGCaMP8f, the L27A and F366H variants the rapid first phase represents the major fraction of the amplitude . In cont ∼200 ms . Therefo 3 and 4 . The lowKd for Ca2+ in the low 1–4 μM range with Hill coefficient (n) values of 2–5 . The F366H variant responded with a ΔF/Fo of 9 (ΔF/Fo measured in response to 2 μM ionomycin was 12), while for L27A the ΔF/Fo was 1 (ΔF/Fo measured in response to 2 μM ionomycin was 2.8). Time to peak values were 1.4 s, 1.8 s and 1 s for jGCaMP8f and its F366H and L27A variants, respectively. Thus, the on-kinetic responses of the three proteins were similar, however, the decay t1/2 for the L27A variant of 4.6 s was 2.3-fold smaller than for jGCaMP8f (10 s) and F366H variant (11 s). Moreover, the amplitude of fluorescence increase for jGCaMP8f F366H was 3.75-fold greater than for jGCaMP8f . Previously, GCaMP3fast and GCaMP6fu were developed which had in vitro decay t1/2 of 11 and 8 ms, respectively, at 20°C  for GCaMP3fast and 0.44 (p = 0.003) for GCaMP6fu (p = 0.0009) and 3.5-fold (p < 0.0001) greater ΔF/Fo than jGCaMP8f. The full dynamic ranges (ΔF/Fo measured in response to 10 μM ionomycin) of the sensors were similar with, interestingly, GCaMP6f exceeding even that of jGCaMP8f F366H (p = 0.002)  ; Table 32+ binding to CaM and hence its interaction with target peptides and proteins is affected by Mg2+ and Mg2+ is also present in the cells, our measurements are carried out in a buffer containing 2 mM MgCl2 and found striking differences in the dynamic ranges when comparing results obtained in the absence of Mg2+. jGCaMP8f is reported to have a significantly greater fluorescence dynamic range than the GCaMP6 generation, in comparable conditions, in the absence of Mg2+ at pH 7.2  of ≥500 s–1 likely represents Ca2+ binding to the CaM EF-1 and EF-2 sites of the N-lobe, the transient drop of fluorescence in the second phase (R2) then corresponds to Ca2+dissociation from the N-lobe as the [Ca2+] is not sufficient for saturating N-lobe Ca2+ binding. Slow Ca2+ binding to the C-lobe follows in phase 3 (R3) and the binding is stabilized in phase 4 (R4). At [Ca2+] > 2.5 μM, the N-lobe can hold onto Ca2+ better and the second phase is no longer detectable. Thus, fluorescence rapidly increases in a more straightforward manner. Our data with the D294A (EF-1) and D330A) (EF-2) variants, which only show the slow 3rd and 4th phases, support this explanation.The complex on-rates and amplitudes is favorable for the L27A and F366H variants with fast initial on-rates (R1) of ≥500 s–1 in the physiologically relevant 1–2.5 μM intracellular [Ca2+], together with jGCaMP8f. The on-rate of jGCaMP8f and its L27A and F366H variants is 3.4 and 1.5-fold faster than that the fast decay GCaMP3fast and GCaMP6fu, respectively, at 20°C  involved in peptide binding to the CaM C-lobe. In contrast, in jGCaMP8f EF-3 or the EF-4 mutation that disabled Ca2+ binding did not significantly affect the properties of jGCaMP8f, while mutating EF-1 and EF-2 resulted in very fast off-rates but drastically slowed-down on-response times, implicating their important role in binding the eNOS peptide which is essential for restoring fluorescence of the cp-EGFP β barrel.jGCaMP8f and the F366H variant have a 13-fold faster GCaMP6fu , 2016. Nfast and GCaMP6fu variants, in each case reduced compared to the parent GCaMP. In contrast, jGCaMP8f F366H shows a 25-fold fluorescence enhancement upon Ca2+ binding, 3-fold greater than jGCaMP8f in our hands (in the presence of Mg2+), but similar off-rate to jGCaMP8f. Thus, simultaneously increasing brightness, dynamic range and off-rate of GCaMPs remains elusive.The fluorescence dynamic range of ∼5 is similar for the fast-decay jGCaMP8f L27A, GCaMPfin vitro. However, response to cell [Ca2+] transients is not always entirely consistent with what the in vitro experiments predict. There are many possible reasons for this, for example there can be affinity differences in the cells compared to what is measured in solution, pH changes can affect sensor responses.The greater amplitude (3.75-fold in HEK293T and 1.9-fold in HUVEC) of the response to ATP stimulation by our F366H variant compared to jGCaMP8f is consistent with the 3-fold difference we measured 2+ transients of different magnitude and time course. Our cell data show the expected range of response times and amplitudes, similar to other reports of GCaMP cell responses , both in vitro and in cellular environments. The off-kinetics of L27A is as fast as that of GCaMP3fast and GCaMP6fu, however, the jGCaMP8f L27A variant may be more advantageous in imaging applications as it has a greater fluorescence dynamic range in the cellular environment than the earlier fast variants. The jGCaMP8f F366H variant gives a 2–3.75-fold greater fluorescence increase in cells than jGCaMP8f whilst preserving its fast kinetics unless saturation distorts its response kinetics.In summary, we present a thorough characterization of jGCaMP8f and two newly developed variants, the faster decay L27A and F366H with an increased dynamic range, through which we highlight the complexities of the kinetics of this sensor generation. We demonstrate how Mg2+ indicators are highly applicable for studying cell signaling involving transient rises of intracellular [Ca2+] to understand physiological or disease conditions. An important consideration for the application for GECIs for imaging neural activity is the kinetics of the intracellular [Ca2+] transient generated by AP-evoked Ca2+ influx and terminated by Ca2+ pumps, which is slower than the ion channel activities driving APs. In cells, the millisecond response times of GECIs translate into transients over seconds. Nevertheless, increasing the off-rate of GCaMPs is important for faithful reporting of intracellular Ca2+ transients and for increasing the resolution of oscillating Ca2+ transients. However, there are still limitations to the sensitivity and fidelity of detection. It is important to understand how closely the fluorescence changes map the neural activity under investigation. We note that many in vivo or in cell studies cannot give detailed kinetic data of Ca2+ signals. This may be explained by the low expression level and/or low brightness of GECIs in vivo, their action as a Ca2+ buffer, slow kinetics, the complexity arising from the non-linearity of the data , respectively, therefore have potential applications to study physiological or disease conditions. For example, simultaneous recording of APs and GCaMP6s or GCaMP6f fluorescence in vivo in the primary visual cortex of transgenic mice revealed that in typical population imaging conditions only ∼10% of single AP events were detected , D330A (EF-2), A18S as well as designed, generated the F366A and F366H variants by SDM, and carried out all This work was funded by BBSRC grant BB/M02556X/1 to KT.