Synechocystis sp. PCC6803 through an adaptive laboratory evolution, in which the cells were repeatedly sub-cultured for 52 days under high light stress conditions (7000 to 9000 μmol m−2 s−1). Although the growth of the parental strain almost stopped when exposed to 9000 μmol m−2 s−1, no growth inhibition was observed in the Tol strain. Excitation-energy flow was affected because of photosystem II damage in the parental strain under high light conditions, whereas the damage was alleviated and normal energy flow was maintained in the Tol strain. The transcriptome data indicated an increase in isiA expression in the Tol strain under high light conditions. Whole genome sequence analysis and reverse engineering revealed two mutations in hik26 and slr1916 involved in high light stress tolerance in the Tol strain.Increased tolerance to light stress in cyanobacteria is a desirable feature for their applications. Here, we obtained a high light tolerant (Tol) strain of  Synechocystis sp. PCC6803 and revealed two key mutations in hik26 and slr1916 using whole genome sequencing and reverse engineering. Here, the high light tolerant strain exhibited no growth inhibition in conditions up to 9000 μmol m−2 s−1, which had no effect on excitation-energy flow and instead showed an increase in isiA expression.In this paper, the authors investigated the high light stress response in  However, excess light, such as in summer, damages their photosystem and inhibits photosynthetic activity2. Because high light (HL) stress is one of the major obstacles in the bio-production of cyanobacteria, it is highly desired to develop an HL tolerant strain for enhancing their growth and production rate of various bio-molecules for industrial applications.Cyanobacteria are promising host microorganisms for the production of industrial bio-molecules because these cells can directly fix carbon dioxide through the Calvin Benson cycle and converted it into target compounds using light energy3 and decreasing contents of chlorophyll and photosystem I (PSI)6. Furthermore, they possess some mechanisms for preventing photoinhibition by allowing excess light energy to escape, such as state transition7, heat dissipation8, energy dissipation from phycobilisome and photosystem II (PSII)10, and synthesis and maintenance of protein complexes in thylakoid membrane by chaperone proteins11. However, because these mechanisms are insufficient for protecting the cellular system from excess light energy under further strong light intensity conditions (more than 2000 μmol m−2 s−1), the cell growth is reduced due to inhibition of photosynthetic activity caused by damage to their photosystems. It has been reported that degradation of the PSII reaction center12 and inhibition of protein synthesis and repair of PSII protein13 by reactive oxygen species occur as by-products of photosynthesis. Although some mutants with reduced light-harvesting pigment content and minimized light-harvesting antenna size have been developed in cyanobacteria for improving the HL stress tolerance in previous studies, such strains have certain drawbacks that cause growth defects under low light conditions15. Furthermore, transcriptome analysis revealed a downregulation of genes related to light-harvesting systems, flagellum synthesis, and transhydrogenase, and upregulation of D1 protein turnover-related genes under excessive HL conditions16. However, effective mechanisms for protecting cells from excess light without decreasing the antenna size are still unknown.Cyanobacteria can adapt their cellular system to survive under HL stress conditions by reducing their light-harvesting antenna size17. A serially passaged culture is performed under a stress environment with decreasing the growth rate, which allows the selection of mutants with improved growth phenotypes. Compared to random mutagenesis approaches using mutagens, this approach can be easily used for identifying key mutations to improve the evolved strains by genome sequence analysis due to their low mutation frequency19. Furthermore, a comparison between the parental and evolved strains employing “Omics” technologies provides useful clues for understanding the tolerance mechanisms. Many tolerant strains of cyanobacteria against various stress conditions, such as alcohol, acid, and heavy metals, have been developed22. In the present study, an evolved strain of Synechocystis sp. PCC6803 (hereafter referred to as PCC6803) was obtained through an ALE experiment under HL stress conditions. We identified key mutations involved in the HL tolerance using whole-genome sequencing and reverse engineering. We also aimed to reveal HL tolerance mechanisms in the evolved strain by fluorescence analyses of their photosystem as well as by transcriptome analysis.Adaptive laboratory evolution (ALE) experiment is a method used to induce tolerance against stress conditions−2 s−1) with bubbling of air. Although cell growth was not inhibited under light conditions of 4000 μmol m−2 s−1, it was decreased under 7000 μmol m−2 s−1 and almost stopped under 9000 μmol m−2 s−1 , suggesting the Tol strain could adapt its photosystem or metabolism to various light intensities  for 15 days following five subcultures , TolS2), Tol(S3), and Tol(S4). All the four strains showed identical growth curves as that of the Tol stain under both low light and HL conditions 23. The chlorophyll contents of the PCC6803 and Tol strains under various light conditions are shown in Fig. −2 s−1 was 52% and 64% lower, respectively, than that under 4000 μmol m−2 s−1, which was consistent with a previous report23. In the Tol strain, chlorophyll content was only decreased by 21% and 42% under 7000 and 9000 μmol m−2 s−1, respectively. These results suggest that the Tol strain possesses a mechanism for HL tolerance other than decreasing the chlorophyll content.The photosynthesis ability of the Tol strain obtained by the ALE experiment was investigated. Chlorophyll is an important pigment for light harvesting during photosynthesis. It has been reported that the amount of chlorophyll decreases to avoid absorption of excess light under HL conditions−2 s−1 were almost identical in the PCC6803 strain . This result suggested that the 4000 μmol m−2 s−1 condition for the Tol strain is also an HL condition for PCC6803. In the comparison between W-7000 and T-7000, the expression levels of 28% genes shows significant difference between PCC6803 and Tol strains under 7000 μmol m−2 s−1.Table isiA and isiB were dramatically increased in the Tol strain (27-fold in isiA and 9.5-fold in isiB). The transcriptome data in a volcano plot for comparing T-7000 and W-7000 are shown in Supplementary Fig. isiA and isiB encode iron-stress chlorophyll-binding protein (CP43′) and flavodoxin, respectively. It has been reported that isiA deletion causes complete destruction of photosynthetic function under HL condition, whereas isiB deletion is not related to HL tolerance24.To identify genes whose expression was significantly altered in the Tol strain compared to those in PCC6803 strain, the ratios of gene expression level were ranked. Table isiA expression level in T-7000 was higher than that in T-4000 and W-7000, the enhanced expression of isiA might have been involved in HL tolerance in the Tol strain. To confirm this hypothesis, an isiA overexpressed strain of PCC6803, named PCC6803/OE-isiA, was constructed by introduction of isiA, controlled under a strong promoter of psbA2, to the neutral site of PCC6803. The PCC6803/OE-isiA showed increased growth under HL conditions (7000 and 9000 μmol m−2 s−1) compared with its parent strain PCC6803 27 showed around 100% coverage of the whole-genome sequence and average 500 read depth for each nucleotide sequence in both the strains  strains, and introduction of mutated hik26 or slr1916 into PCC6803.Effects of the identified mutations on HL tolerance were analyzed by deletion of slr1916 was deleted in both the PCC6803 and Tol(S1), generating 6803Δslr1916 and Tol(S1)Δslr1916, respectively. The 6803Δslr1916 strain could grow under 9000 μmol m−2 s−1 similar to the Tol strain and its culture broth showed higher viscosity Δslr1916 and the 6803Δslr1916 strains may have been caused due to synergy effects of the slr1916 deletion and hik26 mutation.The ity Fig. . The Tolity Fig. . These rhik26 in PCC6803 and Tol(S1), designated as 6803Δhik26 and Tol(S1)Δhik26, showed severe growth inhibition under HL conditions and similar growth rate as that of with PCC6803 strain under low light condition (40 μmol m−2 s−1)  with its own promoter region was amplified from the Tol(S1) strain and introduced into the genome of the 6803Δhik26, generating 6803Δhik26/hik26m. The 6803Δhik26/hik26m showed identical growth as that of the Tol strain under all conditions, even under 9000 μmol m−2 s−1  with its own promoter region was amplified from the Tol(S1) strain and introduced into the genome of the 6803Δslr1916, generating 6803Δslr1916/slr1916m. The 6803Δslr1916/slr1916m showed almost identical growth as that of the Tol and the 6803Δslr1916 strains under all conditions  Fig. h, i. This−1 Fig. ; howeverons Fig. . FurtherSynechocystis sp. PCC6803 was obtained by an ALE experiment under extreme HL stress condition (9000 μmol m−2 s−1). A previous study has reported that size reduction of the light-harvesting antenna increased HL tolerance but decreased the growth under low light condition29. However, we observed that the growth rate of the newly obtained Tol strain was identical to that of the PCC6803 strain under low light condition (40 μmol m−2 s−1), suggesting that the Tol strain possesses a different mechanism to overcome HL stress. This is consistent with the findings that the decrease in chlorophyll content due to HL stress was partially alleviated in the Tol strain. Furthermore, evaluation of photosynthesis machineries revealed the HL tolerance mechanisms, which indicated that the PSII complex and the electron flow to PSI were maintained even under HL conditions in the Tol strain.In the present study, an HL stress tolerant (Tol) strain of 23. Although a comparison between W-4000 and T-4000 showed that the number of genes whose expression was significantly changed was small (0.4%); a comparison between W-7000 and T-7000 showed that the expression levels of many genes were changed (28.0%). Consequently, a change in expression of these genes would have contributed to the superior HL tolerance in the Tol strain. The transcriptome data revealed that isiA and isiB expressions were highly induced in the Tol strain under HL condition. The isiA was originally observed to be an iron deficiency stress-inducible gene30, and it functioned as an antenna protein for PSI under iron-limiting condition31. It has been reported that the isiA and isiB are induced by various stress conditions. For example, the gene expression is highly induced by hydrogen peroxide, which is generated under an extremely HL condition32. The isiA deletion inhibits a redistribution of phycobilisome-absorbed energy between PSII and PSI26 and causes a growth defect under HL condition33. Furthermore, the isiAB deletion resulted in a photosensitive phenotype under HL condition, with accumulation of reactive oxygen species and cell bleaching24. Recently, it has been reported that IsiA is a member of a high light-inducible carotenoid-binding protein complex (HLCC) on a thylakoid membrane, and a lack of HLCC components causes impaired state transition and increased sensitivity to HL stress34. The HLCC also plays a role in protecting the thylakoid membrane and D1 protein of PSII from oxidative stress34. Therefore, the enhanced expression of IsiA in the Tol strain under HL condition would promote the formation of HLCC, which involves in the state transition for processing excess light energy, thus protecting the thylakoid membrane and D1 protein from oxidative stress.The HL tolerance mechanism in the Tol strain was also investigated based on transcriptome data of PCC6803 and Tol strains and known HL-responsive genes which are commonly induced or repressed under HL conditionsslr1916 and hik26 in the Tol strain were identified by whole-genome sequencing, and the function was evaluated by reverse engineering. Especially, Hik26, a histidine kinase of a two-component system, can regulate many genes through a response regulator. Since the expression level of hik26 was not significantly altered in all the conditions, the mutation in hik26 might activate Hik26 at enzymatic level and affect its regulated gene expression level. The non-synonymous mutation in Hik26m (T29K) was located at the sensor domain predicted by NCBI database (ACCESSION No. YP_005652307.1). This suggests that the mutation might activate Hik26 and affect the expression levels of its regulatory genes. Considering functional analysis data and known HL-responsive genes, the known induced HL-responsive genes tended to be highly expressed and repressed HL-responsive genes were poorly expressed in the Tol strain than in PCC6803. The HL tolerance mechanisms related to these genes might have been more activated in the Tol strain, thereby increased HL tolerance. Moreover, these genes may be regulated through Hik26, and the mutation in hik26 might have caused these expression changes. Homology search using BLASTP (KEGG database) indicated that the homologous protein Hik26 was conserved in many cyanobacteria species except in marine Synechococcus and Prochlorococcus species, and was not present in eukaryotic photosynthetic organisms such as green algae. The mutation site was highly conserved among cyanobacteria possessing homologous Hik26. Another histidine kinase, Hik33, was also reported to be necessary for HL tolerance, but hik33 deletion also decreased the growth under low light condition35. Hence, our studies suggest that Hik26 is a different histidine kinase which is known so far, specifically responsive to HL condition. Regarding the slr1916, recently, comprehensive experiments using an inducible CRISPRi gene repression library have shown that pmgA and slr1916 repression clones exhibit similar growth profiles in response various stresses including light conditions36. The PgmA is a regulator involved in HL responses in the PCC680333. A scheme explaining the mechanism of HL tolerance in the Tol strain is summarized in Fig. Two mutations of Synechocystis sp. PCC6803 by the ALE and revealed the mechanisms for HL tolerance by absorption and fluorescence analyses of the photosystem. Transcriptome analysis and whole-genome sequence revealed that isiA overexpression and hik26 mutation enhance HL tolerance in the PCC6803 strain. The Hik26 is a different histidine kinase which is known so far, that responds to HL stress. We expect that further extensive studies to identify a response regulator of Hik26 would reveal the whole picture of regulatory mechanisms responsible for HL stress tolerance in the PCC6803 strain.In conclusion, we obtained an HL tolerant strain of Synechocystis sp. PCC6803 glucose-tolerant, nonmotile37, GT-I strain was used as the parent strain. A slightly modified BG11 medium was used for the culture experiments38. Pre-cultured cells were cultivated in 100 mL Erlenmeyer flasks with 20 mL modified BG11 medium at 34 °C with rotary agitation at 150 rpm , under continuous light illumination using a light-emitting diode (LED) plate . The pre-cultured cells (OD730 = 0.01) were inoculated into fresh modified BG11 medium. The main culture was performed using a test tube  containing 30 mL modified BG11 medium at 34 °C under continuous light illumination at 4000–9000 µmol m−2 s−1 using a point source LED . The spectrum of the point source LED is shown in Supplementary Fig. −2 s−1) used in the present study was significantly higher compared to that used in other studies. In our previous report16, the PCC6803 strain exhibited photoinhibition when cultured under 1100–1300 µmol m−2 s−1. Although a flat LED panel with the photobioreactor was used in the previous study, we here culture the cells in a test tube with a point source LED that can irradiate more HL intensity. The light intensity was measured at the irradiate point on the surface of the test tube. Therefore, the normalized light intensity at the entire test tube becomes low. In the PCC6803 strain, although 70% growth reduction was observed at 1100 μmol m−2 s−1 with the flat LED device, that was observed at 7000 μmol m−2 s−1 with this point source LED device.The 730 for each cultivation. Light intensity was set to 7000 µmol m−2 s−1 at the initial stage of this experiment. After the growth rate increased under a light intensity of 7000 µmol m−2 s−1, the intensity was gradually increased up to 9000 µmol m−2 s−1. After the final cultivation, cells which were designated as the Tol strain, were stored at −80 °C with 15% glycerol solution. A frozen stock of the Tol strain obtained by ALE experiment was streaked on a plate containing modified BG11 medium and incubated under a light intensity of 40 µmol m−2 s−1 at 34 °C using an incubator MIR 154 . Single colonies thus obtained were inoculated in modified BG11 medium and cultivated at 34 °C with rotary agitation at 150 rpm  under continuous light illumination by an LED plate .For the ALE experiment, cells were cultivated in a test tube with 30 mL of modified BG11 medium at 34 °C with aeration under continuous light illumination using a point source LED. Cells were inoculated into fresh medium every 2–3 days to maintain the growth phase. The specific growth rate was calculated from the values obtained at initial and final ODslr1916 with a chloramphenicol-resistance gene. For constructing the 6803Δslr1916/slr1916m strain, mutated slr1916 (slr1916m) with its promoter region was amplified from genome DNA of the Tol(S1) strain and was introduced into a neutral site located in slr0168 of 6803Δslr1916 strain. The hik26-deleted strains of the PCC6803 and Tol(S1) strains, named PCC6803Δhik26 and Tol(S1)Δhik26, were constructed by replacing hik26 with a kanamycin-resistance gene. For constructing the 6803Δhik26/hik26m strain, mutated hik26 (hik26m) with its promoter region was amplified from genome DNA of the Tol(S1) strain and was introduced into a neutral site located at the downstream of the stop codon of ndhB in the 6803Δhik26 strain. For constructing the isiA overexpressed strain, named 6803/OE-isiA, isiA was amplified from genomic DNA of PCC6803. The isiA gene with a strong psbA2 promoter was introduced into a neutral site located in slr0168 of PCC6803 strain. The procedures for strain constructions are described in All strains used in this study are listed in Supplementary Table 730 using a spectrophotometer . For measuring chlorophyll concentration, 1 mL of culture was centrifuged at 15,000 rpm for 10 min at 4 °C, and 1 mL of 100% methanol was added to the cell pellet. After 15 min, the sample was centrifuged at 15,000 rpm for 10 min at 4 °C, and absorbance of the supernatant thus obtained was measured at 660 nm (A660) by a spectrophotometer . Chlorophyll concentration was calculated by the following formula39:−2 s−1, and were subsequently collected at 48 h. Next, the samples were dark adapted for 10 min at 34 °C, and centrifuged. Pellets were suspended in 30% polyethylene glycol solution to final concentrations of OD730 = 2, and 150 μL of the samples was stored in liquid nitrogen. The absorption spectra of whole cells were measured using a spectrometer  at 77 K. Steady-state fluorescence spectra were measured with a spectrofluorometer  at 77 K. The excitation wavelength was 440 nm. Fluorescence decay curves were measured using a time-correlated single-photon counting system at 77-K40; the excitation wavelength was 400 nm.Cell concentration was measured as OD27 using Bowtie 2 software ver. 2.2.3 with default parameters41. SNPs were identified using SAMtools ver 1.0 and BCFtools ver. 1.042 with a threshold of a quality score of >150. The sequence data obtained from MiSeq was deposited in DDBJ Sequence Read Archive under accession number DRA010198. The mutations identified by whole-genome sequencing were confirmed by Sanger sequencing using DNA fragments, including the mutation site amplified by PCR using the primer pair listed in Supplementary Table Total DNA of PCC6803 and tolerant strains were extracted using the Genomic DNA Buffer Set and QIAGEN Genomic-tips 100/G . A DNA library was prepared using Truseq DNA PCR-Free LT sample prep kit  and KAPA DNA library quantification kit , and the sample was sequenced using MiSeq sequencer with MiSeq Reagent kit v2 (Illumina) generating 150 bp paired-end reads. The sequence data were mapped to the genome sequence of PCC6803 GT-I strain (NCBI Reference Sequence No.: NC_017038.1)38. A custom designed microarray of PCC6803 in the Agilent 8 × 60 K platform  was used. This array contains 10 probes of around 60 nucleotides in length for each open reading frame of PCC6803 genome and its cryptic plasmids, which were designed using a web tool eArray (Agilent Technologies) and genomic sequences of Synechocystis sp. PCC6803. The culture broth was mixed with an equal volume of 10% (w/v) phenol in ethanol and then centrifuged to harvest the cells at mid-log growth phase (OD730 = around 0.6). The harvested cells were immediately frozen in liquid nitrogen and stored at −80 °C. Total RNA was extracted using Ambion Ribopure yeast kit . cDNA labeling was performed using Cy3; hybridization and washing were performed using a Low Input Quick Amp WT Labeling Kit (Agilent Technologies), Cyanine 3-CTP (Perkin Elmer), Gene Expression Hybridization Kit (Agilent Technologies), and Gene Expression Wash Buffer (Agilent Technologies). Detection was carried out using an Agilent G2565CA microarray scanner and Feature extraction software (Agilent Technologies). Microarray data were analyzed using Matlab 2014a . In this study, the median of the intensities of the ten probes representing each ORF was defined as the expression level of the corresponding gene.DNA microarray analysis was performed as described in our previous study with minor modifications43 to compare the data to each other. The geometric mean of the median intensity of each gene obtained from triplicate experiments under each condition was used as the representative gene expression level. Gene expression data sets were deposited in Gene Expression Omnibus under accession number GSE149892. The significance of the difference of each gene expression level between the two conditions was analyzed by two-tailed t-test. To correct the p values for multiple comparisons, the q values were calculated by Storey’s method44. The Bioconducter (version 3.11)45 with R software (version 4.0.3) was used for the q values calculation from p values.Expression data were normalized by quantile normalizationFurther information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Aggregates of amyloid-β (Aβ) are characteristic of Alzheimer’s disease, but there is no consensus as to either the nature of the toxic molecular complex or the mechanism by which toxic aggregates are produced. We report on a novel feature of amyloid-lipid interactions where discontinuities in the lipid continuum can serve as catalytic centers for a previously unseen microscale aggregation phenomenon. We show that specific lipid membrane conditions rapidly produce long contours of lipid-bound peptide, even at sub-physiological concentrations of Aβ. Using single molecule fluorescence, time-lapse TIRF microscopy and AFM imaging we characterize this phenomenon and identify some exceptional properties of the aggregation pathway which make it a likely contributor to early oligomer and fibril formation, and thus a potential critical mechanism in the etiology of AD. We infer that these amyloidogenic events occur only at areas of high membrane curvature, which suggests a range of possible mechanisms by which accumulated physiological changes may lead to their inception. The speed of the formation is in hours to days, even at 1 nM peptide concentrations. Lipid features of this type may act like an assembly line for monomeric and small oligomeric subunits of Aβ to increase their aggregation states. We conclude that under lipid environmental conditions, where catalytic centers of the observed type are common, key pathological features of AD may arise on a very short timescale under physiological concentration. The aggregation of Aβ in cell-free solution is well studied and explained by the primary nucleation of monomers, secondary nucleation of monomers on the fibrils and the elongation of fibrils by monomer addition3. For these molecular events to occur, a critical concentration of monomers of ~90 nM in solution is required4, far above the concentration found in vivo. Furthermore, for fibril formation in cell free systems, µM concentration of Aβ is required5; while in the brain, the peptide concentration reaches only up to 4 nM6. Given these numbers it is unlikely that the currently established in vitro mechanisms can account for the formation of plaques and tangles in the brain of AD patients8.A widely adopted framework to understand the biochemical underpinnings of AD is based on the amyloid cascade hypothesis, which assumes that aggregation of the Aβ peptide into oligomeric or fibrous structures triggers a cascade of toxic events that cause functional deterioration of the human brain9, cholesterol10 and gangliosides11 or non-specifically with the lipid membranes themselves20. Previous studies found changes in the lipid composition of the brain in AD patients during disease progression affecting the carbon chain length, linkages, and degree of unsaturation21. This brings to the idea that phospholipid metabolism may be an important factor in the pathogenesis of AD22. As an amphiphilic peptide, Aβ can interact with multiple lipids and membrane proteins23 and the membrane environment also strongly influences the Aβ aggregation24. Aβ can disrupt membrane integrity by enhancing reactive oxygen species (ROS) generation and lipid peroxidation causing shortening of lipid carbon chains27 ) and diseased states of neuronal cell membranes39 . We show that the high curvature edges of DLPC membrane patches facilitate specific attachment of Aβ40 monomers and small oligomers, forming pearl in a necklace-like structures even when Aβ40 concentration is as low as that measured in normal human brains (1 nM). This is the first time that such aggregates have been shown to form within the range of physiological Aβ40 concentrations. We also characterized the kinetics of oligomeric structure formation and determined the stoichiometry of small oligomers attached to the edges of membrane patches by employing a variety of fluorescence imaging techniques and atomic force microscopy (AFM).We studied the formation of AβTo examine the effect of hydrophobic acyl chain length of the lipids on the aggregation behavior of Aβ, 100 nM dye labelled Aβ was added to different surfaces. These were bare glass, brain total lipid extracts (BTLE), DLPC (12-carbon chain) and POPC (16:18-carbon chain) and the samples were imaged every 2 hours for 48 hours. On DLPC bilayers, spontaneous formation of giant, looped oligomeric assemblies was observed only for the DLPC membrane as shown in Fig. Giant loops were only observed for DLPC under physiological ionic strength (150 mM NaCl) and was not detected with other lipids e.g. POPC, or natural lipid mixtures BTLE or with DLPC under 100 mM NaCl ionic strength. For these surfaces, only small Aβ oligomers distributed randomly across the surfaces were observed  to investigate the effect of the fluidity of the membrane to Aβ assembly. Indeed, addition of cholesterol to DLPC facilitates the attachment of Aβ on top of the membrane patches and allowed us to visualize the patch movement kinetics and the evolution of such giant oligomeric structures over 24 hr with time-lapse fluorescence microscopy in the TIRF mode (Supplementary Video 3). We found that in the presence of cholesterol, Aβ assemblies possess sharper edges and are much less round, indicating that the membrane patches are more rigid than that formed by DLPC alone. This observation suggests that the shape of Aβ assemblies resembles that of the membrane patches and follows the evolution of the local topology of the membrane.Giant looped oligomeric structures can grow to different sizes, with the largest size being reached around 12 hr of incubation time Figs. , which iAFM imaging was performed to confirm if there exist lipid membrane patches whose peripheries preferably attract Aβ to bind Fig. .Figure 342. Continuous insertion of Aβ40 monomers and small oligomers into the membrane creates closely packed oligomers on the membrane reaching an apparent height of ~80 nm  with curved membrane not only act as a mechanism to sense the membrane curvature46 but also promote the crowding and assembly of IDPs themselves , where the zeta potential of the DLPC lipid bilayer is positive4. Giant looped oligomeric structures could act as a template for Aβ oligomerization and fibrillation via a variety of pathways and they may even catalyze the secondary nucleation steps  rather than Zn (II) plays an important role in the very early stages of Aβ aggregation in the synapse53. In addition, curved structures could also take place on cell debris. It is well known that glial cells in the AD brain lose their ability to remove impurities and debris from the brain56. Some of these may come from multiple lipid bilayers of the myelin sheath debris55, mitochondrial membrane or endoplasmic reticulum (ER) of neurons53. Aβ aggregation on cell or mitochondrial membrane creates oxidative stress and their interactions with iron and copper generate H2O2. Interaction of H2O2 with Fe2+ or Cu+ generates hydroxyl radical57 (OH*), which contributes to the dysfunction of the endoplasmic reticulum (ER) 62. It is possible that membrane patches from dead cells either from the body itself or the invaders could provide the right curvature for enhanced Aβ/membrane interaction or therefore assist Aβ assembly.Our results strongly suggest that Aβ aggregates It should be noted that our current work is limited by the spatiotemporal resolution of super-resolution fluorescence and AFM instrumentation accessible. It would be highly desirable to resolve the time course of Aβ fibril growth from ring-like oligomers which would help unveil the molecular mechanism of Aβ aggregation on damaged cell membrane or membrane patches.All reagents were purchased from commercial suppliers and used as received. HiLyte Fluor 647-labeled Aβ40 (HPLC purity >95%) was purchased from Anaspec, which we term Aβ for simplicity in the whole manuscript.1,2-Dilauroyl-sn-glycero-3-phosphocholine (DLPC), brain total lipid extracts (BTLE) and 1–palmitoyl-2-oleoyl phosphatidylcholine (POPC) were purchased from Avanti Polar Lipids Inc. . Methanol  was provided by Honeywell. 1 M HEPES sodium salt solution, suitable for cell culture and sodium chloride (≥99%) were purchased from Sigma-Aldrich. Lab-Tek Chambered 1.0 Borosilicate cover glass was provided by Thermo Fisher.10 mg of lyophilized lipid in 1 ml methanol/chloroform was dissolved for a final lipid solution at 10 mg/ml. 100 microliter aliquots of lipid-methanol solution were added to a clean, sterile 2 ml Eppendorf tubes . Methanol/chloroform from Lipid-methanol aliquots was exposed to a stream of clean nitrogen gas, leaving by evaporation a dry lipid film inside the Eppendorf. To ensure all methanol is removed, eppendorfs were placed inside a vacuum desiccator and degas for overnight. Dried lipid films were stored at −20 °C.40 was added to the solutions in the well to a final concentration of 1, 10 or 100 nM respectively.Aliquots were left to warm up to room temperature prior to rehydration. The lipid was then re-suspended in HEPES buffer . The solution was pipetted up and down for multiple times until all the lipids are dissolved in the solution. Rehydrated lipid was vortexed and left for 1 hour at room temperature. After 1 hour, the solution was extruded through a polycarbonate membrane which has holes of 100 nm diameter to form small unilamellar vesicles (SUVs). The solution of SUVs was deposited in an 8-well glass slide for fluorescence measurements or in a petri dish with glued borosilicate glass for AFM measurements. Two incubation conditions were tested. In the first one, the glass wells were incubated overnight at 4 °C to allow the vesicles to collapse onto the glass surfaces. This incubation condition is chosen for all the experiments. In the second one, 10 mM Calcium Chloride were added for rapid formation of lipid bilayer and incubated for 30 minutes. This incubation condition is just chosen to limit the formation of multiple bilayers and the limited formation of giant looped oligomeric structures on this surface was tested and compared with the first incubation condition  microscope based on an inverted Eclipse Ti-E optical microscope  equipped with a 60X NA 1.49 oil immersion objective  and an electron-multiplied CCD  512 ×512 pixels was used to measure the formation and growth of giant looped oligomeric structures. A diode pumped solid state CW laser  at 647 nm is coupled into a single-mode optical fiber which is connected to a motorized TIRF illumination unit . The power at the back aperture is adjusted by neutral density filters to 0.7 mW. Chambered cover glass was loaded with 400 µl pure buffer  onto the sample stage of Nikon Ti for control experiments. Different areas inside each chamber were imaged automatically by an encoded high speed XY stage with focal plane being maintained by a Perfect Focus System (PFS) . 1, 10 or 100 nM of Beta-Amyloid (1–40), HiLyte Fluor 647-labeled Aβ sample was added to wells and the formation of giant looped oligomeric structures were recorded every 2 hr for 48 hr. The contour length of each giant looped oligomeric structures was calculated using imageJ software.44 to automatically analyze single subunit counting data. This software can distinguish individual oligomer spots and analyzes their fluorescence intensity trace for the determination of number of step-wise photobleaching events. It was designed as a fully-automated algorithm that determines the step number distributions, from which the subunit composition can be derived. Thus, large datasets can be analyzed without any user-bias in selection or interpretation of the data. For the analysis of individual molecules, Gaussian blur filter is applied to reduce the effect of imaging noise. We examined the sample quality using single molecule photobleaching analysis to ensure that the percentage of monomeric peptide is always greater than 75%. The rest are small oligomers up to the size of tetramer. Aβ40 was chosen for all the experiments since Aβ42 has a higher tendency to aggregate and can form hexameric or dodecameric structures in contrast to Aβ40 which forms oligomers smaller than hexamers63. Furthermore, the fluorescence tag may influence Aβ/membrane interaction and the behaviour of Aβ aggregation. Aβ40 with HiLyte Fluor 647 tagged in the N-terminus of the peptide was used to minimize the effect.For single molecule photobleaching experiments, a custom TIRF imaging setup based on Nikon Eclipse TE2000-U microscope  was used. The laser source was a 633 nm He-Ne laser with 2.5 mW power. The images were recorded by a sCMOS camera . 80 ms recordings with 2 × 2 binning were taken. Fluorescent spots were analyzed by measuring the integrated intensity of individual spots over the course of all 1000 frames. Traces were produced and analyzed by PIF software written by the Blunck group64. In contrary, DNA points accumulation for imaging nanoscale topography (DNA-PAINT) uses the stochastic binding of a fluorescent ligand65. DNA-PAINT is a quantitative method, because continuous replacement of imager strands from solution avoids photobleaching65. We adopted the later principle to image oligomeric annular structures. Continuous replacement and attachment of monomers and small oligomers creates blinking events. Superresolution fluorescence imaging was performed using a Zeiss Elyra PS-1  super resolution microscope equipped with an Alpha Plan-APO 100×1.46 NA Oil immersion objective at 640 nm excitation and collecting fluorescence through a 650 nm long pass filter. Image acquisition were carried out under TIRF mode with an excitation laser power density of ~2 kW/cm2 and exposure time of 15 ms exposure. 10,000 frames were acquired and single molecule localizations were identified and super-resolution images reconstructed using the Zeiss Zen software. The localization precision used for superresolution image reconstruction is 30 nm.The standard STORM superresolution imaging method achieves increased spatial resolution by sequentially photoswitching individual fluorophores between a fluorescent state and a non-fluorescent state, which leads to a temporal separation of the individual fluorophores from an ensemble of emitters. Basically, it uses the stochastic photo-activation of a permanently bound fluorophoreAFM experiments were performed on a NanoWizard 4 BioScience AFM  integrated in an iX81 optical microscope frame . High-resolution AFM topographical images of lipid bilayers and Aβ oligomers and fibrils were taken using a silicon nitride tip attached to a soft triangular backside gold coated silicon nitride cantilever . Force spectroscopy was performed in the Quantitative Imaging mode (QI) of the JPK system (QI-JPK). Lipid bilayers, Aβ oligomers and fibrils were imaged in QI mode at a resolution of 128 × 128 pixels using the sharp cantilever with a set point of 1.5 nN. The images were analyzed by JPK software and WSxM software.Supplementary Video LegendsSupplementary Figure S1Supplementary Figure S2Supplementary Figure S3Supplementary Figure S4Supplementary Figure S5Supplementary Figure S6Supplementary Figure S7Supplementary FiguresSupplementary Video S1Supplementary Video S2Supplementary Video S3Supplementary Video S4Supplementary Video S5Supplementary Video S6


Solanum pennellii introgression lines (ILs) population, it was previously verified that, under normal (CO2), IL 2–5 and 2–6 display increased photosynthetic rates by up to 20% in comparison with their parental background (M82). However, the physiological mechanisms involved in the enhanced CO2 assimilation exhibited by these lines remained unknown, precluding their use for further biotechnological applications. Thereby, here we attempted to uncover the physiological factors involved in the upregulation of photosynthesis in ILs 2–5 and 2–6 under normal (CO2) as well as under elevated (CO2). The results provide evidence for increased biochemical capacity (higher maximum carboxylation velocity and maximum electron transport rate) in plants from IL 2–5 and 2–6, whereas the diffusive components  were unaltered in these ILs in comparison to M82. Our analyses revealed that the higher photosynthetic rate observed in these ILs was associated with higher levels of starch as well as total protein levels, specially increased RuBisCO content. Further analyses performed in plants under high (CO2) confirmed that biochemical properties are involved in genetic variation on chromosome 2 related to enhanced photosynthesis.Enhanced photosynthesis is strictly associated with to productivity and it can be accomplished by genetic approaches through identification of genetic variation. By using a  A) is the main driving force for plant growth and biomass production having a central position for breeders seeking to increase crop yield  and mesophyll (gm) conductances which are dependent on leaf anatomical and physiological properties and influenced by environmental cues  tend to reduce gs in leaves by an intrinsic property of guard cells  limits photosynthesis. On the other hand, when Ci is higher, CO2 assimilation is limited by maximum electron transport rate (Jmax), or the regeneration of triose phosphate (2) augments RuBisCO carboxylase activity leading to reduced photorespiratory process, improving carbon gains  cultivated under drought stress demonstrated higher transpiration efficiency provided by enchanced gm/gs ratio  regeneration has been proposed as a trait for plant breeding  study performed with a Solanum pennellii population demonstrated that variation in stomatal arrangement may improve internal CO2 diffusion allowing greater photosynthetic efficiency in dry environments  populations, an important genomic tool for Quantitative Trait Locus (QTL) mapping, has been proposed to identify candidate genes associated with improved A . QTL anaA . In a caronments . In addital pore .A was identified in a unique overlapping genomic region, delimited by two ILs (IL 2–5 and IL 2–6) and named as BIN 2K. The IL 2–5 exhibited up to 20% increased A, 50% higher nitrate content and 21% higher starch accumulation at the end of the light period in comparison with the parental line M82. Similarly, the IL 2–6 exhibited 19% increased A, 22% higher gs, 42% higher levels of starch at the end of the light period, and 58% higher starch turnover in comparison with M82. In agreement, the IL 2–6 also displayed higher gs, when evaluating the variation in stomatal responsiveness to desiccation  indicating increased water use efficiency (WUE) in this line . Given that A is highly affected by environmental (CO2) and RuBisCO limitation is reduced by high , but rather to increased photobiochemical capacity. Our findings open important avenues for enhancing our understanding of genetic variation on chromosome 2 that is involved in the regulation of photosynthetic capacity in tomato plants.In order to investigate the physiological basis for the increased igh CO2; , these I®. The plants were allocated in a greenhouse under naturally fluctuating conditions of light intensity (±600 μmol photons m–2 s–1 daily) and ambient CO2 (±400 μmol mol–1) as well as semicontrolled conditions of temperature (±28°C) and relative air humidity (±60%) during spring. After germination, seedlings were transplanted in 1.16 L pots containing the same commercial substrate, supplemented with chemical fertilizer  in the proportion of 0.5 kg of NPK to 10.0 kg of substrate. The plants were irrigated daily as needed and no restriction of root development, and consequently photosynthetic limitation, was observed at the end of the experiment when the physiological analyses and sample harvesting took place (4-week-old plants). Four plants for each genotype were grown side by side and the position of each plant was changed daily.Seeds of the ILs 2–5 and 2–6, as well as the cultivar M82 were sown in trays containing the commercial substrate Tropstrato HT, Vida Verde–2 s–1 daily) and ambient CO2 (±400 μmol mol–1) as well as semicontrolled conditions of temperature (±28°C) and relative air humidity (±60%). The experiment was carried out in two OTCs. One was continuously maintained at the current ambient level of CO2 (∼400 μmol mol–1) and another continuously maintained at an elevated level of CO2 (∼800 μmol mol–1). In the chamber supplemented with elevated (CO2), 800 μmol mol–1 was the setpoint and the (CO2) was monitored and adjusted every day with a portable CO2 meter AZ-77535. CO2 was only injected following the photoperiod, i.e., from 06:00 a.m. to 06:00 p.m. The obtained (CO2) along the experiments are presented in In a second experiment, seeds from the same genotypes were germinated and cultivated as described previoulsy with the following modifications. Pots containing plants with 10 days after transplanting (DAT) were transferred to open top chambers (OTCs) with the following specifications 1.6 m diameter and 1.8 m height. The chambers were placed in a greenhouse under naturally fluctuating conditions of light intensity  while specific leaf area (SLA) was measured as described previously . The drya fluorescence analyses were performed simultaneously using an open-flow infrared gas exchange analyzer system equipped with an integrated fluorescence chamber . These analyses were realized during the light period, from 8 h to 12 h (solar time) using the 2 cm2 leaf chamber at 25°C, flow rate of 300 mol s–1, 0.5 stomatal ratio (amphistomatic leaves), and saturating light intensity of 1,000 μmol photons m2s–1. The leaf-to-air vapor pressure deficit ranged from 1.2 to 2.0 kPa and the amount of blue light was set to 10% of photosynthetic photon flux density (PPFD) to optimize stomatal aperture. The initial fluorescence (F0) was measured in illuminating dark-adapted leaves (for at least 1 h) with weak modulated measuring beams (0.03 μmol m–2 s–1). A saturating white light pulse  was applied for 0.8 s to obtain the maximum fluorescence (Fm), from which the variable-to-maximum Chl fluorescence ratio was then calculated: Fv/Fm = [(Fm–F0)/Fm)]. In light-adapted leaves, the steady-state fluorescence yield was measured with the application of a saturating white light pulse  to achieve the light adapted maximum fluorescence (Fm’). A far-red illumination (2 μmol m–2 s–1) was applied after turn off the actinic light to measure the light-adapted initial fluorescence (F0’). The capture efficiency of excitation energy by open PSII reaction centers (Fv’/Fm’) was estimated following a previously described procedure (Fm’–Fs)/Fm’ , and α is the leaf absorbance. Leaf absorbance (α) was estimated by the chlorophyll content per unit area as follow: α = χ/(χ  +  76), where χ is the chlorophyll content per unit area (Gas exchange and chlorophyll rocedure  and the –Fs)/Fm’ . Electronit area .Pr) according to Rd) was measured by the same gas exchange system after plants being at least 2 h into the dark period.From combined measurements of fluorescence and gas exchange, we estimated the photorespiration rate (A/PPFD) were performed using atmospheric  of 400 μmol CO2 mol–1 and the plants were exposed to a range of PPFD in the sequence: 1,000, 1,500, 1,300, 1,100, 1,000, 800, 600, 400, 300, 200, 100, 50, and 0 μmol photons m–2 s–1. Variables derived from the A/PPFD curves were estimated from adjustments of light response curve by the non-rectangular hyperbolic model  of 400 μmol mol–1 and once the steady state was reached, the Ca was decreased to 300, 200, 100, and 50 μmol mol–1. Then, Ca was increased to 400, 500, 600, 800, 1,000, 1,200, 1,400, and 1,600 μmol mol–1. Curves were obtained using the second terminal leaflet of the third fully expanded leaf from the apex of 4−week−old plants.Photosynthetic light-response curves (ic model . Measure2) experiments, instantaneous gas exchange and chlorophyll a fluorescence analyses were performed as previously described, with minor modifications. All measurements were performed during the light period between 8 h and 12 h (solar time) under 1,000 μmol photons m–2 s–1 at the leaf level (light saturation) of PPFD, determined by A/PPFD curves and at 25°C. The reference (CO2) was set at 400 μmol mol–1 for plants at atmospheric (CO2) and 800 μmol mol–1 for plants under elevated (CO2). Instantaneous measurements were obtained using the second terminal leaflet of the third fully expanded leaf from the apex of 4−week−old plants. Rd was determined as described previously.For the high (CO2 in the carboxylation sites (Cc) was calculated following gm = A/[Ci−(Γ∗(ETR + 8(A + Rd)))/(ETR−4(A + Rd))], and using Γ∗ for tomato measured previously (gm data were employed to convert the A–Ci curves into A–Cc curves using the following equation Cc = Ci−(A/gm). ETR calculation using the proxy of α estimation, by the chlorophyll content empirical equation , as Vcmax and Jmax were calculated by fitting the mechanistic model of CO2 assimilation (Cc based on temperature of kinetic parameters of RuBisCO (Kc and Ko). Vcmax, Jmax, and gm were normalized to 25°C using the temperature response and plug-in equations described by Vcmax single point was only calculated for plants growing under atmospheric , mesophyll (lm), and leaf biochemical (lb) limitations. According to A–Cc curves obtained in the firt experiment, photosynthesis at the growing (CO2) was found at the curvi-linear part of the response, associated to the energy limited RuBP regeneration (Jmax). Since ETR is tightly coupled to Jmax, and should reflect gross photosynthesis  were determined exactly as previously described  for the first experiment and two different time points (middle of the light period and end of the dark period) for the second experiment from 4−week−old plants. The material was frozen in liquid nitrogen and stored at -80°C for further analyses. Samples were homogenized and aliquots of approximately 20 mg were subjected to hot ethanol extraction as described by escribed . Nitrate2O2). All steps were performed at room temperature. Tween-20 in Tris–buffered saline was used for washing  as well as for the dilution of antibodies. The bands on immunoblots were quantified by densitometry using the semi-quantitative ImageJ software (Version 1.1.4) as previously described  of leaf tissue according to the protocol of escribed . Data we2 environment) were considered. The variability of the results is expressed as the mean ± standard error (SE) of n independent biological replicates. Four biological replicates (n = 4) were utilized for each analysis, except for Western blot, where three biological replicate (n = 3) were analyzed. In order to verify the statistical difference between the genotypes, the means were compared using the Tukey test (P < 0.05). Pearson correlation matrices were performed to determine the correlated structure of the different parameters which were considered to be significant when P < 0.05. All analyses were performed using the GENES program . In the second experiment, two levels of the same categorical variable  but only on Cc. Therefore, it was necessary to carry out a complete photosynthetic study of the calculations considering the gm, including to evaluate the photosynthetic limitations for each of the lines. Interestingly, increased photosynthetic rates were observed under specific conditions for the two ILs  and both ILs higher Pr, ETR and lower chloroplastic CO2 concentration (Cc), probably due to the higher carboxylation demand, driven by the maximum RuBisCO carboxylation velocity based on Cc (Vcmax_Cc). In addition, the Jmax was higher in the ILs in comparison to M82.Concerning photosynthetic characterization of plants under atmospheric CO2; , furtherThe total protein levels were consistently increased by up to 21% in the IL 2–5 in comparison to M82 cultivar . Thus, wIncreased starch content in leaves at the end of the light period in the ILs 2–5 and 2–6 was observed when compared to M82 . On the We also evaluated whether the greater photosynthetic efficiency would reflect on total fruit production . We obse2), we comparatively analyzed plants from M82 and the two ILs growing for 3 weeks under atmospheric and elevated (CO2). We noticed under the OTC conditions, which are slightly different from the greenhouse conditions, such as higher air temperature and relative air humidity, a minor decrease in the photosynthetic capacity of all genotypes as they exhibited lower Agross and ETR , an increase of 22% in assimilation rate was observed in the ILs in comparison to M82 plants  among ILs and M82 plants  with A and ETR comparing ILs to M82 as well as the two growing conditions [400 and 800 ppm (CO2)] (2), as expected diffusive limitations were double than biochemicals (ld/lb > 2) where ILs obtained higher A mostly due to lower biochemical limitation  diffusive limitations became less important with all limitations highly balanced (ld/lb∼1), and again ILs even at high (CO2) showed biochemical limitations more reduced than diffusive ones (thus promoted higher ld/lb ratio than M82) when compared to M82 ] . At 400 d to M82 . The vernditions .2; 2), significant increase in the total chlorophyll (IL 2–5), and protein contents (IL 2–5) at the middle of the day, as well as fructose (both ILs) at the end of the dark period was verified (2) compared to M82 (2), an increase in starch levels was observed in plants from both ILs compared to M82 at the middle of the light period (2), nevertheless the ILs exhibited higher starch levels in relation to M82  treatments , and ETR under high  in the lines compared to their respective M82. Regarding photosynthetic results under ambient (CO2), it is well established that modulations of photosynthesis mainly occur in response to environmental factors including light and temperature. In fact, the conditions of the current experiment were characterized by higher temperatures and light intensities in comparison with those previously described , increases in A could not be observed in the ILs at ambient (CO2), as the lower Cc led to higher Pr under these conditions  could be explained, at least partially, by higher ETR values , chlorophyll and protein accumulated in the IL 2–5 as well as starch in leaves from both ILs , followed by the enhanced A and ETR values in ILs exposed to elevated (CO2), it seems reasonable to assume that both ILs optimize their investment in the components involved in RuBP regeneration. Thus, genetic manipulation in these components may be an opportunity to increase photosynthesis reflecting positively on growth in a future environment with high (CO2; 2 fertilization to improve productivity (2 assimilation in the Calvin–Benson cycle is shared among several enzymes, an increase in photosynthetic potential could be achieved combining multigene manipulation (2 fixation and biomass yield dramatically (2 to maximize crop production have been discussed elsewhere (S. pennellii ILs population represents an opportunity to explore the large quantity of traits that affect plant survival as well as yield potential once ILs facilitate the identification of large number of genes affecting single or several plant phenotypes. Perhaps more importantly, our findings not only open up a novel fundamental research avenue, given that they identify a genomic region that lead to significant enhanced photosynthetic capacity, but also have important biotechnological implications, specifically with regard to the potential for increased yield, without increasing the need for arable land.By analyzing the effects of the genetic variation on chromosome 2 of ILs 2–5 and 2–6 that modulates the upregulation of photosynthetic capacity, we have here provided novel insights into the mechanisms that govern photosynthetic capacity in tomato. Considering the higher values of igh CO2; , or curructivity . Noteworpulation . Recent atically , 2017a. lsewhere . In thisAll datasets generated for this study are included in the article/AN-N designed the research. JL-C, FO, and DC characterized the introgression lines under supervision of AN-N and WA. JL-C, WB-S, and RS performed the Western blot analyses. AN-N, FO, and JG analyzed the data. JL-C and AN-N wrote the manuscript with input from all the other authors. JG, DM, SM, and WA complemented the writing.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Validated algorithms to classify type 1 and 2 diabetes  are mostly limited to white pediatric populations. We conducted a large study in Hong Kong among children and adults with diabetes to develop and validate algorithms using electronic health records (EHRs) to classify diabetes type against clinical assessment as the reference standard, and to evaluate performance by age at diagnosis.We included all people with diabetes (age at diagnosis 1.5–100 years during 2002–15) in the Hong Kong Diabetes Register and randomized them to derivation and validation cohorts. We developed candidate algorithms to identify diabetes types using encounter codes, prescriptions, and combinations of these criteria . We identified 3 algorithms with the highest sensitivity, positive predictive value (PPV), and kappa coefficient, and evaluated performance by age at diagnosis in the validation cohort.n = 60, T2D n = 10,136) and 5101  people in the derivation and validation cohorts . Algorithms using codes or prescriptions classified T1D well for age at diagnosis < 20 years, but sensitivity and PPV dropped for older ages at diagnosis. Combination algorithms maximized sensitivity or PPV, but not both. The “high sensitivity for type 1” algorithm  had a sensitivity of 95.3% , while the “high PPV for type 1” algorithm  had a PPV of 100.0% , and the “optimized” algorithm  had a sensitivity of 65.1% (49.1–79.0%) and PPV of 75.7% (58.8–88.2%) across all ages. Accuracy of T2D classification was high for all algorithms.There were 10,196 (T1D Our validated set of algorithms accurately classifies T1D and T2D using EHRs for Hong Kong residents enrolled in a diabetes register. The choice of algorithm should be tailored to the unique requirements of each study question. Administrative health databases are an important resource for population-based diabetes research . Using rMany epidemiological studies apply the untested assumption that findings in adults with diabetes are representative of T2D , 8. HoweConsidering the lifelong and immediate need for insulin treatment in T1D, novel algorithms have been developed to identify T1D using prescriptions and laboratory data from electronic health records (EHRs) . HoweverHong Kong is a special administrative region of China with a population of 7.3 million and an estimated diabetes prevalence of 10.3% (2014) . All res1c ≥ 6.5% [International Statistical Classification of Diseases and Related Health Problems version 9 (ICD-9) codes 72–75), or within 9 months of any pregnancy-related encounter (ICD-9 codes 630–676) outside these periods . We also excluded in-patient glucose measurements to avoid misidentifying acute stress hyperglycemia as diabetes.The Hong Kong Diabetes Surveillance Database (HKDSD) includes all Hong Kong residents with diabetes as identified using the HA’s territory-wide EHR, which includes routinely-collected data on laboratory tests, prescriptions, and hospital visits for the entire population. We defined diabetes onset as the first occurrence of glycated haemoglobin Ac ≥ 6.5% , fastingc ≥ 6.5% , glucosec ≥ 6.5% , 4 excluc ≥ 6.5% , we exclA subset of those in the HKDSD is additionally enrolled in the multicentre Hong Kong Diabetes Register HKDR, . This reBecause the reference standard  was only established for the subset of those enrolled in the HKDR, we restricted the study to this sub-population. To ensure at least 1 year of follow-up data, we included all people with diabetes diagnosed at ages 1.5  to 100 years from 1 January 2002 through 31 December 2015, defined using the HKDSD criteria. The maximum follow-up date was 31 December 2016. We excluded individuals with monogenic or secondary diabetes and those with missing diabetes type in the HKDR  or prescriptions  to improve sensitivity, and combining using “and”  to improve PPV. We then tested all 8 “combination algorithms” in the derivation cohort. Of the 12 code, prescription, and combination algorithms, we identified the 3 algorithms with the highest sensitivity, highest PPV, and highest kappa coefficient  across all ages. Using the validation cohort, we evaluated the performance of these 3 algorithms in classifying T1D and T2D by age at diagnosis.We applied clinical knowledge  and reviewed previous validation studies , 26, 27 We repeated the entire procedure using additional laboratory data (estimated glomerular filtration rate) to determine whether requiring normal renal function with insulin prescriptions would improve the performance of prescription algorithms.n = 357, 2.3%) and handled by complete case analysis. All analyses were performed using the “FREQ” procedure in SAS version 9.4 .We calculated the sensitivity, specificity, PPV, and negative predictive value (NPV) with 95% exact confidence intervals of each selected algorithm for classifying T1D and T2D in the derivation and validation cohorts. We also calculated Cohen’s kappa coefficient, which represents agreement after agreement due to chance is removed . A perfeThere were 15,300 individuals with complete data and diabetes diagnosed during 2002–15 Fig. . More meAmong the prescription algorithms, those specifying “at least 1 insulin prescription” were the most sensitive but lacked PPV for classifying T1D. Nearly everyone with T1D received an insulin prescription at any time , and almost all received it within 90 days of diabetes diagnosis . As these 2 prescription algorithms had the highest sensitivity values and classified everyone identically except for 1 case, we applied the tiebreaker criteria to choose “insulin prescription within 90 days”  based on its greater PPV . Adding criteria for other types of medications improved the PPV of insulin-based prescription algorithms at the expense of sensitivity. In the T1D cohort, 36.7% received at least 1 metformin prescription (versus 88.6% in the T2D cohort), and 16.7% received a glucose-lowering medication prescription other than insulin and metformin (versus 75.7% in the T2D cohort). Of the algorithms that added a condition for no other glucose-lowering medication prescriptions in addition to an insulin prescription, the algorithm “at least 1 insulin prescription with no other glucose-lowering medication prescriptions except for metformin” had the highest PPV . Specifying the type of insulin as multiple daily injections further improved the PPV. “Multiple daily injections with no other glucose-lowering medication prescription”  had a 78.0% PPV (sensitivity 53.3%), which was the highest of the prescription algorithms.Algorithms A–D classified T1D well for age at diagnosis < 20 years in the derivation cohort, but as the proportion of diabetes cases classified as T1D dropped with age, the precision and estimates of sensitivity and PPV also dropped Fig. . For ageAs with algorithms A–D, performance of the combination algorithms also generally dropped at older ages at diagnosis Fig. . For ageAmong the 12 algorithms we tested, “B or C,” “B and D,” and “B and C” had the best sensitivity , PPV , and kappa coefficient  respectively across all ages in the derivation cohort. Table Modifying algorithms with renal function criteria resulted in similar PPV with the same or lower sensitivity, and ultimately did not improve performance . All selThis is one of the largest validation studies of algorithms using EHRs to classify T1D and T2D among children and adults, and the only validation study in an Asian population. Using a systematic approach to generate a set of algorithms maximizing sensitivity and PPV, we revealed that classification performance is best at lower ages at diagnosis and drops as age at diagnosis increases—a finding that has not previously been demonstrated. We developed a “high sensitivity for type 1” algorithm  with > 90% sensitivity across age at diagnosis at the expense of lower PPV, and a “high PPV for type 1” algorithm  with perfect PPV across age at diagnosis at the expense of lower sensitivity. Our optimized algorithm  produced the most accurate estimates of the proportion of T1D cases across all ages at diagnosis. The complementary performance characteristics of these algorithms can inform their application to future studies, and the choice of algorithm should be tailored to the unique requirements of each study question.Among children and adolescents, our diabetes classification algorithms performed similarly to others developed in white populations. Using Canadian administrative and prescription data, Vanderloo et al.  validateBy contrast, classification accuracy of the algorithms was lower among adults versus children. Previous validation studies including adults are limited. Klompas et al.  used a lOur study yielded 3 complementary algorithms, the choice of which can be tailored to different study contexts depending on diabetes type, sensitivity, and PPV requirements. The optimized algorithm  performed highly accurately at ages at diagnosis < 20 years, but it also generated close estimates of the proportion of T1D among adults, as misclassified T1D and T2D cases were approximately balanced. Thus, the optimized algorithm could be applied to diabetes incidence and prevalence studies. Other algorithms may be better suited for cohort studies or other designs. For example, an adult-onset T1D cohort study could use the “high PPV for type 1” algorithm  to maximize PPV. Alternatively, a case-finding study designed to identify as many people with T1D as possible might apply the “high sensitivity for type 1” algorithm . A cohort study of T2D among adults could apply the “high PPV for type 2”  algorithm, although all 3 algorithms performed well considering the relatively high T2D prevalence in adults.Our large register-based validation study is the first to specifically distinguish T1D and T2D in Asians, using routinely available encounter codes and prescriptions in a population-wide EHR within a public universal healthcare context. Unlike previous studies, we demonstrated the critical importance of age at diagnosis, defining separate derivation and validation cohorts to avoid overfitting. However, there are some limitations to note. As in other public healthcare settings, we did not have access to routine autoantibody or C-peptide testing to verify diagnoses of T1D. We could not include the entire HKDSD or externally validate because full chart access was only authorized for the HKDR. However, the HKDR represents a large geographic region of Hong Kong, which has a single publicly administered healthcare system serving its entire population. Although socioeconomic status variables were not captured in our databases, other baseline characteristics were highly similar between the HKDR and HKDSD, supporting the generalizability of our algorithms. Research platforms such as the HA’s Data Collaboration Lab should allow more comprehensive use of EHR data to improve diabetes classification using more complex methodologies and to enhance population research –34.In summary, we developed and validated a set of algorithms to accurately classify diabetes type for different ages at diagnosis using population-level health data. As EHRs become increasingly available, our approach may be applied to generate similar algorithms in other settings. These algorithms can be applied to future studies to characterize incidence, prevalence, and other statistics separately for T1D and T2D—especially in China and other populations where these statistics have never been measured .Additional file 1. Demographic characteristics of the New Territories East Cluster (NTEC) population and the overall Hong Kong population [pulation , 3


Drosophila melanogaster has long been a popular model insect species, due in large part to the availability of genetic tools and is fast becoming the model for insect colour vision. Key to understanding colour reception in Drosophila is in-depth knowledge of spectral inputs and downstream neural processing. While recent studies have sparked renewed interest in colour processing in Drosophila, photoreceptor spectral sensitivity measurements have yet to be carried out in vivo. We have fully characterised the spectral input to the motion and colour vision pathways, and directly measured the effects of spectral modulating factors, screening pigment density and carotenoid-based ocular pigments. All receptor sensitivities had significant shifts in spectral sensitivity compared to previous measurements. Notably, the spectral range of the Rh6 visual pigment is substantially broadened and its peak sensitivity is shifted by 92 nm from 508 to 600 nm. We show that this deviation can be explained by transmission of long wavelengths through the red screening pigment and by the presence of the blue-absorbing filter in the R7y receptors. Further, we tested direct interactions between inner and outer photoreceptors using selective recovery of activity in photoreceptor pairs. Drosophila as the model2. Drosophila melanogaster is fast becoming the model for insect colour vision due to the wide range of genetic tools available. Surprisingly, despite new advances in our understanding of more complex motion and colour visual processing in Drosophila, the characterisation of photoreceptor spectral sensitivity in this species has not been investigated since it was first revealed 20 years ago4.Colour cues in the natural environment are used by many insects, guiding behaviours such as prey detection, mate selection and more general tasks such as flight navigation. To detect and distinguish wavelength cues, the output of two or more photoreceptors with different spectral sensitivities must be compared. This colour-opponency system has been well explored in vertebrates but only more recently has insect colour opponency been characterised, using Drosophila compound eye is formed from approximately 800 ommatidial units, each comprising 6 outer (R1–6) and 2 inner photoreceptors (R7 and R8) with an open rhabdom structure  with an additional peak in the UV due to an associated carotenoid-derived sensitising pigment6. Inner receptor R7 cells express UV-sensitive Rh3 (R7p) or Rh4 (R7y) and the proximal receptor R8 cells express either blue-sensitive Rh5 (R8p) or green-sensitive Rh6 (R8y)4  is short-wavelength shifted when compared to flies with brown screening pigment , as an adaptation to reduce the absorption of red light. The effect of light leakage on the Drosophila Rh6 visual pigment has yet to be tested in vivo.The spectral sensitivity of a cell is not dictated solely by the underlying visual pigment but is shaped also by numerous optical filters, including screening pigment that optically isolates neighbouring ommatidia Fig. c. In theDrosophila has been investigated using behavioural experiments15, modelling16 and by visualising neural responses directly in the fly brain, in response to light stimulation with genetic manipulation2. Colour opponency begins at the first visual synapse, the photoreceptor terminal, with reciprocal inhibition occurring between paired R7 and R8 receptors1. Additionally, there is transfer of information between inner and outer receptors via gap junctions in the lamina that is thought to enhance the sensitivity of the motion detection pathway17. Direct opponency between photoreceptors by way of opponent voltage responses have been demonstrated to occur in other insects and is well characterised in some lepidopteran species19. Such opponency in the photoreceptor soma was not detected in Drosophila in a recent study that inferred photoreceptor response from axonal measurements, using genetic dissection and two-photon calcium imaging2. In this study, we aimed to further test for potential interactions at the level of the photoreceptor soma by directly measuring photoreceptor voltage responses, with activity of photoreceptor pairs restored.Much of what is understood about animal colour vision is derived from studies of vertebrates and only recently have investigations revealed the neural basis of colour information processing in insects. Colour information processing in Drosophila spectral sensitivity, for each receptor type, by selectively restoring photoreceptor activity in flies with no receptor activity (norpA) and testing response using ERG. We characterise the sensitivity of photoreceptors in flies with screening from distal receptors and ocular pigments intact and we test the effect of screening pigment and the blue-absorbing yellow filter on inner receptors. We reveal significant shifts in spectral sensitivity for all receptor sensitivities and a large 92 nm shift in sensitivity of the Rh6 visual pigment when measured in its native photoreceptor (R8y) from 508 to 600 nm. We also find that the blue-absorbing yellow filter refines sensitivity of the Rh4 visual pigment in R7y photoreceptors. Furthermore, we explore the effect of reciprocal inhibition between inner photoreceptors on spectral response at the level of the photoreceptors and to what extent the outer photoreceptors input to this system. Our results indicate that the input from inner photoreceptors to downstream neuronal process is linear and provide no clear evidence for direct interactions at the photoreceptor level. Spectral modulation can be seen however between inner and outer receptors, providing further evidence of possible interactions between motion and spectral channels in the lamina.Here we report the first complete in vivo characterisation of 3). However, they were in good agreement with maximal sensitivities derived from visual pigment extracts (331 and 355 nm4).We generated flies with rescued activity of specific photoreceptor types (single opsin rescue flies) to test the spectral sensitivity of the inner R7p Rh3), R8p (Rh5), R7y (Rh4), R8y (Rh6) and outer receptors (Rh1), using ERG. We were able to measure reliably from all single opsin rescue genotypes. Rh1 rescue flies exhibited characteristic on and off transients at the start and end of the 200 ms light pulse, absent in Rh3–Rh6 rescue flies  was also significantly altered from a simple visual pigment template. There were notably three fluctuations in the waveform around the visual pigment peak  was similar compared to previous measurements of the visual pigment (437 nm)5 nm was To test the effect of screening pigment on photoreceptor response, we examined opsin rescue flies with wild-type (red-eye) and reduced screening pigment (orange-eye). The spectral sensitivities of Rh3 and Rh4 single opsin rescue flies were not affected by a reduction of screening pigment. However, a reduction in screening pigment led to narrowing of the Rh5 rescue response  and inner R7p (Rh3), R7y Rh4) and R8p (Rh5) receptors. The response of the UV-sensitising pigment coupled to visual pigment Rh1 was effectively removed by carotenoid deprivation after one generation on yeast-glucose food and showed no further change in spectral shape after two generations  to 600 nm when the sensitivity was measured in vivo  and the other close to the peak of the flies raised on regular carotenoid rich diet (600 nm)  were compared with the sum of corresponding single photoreceptors. We found no difference in spectral shape when double rescue flies were tested at different intensities, according to the two V-log(I) tests carried out at each visual pigment peak sensitivity, with the exception of a single wavelength in the Rh3 and Rh5 double rescue flies  increased relative to the UV-sensitising pigment, indicating that more light is available to stimulate the visual pigment directly in the blue-green region of the spectrum. This may be explained by the reduction in screening of by off-axis light by the blue-green absorbing yellow pupil  with wild-type (red) screening pigment is far broader than expected, with low levels of sensitivity in the UV and a major peak of sensitivity at 600 nm Fig. a. This l21 Fig. a. By redDrosophila and other red-eyed flies may be sensitive to the longer wavelengths of light that leak through the red screening pigment and as a consequence this would degrade spatial resolution22. At high light levels, the movement of pupillary granules within the photoreceptors cause a short-wavelength shift in the peak sensitivity of the outer receptors and increases photoconversion of metarhodopsin23. While the Rh1 metarhodopsin (λmax = 566 nm) will readily absorb long wavelength light leaking through the screening pigment, this is not the case for Rh6, whose metarhodopsin absorbs maximally in the blue-green (λmax = 468 nm). The effect of light leakage on the Rh1 visual pigment can be seen as a secondary peak close to 575 nm . In both Rh1 and Rh6 opsin rescue flies, response increases above 550 nm  and recording (ERG), compared to on-axis point-source illumination and intracellular recordings of single cells. Additionally, ERG recordings are from the sum of the stimulated photoreceptors in the eye. Thus, with widefield illumination, as light intensity is increased, additional photoreceptors are stimulated (photoreceptor recruitment). It is therefore more difficult to reach photoreceptor saturation with ERG recordings. Consequently, photoreceptors are tested at a higher light intensity than with point-source illumination and intracellular recordings, which will likely cause broadening of spectral curves. Wide-field illumination, as is experienced in the natural environment, more closely resembles natural illumination than on-axis point source illumination. We suggest that in the case of other red-eye flies, we may see similar shifts in sensitivity of long-wavelength photoreceptors when stimulating with wide-field illumination.It is important to note that the broadened spectra we observe in Drosophila has a blue absorbing carotenoid present in the distal R7y retinula cells5. In Musca and Calliphora this serves to reduce the light absorbed by the blue-sensitive visual pigment in R7y, instead conferring sensitivity to the UV by the presence of a UV-sensitising pigment26. This interesting and complex system is not found in Drosophila, rather UV-sensitivity is achieved simply by the presence of a dedicated UV-sensitive visual pigment Rh43. However, the presence of this carotenoid pigment in Drosophila was not previously understood and its effect on R8y (Rh6) has not been tested until now. When carotenoids were removed from the diet of flies with rescued R8y activity, sensitivity was restored close to the peak of the visual pigment (510 nm) but some effect of light leakage remained at 600 nm. This suggests that the blue-absorbing filter reduces light available to Rh6 at its peak sensitivity (508 nm) and instead extends sensitivity towards the red. These findings could not be replicated by simulating the differences in experimental conditions, either reducing or increasing testing intensity. One generation was sufficient to remove the contribution of the Rh1 UV-sensitising pigment and further generations of carotenoid deprivation did not change the response curve of Rh6 flies , opsin expression is still present in flies raised on yeast-glucose food29, which we used here. The apparent lack of reduction in sensitivity of the UV-sensitive Rh3 and Rh4 rescue flies may be explained by the simultaneous reduction in UV-sensitising pigment associated with Rh1, which strongly absorbs UV wavelengths in the outer receptors. This relative increase in available UV light may help to recover sensitivity in Rh3 and Rh4 rescue flies when stimulated with wide-field stimuli. We would not expect to see a similar recovery in Rh5 and Rh6 rescue flies, which are relatively less sensitive to UV-light.Carotenoid deprivation also affects the Rh4 rescue flies, which have active R7y photoreceptors that contain the yellow carotenoid filter. When removed, sensitivity is increased at wavelengths greater than 400 nm, suggesting that this filter narrows sensitivity in the UV, potentially increasing wavelength discrimination in that region of the spectrum. We suggest that this filter both contributes to refining the sensitivity of the UV-sensitive R7y (Rh4) photoreceptor and bathochromically shifts the sensitivity of R8y cells by depleting wavelengths of light between 400 and 540 nm in the R7y receptor. In large flies, the yellow filter only shifts R8y sensitivity from 520 to 540 nm but it is not known whether the filter in 2. This strongly suggests that there is no direct enhancement or inhibition of signal, which could be achieved by gap junctions between neighbouring photoreceptors or by local electrical fields in the surrounding extracellular space. Furthermore, although spectral inhibition occurs between photoreceptor terminals in the medulla2 it is not detectable upstream in Drosophila. If such inhibitory processes typically originate at the terminals and further downstream in other insects, then this may explain why so few studies have described spectral inhibition using intracellular recordings alone.Our findings suggest that at the level of the photoreceptor soma there is no detectable interaction between inner R7/R8 receptor pairs, or inhibition, which has been detected further downstream in the visual pathway at the first visual synapse in the medulla and via the Dm9 pathway31 and was proposed as a mechanism to improve motion discrimination17. We suggest that our findings provide some evidence to support this circuit model but they are not strongly conclusive as only subtle changes between spectral shape was observed in double opsin rescues, compared to the expected sum of the single rescue responses. Results may also be affected by the additional complexity of outer receptor ERG responses, due to the feedback from interneurons (large monopolar cells), which modulate photoreceptor response32. Recent work has explored spectral interactions between inner and outer photoreceptors by directly recording the activity of photoreceptor axons and inferring photoreceptor response by means of genetic dissection2. In these experiments, outer receptors were not shown to significantly contribute to spectral tuning of inner receptors, rather modulation occurred only between inner receptors. Therefore, while we are confident our results do not support direct opponency between inner receptor pairs, we are unable to draw strong conclusions from inner and outer receptor interactions. The addition of responses from all single rescue genotypes is in good agreement with wild type responses, demonstrating that overall, the voltage output of the photoreceptors sum linearly at the level of the retina  under opsin promotors against a norpA background, generating single opsin rescue flies. Opsin rescue flies were generated with wild-type screening pigment (red-eye), w[+] norpA[36]; Rh-norpA and reduced screening pigment (orange eye), by incorporation of the mini-white gene (noted by w[+mC]), w[−] norpA[36]; P{w[+mC], Rh-norpA}. All red-eye single opsin rescue flies were generated in a previous study . Single rescue flies were crossed to generate double opsin rescue genotypes.All ninaE) promotor, in a white eye mutant background, w[−] norpA [36]; PBac{actin88F > RFP, ninaE > norpA}, PBac{actin88F > GFP, ninaE > Rh6}; ninaE [8]. In order to ectopically express Rh6 in outer photoreceptors of w[−] norpA [36] mutant flies, we generated the following two constructs: pigActGFP- ninaE > Rh6 and pigActmCherry- ninaE > norpA. In all the cloning steps, the insert DNA was amplified by PCR using proper primer combinations . Unless otherwise specified, D. melanogaster w1118 genomic DNA was used as a template in the PCR reactions. The pigAct88F-GFP vector stems from the piggyBac transposon-based plasmid pigA3GFP33 through replacement of the 3xP3 synthetic promoter present in pigA3GFP by the promoter and 5′UTR of D. melanogaster Actin88F gene (nucleotides − 1410 to + 647). The Actin88F promoter is active in the thoracic muscles34 and thus Actin88F-GFP can be used as a transgenesis marker without interfering with ERG experiments. The Rh1 promoter and 5′UTR , the Rh6 coding sequence  and the SV40 polyadenylation signal (amplified by PCR from the pigA3GFP vector) were subsequently inserted into pigAct88F-GFP vector in a stepwise manner, giving rise to the final construct pigActGFP- ninaE > Rh6. For the generation of the pigActmCherry- ninaE > norpA construct, the GFP gene present in pigAct88F-GFP was replaced by mCherry (amplified by PCR from the vector pFPV-mCherry. Addgene #20956) to generate the vector pigAct88F-mCherry. This vector was used as a backbone for the cloning of the Rh1 Promoter > norpA cDNA > ninaE 3′UTR cassette (amplified by PCR using genomic DNA isolated from the D. melanogaster Bloomington stock #52276), giving rise to the final construct pigActmCherry- ninaE > norpA. Both pigActGFP- ninaE > Rh6 and pigActmCherry- ninaE > norpA were injected into Drosophila w1118 embryos following standard piggyBac transformation protocols. Oregon R–C flies  were used to test wild-type response and a norpA mutant with no rescue was used as a negative control, w[-] norpA;+;+  was coupled to a monochromator (Cairn Research) with either 1200 or 2400 line-ruled diffraction grating. Long-pass filters were placed in the light path to filter optical harmonics produced by the 2400 grating  and 1200 grating . Intensity of the light was controlled by altering the width of the input and exit slits of the monochromator. Peak wavelength was controlled by the grating angle, yielding a testing range between 315 and 550 nm or 450 and 700 nm in 5 nm steps for the 2400 and 1200 gratings, respectively. Wavelength and photon flux were calibrated at the point of the fly. All spectral measurements were made using spectrophotometers  calibrated to a known light source for measurements of irradiance . Spectral acquisition was controlled using custom MATLAB scripts  and conversions to irradiance were carried out according to the manufacturer’s instructions. Irradiance in µW cm2 and 4 mM MgCl2, all Sigma Aldrich) using pipette fillers . Recordings were measured from near the equator of the eye with a broken pipette touching the eye  and positioned using a motorized micromanipulator (Sensapex). The reference electrode was inserted slightly inside the median ocellus using a manual manipulator. Light was delivered to the fly via a UV transmissive 5 mm liquid light guide (Lumatec series 300) and silica bi-convex lens with 25.4 mm focal length , arranged to maximise light stimulation at the point of the fly. All recordings were made within a Faraday cage and responses were amplified . Both stimuli and data acquisition were controlled using a DAQ board  in conjunction with the software, Ephus36.Animals between 4 and 12 days after eclosion were anesthetised on ice and immobilised on a metal cone using ultraviolet curing adhesive . Electroretinogram (ERG) recordings were made using borosilicate micropipettes  pulled on a Sutter P-2000 laser puller . Electrodes were filled with insect saline  and between 1.14 × 1011 and 3.60 × 1016 photons cm−2 s−1 (2400 grating). Orange eye flies were tested between 3.60 × 1010 and 6.40 × 1015 photons cm−2 s−1. Each intensity was repeated 10 times followed by a pause of 100 s. The wavelength each V-log(I) test was chosen according to previous estimates of peak sensitivity for the test visual pigment: Rh1, 485 nm; Rh3, 345 nm; Rh4, 370 nm; Rh5, 440 nm; Rh6 540 nm4. Peak wavelength for Rh6 was adjusted after preliminary tests indicated longer-wavelength peak sensitivity. The intensity at half maximum response was calculated from the V-log(I) curve and the closest isoquantal test intensity was used for spectral tests. In cases where no obvious photoreceptor saturation had occurred, this value was estimated from the fitted curve.To determine the stimulus–response (V-log(I)) function, each animal was tested with a series of 200 ms light pulses every 10 s that increased in intensity over a possible range of 6 log units. Red eye flies were tested between 1.14 × 10To test spectral response, animals were stimulated with isoquantal flashes every 5 s at all test wavelengths with randomised presentation. Test wavelengths were divided into three blocks from lowest to highest wavelength and randomised within. Stimuli were always presented from these categories in order from low to high, to ensure a balanced order of testing across the wavelength range. Each wavelength was tested 10 times concurrently and the last 5 responses were used for analysis. All genotypes were tested with wavelengths of 315–550 nm and those with long wavelength responses (e.g. Rh1 and Rh6) were also tested with 450–700 nm, in steps of 5 nm. All animals were dark adapted for 30 min prior to the V-log(I) test and subsequently a further 15 min before each spectral sensitivity test.V is the photoreceptor response, Vmax is the maximum response, I is the light intensity and K is the light intensity required to achieve half of Vmax37 and n is the slope. The intensity at half maximum response (K) was used for spectral tests to ensure all testing was carried out in the linear portion of the V-log(I) curve, where possible. Where data did not fit well to a Naka–Rushton function, K was estimated from a fitted sigmoid function. Due to the low response of carotenoid-deprived Rh5 and Rh6 rescue flies, these were tested at the maximum possible intensities Rh5: 7.18 × 1015 photons cm−2 s−1 and Rh6: 1.02 × 1016 photons cm−2 s−1). For spectral sensitivity tests, outliers greater than three scaled median absolute deviations from the median of the 5 responses were first removed then the mean was taken. Data were smoothed using a Savitzky-Golay filter (data window 15 nm) then each spectral curve was normalized per individual and averaged across all individuals in each experiment.The responses from 6 animals were used for each experiment with the exception of the Rh6 rescue flies raised for two generations on yeast-glucose food where 4 animals were used for spectral testing. ERG responses were normalized to a zero baseline using the average of 100 ms prior to stimulus onset. Photoreceptor response was calculated as the change in voltage between the zero baseline and minimum voltage during the 10 ms before the end of the light flash for spectral tests and 20 ms for V-log(I) tests. The last 5 photoreceptor responses from each set of 10 repeats was used for V-log(I) and spectral sensitivity tests. These responses were averaged and mean responses used to compare genotypes. Animals with low or noisy ERG responses indicating a poor-quality preparation or inadequate electrode connection were not used for further analysis. V-log(I) data were fitted to the Naka–Rushton function:38. Two sample Student’s t-tests were carried out with Bonferroni correction for multiple sampling between sensitivity curves with the exception of comparisons made between double opsin rescues tested at different light intensities, which were tested using paired t-tests. All statistical tests were carried out in MATLAB .To combine spectral sensitivity curves from tests using both the lower and higher wavelength gratings all non-normalized curves were joined at the 450–550 nm overlap region and an average fit was derived from the fit of all 21 points. The responses for Rh1 opsin rescue flies were larger in the 450–700 nm range than those in the 315–550 nm range likely a result of increased metarhodopsin stimulation at these longer wavelengths. Thus, to better estimate the long-wavelength tail, a scale factor of 0.5777 was calculated at the mean of both curves at its peak (490 nm) and applied to the longer wavelength spectral curve before combining. The joined curves were then normalized between 0 and 1 and analysed as outlined previously. For the 450–550 nm region of cross over between both curves, means and standard deviations were calculated for all 12 points. For a comparison of photoreceptor pair responses, the mean of non-normalized sensitivity curves from single rescue flies were summed pair-wise according to the order of light stimulus and compared with the response curves of double rescue flies. All analyses were performed in MATLAB , using custom scripts. Visual pigment templates were generated using R package PAVOSupplementary Information.Supplementary Data 1.Supplementary Data 2.