N = 397,385). We identified 38 loci for headaches, of which 34 loci have been reported before and four loci were newly suggested. The LDL receptor related protein 1 (LRP1)—Signal Transducer and Activator of Transcription 6 (STAT6)—Short chainDehydrogenase/Reductase family 9C member 7 (SDR9C7) region in chromosome 12 was the most significantly associated locus with a leading p value of 1.24 × 10–62 of rs11172113. The One Cut homeobox 2 (ONECUT2) gene locus in chromosome 18 was the strongest signal among the four new loci with a p value of 1.29 × 10–9 of rs673939. Our study demonstrated that the genetically correlated phenotypes of self-reported headache and self-reported migraine can be meta-analysed together in theory and in practice to boost study power to identify more variants for headaches. This study has paved way for a large GWAS meta-analysis involving cohorts of different while genetically correlated headache phenotypes.Headache is one of the commonest complaints that doctors need to address in clinical settings. The genetic mechanisms of different types of headache are not well understood while it has been suggested that self-reported headache and self-reported migraine were genetically correlated. In this study, we performed a meta-analysis of genome-wide association studies (GWAS) on the self-reported headache phenotype from the UK Biobank and the self-reported migraine phenotype from the 23andMe using the Unified Score-based Association Test (metaUSAT) software for genetically correlated phenotypes (The online version contains supplementary material available at 10.1007/s43657-022-00078-7. Headache is one of the commonest symptoms that present to clinicians in general practice or in specialist neurology clinics  when considering all ages and both genders in 174 causes (GBD 2019 Diseases and Injuries Collaborators It has been confirmed that headaches such as migraine are heritable. The single nucleotide polymorphism (SNP) based-heritabilities of migraine and self-reported headache were 0.15 and 0.21 in Caucasians, respectively  , defined as those who self-reported headache symptoms affecting daily lives within last month using the UK Biobank online questionnaire; controls , defined as those who did not have any pain affecting daily lives within last month (UK Biobank code 6159). The corresponding GWAS analysis was performed using a linear mixed model adjusting for age, sex, nine population principal components, genotyping arrays, and assessment centers  were as follows: cases , defined as those who self-reported a migraine history (diagnosed by doctors or self-diagnosing) using the 23andMe online questionnaire; controls , those who self-reported having no migraine. The corresponding GWAS was performed using a linear mixed model adjusting for age, sex, and five population principal components  were: cases  .SNPs in both datasets were coded in a forward direction and according to the GRCh37 genome build. In total, 8,500,802 SNPs with minor allele frequency > 0.005 in both datasets were extracted. To ensure the datasets could be jointly meta-analysed, these SNPs were checked for same effect alleles and flipped accordingly in R (https://github.com/RayDebashree/metaUSAT). The metaUSAT software applies a multivariate meta-analysis approach instead of a univariate approach of analysing each related trait separately. Unlike traditional GWAS meta-analysis on a single trait, where several sets of summary statistics on a single trait are combined into a single summary measure for that trait, the multivariate meta-analysis implemented by metaUSAT does not combine the summary statistics; instead, a joint analysis is performed using summary statistics from related traits. It is a statistical inference approach that leverages related traits to provide a p value for the test of no association of any trait with a SNP against the alternative that at least one trait is associated with the SNP. Being a complex data-adaptive approach, the metaUSAT software does not output an overall effect size (Beta) and standard error (SE) values for each SNP. The metaUSAT software is robust to the association structure of correlated traits and potential sample overlap (Ray and Boehnke The Unified Score-based Association Test (metaUSAT) is a software package for performing GWAS meta-analysis studies on genetically correlated phenotypes (Ray and Boehnke https://fuma.ctglab.nl/). FUMA uses “maximum distance of linkage disequilibrium (LD) blocks to merge’’  to determine the number of associated loci and the r2 value  to determine the number of independent significant SNPs. In addition, the gene-based association analysis and the gene-set analysis were performed with Multi-marker Analysis of Genomic Annotation (MAGMA v1.08), which was integrated in FUMA  which was also integrated in FUMA. In the tissue expression analysis, average gene-expression per tissue type was used as gene covariate to test positive relationships between gene expression in a specific tissue type and genetic associations. In addition, regional plots of the suggested new loci were generated by LozusZoom (http://locuszoom.org/).The output generated from metaUSAT was uploaded to Functional Mapping and Annotation (FUMA v1.3.6b) for SNP annotation —Signal Transducer and Activator of Transcription 6 (STAT6)—Short chain Dehydrogenase/Reductase family 9C member 7 (SDR9C7) region in chromosome 12q13.3 being the most significantly associated locus with a leading p value of 1.24 × 10–62 for rs11172113. The Four And A Half LIM Domains 5 (FHL5)—UFM1 Specific Ligase 1 (UFL1) locus in chromosome 6q16.1 was the second most significantly associated, with a p value of 6.57 × 10–39 for rs9486715. A Manhattan plot showing these loci is shown in Fig. p value < 5 × 10–8. Among these SNPs, 113 SNPs were considered as independent associations (r2 < 0.6 with any SNP within the 2228 SNPs) (Supplementary Tables 1 and 2).There were 8,500,802 common SNPs from both cohorts analysed by the metaUSAT software. FUMA reported 38 independent genetic loci across autosomal chromosomes with the One Cut homeobox 2 (ONECUT2) gene locus (18q21.31) was the strongest signal among these four new loci, associated with a p value of 1.29 × 10–9 for rs673939. Table Table p value = 0.05/19,436 = 2.57 × 10–6) that were associated with headaches, with the PR/SET domain 16 (PRDM16) gene showing the strongest association . All significantly associated genes are summarised in Supplementary Table 3.The gene-based association study identified 51 genes . The top 10 pathways are included in the Supplementary Table 4.The gene-set analysis found that no specific pathway was significantly associated with headaches after Bonferroni correction (cut-off p < 0.05/54 = 9.26 × 10–4) . This analysis identified 38 loci associated with headaches, of which 34 had been previously identified —Corticotropin Releasing Hormone Receptor 1 (CRHR1)—Microtubule Associated Protein Tau (MAPT) locus (ranked 10th), although significantly associated, showed a different direction of effect in the two datasets. This might indicate that the role of this locus might be different in these two phenotypes. It is possible that a locus could contribute to non-migraine type headaches while contributing minimally to migraine. However, this assumption definitely needs further lab evidence. Comparing the Z-values could be a novel way to differentiate the genetic impact of certain SNPs in genetically correlated yet different phenotypes  and the METabolic Syndrome In Men (METSIM) datasets   (Supplementary Table 3). Through the tissue expression analysis, both brain and vascular tissues were indicated as being involved in the mechanisms of headaches. Gormley et al.  and the migraine study (Z = –4.05) were in the same direction. The original p values of the top SNP (rs673939) in this region were found to be 9.00 × 10–8 by Meng et al. . It is not uncommon that SNPs can be associated with multiple phenotypes which seem completely unrelated  is a protein-coding gene, which plays an important role when cohesions (chromosome-associated multi-subunit protein complex) try to bind to DNA to carry out a large spectrum of chromatin-related functions, including sister chromatid cohesion, DNA repair, transcriptional regulation, and three-dimensional organization of chromatin  is the nearest gene to the leading SNP of rs72854120 in the third new locus. Variants of this gene have been reported to be associated with ischaemic stroke, cerebral hemorrhage, and arrhythmia  have shown important roles in embryonic development in animal models . Although that study was specifically addressing migraine, rather than the more general headache phenotype that we addressed, this overlap between the new loci suggested in the two studies both helps to confirm the findings of Hautakangas et al. (KCNK17 on chromosome 6p21.2 was not clearly reported and its nearest loci in the paper was potassium two pore domain channel subfamily K member 5 (KCNK5) which is 60 kb away from KCNK17. A recent study on rare variants of migraine showed that significant cis-expression quantitative trait loci (eQTL) in the polycomb response elements (regulatory sites that mediate the silencing of homeotic and other genes) mapped to the KCNK17  are highly genetically correlated. However, the ability of a study to detect more new variants would be reduced if the two phenotypes are not so highly correlated. Second, our two cohorts were both of mainly European descent and with minimum sample overlap; therefore, we avoided some negative impact (such as increasing type I and type II errors) which could be caused by these factors in the study. There are limitations associated with this approach, being novel in its application. For example, although we successfully addressed phenotypes and datasets which are highly genetically correlated, there are insufficient published studies to allow us to determine the strength of correlation which is required to allow this approach for future studies. This will require our approach to be replicated with other phenotypes and datasets, followed by formal statistical appraisal of the results. Similarly, although it was not directly relevant in our study, consideration will need to be given to applying this approach when there is sample overlapping. It is worth mentioning that the genetic correlation between diagnosed cluster headache phenotype from a mixed UK and Swedish cohort and self-reported headache from the UK Biobank was 0.50  to identify further genetic components.Supplementary file1 (PPTX 89 KB)Supplementary file2 (DOCX 181 KB)Supplementary file3 (XLSX 18 KB)Supplementary file4 (XLSX 141 KB)Supplementary file5 (XLSX 15 KB)Supplementary file6 (XLSX 12 KB)Supplementary file7 (DOCX 16 KB)Supplementary file8 (DOCX 23 KB)Supplementary file9 (DOCX 26 KB)Below is the link to the electronic supplementary material.


By learning, through experience, which stimuli coincide with dangers, it is possible to predict outcomes and act pre-emptively to ensure survival. In insects, this process is localized to the mushroom body (MB), the circuitry of which facilitates the coincident detection of sensory stimuli and punishing or rewarding cues and, downstream, the execution of appropriate learned behaviors. Here, we focused our attention on the mushroom body output neurons (MBONs) of the γ-lobes that act as downstream synaptic partners of the MB γ-Kenyon cells (KCs) to ask how the output of the MB γ-lobe is shaped by olfactory associative conditioning, distinguishing this from non-associative stimulus exposure effects, and without the influence of downstream modulation. This was achieved by employing a subcellularly localized calcium sensor to specifically monitor activity at MBON postsynaptic sites. Therein, we identified a robust associative modulation within only one MBON postsynaptic compartment (MBON-γ1pedc > α/β), which displayed a suppressed postsynaptic response to an aversively paired odor. While this MBON did not undergo non-associative modulation, the reverse was true across the remainder of the γ-lobe, where general odor-evoked adaptation was observed, but no conditioned odor-specific modulation. In conclusion, associative synaptic plasticity underlying aversive olfactory learning is localized to one distinct synaptic γKC-to-γMBON connection. Drosophila melanogaster, has proven to be a valuable model organism for testing hypotheses in this area of research. Indeed, despite their relatively modest brain size compared with mammals, fruit flies are able to perform a variety of complex learning tasks. This includes classical, Pavlovian-style conditioning1 in which an odor  is associated with an appetitive or aversive unconditioned stimulus (US)3. Such associative learning forms the basis of the current study. In Drosophila, the mushroom bodies of the central brain have been identified as the site of the neural plasticity that facilitates this learning process5. More specifically, the γ-lobes of the mushroom bodies are required for the acquisition process and the formation of a short-term memory directly after training8. Consequently, not only have intrinsic mushroom body neurons  been identified as essential for associative learning, but so too have their extrinsic, axonal up- and downstream synaptic partners. Ensembles of KCs encode different odor stimuli selectively, as sparsely distributed neuronal activity12. Assigning behavior-instructive values to these odor representations through learning is mediated by axonal upstream US-encoding dopaminergic neurons (DANs) and downstream action-inducing mushroom body output neurons (MBONs)16. Whereas the former signal the presence of the US at the time of conditioning16, the latter have been identified as key players in the guiding of approach towards or repulsion away from the learned odor13. With distinct populations of MBONs signalling positive or negative valence13, current models postulate that the coincident activity of the odor-coding KCs and antagonistically acting (i.e. rewarding or punishing) US-coding DANs leads to a shift in the relative activation of the respective antagonistically acting MBONs18. This implies that the balanced activity of populations of MBONs ultimately represent the behavior-instructive ‘readout’ of the memory trace. Coupled with findings that learning leads to a synaptically localized modulation of KC activity11, and that specifically the synaptic output from the KCs is required for short-term memory recall but not its acquisition20, the KC-to-MBON presynapses have emerged as the site of plasticity mediating this type of learning22. However, determining which exact synapses become modified through the coincidence between the CS and US  has been difficult because electrophysiological or functional imaging approaches typically monitor a neuron’s activity in its entirety, including all excitatory, inhibitory, and modulatory inputs. In this study, we address this issue directly by targeting the genetically encoded calcium indicator GCaMP to the MBON postsynapse, via fusion to the postsynaptic density protein dHomer23 . UAS-dHomerGCaMP3 flies were generated by Pech et al.23.Flies were reared on standard cornmeal food medium at 25 °C and 60% relative humidity with a 12:12 h light/dark cycle. Split-gal4 driver lines used to drive expression in MBONs were obtained from the Bloomington Drosophila Stock Centre: MB112C ), MB077B ), MB083C ), MB298B ), MB210B ). UAS-GCaMP3For behavioral experiments using temperature-sensitive UAS-Shibirets to block synaptic output from MBONs, the same split-gal4 driver lines as above were crossed together with UAS-Shibirets or w1118 . The resulting offspring were reared at 18 °C and 60% relative humidity.2, 2 mM CaCl2, 5 mM Hepes, 36 mM sucrose, pH 7.3) by removing the head capsule and detaching the brain from the ventral nerve cord. Brains were then fixed in 4% paraformaldehyde at room temperature for 45 min, and then washed three times in PBS with 0.6% Triton-X before being incubated for 2 h at room temperature in blocking solution containing 2% bovine serum albumin. To amplify GCaMP signal, brains were incubated with anti-GFP primary antibody  at a concentration of 1:2000. Additionally, an anti-discs large (DLG) primary antibody  was used at a concentration of 1:200 to visualize brain structures. Brains were incubated in primary antibody solution for 1 day at 4 °C, and washed again three times in PBS with Triton-X before incubation in secondary antibody. Secondary antibodies anti-rabbit AlexaFluor488 and anti-mouse AlexaFluor633 were used, both at a concentration of 1:300, for 1 day at 4 °C. Brains were washed again three times in PBS with Triton-X, and then mounted in VectaShield for confocal scanning.Flies were cold anaesthetized on ice for 5 min before being moved to a dissection dish and fixed with insect pins. Brains were extracted in Ringer’s solution  and a Plan-Apochromat 20× water immersion objective (NA = 1). An excitation wavelength of 920 nm was used. Image acquisition was controlled using Zeiss Zen (2011 SP4) software. Images were captured at a framerate of 4 Hz and with a frame size of 512 × 512 pixels. Simultaneous odor delivery and image acquisition was controlled via a custom written LabView program . Three odors were used in all experiments, as follows: 4-methylcyclohexanol (MCH), 3-Octanol (3-Oct), and 1-Octen-3-ol (1-Oct), at concentrations of 1:750, 1:500, and 1:400, respectively, in mineral oil.Female flies aged between 3 and 8 days after eclosion were prepared and imaged as described by Hancock et al.To induce and monitor the effects of aversive olfactory conditioning, a three-stage imaging protocol was used. First, flies were sequentially presented with the three odorants and changes in fluorescence were monitored. Second, the flies were exposed to one of three conditioning protocols. This second step immediately followed the first . Third, the flies were again presented with the three odorants and their responses were measured  were drawn manually to encompass the entire dendritic compartment for each MBON. The fluorescent intensity throughout the recording period was then extracted from these ROIs and used to calculate the normalized relative change in fluorescence over time (ΔF/F0) for each recording. For all measurements, F0 was calculated as the average fluorescence intensity over the 2 s preceding odor delivery, and ΔF was calculated by subtracting this value at each time point. In order to statistically compare responses and provide input for the population decoding analysis below, each response was quantified as the area under the curve (AUC) of each ΔF/F0 time series. The AUC was derived from the 5 s after odor onset. Due to large variability in odor responses between MCH and 3-Octanol, AUC values were normalized before being used in the population decoding analysis, below .Images were processed using ImageJ/Fiji . Any small movements were corrected using the TurboReg pluginWe devised a supervised machine learning approach with the goal to predict punishment (CS+) vs. no punishment (CS−) from the single trial neural population activity across all five distinct MBON types during the post-training phase of the aversive conditioning protocol Fig. a. This aBecause functional imaging could only be performed in a single type of MBON in each individual animal, we had no access to the simultaneous population activity across the five MBON types in the same animal. Per individual and in the post-training phase we are left with two single-trial calcium traces obtained during a single CS+ and a single CS− presentation. For each of the five MBON types we have recordings from nine or ten animals. Thus, we have in total for each MBON type nine or ten per-animal samples . The remaining 45 samples (nine per MBON type) represent the training set. For training, we performed 1000 repeated drawings with replacement to obtain a new training sample, i.e. a pseudo population across the five MBON types as illustrated in Fig. For training and testing a single SVM model we proceeded as follows. We randomly drew one sample from each MBON type to define the test set Fig. a. This dA and B through random assignment of odors to those labels. We again expect an average accuracy at chance level (50%).We performed three controls. For each we repeated the same approach as outlined above to obtain mean accuracy and standard error of the mean Fig. b. We firTo assess the importance of each individual MBON type for the classification, we repeated the classification approach as explained above by excluding each MBON type one by one from the feature space combination of the four remaining MBON types.29.All analysis has been carried out with Python and the implementation of SVM in the Scikit-learn packageShibirets, respectively.To examine the behavioral effects of blocking synaptic output from MBONs during aversive associative conditioning, flies were trained and tested in a custom-built conditioning apparatus. All experiments were carried out at a humidity of 65–85% and either at permissive temperature (22–25 °C) or restrictive temperature (30–32 °C) for control experiments and experiments in which synaptic output was blocked by For the training phase, approximately 30 flies aged between 3 and 7 days were placed into tubes lined with copper wire and connected to an air flow. Flies were conditioned according to the same procedure as the “paired” group described above. Briefly, flies were first presented with an odor (that becomes the CS+ odor) together with a pulsing 90 V electric shock for 60 s. This was followed by a 60 s break before a second odor was presented alone for 60 s (becoming the CS− odor). As in previous experiments, flies were trained reciprocally using the odorants 4-methylcyclohexanol and 3-Octanol. Timely delivery of electric shocks and odor stimuli throughout was controlled by custom-written software.For the testing phase, flies were transferred from the training tubes to the testing arena. This arena comprised multiple lanes, with each being loaded with one of the groups of approximately 30 flies from the training tubes. Flies were initially held in a central carrier before being released into the lanes, into which the CS+ and CS− odors were presented from opposite sides. The flies were allowed two minutes to distribute between the two sides of the arena before a picture was captured using a camera mounted above the arena that would be used to quantify the distribution of the flies between CS+ and CS−.To analyze whether flies exhibit a learned avoidance of the punishment-coupled CS+ odor, the number of flies on each side of each lane was counted. Preference indices were calculated independently for reciprocally trained groups , before being averaged to generate a learning index. Raw data were processed with OriginPro.0 values over time, and shaded areas representing the standard error of the mean (SEM). To quantify and compare responses, the integrated AUC for each ΔF/F0 time series was calculated over the 5 s after odor onset. Tests for significant changes in pre-to-post training AUC were carried out using Wilcoxon signed rank tests.Odor-induced calcium dynamics throughout are presented as line graphs, with lines representing mean ΔF/FFor behavioral experiments Fig. , tests f8. Thus, when looking for a readout of the synaptic plasticity underlying this form of memory, we focused on the population of compartment-specific MBONs that lie directly downstream of the γ-type KCs. These neurons each receive synaptic input from KCs in their respective compartment, the weight of which is modulated by input from DANs and comprises the canonical site of learning-induced plasticity within this circuit . To further verify that the latter can also be used to detect postsynaptic odor-evoked activity in MBONs, flies were presented with MCH and 3-Oct and changes in fluorescence were quantified  nor the control odor (1-Oct) elicited statistically significant changes in response after conditioning   in MBON-γ1pedc > α/β   is confined to a single γ-lobe compartment and not distributed across different compartments.These results go beyond confirmation that the MBONs of the γ-lobe show differential naïve odor responsesGiven this finding, we sought to test in an unbiased manner whether these observed changes in postsynaptic calcium responses are actually indicative of whether the odor-evoked calcium transients have been aversively trained or not. To do so, we applied a machine learning approach, and used pseudo-populations of γ1–γ5 MBON responses to train a classifier. Over multiple training sessions, the classifier was provided with the pre- or post-training odor responses of such pseudo populations of γ-lobe MBONs and was then asked to predict whether the response was from one of two conditions, CS+ or CS− Fig. e,f. This33 we hypothesize that the CS+-specific reduction in odor-evoked input to the γ1 MBON may lead to a disinhibition of its downstream partners that also instruct behavior, meaning that although the plasticity underlying learned avoidance is restricted to a single compartment, its downstream effects are more broad.We have provided evidence so far that aversive associative conditioning leads to localized, specific plasticity within a single postsynaptic compartment of the γ-lobe and that this provides a sufficient basis for the distinction of an odor that is predictive of punishment from one that is not. Unclear thus far, however, is if and how this highly localized plasticity relates to behavioral action. Based on recent evidencets and tested the flies’ ability to distinguish between an odor that was aversively conditioned  and one that was not (CS− odor)Fig. . BlockagFurther mirroring our previous findings, we observed no effect on learning performance when blocking synaptic output from MBONs innervating the γ2, γ3, or γ4 compartments Fig. c–e. The 13, and thus present a logical circuit-level mechanism for the balancing between promotion of approach and promotion of avoidance in response to a conditioned odor. The balance point depends on the strength of KC-to-MBON input in the γ1 compartment and subsequent inhibition exerted by the γ1-innervating MBON on the γ5-innervating MBONs, with both factors determined by the relative (plastic) odor-evoked activity at the γ1 postsynapse.Crucially, we did find that blocking synaptic output from the most distal MBONs of the γ-lobe (innervating γ5 and β′2) caused an inability of flies to distinguish between CS+ and CS− odors when tested for preference  axonal KC presynapses are required for associative odor learning and short-term memory formation40, and (b) γ-lobe KCs are required in particular8. The criterion of sufficiency is difficult to address because the stochastic and sparsely distributed nature of odor representations across all KCs precludes any artificial activation of a selected KC and subsequent test of whether an odor stimulus has been learned. However, artificial activation of defined KCs in coincidence with electric shocks has been used to show that a particular KC activity pattern can be trained, and its subsequent activation causes an aversive behavioral response41. In addition, plasticity in γ-lobe KCs resulting from associative learning has been clearly demonstrated42.Determining the substrate mediating the acquisition and storage of learned information through the properties of single neurons, their synapses, and the neural circuits the neurons are part of is a key challenge in neuroscience. Here, we do not define the term ‘memory trace’ as all potential physiological changes within neurons, synapses, and the entire neuronal circuits that accompany or result from the learning process. Considering that plastic changes in synaptic transmission are the principal substrate underlying learning and memory formation11.However, genetic manipulation of neurons  inevitably affects entire cells; the question of which dendritic or axonal part of a neuron is required or sufficient for mediating learning remains unanswered using such approaches in isolation. This is of importance because the functional compartmentalization of KC axons, with each compartment being innervated by distinct subsets of DANs mediating reward or punishment, and MBONs inducing different behavioral actions, suggests that entire neurons are not the functional units representing the substrate of memory traces (such as ‘engram cells’). Rather, subcellular partitions of neurons, in this case partitions of axons spanning a compartment, represent independently modulated functional units34. We conclude from our data that plasticity in these neurons arises downstream of the KC-to-MBON synapse via previously identified feedforward and feedback loops that exist in the MB circuit. For example, it was shown that plasticity in the specific KC > MBON-γ1pedc > α/β connection does not remain confined to the γ1 compartment, but causes subsequent downstream changes in MBONβ′2mp and MBONγ5β′2a as well via a feedforward inhibition loop33. Our behavioral data support the presence and potential significance of such a circuit motif , have also reported bidirectional modulation of MBONs as a result of associative olfactory conditioningtif Fig. . As a co18 assumed downregulation of the synaptic input from KCs to MBON-γ1pedc > α/β through a plasticity mechanism that requires pairing of the CS+ odor stimulus with neuromodulatory input from the aversive US-signaling PPL1-γ1pedc DAN. This simulation showed a significant reduction in MBON-γ1pedc > α/β synaptic input and avoidance behavior after a single pairing, as confirmed in our experiments . Of course, our data do not rule out the possibility for associative learning-induced modulation of other MBONs in other forms of learning, e.g., over longer time periods, conditioning of different valences such as reward, or context. For example, it is well-established that long-term memory formation requires KCs of the vertical α/α′-lobes48. It must be pointed out that our data refer only to the acquisition process and short-term memory formation of aversive olfactory learning.Our results directly support recent computational model studies. A circuit model by Springer and Nawrotnts Fig. . In this11; axonal KC boutons in the γ1 compartment could not be recorded due to technical limitations, which leaves open the question of whether axonal boutons in this compartment similarly change their correlated activity. However, the fact that differential training affects odor coding across KC populations differentially implies that the learned change in odor-coding of mushroom body circuit and the learned behavior-instructive properties of the MBON signals are two distinct aspects of associative learning that can be experimentally dissociated.The broad and generalized suppression of odor responses in all more distal MBON postsynapses indicates that they, unlike MBON-γ1pedc > α/β, possess properties more tuned to signaling non-associative events, such as adaptation resulting from prolonged odor stimulation. This distinction is apparent when comparing postsynaptic responses in flies that received only prolonged odor presentation and those that received that prolonged stimulation together with punishment, with only the γ1-innervating MBON showing a specific depression in response to the punished odor whereas a generalized adaption predominated all other MBON post-training responses. Two key observations indicate that the non-associative changes manifested principally as strong reductions in odor-evoked postsynaptic calcium responses in post-training recordings in the majority of MBONs (namely: MBON-γ2α′1, MBON-γ3 and MBON-γ3β′1, and MBON-γ4 > γ1γ2; see Fig. Supplementary Figures.


Natural attenuation, involving microbial adaptation, helps mitigating the effect of oil contamination of surface soils. We hypothesized that in soils under fluctuating conditions and receiving oil from seeps, aerobic and anaerobic bacteria as well as fungi could coexist to efficiently degrade hydrocarbons and prevent the spread of pollution. Microbial community diversity was studied in soil longitudinal and depth gradients contaminated with petroleum seeps for at least a century. Hydrocarbon contamination was high just next to the petroleum seeps but this level drastically lowered from 2 m distance and beyond. Fungal abundance and alpha-diversity indices were constant along the gradients. Bacterial abundance was constant but alpha-diversity indices were lower next to the oil seeps. Hydrocarbon contamination was the main driver of microbial community assemblage. 281 bacterial OTUs were identified as indicator taxa, tolerant to hydrocarbon, potentially involved in hydrocarbon-degradation or benefiting from the degradation by-products. These taxa belonging to lineages of aerobic and anaerobic bacteria, have specific functional traits indicating the development of a complex community adapted to the biodegradation of petroleum hydrocarbons and to fluctuating conditions. Fungi are less impacted by oil contamination but few taxa should contribute to the metabolic complementary within the microbial consortia forming an efficient barrier against petroleum dissemination. These natural phenomena occur in environments contaminated by many different compounds such as polychlorobiphenyls2 or hydrocarbons4. Natural attenuation reflects the fact that biota can adapt over time to resist contamination and efficiently degrade it in various environments and conditions. Most of the reported studies on microbiology of sites contaminated with hydrocarbons are focused on the short-term effect of the crude oil after a major contamination event 7, but very few have studied the long-term effect of chronic crude oil seeps on the composition of microbial communities. Studies focusing on petroleum-contaminated soils in oil exploitation area have mostly tended to isolate microbes with hydrocarbon degradation properties for bioremediation application8. But there has been little in the way of attempts to analyse the composition of microbial communities in surface soils9, such as those contaminated for decades by oil seeps, to identify who are the best-adapted microbes and tend to found taxa as bioindicator of efficient petroleum degradation.The expansion of the oil industry in the first half of the twentieth century left behind sites where oil continues to seep to the surface. Contamination of surface soils by oil from leakage of old petroleum pumping wells or natural petroleum seeps is not necessarily a major problem because natural attenuation often limits the extent of the damage. During natural attenuation process, indigenous microorganisms degrade organic contaminants without external inputs that can promote bioremediation11. Although aerobic hydrocarbon biodegradation12 is the most energy-efficient respiration process13, the contaminated subsurface environments are often naturally anaerobic14, especially within oil plumes where the available oxygen has been depleted. Anaerobic bacterial degradation of petroleum-related compounds such as benzene15, toluene16, polycyclic aromatic hydrocarbons17 and alkanes18 has been demonstrated in aquifers, sediment and soil environments. Local consumption of oxygen19 or fluctuating conditions20 could create aerobic and anaerobic microniches in soil, then a multitude of degradation processes could coexist at a small scale, involving aerobic and anaerobic microbial functional populations21. Fungi could also be key players in degrading recalcitrant organic contaminants because of their variety of extracellular enzymes22. The fungal mobilization and degradation of contaminants could contribute to the release of bioavailable intermediates that could be further degraded by bacteria24. Hence, catabolic interactions among different microbial groups during biodegradation are extremely important. In this context, we therefore hypothesized that in environments under fluctuating conditions and receiving constant input of petroleum, both bacteria and fungi and, among bacteria, both aerobic and anaerobic taxa could coexist and be involved in hydrocarbon degradation processes. It is therefore essential to identify the whole microbial community adapted to chronic soil petroleum contamination and to estimate how far hydrocarbon contamination and its impact on communities extend around oil seeps.A wide diversity of microorganisms  are able to degrade and use petroleum  as sole carbon source using specific enzymatic machineryIn this study, we present a detailed spatial analysis of the microbial community structure in two forest soils which have both been impacted by petroleum seeps for at least a century but with contrasting soil characteristics. For both sites, we studied the hydrocarbon contamination longitudinal gradients away from oil seeps. Bacterial and fungal community composition was studied through high throughput sequencing of 16S and 18S rDNA and indicator taxa developing mainly next to the oil seeps and potential hydrocarbon-degraders were identified thanks to an original analysis rarely used in microbial ecology (TITAN) but well-adapted to environmental gradients.−1 on average in GUN and ETV soils, respectively. Continuous oil seeps created ponds of 5–10 m of diameter  site and was carried out in May 2017. Only the first zone , the closest to the oil seep, was sampled  were sieved at 4 mm, aliquots of fresh soil sample were kept at 4 °C until the return to the laboratory for mineralization activity measurements and estimation of the percentage of humidity. The rest was immediately frozen at -80 °C for further analyses (DNA and organic extractions). Texture (NF X 31–107), total nitrogen and organic carbon content  and pH  were measured at the LAS INRAE at Arras (France).2/H2 (95:5) gas mix. Empty control flasks without soil (similar atmosphere than for aerobic and anaerobic tests) were run in the same way to correct CO2 measurements. Flasks were incubated at 24 °C for 7 days. Carbon dioxide (CO2) release was measured after 1, 2, 3, 4 and 7 days on a 2 ml portion of the flask atmosphere (sampled using a syringe) by infrared spectrophotometer 25. The mineralization activities were represented by the produced CO2 expressed as carbon mass per gram of dry weight soil per day (µg C g−1 d−1).Two replicates of each soil samples (10 g fresh weight corresponding to 5.8 to 7.8 g dry weight) collected in May 2016 on longitudinal transects were incubated into 125 ml glass flasks sealed hermetically with Teflon septum. One replicate was incubated under aerobic condition (ambient atmosphere) and the other under anaerobic condition by flushing and replacing the atmosphere by N2SO4 (1 g) were added to 2 to 10 g of soil in the extraction cells to remove the molecular sulphur and the residual water, respectively. Extractions were performed twice at 130 °C and 100 bars with HPLC grade dichloromethane (DCM) using a static time of 5 min. Organic extracts were diluted with DCM to reach 20 ml. An aliquot (3 ml) was dried in a fume hood (36 h) to quantify the mass of the extractable organic matter (EOM)26.Solvent extractions were performed using an automated solvent extractor Dionex® ASE 350. Before extraction procedure, activated copper powder (1 g) and Na27. The integration limits of the chromatogram area, corresponding to the n-decane (C10H22) and n-tetracontane (C40H82), were defined using a standard mixture (ASTM D5307). For external calibration an ISO 11,046 standardized oil was used. Twelve solutions of different concentrations (from 1 to 31 mg ml−1) were injected into the GC-FID in order to obtain calibration curves. The HI was estimated from integration of the area under each chromatogram and corrected by the background noise (by integrating the area chromatogram obtained with a sample of pure dichloromethane that serves as a control).The Hydrocarbon Index (HI) was measured according to the ISO 16,703:2004 procedure using a GC-FID 7890A Agilent technologies−1 constant flow. Compound identification was confirmed by the mass spectra obtained with the MS detector in comparison with the NIST08 and Wiley database spectra. Carbon preference index (CPI) was calculated according to Colombo et al.28 with the n-alkanes areas obtained on the GC–MS extracted ion chromatogram (m/z 57) of EOM from the different sampling points, using the formula: CPI = 2 × (n − C27 + n − C29)/(n − C26 + 2 × n − C28 + n − C30). CPI is a ratio classically used for EOM source and contribution type29, a ratio close to one is indicator of crude oil signature and higher values (> 5) indicates biogenic contribution.Compound molecular distribution of soil EOM and crude oil (sampled directly in the middle of the oil seep) was determined through injection in a Shimadzu gas chromatograph (GC2010) equipped with a capillary column in silica glass DB-5MS (60 m × 0.25 mm i.d. × 0.1 µm film thickness) coupled with a mass spectrometer (QP2010) operating at 70 eV in fullscan mode. The inlet temperature was set at 300 °C and injections were run in split mode (split ratio of 1:5). The oven temperature program was as followed: 70 °C for 2 min, from 70 to 130 °C at 15 °C/min, then 130–315 °C at 4 °C/min followed by an isothermal stage at 315 °C for 25 min. The carrier gas was helium at 1.5 ml.minca. 0.5 g of the soil samples using a Fast DNA spin kit for Soil . Genomic DNA extracts were diluted to 5 ng µl−1 to be used as templates for further PCR and qPCR analyses.Genomic DNA was extracted from 30 and Thion et al. 31. PAH-RHDalkB genes encoding alkane monooxygenases32 and bssA genes encoding benzylsuccinate synthases were quantified using PAH-RHD30, Gielnik et al.33 and Winderl et al.34, respectively. Real-time PCR assays were performed using iQ SybrGreen Supermix (Biorad) and CFX96 apparatus (BioRad). Data were expressed as gene copy number per gram of dry weight soil thanks to ten-time dilutions (from 108 to 102 copies/µl) of standard plasmids.Quantification of bacterial and fungal communities was done using real-time PCR quantification using primer sets 968F/1401R and Fung5F/FF390R for 16S and 18S rRNA gene quantification, as described previously by Cébron et al.35 using PCR primers with an Illumina adaptor, pad and index sequences36. PCR reactions were performed on 2 μl of diluted gDNA using Phusion high-fidelity polymerase (Thermo Scientific). PCR reactions consisted of 31 cycles with touchdown annealing temperature for 18 cycles (63–54 °C with a decrease of 0.5 °C/cycle) and 13 cycles at 54 °C. Amplification products were checked on 1% agarose gel electrophoresis and purified using the UltraClean-htp 96Well PCR Clean-Up kit (Qiagen) following the manufacturer’s instructions. After Quant-iT PicoGreen ds-DNA Assay Kit (Invitrogen) quantification, an amplicon library was prepared (equimolar pool at 10 nM), purified on QIAquick PCR purification Kit Column (Qiagen) and sent for sequencing to the Genewiz platform  which used an Illumina MiSeq V2 Kit for 2 × 250 bp paired-end sequencing.The V3/V4 region of bacterial 16S rRNA genes (ca. 450 bp) was amplified using S-D-Bact-0341-a-S-17 and S-D-Bact-0787-b-A-20 primers following a previously described dual-index strategyca. 390 bp) using FR1 and FF390 primers37 fused with a partial sequencing adapter. This first PCR was performed using Phusion high-fidelity polymerase (Thermo Scientific). The PCR reactions consisted of 25 to 30 cycles (depending on the samples) with an annealing temperature of 54 °C. Amplification products were checked by 1% agarose gel electrophoresis before being sent to Microsynth AG  for further steps namely second PCR, library preparation and Illumina MiSeq 2 × 250 bp paired-end sequencing.The V7–V8 region of fungal 18S rRNA genes was amplified  and joined using the following criteria: no ambiguous bases and 404 bp < length < 430 bp or 348 bp < length < 359 bp for 16S or 18S rDNA paired-end reads, respectively. After, alignment of unique sequence representatives to Silva V132 fasta data (released in Dec. 2017) and pre-clustering of sequences (sequences that are within 2 nt of each other), chimeras were detected and removed using Uchime and singletons  were removed. Taxonomy was assigned using the Silva V132 tax data with a cut-off = 80 and using the Wang method. Sequences affiliated to archaea, chloroplasts, unknown, mitochondria and eukaryota were removed for 16S rDNA analysis, while sequences affiliated to archaea, chloroplasts, unknown, mitochondria and bacteria were removed for 18S rDNA analysis. After uncorrected pairwise distances calculation between aligned sequences, the bacterial and fungal sequences were clustered in Operational Taxonomic Units (OTUs) at 97% similarity, and consensus taxonomy for each OTU was determined using classify.otu. Finally, datasets were rarefied to the lowest number of sequences per sample .Sequence data were analysed following the MiSeq SOP procedure available in March 2017 and described in Kozich et al.The number of reads for each OTU was expressed as a percentage to express the relative proportion or relative abundance of each OTU within the total community. Alpha diversity was expressed by calculating Chao1 richness, Pielou’s evenness J’ and Shannon diversity H’ indices using Mothur.Functional traits were compared between increasing indicator taxa found in ETV0 and GUN0 samples  and the whole bacterial community inhabiting the control forest samples ETV15 and GUN15.39. The relative proportion of each trait attributes for all bacterial OTUs were calculated and attributed based on their taxonomic affiliation to get OTUs trait profiles. Based on the relative abundance of each OTU, the relative abundance of each trait attributes for the 19 traits was calculated and compared for the two different populations by Wilcoxon rank-sum test .Inference of 19 bacterial traits  was performed using the BactoTraits database40 (v 1.0.1) run on Galaxy (X. SIGENAE [http://www.sigenae.org/]). Differences in the level 1, level 2 and level 3 KEGG predicted functions of increasing indicator taxa and of control forest soil communities were compared and analyzed by Wilcoxon rank-sum test .The potential functional pathways of the two bacterial populations were profiled using Tax4Fun41. Values of soil parameters , mineralization activities, bacterial and fungal quantification (16S and 18S rRNA gene copy numbers), alpha-diversity estimators , relative abundance of bacteria phyla and fungal divisions and the relative abundance of the bacterial functional trait attributes were compared among the 5 different zones  for both sites independently (GUN and ETV) and for different sampling depths at the ETV site. This was carried out through one-way analyses of variance (Anova) with p < 0.05, followed by Tukey HDS PostHoc tests.Statistical analyses were performed using R studio version 3.6.042, non-metric multidimensional scaling (NMDS) analyses were performed (‘metaMDS’ function) based on the Bray–Curtis dissimilarity matrix generated from bacterial and fungal relative abundances to estimate the dissimilarity in structure between all samples. For bacteria, OTUs having at least 0.1% relative abundance (≥ 54 reads) were kept for this analysis. The environmental gradients of hydrocarbon content were built on NMDS ordination space using the ‘Ordisurf’ function of the Vegan R package which used generalized addictive modelling (GAM) to overlay environmental variables43. Permutational multivariate analyses of variance (PERMANOVA) were performed with 999 permutations using the ‘adonis’ function in Vegan R package to determine if bacterial and fungal community composition differed significantly between sites (next to the oil seeps vs. the others).Using the Vegan R package44. TITAN 2.4 package in R45 was used. Association was measured by taxon abundances weighted by their occurrence in each sample as for Indicator taxa determination46 and standardized as z-scores to facilitate cross-taxon comparison via permutation of samples along the predictor. The number of bootstraps and permutations performed in our TITAN2 analysis was 500 and 250, respectively. Analysis was performed at the OTU taxonomic level. Only OTUs observed more than 3 times across all sites were used and thus 3,306 and 861 OTUs were considered for bacterial and fungal analyses respectively. TITAN2 distinguishes declining and increasing Indicator Taxa along environmental gradient.Threshold Indicator Taxa ANalysis (TITAN) was performed to identify environmental variable values  maximizing taxa frequency and abundance using bootstrapping to identify reliable indicator taxaThe significance and novelty of our work is the study of bacterial and fungal diversity and the identification of indicator taxa developing next to petroleum seeps and most likely involved in the hydrocarbon degradation. The originality is that these indicator taxa are well-known aerobic and anaerobic bacteria having specific functional traits making them well-adapted to hydrocarbon-contaminated environment as well as fluctuating redox conditions.48. At the first zone next to the petroleum seep, at both sites, the average quantity of extractable organic matter , and the hydrocarbon index  values were significantly higher than in the samples farthest from the seeps at a distance of 10 or 15 m which revealed high oil contamination of the soil at the vicinity of the seeps. The EOM and HI values did not decrease gradually with distance but dropped by more than 90% from the second sampling zone (a 2 m distance from the oil seeps). In opposite way, an increase in the carbon preference index (CPI) with increasing distance from the oil seep (Table 29. The low ratio (around 1) as measured next to the oil seeps is indicator of crude oil signature and higher values (> 5) indicates a major contribution of biogenic compounds to the soil organic matter. For both sites, samples closest to the oil seeps were dominated by n-alkanes similar to those of the crude oil  petroleum signature completely disappeared  compared to the samples taken at a depth of 15 and 30 cm  were found between organic matter mineralization activities and HI values . As much of the organic matter found next to the oil seep is composed of petroleum hydrocarbons we can assume that microbial activities were mainly dedicated to hydrocarbon degradation both in aerobic and anaerobic conditions.To evaluate the functional potential of hydrocarbon degradation, we measured the microbial activity of organic matter mineralization under aerobic and anaerobic conditions. Both aerobic and anaerobic microbial mineralization activities were detected. Mineralization activity was 3–7 times greater under aerobic than under anaerobic conditions for both sites Table . Both ae10 to 10 × 1010 copies of 16S rRNA genes per gram of soil in both soil transects. Fungal 18S rRNA gene copy number was 100 times lower than for 16S rRNA one. The quantification of bacteria showed a significant decrease with soil depth from 10 × 1010 to 2 × 1010 16S rRNA gene copy numbers per gram of soil from the surface (0–2 cm) to the 15–30 cm depth samples.The distance to the petroleum seep was not found to significantly influence the number of bacteria and fungi Table . We quanalkB) and benzylsuccinate synthase-like (bssA) were quantified to estimate the functional population potentially involved in hydrocarbon degradation in aerobic and anaerobic conditions  as well as the metabolic complementarity of the taxa could prevent the analysis of such complex metagenomes, especially as there seem to be both anaerobic and aerobic processes taking place in our study sites.The well-known bacterial genes encoding PAH dioxygenase . No general trend was found for fungal alpha-diversity indicators  members  was detected between GUN and ETV sites at the phylum level. The distance to the petroleum seep positively or negatively influenced many bacterial phyla Fig. A. The re56 and were isolated57 in diesel-contaminated soil, maybe because of their hydrocarbon-degradation pathways58. Modification of the fungal community structure at the OUT level was less pronounced than for bacteria, but a similar gradient was observed according to the hydrocarbon content  was detected between GUN and ETV sites at the division level. Fewer changes were observed for the fungal community composition at the division and sub-division levels along longitudinal and depth gradients Fig. B. Althouent Fig. B. The lo60. The presence of all these microorganisms with varied metabolisms should form an efficient barrier against dissemination of the petroleum contamination under fluctuating environmental conditions.As bacterial and fungal community composition are shaped by petroleum contamination, indicator taxa (IT) relative to the hydrocarbon level were sought to further understand which microorganisms are tolerant/sensitive to oil contamination, potentially involved in hydrocarbon degradation or benefiting of degradation metabolites. Data from both sampling sites were combined and indicator taxa were sorted using TITAN2, a well-adapted tool for gradient studies. Bacterial and fungal indicator taxa increasing Fig.  or decreAcidithiobacillus genera are chemolithoautrophes that can oxidize elemental sulphur or oxidize minerals containing ferrous iron61. Among the Rhodocyclaceae family, OTUs belonging to Dechloromonas and Sulfuritalea genera were identified as increasing IT. These genera were previously detected in hydrocarbon-polluted sites62 and could degrade a wide variety of aromatic compounds under anaerobic nitrate-reducing conditions65 through potentially original pathways66. Moreover, Sulfuritalea are also well recognized as chemolithoautotrophic sulfur-oxidizing bacteria67. Some OTUs affiliated to the Chloroflexi and belonging to the Anaerolineae were also identified as increasing IT. This lineage is composed of bacteria known as strictly anaerobic fermentative and sulphate-reducing bacteria, previously found in hydrocarbon-rich sludge samples68 and in anaerobic alkane-degrading culture69. Other IT mostly known as aerobic or facultative aerobic taxa had their relative abundance increasing next to the oil seeps. OTUs belonging to Gamma-Proteobacteria were identified, they are affiliated to the Porticoccaceae family and to Immundisolibacter, Rhodanobacter and Pseudoxanthomonas genera. Members of Porticoccaceae family have been described as aromatic-hydrocarbon degraders70 and alkane-degraders71, but they also possess in their genomes a high number of secondary alkane-degrading genes72. Immundisolibacter strain have been isolated from aerobic bioreactor for its capacity to degrade high-molecular-weight PAHs73. Even if Rhodanobacter was previously identified as a member of a PAH-degrading consortium74 it would more likely degrade intermediate metabolites75 because no PAHs degradation gene have been detected in sequenced genomes76. The Pseudoxanthomonas species have been found to degrade different PAHs78 and BTEX compounds79 and some strains could produce a rhamolipid surfactant that enhances the hydrocarbon degradation80. Two types of Alpha-Proteobacteria were also identified as aerobe increasing IT. The Methyloligellaceae members that are well-known methylotrophs81, and the Parvibaculum genus possessing genes involved in alkane utilisation82 that was detected in situ83 and in aerobic enrichment culture degrading crude oil84. Well known aerobic alkanes and PAHs degraders, such as the Actinobacteria belonging to Williamsia86, Cellulomonas88, Agromyces89 and Mycobacterium90 genera, were almost undetected in the forest soil but became abundant next to the oil seeps and identified as increasing IT. These Actinobacteria are equipped with hydroxylase enzymes such as monooxygenases and dioxygenases, to initiate the aerobic oxidation of hydrocarbon molecules91. In addition, some of these genera can also produce exopolysaccharides (EPS) which serve as bioemulsifiers such as glycolipides or curdlan-like EPS produced by Cellulomonas spp.92. Finally, two types of Acidobacteria appeared as increasing IT, the members of Solibacterales order with undemonstrated hydrocarbon-degradation capacities but that were previously found in oil contaminated soil and sediments94 and the members of the Acidobacteriales order that were identified as aerobic benzene-degraders using stable isotope probing technique95.TITAN2 sorted 281 and 664 bacterial OTUs as indicator taxa which were found to increase and decrease respectively with HI values  and another belonging to Cryptomycota LKM11. Lactarius genera are well-known forest soil symbiotroph ectomycorrhizal fungi but can also be facultative saprotroph96. Lactarius species increased up to 33.8% close to the oil seep indicating an essential role in the biodegradation of hydrocarbons. Indeed, the role of Agaricomycetes in bioremediation of crude oil-contaminated soil was demonstrated97 and the in vitro degradation of various hydrocarbons was reported for many Basidiomycetes98. Cryptomycota LKM11 represented 1.8 to 12.7% at the sampling sites close to the oil seeps. This fungal sub-division was recently found in PAH contaminated soils99, anoxic and hydrocarbon-enriched oil sand tailings ponds100 or creosote polluted site101.As shown before, the fungal community structure was less impacted by petroleum contamination and thus a low number of OTUs indicator taxa was found. TITAN2 sorted 61 and 40 fungal OTUs as indicator taxa which were found to increase Fig.  respecti39  and Tax4Fun40  tools to better characterize the bacterial indicator taxa increasing and dominant next to the oil seeps (Fig. Functional traits were predicted from BactoTraitseps Fig. . We hypo39. The higher proportion of bacteria belonging to these two functional groups among the IT could confirm the presence of two complementary populations for efficient hydrocarbon degradation next to the oil seeps.Among the bacterial IT increasing next to the oil seeps, a higher proportion of Gram positive and non-motile bacteria were found Fig. A and con102. Moreover, among bacterial IT, both aerobic and anaerobic/facultative anaerobic taxa were identified representing 62.5 and 33.1% respectively, while control bacterial community was mainly composed of aerobic bacteria. This finding agrees with higher rates of both aerobic and anaerobic hydrocarbon mineralization that were measured next to the oil seep (Table 104. The co-occurrence of different metabolisms has the advantage of reducing the concentrations of both toxic contaminants and of degradation metabolites105. Aerobic and anaerobic populations could alternatively be active depending on fluctuating redox conditions in the soil or could cooperate for hydrocarbon degradation such as in syntrophic metabolisms21 by substrates sharing106. Joint anaerobic and aerobic metabolisms were rarely suggested107, but recent evidence of the co-transcription of genes involved in both aerobic and anaerobic benzene degradation in continuous culture108 would explain our findings as well as previous ones of co-existence of aerobic and anaerobic bacteria in oil contaminated desert soils55.There was also a higher proportion of known lithotrophs and of anaerobic or facultative anaerobic bacteria close to the oil seeps Fig. A. Chemol109 showed that in sediment supplemented with pyrene, genes involved into almost every metabolic pathways showed a dramatic increase. Our finding indicates the possible availability of simple carbon sources besides hydrocarbons because these functions are dedicated to optimize the use of available resources and underline the large genetic investment of bacteria in the processing of environmental information important in these oil polluted environments110.At level 1 of KEGG functional categories, functions related to “Metabolism” and “Genetic information processing GIP” were detected in higher proportion while “Environmental information processing” and “Cellular processes” were detected in lower proportion for bacterial IT next to the oil seeps than for the control communities Fig. B. WithinPseudomonas aeruginosa N002 strain, authors found among other gene categories, a very high abundance of genes for transcription111.Moreover, within the “xenobiotic biodegradation and metabolism” category, functions involved in polycyclic aromatic hydrocarbon degradation as well as drug metabolism were significantly more present in bacterial IT increasing next to the oil seeps than in the control communities Fig. B. All thAt this study site, where petroleum seeps have been contaminating the soil for at least a century, we were able to demonstrate the natural attenuation phenomenon. Indeed, the presence of microbial communities, that are well adapted to oil and its degradation, limits the extent of the contamination to a few meters around the seeps. We identified bacterial taxa that are well known for their capacity to biodegrade hydrocarbons, which it would be interesting to isolate to study their metabolism and use them for bioremediation purposes. Amongst the indicator taxa, which are highly favoured in the vicinity of oil seeps, we have shown the coexistence and possible cooperation of fungi as well as aerobic and anaerobic bacteria having specific functional traits that make them well adapted to the hydrocarbon contamination and to fluctuating redox conditions.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


Moreover, the power and polarization of the incident optical beams can be used to tune the output chirality and modulation performance. Major performance of our demonstration reaches the fundamental limits of optical modulation: near-unity modulation depth, instantaneous speed (ultra-fast coherent interaction), compact footprint (atomic thickness), and unlimited operation bandwidth, which hold an ideal optical modulation solution for emerging and future nonlinear optical applications .Light modulation is of paramount importance for photonics and optoelectronics. Here we report all-optical coherent modulation of third-harmonic generation (THG) with chiral light via the symmetry enabled polarization selectivity. The concept is experimentally validated in monolayer materials , unlimited bandwidth, perfect modulation depth (~100%), and compact footprint (atomic thickness). To meet the increasing demand for high-performance optical signal processing in the information era, optical modulators with compact footprint, ultra-fast speed, high efficiency, and broadband operations are highly desired1. Among them, nonlinear optics based optical modulation attracts much attention, due to the performance advantages of the coherent light-matter interaction5. For example, all-optical modulations utilizing third-order nonlinear optics  have been demonstrated for various applications, such as ultra-fast lasers6 and phase shifters7. As a common and widely used third-order nonlinear optical process, third-harmonic generation (THG) is always present in a large range of materials , in contrast to the second-harmonic generation (SHG) that only exists in non-centrosymmetric materials5. Recently, all-optical modulations of THG in hybrid micro-systems18, gold metasurfaces19, and 2D layered materials21 have been explored. However, these all-optical modulation methods typically involve carrier relaxation, and thus are naturally incoherent with a relatively slow modulation speed (>~ps), a typical modulation depth of ~90%21, and polarization non-selectivity. Therefore, novel all-optical modulation strategies with high performance in speed and modulation depth, and polarization selection capability are thus of great importance.Optical modulation plays an important role in modern photonics and optoelectronics, such as optical interconnection, imaging, computing, and quantum technologiesσ−) and right-handed (σ+) circular polarization, has been long employed to explore chiral light-matter interaction both in linear optics  and nonlinear optics . Chiral nonlinear optics is becoming increasingly important in the rapidly growing fields of optics and optoelectronics26. Accordingly, the efficient control of chiral nonlinear optical responses is desirable for practical applications, but still remains elusive. Here we report all-optical coherent modulation of THG with chiral light via the polarization selectivity induced by Dh3 symmetry of monolayer MoS2. All-optical modulation with performance reaching fundamental limits  is demonstrated with a minimum pulse energy of ~50 pJ. Moreover, the modulation performance can be highly tuned by the incident light power and polarization. Our approach depends only on the symmetry of the crystal structure and thus can be applied to other materials and structures with similar symmetries. This demonstration paves the way toward high-performance all-optical modulation, and also offers a new alternative for ultra-short pulse characterization different from current methods based on non-centrosymmetric materials.As a fundamental property of the electromagnetic field, optical chirality, embodied by left-handed . The THG in the material is thus modulated by the time delay (Δτ) and reaches the maximum at Δτ = 0  due to the coherent modulation process. Our modulation mechanism is based on the material symmetry and polarization selectivity of the third-order susceptibility. Taking monolayer MoS2 (3 can be obtained via i(j), k, and l run over x, y, and z coordinates, Ej denotes the electric component of the incident pump beam projecting on x  direction. It has been demonstrated that Pω). For simplicity, we only consider that the incident power of one beam is variable. Therefore the total synthesized electrical field (Ein) of the two input beams can be expressed as σ+ (σ−) circularly polarized input beam, and m is a variable that denotes the magnitude ratio of the electrical field of the σ− and σ+ circularly polarized input beam. Note that the measured average power in the experiment of the variable incident beam is proportional to m2. In this case, the modulated THG intensity (when Δτ = 0) can be expressed (see Section 1 of SI for details) as:We then theoretically analyze two typical cases of incident light polarization states. For Case 1, both incident beams are circularly polarized but with opposite chirality  can be expressed as:For Case 2, one incident beam is linearly polarized, and the other beam is circularly polarized. Again, for simplicity, we consider that the circularly- (linearly-) polarized incident beam power is fixed (variable). Thus, the total synthesized electrical field is m2  and the incident polarization states for both cases. For example, Iω)(3 is zero when m is zero (THG is absent with only one circularly polarized input), while Iω)(3 can be large when m is nonzero (THG is significantly modulated with the simultaneous presence of both input beams).From Eqs.  and 2),,2), we c2  two incident beams possess the opposite (green curve) and identical  circular polarization, which generates strong and negligible THG signal. The very small  signal in the orange curve may be caused by the non-perfect circular polarization generated by the broadband (~1100–2000 nm) quarter-wave plate (QWP) used in our experiment. Figure τ. Its full width at half maximum (FWHM) is ~130 fs, agreeing well with the THG autocorrelation results of the incident pulses with an FWHM of ~81 fs. This not only demonstrates that our THG modulation method can be used for the current autocorrelation-based pulse characterization method besides the modulation applications, but also shows the intrinsic ultra-fast speed (coherent process) of our modulation mechanism that is limited only by the incident pulse duration. Moreover, our all-optical modulation method is completely background free  calculated from Fig. −7% and the third-order susceptibility −19 m2/V2 when the incident beams are at ~1560 nm , which are comparable to previous results (~10−19 m2/V2)33.We now experimentally verify the modulation principle Fig. . Figure 2 as schematically illustrated in Fig. τ = 0, pink curve) and with  time delay between the linearly- and circularly- polarized incident beams. The THG intensity is significantly enhanced when the two incident beams are synchronized in the time domain. Figure To further demonstrate the variability of this modulation approach, we also use one circularly polarized beam and one linearly polarized beam at ~1560 nm with the same power ~2 μW) impinging on MoS μW impinDh3 symmetry of MoS2, which implies that this method can work at a wide operation bandwidth. Figure The coherent modulation of THG enabled by optical chirality is governed solely by the 2 by changing the incident power of the σ− circularly polarized beam is illustrated in Fig. σ+ circularly polarized beam keeps at ~2 μW. As expected, the almost perfect modulation depth of ~100%  is demonstrated at different incident powers. Note that the intensity of THG is almost unchanged when the power of the σ− circularly polarized beam changes from ~4 μW (corresponding to a pulse energy of 2 nJ) to ~5 μW (corresponding to a pulse energy of 2.5 nJ), which is caused by the saturation of the THG process in monolayer MoS2 due to (nonlinear) absorption34. When the power of the σ+ circularly polarized beam is ~2 μW, the THG modulation is observed with a minimum power of the σ− circularly polarized beam ~0.1 μW  matches well with the theoretically calculated value of 1.92, which is obtained by plotting the THG intensity according to Eq. . Note that the dependence of modulated THG’s ellipticity on the σ+ circularly polarized incident beam shows the same variation trend but with an output opposite chirality.Moreover, we analyze the polarization state of modulated THG by combining a QWP and a polarizer. Figure mentclass2pt{minim2 as a function of the time delay at different incident powers is illustrated in Fig. σ+ circularly- (the linearly-) polarized beam when the power of the linearly- (the σ+ circularly-) polarized beam is fixed at ~2 μW. For the upper panel (lower panel), the background is almost fixed (increases significantly) and illustrated by the shaded green (yellow) area, which signifies an increasing (decreasing) modulation depth with the incident power. This is expected because the background comes from the linearly polarized incident beam’s THG. Nevertheless, we can adjust the modulation depth on demand for different applications . The slope of the experimental results is ~1.7 (~2.5), which matches well with the theoretical calculation of ~1.8 (~2.5) obtained by plotting the THG intensity calculated in Section 1 of SI versus m2 in the logarithmic coordinate. The deviations from the typical cubic power law between the incident beam power and modulated THG signal are identical to what has been explained for Case 1  polarized input beam, where the dots are measured results, and the curves are theoretical calculations. The ellipticity decreases (increases) as the incident power of the linearly (σ+ circularly) polarized beam increases. Note that the ellipticity variation is not symmetrical with each other by changing the power of linearly- and σ+ circularly-polarized beam, which follows well with our theoretical calculation (Section 1 in SI).Further, the polarization of the output modulated THG signal is investigated for Case 2. Figure 2 across a broadband wavelength range, which can be applied to other 2D materials and even bulk material  and then passes through BS2. The HWP changes the vertically polarized beam into a horizontally polarized beam. After BS2, the recombined vertically polarized and horizontally polarized beams are changed into σ− and σ+ circular polarization by a QWP . The spatially overlapped beams are synchronized in time by moving a homemade delay line, consisting of two mirrors mounted on a precision motorized translation stage . The spatially and temporally overlapped beams are focused onto the sample by the objective lens obj. 1 (Nikon). The generated THG signal is collected by another objective lens obj. 2 (Nikon), which is delivered to a monochromator (Andor) equipped with a photomultiplier tube (Hamamatsu) connected to a lock-in amplifier (Stanford research system). Two 600-nm short pass filters (Edmund) after obj. 2 block the two incident beams. The ellipticity of the THG is analyzed by a homemade ellipticity analyzer consisting of a QWP  and a polarizer, which is placed behind the filter as shown in the inset of Fig. Figure 2 flakes are grown on a quartz substrate (a thickness of 500 μm) by chemical vapor deposition. First, a 5 mg/ml Na2MoO4 aqueous solution is spin-coated on the substrate, and then it is heated at 800 °C. Second, ~10 mg of sulfur is added and heated at 170 °C (5 min) under high-purity argon. Figure 2 excited by a laser at 532 nm. The Raman spectrum possesses two characteristic peaks representing the in-plane mode (E2g) and out-of-plane mode (A1g) at ∼384 cm−1 and ∼404 cm−1. The inset in Fig. 2 flake on the quartz substrate. Figure 2 excited by a laser at 532 nm. The Gauss fit curves show two characteristic peaks corresponding to the A-exciton  and B-exciton , respectively.Monolayer MoSSUPPLEMENTAL MATERIAL


Scientific Reports 10.1038/s41598-022-17470-y, published online 22 August 2022Correction to: The original version of this Article contained errors.In the Results and discussion,(5%) bionanocomposite as a selected example as illustrated in Fig. 7.”“Also, the existence of Ag and Zn has been additionally confirmed by EDX analysis of specific spots during SEM imaging of PHBV/Ag–ZnOnow reads:(5%) bionanocomposite as a selected example as illustrated in Fig. 7. Ag peaks are not visible as its doping percentage is below the detection limit. The Au peaks are due to the gold coating used for SEM sample preparation.”“Also, the existence of Zn has been confirmed by EDX analysis of specific spots during SEM imaging of PHBV/Ag–ZnOIn addition, the caption of Figure 7 in the original version of this Article was incomplete.The caption of Figure 7,(5%) bionanocomposite.”“EDX analysis of PHBV/Ag–ZnOnow reads:(5%) bionanocomposite. The Au peaks are due to the gold coating used for SEM sample preparation. Ag peaks are not visible, as Ag doping was constant at 1 mol% which is below the detection limit.”“EDX analysis of PHBV/Ag–ZnOThe Article has been corrected.


It also shows a high energy density of 14.2 Wh kg−1 with good rate capability even at a current density of 10 A g−1, which is superior to commercial devices (3–10 Wh kg−1). Moreover, the device delivers a stable energy storage capacity when encountering different flexible conditions, such as elongated, tangled and bent states, showing wide potentials in flexible and even wearable applications. Especially, it retains stable specific capacitance even after 500 bending cycles with a bending angle of 180°. The two-step fabrication method of these flexible tubular supercapacitors may allow for possible mass production, as they could be easily integrated with other functional components, and used in realistic scenarios that conventional film devices struggle to realize.Flexible supercapacitors have been demonstrated to be ideal energy storage devices owing to their lightweight and flexible nature and their high power density. However, conventional film-shaped devices struggle to meet the requirements of application in complicated situations, including medical instruments and wearable electronics. Here we report a hollow-structured flexible tubular supercapacitor prepared from a scalable method with the same diameter as electric wires. This new supercapacitor design allows for a large specific capacitance of 102 F g Flexible supercapacitors are versatile energy storage devices owing to their light weight, flexibility and high power density. Here a scalable, flexible, tubular supercapacitor possessing the diameter of an electrical wire is presented. It is critical to develop flexible supercapacitors with fast charging rate, high energy capacity, and long life span, as well as good mechanical properties that allows their practical applications under flexible conditions6. Comparing with conventional battery systems, the major challenge impeded applications of flexible supercapacitors is their relatively lower energy density, which is mainly controlled by intrinsic properties of the electrode materials8. To address this issue, tremendous efforts have been made for the purpose of developing high energy density electrode materials, such as conducting polymers10, CNTs11, graphene12, Co3O413, MoS214, graphitic carbon nitride16 and so on. Resultly, these nanostructural electrode materials with good electrical conductivity, high surface area and porous structure greatly improved energy density of supercapacitors to the practical application level competitive to batteries18. But it is still challenging to fabricate flexible supercapacitors with proper morphology and superior mechanical properties that can accommodate complicated deformations, such as bending, twisting and stretching.Over the past decades, flexible supercapacitors have witnessed an enormous development owing to the dramatic innovation of wearable electronic devices, which require to work in close contact with the human body19. Compared with two-dimensional (2D) and three-dimensional (3D) devices, 1D devices are more flexible to accommodate deformations and can even be woven with textiles and fabrics with different materials that fit the different curved targeted surfaces21. For instance, a 1D supercapacitor based on electrospinning technique was reported with high energy density and good flexible energy storage properties22. Another coaxial asymmetric 1D supercapacitors prepared with electrical machinery was reported with a high volumetric energy density and ultrafast charging rates23. Recently, wearable asymmetric fiber-shaped supercapacitors have extensively investigated based on various nanomaterials, including MOF-derived carbon nanomaterials24, core-shell nanomaterials25, and VN/CNTF26. The fiber supercapacitors all display excellent flexibility, superior integratability, long life and high energy density, and present great potential as power sources for wearable applications. These previously reported devices showed high energy storage capacity and flexible performances for potential applications, but the fabrication processes of supercapacitors always referred to complicated materials synthesis methods and relied on advanced instruments and equipment. The drawbacks made mass-production of flexible devices impossible and seriously hindered their practical applications28. Thus it is urgent to put forward a simple and effective fabrication method of 1D flexible supercapacitors without complicated materials and large-scale instruments.One-dimensional (1D) flexible supercapacitors are promising to meet the requirements of mechanical properties for wearable applications−1 at the current density of 1 A g−1 and retains a stable energy storage capacity even after working 10000 times in air. The high rate capability from 1 to 10 A g−1 makes its applications under high charging rates possible, which satisfies the requirements of fast charge for energy storage systems in electronic products. The achieved energy density is as high as 14.2 Wh kg−1 and is superior to that of commercial capacitors (3–10 Wh kg−1). It is noteworthy that this tubular supercapacitor delivers a stable energy storage capacity under different flexible conditions, including elongation, tangle and bending, showing wide potentials in flexible and even wearable applications. Its specific capacitance retention remains at 92% after 500 bending cycles under a bending angle of 180°. Moreover, the simple and cost-effective fabrication strategy would make mass production of flexible supercapacitors possible and could promote their practical applications in future. This work presents an insight into the morphology and design of 1D flexible energy devices and hopes to accelerate the development of other flexible energy storage systems, such as in solar cell, fuel cell, and lithium battery applications.In this work, we report a high-performance flexible tubular supercapacitor prepared through a simple and scalable strategy. The as-fabricated 1D device with a diameter of about 3 mm is very different from conventional 2D film devices, as its hollow structure makes it suitable for integration with other electrical devices in various application scenarios. It exhibits a large specific capacitance of 102 F g3 solution for polymerization of PEDOT ) electrode. Chemical structures of Nafion and PEDOT materials are presented in Supplementary Fig. 30. From the Raman spectra of PEDOT materials, the sharp peak visible at 1429 cm−1 is attributed to the characteristic symmetric Cα=Cβ (–O) stretching31. The broad peak at 1505 cm−1 represents asymmetric C=C stretching mode of PEDOT32. The medium and weak bands near 437, 573, 699, 988, 1110, and 1261 cm−1 are likewise typical for PEDOT33. It is found that the device can be twisted around an iron rod, showing its excellent flexibility  and macropores (20–90 nm) range. The porous structure with high surface area will facilitate ion transfer kinetics in the electrochemical devices.Commercial Nafion tube was etched with plasma method to obtain a rough surface, which is vital for the next-up polymerization process of PEDOT electrodes. Previous studies found that physical etching treatment did not alter the chemical structure of materials and thus performance of the device37. Nyquist plot of the device is shown in Fig. −1 and the rectangular shapes verify capacitive mechanism. Galvanostatic charge-discharge (GCD) curves of the supercapacitor are obtained under various current densities from 1 to 10 A g−1 in Fig. −1 at the current density of 1 A g−1 and achieves at 57 F g−1 at the high current density of 10 A g−1. These results indicate that the tubular supercapacitors get high energy storage capacity and good rate capability.Working mechanism of tubular supercapacitor is illustrated in Supplementary Fig. −1 within potential window of 1.0 V. It is found that the device shows only a slight degradation of specific capacitance after 10,000 working cycles in air. As shown in Fig. −1 at a current density of 1 A g−1, which exceed to that of commercially available devices (mainly 3–10 Wh kg−1)38 and achieved relative high-level energy storage capacity of flexible supercapacitor. These results manifest that the tubular supercapacitor can reach high energy densities with excellent air-working stability.Additionally, long-term working stability of tubular supercapacitor was evaluated in Fig. 3/2 and S2p1/2 characteristic peaks for –C-S-C– covalent bond of thiophene-type sulfur owning to the spin-orbit splitting39. The other two positions at 166.0 and 168.3 eV are assigned to –C-S(O)2-C– sulfone bridges40. These characteristic peaks all belong to PEDOT materials and do not exist in Nafion materials. The results verify that the device interface contains PEDOT materials, which indicates that the PEDOT electrode layer forms tight interface coupling with Nafion polyelectrolyte layers (as shown in the inset) and thus leads to stable electrochemical performance of device.To clarify the reason for stable electrochemical performance of the device, we have characterized the device interface with SEM method at higher magnification. As display in Supplementary Fig. 42.Mechanical performances of tubular supercapacitors determine whether they can be applied and integrated with flexible and even wearable electronic devices in practical applications. Flexible energy storage performances of the device were investigated using electrochemical charge-discharge method under different deformation conditions, including bending and twisting. The tubular supercapacitor is designed to integrate with other tubular electrical devices for specific applications, such as medical catheters, tubular robots, and unmanned aerial vehicle. But the widely studied fiber-shaped solid-wire devices with micro/nano sizes can not realize such integration property with macroscopic devices. This tubular supercapacitor is made with polymer electrode and electrolyte, which are both flexible enough to endure external forces/stresses. The compatibility of two polymer layers would be beneficial to robust interface of the device, which guarantees the stable energy storage property under flexible conditions. On the one hand, graphene and CNT fibers are much more expansive than PEDOT electrode and not easy to integrate onto solid-state polymer electrolytes because of modulus mismatch of organic and inorganic materials−1 in Fig. 44.Figure −1 at current density of 1 A g−1 if the two devices were connected in parallel, as displayed in Fig. −1. Voltage output of the system increases linearly with increasing unit numbers. The multi-unit system can provide higher energy capacity by connecting these units in parallel and its specific capacitance improves linearly with unit numbers in Fig. To satisfy voltage and current needs in practical scenarios, sometimes many devices need to integrate in series and parallel and the corresponding equivalent circuit diagrams are displayed in Supplementary Figs. −1 at the current density of 1 A g−1 and displays excellent working stability over 10000 cycles in air. Its energy density achieves as high as 14.2 Wh kg−1 with good rate capability and surpasses most of the commercially available devices. The device retains its energy capacity with negligible degradation when encountering flexible conditions, including bending or twisting states. It also kept delivering stable energy supply under bending angle of 180° after 500 cycles, showing wide potential in wearable applications. This work not only paves a way for design of novel stereotype of flexible supercapacitors toward wearable electric devices, but also presents a scalable strategy for mass production of energy storage devices for practical applications.In summary, a tubular supercapacitor with high energy storage capacity and flexibility has been developed by a simple and effective preparation strategy. The proposed two-step preparation method does not involve any complicated material synthesis or large-scale instruments and has potential for scale-up experiments. The as-fabricated supercapacitor with diameter of 3 mm delivers a large specific capacitance of 102 F g2O2), sulfuric acid (H2SO4) and Ferric chloride (FeCl3) were obtained from Sigma-Aldrich Co., Ltd. 3,4-Ethylenedioxythiophene (EDOT) monomer was purchased from Sinopharm Chemical Reagent Co., Ltd. Deionized water was homemade.Nafion tube was purchased from the HALMA company. Hydrogen peroxide  and motorized translation stages (MTS121).Supplementary Information