Escherichia coli. While individual perturbations improved fitness during antibiotic exposure, multiplexed perturbations caused large fitness loss in a significant epistatic fashion. Strains exhibiting epistasis adapted significantly more slowly over three to fourteen days, and loss in adaptive potential was shown to be sustainable. Finally, we show that multiplexed peptide nucleic acids increase the antibiotic susceptibility of clinically isolated Carbapenem-resistant E. coli in an epistatic fashion. Together, these results suggest a new therapeutic strategy for restricting the evolution of antibiotic resistance.The ever-increasing threat of multi-drug resistant bacteria, a shrinking antibiotic pipeline, and the innate ability of microorganisms to adapt necessitates long-term strategies to slow the evolution of antibiotic resistance. Here we develop an approach, dubbed Controlled Hindrance of Adaptation of OrganismS or CHAOS, involving induction of epistasis between gene perturbations to deter adaption. We construct a combinatorial library of multiplexed, deactivated CRISPR-Cas9 devices to systematically perturb gene expression in  E. coli through CRISPR-Cas9-based perturbation of gene expression. They show that multiplexed perturbations decrease fitness of clinically-isolated Carbapenem-resistant E. coli upon antibiotic exposure.Peter Otoupal et al. present CHAOS, an approach for preventing the development of antibiotic resistance in  Bacteria are constantly driven to adapt to new treatments, establishing a biological arms race between evolution and our ability to develop new antimicrobial treatments. As the balance of these processes has trended towards more resistance and fewer novel therapies2, attention must be turned to new strategies which hinder the evolution of antibiotic resistance if the utility of our antibiotic arsenal is to be preserved. Current approaches are largely limited to the cycling of antibiotics, which rely upon high levels of coordination between clinicians and has been found to be difficult to implement in practice4. We propose an alternative strategy that we term Controlled Hindrance of Adaptation of OrganismS or CHAOS that involves introducing multiplexed gene expression perturbations to disrupt bacterial homeostasis by manipulating a fundamental process underpinning evolution: epistasis.The rapid emergence of multi-drug resistant superbugs poses a serious threat to millions and constitutes an impending international health crisis7. Such landscapes project an organism’s fitness at every unique genetic state for a pair of genes, with epistatic interactions influencing the landscape’s shape. Epistasis has been found to emerge frequently upon introduction of simultaneous mutations and is widely recognized to influence evolutionary trajectories10. Negative epistasis in particular has been shown to restrict rates at which bacteria adapt12. Research regarding epistasis has thus far focused on the theoretical side, using epistasis to explain how particular mutations emerged during evolution. Here, we demonstrate that artificial induction of epistasis has the potential for curtailing evolutionary adaptation by natural selection. This is accomplished by introducing multiplexed gene expression perturbations into a bacterium leading to cascading epistatic effects and restricting its ability to evolve  epistasis occurs if the combined changes produce a worse (or better) fitness than expected based upon their individual impacts. This phenomenon creates the rugged shapes of fitness landscapes used to describe evolutionlve Fig. .Fig. 1Co17. However, such noise in gene expression comes at a fitness cost, as deviations from basal gene expression are often more deleterious than beneficial21  diminished adaptation rates over three to fourteen days and that such effects are sustainable, suggesting that multiplexed gene perturbations can be employed to restrict the evolution of antibiotic resistance. Finally, we use peptide nucleic acids (PNAs) to repress expression of a different set of gene perturbations in a multi-drug resistant clinical isolate of E. coli, demonstrating that CHAOS is applicable to other modes of gene expression manipulation. We successfully re-sensitized this antibiotic-resistant isolate to treatment, suggesting the benefit of CHAOS in clinical settings.Here we explore CHAOS-induced epistasis to restrict bacterial adaptation to antibiotics. We employ deactivated Cas9 (dCas9) clustered regularly interspaced short palindromic repeats (CRISPR) technology to selectively inhibit or activate mRNA production of specific genes. We construct a library of CRISPR plasmids to express dCas9 constructs for inducing gene expression perturbations and systematically quantify the impact of multiplexed gene perturbations on bacterial fitness with and without antibiotic exposure. We show that increasing the number of genes perturbed from their basal levels strongly correlates with reduced fitness during antibiotic exposure. We demonstrate that this phenomenon arises in a negative epistatic fashion, wherein compounding perturbations exacerbate fitness loss. The degree of negative epistasis correlated with the number of downstream genetic partners impacted by the primary gene perturbation(s). Disruption of genes relating to metabolic processes tended to induce the greatest level of negative epistasis. We demonstrate that negative epistasis correlated with significantly (Escherichia coli strains harboring dCas930 or dCas9-ω26 devices to inhibit or activate mRNA production respectively. Each strain hosts a unique array of one or more single guide RNAs (sgRNAs) to direct the CRISPR enzymes to particular genomic loci , soxS (SOX pathway regulator), tolC (multidrug efflux pump), and recA (SOS response activator) , topA , zwf (a key glycolysis enzyme), and frr . These genes are monocistronic (except tolC and recA) and possess little direct interactions between one another (except mutS/recA and soxS/tolC)  Fig. . These fays Fig. . We bega32. Multiplexing perturbation of conserved genes was predicted to cause more cascading impacts than multiplexing perturbation of stress response genes  gene, which was absent in all strains. These strains harbored one or four copies of rfp targeting sgRNAs . Another control strain including constitutively expressed mCherry and one copy of rfp perturbation  was created to enable tracking of the control population during strain competition. Finally, two control strains harboring either one or four sgRNAs to inhibit lacZ were constructed and used to demonstrate that no differences arose from targeting our control to a nonsense perturbation (rfp) or a gene irrelevant to fitness   allowing for moderate growth while still imparting selective pressure. We chose ciprofloxacin as it is a clinically-relevant antibiotic treatment which selects for resistant populations at very low concentrations33 via specific mutations in the gyrA gene34, thus allowing us to assess adaptation of strains. The CHAOS and controls strains were competed for one day followed by plating on solid media. Fitness impacts were quantified by measuring the relative changes in colony forming units of both control (Cmcherry—red colonies), and the competed CHAOS strain before and after exposure to sub-MIC of ciprofloxacin using fluorescence imaging (see methods). No significant differences were observed between either control strains control or CCCC .We evaluated the fitness impacts caused by each CHAOS construct by competing these experimental strains with the fluorescent control strain soxS, tolC) or increased fitness  in relation to the control , their benefits were abated when combined in pairs , triplets , and all at once  , while another two (soxS-tolC and tolC-recA) resulted in significant fitness losses. Furthermore, half of the triple perturbations reduced fitness (mutS-tolC-recA and soxS-tolC-recA).Individual gene perturbations of stress response genes either had no statistically significant impact  Fig. . Only onzwf, topA, and frr), with inhibition of topA providing a strong fitness benefit , fitness decreased when these were combined in pairs  or three at a time . Combining all four perturbations resulted in a particularly severe diminishment of fitness , suggesting drastic epistatic effects   did not demonstrate significant epistasis, indicating that direct interaction is not required to produce negative epistatic effects. The degree of negative epistasis also appeared to increase as more genes were perturbed. Inhibition of conserved genes resulted in statistically greater levels of negative epistasis than activation of stress response genes for triple perturbation constructs (P = 0.01). The high degree of negative epistasis across both sets appears compounded by sign-epistasis, wherein individually beneficial perturbations become deleterious once combined. Raw epistasis values and significance are presented in the Supplementary Data We next quantified epistasis between simultaneous perturbations by calculating deviations between the measured fitness of multiple perturbation strains, and their expected fitness based upon single perturbation (see methods). Comparing these fitness values, no deviation between expected and actual fitness of strain CCCC was observed, while multiple perturbation strains clearly exhibited lower fitness than was expected Fig. . This trs P = 0.0. The hig32. Functional differences between the affected pathways from each set could explain these differences, as conserved gene perturbations disrupted pathways more central to cell survival. Exploring the functionality of each gene within the impacted networks, we observed that all simultaneous perturbation constructs impacted pathways related to DNA maintenance, RNA production, and protein synthesis, but metabolism and membrane transport were unique to conserved gene perturbations and stress response perturbations respectively  between the total amount of affected partners and the degree of negative epistasis exhibited by each CHAOS strain  disrupted by CRISPR perturbation(s) as characterized by the STRING databaseely Fig. . As expeain Fig. . This suP = 8 × 10−7), and a significant correlation between metabolic activity and the degree of epistasis . A potential mechanism for this could be reduced efficacy of resource dedication towards surviving antibiotic exposure. This is supported by previous work correlating increased metabolic rates to potentiation of bactericidal antibiotics36 and another study correlating antibiotic efficacy to the presence of metabolites37. Furthermore, 13 of the affected metabolic genes in strain dzTf are essential. Adapting bacteria exhibit less stochastic expression in essential genes22, and negative epistasis has been proposed as a mechanism constraining this heterogeneity24 due to essential genes exhibiting stronger overall genetic interactions38. Indeed, genetic interactions are statistically greater in the conserved gene set than stress response set . Collectively, these studies appear to corroborate our finding that accumulating gene expression deviations in diverse cellular pathways has a fundamental tendency to influence fitness detrimentally in an epistatic fashion.As central metabolic pathways were impacted only by conserved gene perturbations, we quantified metabolic rates of each strain after 20 h of exposure to sub-MIC levels of ciprofloxacin Fig. . We obser, see methods). Statistical differences in fits between the control and CHAOS strains were estimated using F-tests .To test the hypothesis that induced negative epistasis can restrict the adaptive potential of bacteria, we exposed single and quadruple perturbation strains to ciprofloxacin over three days (D1–3) and quantified changes in MIC  and D3 (P = 2 × 10−3), and the rate of this increase was significantly faster than the control (P > F = 9 × 10−5) , the strain quickly adjusted back to control levels resulting in a statistically faster rate of increase than the control (P > F = 5 × 10−7)  Fig. . While t−7) Fig. . The disins Fig. .mutS, soxS, tolC, and recA) exhibited a positive correlation that was weaker than each individual perturbation  exhibited a completely flat MIC trajectory, and always survived statistically lower ciprofloxacin concentrations than the control. Strain dzTf  presented the most striking results, as not only were MICs statistically lower than the control, average MIC actually decreased over time . This was primarily due to death of fifteen replicates by the end of adaptation. Nevertheless, excluding replicates that died during the course of the experiment showed a neutral trajectory of MIC over time , indicating that the living population was adapting slower   have been found as the first genetic changes during the evolution of ciprofloxacin resistance34. The vast majority of strains exhibited no gyrA mutations  sensitive/resistant breakpoint values40 showed resistance to at least eight classes of antibiotics42. For this study, we tested the response of this strain to chloramphenicol as it exhibited greater than 8-fold higher MIC (>256 µg mL−1) than the corresponding CLSI breakpoint of 32 µg mL−1  gene). These PNAs target and bind to the translation start codon of these genes, thus inhibiting translation of these genes’ mRNAs into protein45 (see Methods). We chose concentrations of PNA at 2.5 µM at which individual perturbations had minimal to no effect on cell growth of standard MG1655 E. coli. These PNAs were conjugated via an O-linker to a cell-penetrating peptide (CPP) motif for direct intracellular delivery. We exposed the clinically isolated CRE E. coli to these PNAs individually and in combination for 24 h, before plating in both the presence and absence of chloramphenicol , with no apparent exacerbation of α-ffh’s toxic effect upon multiplexing with other PNAs  compared to the best individual perturbation. We note that overall PNA concentration was increased under this condition, leading to the potential of slightly increased antibiotic permeability  emerged in 2013topA and zwf, both plausibly influence DNA folding rates through different mechanisms—topA through altered topoisomerase expression, and zwf through altered NADPH production, influencing flux through the thioredoxin pathway that regulates gyrA expression47. Such collateral effects help explain why independent perturbation of topA and zwf appears to increase fitness during ciprofloxacin exposure, but their combined perturbation results in negative epistasis. Furthermore, this disruption of homeostasis via gene expression changes could explain why essential genes are conspicuously restricted in heterogeneity24, particularly after stress exposure22.An intuitive explanation for this phenomenon is that increasing perturbations generally leads to a breakdown in homeostasis, as shifts in independent pathways create cascading detrimental impacts. For example, despite no direct interaction between 27. This raised the idea that gene expression based epistasis might be a fundamental phenomenon, paving the way for this work in which we significantly expand this idea.Our previous results also corroborate this notion, wherein a smaller set of five strains harboring multiplexed inhibitions and activations of two to three stress response genes tended to exhibit negative epistasis when exposed to various stressors including both antibiotics and biofuels48, but is limited to mass alterations of genetically related genes regulated by the same native processes bacteria have naturally evolved. In contrast, CHAOS can theoretically be applied to modify any set of genes and can be interfaced with either a CRISPR or PNA platform.Artificially engineering bacterial gene expression to increase antibiotic susceptibility is a relatively new idea. Previous work has shown multiplexing transcription factor overexpression can successfully re-sensitize antibiotic resistant bacteria54 and PNAs57, and there is reasonable potential that these approaches could be applied to making CHAOS a viable therapy. This is best demonstrated by our application of CHAOS to increase the susceptibility of a clinically isolated multi-drug resistant CRE E. coli. Despite high levels of resistance, we were able to increase its susceptibility to chloramphenicol with multiplexed perturbations of four essential genes.One of the major limitations to implementing CHAOS in a clinical setting is the delivery of these gene perturbation systems into the target cells, especially for intracellular infections. Significant work is being undertaken to improve the therapeutic delivery of CRISPR systems58, and engineered negative epistasis affords an ancillary but functionally separate strategy to achieve this goal. Exploiting genetic interactions is ripe for use in therapeutic strategies, as evidenced by the growing interest in synthetically lethal mutations that are detrimental to cancer mutated cells but benign to healthy cells59. Future studies involving a systematic investigation of transcriptome-wide epistasis would help in this regard by identifying optimal gene combinations to achieve detrimental interactions with minimal invasiveness. Such studies will help to confirm whether the sign of perturbation (activation vs. inhibition) or the degree of perturbation could influence the level of emergent epistasis. Overall, our results demonstrate that applying a more sophisticated understanding of gene expression enables one to induce negative epistasis to gain control over adaptation rates, and could preserve the efficacy of last-resort antibiotics when employed in co-therapy strategies.Hindering the evolution of antibiotic resistance is a major goal of antibiotic synergy in combination therapy approachesrfp inhibition (the Control—C) and served as the starting plasmid for creating all subsequent sgRNA plasmids. Addgene plasmid 44,251 was used directly for providing dCas9, while the previously constructed pPO-dCas9ω27 plasmid was used directly for providing dCas9-ω. New sgRNA target plasmids were created by replacing the RFP-targeting sequence in 44,251 with new gene sequences specific to the target of interest. This was accomplished by designing unique forward primers flanked with a SpeI restriction site and encoding the new target sequence. A common reverse primer flanked with ApaI was used alongside these primers to perform PCR amplification with Phusion High-Fidelity DNA Polymerase (New England Biolabs) of DNA inserts, which were subsequently digested with Cutsmart SpeI and ApaI (New England Biolabs) alongside 44,251 backbone. Ligations of these pieces were performed using T4 DNA Ligase (Thermo Scientific), which were subsequently transformed into electrocompetent NEB 10-β. Transformants were minipreped using Zyppy Plasmid Miniprep Kit (Zymo Research Corporation) and submitted for sequencing to confirm successful insertion (GENEWIZ). sgRNA plasmids targeting individual genes were used to construct sgRNA plasmids targeting two or more genes via Gibson Assembly, for which a common forward and reverse primer was used to amplify the first sgRNA target plasmid while introducing overhangs downstream of the terminator sequence following the first target. A common set of primers were then utilized to amplify sgRNA targets to the second, third, and fourth targets depending on the intended number of final sgRNA targets. A batch Gibson reaction was performed at 50 °C for 3 h with T5 exonuclease (New England Biolabs), Phusion polymerase and Taq ligase (New England Biolabs) on this one backbone and one to three inserts to stitch all pieces together  harboring either 44,249 or pPO-dCas9ω if the target was meant to inhibit or activate expression respectively. This process was used to construct all control and experimental strains used in the study.A list of plasmids and primers used in this study can be found in Supplementary Tables 4, and 0.1 mM CaCl2 supplemented with 0.4% weight/vol glucose). Plates and media were supplemented with ampicillin (100 µg mL−1) or chloramphenicol (35 µg mL−1) to maintain selection of sgRNA plasmids or dCas9/dCas9-ω plasmids respectively. aTc was used to induce CRISPR expression at a final concentration of 10 ng mL−1, except where otherwise noted. The authors also note that the aTc-inducible promoter driving expression of dCas9 is not PLtetO-1 as originally reported30, but rather a tet-promoter variant with only one Tet binding site highly similar to the original tet-promoter, indicating that slightly higher leaky expression is expected of dCas9 and dCas9-ω. All cultures were grown at 37 °C, with shaking at 225 rpm. Cultures for competition were grown in 200 µL cultures in 96-well conical-bottom microplates. Cultures for RT-qPCR and for batch growth comparisons to microplates were grown in 3 mL cultures. Cultures for CFU and MIC screens were grown in 100 µL cultures in 384 well flat-bottom microplates. Cultures for growth rate calculations were grown in 100 µL cultures in 384 well flat-bottom microplates in a GENios plate reader (Tecan Group Ltd.) operating under Magellan software (version 7.2) with 16.6 min of shaking before measurement of optical densities at 590 nm absorbance every 20 min.All cultures were grown in Lennox Luria-Bertani Broth (LB) (Sigma-Aldrich), with the exception of RT-qPCR samples and certain samples for growth calculations which were grown in M9 minimal media  and purification using the Turbo DNA-free kit (Ambion). Separate biological replicates of topA perturbation and the single target rfp control strain were also grown for 24 h to stationary phase and collected as before. Purified RNA was used to create cDNA using the Maxima First Strand cDNA Synthesis Kit for RT-qPCR (Thermo Scientific). Technical duplicates of each replicate were subjected to RT-qPCR reactions from the Maxima SYBR Green qPCR Master Mix (Thermo Scientific) using 2 ng of cDNA in 12.5 µL reactions run on a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems) in the CU Core Sequencing Facility. Reactions were allowed to run for 40 cycles with Rox normalization. Gene expression changes were calculated using 2-ΔΔCq values calculated from averages of technical duplicates, relative to the control strain targeting rfp.The degree of gene expression perturbation was confirmed by subjecting biological triplicates of each individual gene perturbation to RT-qPCR, as well as constructs perturbing four genes simultaneously and the control strain targeting mCherry. A total of eight biological replicates were inoculated from colonies into 200 µL in 96 well plates and grown overnight for 16 h with selection. Cultures were then diluted 1:100 into 200 µL of media with selection and 10 ng mL-1 aTc to induce gene perturbation and grown for another 24 h. Competition was initiated by diluting cultures 1:100 and mixing equal cell ratios of the red control strain with each experimental strain into 200 µL of media containing selection, 10 ng mL−1 aTc and 0.005 µg mL−1 ciprofloxacin. To determine starting ratios of each strain, two µL were diluted into 198 µL of water, from which 10-fold serial dilutions up to 1:100,000 were created each using a total volume of 200 µL for each dilution. 50 µL of both the 1:10,000 and 1:100,000 dilutions were plated to determine CFUs (and by extension starting ratios) of each strain. The remaining 198 µL of culture not used for serial dilutions was grown for another 24 h, diluted 1:100 into the 198 µL of the same media, and grown again for 24 h. At the end of this growth period, CFUs for each strain were determined using the previously outlined dilution scheme. Plates were incubated for 24 h after inoculation. Two images were taken of each plate with fluorescence activation at 540 nm, one with emission filtering at 590 nm and the other without. These images were overlaid to facilitate colony counting. Colony counts were used to determine fitness values (ω) using the standard Malthusian fitness equation:60N: CFU, E: experimental strain, C: control strain, 1: after exposure, and 0: before exposure. Fitness values were calculated as such for all experimental strains, as well as the control strains targeting rfp with one or four sgRNAs and expressing no fluorescence. This same protocol was repeated in the absence of ciprofloxacin for all combinations of conserved gene perturbationsThe fitness of each perturbed strain was calculated by competing said strain against the red fluorescent control strain Cωe) for strains with perturbation of two or more genes were calculated assuming a multiplicative model as follows:n expands to all sets of genes perturbed. For instance, ωe of strain dzf would be calculated as the product of fitness from each individual gene perturbation (ωd × ωz × ωf). Epistasis (E) was calculated as the difference between measured fitness and expected fitness (E = ω − ωe).Expected fitness values . After 20 h of growth, 10x Resazurin  was added to each well , and fluorescence (excitation 485 nm and emission 610 nm) was measured in 5-minute intervals. The slope of the curve from the first five data points was used to quantify metabolic activity during the initial stages of ciprofloxacin exposure.Four individual colonies were cultured overnight (16 h) in 100 µL of LB supplemented 35 µg mL−1 aTc in 384 well plates and grown another 24 h. The following day, each replicate was diluted 1:50 into fresh media containing selection, aTc, and a range of ciprofloxacin concentrations including 0, 0.005, 0.01, 0.02, 0.04, 0.08, and 0.16 µg mL−1 ciprofloxacin to begin the MIC screen. The new 384 well plate containing variable ciprofloxacin concentrations was grown for 24 h (Day 0–Day 1), after which absorbance was measured at 590 nm. Cultures expressing ODs greater than 0.20 were determined to have survived. The highest concentration at which each replicate survived was used to inoculate the same plate setup as defined previously (Day 1–Day 2), while the next highest concentration was determined to be the MIC. This process was repeated for one more day to obtain MICs for 22 cultures at the end of each day of growth for all three days. Three replicates of each strain growing at the highest MICs were saved as glycerol stocks for subsequent sequencing.MIC assays were performed using 22 biological replicates per strain. Individual colonies were inoculated into 100 µL LB cultures containing selection and grown for 16 h overnight. The following morning, cultures were diluted 1:50 into 100 µL of fresh media containing 10 ng mL−1 aTc, chloramphenicol, ampicillin and no ciprofloxacin. These cultures were grown for 24 h, diluted into fresh media, and grown for another 24 h. After two days of growth in the absence of ciprofloxacin, these cultures were transferred back into the ciprofloxacin concentration gradient using the aforementioned protocol.Samples for re-exposure to ciprofloxacin were diluted 1:50 into fresh media containing 10 ng mLdfp, zwf, topA, and frr were continued on for up to 14 days using the same protocol as before, but increasing the maximum concentration of the gradient up to 20.48 µg mL−1 ciprofloxacin. For strain dzTf, we biased our experiment by picking the six best replicates as quantified by maximum MIC reached.The same batch of 22 cultures used for re-exposure to ciprofloxacin experiments was used for the fourteen-day adaptation experiment. For this, the first six replicates by order of the control strain, as well as individual perturbations of gyrA in the 1203 bp region surrounding S83 and D87, the most likely regions for mutations conferring ciprofloxacin resistance to arise. PCR samples were purified and submitted for sequencing (GENEWIZ), for a total of six samples per strain.Glycerol stocks of strains saved after three days of ciprofloxacin exposure were streaked onto LB agar plates with selection and grown overnight. Two colonies from each plate were used to perform colony PCR amplification of 61. Individual colonies were grown in three mL LB without selection for 16 h and subsequently adjusted to normalized ODs with the addition of LB to denser cultures. Each culture was used to dilute 1:10,000 into 33 parallel 100 µL cultures of LB supplemented with 10 ng mL−1 aTc and grown for 24 h. Colony forming units were estimated from three replicates on plain LB agar plates, while the remaining 30 cultures were plated on LB agar containing 100 µg mL−1 rifampicin. Colonies were counted after 48 h of exposure, and the FALCOR web tool was used to estimate mutation rates62.Mutation rates were estimated using the rifampicin exposure approach outlined by Luria and DelbruckP values reported were calculated using a standard two-tailed type II student’s t-test. Pearson correlation coefficients and their corresponding P values were calculated using linear fits with no weighting (OriginPro 9.3.226 software). Comparisons of linear fits  were performed using F-tests of results from linear fits in OriginPro. Grubb’s test for outliers was used to remove individual data points prior to calculations where indicated. Standard deviations of expected fitness (σe) of simultaneous gene perturbations were calculated from standard deviations of single gene perturbations (σi) using error propagation following the equation: E) were also estimated using error propagation following the equation: t-test to obtain P-values (assuming a two-tailed distribution) using the standard formula t is the test statistic, E is the average sample epistasis, μ is the null hypothesis , σE is the estimated standard deviation of Epistasis, and n is the number of replicates .Unless otherwise indicated, all 3K connected to the C-terminus by an O-linker and supplied as a lyophilized powder. Upon arrival, PNA was resuspended in 5% DMSO to a final concentration of 100 µM. PNA sequences were designed to bind to 12 nts surrounding the translation start site of the targeted gene, keeping minimal off-target homology. These PNA sequences from N to C terminus are as follows: (KFF)3K-O-ATACCATGATTAT (folC), (KFF)3K-O-CTCTTGCAGGTG (fnrS), (KFF)3K-O-GACAATGTTTGA (ffh), and (KFF)3K-O-GTTGATGTCGAA (gyrB). The nonsense PNA targeting a non-existent RNA sequence was designed using the sequence GAATAAGGGCGA and also included the (KFF)3K motif on the N terminus. This PNA was graciously donated to us from Dr. Teruna Siahaan of the University of Kansas.PNA polymers, peptide backbones connecting standard ATCG nucleosides which bind strongly to RNA and act as effective translation inhibitors, were purchased pre-synthesized (PNA Bio Inc.). These polymers were bought conjugated to a cell-penetrating-peptide motif (KFF)E. coli was obtained resistance levels was obtained from the lab of Dr. Nancy Madinger at the University of Colorado Anschutz Medical Campus, and its resistance profile was characterized in previous work42. For testing CHAOS against this strain, the clinical isolate was plated on cation-adjusted Muller Hinton Broth (caMHB). Three individual colonies were picked from this plate and grown in 3 mL caMHB overnight for 16 h. Each culture was then diluted 1:100 into a new 50 µL cultures of fresh caMHB in a 384 well plate and grown for another 24 h. Each culture was then diluted 1:100 again into six new 50 µL cultures of fresh caMHB in a 384 well plate, this time supplemented with either no PNA, 2.5 µM of each PNA individually, or 2.5 µM of each PNA mixed together . These cultures were then grown for another 24 h before performing CFU analysis on plates supplemented with either no chloramphenicol or supplemented with 16 µg mL−1 chloramphenicol. Plates were incubated for 24 h after plating before performing CFU analysis.Carbapenem-resistant Enterobacteriaceae (CRE) E. coli were picked and used to inoculate three 3 mL caMHB overnight for 16 h. Cultures were then diluted 1:100 into fresh 50 µL caMHB supplemented with either 0, 2.5, or 10 µM of the nonsense PNA in both the presence and absence of 16 µg mL-1 chloramphenicol in a 384 well microplate. These cultures were then grown in a microplate for 24 h with continuous shaking while tracking ODs.A control experiment testing the effect of increased PNA concentration was also run using the control nonsense targeting PNA. Individual colonies of the CRE Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1


As the observations are sparse in the early decades, available records were combined into a ‘quasi-homogeneous’ dataset based on similarity in their signals, to minimize spurious variations from beginning or ending data records. During El Niño events, CO2 is anomalously released from the tropical band, and a few months later also in the northern extratropical band. This behaviour can approximately be represented by a linear relationship of the NEE anomalies and local air temperature anomalies, with sensitivity coefficients depending on geographical location and season. The apparent climate sensitivity of global total NEE against variations in pan-tropically averaged annual air temperature slowly changed over time during the 1957–2017 period, first increasing (though less strongly than in previous studies) but then decreasing again. However, only part of this change can be attributed to actual changes in local physiological or ecosystem processes, the rest probably arising from shifts in the geographical area of dominating temperature variations.Interannual variations in the large-scale net ecosystem exchange (NEE) of COThis article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’. Climate anomalies cause NEE anomalies through enhancement or suppression of photosynthesis, autotrophic and heterotrophic respiration, biomass burning, and mortality [The El Niño Southern Oscillation (ENSO) is the largest mode of interannual variability both in the climate system  and in t) of CO2 . NEE is ortality . These s2 measurements, started by Keeling [2 growth rate in any of these records, approximately reflecting global total CO2 flux variations, are clearly ENSO-related [For this aim, an analysis period as long as possible is necessary. The longest available observations of carbon cycle variability are sustained atmospheric CO Keeling  at La Jo-related .2 records, inversion techniques have been applied [2 to the underlying CO2 exchanges at specific locations and times (instead of assuming instantaneous mixing throughout the atmosphere). However, while there are more than 100 stations globally that regularly measure atmospheric CO2 mixing ratios today, only a few of them were in operation before 1980, and even fewer in the 1960s and early 1970s. Owing to this, available inverse estimates of NEE variations do not start much before the 1980s. The first aim of this study, therefore, is to quantify the interannual NEE anomalies on large spatial scales over the full 1957–2017 time period since the start of atmospheric CO2 measurements, applying the inversion technique on as many early data as possible, and to present their typical temporal patterns (§3a).In order to disentangle the contributions of different geographical areas to the variability in atmospheric CO2 growth rate at Mauna Loa as a representation of global NEE, Wang et al. [T). In order (1) to take into account that the link between climate and NEE acts on local  scales and that NEE–T relationships may depend on geographical location and season, and (2) to make systematic use of the additional information in multiple atmospheric CO2 records as discussed above, Rödenbeck et al. [T variations with an atmospheric inversion. In that study, we found that this ‘NEE–T inversion’ captures a large fraction of the interannual NEE variations as seen by a ‘standard inversion’ having explicit interannual degrees of freedom, for both tropical and northern extratropical NEE. Temperature acts as a proxy of climate variations here, representing both direct temperature effects and effects of covarying climate variables such as moisture and incoming radiation .Using the observed COg et al.  showed ak et al.  extendedssion in ). In thi2 (via CO2 fertilization and/or changing water use efficiency), changes in vegetation greenness or density, species composition and other factors. Wang et al. [2 growth rate anomalies at Mauna Loa and the South Pole to anomalies in the tropical annual mean temperature between the 1960s and the 2000s. By contrast, Chylek et al. [2 growth rate to the temperature variations during all individual El Niño events since 1960. Here, we re-assess decadal changes in the interannual climate sensitivity of NEE using the multi-station inversions (§3c).Over the 61-year period 1957–2017, however, carbon cycle responses to climatic variations may concievably have been slowly changing due to rising atmospheric COg et al.  reportedk et al.  found noFinally, we present typical spatial patterns of the NEE anomalies during El Niño events, with particular attention to the 2015/2016 El Niño event (§3d).2.(a)2 measurements at a set of sites, using an inversion of atmospheric transport . We performed several inversion runs, listed in et al. [We estimated spatio-temporal variations of NEE from long-term atmospheric COpdate of ,16, see n et al. , except Calculation period: All inversion runs were done over the period 1955–2018  [in situ data till 1962 and flask data from 1970, both by SIO) and a combined ‘northern record’ (NR)), as motivated and described in the appendix. It forms a ‘quasi-homogeneous’ dataset that can constrain interannual NEE variations over the full period at least in a coarse latitudinal resolution.stations . The sethy (SIO) ) and thePrescribed ocean flux: As the ocean flux product based on pCO2 data . From the simulations, we only used the interannual anomalies (including the flux trend), by subtracting the 1992–2016 mean and mean seasonal cycle and adding instead the corresponding mean and mean seasonal cycle of the pCO2 data-based product oc_v1.6 (update of [ used in ) is onlypdate of ) forced pdate of . The Pla(b)2 signals, the NEE–T inversion instead effectively performs a linear regression of interannual NEE anomalies against interannual anomalies of air temperature (see [γTNEE–) formally represent the local and season-specific sensitivities of NEE to interannual variations in temperature, but include the sensitivities to other climate variables covarying with temperature. The NEE–T inversion is considerably more strongly regularized than the standard inversion, because the regression term involving only 13 degrees of freedom per spatial discretization unit replaces the explicit interannual term with 1320 degrees of freedom per spatial discretization unit.While the standard inversion directly estimates the interannual variations of NEE from the atmospheric COure No El Niño data: To investigate whether the estimated sensitivity parameters γTNEE– only reflect the large NEE and T variations during El Niño events but differ for the smaller non-ENSO variations, a specific run was performed where all data points around the seven El Niño events with the largest values of the Multivariate El Niño Index .ex  are excVariableγTNEE–: By default, the sensitivity parameters γTNEE– of the NEE–T inversion are identical in every year of the calculation. To investigate possible long-term changes in the climate sensitivity, we ran the NEE–T inversion also with separate independent γTNEE– parameters for 20-year windows starting at 1957, 1967, 1977, 1987 and 1997, respectively . As each of the 20-year windows is only constrained by a third of the data, we increased the a priori uncertainties of γTNEE– by 61/20 for compensation. Like in the default set-up, NEE variations outside these intervals are represented by explicit degrees of freedom as in the standard inversion [nversion .(d)2 flux fields nominally on a daily and pixel-scale resolution. Here, we only consider the interannual variations of the land flux (NEE), obtained by applying both running yearly averages  and a Gaussian spectral filter removing variations faster than about three months. Together, these two filters leave NEE variations on time scales of about 15 months or slower.All inversions give spatio-temporal COFor showing time series, we integrated the interannual NEE variations over three regions: globally, over the northern extratropics (90° N–25° N), and over the tropics .3.(a)2 records . a). Further, maps of interannual amplitudes  confirm this view. Keeping the deficiency in its amplitude in mind, we therefore take the inversion with station set s57X (henceforth referred to as ‘quasi-homogeneous’ set) as default estimate, as it offers the best available compromise between the necessities to use temporally homogeneous data constraints over 1957–2017 but also to include stations north of MLO as shown above.Though the s57X inversion still has too small interannual variations in the tropics, their actual temporal course in the two latitude bands does not differ much from that of the s85 inversion a. Furtheplitudes  reveal t2 emissions in the tropics during El Niño events, and of enhanced CO2 emissions in the northern extratropics some months after El Niño events. This pattern is even better seen on the magnified time axis in figure 4a.Like the recent decades, the early decades also follow the pattern of enhanced CO(b)T inversion, whose NEE variability originates entirely from temperature variability (§2b), aligns for almost all peaks and troughs with the interannual NEE variability inferred from the ‘quasi-homogeneous’ set s57X by the standard inversion having explicit interannual degrees of freedom (T inversion (bottom right) not present in the standard inversion (bottom left). This double-peak structure seems to arise because various subregions of the 90° S–25° N band have their peaks at slightly different times. The information about this different timing is added to the NEE–T inversion by the temperature field, while the standard inversion based on very few stations would not be able to resolve such geographical differences. This is also seen in the more heterogeneous structure of amplitudes of the NEE–T inversion , the IAV amplitude of NEE from the NEE–are used . Since tT inversion discarding the data from 2-year periods around the seven El Niño events with the largest MEI [Are the largest El Niño events dominating the inferred relation between NEE variations and climate variations? We tested this by a specific run of the NEE–gest MEI ) within (c)T inversion runs presented so far, the estimated sensitivity parameters γTNEE– are set up to be temporally constant over all the analysis period 1957–2017. To investigate potential changes in the climate sensitivity, we performed specific test runs of the NEE–T inversion with separate independent γTNEE– parameters for the 20-year windows starting at 1957, 1967, 1977, 1987 and 1997, respectively (§2c). Indeed, we found changes in the local sensitivities over time. These changes are different in different geographical areas and different seasons, and mostly do not follow monotonic temporal trends (not shown).In the NEE–T inversions with decadal variations removed, averaged over the 25° N–25° S land areas. Before the regression, both NEE and temperature time series were detrended, and interannually filtered as described in §2d.In order to see the global effect of these changes, we looked at regression coefficients between the interannual variations in global total NEE and interannual variations in tropical air temperature, calculated over the before-mentioned 20-year windows. For tropical air temperature, we used the temperature field from the NEE–T inversion with variable γTNEE– (green-to-blue bars) agree relatively well with each other, and indeed rise from the 1957–1976 window to the 1987–2006 window. The rise by about 1.5 (1.6) is somewhat lower than the factor of 1.9 ± 0.3 reported by Wang et al. [g et al. , possiblg et al. ), howeveT inversion with constant γTNEE– (orange bars) change over time in a similar pattern, just less strongly (e.g. a 1.2-fold increase from 1967–1986 to 1987–2006 instead of 1.6-fold when γTNEE– is variable). Despite limited confidence in the absolute ratios (see below), this indicates that by far not all the changes in sensitivity inferred from the data are actually related to decadal physiological changes, while the remaining changes must just arise from shifts in the geographical areas or seasons that most contribute to the tropical mean temperature: if the dominant areas (or seasons) have lower/higher local (or season-specific) climate sensitivity, the apparent sensitivity calculated from large-scale annual NEE and large-scale annual temperature will be lower/higher as well, even without any actual physiological change.Maybe surprisingly, the 20-year regression slopes from the NEE–All runs for (d)a shows maps of the NEE anomalies for individual large El Niño events. We use here the NEE–T inversion with the large sEXT station set as discussed in §3b. Corresponding to the specific climate anomaly patterns of the individual events, the spatial patterns of NEE are different in the details, but the mean over these events (b) reveals systematic responses mainly in the tropics, in particular, South America, tropical and Southern Hemispheric Africa, and south Asia. The 2015/2016 El Niño event focused on in this special issue conforms with the mean spatial pattern, with one of the largest amplitudes. Particularly large responses are estimated in the Amazon basin and in Southern Hemisphere Africa.Figure 8e events b reveals4.2 over the period 1957–2017 from atmospheric CO2 measurements. From the few observational records already available in the early part of this period, we formed a ‘quasi-homogeneous’ set as a compromise between avoiding spurious jumps from starting/ending records and nevertheless offering sufficient information to distinguish at least northern extratropical and tropical variability. Consistent with previous findings, the estimates show enhanced CO2 outgassing during El Niño events in the tropical band, and enhanced CO2 outgassing a few months later also in the northern extratropical band, throughout 1957–2017.We estimated interannual variations of terrestrial NEE of COT anomalies taken as a climate proxy. The regression coefficients γTNEE–, interpreted as ‘interannual climate sensitivity’, not only depend on the largest anomalies, but can also be inferred by excluding the data during the large El Niño events.Despite the complexities of the underlying processes, the response of the terrestrial carbon cycle to El Niño climate anomalies is well approximated by a spatially/seasonally resolved linear relationship between NEE anomalies and et al. [The apparent climate sensitivity of global NEE with respect to tropical annual mean air temperature increased from the 1960s and 1970s to the 1990s and early 2000s , but de


Subduction of seasonally accumulated semi-labile DOC was estimated to be responsible for ∼20% of the oxygen consumption mostly occurring at the low epipelagic-upper mesopelagic boundary layer. Variability in mesopelagic waters was higher than expected (ca. 20 µmol C L−1) evidencing a more active realm than previously thought, with consequences for carbon sequestration.In oligotrophic waters, dissolved organic carbon (DOC) is mostly produced in the surface layers by phytoplankton and remineralized by heterotrophic prokaryotes throughout the water column. DOC surface excess is subducted and exported to deeper layers where a semi-labile fraction is further processed contributing to oxygen consumption. How this cycling of DOC occurs in the Red Sea, one of the warmest oligotrophic marine basins, is virtually unknown. We examined DOC vertical and seasonal variability in a mesopelagic station (ca. 700 m depth) of the central Red Sea performing monthly profile samplings over a two-year period. Together with DOC vertical and seasonal distribution we evaluated the interaction with heterotrophic prokaryotes and contribution to oxygen respiration. DOC values ranged from 41.4 to 95.4 µmol C L In the oligotrophic ocean, organic carbon is mostly produced by phytoplankton in the surface layers. Heterotrophic prokaryotes (HP) are in turn responsible for most of DOC remineralization throughout the water column, although photochemical oxidation could also play a role in highly UV-irradiated surface waters by making some organic molecules more susceptible to microbial remineralization3. The net conversion of CO2 into organic material through photosynthesis in the upper layers and ulterior export and processing in the ocean’s interior, known as the biological carbon pump, represents a net removal and long-term storage of atmospheric CO21, playing a key role in the regulation of global climate5. The efficiency of carbon transport and storage depends on the fraction of the system’s net production removed from the surface waters via export mechanisms. It is therefore essential to quantify the magnitude of carbon export from the surface waters to the deep ocean, not only to understand the current Earth’s carbon cycle but to be able to better assess the impact of continuously increasing atmospheric CO2 levels.The importance of the interactions between dissolved organic matter (DOM) and planktonic microbes in marine biogeochemical cycles is well recognized. With an estimate of 662 Pg C6, and the enzymatic and mechanical solubilisation of POC throughout the water column7, all contribute to carbon sequestration. However, active transport of both POC and DOC by vertically migrating animals is another known but not yet quantified carbon transfer mechanism to the ocean interior9 with consequences for the microbial community11. The relative contributions of POC and DOC transport to carbon sequestration are still unclear. DOC is estimated to account for ~20% of global export, but it can vary largely (more than threefold) across oceanographic regions12, accounting for as much as half of the total export in oligotrophic subtropical gyres13. As these stratified and nutrient-depleted regions may expand with increased warming14, the role of DOC in the global carbon transfer to the deep sea may be intensified. However, our understanding of DOC dynamics and cycling in oceanic regions experiencing increase stratification is still scarce and needs to be addressed. This study focuses on DOC dynamics, export and cycling in the central Red Sea, a tropical oligotrophic semi-enclosed marine basin undergoing faster warming rates (0.17 ± 0.07 °C decade−1) than the global ocean (0.11 °C decade−1)15 and experiencing intense stratification in the upper layers. Altogether, this makes the Red Sea a marine ecosystem that can serve as a model to study other tropical marine basins threatened by rapid surface warming.Well-recognized export processes from the surface to the oceans interior are the sinking of particulate organic carbon (POC) and the transport of DOC by turbulent mixing. Together with the production of long-lasting DOC through microbial processes, known as the microbial carbon pump16. The lability continuum of DOC pool in the oceans is defined by the capability of heterotrophic prokaryotes to breakdown its compounds and use them for respiration and/or self-biomass production. The labile DOC fraction is freshly produced mostly in the photic layer as a result of photosynthesis and food web processes17, and consumed very quickly; therefore, it does not accumulate and is usually studied in incubation experiments19. The semi-labile DOC fraction escapes rapid microbial degradation and it can seasonally accumulate in the surface waters in conditions of high stratification. This accumulated fraction can be transported downward by vertical mixing and convective overturn and plays the most important role from the carbon transport perspective16. The subducted semi-labile DOC can be then remineralized when exposed to subsurface nutrients and heterotrophic prokaryote community with the right enzymatic activities21. The refractory fraction, the nature of which is still under debate22, is the most ubiquitous one, present in all depths. Refractory DOC has a mean age of 3700–6000 years according to 14C records23, making it the biggest and longer lasting fraction stored in the deep ocean. However, this is the average age of a non-homogenous pool that comprises from relatively young compounds to truly ancient carbon sources (>20000 years old)24. The DOC lability and potential for degradation, transport and accumulation depends on its nature and chemical composition, not easy to disentangle due to its high heterogeneity. However, different molecular, spectroscopic and separation techniques are in continuous development in order to shed light on its chemical structure25. Among others, the study of the optical fraction of DOM that can emit fluorescence after absorbing light in the UV and visible range, called fluorescent DOM (FDOM) and ubiquitously present in the oceans, has proved a valuable tool to provide information on the reactivity and composition of DOC in the oceans27. In this study we examine FDOM properties in order to diagnose the lability and degradation state of the bulk DOC pool.To understand DOC dynamics it is necessary to somehow decipher the heterogeneous mix of molecules with turnover times ranging from minutes to millennia that comprise the bulk DOC pool. In order to describe its complexity, three major fractions are commonly distinguished according to turnover or life time: labile (minutes to days), semi-labile (months to years) and refractory (centuries to millennia)28. Relationships between DOC and AOU have been widely studied33. The contribution of DOC remineralization to changes in AOU appears to vary largely, with values ranging from <20% in equatorial regions to ~70% in subtropical gyres34. The large spatial variability observed across different oceanographic regions makes the contribution of DOC to respiratory processes still unclear. The higher the accumulation of semi-labile DOC in stratified oligotrophic waters29, the higher would be the net DOC transport downward that can potentially be respired by heterotrophic prokaryotes. Most of the studies examining the relationship of DOC and AOU mentioned above have used measurements restricted to one or two consecutive months of a particular year. However, DOC accumulation depends on the decoupling of microbial consumption from primary production in the euphotic layer, which is tightly coupled to seasonal changes35. Thus, we believe that to properly assess the contribution of DOC to AOU at a particular location, seasonal studies measuring DOC and AOU along the water column covering a full annual period would more realistically predict DOC contribution to respiration.Besides analyzing its chemical properties, the lability of subducted DOC can also be explored by evaluating how much of it is being used by heterotrophic prokaryotes through respiration or oxygen utilization. The relationships between changes in DOC concentration and apparent oxygen utilization (AOU) below the thermocline allow us to quantify the remineralization of DOC by microbial respiration and its relative contribution to total oxygen utilization. AOU integrates all respiratory processes within a water body since it is isolated from the atmosphere, therefore precluding oxygen exchanges with it, and it reflects the oxidation of the total biogenic carbon (POC and DOC)In this study we have simultaneously evaluated the vertical and temporal changes of DOC and AOU in a mesopelagic station (ca. 700 m depth) of the central Red Sea over a two-year period, and examined their relationships with the communities of heterotrophic prokaryotes inhabiting both the epipelagic and mesopelagic layers. We provide a detailed assessment of the seasonality of DOC, HP and AOU in a tropical open water stratified ecosystem off the Red Sea central coast.R.V. Thuwal and KAUST R.V. Explorer.The data were obtained from repeated samplings performed at a mesopelagic station in the central Red Sea, off King Abdullah Economic City  between November 2014 and May 2017, covering a two-year period. All samplings were performed around midday on board the KAUST 2, mg L−1), and vivo fluorescence  were recorded continuously in a conductivity-temperature-depth (CTD) probe SeaBird 11plus V 5.2 system, with a pre-calibrated through Winkler titration polarographic membrane oxygen sensor SeaBird-43 and a Wet Labs ECO-AFL/FL fluorometer installed in the rosette sampler. Apparent Oxygen Utilization (AOU) was calculated as the difference between the oxygen saturation and the measured dissolved oxygen. Oxygen saturation represents the oxygen concentration at equilibrium with the atmosphere at the temperature and salinity observed and was calculated using the equation of Benson and Krause36. Potential density or Sigma-Theta  was calculated from in situ salinity, potential temperature, and pressure = 0, minus 1000 Kg m−3 37. Potential density gradient was used to estimate the mixed layer depth (MLD). MLD was determined for each profile as the depth where a change in density higher than or equal to 0.05 Kg m−3 was first observed for a 5 m interval.Vertical profiles down to 700 meters depth (site seabed) of salinity , water temperature , dissolved oxygen (Oa), dissolved organic carbon, inorganic nutrients and heterotrophic prokaryote abundance.Discrete water samples were collected from niskin bottles at 12–16 depths distributed throughout the whole water column for the analysis of chlorophyll a (Chl a (Chl a) analysis were collected from 8 profiles at 5–6 depths distributed above 100 meters and including the depth of maximum fluorescence or deep Chlorophyll maximum (DCM) identified by the CTD attached fluorometer. 250 ml of water from each depth was collected from the niskin bottles and kept at 4 °C until filtration (performed within the next 2 hours of sample collection). Unfortunately Chl a samples from summer time were not collected. Chl a concentration was obtained after filtration of 200 ml water samples through 0.22 µm size filter (IsoporeTM Membrane Filters (RTTP)). Filters were collected and kept frozen (−80 °C) until further analysis. Pigments were then extracted in 90% acetone solution for 24 h in darkness at 4 °C and Chl a pigment concentration was measured with a Turner Trilogy fluorometer calibrated with pure Chl a standard38.Samples for Chlorophyll 3PO4 until pH 1–2, and kept in the dark at 4 °C until analysis at the laboratory. A total of 296 DOC samples collected were analysed by high temperature catalytic oxidation (HTCO) in a Shimadzu TOC-L. All glass material used was previously acid cleaned and burned . Three to five replicate 100 µl of sample were injected into the combustion tube pre-heated to 680 °C. The resulting peak area was then integrated with Shimadzu chromatographic software. A five-point calibration curve of potassium hydrogen phthalate solution was performed daily to standardize the instrument response. All samples were systematically referenced against Consensus Reference material of deep-sea water  and low carbon water , provided by D. A. Hansell (Univ. of Miami), to monitor the ultimate accuracy of the measurements, allowing a resolution of 1.4 µmol L−1.Samples for DOC analysis were passed through an online acid-cleaned polycarbonate filter cartridge, holding a pre-combusted  GF/F filter, attached directly to the Niskin bottle, and collected into acid cleaned and pre-combusted glass vials. Samples were acidified with H−1 m−1.Changes in DOC concentration along the thermocline (∆DOC/∆z) were calculated and used as a proxy for vertical export. The decrease of DOC concentration per meter was calculated for each profile as the absolute slope of the DOC concentration change with depth (∆DOC/∆z) within the thermocline, and its absolute value expressed in µmol C Let al.39. Briefly, Raman-normalized Milli-Q blanks were subtracted to remove the Raman scattering signal40. All fluorescence spectra were Raman area (RA) normalized by subtracting daily blanks that were performed using Ultra-Pure Milli-Q sealed water . To extract information of the fluorescent signal of obtained EEMs the fluorescent, humification and biological indices were evaluated. The fluorescence index (FI) was calculated as the fluorescence intensity ratio at 450 nm and 500 nm emission and 370 nm excitation41. The humification index (HIX) was obtained as the ratio of peak area under emission spectra between 435 and 480 300–345 nm at an excitation wavelength of 254 nm42. HIX index has been long used as an indicator of the humic substance content or the extent of humification43. However, its significance in the oligotrophic ocean is still unclear and novel interpretations have been recently put forward44, which will be further discussed. The biological index (BIX) was obtained as the ratio of emission at 380 and 430 nm at 310 nm of excitation wavelength45 and is an indicator of autotrophic productivity, with values >1 revealing recently produced DOM of autochthonous origin46.Samples for FDOM analysis were collected from the same GF/F filtrate into acid-cleaned and pre-combusted 40 ml glass bottle. UV-VIS fluorescence spectroscopy was measured immediately after collection using a HORIBA Jobin Yvon AquaLog spectrofluorometer installed onboard, with a 1 cm path length quartz cuvette. Three dimensional fluorescence excitation emission matrices (EEMs) were recorded by scanning the excitation range between 240 and 600 nm and emission wavelength range of 250–600 nm, both at 3 nm increments and using an integration time of 8 seconds. To correct and calibrate the fluorescence spectra post-processing steps were followed according to Murphy 3−) nitrite (NO2−) and phosphate (PO43−) were analyzed by colorimetry on a Bruan and Luebbe® Autoanalyzer following the methods described at Hansen and Koroleff47 for the automated analysis in segmented flow. Limits of quantification were 0.2, 0.06 and 0.01 µmol L−1 for NO3−, NO2− and PO43− respectively. All standards were prepared with a nutrient-free artificial seawater matrix in acid-washed glassware. DIN = [NO−3] + [NO−2] (note that NH4+ was not measured), and DIP = [PO43−], in μmol N L−1 and μmol P L−1, respectively.Samples for dissolved inorganic nutrients analysis were collected from the same GF/F filtrate into 15 ml acid cleaned Falcon tubes and stored frozen at −20 °C until analysis. Nitrate  were added to each sample as internal standards used to calibrate the flow cytometric size signals. Samples were analyzed at low flow rates until the acquisition of 10000 events occurred and abundance was estimated after daily calibrating the flow rate. Flow cytometry files were further processed using the FCSExpress 5 software.Heterotrophic prokaryote samples were collected in sterile Falcon tubes from each niskin. Subsamples were then fixed with 1% paraformaldehyde + 0.05% glutaraldehyde , kept for 10–15 min in the dark, deep-frozen in liquid nitrogen, and stored at −80 °C until further analysis. In the laboratory aliquots of 0.4 ml were stained with SYBR Green I (Molecular Probes)  and run using a BD FACSCanto flow cytometer following the methodology described in more detail in Gasol and MoránContouring of the data, statistical analysis and graphs were performed using the JMP statistical package and software (SAS Institute Inc.). Correlations between the investigated variables were examined with parametric Pearson correlations. p-values below 0.05 are considered statistically significant. A linear regression (model II) was performed to infer the discussed equations. To study the seasonal variability data was grouped in northern meteorological seasons. To evaluate significant differences between seasons a one-way analysis of variance (ANOVA) followed by a Fisher’s Least Significant Differences post hoc test (posthoc Fisher LSD test) was performed using MATLAB_R2015b. To study vertical variability data was grouped in two main layers: the water layer above 200 m, or epipelagic (<200 m); and the water layer bellow 200 m, or mesopelagic (200–700 m). Supplementary Table θ, Kg m−3) varied from 24.10 to 28.80  compared to winter and spring, when values for the same layer were generally lower than 70 µmol Kg−1. Values close to zero were found during spring and winter-time in the upper 75 meters. Deeper in the water column AOU values up to 200 µmol Kg−1, indicative of highly oxygen under-saturated water, were observed from 300 m downwards.AOU vertical and seasonal distribution is illustrated in Fig. −1, respectively. Their values increased with depth along the thermocline to mean values of 19.6 ± 3.8 and 0.94 ± 0.29 µmol L−1 in mesopelagic waters. No seasonality was observed for DIN or DIP in surface waters, which remained depleted in inorganic N and P year round, or in mesopelagic waters. DIN:DIP ratios in the epipelagic (11 ± 10), however, were seasonally variable, with significantly higher values in summer (20 ± 7) than in the rest of the seasons (10 ± 2) (Supplementary Table DIN and DIP exhibited surface concentrations ranging from below detection limits to 1.21 and 0.17 µmol L5 cells ml−1) than in the mesopelagic layer (1.9 to 9.7 × 104 cells ml−1). Seasonal changes were significant in the mesopelagic  and marine microbial origin (FI∼1.8)43. FI was highly variable in the epipelagic, with lowest values at the surface and a gradual increase until around the DCM. FI slightly decreased again from 100 to 200 m and remained relatively constant (1.53 ± 1.38) in mesopelagic waters. The humification index (HIX), indicative of DOM of lower C/N ratios43, ranged between 0.8 and 8.9 , indicative of recent biological activity or fresh DOM, varied between 0.9 and 1.3  value of 1.25, the FI ranged between 1.3 and 1.6 Fig. , notably8.9 Fig. , similar1.3 Fig. , slightl49), we have used changes in DOC concentration along the thermocline (∆DOC/∆z) as a proxy for vertical export. Higher DOC vertical export will occur at higher DOC concentration gradients. Pooling all profiles together, the ∆DOC/∆z averaged 0.143 ± 0.029 µmol C L−1 m−1, and it showed a clear seasonal pattern  compared to winter (0.122 ± 0.016 µmol C L−1 m−1)  depends on the decrease of DOC concentration within the thermocline and the water renewal time or ventilation time. Assuming a constant ventilation time for the two-year sampling period . However, a DOC-AOU relationship for the entire water column is of limited value because it combines depth layers where it may differ. Therefore, an evaluation of the two properties along isopycnal layers was performed. To better understand processes occurring during DOC remineralization, the relationship between fluorescent indices (HIX and BIX), and dissolved inorganic nutrients  with AOU were similarly evaluated when DOC and AOU showed a significant correlation. Pearson correlations results of all variables in each isopycnal are shown in the Supplementary Table θ) shown in Fig. Five different isopycnal layers were evaluated according to the density distribution (σθ = 24–26.5 Kg m−3), mostly comprising the upper 60 m. Within this layer DOC tended to increase with increasing AOU, but the correlation was weak and not significant, probably due to processes other than heterotrophic respiration, such as primary production oxygen, exchange with the atmosphere, and DOC photo-oxidation, affecting changes in both DOC and dissolved oxygen concentrations in the surface waters.Isopycnal 1 was the shallowest density layer (σθ = 26.5–27.1 Kg m−3), mostly between 60 and 100 m, thus encompassing the DCM, and isopycnal 3 (σθ = 27.1–27.8 Kg m−3), a thin layer around 100 m, displayed no clear relationship between DOC and AOU, indicating that heterotrophic oxygen consumption was not yet enough to significantly deplete DOC concentrations within these depth intervals. Another possible explanation could be that DOC may simultaneously increase as a consequence of POC solubilization from marine snow aggregates50, thus obscuring the relationship between DOC consumption and AOU buildup.Isopycnal 2 (σθ = 27.8–28.4 Kg m−3), mostly between 100 and 200 m, we found DOC concentration to significantly decline with increasing AOU (DOC = −0.15 (±0.03) AOU + 75.87 (±3.24), r = −0.67, p < 0.0001, n = 31, Fig. 51. A slope of −0.72 would be expected if all oxygen consumption was related to the remineralisation of DOC. Thus, our slope (−0.15) indicates that 21% of changes in AOU were driven by DOC respiration at this density horizon. Significant increase in HIX  and decrease in BIX  with increasing AOU were also concurrently observed  than summer (120 ± 17 µmol Kg−1) . DOC concentration and HP abundance displayed a quasi-identical and inverse seasonal pattern to AOU, both showing significantly higher mean values in spring (65.3 ± 7.5 µmol C L−1) than winter and summer (ranging from 51.4 to 65.5 µmol C L−1)   Fig. .Figure 5θ = 28.4–28.8 Kg m−3), from around 200 m all the way down to the seafloor (i.e. coincident with most of the mesopelagic layer), showed no correlation between DOC and AOU, but here HIX also increased significantly with increasing AOU levels  . The correlation was weaker but still significant in the epipelagic layer  but it was lost in mesopelagic waters. If we zoom into the previously defined isopycnal layers, we observe that HP biomass was significantly and positively correlated with DOC  only at the isopycnal 4 where DOC remineralization significantly contributed to changes in AOU as Fig. 52. The inflow of Indian ocean surface waters through the Gulf of Aden due to wintertime northward winds was exhibited in surface waters (above 100 m) with low salinity and temperature values in that season 55 and seasonally contributes to highly saline deep waters in the Indian Ocean with sigma theta values between 27 and 28 Kg m−3, very similar to the values observed at isopycnal 3 (27.1–27.8 Kg m−3).The seasonal hydrography observed Fig.  revealedrs above 00 m withrs above 00 m withrs above 00 m with56), likely as a consequence of the recent fast warming15. Our data also likely captured the influence of 2015 El Niño event (2015 displayed maximum temperatures 1.7 °C higher than those observed during 2016), when maximum temperature anomalies had dramatic consequences for the health of coastal ecosystems of the Red Sea15. This supports the mechanism proposed by Raitsos et al.57 suggesting that the monsoon’s seasonality and the global climate Multivariate El Niño-Southern Oscillation (ENSO) are influencing the Red Sea marine ecosystem functioning. We can infer from the hydrological features observed that our station was not isolated from the seasonal or inter-annual processes affecting the Red Sea as a whole.The seasonal thermocline observed over the period studied (2014–2017) was shallower (between 20 and 100 m) than documented three decades ago for the central Red Sea , higher than the Redfield ratio and suggesting phosphorus limitation, were similar to previous measurements in the mesopelagic Red Sea60 and comparable to those in the ultraoligotrophic Eastern Mediterranean Sea subsurface waters31. Epipelagic waters, on the contrary, generally showed lower DIN:DIP ratios (∼11), indicating N limitation for most of the year except in summer (DIN:DIP up to 19), with implications for the distribution and dynamics of nitrogen fixing diazotrophs (Trichodesmium), commonly abundant in N limiting waters of the Red Sea60.As usual for most tropical environments unaffected by upwelling, close to zero or below detection limit values of DIN and DIP at the surface layer were observed35 but similar to those in the Pacific Ocean for the year 1992 surveyed in Campbell et al.61. The lower summer HP abundance might indeed have allowed the accumulation of DOC observed in this season  observed in the epipelagic in summer could have different explanations. According to the traditional interpretation43, low values of both indices may suggest a smaller proportion of freshly produced, most likely labile phytoplanktonic DOM accompanied by a stronger terrestrial signature, which we could attribute to the influence of mangrove-derived material from nearby coastal waters64. However, this interpretation should be taken with caution since recent research has identified other/novel microbial processes affecting the FDOM signature in the oligotrophic ocean. Picocyanobacteria (Synechococcus and Prochlorococcus) abundant in the Red Sea can release FDOM with a humic-like appearance44, while Trichodesmium, also important in these tropical N-limited waters, can as well produce chromophoric DOM in the open ocean65. Higher predation on HP or mortality caused by viruses could also be partly responsible for the decrease in HP abundance and subsequent DOC accumulation62.The seasonality of HP abundance in the epipelagic displayed a slightly different pattern than DOC, with an increase in abundance from winter to spring followed by a decrease in summer Fig. , contrarson Fig. . Summer 67. The slope of the DOC change with depth along the thermocline, smaller in winter than in summer  . The correlation of DOC concentration with HP abundance and their similar seasonal pattern suggests that temporal changes in available DOC were driving changes in HP abundance evidencing a tight bottom-up control of HP by substrate availability within this layer. Optical indices indicate that while DOC was being consumed, the remains of it were enriched in humic-like substances (HIX increased) and depleted in autotrophic freshly produced DOC (BIX decreased)  Fig. . We hypoed) Fig. . This is−1) all the way down to the sea floor (ca. 700 m). However, DOC concentrations below 200 m displayed values varying between 42 and 58 µmol C L−1  for what is commonly regarded as an homogenous mesopelagic layer (with DOC variability ∼5 µmol C L−1 25). This observation, together with the fact that no significant relationship between DOC and AOU was found, neither with HP that remained relatively constant in the mesopelagic , which feed year-round at the surface at night and rest between 400 and 600 m during daytime forming a conspicuous deep scattering layer (DSL)71. Recent reports of high bacterial growth efficiencies measured at the DSL of the same study site seemingly sustained by DOC supplied through fish migration10, suggest the existence of complex and still poorly explored interactions between daily fluxes of DOC and the microbial communities thriving in these layers. The significant increase in humification index with AOU  chromophoric DOM release from vertically migrating fish through excretion, fecal pellet dissolution and/or mucus production, all of them recognized mechanisms of DOM supply from migrating animals72, likely fuelling bacterial activity in the mesopelagic; iii) picocyanobacteria derived-DOM efficiently transported to the deep sea73 with humic-like FDOM properties44. Calleja and co-workers (2018)10 also reported high relative abundances of Candidatus “Nitrosopumilus maritimus” in these waters , an archaeon described to be responsible for most of ammonium oxidation and inorganic carbon fixation in the deep ocean74. We hypothesize that the DOC variability observed in the mesopelagic could be explained by the mixotrophic metabolic capabilities of the abundant marine archaeal community76 most probably fuelled by the supply of ammonia from the migrating fish77, further contributing to reduced oxygen levels  and enhanced DIN values in the mesopelagic.In the mesopelagic zone (>200 m), below the above-mentioned isopycnal layer, AOU values reached its maximum at around 300 m and remained relatively constant  and are important to consider when evaluating deep sea carbon sequestrationSupplementary Information