KITLG, MIM 184745) gene are responsible for FPHH. To date, only eight KITLG mutations have been reported to be associated with FPHH, and no clear genotype–phenotype correlations have been established. This study aimed to identify the causative mutations in the KITLG gene in two Chinese FPHH patients.Familial progressive hyper- and hypopigmentation  is a rare hereditary skin disorder that is predominantly characterized by progressive, diffuse, partly blotchy hyperpigmented lesions intermingled with scattered hypopigmented spots, lentigines and sometimes Cafe-au-lait spots (CALs). Heterozygous mutations of the KIT ligand  guidelines.Direct sequencing of the coding regions of KITLG were identified. As shown using SIFT and Polyphen-2 software, both mutations identified in this study were predicted to be detrimental variations. Three-dimensional protein structure modeling indicated that the mutant KITLG proteins might affect the affinity of KITLG for its receptor, c-KIT. According to the 2015 ACMG guidelines, the novel mutation c.104A > T was ‘likely pathogenic’.The novel mutation c.104A > T (p.Asn35Ile) and the recurrent mutation c.101C > T (p.Thr34Ile) in KITLG mutations have been clustered within the conserved VTNNV motif (amino acids 33–37) in exon 2. The known mutations are only involved in 33 V, 34 T, 36 N, and 37 V but not 35 N. We have now identified a novel mutation in KITLG, c.104A > T, that was first reported in FPHH within the conserved 35 N motif. These results strengthen our understanding of FPHH and expand the mutational spectrum of the KITLG gene.To date, most of the identified  Familial progressive hyper- and hypopigmentation  is a rare genetic skin pigmentation anomaly disorder characterized by progressive, diffuse, partly blotchy hyperpigmented lesions that are intermixed with multiple café-au-lait spots, hypopigmented maculae and lentigines that are located on the face, neck, trunk and limbs and frequently on the palms, soles and oral mucosa. Dispigmentation patterns can range from well-isolated café-au-lait/hypopigmented patches on a background of normal-appearing skin to a confetti-like or mottled appearance [KITLG, MIM 184745) gene is responsible for this disorder [The FPHH locus was mapped at chromosome 12q21.31-q23.1 by genome-wide linkage analysis in a six-generation Chinese family. Positional candidate gene screening revealed that a heterozygous transversion  in exon 2 of the KIT ligand  and mast cell growth factor, encodes the ligand for the KIT receptor tyrosine kinase. By KITLG binding, KIT dimerizes and initiates diverse cellular responses and plays a crucial role in the development and maintenance of the melanocyte lineage in adult skin. Injection of the soluble form of sKITLG resulted in hyperpigmentation of the grafted skin tissue, while injection of the KIT- or KITLG-blocking antibodies into the explanted human skin led to a loss of melanocytes [The anocytes . Functioanocytes .http://www.hgmd.cf.ac.uk) in the KITLG gene have been documented in a few FPHH families [KITLG are clustered within the conserved VTNNV motif (amino acids 33–37) in exon 2, and a mutated VTNNV domain may increase the affinity of KITLG to the c-Kit receptor, suggesting that the mutation causes a downstream gain-of-function effect. However, many FPHH families without KITLG mutations have been identified, indicating additional locus heterogeneity for this disorder [Several mutations  and a recurrent mutation c.101C > T (p.Thr34Ile) in the Family 1 was a four-generation Chinese FPHH pedigree with seven affected individuals (three men and four women) . All exons and their flanking intronic sequences of the KITLG gene were amplified by polymerase chain reaction (PCR) as described previously [® 3730 automated sequencer . DNA sequences were analyzed by comparison to the human KITLG reference sequence . The mutations were checked with HGMD, Clinvar (https://www.ncbi.nlm.nih.gov/clinvar/) and the 1000 Genomes project (http://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/). Furthermore, samples from 100 unrelated normal Chinese Han individuals were also sequenced to exclude polymorphic variants.Genomic DNA was extracted from blood samples of family 1 and a sporadic case using the QIA-ampeviously . Purifiehttp://genetics.bwh.harvard.edu/pph2/) and SIFT (http://sift.jcvi.org/), respectively. Furthermore, we performed analysis using an online server, SWISS-MODEL (http://swissmodel.expasy.org/), to construct the three-dimensional structure of KITLG. The pathogenicity prediction was evaluated following the American College of Medical Genetics and Genomics (ACMG) guidelines of 2015.Online in silico programs were applied to predict the potential impact of an amino acid substitution on the structure and function of the KITLG protein with Polyphen-2  and a recurrent mutation c.101C > T (p.Thr34Ile) in the y 1 Fig. a and in y 1 Fig. b. These y 1 Fig. c. The vay 1 Fig. a.Fig. 2KAccording to the 2015 ACMG guidelines, the prevalence of the variant in affected individuals was significantly increased compared with the prevalence in controls, meeting the criterion of pathogenic strong 4 (PS4). These two mutations match the criterion of pathogenic moderate 1 (PM1) since all of them are in hot spot regions. A novel heterozygous missense mutation c.104A > T (p.Asn35Ile) was cosegregated in each affected family member, indicating that they matched the criterion of pathogenic supporting 2 (PP2).http://genetics.bwh.harvard.edu/pph2/) and SIFT (http://sift.jcvi.org/), respectively. These predictions indicated that the two variants may have an effect on protein function, meeting the criterion of pathogenic supporting 3 (PP3) according to the ACMG guidelines of 2015.The mutations c.104A > T (p.Asn35Ile) and c.101C > T (p.Thr34Ile) were predicted to be ‘possible damaging’ and ‘deleterious’ with Polyphen-2 (http://swissmodel.expasy.org/ ).The substitutions 34Thr and 35Asn were identified in KITLG  and a recurrent mutation c.101C > T (p.Thr34Ile) in the y 1 Fig. a and in 5Ile and KITLG gene, which encodes the C-Kit ligand [FPHH is a rare autosomal dominant disorder with variable penetrance caused by mutations in the t ligand . Becauset ligand . A 2-yeat ligand .https://www.ncbi.nlm.nih.gov/pubmed) were related to pathogenic mutations of the KITLG gene. Mutations in the KITLG gene are associated with autosomal dominant nonsyndromic deafness-69 , Waardenburg syndrome-2 , and FPHH. Seco Z et al. [KITLG, including c.286_303delinsT (p.Ser96Ter), c.200_202del (p.His67_Cys68delinsArg), and c.310C > G , cause asymmetric and unilateral hearing loss and Waardenburg syndrome type 2 (WS2). Ogawa Y et al. [KITLG mutation . It was speculated that the mechanism of the mutation underlying WS2 leading to membrane incorporation and reducing secretion of KITLG occurs via a gain-of-function or dominant-negative effect. A de novo mosaic KITLG variant  was found in a 6-year-old boy who had congenital linear and mottled hyperpigmentation [KITLG are not defined as clearly associated with FPHH; therefore, we only summarize all the other KITLG mutations associated with FPHH here in this study.Nine publications in PubMed  and lie within the third b-strand of the protein. Only the p.Val37 change represents the first amino acid of the second a-helix (amino acids 37–46). To date, the reported mutations are only involved in 33 V, 34 T, 36 N, and 37 V but not 35 N. We are the first to report the c.104A > T (p.Asn35Ile) mutation at 35 N  we reported in this study, eight different missense mutations in the Except for diffuse hyper- and hypopigmentation, vast CAL-like lesions have been detected as the most common skin problems present in FPHH patients. Vitiligo was found in one family. Sparse lateral eyebrows and malignancy  were found in two families. Short sutures were found in only one family, and mental retardation was not present in these FPHH patients , autosomal dominant nonsyndromic deafness-69, WS2 and FPHH.KITLG, as KIT LIGAND, is produced locally in human skin by epidermal keratinocytes and endothelial cells, where it induces the migration, development and survival of melanocytes. The signaling of oduction , 16. Theoduction , 17. Aftoduction . The KIToduction , 20. MutKITLG in a Chinese FPHH family. According to the 2015 ACMG guidelines, the mutation was initially identified as a ‘likely pathogenic’ mutation [KITLG mutations have been reported to cause FPHH  mutation of mutation . To the Ile mutatKITLG gene was reported in a Chinese FPHH patient. The correlations between genotypes and phenotypes of FPHH were summarized. These results strengthen our understanding of FPHH and expand the mutational spectrum of the KITLG gene.In summary, a novel mutation c.104A > T (p.Asn35Ile) in the Additional file 1. Fig. S1: Sequencing results of seven individuals from family 1. A: II1, B: II4, C: III1, D: III4, E: III5, F: IV1, G: IV3Additional file 2. Fig. S2: Sequencing results of the proband’s parents from the sporadic case. A: Proband’s father, B: Proband’s mother.Additional file 3. Table S1: Classification of KITLG mutations in this study according to the 2015 ACMG guidelines.Additional file 4. Table S2: Family 1 (A) and the sporadic case (B) genotype information details.Additional file 5. Table S3: The reported cases and KITLG mutations of familial progressive hyper- and hypopigmentation to date.


Proteins have long been considered to be the most prominent factors regulating so-called invasive genes involved in host-pathogen interactions. The possible role of small non-coding RNAs (sRNAs), either intracellular, secreted or packaged in outer membrane vesicles (OMVs), remained unclear until recently. The advent of high-throughput RNA-sequencing (RNA-seq) techniques has accelerated sRNA discovery. RNA-seq radically changed the paradigm on bacterial virulence and pathogenicity to the point that sRNAs are emerging as an important, distinct class of virulence factors in both gram-positive and gram-negative bacteria. The potential of OMVs, as protectors and carriers of these functional, gene regulatory sRNAs between cells, has also provided an additional layer of complexity to the dynamic host-pathogen relationship. Using a non-exhaustive approach and through examples, this review aims to discuss the involvement of sRNAs, either free or loaded in OMVs, in the mechanisms of virulence and pathogenicity during bacterial infection. We provide a brief overview of sRNA origin and importance and describe the classical and more recent methods of identification that have enabled their discovery, with an emphasis on the theoretical lower limit of RNA sizes considered for RNA sequencing and bioinformatics analyses. More than 60 years ago, RNA was already mentioned as a critical player in the regulation of gene expression . AccordiBacterial small regulatory RNAs (sRNAs), which are the subject of this review, were characterized in prokaryotes ,5,6 wellcis as well  rarely included sRNAs. The development of HTS repositioned sRNAs at the heart of gene regulation through their unique properties; they are increasingly recognized as a distinct class of virulence factors. sRNAs are effective in the regulation of virulence genes and adaptive responses within the bacterium. They may also be transferred, via different mechanisms, to host cells, where sRNAs may affect the immunomodulatory, metabolic, and apoptotic functions of the cell, with multiple consequences . sRNAs may be delivered to the host through injection via TSS, secretion via channels or packaging in OMVs. IM = Inner Membrane; OM = Outer Membrane; OMV = Outer Membrane Vesicle; TSS = Type  Secretion System; V= Vaccines; DDV = Drug Delivery Vehicles. Original figure created with BioRender.com and inspired by the references ,132,133,


A wide variety of post-transcriptional regulatory events in the life of an mRNA have emerged as major checkpoints during its temporal and spatial journey within the cell. The advent of deep sequencing technologies combined with various fractionation or enrichment protocols has produced a wealth of data regarding transcripts, their variants and their interactomes. Yet, these data must be integrated with mechanistic and biological frameworks in order to better understand complex and dynamic regulatory networks that tailor mRNA metabolism and shape the cell proteome in healthy and diseased states.The articles in this Research Topic review our current knowledge in eukaryotic post-transcriptional gene regulation, from mRNA export out of the nucleus to its localization, translation, and eventual decay. Among several topics related to RNA regulation, this article collection puts a particularly strong emphasis on translational control  and its impact on localized protein synthesis, downstream transcriptional programs, cellular metabolism, organismal development, and disease pathogenesis.Palazzo and Lee describe the various cis-acting determinants regulating nuclear retention or export of both long non-coding and coding RNAs. Once in the cytoplasm, mRNAs can be sorted to specific subcellular domains, allowing localized translation of these transcripts. In their article, Neriec and Percipalle present the different mechanisms behind this process, focusing on CBF-A/hnRNP AB-mediated mRNA transport and localization. The role of the 3'UTR in modulating mRNA localization, but also its translation and fate, are reviewed by Mayya and Duchaine. Finally, Karamyshev and Karamysheva discuss various mechanisms involved in quality control of both mRNAs and proteins during translation to prevent production of abnormal proteins.First, several articles review fundamental RNA-based mechanisms of post-transcriptional gene regulation. Starting in the nucleus, Cao discusses novel rhythmic functions of mTOR signaling in translational control in neurons, as they regulate their metabolism to suit circadian functions. Another example is the role of ribosome availability in regulating cellular metabolism and the cell cycle. While ribosomes have been considered for a long time as mere executants in the translation program, Calamita et al. discuss novel evidence of ribosome heterogeneity and its impact on differential mRNA translation and ribosomopathies, diseases in which these processes malfunction.A second group of articles in this collection focuses more specifically on the roles of RNA-mediated control of cellular metabolism and organismal development. Necessary to produce biological building blocks, regulated translation is key for cell growth and is a downstream target of several signaling pathways that control cellular metabolism. One example is the mammalian or mechanistic target of rapamycin (mTOR) signaling pathway. A review by Tahmasebi et al., who describe several mechanisms that control mRNA translation to coordinate stem cell renewal and differentiation. Particularly important during early development, mRNA localization has been extensively researched in Drosophila. While original studies were carried out in the oocyte and early embryo, most mRNA localization factors are conserved evolutionarily and are expressed in multiple tissues at late developmental stages and the adult, suggesting that RNA localization may be necessary throughout the lifespan of many organisms to enable structural and functional cellular asymmetries. Hughes and Simmonds illustrate the diversity of mRNA localization patterns in Drosophila, its role of sorting proteins to various subcellular compartments and reflect on the conservation of the underlying regulation. Finally, this section also includes two original research articles, one on the global transcriptome of adipogenic differentiation in cattle by Cai et al., and a second article by Alard et al. on the regulation of the translation initiation factors eIF4Gs by the proteasome.Translational control also plays important developmental functions such as stem cell differentiation, which is the topic of a review by Culjkovic-Kraljacic and Borden, who discuss the concepts of one- and two-tier RNA regulons and explain how their mis-regulation is a feature of diseases such as cancer. Moreover, the authors highlight how the advent and integration of “OMICS” approaches  has contributed to uncover the RNA-interactome of RNA-binding proteins and the therapeutic potential of redirecting RNA regulons. Dysregulation of signaling cascades or mis-expression of translation initiation factors frequently occurs in cancers, which impact translation initiation  and cell growth. This topic is reviewed by Hernández et al., with a focus on the development of pharmacological inhibitors of translation initiation as a potential treatment for prostate cancer. Translational output can also be affected by mutations in the sequence of a transcript, and a review article by Robert and Pelletier discusses how single nucleotide polymorphisms (SNPs) in regulatory elements of an mRNA  can impact its translation.The third section of this collection includes several articles on the roles of RNA regulation and mis-regulation in diseases. There is growing appreciation that sets of mRNAs encoding functionally related proteins are coordinately regulated through Untranslated Sequence Elements for Regulation (USER) codes that are “read” by specific RNA-binding proteins. This post-transcriptional regulatory mechanism, referred to as the RNA regulon model, is reviewed by Ostareck and Ostareck-Lederer describe in their review key RNA-binding proteins  that coordinate macrophage inflammatory responses to the Gram-negative bacterial endotoxin lipopolysaccharide by controlling turnover and translation of immune-related transcripts. Non-coding RNAs from bacteria such as ribozymes, riboswitches, and CRISPR-Cas9 systems are being developed as potential antimicrobials to curb acquired multi-drug resistance in pathogens without harming beneficial microbiota, as reviewed by Di Noto et al.. Finally, the current COVID-19 pandemic reminds us that RNA viruses, such as coronavirus and flavivirus, remain among the most formidable challenges faced by today's world health system. Flavivirus  and the fates of the flavivirus RNA genome are the topic of a review by Mazeaud et al..RNA molecules are also at the forefront of human action against infectious diseases. Efficient innate immune responses to bacterial, protozoan, fungal, and viral pathogens are largely dependent on a delicate balance between transcriptional and post-transcriptional regulation of genes encoding pro- and anti-inflammatory mediators. Uttam et al.). Last but not least, RNA dysregulation is a key contributor in several neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS), frontotemporal degeneration (FTD), and microsatellite expansion disorders such as Fragile X syndrome. A review by Butti and Patten describes how major genes mutated in ALS, such as SOD1, TARDBP, FUS, and C9orf72, are all involved in various aspects of RNA metabolism.The last two review articles focus on RNA regulation in the nervous system, where eIF4E-dependent translational control plays a major role in regulating the brain response to pain and the development of chronic pain diseases (As the field of RNA Biology advances very rapidly and the integrative analysis of global-scale RNA-protein interactions continues to evolve, novel examples of sophisticated regulatory mechanisms of RNA metabolism will emerge and thereby improve fundamental knowledge of cellular and organismal physiology. We may thus begin to comprehend how widespread, yet selective changes in transcriptional and translational programs underscore normal biological rhythms and adaptations to the changing environment. A better understanding of the role of dysregulated RNA metabolism in disease pathogenesis will be instrumental to design targeted RNA-based therapeutics to combat morbidity and mortality related to pathological conditions that affect millions of people around the world.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Drosophila melanogaster. By genetically encoding CasRx in flies, we demonstrate moderate transcript targeting of known phenotypic genes in addition to unexpected toxicity and lethality. We also report on the off-target effects following on-target transcript cleavage by CasRx. Taken together, our results present the current state and limitations of a genetically encoded programmable RNA-targeting Cas system in Drosophila melanogaster, paving the way for future optimization of the system.CRISPR-Cas genome editing technologies have revolutionized the fields of functional genetics and genome engineering, but with the recent discovery and optimization of RNA-targeting Cas ribonucleases, we may soon see a similar revolution in the study of RNA function and transcriptome engineering. However, to date, successful proof of principle for Cas ribonuclease RNA targeting in eukaryotic systems has been limited. Only recently has successful modification of RNA expression by a Cas ribonuclease been demonstrated in animal embryos. This previous work, however, did not evaluate endogenous expression of Cas ribonucleases and only focused on function in early developmental stages. A more comprehensive evaluation of this technology is needed to assess its potential impact. Here we report on our efforts to develop a programmable platform for RNA targeting using a Cas ribonuclease, CasRx, in the model organism  However, this technology has yet to be comprehensively adapted for facile use in other systems , such as Drosophila melanogaster (flies), which are a common tool for exploring new biological questions and developing novel bioengineering tools in vivo. Non-RNAi-based techniques for reducing gene expression  in animals would provide for a more flexible technique to modulate gene expression in a biologically relevant way.The development of CRISPR as a programmable genome engineering tool has revolutionized the life sciences by providing transformative applications for both medicine and biotechnology.6 Though these enzymes possess promiscuous RNase activity resulting in cleavage of non-target RNA,7–9 a possible drawback for applying Cas13 ribonuclease-based transcriptome engineering technologies, they may serve as a starting point for optimizing these RNA-targeting platforms for in vivo applications. For example, RNA-targeting CRISPR technologies could enable the development of robust gene silencing techniques in animals in which RNAi poorly functions.10 Another potential application may involve using RNA-targeting CRISPR technologies to engineer mosquito populations resistant to infection with RNA viruses. Numerous RNA viruses of global medical importance, such as dengue, Zika, and chikungunya virus, are transmitted primarily by one species of Aedes mosquito. Engineering this mosquito population with virus resistance may be a tool to reduce disease transmission;11 however, no current technologies have successfully targeted all of these viruses simultaneously.12–16 RNA-targeting CRISPR systems may provide a platform to reduce the spread of mosquito-borne viruses by targeting viral RNA genomes in a programmable manner. Therefore, it is of high priority to further understand the utility of RNA-targeting CRISPR systems in relevant model organisms.CasRx belongs to the Cas13 family of RNA-targeting Cas enzymes, a group of highly specific ribonucleases.Drosophila. This technology has increased our understanding of the function and regulation of many genes,17–20 yet RNAi was reported to show occasional high false negative rates, particularly in highly expressed genes due to insufficient small RNA expression,21 and at some other times high false positive rates due to positional or off-target effects.22–25 Co-expression of Dicer2 can reduce false negative rates, but would in turn increase the prevalence of false positives17 and render the entire process not as clean. Ideally, an RNA-targeting system should be easily programmable, not require expression of multiple factors, and should work in a simplified manner. CasRx, like other CRISPR systems, is easily programmable27 and is capable of targeting nearly any coding gene, but unlike other Cas13 enzymes, it lacks a protospacer flanking sequence requirement,4 making it more versatile for programmable targeting. Additionally, CasRx is a simplified RNA-targeting system as it requires no additional helper enzymes to efficiently target and degrade RNA.4 For these reasons, the CasRx ribonuclease is a practical starting point for establishing a single-effector RNA-targeting platform for in vivo gene reduction studies. Here we report the first use of a CasRx-mediated RNA-targeting system in flies. We show that separately encoding CasRx and guide RNA arrays (gRNAarray) in the genome promotes robust expression of these elements throughout development. Furthermore, we demonstrate that binary genetic crosses with ubiquitous and tissue-specific CasRx- and gRNAarray-expressing fly lines can produce clear, highly penetrant phenotypes and by using RNA sequencing (RNAseq) we demonstrate that CasRx is capable of moderate targeted transcript reduction at various stages of fly development, albeit with various degrees of off-target activity. Moreover, we also found that CasRx expression and targeting was often toxic and resulted in unexpected lethality indicating further optimization will be necessary for this to be a versatile tool for Drosophila genetics.RNAi-based approaches are the current standard for transcriptome modification in 28 For transgenic gRNA arrays, four targets per gene were selected to ensure efficient targeting. We assembled four CasRx- and catalytically inactive negative control (dCasRx)–expressing constructs under the control of one of two promoters: Ubiquitin-63E (Ubiq) or the original Upstream Activation Sequence (UAS) promoter developed in Brand and Perrimon29  using the Gibson enzymatic assembly method.30 A base vector (Addgene plasmid 112686) containing piggyBac and an attB-docking site, the Ubiq promoter fragment, SpCas9-T2A-GFP, and the Opie2-dsRed transformation marker was used as a template to build all four constructs.31 To assemble constructs OA-1050E , and OA-1050R , the SpCas9-T2A-GFP fragment was removed from the base vector by cutting with restriction enzymes SwaI and PacI and replaced with CasRx and dCasRx fragments amplified with primers 1050E.C3 and 1050E.C4  and OA-1050S , the base vector described above (Addgene plasmid 112686) was digested with restriction enzymes NotI and PacI to remove the Ubiq promoter and SpCas9-T2A-GFP fragments. Then the UASt promoter fragment and CasRx or dCasRx fragments were cloned in. The UASt promoter fragment was amplified from plasmid pJFRC8132 using primers 1041.C9 and 1041.C11  regions that had no poly-U stretches greater than four base pairs, had GC base content between 30% and 70%, and were not predicted to form strong RNA hairpin structures. Care was also taken to select target sites in RNA regions that were predicted to be accessible, such as regions without predicted RNA secondary or tertiary structure . All RNA1050E.C4  from con1041.C11 . The Cas1041.C11  from conwhite, Notch, GFP, and yellow, respectively. To generate a base plasmid, OA-1043, which was used to build all constructs carrying the four-gRNA array, Addgene plasmid 112688 containing the miniwhite gene as a marker, an attB-docking site, a D. melanogaster polymerase-3 U6 (U6:3) promoter fragment, and a gRNA fragment with a target, scaffold, and terminator sequence was digested with restriction enzymes AscI and XbaI to remove the U6:3 promoter and gRNA fragments. Then, the U6:3 promoter fragment was amplified from the same Addgene plasmid 112688 with primers 1043.C1 and 1043.C23 . We have also made all plasmids and sequence maps available for download and/or order from Addgene using the identification numbers listed in We designed four constructs, each carrying a four-gRNA array: OA-1050G (Addgene plasmid 132420), OA-1050I (Addgene plasmid 132421), OA-1050J (Addgene plasmid 133304), and OA-1050Z4 (Addgene plasmid 132425), targeting transcripts of 1043.C23  and was array-expressing lines were generated by site-specific integrating constructs at an available φC31 integration site on the third chromosome (site 8622). Homozygous lines were established for all gRNAarray-expressing flies.Flies were maintained under standard conditions at 26°C. Embryo injections were performed at Rainbow Transgenic Flies, Inc.. All CasRx and dCasRx-expressing lines were generated by site-specifically integrating our constructs at available φC31 integration sites on the 2nd chromosome (sites 8621 [UAS/-(d)CasRx] and attp40w [Ubiq-(d)CasRx]). Homozygous lines were established for UASt-CasRx and UASt-dCasRx, and heterozygous balanced lines were established for Ubiq-CasRx and Ubiq-dCasRx (over Curly of Oster: CyO). All gRNAarray-expressing lines at 26°C. F1 transheterozygotes were scored for the inheritance and penetrance of observable phenotypes. Embryo, larvae, and pupae counts were preceded by crossing male Ubiq-CasRx- or Ubiq-dCasRx-expressing flies to female gRNAarray-expressing flies. Flies were incubated at 26°C for 48 h with yeast to induce embryo laying. Flies were then transferred to embryo collection chambers containing yeast-smeared grape-juice plates and were incubated at 26°C overnight (16 h). The grape-juice plates were then removed, the embryos were counted, and the grape-juice plates were incubated for 24 h at 26°C. Total larvae and transheterozygote larvae were then counted, and the grape-juice plates were transferred to jars and incubated at 26°C. Once all larvae reached the pupal stage, total, and transhet pupae were counted. Finally, total adult flies and total adult transheterozygotes were counted 20 days post initial lay. Each genetic cross was set using 5 male and 10 female  or 4 male and 8 female  flies in triplicate.To genetically assess the efficiency of CasRx ribonuclease activity, we bidirectionally crossed Ubiq-CasRx- and Ubiq-dCasRx-expressing lines to gRNA2 triple transheterozygotes  to generate F1 transheterozygote males carrying the TM6-balancer chromosome. The F1 transheterozygote males carrying TM6 were then crossed with a Gal4–driver-expressing line. F2 triple transheterozygous inheritance and phenotype penetrance was scored based on visible phenotypes manifesting in flies F2 flies with red eyes, a lack of the TM6 balancer chromosome, and red fluorescent protein (dsRed) expression. Marked by the presence of dsRed (for UASt-CasRx or UASt-dCasRx), red eyes (to mark the gRNA), and the lack of TM6, F2 triple transheterozygotes' inheritance and phenotype penetrance was scored. Each cross was set using 1 female and 10 male flies in triplicate. The flies were imaged on the Leica M165FC fluorescent stereomicroscope equipped with a Leica DMC4500 color camera. Image stacks of adult flies were taken in Leica Application Suite X (LAS X) and compiled in Helicon Focus 7. Stacked images were then cropped and edited in Adobe Photoshop CC 2018.To investigate the tissue-specific activity of CasRx, we designed a two-step crossing scheme to generate Fozygotes . First, 1 transheterozygous flies at different developmental stages based on the expression data available through modENCODE analysis (white gene (gRNAw) flies, transheterozygous adult heads were removed one day after emerging and were frozen at −80°C. For guide RNA targeting the yellow gene (gRNAy) flies, the flies were incubated in vials for 48 h with yeast to induce embryo laying. The flies were then transferred to embryo collection chambers containing yeast-smeared grape-juice plates and incubated at 26°C for 3 h. The flies were then removed, and the embryos were aged on the grape-juice plates . The embryos were removed from the grape-juice plates, washed with deionized H2O, and frozen at −80°C. The guide RNA targeting the Notch gene (gRNAN) and guide RNA targeting the Green Fluorescent Protein gene (gRNAGFP) flies were incubated in vials with yeast for 48 h to induce embryo laying. The flies were then transferred to a new vial and allowed to lay overnight (16 h). The adults were removed, and the vials were incubated at 26°C for 24 h. Transheterozygote first instar larvae were then picked (based on dsRed expression) and frozen at −80°C. For all samples, the total RNA was extracted using Qiagen RNeasy Mini Kit (Qiagen 74104). Following extraction, the total RNA was treated with Invitrogen TurboTM DNase (Invitrogen AM2238). The RNA concentration was analyzed using a Nanodrop OneC UV-vis spectrophotometer (ThermoFisher ND-ONEC-W). The RNA integrity was assessed using an RNA 6000 Pico Kit for Bioanalyzer (Agilent Technologies 5067-1513). The RNA-seq libraries were constructed using NEBNext Ultra II RNA Library Prep Kit for Illumina (NEB E7770) following the manufacturer's instructions.33 The libraries were sequenced on an Illumina HiSeq2500 in single read mode with a read length of 50 nt and a sequencing depth of 20 million reads per library following the manufacturer's instructions. Base calls were performed with RTA 1.18.64 followed by conversion to FASTQ with bcl2fastq 1.8.4. We sequenced and analyzed three replicates for all CasRx and dCasRx samples. In total, we sequenced and analyzed 24 samples: 12 CasRx experimental samples and 12 dCasRx control samples. All raw sequencing data can be accessed at the National Center for Biotechnology Information Sequence Read Archive .The total RNA was extracted from Fanalysis . For guiD. melanogaster genome  supplemented with cDNA sequences of CasRx and GFP using the default parameters of STAR aligner34 with the addition of the “–outFilterType BySJout” filtering option and “–sjdbGTFfile Drosophila_melanogaster.BDGP6.22.97.gtf” gene transfer format (GTF) file downloaded from ENSEMBL. The expression levels were determined with featureCounts35 using “-t exon -g gene_id -M -O –fraction” options. The raw transcript counts were normalized to transcripts per million (TPM), which were calculated from count data using the “addTpmFpkmToFeatureCounts.pl” Perl script  if one replicate of a transcript is an outlier with an extreme count (detected by Cook's distance), this transcript's P value and padj will be set to NA; or (3) if a transcript is found to have a low mean normalized count after automatic independent filtering, this transcript's padj will be set to NA.Reads were mapped to the Material , and thein vivo, we engineered flies encoding two versions of the CasRx ribonuclease: the active enzyme and a catalytically inactive negative control (dCasRx). We did this by generating transgenes that use a broadly expressing ubiquitin (Ubiq) promoter37 to drive expression of either CasRx (Ubiq-CasRx) or dCasRx (Ubiq-dCasRx)  (white (w), Notch (N), and yellow (y).38–41 To target these genes with CasRx, we designed a gRNAarray for each gene driven by a ubiquitously expressed polymerase-3 U6 (U6:3) promoter42  and established a homozygous transgenic line  . CasRx eattp40w) . These fattp40w) . To geneomoter42 . Each arter42  expressing flies crossed to either Ubiq-CasRx  or Ubiq-dCasRx  expressing flies (array /+) for w suggesting that CasRx exhibits programmable on-target RNA cleavage capabilities  were 100% lethal and did not develop beyond the second instar larval stage  from our negative control crosses using all arrays tested, indicating that a catalytically active form of the ribonuclease is necessary for visual phenotypes to be observed  or dCasRx (UASt-dCasRx) as a negative control ;47  (array (gRNAarray/gRNAarray) expressing flies to heterozygous, double-balanced UASt-CasRx  flies, or for the negative control, heterozygous, double-balanced UASt-dCasRx  flies. The second step was to cross the F1 transheterozygote males expressing both a CasRx ribonuclease and the gRNAarray  to respective homozygous Gal4 driver lines to generate F2 triple transheterozygotes  to be scored for phenotypes ;47 . These l y, or N . To testay/Gal4) . This coenotypes .1 cross between gRNAw  and GMR-Gal4 , we observed survival of only 0.57% viable F2 triple transheterozygotes , all of which displayed severe eye specific pigmentation and morphology phenotypes  inheritance rate, which was closer to the expected 25% Mendelian inheritance (27.6%)  compared to the 29.3% inheritance rate for the negative control F2 triple transheterozygotes  , we observed marginal chitin pigment reduction as small patches of yellow cuticle on the thorax and abdomen in F2 triple transheterozygotes   inheritance was significantly lower (2.67%) when compared to the control F2 triple transheterozygote  inheritance (25.2%), indicating a substantial degree of lethality  again indicating that functional catalytic residues of the HEPN motifs are necessary for generating phenotypes observed  . MoreoveMR-Gal4) . Finally/y-Gal4) , arrows ethality . In all observed . Taken tarray targeting GFP along with GFP expression driven by the broadly expressing OpIE2 promoter (gRNAGFP)  by site-specifically integrating the construct at an available φC31 docking site located on the 3rd chromosome (site 8622)  to heterozygous Ubiq-CasRx-expressing flies  or heterozygous Ubiq-dCasRx-expressing flies  as a negative control , while larval lethality was eliminated in F1 progeny that did not inherit Ubiq-CasRx  in addition to the transheterozygote controls . Lethality was also observed regardless of the maternal or paternal deposition of CasRx  larvae and observed near-complete reduction in GFP expression for Ubiq-CasRx transheterozygote larvae as compared to Ubiq-dCasRx transheterozygote  control larvae indicating robust CasRx mediated target transcript (GFP) reduction  reporter assay to assess the necessity of a target sequence in CasRx-mediated lethality while simultaneously visualizing on-target transcript reduction. We designed a binary GFP reporter construct consisting of both a CasRx gRNAgRNAGFP) .48 We este 8622) . To test control . With thof CasRx . Given tt beyond . Given teduction . Taken tarray target genes from our binary crosses producing either highly penetrant, visible phenotypes (w) or lethal phenotypes   to control F1 Ubiq-dCasRx transheterozygotes  (D. melanogaster (modENCODE),49 we extracted total RNA stages of development when high transcript expression levels were expected for each target gene with the exception of GFP, where we sequenced first instar larvae  . To do srray /+) . Using tr larvae . In totar larvae . From our larvae . For exar larvae  the targ and GFP , S8, S9.observed , S8,S9. rved , 300 (N), 41 (y), and 5,880 (GFP), representing 1.4% (w), 1.7% (N), 0.23% (y), and 33% (GFP) of the total transcripts , N , y  and w  . Specifically, RNA targeting was demonstrated with ubiquitous, inducible, and tissue-specific CasRx expression systems against native and synthetic RNA targets, which are prerequisites for enabling comprehensive studies of gene function. However, we did also consistently observe both cellular toxicity from the ubiquitous expression of CasRx and dCasRx as we could not generate homozygous strains for either, and unexpected lethality and tissue necrosis, presumably due collateral off target effects which have been a feature previously observed for many CRISPR ribonucleases including CasRx.51 Nevertheless, in both bidirectional and Gal4/UAS crosses, we were able to obtain visible phenotypes as well as quantitative evidence  indicating that the CasRx is capable of targeting and degrading target RNA in flies. It is interesting to note that for one of the targeted genes (w), while the observed phenotype indicated consistent on-target transcript reduction, DESeq2 analysis did not reveal significant on-target reduction, which may be due to the timing of sample collection for RNAseq since expression levels of these genes vary over development.Our results demonstrate that CasRx has some potential for programmable RNA targeting in flies, as we did observe some expected phenotypes for each target transcript, including y, N, and GFP. Interestingly, transheterozygotes  for y, N, and GFP also had many other misexpressed non-target genes, possibly indicating that target cleavage results in increased collateral off-target activity that is detrimental to development as these individuals were adult lethal. For example, targeting GFP, a nonessential gene, produced the largest number of misexpressed genes as well as the most significant fold change in expression compared to all other gene targets analyzed. Additionally, because Gadd45, a gene involved in cellular arrest and apoptosis in D. melanogaster,52 was also significantly misexpressed in four samples   , S8, S9,GFP-expressing flies. These results recapitulate previous mechanistic analyses of CasRx and other Cas13 ribonucleases, demonstrating that collateral off-target activity following targeted transcript cleavage is a native feature of Cas13 ribonucleases.51 While this feature may not be desirable for generating tools for targeting specific transcripts of genes, this may be useful for generating sensors that get activated in response to a target transcript  such as diagnostic tools that could alert the presence of a nucleic acid target and activate a marker, or even organismal lethality acting as an in vivo ribonucleic acid sensor.Through this study, we identified two main factors contributing to CasRx-mediated lethality: (i) the catalytic activity of the CasRx HEPN domains, as lethality and tissue necrosis phenotypes were eliminated in dCasRx compared to CasRx crosses, and (ii) the presence of the guide RNA and target transcript resulting in on-target cleavage, as lethality was only observed when crossing Ubiq-CasRx-expressing flies to gRNAD. melanogaster. In the future, optimization of the strength and timing of CasRx expression could mitigate some of the off-target-associated lethality in this system. Stricter and more tunable regulation of CasRx expression may also improve phenotype penetrance as it appears to be dosage dependent in both our system and other CasRx systems.5 For example, the phenotypes of y varied by expression, with ubiquitous expression of CasRx resulting in a Ubiq-CasRx/+; gRNAy /+ lethal phenotype and embryo and wing and body specific expression mitigated lethality phenotype seen in Ubiq-CasRx expression. Optimization of gRNA design may further improve these systems as CasRx gRNAs have been shown to have variable knockdown efficiency.53 Nevertheless, this is an important first step towards making transcriptome engineering a viable in vivo technology and provides a foundation for future experiments to mitigate the off-target and toxic attributes of the enzyme to make a new, viable tool in the expanding gene-editing toolbox.Taken together, further optimization will be required to increase the CasRx on-target cleavage rates and decrease cellular toxicity and off-target effects, but this is the first demonstration of a genetically encoded programmable RNA-targeting Cas system in


Felis catus) being able to successfully learn to reproduce human-demonstrated actions based on the Do as I Do paradigm. The subject was trained to reproduce a small set of familiar actions on command “Do it!” before the study began. To test feature–contingent behavioural similarity and control for stimulus enhancement, our test consisted of a modified version of the two-action procedure, combined with the Do as I Do paradigm. Instead of showing two different actions on an object to different subjects, we applied a within-subject design and showed the two actions to the same subject in separate trials. We show evidence that a well-socialized companion cat was able to reproduce actions demonstrated by a human model by reproducing two different actions that were demonstrated on the same object. Our experiment provides the first evidence that the Do as I Do paradigm can be applied to cats, suggesting that the ability to recognize behavioural similarity may fall within the range of the socio-cognitive skills of this species. The ability of reproducing the actions of a heterospecific human model in well-socialized cats may pave the way for future studies addressing cats’ imitative skills.This study shows evidence of a domestic cat (The online version of this article (10.1007/s10071-020-01428-6) contains supplementary material, which is available to authorized users. The owner, Fumi Higaki, is a professional dog trainer, experienced with the use of the Do as I Do method to train dogs. She reported that the cat had always been exceptionally motivated for food, a condition that allowed her to train the cat relatively easily by applying the same operant conditioning-based training methods commonly used for dogs. Ebisu was trained by her owner with the Do as I Do protocol to match her behaviour to actions that were demonstrated by a human , but the owner reported that her motivation for food and her physical condition had been apparently good and stable until September 2019, when the cat started to show a decreased appetite and a lower level of activity.The tests were conducted in December 2019 in the pet shop where the training took place, in the evenings, when the shop was closed to the public. In each test, a cardboard box (27 × 18 × 10 cm) was displayed on a table . The owner was standing in front of the table and provided cues to induce the cat to go on it and to sit in front of her. The owner and the cat were facing each other, and the box was placed laterally at 50 cm from the cat’s position , the owner performed the demonstration, then returned to her starting position facing the cat and gave the “Do it!” command. The cat’s behaviour was then observed for maximum 20 s (or until the cat performed an action). To avoid any possible inadvertent cueing on the part of the owner, the owner looked straight ahead while giving the “Do it!” command . This forced us to expose her to a limited amount of trials per test occasion and to a limited number of test occasions as well. Every test session consisted of 3 trials in which the same action (either A or B) was demonstrated, every time followed by the “Do it!” command. We planned to run 6 test sessions of 3 trials each, one test session per day with an inter-test interval of minimum 1 day, maximum 2 days. In half of the test sessions, we demonstrated action A and in the other half, we demonstrated action B. The action demonstrated (A or B) was semi-randomized, so that the same action would not be demonstrated in more than 2 sessions in a row. We kept the number of sessions and trials limited to ensure that the cat would remain motivated. Due to unforeseen circumstances, on one day, it was not possible to test the cat . Therefore, we did not test the cat on that day and, instead, we ran two test sessions in the next test occasion, two days later. Ebisu’s health condition did not allow further testing; therefore, we had to refrain from carrying out more test sessions and from testing her on other actions.Video recordings of the cat’s behaviour following the ‘Do it!’ command were used to investigate whether she matched the demonstrated action. Out of the 18 trials, 2 trials were excluded from analysis, one because the cat did not watch the demonstration, and the other because the cat responded by touching the object with both her face and her paw .We carried out two analyses. Although our aim was testing presence of action matching, we observed that, unexpectedly, in some trials, the cat matched the demonstrated movements but did not perform them on the object (see results). Therefore, we carried out two separate analysis, one for action matching and one for object matching.To ensure unbiased coding, videos of the cats’ behaviour after the “Do it!” were watched by the coder without knowing what action had been demonstrated. The cat’s behaviour was noted and then compared to the description of the demonstrations to determine action matching. For the action matching analysis, the response of the cat was considered as matching the demonstrated action if the cat performed a movement similar to the one demonstrated by the owner, using a matching body part: when the human demonstrator raised a hand to touch the object, the cat’s response was considered as matching if it raised a front paw either to touch an object or mimicking the movement of raising a hand/paw without touching an object (both coded as “paw action”—action A); when the human demonstrator rubbed her face on the object, the cat’s response was considered as matching if she rubbed her face on something, or if she mimicked the face-rubbing movement without touching any object (both coded as “face action”—action B). If the cat performed any other than the demonstrated action, including action A when action B was demonstrated or vice versa, an action that differed from action A and B, or no action at all, action matching was considered unsuccessful. For the object matching analysis, the response of the cat was considered as matching the object if the cat interacted with any body part with the object touched by the demonstrator.Statistical analyses were carried out using the R statistical environment . Out of the 9 trials in which a hand movement was demonstrated, the cat responded with a paw movement in 7 trials (77.8%) and out of the 7 trials in which face-rubbing was demonstrated, the cat responded with rubbing her face in 6 trials (85.7%).Overall, the cat matched her behaviour to the demonstrated actions in 81.2% trials ; our statistical analysis confirmed that the demonstrated action explained most of the variations in performed action  and we also did not find any effect of trial ; therefore, these variables were excluded from the final model.We did not find a significant effect of test session . In 3 trials in which face-rubbing on the object was demonstrated, the cat responded by rubbing her face on the floor  rather than on the object, and in 1 trial in which the demonstrator raised her hand to touch the object, the cat responded by raising her paw without approaching the object.Out of the 17 trials analysed for object matching , the cat interacted with the object that was touched by the demonstrator in 11 trials Below is the link to the electronic supplementary material.


N-methyl-D-aspartate receptor (NMDAR) antagonist MK-801 itself increased γ power and occluded DA-mediated enhancement of γ power in WTM but partially restored DA modulation of γ oscillations in HRM. Third, inhibition of phosphatidylinositol 3-kinase (PI3K), a downstream molecule of NMDAR, increased γ power and blocked the effects of DA on γ oscillation in WTM and had no significant effect on γ power but largely restored DA modulation of γ oscillations in HRM. Our results reveal that impaired DA function in HRM is associated with dysregulated NMDAR–PI3K signaling, a mechanism that may be relevant in the pathology of schizophrenia.The reelin haploinsufficient heterozygous reeler mice (HRM), an animal model of schizophrenia, have altered mesolimbic dopaminergic pathways and share similar neurochemical and behavioral properties with patients with schizophrenia. Dysfunctional neural circuitry with impaired gamma (γ) oscillation (30–80 Hz) has been implicated in abnormal cognition in patients with schizophrenia. However, the function of neural circuitry in terms of γ oscillation and its modulation by dopamine (DA) has not been reported in HRM. In this study, first, we recorded γ oscillations in CA3 from wild-type mice (WTM) and HRM hippocampal slices, and we studied the effects of DA on γ oscillations. We found that there was no difference in γ power between WTM and HRM and that DA increased γ power of WTM but not HRM, suggesting that DA modulations of network oscillations in HRM are impaired. Second, we found that  Reelin, a glycoprotein of the extracellular matrix, controls cell migration and layering in the developing brain, promotes the formation of synaptic circuits, and regulates synaptic transmission and plasticity in the postnatal and adult brain . During The reelin gene is a susceptibility factor for early-onset psychiatric disorders, such as schizophrenia and autism. The heterozygous reeler mice (HRM) have a single reelin allele deficiency . SimilarThe hippocampal CA3 region plays a specific role in memory processes  and atteSchizophrenia has been suggested to be caused by the failure of integrating local and distributed neural circuits . In factAs an animal model of schizophrenia, HRM shows abnormal dopaminergic function, including reduced DA D1 and D2 receptors (D1R and D2R) in the striatum, reduced D1R- and D2R-mediated locomotor response , and incN-methyl-D-aspartate receptor (NMDAR)-dependent mechanism , reelin activates different signaling cascades, one of which is phosphatidylinositol 3-kinase (PI3K) signaling pathway, and increases synaptic transmission by enhancing PI3K-dependent postsynaptic AMPAR insertion . PI3K isIn this study, we investigated γ oscillation and its modulation by DA in HRM using extracellular field potential recording to determine whether there are altered dopaminergic modulations of γ oscillation in HRM and the possible mechanisms associated with it.ad libitum, under a normal 6 AM light–6 PM dark cycle. The animals were anesthetized by intraperitoneal injection of Sagatal . When all pedal reflexes were abolished, the animals were perfused intracardially with chilled (4°C), oxygenated artificial cerebrospinal fluid (ACSF), in which sodium chloride had been replaced by iso-osmotic sucrose. This sucrose-ACSF contained (in mM) the following: 225 sucrose, 3 KCl, 1.25 NaH2PO4, 24 NaHCO3, 6 MgSO4, 0.5 CaCl2, and 10 glucose (pH 7.4). Horizontal slices (350 μm) of mouse brain containing the ventral hippocampus were cut at 4°C in sucrose-ACSF, using a Leica VT1000S vibratome , and stored at room temperature at the interface between recording of ACSF and humidified carbogen (95% O2–5% CO2) until these transferred to the recording chamber. The recorded ACSF contained (in mM) the following: 126 NaCl, 3 KCl, 1.25 NaH2PO4, 24 NaHCO3, 2 MgSO4, 2 CaCl2, and 10 glucose (pH 7.4).Wild-type (WT) mice (c57BL/6N) and HRM (reelin+/−), 3- to 6-month-old male and female mice, were purchased from Model Animal Research Center of Nanjing University. WT animals used in this study are littermates of HRM. Mice were kept in standard housing conditions, with normal chow and water S,10R)-(+)-5-methyl-10,11-dihydro-5H-dibenzocyclohepten-5,10-imine or dizocilpine hydrogen maleate (MK-801); and the PI3K inhibitor, 11-(acetyloxy)-1S,6bR,7,8,9aS,10,11R,11bR-octahydro-1-(methoxymethyl)-9a,11b-dimethyl-3H-furoindeno-2-benzopyran-3,6,9-trione (wortmannin), were purchased from Tocris Cookson Ltd. . All other drugs and ACSF salts were purchased from Sigma-Aldrich . Stock solutions, at thousand times the final concentration, were made in water or DMSO and stored in individual aliquots at −20°C. The final solutions were freshly prepared on the day of the experiment.Carbachol; DA hydrochloride; the non-competitive NMDAR antagonist,  of the hippocampus, using glass microelectrodes containing ACSF . Field potentials were amplified using NeuroLog NL106 AC/DC amplifiers  and band-pass filtered between 0.5 and 500 Hz using NeuroLog NL125 filters . Electromagnetic interference from the main supply was eliminated from the recordings with the use of HumBug 50-Hz noise eliminators . The recordings were digitized at a sample rate of 2 kHz using a CED 1401-plus ADC board .Data were analyzed offline using the Spike2 software (Cambridge Electronic Design). Power spectra were generated to provide a quantitative measure of the frequency components. Power spectra were constructed for 60-s epochs using a fast Fourier transform algorithm.in vitro γ oscillations ranged from 20 to 80 Hz, because the recorded γ oscillations in brain slices are temperature dependent and the slice recordings performed mostly at 32°C rather than 37°C. There is a linear relationship between peak frequency of network oscillations and temperature, in which an increase of 1°C in temperature of brain slices corresponds to an increase of 2.3 ± 0.4 Hz in the oscillation frequency (It has been widely accepted that requency .Y = exp(−a∗X).The area under the curve between 20 and 60 Hz was used to quantify the γ power. Autocorrelograms were calculated in Spike2 using a 500-ms lag from the same local field potential trace used for γ power calculation. The decay time constant (tau) of the autocorrelation peaks is a measure of the regularity of the oscillation and generated by fitting the autocorrelation peaks with an exponential function: t-tests were used to compare two groups of parametric data. One-way analysis of variance (ANOVA) and repeated-measures (RM) ANOVA were used to compare three or more group means. Non-parametric data were expressed as median ± interquartile range. The Wilcoxon rank-sum and signed-rank tests were used to compare the two groups of non-parametric data. One-way and RM ANOVAs on ranks were used for three or more group comparisons. The parametric two-way ANOVA was used to analyze experimental data derived from two-factor designs with or without RM. The two-way ANOVA on ranks was used to analyze non-parametric data. A P-value < 0.05 was considered statistically significant.All statistical analyses were performed using IBM SPSS Statistics 22 software . The Shapiro–Wilk test was used in testing the normality of the data. Parametric data were expressed as mean ± standard error of the mean. The paired and unpaired Student’s 2, n = 45 slices from 22 mice, vs. HRM, 646.30  μV2, n = 37 slices from 19 mice, Mann–Whitney U-statistic = 744, T = 1,513.00, P = 0.749, t(28) = 0.373; P = 0.712; t(12) = 0.726, P = 0.482, To induce stable γ oscillations in the CA3 area of mouse hippocampal slices, the cholinergic agonist carbachol at 10 μM, half of the concentration used in γ induction in rat hippocampal slices , has bee2, Z-statistic = 3.233, n = 14 slices from six mice, P < 0.001, Wilcoxon signed-rank test, 2, Z-statistic = −1.363, n = 13 slices from five mice, P = 0.191, Wilcoxon signed-rank test, t(25) = 4.626, P = 0.0001, t-test, F = 25.559, P < 0.0001) and a significant main effect of 20 μM of DA  = 5.279, P = 0.026). Moreover, there was a significant interaction effect between genotype and 20 μM of DA  = 25.559, P < 0.0001). These results indicate that DA increased γ power in WTM but not in HRM.Defective reelin signaling influences the mesolimbic dopaminergic pathways . Thus, wt(7) = −0.287, P = 0.783, paired t-test] but slightly increased peak frequency in HRM  or HRM .Dopamine had no effect on peak frequency in WTM  without affecting peak frequency  in WTM and had no significant effect on either γ power  or peak frequency  in HRM. A two-way non-parametric ANOVA for γ powers revealed a significant main effect of genotype  = 33.749, P < 0.0001) and no significant main effect of DA concentrations  = 1.925, P = 0.174). There was no significant interaction effect between genotype and DA concentrations  = 0.896, P = 0.350).Previous studies indicate that a high DA concentration (200 μM) actually inhibited CCh-induced γ oscillations in rat hippocampus . Thus, w2 vs. CCh, 459.55  μV2, q = 4.648, P < 0.05, RM ANOVA on ranks, followed by Tukey’s test, 2 vs. CCh + MK-801, 737.23  μV2, q = 1.549, P > 0.05, RM ANOVA on ranks, followed by Tukey’s test, t(27) = 0.758, P = 0.455, t-test], and the net increase of γ power caused by DA after deducting the effect of MK-801 was significantly smaller than that of DA alone in WTM . Neither MK-801 nor MK-801 + DA had any effect on the peak frequency in WTM  μV2, q = 3.000, P > 0.05, RM ANOVA on ranks, followed by Tukey’s test, F = 4.618, P = 0.037). A further application of DA (20 μM) caused an additional 21.3 ± 3.8% increase (over MK-801) or a total increase of 50.4 ± 13.3% (over CCh) on γ power , and such an increase was comparable with that of DA in WTM . The net increase of γ power caused by DA after deducting the effect of MK-801 was also significantly larger than that of DA alone in HRM . These results suggest that MK-801 restored partial sensitivity of γ power to DA in HRM despite the fact that the effect of MK-801 on γ power in HRM is significantly less than that of MK-801 in WTM .Perfusion of hippocampal slices of HRM with MK-801 (20 μM) caused a 24.6 ± 11.3% change in the γ power without statistical significance . DA mediated an increase in γ power in the presence of wortmannin in HRM at a level that is comparable with that of DA effect on γ power in WTM, which suggests that wortmannin restored the response of hippocampal γ oscillations to DA in HRM. Neither wortmannin nor wortmannin + DA had any effect on peak frequency of γ oscillations in HRM  increased γ power by 20 ± 7.5% without statistical significance [716.87  μVs in HRM .Our main findings are as follows: (1) DA enhanced γ power in WTM but not in HRM. (2) MK-801 induced a larger increase in γ power, occluded the effect of DA in WTM, induced a small increase in γ power, and partially restored the effect of DA in HRM. (3) Wortmannin induced a larger increase in γ power, blocked the effect of DA in WTM, and caused no significant increase in γ power but largely restored the effect of DA in HRM.Dopamine at a concentration of 20 or 200 μM increased γ power in hippocampal slices in WTM, which differs from the observation that DA at a concentration of 200 μM reduced γ oscillations induced by carbachol in area CA3 of rat hippocampus , suggestin vitro hippocampal γ oscillation was intact in HRM but that DA modulation of γ oscillations was impaired. Loss of sensitivity to DA for hippocampal γ power in HRM may be related to altered densities of dopaminergic fibers in different brain areas: increased in the hippocampus but reduced in the ventral tegmental area and nucleus accumbens in HRM  inhibition of γ oscillations . HoweverDopamine alone increased the peak frequency of oscillatory activity in HRM. This effect was reversed, whereas DA effect on γ power was partially restored in the presence of MK-801, which suggests that NMDAR activation is required for DA-mediated oscillatory frequency.Similar to the effects of MK-801 on γ oscillations, wortmannin, a PI3K inhibitor, caused a substantial increase in γ power in WTM and a small, insignificant increase in HRM, indicating that the endogenous PI3K activity is different between WTM and HRM and that sensitivity of γ oscillations to PI3K activity is reduced in HRM.In WTM, wortmannin was able to occlude DA enhancement of γ power, which indicates that PI3K is also involved in DA modulation of γ oscillation. In HRM, DA-mediated response was largely increased in the presence of wortmannin. Our result is in agreement with the report that blocking NMDAR and its downstream signaling molecule, the mTOR, rescued the deficit of function and behavior in HRM . StudiesAs reelin activates PI3K  and enhaThe results of this study demonstrated that the altered DA modulation of γ oscillations in HRM is associated with dysregulated NMDAR–PI3K signaling, establishing a link between DA- and NMDAR-mediated signaling, network oscillations, and reelin, which might be relevant to the field of schizophrenia research.The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.The animal study was reviewed and approved by the Animal Experimentation Ethics Committee of Xinxiang Medical University (protocol number: 11401300017419). All experiments were performed in accordance with the guidelines of the Animal Care and Use Committee of Xinxiang Medical University.LW, DZ, MW, and YW performed the research. CL and JL designed the research. CL, LW, DZ, and MV analyzed the data. LW, DZ, and CL wrote the manuscript. CL, LW, and MV revised the manuscript. All authors approved the final manuscript for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Microorganisms are fundamental drivers of biogeochemical cycling, though their contribution to coral reef ecosystem functioning is poorly understood. Here, we infer predictors of bacterioplankton community dynamics across surface-waters of the Great Barrier Reef (GBR) through a meta-analysis, combining microbial with environmental data from the eReefs platform. Nutrient dynamics and temperature explained 41.4% of inter-seasonal and cross-shelf variation in bacterial assemblages. Bacterial families OCS155, Cryomorphaceae, Flavobacteriaceae, Synechococcaceae and Rhodobacteraceae dominated inshore reefs and their relative abundances positively correlated with nutrient loads. In contrast, Prochlorococcaceae negatively correlated with nutrients and became increasingly dominant towards outershelf reefs. Cyanobacteria in Prochlorococcaceae and Synechococcaceae families occupy complementary cross-shelf biogeochemical niches; their abundance ratios representing a potential indicator of GBR nutrient levels. One Flavobacteriaceae-affiliated taxa was putatively identified as diagnostic for ecosystem degradation. Establishing microbial observatories along GBR environmental gradients will facilitate robust assessments of microbial contributions to reef health and inform tipping-points in reef condition. Pedro R. Frade et al. analyze published bacterial data and environmental data from the eReefs platform to study bacterioplankton community dynamics across the Great Barrier Reef. They identify communities within inshore and outershelf reefs that are susceptible to nutrient and temperature changes, highlighting the importance of understanding microbial ecosystems dynamics informing reef health. A combination of global disturbances, such as rising seawater temperatures2, and local pressures including overfishing, declining water quality, disease and outbreaks of coral predating crown-of-thorns starfish3, are driving declines in coral condition. Despite being considered among the best-managed marine areas, parts of the Great Barrier Reef (GBR) are threatened by nutrient and sediment inputs from land-based sources5. The GBR was also impacted by back-to-back bleaching events in 2016–2017, resulting in the mortality of one-third of all its shallow-water corals6 and dramatic impairment of recruitment capacity of the surviving coral7. Another mass bleaching was reported in early 2020, representing three events in just 5 years, each with extensive spatial impacts on the ecosystem values of the GBR8. There is currently an urgent requirement to better understand the functioning of coral reefs and identify the factors underpinning their resilience or susceptibility, to determine how the socio-economic and ecological value of these ecosystems will change.Coral reef ecosystems are under increasing anthropogenic pressure leading to widespread global degradation10 and a crucial component of the coral holobiont11. However, their contribution to the functioning and resilience of reefs is not well understood14. Shifts in the compositional and functional diversity of both coral-associated16 and free-living planktonic17 microbial communities have been linked to varying levels of anthropogenic impact, including changes in seawater nutrient levels18. For example, chronic nutrient exposure has been correlated with increased prevalence of coral disease in Caribbean reef systems19. Protection from fishing has led to improved reef health through the promotion of microbial diversity as opposed to the growth and rapid development of opportunistic microbial pathogens in reefs open to fishing pressures20. These findings were validated in a field experiment simulating overfishing and nutrient pollution, which interacted with sea surface temperatures to drive changes in coral microbiomes and an increase in coral mortality21. The health and condition of corals, and resilience of reefs to environmental stressors more broadly, is inherently linked to microbial functioning in these ecosystems.Microorganisms are fundamental drivers of biogeochemical cycling in coral reef waters23. These phase-shifts are reinforced by the positive feedback loop proposed in the DDAM model , through which macroalgae-derived labile dissolved organic carbon supports copiotrophic and potentially pathogenic bacterioplankton communities that harm corals, therefore promoting algal competitive dominance25. The concomitant increase in microbial abundances on algal-dominated reefs worldwide results in a switch from autotrophic to heterotrophic microbial processes and a shift in trophic structure towards higher microbial biomass and energy use, a phenomenon coined microbialization27. An additional hypothesis considers the self-reinforcement of macroalgal dominance through microbial pathways. Specifically, positive microbial responses to the photosynthates leached from algae may increase the vertical attenuation of light, thereby suppressing coral calcification and elevating stress on the coral28.Pressures such as overfishing and nutrient pollution can ultimately contribute to the top down and bottom up processes that drive phase-shifts from coral-dominated to macroalgal-dominated reef ecosystems12, features characteristic of near-shore environments. In addition, the types of bacterial autotrophs changed from Prochlorococcus-dominated assemblages in the most pristine regions to Synechococcus-dominated communities at atolls with human-influences such as increased concentrations of nitrogen and phosphate12. Higher nutrient availability also enriched for nutrient-related microbial metabolic traits such as nitrate and nitrite ammonification29. In the Caribbean, microbial signatures were also clearly distinct between protected and offshore Cuban reefs compared to human impacted reefs in the Florida Keys18. Similarly, a study across three ocean basins observed that algal-dominated sites were enriched in copiotrophic microbial taxa, including Gammaproteobacterial families such as Enterobacteriaceae, Vibrionaceae, Shewanellaceae and Pasteurellaceae, and Bacteroidetes such as Cytophagaceae and Flavobacteriaceae, whereas coral-dominated reefs were enriched in oligotrophic Alphaproteobacterial families such as Caulobacteriaceae, Sphingomonadaceae, Hyphomonadaceae, Bradyrhizobiaceae, Acetobacteriaceae, Phyllobacteriaceae, Rhodospirillaceae, Pelagibacteraceae, Rhizobiaceae and Rhodobacteriaceae26.Shifts in free-living microbial lineages in response to seawater nutrient gradients and benthic composition in reef systems have previously been reported. For example, atolls of the Line Island chain that experience the highest levels of coral disease, nitrogen and phosphate, and lowest coral cover also display microbial abundances that are tenfold higher and communities dominated by heterotrophs, including a large percentage of potential pathogens30. In the southern GBR, along cross-shelf gradients in the Mackay region, bacterioplankton numbers correlated with dissolved organic carbon and particulate carbon, nitrogen and phosphorus31. Seawater microbiomes from inshore reefs in the central GBR were investigated for their predictive power to identify environmental perturbations affecting reefs, with bacterial compositional variability significantly explained by temperature in addition to water quality parameters such as total suspended solids, particulate organic carbon or chlorophyll concentrations32. Current evidence from a number of global studies indicates that cumulative environmental pressures and the ability of microbial communities to tolerate and respond to various abiotic and biotic conditions structure the microbial community33. However, the lack of available microbial data collected at sufficient spatial and temporal resolution, and supported by a comprehensive suite of contextual parameters, limits our understanding of the role of microbes in the functioning and resilience of the GBR and coral reef ecosystems more generally11.Temporal and environmental variability in microbial assemblages at specific GBR sites have been identified, however the drivers of bacterioplankton community change are poorly resolved across the large expanse of the GBR. In the northern GBR (Tully River region), microbial communities in proximity to reefs followed seasonal dynamics and responded to riverine inputs, with rainfall, water quality , salinity and temperature implicated as drivers of bacterial community composition34 and mapped their impacts on coral reef communities across the continental shelf35. Models of water quality such as those integrated in the eReefs collaborative data platform are now available (https://ereefs.org.au/), allowing for interpretation and prediction of ecosystem patterns at large temporal and spatial scales. As contributors to coral reef functioning, it is important that microbial communities are incorporated into modelling frameworks to elucidate their applicability for ecosystem management and bioremediation37. A first step towards a global understanding of microbial communities in the context of ecosystem functioning in the GBR is to evaluate how much we know and, importantly, identify knowledge gaps in microbial dynamics across this threatened ecosystem38.In recent years, the need to scale-up management of natural ecosystems has been increasingly aided by modelling efforts. For instance, ecosystem models have inferred relationships between long-term anthropogenic pollution and reef resilience35 as the categorization framework. This classification splits reefs surveyed across the GBR as part of the Long-Term Monitoring Program (LTMP) of the Australian Institute of Marine Science (AIMS)39 into six main reef benthic categories according to environmental predictors   groups of reef microbes across known environmental gradients and for distinct reef categories. We use the coral community mapping of Mellin et al.see Fig. . These rhttps://research.csiro.au/ereefs/models/model-outputs/gbr1/) for the microbial case study (n = 37) and LTMP (n = 109) sites . Out-Soft and Out-Digit were devoid of seasonal effects, with the exception of temperature differences between the wet and dry seasons. Inshore reef categories in contrast, showed strong seasonal differences for all variables measured.Modelled estimates of the environmental conditions of surface seawater retrieved from the eReefs hydrodynamic and biogeochemical model  and the Coral Sea (n = 6 sites), which comprised n = 69 samples across two seasons (wet and dry), were used to assess broad microbial community assemblages across environmental categories. These sites span the inshore to offshore cross-shelf gradient and cover five out of six defined benthic reef categories 41]. Alpha diversity  = 14.43, p < 0.001; see Supplementary Table F = 14.04, p < 0.001; see Supplementary Table Locations in the Burdekin region of the GBR  = 6.15, p < 0.01) and included the variables salinity, temperature, total chlorophyll a, dissolved inorganic carbon (DIC), ammonium (NH4), dissolved organic carbon (DOC) and the vertical attenuation coefficient of light (Kd_490)  with community patterns. After dimensional reduction based on pairwise correlations between variables  and positively with OCS155 and Prochlorococcaceae, and negatively with Cryomorphaceae , both at broad geographical scales (i.e. across reef categories), and at the level of temporal-seasonal dynamics. A trend of increasing terrestrial input near-shore resulted in reduced richness and diversity of bacterial communities in In-MA and In-Porites (inshore) reefs. Reduced microbial diversity was also evident at riverine stations  identified as the strongest drivers of microbial community composition42. Changes in bacterioplankton community structure related to freshwater runoff have also been described for Hawaiian reefs48.Riverine outflows are known to impact the health of inshore reefs as they carry organic and inorganic nutrients of terrestrial origin  onto the reef systemsa and salinity to bacterial seasonal dynamics. Seasonal changes in community structure may be the result of event-related phytoplankton blooms, which are known to quickly assimilate and turnover available pools of dissolved inorganic nutrients in the GBR lagoon at the beginning of the warm wet season50. Seasonal influence on microbial diversity (lower in the wet season) and community structure was also confirmed for In-Porites reefs located under the influence of river plumes (see Supplementary Discussion). These seasonal patterns in microbial community dynamics, even at smaller spatial scales of river influence, are consistent with previous studies on the GBR32. Irrespective of their spatial and temporal drivers, temperature and nutrient availability likely interact to establish selective processes that determine the assemblage of prevalent bacterial groups for each reef category. Inshore reefs are typically light limited (high attenuation of light due to high particulate and dissolved nutrients) and richer in heterotrophic processes, whereas outershelf reefs are more nutrient limited, particularly nitrogen limited (low DIN), and richer in autotrophic processes50.Seasonal variation influenced the richness and structure of bacterial communities, though trends were not as strong as observed for cross-shelf spatial variation  and midshelf (Mid-Mixed) reef waters. Prochlorococcaceae and Synechococcaceae represent the main photosynthetic bacteria in the ocean53. Prochlorococcaceae reached mean relative abundances of ~25% in outershelf reef waters (Out-Digit and Out-Soft), while Synechococcaceae populations were >30% in inshore reefs (In-MA and In-Porites). Metabolic properties of the dominant bacterial taxa likely influenced these observed distribution patterns across the GBR shelf.The shallow-water pelagic microbiomes of the GBR were dominated by autotrophic cyanobacterial families Prochlorococcaceae and Synechococcaceae, as well as the oligotrophic Pelagibacteraceae (Alphaproteobacteria). These bacterial groups are highly abundant in the global ocean, with Pelagibacteraceae (formerly SAR11 clade) accounting for up to a third of all cells present in the oceans’ surface waters54, and was previously identified as being more abundant in marine coastal zones than in open-ocean surface waters55. Rhodobacteraceae are often characterised as opportunistic microbes correlated with poor reef health21 and are commonly enriched in diseased corals56. Both these lineages were found at higher relative abundances on In-MA than In-Porites reefs. The high abundance of the Bacteroidetes families Cryomorphaceae and Flavobacteriaceae in inshore reefs is consistent with previous studies. Bacteroidetes have been used as fecal indicators and can act as a reservoir of resistance genes for other more pathogenic bacterial strains57. A recent study has proposed that increased levels of Bacteroidetes in reef waters are indicative of enhanced macroalgal growth and the onset of microbialisation in coral reefs58. Proximity to river mouths has also been linked to increases in microbial taxa implicated in coral disease30. In contrast, Prochlorococcaceae showed increasing relative abundance with decreasing nutrients, being more abundant on outershelf reefs likely as a result of their photoautotrophic metabolism. Rhodospirillaceae also showed a tendency to increase with decreasing nutrients, but their known diversified lifestyles , likely support their constant relative abundances across reef categories. The increase in Pelagibacteraceae and SAR86, two abundant marine bacterial lineages that exhibit metabolic streamlining60, is consistent with the oligotrophic conditions found in the GBR lagoon and Coral Sea. Nutrient levels measured in these habitats were the lowest found across all parameters and throughout all datasets included in our meta-analysis  and thus dominated inshore reefs. OM60 is an oligotrophic gammaproteobacterial family known to encompass diverse metabolisms including aerobic anoxygenic photosynthesis32.The population of indicators identified for each reef category includes particular OTUs within the families Rhodobacteraceae, Synechococcaceae, Cryomorphaceae, Flavobacteriaceae, Pelagibacteraceae and Halomonadaceae, among others. Counter-intuitively, the less rich and less diverse inshore communities yielded a larger number of microbial indicators than the taxa-rich and diverse outershelf reefs. This pattern strongly relates to the number of unique OTUs found in the different reef areas. The outershelf reefs were characterised by only 15 indicator OTUs from a restricted number of families and appeared to support a higher abundance of ubiquitously distributed bacterial taxa. In contrast, inshore environments harboured a greater relative abundance of autochthonous microbial taxa that represented indicators of inshore and potentially degraded systems. The absence of diagnostic microbial taxa for individual reef categories is a good illustration of the spatial continuity observed for free-living microbial communities among reef categories. This strongly contrasts with host-associated microbial data, where the specificity of bacterial taxa towards their host is usually high and indicator taxa are commonProchlorococcus is commonly reported from oligotrophic waters, due to its capacity to take up low levels of organic nitrogen, whereas Synechococcus becomes increasingly dominant in nutrient rich waters61. The Prochlorococcaceae:Synechococcaceae relative abundance ratio represents a potential indicator for the contribution of nutrient concentration in coral reef waters. These patterns have also been observed for pristine versus human-influenced reef atolls, with a four-fold increase in nitrogen and phosphate concentrations associated with Synechococcus dominance from 9–15% to 64–66% of the cyanobacterial population12. However, even within a particular bacterial genus or species, there can be different lineages (i.e. 16S rRNA sequence variants) that are associated with distinct environmental conditions62. For example, the Prochlorococcus group is comprised of various ecotypes that are phylogenetically and physiologically distinct and whose abundance distributions respond according to environmental gradients63. These patterns are often attributed to the partitioning of environmental resources and niche spaces among taxa64. An index could be established that categorizes this ratio into levels that broadly relate to the availability of nutrients/contribution of terrestrial run-off. Additional indices could be developed to monitor eutrophication of GBR waters, for instance by including particular lineages  with different substrate affinities. Comparison of broader trophic groups may also prove valuable indicators of ecosystem health and/or function. For example, levels of typical copiotrophs such as families OCS155, Flavobacteraceae, Cryomorphaceae and Rhodobacteraceae, could be modelled against levels of oligotrophs such as Pelagibacteraceae and SAR86 to generate a complementary index for eutrophication . Typical opportunistic bacteria, such as those exhibiting virulence towards benthic organisms , could also be used as indicators of reef health and/or degradation.The cyanobacterial families Prochlorococcaceae and Synechococcaceae show opposing correlations to many of the environmental parameters included in the analyses Fig. , indicat65 causes changes in the microbial communities26. For instance, a reduced Prochlorococcaceae:Synechococcaceae ratio at In-MA reefs may be related to the increasing contribution of organic nutrients of macroalgal origin. In-MA reefs are potentially influenced by positive feedback loops through which microbial changes related to increased macroalgae cover promote more advanced states of macroalgae domination66. Although the identification of microbial taxa and functions that contribute to a functioning reef (or to disturbed reef states) is a major objective of this work, further analyses are required to identify potential microbial indicators of reef health, microbialization, degraded environments and ecological tipping points. The Flavobacteriaceae-affiliated OTU161 was the only indicator for a single reef category (i.e. In-MA) and may represent a microbial indicator of ecosystem degradation on the GBR. Further work should be undertaken to assess the robustness of this indicator by establishing comprehensive baselines and additional experimental validation. Future research should also resolve dynamic causal relationships between environmental parameters, microbial communities and underlying coral reef ecosystem health.Analyses of GBR microbial communities with extensive environmental metadata identified bacterial taxa that are indicative of particular conditions on the reef, either because they contribute to the processes underlying reef health, or because they occur as a consequence of those underlying processes. The causality of these relationships is difficult to attribute directly, though a non-mutually exclusive alternative explanation could be that variability in benthic cover and associated availability of labile organic matter released by dominant benthic primary producers35, with frequent sampling to capture inter-seasonal patterns, is proposed. Extension of microbial data to the far northern section of the GBR is required to ascertain whether pelagic microbial communities in this region follow the patterns identified here for other regions of the GBR. Sampling effort should also be directed towards characterising pelagic microbial communities over the outershelf tabular and corymbose hard coral (Out-Tab) and the outershelf branching hard coral (Out-Digit) communities, for which we could not robustly predict a microbial community. However, we suggest that inshore reefs demand the most intensive monitoring program, as this is the area exposed to the broadest range of environmental variation, and the highest degree of uncertainty due to proximity to land and human activities affecting inshore reefs. These interactions drive higher spatial heterogeneity of bacterial communities in inshore reefs as compared to more homogeneous outershelf and open ocean sites67.Robust mapping of microbial communities across the GBR requires extensive baselines across temporal periods and spatial scales that reflect its expanse of 2300 Km and almost 3000 individual reefs. Microbial baselines could be achieved through a series of microbial observatories spanning key habitats to complement current reef-monitoring efforts (e.g. AIMS LTMP). Establishment of parallel cross-shelf transects that capture all six reef communities previously identified11. Establishment of a GBR microbial observatory network would aid identification and validation of microbial indicators for environmental disturbance and facilitate early identification of ecosystem conditions leading to tipping-points in reef condition. Extending beyond taxa to also characterise the functional traits of these microorganisms would determine if changing community composition translates to changes in biogeochemical cycling and/or other microbial processes important for ecosystem health58. Finally, it is crucial to generate global reef microbial data to understand the ubiquity of the patterns described here for the GBR.Microorganisms represent the first responders to environmental change and may mitigate or exacerbate the impacts of disturbance for higher trophic levels35. Our approach comprised: (1) deriving water chemistry data for sites with microbial data as well as for broad reef categories as monitored by the LTMP of the AIMS; (2) training a classification model that establishes a relationship between reef categories and prevailing environmental conditions; (3) predicting reef category for the microbial case study sites; and (4) summarizing microbial community data for broad reef categories across the wider GBR.To estimate the compositional variation of microbial communities in GBR waters across different environments from inshore to outershelf reefs, we undertook a modelling exercise extrapolating the publically available microbial data (for a number of sites) into the larger extent of the GBR. The crucial step here was to ascertain the spatial representativeness of the available case study sites in relation to the wider GBR, which we did by modelling their environmental variation and comparing it to that of known GBR reef habitats as categorized in a recent study4. The microbial datasets available as case studies for the GBR are spatially restricted, covering localised sites on Heron Island40 and the Mackay region31 in the southern GBR, the Burdekin region32 in the central GBR, and the Tully region30 in the northern GBR  and a biogeochemical model  to generate estimates with a high spatial and temporal resolution for a vast variety of environmental conditions across the GBR, from inshore reefs to the open ocean. Aggregations of such data are available over hourly, daily, monthly, annual or all-time periods (https://ereefs.org.au/).The GBR represents an extensive reef system with large longitudinal temperature gradients and a seawater productivity gradient mostly defined by distance to shore35 as a categorization framework. This classification splits reefs surveyed across the GBR as part of the LTMP of the AIMS39 into six main reef benthic categories according to environmental predictors   were extracted using the R package ereefs (https://github.com/AIMS/ereefs). Specifically, surface seawater temperature, salinity, total chlorophyll a, dissolved inorganic carbon (DIC), nitrogen (DIN) and phosphorus (DIP), ammonium (NH4), nitrate (NO3), dissolved organic carbon (DOC), nitrogen (DON) and phosphorus (DOP), total carbon (TC), nitrogen (TN) and phosphorus (TP), total suspended solids (TSS) and the vertical attenuation coefficient of light (Kd_490) were retrieved. For each of these 16 environmental variables, data spanning the period Jan 2015–Jan 2018, were extracted for each 1 × 1 Km grid cell matching the n = 37 sites that are part of the analysed case studies  and n = 109 reference sites included in the GBR-wide analysis of Mellin et al.35 (further referred to as “LTMP” sites) using an adapted R script . Data for each site were then averaged for two seasons, wet and dry, as formally defined by the Australian Bureau of Meteorology (http://www.bom.gov.au/climate/glossary/seasons.shtml), i.e., warmer wet season defined as Dec-Feb, and cooler dry season as Jun-Aug. After assessing collinearity among the 16 environmental variables , non-collinear variables were included in a Linear Discriminant Analysis . LDA identifies the component axes that maximize the variance of the data, but additionally finds the axes that maximize the separation between multiple data classes. LDA was initially trained with the LTMP dataset to predict the reef category in the classification system of Mellin et al.35 based on the reduced set of environmental variables. LDA performance was evaluated using “leave-one-out” cross validation (LOOCV70), with function “table”, and with Cohen’s weighted Kappa for interrater agreement71, via function “kappa2” in package irr, where disagreements were weighted according to their squared distance from perfect agreement. The trained LDA model was then used to predict reef categories for the reefs included in the microbial dataset using the prevailing environmental data as model input (using function “predict”). This allowed identification of the reef community classes of Mellin et al.35 that show environmental variation similar to, or in the range of that occurring in the microbial sites, thus facilitating extrapolation of microbial community data to each of the reef benthic categories to predict putative community composition across the wider GBR.To compare surface seawater conditions across sites for the available microbial case studies with those included in the LTMP program, environmental data available from the eReefs hydrodynamic and biogeochemical model  for which microbial taxonomy had previously been assigned using 16S rRNA phylogenetic marker genes. OTU tables quantified abundance of the 16S rRNA gene sequences affiliated with each microbial lineage across sampling sites, which provided an accepted proxy for relative abundances of distinct microbial taxa. Limitations of this metadata approach are that methods differed among case studies . OTUs with one single occurrence (singletons) were removed to avoid including spurious data originating from sequencing errors. All data were rarefied for within-study comparisons (see Supplementary Table https://data.bioplatforms.com/organization/pages/bpa-marine-microbes/methods). Otherwise, all pre-processing data analyses are detailed in the respective publications for each of the GBR regions: Tully30, Burdekin32, Mackay31 and Heron Island40.All OTUs derived from cellular plastids (mitochondria and chloroplast) were removed from the analyses and, because Archaea were only reported for the Tully region72 and “rand” of lmerTest package73) applied to test for the effect of reef category and season (fixed factors) on each estimator . Venn diagrams were constructed with VennDiagram package74 to depict the number of unique and shared OTUs. Beta-diversity statistics and visualizations were calculated from Bray-Curtis similarity matrices based on Hellinger-transformed abundance data to reduce influence of dominant lineages. Non-metric Multidimensional Scaling (NMDS—function “metaMDS” of vegan package75) limited to two dimensions was used to visualize the microbial community structure. Permutational Multivariate Analysis of Variance  was used to test  for differences in community structure between reef categories and seasons within each dataset . All beta-diversity statistics and follow-up analysis (see below) included only OTUs seen more than five times in at least 50% of the samples (dominant taxa). However, data obtained by 454 sequencing technology (Tully dataset) yielded a low number of OTUs and all OTUs were included in beta-diversity statistics and follow-up analysis.All microbial data were transformed into relative abundance data and presented figures summarize the most abundant microbial taxa in each case study. Alpha-diversity estimators  were calculated after rarefying , and a mixed-effects Analysis of Variance . All surface seawater data were extracted using 1 × 1 Km resolution except for those cases where only the 4 × 4 Km model could provide data, such as for the Tully region (1 × 1 Km model only available from 2015 onwards). In all cases, data were averaged across the 3 days leading up to (and including) the actual sampling dates reported in the case studies. eReefs only models data obtained since 2011 (using 4 × 4 Km model), so microbiome-environment links could not be established for the Mackay region as this study76) of non-collinear variables (chosen as explained above for the GBR-wide dataset). Before PCA, abiotic variables were first checked for normality using a graphical check (and transformed if needed) and then scaled to zero mean and unit variance (z-scores standardization). To determine the contribution of abiotic parameters to the structure of microbial communities, a Bray-Curtis distance-based redundancy analysis (db-RDA) was applied on Hellinger-transformed relative abundance and the abovementioned non-collinear environmental metadata using functions “dbrda” and “capscale” of vegan package75. Samples with missing values for at least one of the environmental parameters were removed. A model selection tool based on the explained variance (function “ordiR2step”) was used to select the environmental variables (constraints) significantly explaining variation in the microbial community , after which the significance of each constraint was confirmed with analysis of variance (ANOVA) for db-RDA (function “anova.cca” in the vegan package75). An ANOVA-like permutational test (function “permutest”) for dbRDA was used to assess the significance of the full model. Explanatory value (in %) of significant constraints  was assessed with a Variation Partitioning Analysis of the vegan package75. The function “taxa.env.correlation” of microbiomeSeq package was used to compute (Spearman) correlation coefficients between environmental gradients and the relative abundance of the 20 most abundant OTUs (non-transformed data), for the environmental variables that were significant constraints of the community (“ordiR2step” above). The Benjamini-Hochberg procedure was used to calculate adjusted p-values for multiple comparisons.Environmental variation was visualized by Principal Component Analysis  was performed on relative abundance data (non-transformed). IndVal identifies microbial taxa based on their significant specificity and fidelity to particular treatments78 and has been previously used in the coral microbiome literature79.Finally, to identify groups of microbes diagnostic of the different reef categories, an indicator value analysis (IndVal; indicspecies package80 using the phyloseq package81, and graphical outputs were generated with ggplot2 package82.No experiments were performed. A high number of permutations was applied to deal with hypothesis-driven statistical tests. All meta-analyses of available microbial community composition and contextual environmental data were performed in R version 3.4.3Further information on research design is available in the Supplementary InformationReporting SummarySupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Description of Additional Supplementary FilesPeer Review File