Network-based integration of omics, physiological and environmental data in real-world Elbe estuarine Zander

Network-based integration of omics, physiological and environmental data in real-world Elbe estuarine Zander | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Network-based integration of omics, physiological and environmental data in real-world Elbe estuarine Zander Raphael Koll, Jesse Theilen, Elena Hauten, Jason Woodhouse, Ralf Thiel, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3990815/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jun, 2024 Read the published version in Science of The Total Environment → Version 1 posted You are reading this latest preprint version Abstract Coastal and estuarine environments are under endogenic and exogenic pressures jeopardizing survival and diversity of inhabiting biota. Information of possible synergistic effects of multiple (a)biotic stressors and holobiont interaction are largely missing in the Elbe estuary but are of importance to estimate unforeseen effects on animals’ physiology. Here, we seek to leverage host-transcriptional RNA-seq and gill mucus microbial 16S rRNA metabarcoding data coupled with physiological and abiotic measurements in a network analysis approach to deconvolute the impact of multiple stressors on the health of juvenile Sander lucioperca along one of the largest European estuaries. We find mesohaline areas characterized by gill tissue specific transcriptional responses matching osmosensing and tissue remodeling. Liver transcriptomes instead emphasized that zander from highly turbid areas were undergoing starvation which was supported by compromised body condition. Potential pathogenic bacteria, including Shewanella , Acinetobacter , Aeromonas and Chryseobacterium , dominated the gill microbiome along the freshwater transition and oxygen minimum zone. Their occurrence coincided with a strong adaptive and innate transcriptional immune response in host gill and enhanced energy demand in liver tissue supporting their potential pathogenicity. Overall, we demonstrate the information gain from integration of omics data into biomonitoring of fish and point out bacterial species with disease potential. Biological sciences/Ecology/Conservation biology Biological sciences/Molecular biology/Transcriptomics Biological sciences/Microbiology/Bacteria/Bacterial host response RNAseq Metabarcoding Network analysis Hypoxia Holobiont Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Structure and functioning of coastal and estuarine environments have dramatically changed over the last century driven by overexploitation, habitat fragmentation, chemical pollution, and species invasion [ 2 ]. As transitional ecosystems estuaries experience high spatiotemporal variation in physicochemical conditions (i.a. hydrology, salinity, turbidity, temperature, dissolved oxygen, habitat availability) [ 3 ]. Global change is expected to increase pressures on inhabiting fish communities in estuaries, especially due to changes in temperature [ 4 ], dissolved oxygen (DO) levels [ 5 ] and salinity [ 6 ]. The tidal Elbe is described as an alluvial, turbid, well-mixed and macro-tidal estuary [ 7 ]. Over the last centuries, extensive engineering, notably by diking and deepening to improve access to the Hamburg harbor located about 100 km inland, has placed substantial pressure on the estuary. Since the 1980s regions of low dissolved oxygen, in some instances hypoxia, have been reported especially in upstream freshwater areas [ 8 ]. During summer, the otherwise well-mixed estuary experiences stratification, particularly in the port area and deepened navigation channel. Under these conditions it is common to observe oxygen depletion and ammonium accumulation in the hypolimnic waters [ 9 ]. Efforts to abate these environmental impacts are limited. In 2021 alone, deepening and maintenance dredging of navigation channels resulted in relocation of ca. 23 million m 3 sediment [ 10 ] of which a proportion was resuspended into the water column [ 11 ]. Fish are responsive to physical and chemical changes in the environment and are critical components of aquatic food webs. Through coupling effects, nutrient sequestration and top-down impact predatory fishes strongly influence nutrient cycling, carbon storage and overall habitat maintenance [ 12 ], [ 13 ]. Major changes in abundance, life strategy and composition of estuarine fish communities in Europe have been detected recently, with declines of up to 80% overall fish biomass, affecting nutrient cycling and overall functioning of these ecosystems [ 14 ], [ 15 ]. For the Elbe estuary, the first time series data were analyzed for only one anadromous fish species ( Osmerus eperlanus (Linnaeus, 1758)), indicating a decline in abundance due to the combined effects of several stressors, such as the reduction of shallow water areas, the abstraction of cooling water for power plants, increased dredging, increased turbidity and re-emerging hypoxia [ 16 ]. In this study we use juvenile zander Sander lucioperca (Linnaeus, 1758) (0 + cohort) sampled in late summer 2021 from the Elbe estuary as model organism to explore gene expression patterns and holobiont interaction linked to end-point measurements of physiological health in response to steep abiotic gradients. The zander is among the most abundant fish in the ecosystem and among the top predatory species in the Elbe exerting a strong top-down control on other species [ 17 ]. In contrast to their embryonic stages, which require a salinity of less than 7 psu for their development [ 18 ], juvenile and adult walleye pollock tolerate mesohaline conditions and strong osmotic fluctuations [ 19 ], allowing them to inhabit the entire estuary. Life history strategies analyzed in the vicinity of the Kiel Canal indicate either brackish residency or a freshwater-brackish water strategy, where reproduction and larval growth occur in freshwater with later dispersal to more saline habitats [ 20 ]. The spawning migration takes place in early spring and extends usually over about 30 km [ 21 ]. After the switch from planktonic to piscivorous nutrition within the first year [ 22 ], O. eperlanus becomes the preferred prey for zander in the Elbe estuary [ 23 ]. The summer period is characterized by intensive growth (16 ± 2 cm) with high metabolic activity susceptible to be altered by multiple stressors [ 24 ]. Despite a well-found knowledge of the abiotic and biotic changes occurring within the estuary, many spatio-temporal factors, i.e. temperature, salinity, DO levels, are confounding and make it difficult to determine the precise mechanisms underlying for example losses in fish biomass. Omics are increasingly considered in the context of biomonitoring and conservation biology, as they enable a deep understanding of processes at all levels of biological organization and of previously overlooked interactions [ 25 ], [ 26 ]. Co-working of multiple stressors is expected to lead to unforeseen synergistic or antagonistic effects in organisms [ 27 ], sublethal impacts of which can contribute to population declines over generations [ 25 ]. Molecular adjustments precede high level responses thereby probably allowing earlier detection of adverse events or situations [ 28 ]. Real-world omics studies indicate molecular mechanisms used by fish to cope with adverse effects of multi-stress environment. Reversely they inform about the environmental conditions themselves allowing identification of unexpected stressors as shown in recent studies on range expansion in invasive species [ 29 ] or xenobiotic response in estuarine flounder [ 24 ]. Biotic interaction with complex communities of microbiota inhabiting every mucosal surface of teleost fish strongly influence the host metabolism, growth and health [ 30 ]. The gill mucosal surface constitutes a special habitat for host-associated microbes due to its unique functions in waste excretion, gas exchange and local immune activity [ 31 ], [ 32 ]. Local immune cell populations composed by adaptive parts (cytotoxic CD8 + and CD4 + T helper cells (Th) & B cells [ 33 ]) as well as innate immune cells form the gill associated lymphoid tissue (GIALT) [ 32 ]. It enables fish to discriminate between beneficial and pathogenic bacteria keeping microbiome homeostasis [ 34 ]. However, changes in the environment, including water conditions, temperature, seasonal changes, physiological stress can lead to compositional disturbances that turn commensals into pathogens [ 30 ]. Hypoxia led to dysbiosis in captive salmonid skin microbiome [ 35 ], in gills of clownfish suspended sediment load led to a microbial shift towards pathogenic communities [ 36 ]. This sensitivity to environmental parameters makes the external mucus composition a biomarker for fish health built by a stable community of core taxa of low number but shared over a large range of conditions and a variable community strongly influenced by environmental conditions [ 37 ]. Investigating changes in the core microbiome may deliver indication for deep reorganization and dysbiosis, while analysis of variable taxa might deliver potential indicators for specific stressors [ 37 ]. The objective of this study is to apply a network-based dimensionality reduction method on real-world holobiont omics data based on host RNA and 16S rRNA sequencing. The unsupervised approach allows high resolution detection of tissue specific gene profiles and relate those to prevailing abiotic conditions. We describe the microbiota dynamics in gill mucus community along the estuary in relation to the bacterioplankton and incorporate them with identified host immune responses. Different levels of biological organization are integrated to assess the fish’s health on individual level, allowing identification of individual responses. 2. Material and methods 2.1 Sample Collection Fish were caught with a stow-net fishing vessel (opening area of 135 m², mesh size of 10 mm at the cod end) at four stations along the Elbe estuary ( Fig. 1 ) in the summer period from 25–29.08.2021. Sampling stations were chosen within different estuarine sections classified by dominating abiotic drivers [ 38 ]: Station ML-Elbe kilometer 663 within oxygen minimum zone (OMZ) (stream km 620–650, low oxygen, summer < 2 mg/L, salinity 0.5 1 < 20 psu). See Fig. 1 for sampling overview map and Fig. 4 C for abiotic conditions at sampling times. Sampling procedures followed the standards described in the German Animal Welfare Act (§ 4 TierSchG). Exemptions to the ordinances on nature reserves were obtained ( see Permits ). 534 individuals of age 0 + were caught with even distribution over stations. Six individuals from each ebb haul (n = 24) were processed immediately on board in the following standardized manner: Fish were measured and weighed before recovering gill bacteria with sterile cotton swabs from the middle part of the second and third arch. The same part of the gill tissue as well as the right most edge of the liver were resected for preservation. All samples were kept on dry ice on board until they were moved to -80°C until further procession. Tissue samples were first placed in tubes with RNAlater (Macherey-Nagel) for one hour before they were moved to dry ice. Abiotic conditions (oxygen, salinity, Secchi depth, temperature, pH) were measured at start and end of each haul using a multi-probe (Hanna HI 9829 and Secchi disc) at the water surface. Water samples (n = 4) obtained from the water column were vacuum filtered onto 0.2 µm polycarbonate membranes (Whatman ™ Nucleopore ™ Track-Etch). Whole fish samples were frozen at -20°C and further analyzed in the lab: body indices were determined (Fulton’s body condition (FultonK), hepato-somatic - (HSI) and spleno-somatic indices (SSI) see [ 39 ]), stomach content was weighed and age determination from otoliths and scales was performed. 2.1 Transcriptome RNA extraction Total RNA extraction from gill and liver tissues was performed via sample homogenization (45 seconds) with ceramic beads in peqGOLD Trifast (Peqlab), RNA dissolving in chloroform and mixing with 70% ethanol before immobilization on a silica membrane (Crystal RNA Mini Kit, BiolabProducts). On-column DNA digestion with RNase free DNase I (Qiagen) was performed for all samples. RNA quantity and integrity were controlled by spectroscopy (Nanodrop, Thermo Fisher) and on denaturing agarose gels and Bioanalyzer (Agilent Technologies) (avg. RIN 9.6 ± 0.6 over 48 samples). For every individual (n = 24) liver and gill mRNA was isolated using either (NEBNext Poly(A) Magnetic Isolation Module (New England BioLabs) followed by stranded cDNA library creation (NEBNext Ultra II Directional RNA Library Preparation kit (New England BioLabs) or the Illumina RNA stranded mRNA kit including polyA enrichment. Individually barcoded libraries were 2x150 paired-end sequenced on NovaSeq6000 (Illumina) (300 cycles, v1.5 reagents, S4 FlowCell) on three different lanes resulting in 3.4 billion reads (avg. ca. 25 ± 6.8 million). To account for possible batch effects from library preparation and sequencing runs, biological replicates from sampling location are spread over runs. Transcriptome analysis Transcriptome analysis was performed on the University Hamburg Hummel HPC. Read files were quality controlled using FASTQC (v 0.11.9) and MultiQC (1.12) followed by removal of adapter sequences, clipping of first and last base pairs, quality filtering bases lower Phred score 20 and discarding reads lower than 20 base pairs in length via TrimGalore (v 0.6.10) wrapper of cutadapt[ 40 ] and FastQC [ 41 ]. Clean reads were mapped to the genome of Sander lucioperca SLUC_FBN_1.2[ 42 ] using Hisat2 (v.2.2.1)[ 43 ] (overall mapping rate 99 ± 1%) followed by expression determination via featureCounts (subread v.2.0.2)[ 44 ] (assignment rate 51 ± 5.4 %; 18 ± 5 million asignments). All downstream analyses were performed in R (v.4.3.0) using visualization packages ggplot2 (v.3.4.2) and cowplot (v.1.1.1). Genome wide annotation databases org.XX.eg.db (v.3.17.2)[ 45 ] for human, mouse, and zebrafish as well as AnnotationHub (v3.17)[ 46 ] entries for zander were used to translate between different gene identifiers, names and symbols. CDS regions from the reference genome were annotated using diamond (v.2.0.15) blast against UniProt, UniRef90, TrEMBL, RefSeq-Protein and Human EMBL-EBI reference proteome databases. Blast results were filtered for E-value < 1e-6 and bit score higher 50, for multiple hits to the same transcript, the one with the lowest E-value was retained. Wherever human genes could be assigned to transcripts on gene-level the human annotation was used for pathway analyses. DEA & Functional term enrichment Differential expression analysis was performed to verify network analysis results via DESeq2 (v. 1.41.4)[ 47 ] (significance when FDR adjusted p value < 0.05 & Log2FoldChange 0.05 or 1) contrasting sampling locations along the estuary. Genes failing to meet expression threshold of transcripts per million mapped reads (TPM) of 1 in at least three samples were excluded from downstream analyses [ 48 ]. Genes were aggregated and hierarchical clustered using in ComplexHeatmap (v.2.17) [ 49 ]. Functional enrichment of heatmap clusters was performed via overrepresentation analyses as described in section 2.3. For results see S.2_Fig & S.3_Fig . 2.2 Microbiome DNA extraction Swabs were incubated in a mix of lysozyme, mutanolysin and lysostaphin before the cells were physically disrupted in a bead mill [ 50 ]. Proteinase K digestion in CTAB buffer was followed by chlorophorm-phenol extraction. Nucleic acids were precipitated in PEG 6000 using LPA-carrier and recovered and washed repeatedly with 95% and 70% ethanol before resuspension in water [ 51 ]. QC was performed via spectroscopy (Nanodrop, Thermo Fisher) and gel electrophoreses. DNA Sequencing The V3-V4 variable regions of the 16S rRNA gene were amplified in a one-step PCR using the primer pair 341F-806R (dual-barcoding approach [ 52 ]; primer sequences: 5’-CCTACGGGAGG-CAGCAG-30 and 5’-GGACTACHVGGGTWTCTAAT-30). After verification of the presence of PCR-products by gel electrophoresis, normalization (Sequal Prep Normalization Plate Kit; Thermo Fisher Scientific, Waltham, USA) and equimolar pooling was performed. Sequencing was conducted on the MiSeq platform (MiSeqFGx; Illumina, San Diego, USA) with v3 chemistry (2x300 bp). The settings for demultiplexing were 0 mismatches in the barcode sequences. DNA Bioinformatics Sequence data trimming, amplicon sequence variant (ASV) prediction and taxonomic identification were performed using cutadapt, FastQC and DADA2 (v.1.29.0) [ 53 ]. Quality profiles of paired reads were inspected and truncated at 270 and 190 for forward and reverse reads. ASV inference was performed with pooling method and taxonomy assignment used the SILVA SSU v138 taxonomic database [ 54 ]. Accuracy of DNA extraction, sequencing until taxonomic assignment was validated via inclusion of mock community samples (ZYMO research). ASV table and sample data were parsed to phyloseq (v 1.45.0) [ 55 ] removing ASVs taxonomically assigned to non-bacteria. Low abundance taxa with sum of counts lower than 0.005% of total sum of all counts were filtered reducing the number of zeros in the dataset by 50% to improve interpretability and minimize the risk of spurious correlations. A total of 1285 bacterial ASVs were included in further analyses, minimum sampling depth was ca. 7000, (avg. ca. 21000 ± 13000) clean sequences for fish samples. Alpha diversity measures were calculated via vegan package (v. 2.6.4) [ 56 ]. The core microbiome was determined from relative abundance data via microbiome package (v. 1.23.0) [ 57 ] with filtering detection threshold to 0.0001% within samples and 90% prevalence. Centered log-ratio (CLR) transformation to the ASV matrix was applied following best practices for handling of compositional data [ 58 ] using the microbiome package [ 57 ]. The transformed data were used for visualization and network analyses. Permutation multivariate analysis of variance was used to test differences in community structure between fish and bacterioplankton as well as between fish sampling groups in vegan followed by post hoc pairwise t-test via pairwiseAdonis [ 59 ]. Differential Abundance testing Negative binominal GLM fitting and Wald statistics in DESeq2 (v. 1.41.4) [ 47 ] were used to identify taxa with significantly changing abundances between conditions. Although DESeq2 designed for smaller RNAseq-Datasets tends to show higher false discovery rates with uneven library size (> 10x on average), sensitivity for differential abundance testing for smaller datasets (< 20 samples per group) is increased [ 60 ]. As sparsity and the assumption that most taxa do not change in the dataset are attributed to inflate false positive rates in DESeq2 [ 60 ], [ 61 ] abundance filter was applied as described above. 2.3 Holobiont analyses: WGCNA Weighted gene co-expression network analysis (WGCNA) (v.1.77-1) [ 62 ] was used for network inference using the following steps and parameters: 1. Signed network construction using biweight midcorrelation measure [ 63 ]. Weighted adjacency is created via soft-thresholding, multiplying the co-expression matrix by a power that leads to approximate scale-free topology. 2. Signed Topological Overlap Measure (TOM) is then used to measure interconnectedness accounting for indirect interactions and assignment of genes and bacterial clusters was performed via hierarchical clustering. Three samples were identified as outliers in both tissue RNAseq datasets by PCA and Euclidean distance clustering and excluded for network analysis and DEA. Parameter list: networkType = "signed", TOMType = "signed", corType = "bicor", minModuleSize = 30/5 (RNA/SSU), reassignThreshold = 0, deepSplit = 3, mergeCutHeight = 0.25. Our parameters were set to allow highest reproducibility between the different datasets in this study and upcoming samplings. Minimum module sizes of 30 and 5 for transcriptomic and SSU (Small subunit rRNA) datasets were chosen to facilitate interpretability. DeepSplit controlling sensitivity for module detection by hierarchical clustering was slightly increased from default. The first principal components of each module (Eigengene/EigenASV) are considered representative for the gene expression or bacteria abundance profile. Functional roles were assigned to the network modules by overrepresentation analyses against the KEGG-[ 64 ] and GO-databases[ 65 ] using ClusterProfiler (v.4.9.0) [ 66 ]. A subset of genes with correlation between gene expression profile and module eigengene (module membership) higher 0.8 was used for function annotation to increase interpretability. From each module, nodes with highest intramodular connectivity from the adjacency (hubs) were inspected as they are considered and shown functionally important [ 67 ]. Module eigengenes/ASVs where correlated between the different networks and to physiological traits of the fish (Fulton`s body condition, weight, length, stomach content weight, HSI, SSI) and external abiotic factors (DO levels, salinity, Secchi depth) via Pearson correlation. An integrated heatmap analysis approach was used to visualize host responses and holobiont interaction following a workflow developed by [ 68 ]. Principal component analysis Principal component analyses (PCA) were performed on variance stabilizing transformed counts for RNAseq data or CLR transformed counts for 16S data using PCAexplorer and PCAtools [ 69 ], [ 70 ]. Evaluation of clustering in PCA-plots indicated no obvious batch effects between sequencing runs. Nevertheless, ComBat-seq [ 71 ] batch effect adjustment tool was applied on the datasets but lead to an overall non-batch effect related reduction in variance and was discarded for further analyses. Data availability Sequence data are deposited in the ENA Sequence Read archive under study PRJEB71116. We made the full analysis available in stepwise R markdown files, including metadata and supplementary lists under https://github.com/vollkorrn/ElbeEstuarineZander 3. Results To gain insight into fish response and fish-microbiota interactions under steep abiotic gradients in an estuarine habitat, we applied an integrative approach of tissue specific RNA-seq and gill epithelial surface 16S rRNA amplicon sequencing collected from juvenile zander from Elbe estuary during late summer. The sampling sites spanned mesohaline to freshwater habitats [ 72 ] with DO levels, turbidity and salinity showing variation between the discreet estuarine areas [ 38 ]: The post-maximum turbidity zone (post-MTZ, station: MG), the maximum turbidity zone (MTZ, stations: SS, BB) as well as the oxygen minimum zone around the Hamburg harbor area (station ML) (Fig. 1 ). 3.1 Network analysis 3.1.1 Post-maximum turbidity zone – salinity adaptation The post-MTZ was characterized by mesohaline conditions ranging from 9.0–14.9 psu and stable DO levels between 8.2–8.5 mg/L (see Fig. 2 C). Only a few RNAseq modules positively correlated to salinity levels exhibited functional enrichment. Gill-RNA-1 (3848 genes, 753 with module membership MM > 0.8, correlation to salinity value r = 0.54, correlation p-value 0.01) was enriched in cellular communication and signaling (focal adhesion, extracellular matrix (ECM)-receptor interaction, cell adhesion), signal transduction (PI3K-AKT, MAPK, cGMP-PKG) and axon guidance (compare Fig. 2 A & B & Fig. 3 C). Module hub genes were transmembrane protein 204 ( tmem204 ) and protein tyrosine kinase 2 ( ptk2aa ), while slc5a3b shows highest correlation to salinity values. Genes in Gill-RNA-5 (888 genes, 144 MM > 0.8, salinity r = 0.67, correlation p-value 7.8E-04) were involved in ER protein processing and Gill-RNA-37 (41 genes, 22 MM > 0.8, salinity r = -0.46, correlation p-value 3.4E-02) in glutathione metabolism ( gpx7 , mgst3a ). The liver tissue-specific response in RNA-Liver-10 (418 genes, 22 MM > 0.8, salinity r = 0.49, correlation p-value 0.022) was related to fatty acid metabolism ( elovl5 , scdb ). 3.1.2 Maximum turbidity zone – enhanced energy metabolism The high turbidity zone was marked by high particle load (Secchi depth of 10–30 cm), salinity fluctuation between 5–0.6 psu (SS: 0.9–0.6 psu, BB: 1.6–5.2 psu) but stable DO levels 7.6–8.8 mg/L (see Fig. 2 C). Gill-RNA modules with significant negative correlation to Secchi depth values comprised Gill-RNA-3 (1675 genes, 1469 MM > 0.8, Secchi depth r = -0.55, correlation p-value 9E-3) enriched in ribosomal genes of both subunits and Gill-RNA-13 (499 genes, 50 MM > 0.8, Secchi depth r = -0.69, correlation p-value 5E-4) contained genes involved in axon guidance (compare Fig. 2 A & B & Fig. 3 C). Liver-RNA-1 (3334 genes, 754 MM > 0.8, Secchi depth r = -0.57, correlation p-value 5E-3) comprised genes related to autophagy (AMPK, mTOR, Insulin, FoxO, adipocytokine), mitophagy and peroxisome, hub gene was the autophagy related bnip4 . Liver-RNA-6 (716 genes, 118 MM > 0.8, Secchi depth r = -0.56, correlation p-value 6.6E-3) was composed of ribosomal subunit genes. Liver-RNA-9 (519 genes, 138 MM > 0.8, Secchi depth r = -0.45, correlation p-value 3.6E-2) was highly enriched in metabolic pathways comprising fatty and amino acid degradation (tryptophan metabolism, valine, leucine and isoleucine degradation, lysine degradation, alanine, aspartate and glutamate metabolism). 3.1.3 Oxygen minimum zone – cellular stress and immune reactions The oxygen minimum zone was characterized by low DO levels between 4.6–5.0 mg/l (46–52% saturation) and stable salinity levels (0.46 psu). Seven Gill-RNA modules were negatively correlated to oxygen levels in the surrounding milieu, five of them were also negatively correlated to salinity (see Fig. 2 A & B). Gill-RNA-Module 2 (2735 genes, 455 module membership > 0.8, DO correlation r = -0.52, correlation p-value 0.015) was highly enriched in replication and repair pathways (DNA replication, base excision repair, mismatch repair, nucleotide excision repair), cell growth and death (cell cycle, apoptosis, necroptosis), folding, sorting and degradation (proteasome), immune system (antigen processing and presentation) as well as signal transduction (NF-kappa B signaling pathway). Hub genes comprised proteasomal subunits ( psmb9a , psma6l , psmb10 , psme1 ), innate immunity related irf1b and erap2 . Gill-RNA- 4 (893 genes, 326 MM > 0.8, DO r = -0.51, correlation p-value 0.017) showed enrichment in protein procession (proteasome, protein processing in ER, Protein export), genetic information processing (nucleocytoplasmic transport and spliceosome). Hub genes were psmd2 , ups5 , usp14 involved in protein processing as well as cell proliferation related ppp5c . The immune response in gill tissue was divided between two modules. Gill-RNA-7 (637 genes, 123 MM > 0.8, DO r = -0.79, correlation p-value 2.6e-05) included innate immune response parts (chemokine signaling and viral protein interaction with chemokine). Hub genes were microbicidal oxidase system of phagocytes ( cyba , nox1 ) and MHC class II paralogues. Three variants encoding for eosinophil peroxidase ( epx -like) and mucin-2 like isoforms were most correlated to DO levels (r -0.85) and highly upregulated in the Hamburg area (LFC > 1, see S.2_Fig ). Gill-RNA-11 (587 genes, 213 MM > 0.8, DO r = -0.59, correlation p-value 4.5e-03) was enriched in adaptive immune system processes specifically T cell receptor signaling, T-helper cell differentiation, NK cell mediated cytotoxicity and general chemokine signaling. Hub genes include the protein tyrosine phosphatase ptprc and lymphocyte cytosolic protein 2 lcp2a . Gill-RNA-21 (186 genes, 64 MM > 0.8, DO r = -0.77, correlation p-value 4.6e-05) was enriched in porphyrin metabolism (hubs: rhd and alas2 ) and strongly upregulated hemoglobin subunit alpha-2-like and beta-2-like. Gill-RNA-25 (102 genes, 19 MM > 0.8, DO r = -0.6, correlation p-value 4e-03) shows enrichment in lysine degradation ( kmt2d , ezh2 , kmt2bb ). Liver specific response was less pronounced than gill, Liver-RNA-2 (2571 genes, 2327 MM > 0.8, DO r = -0.45, correlation p-value 0.03) was enriched in genetic information processing pathways (proteasome, protein processing in ER, DNA replication, spliceosome, base excision repair), cellular processes (cell cycle) and metabolism (carbon metabolism, N-glycan biosynthesis, TCA cycle). Hub genes include again usp14 and ppp5c . Liver-RNA-8 (728 genes, 81 MM > 0.8, DO r = -0.49, correlation p-value 0.02) was enriched in energy metabolism (oxidative phosphorylation), metabolism (of amino acids, cofactors, vitamins), mismatch repair as well as protein procession (proteasome, protein export, N-glycan biosynthesis). 3.2 Gill microbial community structure Approximately 630,000 trimmed, filtered and merged reads across 24 zander and four water samples resulted in 42.000 ASVs. Filtering for ASVs contributing at least 0,005% of overall abundance reduced sparsity in the dataset by 50% and retained 1285 ASVs for further analysis. Species accumulation analysis indicated that the rare microbiome was not sufficiently captured in the dataset ( see S.1_Fig A ) but overall composition was when excluding rare taxa ( S.1_Fig B ). 3.2.1 Host-associated differ from free-living bacterial communities We identified seven bacterial phyla associated with the fish microbiome and eight phyla in the surrounding water column ( S.1_Table ). There was a significant difference between the microbiome of the fish and that of the surrounding water column (DF 1, F 7.5686, p 0.001) (Fig. 2 , D, S .2_Table ). Of the 1285 ASVs identified 51 were found only in the water column while 600 were unique to the fish gill microbiome (Fig. 2 , B). The water column was characterized by a large abundance of Actinobacteriota (25% in water samples, 1–6% in fish), Proteobacteria (38%, fish: 53–78%), Bacteroidota (15%, fish: 14–32%), Verrucomicrobiota (8%, fish: 1–3% ) and several phyla not represented in fish samples at all: Cyanobacteria (4%), Nitrospirota (3%), Acidobacteriota (2%), Planctomycetota (1%). Firmicutes, Deinococcota and Desulfobacterota in the contrary were only recovered from fish gills. 3.2.2 Host-associated communities diverge along the course of the estuary We found a significant site-dependent difference in the bacterial community composition of the fish gill (DF 3, F 3.5533, p 0.001) between all sampling locations (Fig. 4 D, PC2, S.2_Table ). 45% of ASVs occurred in at least one fish from each sampling site and there were few site-specific taxa (1–6%) (Fig. 4 C). The community was dominated by Proteobacteria followed by Bacteroidota and Actinobacteria which were the only phyla present on all individuals. The ratio of abundances between Proteobacteria and Bacteroidota was subjected to strong changes (MG: 62/21%, BB: 53/20%, SS: 54/32%, ML: 78/14%). Enterobacterales (32%), Flavobacteriales (19%), Burkholderiales (13%), Pseudomonadales (9%) and Clostridiales (6%) make up 80% of the relative bacterial abundance over the estuary. Enterobacterales (49% in most downstream – 12% in most upstream sampling station) steadily decrease over the course of the estuary with Photobacterium (27% – 0%), Citrobacter (8–3%), Enterobacter (7–2.5%), Lelliottia (4–2%) being the most abundant genera. Burkholderiales (3–36%) with the genera Polynucleobacter (0–28%) Verticiella (0–7%) and the Pseudomonadales (5–17%) with the genera Acinetobacter (0–14%) and Psychrobacter (0–2%, SS 8%) show increasing relative abundance in fish gill swabs in upstream direction. Flavobacteriales show differing patterns with the genera Elizabethkingia (16–8%) steeply decreasing over estuary and the genus Chryseobacterium (0–6%, SS 9%) increasing with a peak in the middle of the estuary. Alpha diversity slightly changes along the course of the estuary, observed richness steadily increased from 364 to 502 ASVs (sampling group means) in upstream direction with slight changes in Shannon index: MG: 3.9; BB: 3,6; SS: 4.3; ML: 4.0. 3.2.1. The core microbiome community of estuarine zander gills The core microbiome comprised 43 ASVs from 11 orders predominated by roughly even relative abundance of Proteobacteria and Bacteroidota ( Supplement S.1_Fig & S.2_List ): Proteobacteria (Enterobacterales (18%), Pseudomonadales (1%), Burkholderiales (1%), Caulobacterales, Sphingomonadales), Bacteroidota (Flavobacteriales (16%), Chitinophagales (1%), Sphingobacteriales), Actinobacteriota (Microtrichales (1%), Corynebacteriales, Micrococcales), Deinococcota (Deinococcales) & Verrucomicrobiota (Chthoniobacterales). While the relative proportion of core taxa in the total microbiome declined along the course of the estuary (56 to 28%), the relative proportions within core taxa stay overall stable (Bacteroidota/Proteobacteria from 55/40 to 60/35%). The Hamburg harbor area has the largest impact on the core microbiome, with slight increases in Bacteroidota. CL500-29 marine group was the only taxon shared in large amounts (> 1%) between core fish gill microbiome and surrounding water column. 3.2.1 Differential abundant taxa The differential abundant taxa from pairwise comparisons split into four clusters. Taxa from cluster 1 were abundant in fish samples, most enriched in mesohaline habitat and comprising most of the core microbiome (Fig. 4 E, Table 2 ). Bacteroidota ( Elizabethkingia , Asinibacterium ) and Proteobacteria ( Citrobacter , Photobacterium , Lelliottia and Enterobacteriaceae) were the most abundant taxa (> 0.5%). Cluster 2 comprised taxa more related to the upstream region of the maximum turbidity zone dominated by Proteobacteria ( Psychrobacter , Acinetobacter , Alkanindiges , Polynucleobacter ), Bacteroidota ( Chryseobacterium , Flavobacterium , Ornithobacterium , Weeksellaceae) and Verrucomicrobiota ( Luteolibacter ). Cluster 3 was composed of taxa enriched in freshwater transition and OMZ comprising Proteobacteria ( Polynucleobacter , Verticiella , Alcaligenaceae, Caedibacter , Candidatus Megaira, Methylococcaceae, Acinetobacter ) and Bacteroidota ( Chryseobacterium ). Taxa in cluster 4 were predominantly found in the bacterioplankton comprising Actinobacteriota (Sporichthyaceae, hgcI clade), Candidatus Planktophila), Proteobacteria (CL500-29 marine group, Candidatus Methylopumilus, Limnobacter , Polynucleobacter , Limnohabitans , Candidatus Symbiobacter, Rhizobiales Incertae Sedis, A0839, Clade III) and Nitrospirota ( Nitrospira ). Table 2 Differential abundant taxa . Phylum, Order and lowest taxonomic level of differential abundant ASVs with individual relative abundance higher > 0.5% as sampling group mean. Taxa belonging to the core microbiome are marked in thick font. Relative abundance is calculated over all samples. Complete lists are available in supplemental material S.3_List. Location Phylum Order Lowest Taxonomic level Rel. Abundance [%] Cluster 1 Fish specific Mesohaline enriched Bacteroidota Flavobacteriales Elizabethkingia (2 ASVs) 14.6 Chitinophagales Asinibacterium (1 ASV) 0.7 Proteobacteria Enterobacterales Citrobacter (3 ASVs) Photobacterium leiognathi Lelliottia Enterobacteriaceae (6 ASVs) Enterobacter (3 ASVs) Enterobacter cancerogenus 3.7 0.9 0.8 7.8 2.9 1.0 Burkholderiales Microvirgula 0.9 Cluster 2 SS Ekm-665 Oligohaline Proteobacteria Pseudomonadales Psychrobacter (4 ASVs) Psychrobacter maritimus Acinetobacter Acinetobacter johnsonii Alkanindiges 1.5 0.1 0.9 0,7 0.3 Burkholderiales Polynucleobacter 0.4 Bacteroidota Flavobacteriales Chryseobacterium (4 ASVs) Chryseobacterium antarcticum Flavobacterium Ornithobacterium Weeksellaceae 1.7 0.7 0.6 0.3 0.3 Verrucomicrobiota Verrucomicrobiales Luteolibacter 0.2 Cluster 3 Freshwater transition ML Ekm-633 Proteobacteria Burkholderiales Polynucleobacter (3 ASVs) Verticiella Alcaligenaceae 4.1 1.1 0.5 Caedibacterales Caedibacter varicaedens 0.5 Rickettsiales Candidatus Megaira 0.2 Methylococcales Methylococcaceae 0.3 Pseudomonadales Acinetobacter Acinetobacter lwoffii 0.1 0.5 Bacteroidota Flavobacteriales Chryseobacterium 0.2 Cluster 4 Bacterioplankton Actinobacteriota Frankiales Sporichthyaceae hgcI clade (4 ASVs) Candidatus Planktophila 0.5 0.8 0.2 Proteobacteria Microtrichales CL500-29 marine group (4 ASVs) 1.3 Burkholderiales Candidatus Methylopumilus Limnobacter Polynucleobacter Limnohabitans Candidatus Symbiobacter 0.3 0.1 0.1 0.1 0.2 Rhizobiales Rhizobiales Incertae Sedis A0839 0.2 0.1 SAR11 clade Clade III 0.2 Nitrospirota Nitrospirales Nitrospira 0.3 3.2.1 Potential pathogens correlate to host immune response The bacterial network modules Gill-SSU-2 & 3 were correlated (Fig. 5 A) with the gill immune modules (Gill-RNA-7 & 11), bacterial taxa of highest Pearson correlation (> 0.6) and intramodular connectivity (> 0.8) comprise 17 genera (Fig. 7C, S.4_List & S.5_List ). Of these Verticiella (4 ASVs), Shewanella spp ., S. baltica and S. putrefaciens (9 ASVs), Aeromonas (8 ASVs), Acinetobacter spp ., A. johnsonii , A. tjernbergiae , A. lwoffii (24 ASVs), Polynucleobacter (4 ASVs), Plesiomonas (2 ASVs) and Chryseobacterium spp ., C. piscicola , C. haifense (13 ASVs) were with almost all strains absent from the bacterioplankton. In the contrary, Methylococcaceae (4 ASVs), Gemmataceae (4 ASVs), Luteolibacter (5 ASVs), Terrimicrobium (9 ASVs), Cyanobium (3 ASVs) and Legionella (7 ASVs) were abundant in both water and fish samples. 4. Discussion In this study we sought to understand, through OMICs, how the predatory fish zander responds to changing abiotic and biotic (microbiome) conditions along an estuary, as a means for understanding existing and future paradigms of environmental change and fish biomass losses. Here we were able, using a global molecular approach, to demonstrate how fish physiology is impacted in a tissue-specific manner, both by different environmental impacts (salinity, turbidity and oxygen availability) and by changes in the relative contribution of core and potentially pathogenic bacteria on the fish gills. 4.1 Salinity adaptation While zander larvae are stenohaline bound to freshwater and oligohaline areas, juveniles acquire the capacity to adapt to a wider range of osmotic fluctuations. Osmoregulation requires synergistic action of a complex sensing and signal transduction network that could be achieved by enhanced osmosensing in the juvenile zander via mitogen-activated protein kinase (MAPK) pathway [ 73 ] and PI3k-Akt regulating involved salinity stress response [ 74 ]. Further, we found cell-cell/ECM interactions related to gill remodeling processes during salinity fluctuations [ 75 ], [ 76 ], [ 77 ] and protein tyrosine kinase 2 (here ptk2aa) as hub gene in gill remodeling [ 78 ]. Major epithelial cells in fish gills, pavement and mitochondria-rich cells, play complementary roles in ion transport [ 79 ]. Their growth and differentiation may be organized by axonal guidance contributing to functional plasticity in estuarine fish [ 80 ]. In accordance, axonal guidance was enriched in zander gill from mesohaline till oligohaline areas. Whilst osmoregulation is among the most energy-intensive metabolic activities in teleosts [ 81 ], mesohaline zander in this study were in good physiological condition with liver-specific gene expression related to hepatic fat accumulation [ 82 ]. 4.2 Starvation in murky waters The mid estuary maximum turbidity zone is marked by liver specific elevated metabolic pathways, autophagy and regulation of ribosomal genes. These correlate with low HSI and compromised body condition indicative for nutritional stress [ 83 ]. During periods of nutrient deprivation, fish exhibit shifts in energy utilization, depleting liver glycogen[ 84 ], [ 85 ] and relying on lipids as major energy source [ 86 ]. Lipid droplets are broken down to free fatty acids via lysosomal-autophagy pathway [ 87 ]. In mammalian cells, starvation causes disruption of mitochondrial structure[ 88 ] thus increases ROS and induces autophagy via AMPK pathway [ 89 ]. Likewise, fish show increased ROS[ 90 ] and induced autophagy and mitophagy [ 91 ]. Autophagy is a key pathway mediating damage control and metabolic adaptation[ 92 ] triggered i.a. by hypoxic conditions involving BNIP3 and Beclin1 signaling while nutrient depletion activates AMPK-dependent ULK1 and Beclin1[ 93 ] in mammals. Zebrafish ortholog genes include hypoxia-induced bnip3a, closest to human BNIP3, and hypoxia-independent bnip3 and bnip4 [ 94 ]. Here we identified bnip4 and sirt5 -like as hub genes related to autophagy. Sirtulin enzymes, such as mitochondria-specific SIRT5, play pivotal role controlling lipid metabolism, mitophagy and apoptosis possibly via AMPK pathway[ 95 ], [ 96 ] indicating nutrient depletion driven autophagy activation in zander liver. The upregulation of ribosomal genes appears seemingly counterintuitive as ribosome biogenesis is most energy-consuming [ 97 ] and downregulated in starving fish [ 98 ]. However, severely reduced translation might be detrimental for acclimatization responses [ 99 ], especially in a multi-stress real-world setting. Ribosomes occupy large amounts of amino acids that can be tapped by autophagy during starvation delivering fuel for proteome remodeling [ 100 ]. However, autophagic tissue degradation has to be balanced by macromolecule synthesis to maintain homeostasis [ 100 ]. The cause of the starvation situation remains unclear. Turbidity, in general exerting negative effects on prey capture [ 101 ], did not affect foraging success in juvenile zander across a range of turbidity levels [ 102 ]. We did not investigate potential influences on prey availability or altered food spectra, observed in other estuarine predatory fish during increased turbidity [ 103 ]. As tissue fat content is a crucial factor for overwinter survival of juvenile zander [ 104 ], the low HSI and body condition together with fat and other energy source degradation pathways activation can be expected to affect survival rates of MTZ zander. Sediment load was shown to damage gill tissue in different species [ 36 ], [ 103 ], however an anticipated immune response linked to turbidity was not evident here. The relationship between starvation and immune regulation is complex and might relate to suppression of tissue [ 98 ] and external mucus immune functions [ 105 ]. We cannot rule out starvation related immunosuppression masking underlying processes. 4.3 Cellular stress in oxygen minimum zone Gills are the primary organ for physiological exchanges and first response to stressors like hypoxia [ 106 ]. Low DO acclimatization involves optimizing uptake and distribution, reflected in enhanced erythrocyte and hemoglobin concentrations [ 107 ]. Identified genes, including hemoglobin subunit beta-2-like hbb2 , linked to oxygen transfer and upregulated in OMZ, mirror findings in other percid species like Elbe estuarine ruffe and yellow croaker [ 108 ], [ 109 ]. Hypoxic conditions also induce epigenetic modulation by KMT2 family genes [ 110 ], proposed as adaptive coping mechanism in rainbow trout [ 111 ] and identified here as module hubs ( kmt2d and kmt2bb ). The upregulation of carbon and amino acid metabolism pathways together with TCA and OXPHOS in juvenile zander liver from the estuarine OMZ indicates a locally enhanced energy demand. Although there is no significant correlation with body condition and HSI, the latter is especially low in several animals from the oxygen minimum zone, which could indicate the first signs of depletion of energy reserves. Remodeling of energy metabolism is an effective strategy in fish to compensate increased demand during hypoxic conditions [ 112 ] or infection [ 113 ]. The liver plays a pivotal role controlling energy reserves utilizing amino acids as primary fuel [ 114 ] with upregulated metabolism [ 115 ], [ 116 ] observed during hypoxic episodes. Prolonged hypoxia increased dominance of aerobic TCA and OXPHOS [ 112 ]. Local response was further marked by different aspects of cellular stress response[ 117 ] including cell cycle arrest and DNA repair accompanied by affected proteostasis in both tissues. Halting energy-intensive genome duplication processes leverages time for macromolecular repair and allows for energy redirection [ 118 ]. Recovery from stressors on the other hand is accompanied by sustained repair processes [ 119 ], [ 120 ]. The ppp5c is a cross-tissue hub gene in our study involved in cell cycle arrest and DNA damage repair[ 121 ] shown dysregulated in fish under severe stress [ 122 ]. The ATP-dependent ubiquitin-proteasome system (UPS) mitigates proteotoxicity from damaged proteins[ 123 ] resulting i.a. from disrupted endoplasmic reticulum (ER) [ 124 ]. In both tissues we found moderate hypoxia correlated with UPS and endoplasmic reticulum (ER) stress response[ 125 ] characterized by luminal chaperone and ER-associated degradation (ERAD) activation. Besides ubiquitin-proteasome subunits, deubiquitinase genes (DUBs) usp5 & usp14 showed highest intramodular connectivity linking enhanced protein turnover with DNA repair [ 126 ], immune response[ 127 ] and autophagic processes [ 128 ]. Enrichment in apoptosis and necrosis pathways specific in gill indicates stress exceeding cellular tolerance levels[ 117 ] in the exposed tissue. Adaptive modification of the ubiquitin-proteasome composition by disassembly into the 20S unit increases recognition capacity for oxidatively damaged proteins [ 129 ]. Inflammation as well as oxidative stress induce transcriptional upregulation of PSMB8 , PSMB9 , PSMB10 , encoding specialized beta subunits in the 20S proteasome core creating the so-called immunoproteasome [ 130 ]. Immunoproteasomes are central to processing antigenic peptides presented by major histocompatibility complex class (MHC) I molecules, clearance of oxidized proteins and protection of cells from inflammation induced damage [ 131 ]. In mammals, transcription of the inducible B-type subunits is suspected to be controlled by the Interferon regulation factor-1 (IRF-1) signal transduction pathway [ 132 ]. In line, we identified B-type subunits ( psmb8a & b , psmb9a , psmb10 ) together with irf1b as hub genes in the largest oxygen correlated gill module. So far there is relatively little research focusing in depth on proteasomal gene expression in fish. Enhanced expression of psmb (8, 9, 9-L, 10) was shown during antibacterial and antiviral response [ 133 ] used as discriminative markers for infection monitoring [ 134 ]. The expression and high intramodular connectivity of a full set of 20S proteasome, proteasome activator and inducible immunoproteasome indicate elevation of oxidized proteins due to oxygen stress and elevated ROS-levels and increased activity of immunologically active cells in the gill tissue of Hamburg harbor fish coordinated by irf1b signaling. 4.3.1 Gill immune response Both the co-expression networks and the differential gene expression analyses identified specific marker genes for T and B cell signaling upregulated in Hamburg area. Adaptive immune module hubs ptprc as essential regulator of T and B cell antigen receptor [ 135 ] and T cell signaling related LCP2 ( lcp2a ) were also identified as immune hubs in bacteria challenged flounder [ 136 ]. The latter was also found regulated upon infection with eukaryotic parasites [ 137 ] and bacteria [ 138 ] indicating cross-species and cross-tissue importance in teleost immune response. Immune cells use reactive oxygen species (ROS) for destruction of pathogen cells involving CYBA and NOX2 encoded phagocyte NADPH oxidase multiprotein complex [ 139 ]. In mucosal immunity NOX1 is hypothesized to replace NOX2 [ 140 ], activity of which requires CYBA, NOXO1 and NOXA1 stimulated by IFN-γ [ 141 ]. Phagocyte NADPH oxidase has key regulatory function in innate immune response via ROS mediated signaling in mammals [ 142 ], matching identification of this gene set as innate immune module hubs in juvenile zander. The highly upregulated eosinophil peroxidase variants in Hamburg area indicate involvement of eosinophils in anti-microbial immune response [ 143 ]. Eosinophils are associated with parasitic infections, controlling inflammation and maintaining epithelial barrier [ 144 ] but have only been studied in a limited number of teleosts including zebrafish [ 145 ], turbot [ 146 ] and flounder [ 147 ]. All variants show a strong negative correlation with DO, indicative for eukaryotic infestation in the OMZ. Strong upregulation of Mucin-2 like isoforms involved in GIALT physical barrier in Hamburg area further support the local immune response. These variants have already been found enhanced in skin mucus of salmonids under physiological stress going along with immune suppression and overgrowth in bacteria [ 148 ]. 4.4 The zander gill microbiome Juvenile zander host a complex gill microbiome distinct from that of the surrounding water column which was clearly impacted by the sampling location along the estuary. Although influenced by the bacterioplankton [ 31 ], gill structure and function support a unique and highly diverse bacterial composition in wild fish [ 149 ]. Differing tolerances of inhabiting taxa to physiochemical gradients influence the composition of fish mucus communities [ 150 ]. The strong variation in bacterial composition along the estuary identified in this study is accompanied by only a slight increase in observed taxa with decreasing salinity. The significant differentiation between sampling sites combined with relatively high similarity of bacterial composition on individuals from the same section might be indicative for low movement patterns of juvenile zander within the course of the estuary. Overall, the oxygen minimum zone and freshwater transition in the Hamburg harbor area were found most influential for variable as well as core bacterial composition, where Proteobacteria show a large increase in relative abundance from 50 to 80 percent. This was on one hand driven by the decline in large parts of the core microbiome identified in this study. Core species are assumed to serve beneficial roles in the host, disruption in composition could theoretically be used to identify diseased animals [ 37 ]. In line, Asinibacterium and Enterobacter taxa are considered to inhibit pathogen growth [ 151 ], [ 152 ], Lelliottia is described as critical compartment in juvenile percides gut microbiome [ 153 ]. Elizabethkingia is a well-known component in mucus communities in freshwater and marine fish [ 154 ], [ 155 ] and the most abundant taxon overall in this study (rel. abundance 31–4%, WF: 0–1 %). On the other hand, known freshwater taxa lke Polynucleobacter [ 156 ] and Verticella emerge together with many opportunistic pathogenic taxa in the OMZ. 4.4.1 Holobiont interaction Host-microbe and microbe-microbe interaction are gaining more and more attention due to their tremendous importance for fish health [ 30 ]. A few recent studies focused on the bacterial composition on gills in wild fish[ 31 ] assessing pathogen load from the presence of specific genera and species [ 149 ], [ 157 ]. Here, we aimed to describe the holobiont incorporating bacterial abundance with tissue specific gene expression patterns in the host. Stressful conditions are expected to affect the interplay [ 158 ], oxygen deficiency in aquaculture for example has been linked to immunosuppressive effects[ 159 ], [ 160 ] and increased abundance of potentially pathogenic taxa [ 35 ]. As such, a substantial number of suspected or confirmed fish pathogens on zander aligned with an intensified host immune response in the oxygen minimum zone. Taxa with suspected opportunistic pathogenic functions comprise Pseudomonas [ 161 ], Chryseobacterium [ 162 ], Acinetobacter [ 163 ] and Psychrobacter [ 164 ] and Aeromonas (8 ASVs), the latter known for causing multi-tissue damage in freshwater fish [ 165 ]. Kidney specific transcriptome studies indicated OXPHOS and proteasome to be strongly activated upon Aeromonas infection followed by cellular senescence and apoptosis pathways[ 113 ] matching our results. Shewanella strains (16 ASVs), including the species S. putrefaciens and S. baltica , showed highest correlation with abundances of up to 3.5% in fish while being scarce in bacterioplankton. Shewanella is recognized for its role in organic matter turnover under hypoxic conditions[ 166 ] and polyunsaturated fatty acids (PUFA) production [ 167 ]. Different strains were isolated from freshwater walleye gastrointestinal tracts[ 168 ] and gill mucus communities of different marine fish species [ 31 ], [ 157 ], with substantial numbers of ASVs resembling potentially pathogenic species [ 149 ]. The pathogenic role of Shewanella remains unclear[ 169 ] despite repeated recovery from diseased fish [ 170 ], [ 171 ]. Notably, Esteve et al. recovered S. putrefaciens strains from diseased eel in a freshwater lake system where infection rates increased from zero to 64% morbidity over a period of ten years especially when DO values felt below 5 mg/L. Isolates exhibited pathogenicity capable of killing healthy fish at doses similar to well-known pathogens [ 172 ]. The activated immune response, the bacterial load and the cellular stress response in the OMZ fish are not matched compromised physiological end point markers in our study. However, the overall analysis constitutes only a snapshot and a continuous monitoring is required to understand the relationships in ongoing processes. 5. Conclusion This is the first network-based study co-analyzing matching host and microbial data in relation to physiological and abiotic factors in an estuarine wild-fish population. We show local adaptations of zander to salinity gradients and moderate hypoxia and confirm regulatory key genes in signal transduction pathways. Implications for the health situation by starvation in turbid waters and cellular stress and immune response combined with potential pathogenic bacteria in freshwater transition and low oxygen areas are identified. Responses show strong tissue specificity. As expected, metabolic responses correlated with physiological measurements were more prevalent in the liver. The changes in the gill transcriptome and its associated microbiome provide deep insights into the holobiont and can be monitored non-invasively. With expected increases in temperature and related decline in DO in estuarine habitats, diseases like shewanellosis might become prominent in inhabiting fish species. Meta-data analyses indicate lower tolerance to fluctuating conditions in embryos and breeding adults [ 173 ]. In general, it is largely unknown how species in dynamic systems such as estuaries respond to changes in the abiotic environment [ 174 ]. We propose a continuous monitoring of particularly pathogenic bacteria and a future inclusion of further chemical, hydrological, geographical data in a time series analysis of the holobiont over different life stages to understand ongoing processes and the decline of the total fish biomass in estuaries like the tidal Elbe. Abbreviations DEG differentially expressed genes, DEA differential expression analysis, SSU Small subunit rRNA , WGCNA Weighted gene correlation network analysis, DO dissolved oxygen, ER endoplasmatic reticulum, TCA tricarboxylic acid cycle, TPM million mapped reads FDR false discovery rate Declarations Funding This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the Research Training Group 2530: “Biota-mediated effects on Carbon cycling in Estuaries” (project number 407270017; contribution to Universität Hamburg and Leibniz-Institut für Gewässerökologie und Binnenfischerei im Forschungsverbund Berlin e.V.). This work was also supported by the DFG research grand “Large Scale Sequencing to Unravel Carbon Cycling in the Elbe estuary (Micro)biota” Project number: 496691966 / FA 1568. and by the project “Blue Estuaries” funded by the Federal Ministry for Education and Research under funding code 03F0864F. Permits Sampling procedures were according to the standards described in the German Animal Welfare Act (§4 TierSchG). In detail, after being brought on board, the fish are stunned with a blow to the head, before being killed with a heart stab. Samples were cooled to 4° until sampling immediately afterwards to ensure best RNA quality. The implementation of the stow-net fishing for scientific purposes is approved in accordance with § 10 Regulation for the implementation of the Hamburg Fisheries Act in the Elbe estuary by the Authority for the Environment Climate, Energy and Agarwirtschaft (A132-Supreme Fisheries Authority, Stadthausbrücke 12, 20355 Hamburg), by the State Fisheries Office Bremerhaven (Fischkai 31, 27572 Bremerhaven) according to § 10 of the Lower Saxony Coastal Fisheries Ordinance and by the State Office for Agriculture, Environment and Rural Areas of Schleswig-Holstein (Department 3, Fisheries, Hamburger Chaussee 25, 24200 Flintbek). Exemptions to the ordinances on nature reserves Mühlenberger Loch/Neßsand (Amt für Naturschutz, Grünplanung und Bodenschutz, Abteilung Naturschutz, Neuenfelder Strasse 19, 21109 Hamburg) as well as a nature conservation permit to conduct research fishing in protected areas in the NSG "Rhinplate und Elbufer südlich Glückstadt"/FHH area DE 2393-393 "Schleswig-Holsteinisches Elbästuar mit angrenzenden Flächen" from the Office of Environmental Protection (Department of Nature Conservation, Langer Peter 27a, 25506 Itzehoe). Author contributions RK: Conceptualization, Methodology, Investigation, Formal analysis, Data curation, Validation, Writing- original draft, Visualization, Project administration. JT: Investigation, Permit acquisition, Review & Editing. EH: Investigation, Visualization, Review & Editing. JW: Supervision, Validation, Review & Editing. AF: Funding acquisition, Conceptualization, Supervision, Validation, Review & Editing, Project administration. 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Pörtner, “Thermal bottlenecks in the life cycle define climate vulnerability of fish,” Science (1979) , vol. 369, no. 6499, pp. 65–70, Jul. 2020, doi: 10.1126/SCIENCE.AAZ3658/SUPPL_FILE/AAZ3658_DAHLKE_SM.PDF. S. S. Lauchlan and I. Nagelkerken, “Species range shifts along multistressor mosaics in estuarine environments under future climate,” Fish and Fisheries , vol. 21, no. 1, pp. 32–46, Jan. 2020, doi: 10.1111/faf.12412. Supplementary Materials Figure S1 and List S1 to S5 are not available with this version. Additional Declarations There is NO Competing Interest. Supplementary Files GraphicalAbstract.png Conceptual study scheme adapted from [1] using R (v.4.3.0) and PowerPoint. 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Integrated heatmap of gill network analysis (A) showing correlation between host gene expression module eigengenes (middle panel: Gill-RNA0-38), physiological traits (left panel: FultonK, StomachContent, HSI (hepatosomatic index)) and relevant abiotic factors (left panel: O2, salinity, Secchi depth) as well as microbiome abundance module eigenASVs (right panel SSU0-12). The middle panel shows Z-score of eigengene values per module as rows for individual fish ordered for sampling location in upstream direction as columns. The left panel shows Pearson correlation strength between eigengene and host/external traits, the right panel shows Pearson correlation to SSU eigenASVs (brown representing positive, zero white and green negative correlation) and statistical significance indicated by stars: * p \u0026lt; 0.05, ** p \u0026lt;= 0.01, *** p \u0026lt;= 0.001. Integrated heatmap of liver network analysis of matching fish (B) with host module eigengenes (right panel: Liver-RNA0-39) correlated to physiological traits and abiotic factors (left panel). Most relevant abiotic factors in the estuary are depicted for sampling stations (C) as well as selected physiological measurements of FultonK and HSI per individual (D).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3990815/v1/3b20e738724aa031a119a213.png"},{"id":52049755,"identity":"d071ff87-2ae1-42d7-b4ac-a3741cd4eb7d","added_by":"auto","created_at":"2024-03-05 21:50:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":726989,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTissue specific gene regulation in relation to estuarine section\u003c/strong\u003e. KEGG pathway enrichments from modules correlated with abiotic traits from tissue specific network analysis. Modules with negative correlation to dissolved oxygen levels (A) and positive correlation to turbidity (B) and salinity (C) indicative for gene expression patterns within the oxygen minimum zone, the maximum turbidity zone and the post MTZ. In each plot KEGG-level 1 (left y Axis) and KEGG- level 2 (right y Axis) against the number of genes within each module with \u0026gt; 0.8 Module membership are depicted.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3990815/v1/84a44c0bf7a1b713219f4d88.png"},{"id":52049756,"identity":"c037223d-00d9-4801-a7b4-82d5c62ce9ea","added_by":"auto","created_at":"2024-03-05 21:50:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1004342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBacterial composition and differential abundant taxa from host gill and bacterioplankton. \u003c/strong\u003eCompositional barplot\u003cstrong\u003e \u003c/strong\u003e(A) shows genus accumulated relative abundance (\u0026gt; 1%) per sample. Venn Diagrams depict presence/absence of bacterial ASVs found in water samples (WF) and on fish (SL) (B), and in fish swabs along the estuary (C). Principle component analysis of CLR transformed count data (D) indicates overall sample similarity. Differential abundant taxa (p.adjust \u0026lt; 0.05) tested by DESeq2 negative binominal distribution in pairwise comparison are shown as cluster-heatmap (E) with core taxa indicated.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-3990815/v1/52d8a2fa82474ac22e52c61c.png"},{"id":52049757,"identity":"26a0602e-15c8-42f7-a3b0-22c8b073a213","added_by":"auto","created_at":"2024-03-05 21:50:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2856960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecific bacterial strains are highly correlated with innate and adaptive immune response in host gill\u003c/strong\u003e. Bacterial abundance correlated to host gill innate immune response Gill-RNA-7 module eigengene (\u003cstrong\u003eA\u003c/strong\u003e). ASV module membership on X-axis and Pearson correlation on Y-Axis, point size indicates intramodular connectivity, color core depicts module membership. Co-occurrence network module Gill-SSU-2 highly correlated with host immune response. For visualization purposes an adjacency threshold for including edge of 0.1 was chosen (\u003cstrong\u003eB\u003c/strong\u003e). ASVs with highest correlation to host immune response modules Gill-RNA-7 and 11 (\u003cstrong\u003eC\u003c/strong\u003e). Relative abundance values over all samples (Avg.) and for the highest sample (max.) are shown. Only first strain per lowest taxonomic level with Module Membership \u0026gt; 0.8 and Pearson correlation \u0026gt; 0.6 is depicted. For full list see \u003cstrong\u003eS.4_List\u003c/strong\u003e and \u003cstrong\u003eS.5_List\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-3990815/v1/ec6a67c2d0cedce94407184e.png"},{"id":57570082,"identity":"a220b78f-0c44-4dca-ad5c-34b7a1805adb","added_by":"auto","created_at":"2024-06-02 14:05:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5502842,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3990815/v1/44e5ea3d-9d06-4107-b96e-69159692f9dd.pdf"},{"id":52049752,"identity":"c7134bf5-1e04-4428-b318-ad610b8d94f5","added_by":"auto","created_at":"2024-03-05 21:50:41","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1011918,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual study scheme adapted from [1] using R (v.4.3.0) and PowerPoint.\u003c/p\u003e","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-3990815/v1/cbf4d1059bf27bd4a935f8b1.png"},{"id":52049754,"identity":"3ae8eb3a-c057-4781-b5a4-0cc5c0d50506","added_by":"auto","created_at":"2024-03-05 21:50:41","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1191534,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-3990815/v1/12832a5f65d9e1753e10b641.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Network-based integration of omics, physiological and environmental data in real-world Elbe estuarine Zander","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eStructure and functioning of coastal and estuarine environments have dramatically changed over the last century driven by overexploitation, habitat fragmentation, chemical pollution, and species invasion [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As transitional ecosystems estuaries experience high spatiotemporal variation in physicochemical conditions (i.a. hydrology, salinity, turbidity, temperature, dissolved oxygen, habitat availability) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Global change is expected to increase pressures on inhabiting fish communities in estuaries, especially due to changes in temperature [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], dissolved oxygen (DO) levels [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and salinity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe tidal Elbe is described as an alluvial, turbid, well-mixed and macro-tidal estuary [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Over the last centuries, extensive engineering, notably by diking and deepening to improve access to the Hamburg harbor located about 100 km inland, has placed substantial pressure on the estuary. Since the 1980s regions of low dissolved oxygen, in some instances hypoxia, have been reported especially in upstream freshwater areas [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. During summer, the otherwise well-mixed estuary experiences stratification, particularly in the port area and deepened navigation channel. Under these conditions it is common to observe oxygen depletion and ammonium accumulation in the hypolimnic waters [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Efforts to abate these environmental impacts are limited. In 2021 alone, deepening and maintenance dredging of navigation channels resulted in relocation of ca. 23\u0026nbsp;million m\u003csup\u003e3\u003c/sup\u003e sediment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] of which a proportion was resuspended into the water column [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFish are responsive to physical and chemical changes in the environment and are critical components of aquatic food webs. Through coupling effects, nutrient sequestration and top-down impact predatory fishes strongly influence nutrient cycling, carbon storage and overall habitat maintenance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Major changes in abundance, life strategy and composition of estuarine fish communities in Europe have been detected recently, with declines of up to 80% overall fish biomass, affecting nutrient cycling and overall functioning of these ecosystems [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For the Elbe estuary, the first time series data were analyzed for only one anadromous fish species (\u003cem\u003eOsmerus eperlanus\u003c/em\u003e (Linnaeus, 1758)), indicating a decline in abundance due to the combined effects of several stressors, such as the reduction of shallow water areas, the abstraction of cooling water for power plants, increased dredging, increased turbidity and re-emerging hypoxia [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study we use juvenile zander \u003cem\u003eSander lucioperca\u003c/em\u003e (Linnaeus, 1758) (0\u0026thinsp;+\u0026thinsp;cohort) sampled in late summer 2021 from the Elbe estuary as model organism to explore gene expression patterns and holobiont interaction linked to end-point measurements of physiological health in response to steep abiotic gradients. The zander is among the most abundant fish in the ecosystem and among the top predatory species in the Elbe exerting a strong top-down control on other species [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In contrast to their embryonic stages, which require a salinity of less than 7 psu for their development [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], juvenile and adult walleye pollock tolerate mesohaline conditions and strong osmotic fluctuations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], allowing them to inhabit the entire estuary. Life history strategies analyzed in the vicinity of the Kiel Canal indicate either brackish residency or a freshwater-brackish water strategy, where reproduction and larval growth occur in freshwater with later dispersal to more saline habitats [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The spawning migration takes place in early spring and extends usually over about 30 km [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. After the switch from planktonic to piscivorous nutrition within the first year [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], \u003cem\u003eO. eperlanus\u003c/em\u003e becomes the preferred prey for zander in the Elbe estuary [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The summer period is characterized by intensive growth (16\u0026thinsp;\u0026plusmn;\u0026thinsp;2 cm) with high metabolic activity susceptible to be altered by multiple stressors [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite a well-found knowledge of the abiotic and biotic changes occurring within the estuary, many spatio-temporal factors, i.e. temperature, salinity, DO levels, are confounding and make it difficult to determine the precise mechanisms underlying for example losses in fish biomass. Omics are increasingly considered in the context of biomonitoring and conservation biology, as they enable a deep understanding of processes at all levels of biological organization and of previously overlooked interactions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Co-working of multiple stressors is expected to lead to unforeseen synergistic or antagonistic effects in organisms [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], sublethal impacts of which can contribute to population declines over generations [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Molecular adjustments precede high level responses thereby probably allowing earlier detection of adverse events or situations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Real-world omics studies indicate molecular mechanisms used by fish to cope with adverse effects of multi-stress environment. Reversely they inform about the environmental conditions themselves allowing identification of unexpected stressors as shown in recent studies on range expansion in invasive species [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] or xenobiotic response in estuarine flounder [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBiotic interaction with complex communities of microbiota inhabiting every mucosal surface of teleost fish strongly influence the host metabolism, growth and health [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The gill mucosal surface constitutes a special habitat for host-associated microbes due to its unique functions in waste excretion, gas exchange and local immune activity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Local immune cell populations composed by adaptive parts (cytotoxic CD8\u0026thinsp;+\u0026thinsp;and CD4\u0026thinsp;+\u0026thinsp;T helper cells (Th) \u0026amp; B cells [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]) as well as innate immune cells form the gill associated lymphoid tissue (GIALT) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. It enables fish to discriminate between beneficial and pathogenic bacteria keeping microbiome homeostasis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, changes in the environment, including water conditions, temperature, seasonal changes, physiological stress can lead to compositional disturbances that turn commensals into pathogens [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Hypoxia led to dysbiosis in captive salmonid skin microbiome [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], in gills of clownfish suspended sediment load led to a microbial shift towards pathogenic communities [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This sensitivity to environmental parameters makes the external mucus composition a biomarker for fish health built by a stable community of core taxa of low number but shared over a large range of conditions and a variable community strongly influenced by environmental conditions [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Investigating changes in the core microbiome may deliver indication for deep reorganization and dysbiosis, while analysis of variable taxa might deliver potential indicators for specific stressors [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe objective of this study is to apply a network-based dimensionality reduction method on real-world holobiont omics data based on host RNA and 16S rRNA sequencing. The unsupervised approach allows high resolution detection of tissue specific gene profiles and relate those to prevailing abiotic conditions. We describe the microbiota dynamics in gill mucus community along the estuary in relation to the bacterioplankton and incorporate them with identified host immune responses. Different levels of biological organization are integrated to assess the fish\u0026rsquo;s health on individual level, allowing identification of individual responses.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Sample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFish were caught with a stow-net fishing vessel (opening area of 135 m\u0026sup2;, mesh size of 10 mm at the cod end) at four stations along the Elbe estuary \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e in the summer period from 25\u0026ndash;29.08.2021. Sampling stations were chosen within different estuarine sections classified by dominating abiotic drivers [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]: Station ML-Elbe kilometer 663 within oxygen minimum zone (OMZ) (stream km 620\u0026ndash;650, low oxygen, summer\u0026thinsp;\u0026lt;\u0026thinsp;2 mg/L, salinity\u0026thinsp;\u0026lt;\u0026thinsp;0.5 psu), stations SS-Ekm 665 and BB-Ekm-692 within the maximum turbidity zone (MTZ) (stream-km 650\u0026ndash;705, high loads of suspended matter, salinity\u0026thinsp;\u0026gt;\u0026thinsp;0.5\u0026thinsp;\u0026lt;\u0026thinsp;5 psu) and station MG-Ekm 715 within post-MTZ (stream km 705\u0026ndash;730, transition full marine, salinity\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026thinsp;\u0026lt;\u0026thinsp;20 psu). See Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for sampling overview map and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC for abiotic conditions at sampling times. Sampling procedures followed the standards described in the German Animal Welfare Act (\u0026sect;\u0026nbsp;4 TierSchG). Exemptions to the ordinances on nature reserves were obtained (\u003cstrong\u003esee Permits\u003c/strong\u003e). 534 individuals of age 0\u0026thinsp;+\u0026thinsp;were caught with even distribution over stations. Six individuals from each ebb haul (n\u0026thinsp;=\u0026thinsp;24) were processed immediately on board in the following standardized manner: Fish were measured and weighed before recovering gill bacteria with sterile cotton swabs from the middle part of the second and third arch. The same part of the gill tissue as well as the right most edge of the liver were resected for preservation. All samples were kept on dry ice on board until they were moved to -80\u0026deg;C until further procession. Tissue samples were first placed in tubes with RNAlater (Macherey-Nagel) for one hour before they were moved to dry ice. Abiotic conditions (oxygen, salinity, Secchi depth, temperature, pH) were measured at start and end of each haul using a multi-probe (Hanna HI 9829 and Secchi disc) at the water surface. Water samples (n\u0026thinsp;=\u0026thinsp;4) obtained from the water column were vacuum filtered onto 0.2 \u0026micro;m polycarbonate membranes (Whatman\u003csup\u003e\u0026trade;\u003c/sup\u003e Nucleopore\u003csup\u003e\u0026trade;\u003c/sup\u003e Track-Etch). Whole fish samples were frozen at -20\u0026deg;C and further analyzed in the lab: body indices were determined (Fulton\u0026rsquo;s body condition (FultonK), hepato-somatic - (HSI) and spleno-somatic indices (SSI) see [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]), stomach content was weighed and age determination from otoliths and scales was performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Transcriptome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA extraction from gill and liver tissues was performed via sample homogenization (45 seconds) with ceramic beads in peqGOLD Trifast (Peqlab), RNA dissolving in chloroform and mixing with 70% ethanol before immobilization on a silica membrane (Crystal RNA Mini Kit, BiolabProducts). On-column DNA digestion with RNase free DNase I (Qiagen) was performed for all samples. RNA quantity and integrity were controlled by spectroscopy (Nanodrop, Thermo Fisher) and on denaturing agarose gels and Bioanalyzer (Agilent Technologies) (avg. RIN 9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 over 48 samples). For every individual (n\u0026thinsp;=\u0026thinsp;24) liver and gill mRNA was isolated using either (NEBNext Poly(A) Magnetic Isolation Module (New England BioLabs) followed by stranded cDNA library creation (NEBNext Ultra II Directional RNA Library Preparation kit (New England BioLabs) or the Illumina RNA stranded mRNA kit including polyA enrichment. Individually barcoded libraries were 2x150 paired-end sequenced on NovaSeq6000 (Illumina) (300 cycles, v1.5 reagents, S4 FlowCell) on three different lanes resulting in 3.4\u0026nbsp;billion reads (avg. ca. 25\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u0026nbsp;million). To account for possible batch effects from library preparation and sequencing runs, biological replicates from sampling location are spread over runs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptome analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptome analysis was performed on the University Hamburg Hummel HPC. Read files were quality controlled using FASTQC (v 0.11.9) and MultiQC (1.12) followed by removal of adapter sequences, clipping of first and last base pairs, quality filtering bases lower Phred score 20 and discarding reads lower than 20 base pairs in length via TrimGalore (v 0.6.10) wrapper of cutadapt[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e] and FastQC [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. Clean reads were mapped to the genome of \u003cem\u003eSander lucioperca\u003c/em\u003e SLUC_FBN_1.2[\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e] using Hisat2 (v.2.2.1)[\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] (overall mapping rate 99\u0026thinsp;\u0026plusmn;\u0026thinsp;1%) followed by expression determination via featureCounts (subread v.2.0.2)[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e] (assignment rate 51\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4 %; 18\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026nbsp;million asignments). All downstream analyses were performed in R (v.4.3.0) using visualization packages ggplot2 (v.3.4.2) and cowplot (v.1.1.1). Genome wide annotation databases org.XX.eg.db (v.3.17.2)[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e] for human, mouse, and zebrafish as well as AnnotationHub (v3.17)[\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e] entries for zander were used to translate between different gene identifiers, names and symbols. CDS regions from the reference genome were annotated using diamond (v.2.0.15) blast against UniProt, UniRef90, TrEMBL, RefSeq-Protein and Human EMBL-EBI reference proteome databases. Blast results were filtered for E-value\u0026thinsp;\u0026lt;\u0026thinsp;1e-6 and bit score higher 50, for multiple hits to the same transcript, the one with the lowest E-value was retained. Wherever human genes could be assigned to transcripts on gene-level the human annotation was used for pathway analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEA \u0026amp; Functional term enrichment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis was performed to verify network analysis results via DESeq2 (v. 1.41.4)[\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e] (significance when FDR adjusted p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 \u0026amp; Log2FoldChange 0.05 or 1) contrasting sampling locations along the estuary. Genes failing to meet expression threshold of transcripts per million mapped reads (TPM) of 1 in at least three samples were excluded from downstream analyses [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. Genes were aggregated and hierarchical clustered using in ComplexHeatmap (v.2.17) [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. Functional enrichment of heatmap clusters was performed via overrepresentation analyses as described in section 2.3. For results see \u003cstrong\u003eS.2_Fig \u0026amp; S.3_Fig\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Microbiome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSwabs were incubated in a mix of lysozyme, mutanolysin and lysostaphin before the cells were physically disrupted in a bead mill [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]. Proteinase K digestion in CTAB buffer was followed by chlorophorm-phenol extraction. Nucleic acids were precipitated in PEG 6000 using LPA-carrier and recovered and washed repeatedly with 95% and 70% ethanol before resuspension in water [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. QC was performed via spectroscopy (Nanodrop, Thermo Fisher) and gel electrophoreses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA Sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe V3-V4 variable regions of the 16S rRNA gene were amplified in a one-step PCR using the primer pair 341F-806R (dual-barcoding approach [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]; primer sequences: 5\u0026rsquo;-CCTACGGGAGG-CAGCAG-30 and 5\u0026rsquo;-GGACTACHVGGGTWTCTAAT-30). After verification of the presence of PCR-products by gel electrophoresis, normalization (Sequal Prep Normalization Plate Kit; Thermo Fisher Scientific, Waltham, USA) and equimolar pooling was performed. Sequencing was conducted on the MiSeq platform (MiSeqFGx; Illumina, San Diego, USA) with v3 chemistry (2x300 bp). The settings for demultiplexing were 0 mismatches in the barcode sequences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA Bioinformatics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequence data trimming, amplicon sequence variant (ASV) prediction and taxonomic identification were performed using cutadapt, FastQC and DADA2 (v.1.29.0) [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. Quality profiles of paired reads were inspected and truncated at 270 and 190 for forward and reverse reads. ASV inference was performed with pooling method and taxonomy assignment used the SILVA SSU v138 taxonomic database [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. Accuracy of DNA extraction, sequencing until taxonomic assignment was validated via inclusion of mock community samples (ZYMO research). ASV table and sample data were parsed to phyloseq (v 1.45.0) [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e] removing ASVs taxonomically assigned to non-bacteria. Low abundance taxa with sum of counts lower than 0.005% of total sum of all counts were filtered reducing the number of zeros in the dataset by 50% to improve interpretability and minimize the risk of spurious correlations. A total of 1285 bacterial ASVs were included in further analyses, minimum sampling depth was ca. 7000, (avg. ca. 21000\u0026thinsp;\u0026plusmn;\u0026thinsp;13000) clean sequences for fish samples. Alpha diversity measures were calculated via vegan package (v. 2.6.4) [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]. The core microbiome was determined from relative abundance data via microbiome package (v. 1.23.0) [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e] with filtering detection threshold to 0.0001% within samples and 90% prevalence. Centered log-ratio (CLR) transformation to the ASV matrix was applied following best practices for handling of compositional data [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e] using the microbiome package [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]. The transformed data were used for visualization and network analyses. Permutation multivariate analysis of variance was used to test differences in community structure between fish and bacterioplankton as well as between fish sampling groups in vegan followed by post hoc pairwise t-test via pairwiseAdonis [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential Abundance testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNegative binominal GLM fitting and Wald statistics in DESeq2 (v. 1.41.4) [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e] were used to identify taxa with significantly changing abundances between conditions. Although DESeq2 designed for smaller RNAseq-Datasets tends to show higher false discovery rates with uneven library size (\u0026gt;\u0026thinsp;10x on average), sensitivity for differential abundance testing for smaller datasets (\u0026lt;\u0026thinsp;20 samples per group) is increased [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e]. As sparsity and the assumption that most taxa do not change in the dataset are attributed to inflate false positive rates in DESeq2 [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e] abundance filter was applied as described above.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Holobiont analyses: WGCNA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeighted gene co-expression network analysis (WGCNA) (v.1.77-1) [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e] was used for network inference using the following steps and parameters: 1. Signed network construction using biweight midcorrelation measure [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e]. Weighted adjacency is created via soft-thresholding, multiplying the co-expression matrix by a power that leads to approximate scale-free topology. 2. Signed Topological Overlap Measure (TOM) is then used to measure interconnectedness accounting for indirect interactions and assignment of genes and bacterial clusters was performed via hierarchical clustering.\u003c/p\u003e\n\u003cp\u003eThree samples were identified as outliers in both tissue RNAseq datasets by PCA and Euclidean distance clustering and excluded for network analysis and DEA. Parameter list: networkType = \u0026quot;signed\u0026quot;, TOMType = \u0026quot;signed\u0026quot;, corType = \u0026quot;bicor\u0026quot;, minModuleSize\u0026thinsp;=\u0026thinsp;30/5 (RNA/SSU), reassignThreshold\u0026thinsp;=\u0026thinsp;0, deepSplit\u0026thinsp;=\u0026thinsp;3, mergeCutHeight\u0026thinsp;=\u0026thinsp;0.25. Our parameters were set to allow highest reproducibility between the different datasets in this study and upcoming samplings. Minimum module sizes of 30 and 5 for transcriptomic and SSU (Small subunit rRNA) datasets were chosen to facilitate interpretability. DeepSplit controlling sensitivity for module detection by hierarchical clustering was slightly increased from default. The first principal components of each module (Eigengene/EigenASV) are considered representative for the gene expression or bacteria abundance profile. Functional roles were assigned to the network modules by overrepresentation analyses against the KEGG-[\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e] and GO-databases[\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e] using ClusterProfiler (v.4.9.0) [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e]. A subset of genes with correlation between gene expression profile and module eigengene (module membership) higher 0.8 was used for function annotation to increase interpretability. From each module, nodes with highest intramodular connectivity from the adjacency (hubs) were inspected as they are considered and shown functionally important [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]. Module eigengenes/ASVs where correlated between the different networks and to physiological traits of the fish (Fulton`s body condition, weight, length, stomach content weight, HSI, SSI) and external abiotic factors (DO levels, salinity, Secchi depth) via Pearson correlation. An integrated heatmap analysis approach was used to visualize host responses and holobiont interaction following a workflow developed by [\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrincipal component analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal component analyses (PCA) were performed on variance stabilizing transformed counts for RNAseq data or CLR transformed counts for 16S data using PCAexplorer and PCAtools [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e]. Evaluation of clustering in PCA-plots indicated no obvious batch effects between sequencing runs. Nevertheless, ComBat-seq [\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e] batch effect adjustment tool was applied on the datasets but lead to an overall non-batch effect related reduction in variance and was discarded for further analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequence data are deposited in the ENA Sequence Read archive under study PRJEB71116. We made the full analysis available in stepwise R markdown files, including metadata and supplementary lists under https://github.com/vollkorrn/ElbeEstuarineZander\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eTo gain insight into fish response and fish-microbiota interactions under steep abiotic gradients in an estuarine habitat, we applied an integrative approach of tissue specific RNA-seq and gill epithelial surface 16S rRNA amplicon sequencing collected from juvenile zander from Elbe estuary during late summer. The sampling sites spanned mesohaline to freshwater habitats [\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e] with DO levels, turbidity and salinity showing variation between the discreet estuarine areas [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]: The post-maximum turbidity zone (post-MTZ, station: MG), the maximum turbidity zone (MTZ, stations: SS, BB) as well as the oxygen minimum zone around the Hamburg harbor area (station ML) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Network analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.1 Post-maximum turbidity zone \u0026ndash; salinity adaptation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe post-MTZ was characterized by mesohaline conditions ranging from 9.0\u0026ndash;14.9 psu and stable DO levels between 8.2\u0026ndash;8.5 mg/L (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Only a few RNAseq modules positively correlated to salinity levels exhibited functional enrichment. Gill-RNA-1 (3848 genes, 753 with module membership MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, correlation to salinity value r\u0026thinsp;=\u0026thinsp;0.54, correlation p-value 0.01) was enriched in cellular communication and signaling (focal adhesion, extracellular matrix (ECM)-receptor interaction, cell adhesion), signal transduction (PI3K-AKT, MAPK, cGMP-PKG) and axon guidance (compare Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA \u0026amp; B \u0026amp; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Module hub genes were transmembrane protein 204 (\u003cem\u003etmem204\u003c/em\u003e) and protein tyrosine kinase 2 (\u003cem\u003eptk2aa\u003c/em\u003e), while \u003cem\u003eslc5a3b\u003c/em\u003e shows highest correlation to salinity values. Genes in Gill-RNA-5 (888 genes, 144 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, salinity r\u0026thinsp;=\u0026thinsp;0.67, correlation p-value 7.8E-04) were involved in ER protein processing and Gill-RNA-37 (41 genes, 22 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, salinity r = -0.46, correlation p-value 3.4E-02) in glutathione metabolism (\u003cem\u003egpx7\u003c/em\u003e, \u003cem\u003emgst3a\u003c/em\u003e). The liver tissue-specific response in RNA-Liver-10 (418 genes, 22 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, salinity r\u0026thinsp;=\u0026thinsp;0.49, correlation p-value 0.022) was related to fatty acid metabolism (\u003cem\u003eelovl5\u003c/em\u003e, \u003cem\u003escdb\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.2 Maximum turbidity zone \u0026ndash; enhanced energy metabolism\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe high turbidity zone was marked by high particle load (Secchi depth of 10\u0026ndash;30 cm), salinity fluctuation between 5\u0026ndash;0.6 psu (SS: 0.9\u0026ndash;0.6 psu, BB: 1.6\u0026ndash;5.2 psu) but stable DO levels 7.6\u0026ndash;8.8 mg/L (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Gill-RNA modules with significant negative correlation to Secchi depth values comprised Gill-RNA-3 (1675 genes, 1469 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, Secchi depth r = -0.55, correlation p-value 9E-3) enriched in ribosomal genes of both subunits and Gill-RNA-13 (499 genes, 50 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, Secchi depth r = -0.69, correlation p-value 5E-4) contained genes involved in axon guidance (compare Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA \u0026amp; B \u0026amp; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Liver-RNA-1 (3334 genes, 754 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, Secchi depth r = -0.57, correlation p-value 5E-3) comprised genes related to autophagy (AMPK, mTOR, Insulin, FoxO, adipocytokine), mitophagy and peroxisome, hub gene was the autophagy related \u003cem\u003ebnip4\u003c/em\u003e. Liver-RNA-6 (716 genes, 118 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, Secchi depth r = -0.56, correlation p-value 6.6E-3) was composed of ribosomal subunit genes. Liver-RNA-9 (519 genes, 138 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, Secchi depth r = -0.45, correlation p-value 3.6E-2) was highly enriched in metabolic pathways comprising fatty and amino acid degradation (tryptophan metabolism, valine, leucine and isoleucine degradation, lysine degradation, alanine, aspartate and glutamate metabolism).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.3 Oxygen minimum zone \u0026ndash; cellular stress and immune reactions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe oxygen minimum zone was characterized by low DO levels between 4.6\u0026ndash;5.0 mg/l (46\u0026ndash;52% saturation) and stable salinity levels (0.46 psu). Seven Gill-RNA modules were negatively correlated to oxygen levels in the surrounding milieu, five of them were also negatively correlated to salinity (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA \u0026amp; B). Gill-RNA-Module 2 (2735 genes, 455 module membership\u0026thinsp;\u0026gt;\u0026thinsp;0.8, DO correlation r = -0.52, correlation p-value 0.015) was highly enriched in replication and repair pathways (DNA replication, base excision repair, mismatch repair, nucleotide excision repair), cell growth and death (cell cycle, apoptosis, necroptosis), folding, sorting and degradation (proteasome), immune system (antigen processing and presentation) as well as signal transduction (NF-kappa B signaling pathway). Hub genes comprised proteasomal subunits (\u003cem\u003epsmb9a\u003c/em\u003e, \u003cem\u003epsma6l\u003c/em\u003e, \u003cem\u003epsmb10\u003c/em\u003e, \u003cem\u003epsme1\u003c/em\u003e), innate immunity related \u003cem\u003eirf1b\u003c/em\u003e and \u003cem\u003eerap2\u003c/em\u003e. Gill-RNA- 4 (893 genes, 326 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, DO r = -0.51, correlation p-value 0.017) showed enrichment in protein procession (proteasome, protein processing in ER, Protein export), genetic information processing (nucleocytoplasmic transport and spliceosome). Hub genes were \u003cem\u003epsmd2\u003c/em\u003e, \u003cem\u003eups5\u003c/em\u003e, \u003cem\u003eusp14\u003c/em\u003e involved in protein processing as well as cell proliferation related \u003cem\u003eppp5c\u003c/em\u003e. The immune response in gill tissue was divided between two modules. Gill-RNA-7 (637 genes, 123 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, DO r = -0.79, correlation p-value 2.6e-05) included innate immune response parts (chemokine signaling and viral protein interaction with chemokine). Hub genes were microbicidal oxidase system of phagocytes (\u003cem\u003ecyba\u003c/em\u003e, \u003cem\u003enox1\u003c/em\u003e) and MHC class II paralogues. Three variants encoding for eosinophil peroxidase (\u003cem\u003eepx\u003c/em\u003e-like) and mucin-2 like isoforms were most correlated to DO levels (r -0.85) and highly upregulated in the Hamburg area (LFC\u0026thinsp;\u0026gt;\u0026thinsp;1, see \u003cstrong\u003eS.2_Fig\u003c/strong\u003e). Gill-RNA-11 (587 genes, 213 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, DO r = -0.59, correlation p-value 4.5e-03) was enriched in adaptive immune system processes specifically T cell receptor signaling, T-helper cell differentiation, NK cell mediated cytotoxicity and general chemokine signaling. Hub genes include the protein tyrosine phosphatase \u003cem\u003eptprc\u003c/em\u003e and lymphocyte cytosolic protein 2 \u003cem\u003elcp2a\u003c/em\u003e. Gill-RNA-21 (186 genes, 64 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, DO r = -0.77, correlation p-value 4.6e-05) was enriched in porphyrin metabolism (hubs: \u003cem\u003erhd\u003c/em\u003e and \u003cem\u003ealas2\u003c/em\u003e) and strongly upregulated hemoglobin subunit alpha-2-like and beta-2-like. Gill-RNA-25 (102 genes, 19 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, DO r = -0.6, correlation p-value 4e-03) shows enrichment in lysine degradation (\u003cem\u003ekmt2d\u003c/em\u003e, \u003cem\u003eezh2\u003c/em\u003e, \u003cem\u003ekmt2bb\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eLiver specific response was less pronounced than gill, Liver-RNA-2 (2571 genes, 2327 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, DO r = -0.45, correlation p-value 0.03) was enriched in genetic information processing pathways (proteasome, protein processing in ER, DNA replication, spliceosome, base excision repair), cellular processes (cell cycle) and metabolism (carbon metabolism, N-glycan biosynthesis, TCA cycle). Hub genes include again \u003cem\u003eusp14\u003c/em\u003e and \u003cem\u003eppp5c\u003c/em\u003e. Liver-RNA-8 (728 genes, 81 MM\u0026thinsp;\u0026gt;\u0026thinsp;0.8, DO r = -0.49, correlation p-value 0.02) was enriched in energy metabolism (oxidative phosphorylation), metabolism (of amino acids, cofactors, vitamins), mismatch repair as well as protein procession (proteasome, protein export, N-glycan biosynthesis).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Gill microbial community structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproximately 630,000 trimmed, filtered and merged reads across 24 zander and four water samples resulted in 42.000 ASVs. Filtering for ASVs contributing at least 0,005% of overall abundance reduced sparsity in the dataset by 50% and retained 1285 ASVs for further analysis. Species accumulation analysis indicated that the rare microbiome was not sufficiently captured in the dataset (\u003cstrong\u003esee S.1_Fig A\u003c/strong\u003e) but overall composition was when excluding rare taxa (\u003cstrong\u003eS.1_Fig B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1 Host-associated differ from free-living bacterial communities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified seven bacterial phyla associated with the fish microbiome and eight phyla in the surrounding water column (\u003cstrong\u003eS.1_Table\u003c/strong\u003e). There was a significant difference between the microbiome of the fish and that of the surrounding water column (DF 1, F 7.5686, p 0.001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, D, S\u003cstrong\u003e.2_Table\u003c/strong\u003e). Of the 1285 ASVs identified 51 were found only in the water column while 600 were unique to the fish gill microbiome (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, B). The water column was characterized by a large abundance of Actinobacteriota (25% in water samples, 1\u0026ndash;6% in fish), Proteobacteria (38%, fish: 53\u0026ndash;78%), Bacteroidota (15%, fish: 14\u0026ndash;32%), Verrucomicrobiota (8%, fish: 1\u0026ndash;3% ) and several phyla not represented in fish samples at all: Cyanobacteria (4%), Nitrospirota (3%), Acidobacteriota (2%), Planctomycetota (1%). Firmicutes, Deinococcota and Desulfobacterota in the contrary were only recovered from fish gills.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2 Host-associated communities diverge along the course of the estuary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found a significant site-dependent difference in the bacterial community composition of the fish gill (DF 3, F 3.5533, p 0.001) between all sampling locations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD, \u003cstrong\u003ePC2, S.2_Table\u003c/strong\u003e). 45% of ASVs occurred in at least one fish from each sampling site and there were few site-specific taxa (1\u0026ndash;6%) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). The community was dominated by Proteobacteria followed by Bacteroidota and Actinobacteria which were the only phyla present on all individuals. The ratio of abundances between Proteobacteria and Bacteroidota was subjected to strong changes (MG: 62/21%, BB: 53/20%, SS: 54/32%, ML: 78/14%). Enterobacterales (32%), Flavobacteriales (19%), Burkholderiales (13%), Pseudomonadales (9%) and Clostridiales (6%) make up 80% of the relative bacterial abundance over the estuary. Enterobacterales (49% in most downstream \u0026ndash; 12% in most upstream sampling station) steadily decrease over the course of the estuary with Photobacterium (27% \u0026ndash; 0%), Citrobacter (8\u0026ndash;3%), Enterobacter (7\u0026ndash;2.5%), Lelliottia (4\u0026ndash;2%) being the most abundant genera. Burkholderiales (3\u0026ndash;36%) with the genera Polynucleobacter (0\u0026ndash;28%) Verticiella (0\u0026ndash;7%) and the Pseudomonadales (5\u0026ndash;17%) with the genera Acinetobacter (0\u0026ndash;14%) and Psychrobacter (0\u0026ndash;2%, SS 8%) show increasing relative abundance in fish gill swabs in upstream direction. Flavobacteriales show differing patterns with the genera Elizabethkingia (16\u0026ndash;8%) steeply decreasing over estuary and the genus Chryseobacterium (0\u0026ndash;6%, SS 9%) increasing with a peak in the middle of the estuary. Alpha diversity slightly changes along the course of the estuary, observed richness steadily increased from 364 to 502 ASVs (sampling group means) in upstream direction with slight changes in Shannon index: MG: 3.9; BB: 3,6; SS: 4.3; ML: 4.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1. The core microbiome community of estuarine zander gills\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe core microbiome comprised 43 ASVs from 11 orders predominated by roughly even relative abundance of Proteobacteria and Bacteroidota (\u003cstrong\u003eSupplement S.1_Fig \u0026amp; S.2_List\u003c/strong\u003e): Proteobacteria (Enterobacterales (18%), Pseudomonadales (1%), Burkholderiales (1%), Caulobacterales, Sphingomonadales), Bacteroidota (Flavobacteriales (16%), Chitinophagales (1%), Sphingobacteriales), Actinobacteriota (Microtrichales (1%), Corynebacteriales, Micrococcales), Deinococcota (Deinococcales) \u0026amp; Verrucomicrobiota (Chthoniobacterales). While the relative proportion of core taxa in the total microbiome declined along the course of the estuary (56 to 28%), the relative proportions within core taxa stay overall stable (Bacteroidota/Proteobacteria from 55/40 to 60/35%). The Hamburg harbor area has the largest impact on the core microbiome, with slight increases in Bacteroidota. CL500-29 marine group was the only taxon shared in large amounts (\u0026gt;\u0026thinsp;1%) between core fish gill microbiome and surrounding water column.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1 Differential abundant taxa\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe differential abundant taxa from pairwise comparisons split into four clusters. Taxa from cluster 1 were abundant in fish samples, most enriched in mesohaline habitat and comprising most of the core microbiome (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Bacteroidota (\u003cem\u003eElizabethkingia\u003c/em\u003e, \u003cem\u003eAsinibacterium\u003c/em\u003e) and Proteobacteria (\u003cem\u003eCitrobacter\u003c/em\u003e, \u003cem\u003ePhotobacterium\u003c/em\u003e, \u003cem\u003eLelliottia\u003c/em\u003e and Enterobacteriaceae) were the most abundant taxa (\u0026gt;\u0026thinsp;0.5%). Cluster 2 comprised taxa more related to the upstream region of the maximum turbidity zone dominated by Proteobacteria (\u003cem\u003ePsychrobacter\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eAlkanindiges\u003c/em\u003e, \u003cem\u003ePolynucleobacter\u003c/em\u003e), Bacteroidota (\u003cem\u003eChryseobacterium\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eOrnithobacterium\u003c/em\u003e, Weeksellaceae) and Verrucomicrobiota (\u003cem\u003eLuteolibacter\u003c/em\u003e). Cluster 3 was composed of taxa enriched in freshwater transition and OMZ comprising Proteobacteria (\u003cem\u003ePolynucleobacter\u003c/em\u003e, \u003cem\u003eVerticiella\u003c/em\u003e, Alcaligenaceae, \u003cem\u003eCaedibacter\u003c/em\u003e, Candidatus Megaira, Methylococcaceae, \u003cem\u003eAcinetobacter\u003c/em\u003e) and Bacteroidota (\u003cem\u003eChryseobacterium\u003c/em\u003e). Taxa in cluster 4 were predominantly found in the bacterioplankton comprising Actinobacteriota (Sporichthyaceae, hgcI clade), Candidatus Planktophila), Proteobacteria (CL500-29 marine group, Candidatus Methylopumilus, \u003cem\u003eLimnobacter\u003c/em\u003e, \u003cem\u003ePolynucleobacter\u003c/em\u003e, \u003cem\u003eLimnohabitans\u003c/em\u003e, Candidatus Symbiobacter, Rhizobiales Incertae Sedis, A0839, Clade III) and Nitrospirota (\u003cem\u003eNitrospira\u003c/em\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifferential abundant taxa\u003c/strong\u003e. Phylum, Order and lowest taxonomic level of differential abundant ASVs with individual relative abundance higher\u0026thinsp;\u0026gt;\u0026thinsp;0.5% as sampling group mean. Taxa belonging to the core microbiome are marked in thick font. Relative abundance is calculated over all samples. Complete lists are available in supplemental material \u003cstrong\u003eS.3_List.\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOrder\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLowest Taxonomic level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRel. Abundance [%]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eCluster 1\u003c/p\u003e\n \u003cp\u003eFish specific\u003c/p\u003e\n \u003cp\u003eMesohaline enriched\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBacteroidota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlavobacteriales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eElizabethkingia\u003c/strong\u003e \u003cstrong\u003e(2 ASVs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChitinophagales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsinibacterium\u003c/strong\u003e \u003cstrong\u003e(1 ASV)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProteobacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnterobacterales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCitrobacter\u003c/strong\u003e \u003cstrong\u003e(3 ASVs)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePhotobacterium leiognathi\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLelliottia\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEnterobacteriaceae\u003c/strong\u003e \u003cstrong\u003e(6 ASVs)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEnterobacter\u003c/strong\u003e \u003cstrong\u003e(3 ASVs)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEnterobacter cancerogenus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.7\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.9\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.8\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e7.8\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.9\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurkholderiales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMicrovirgula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eCluster 2\u003c/p\u003e\n \u003cp\u003eSS Ekm-665\u003c/p\u003e\n \u003cp\u003eOligohaline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProteobacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePseudomonadales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePsychrobacter\u003c/em\u003e (4 ASVs)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePsychrobacter maritimus\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter johnsonii\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eAlkanindiges\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003cp\u003e0,7\u003c/p\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurkholderiales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePolynucleobacter\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBacteroidota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlavobacteriales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eChryseobacterium\u003c/em\u003e (4 ASVs)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eChryseobacterium antarcticum\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eFlavobacterium\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eOrnithobacterium\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eWeeksellaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVerrucomicrobiota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVerrucomicrobiales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLuteolibacter\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eCluster 3\u003c/p\u003e\n \u003cp\u003eFreshwater transition\u003c/p\u003e\n \u003cp\u003eML Ekm-633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProteobacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurkholderiales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePolynucleobacter\u003c/em\u003e (3 ASVs)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eVerticiella\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eAlcaligenaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaedibacterales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCaedibacter varicaedens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRickettsiales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCandidatus Megaira\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethylococcales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMethylococcaceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePseudomonadales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter lwoffii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBacteroidota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlavobacteriales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eChryseobacterium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eCluster 4\u003c/p\u003e\n \u003cp\u003eBacterioplankton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActinobacteriota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrankiales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSporichthyaceae\u003c/p\u003e\n \u003cp\u003ehgcI clade (4 ASVs)\u003c/p\u003e\n \u003cp\u003eCandidatus Planktophila\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProteobacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMicrotrichales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCL500-29 marine group (4 ASVs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurkholderiales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCandidatus Methylopumilus\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eLimnobacter\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePolynucleobacter\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eLimnohabitans\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eCandidatus Symbiobacter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRhizobiales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRhizobiales Incertae Sedis\u003c/p\u003e\n \u003cp\u003eA0839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAR11 clade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClade III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrospirota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrospirales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNitrospira\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1 Potential pathogens correlate to host immune response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bacterial network modules Gill-SSU-2 \u0026amp; 3 were correlated (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA) with the gill immune modules (Gill-RNA-7 \u0026amp; 11), bacterial taxa of highest Pearson correlation (\u0026gt;\u0026thinsp;0.6) and intramodular connectivity (\u0026gt;\u0026thinsp;0.8) comprise 17 genera (Fig. 7C, \u003cstrong\u003eS.4_List \u0026amp; S.5_List\u003c/strong\u003e). Of these \u003cem\u003eVerticiella\u003c/em\u003e (4 ASVs), \u003cem\u003eShewanella spp\u003c/em\u003e., \u003cem\u003eS. baltica\u003c/em\u003e and \u003cem\u003eS. putrefaciens\u003c/em\u003e (9 ASVs), \u003cem\u003eAeromonas\u003c/em\u003e (8 ASVs), \u003cem\u003eAcinetobacter spp\u003c/em\u003e., \u003cem\u003eA. johnsonii\u003c/em\u003e, \u003cem\u003eA. tjernbergiae\u003c/em\u003e, \u003cem\u003eA. lwoffii\u003c/em\u003e (24 ASVs), \u003cem\u003ePolynucleobacter\u003c/em\u003e (4 ASVs), \u003cem\u003ePlesiomonas\u003c/em\u003e (2 ASVs) and \u003cem\u003eChryseobacterium spp\u003c/em\u003e., \u003cem\u003eC. piscicola\u003c/em\u003e, \u003cem\u003eC. haifense\u003c/em\u003e (13 ASVs) were with almost all strains absent from the bacterioplankton. In the contrary, Methylococcaceae (4 ASVs), Gemmataceae (4 ASVs), \u003cem\u003eLuteolibacter\u003c/em\u003e (5 ASVs), \u003cem\u003eTerrimicrobium\u003c/em\u003e (9 ASVs), \u003cem\u003eCyanobium\u003c/em\u003e (3 ASVs) and \u003cem\u003eLegionella\u003c/em\u003e (7 ASVs) were abundant in both water and fish samples.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study we sought to understand, through OMICs, how the predatory fish zander responds to changing abiotic and biotic (microbiome) conditions along an estuary, as a means for understanding existing and future paradigms of environmental change and fish biomass losses. Here we were able, using a global molecular approach, to demonstrate how fish physiology is impacted in a tissue-specific manner, both by different environmental impacts (salinity, turbidity and oxygen availability) and by changes in the relative contribution of core and potentially pathogenic bacteria on the fish gills.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Salinity adaptation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile zander larvae are stenohaline bound to freshwater and oligohaline areas, juveniles acquire the capacity to adapt to a wider range of osmotic fluctuations. Osmoregulation requires synergistic action of a complex sensing and signal transduction network that could be achieved by enhanced osmosensing in the juvenile zander via mitogen-activated protein kinase (MAPK) pathway [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e] and PI3k-Akt regulating involved salinity stress response [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]. Further, we found cell-cell/ECM interactions related to gill remodeling processes during salinity fluctuations [\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e] and protein tyrosine kinase 2 (here ptk2aa) as hub gene in gill remodeling [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e]. Major epithelial cells in fish gills, pavement and mitochondria-rich cells, play complementary roles in ion transport [\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e]. Their growth and differentiation may be organized by axonal guidance contributing to functional plasticity in estuarine fish [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e]. In accordance, axonal guidance was enriched in zander gill from mesohaline till oligohaline areas. Whilst osmoregulation is among the most energy-intensive metabolic activities in teleosts [\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e], mesohaline zander in this study were in good physiological condition with liver-specific gene expression related to hepatic fat accumulation [\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Starvation in murky waters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mid estuary maximum turbidity zone is marked by liver specific elevated metabolic pathways, autophagy and regulation of ribosomal genes. These correlate with low HSI and compromised body condition indicative for nutritional stress [\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e]. During periods of nutrient deprivation, fish exhibit shifts in energy utilization, depleting liver glycogen[\u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e] and relying on lipids as major energy source [\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e]. Lipid droplets are broken down to free fatty acids via lysosomal-autophagy pathway [\u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e]. In mammalian cells, starvation causes disruption of mitochondrial structure[\u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e] thus increases ROS and induces autophagy via AMPK pathway [\u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e]. Likewise, fish show increased ROS[\u003cspan class=\"CitationRef\"\u003e90\u003c/span\u003e] and induced autophagy and mitophagy [\u003cspan class=\"CitationRef\"\u003e91\u003c/span\u003e]. Autophagy is a key pathway mediating damage control and metabolic adaptation[\u003cspan class=\"CitationRef\"\u003e92\u003c/span\u003e] triggered i.a. by hypoxic conditions involving BNIP3 and Beclin1 signaling while nutrient depletion activates AMPK-dependent ULK1 and Beclin1[\u003cspan class=\"CitationRef\"\u003e93\u003c/span\u003e] in mammals. Zebrafish ortholog genes include hypoxia-induced bnip3a, closest to human BNIP3, and hypoxia-independent bnip3 and bnip4 [\u003cspan class=\"CitationRef\"\u003e94\u003c/span\u003e]. Here we identified \u003cem\u003ebnip4\u003c/em\u003e and \u003cem\u003esirt5\u003c/em\u003e-like as hub genes related to autophagy. Sirtulin enzymes, such as mitochondria-specific SIRT5, play pivotal role controlling lipid metabolism, mitophagy and apoptosis possibly via AMPK pathway[\u003cspan class=\"CitationRef\"\u003e95\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e96\u003c/span\u003e] indicating nutrient depletion driven autophagy activation in zander liver.\u003c/p\u003e\n\u003cp\u003eThe upregulation of ribosomal genes appears seemingly counterintuitive as ribosome biogenesis is most energy-consuming [\u003cspan class=\"CitationRef\"\u003e97\u003c/span\u003e] and downregulated in starving fish [\u003cspan class=\"CitationRef\"\u003e98\u003c/span\u003e]. However, severely reduced translation might be detrimental for acclimatization responses [\u003cspan class=\"CitationRef\"\u003e99\u003c/span\u003e], especially in a multi-stress real-world setting. Ribosomes occupy large amounts of amino acids that can be tapped by autophagy during starvation delivering fuel for proteome remodeling [\u003cspan class=\"CitationRef\"\u003e100\u003c/span\u003e]. However, autophagic tissue degradation has to be balanced by macromolecule synthesis to maintain homeostasis [\u003cspan class=\"CitationRef\"\u003e100\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe cause of the starvation situation remains unclear. Turbidity, in general exerting negative effects on prey capture [\u003cspan class=\"CitationRef\"\u003e101\u003c/span\u003e], did not affect foraging success in juvenile zander across a range of turbidity levels [\u003cspan class=\"CitationRef\"\u003e102\u003c/span\u003e]. We did not investigate potential influences on prey availability or altered food spectra, observed in other estuarine predatory fish during increased turbidity [\u003cspan class=\"CitationRef\"\u003e103\u003c/span\u003e]. As tissue fat content is a crucial factor for overwinter survival of juvenile zander [\u003cspan class=\"CitationRef\"\u003e104\u003c/span\u003e], the low HSI and body condition together with fat and other energy source degradation pathways activation can be expected to affect survival rates of MTZ zander. Sediment load was shown to damage gill tissue in different species [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e103\u003c/span\u003e], however an anticipated immune response linked to turbidity was not evident here. The relationship between starvation and immune regulation is complex and might relate to suppression of tissue [\u003cspan class=\"CitationRef\"\u003e98\u003c/span\u003e] and external mucus immune functions [\u003cspan class=\"CitationRef\"\u003e105\u003c/span\u003e]. We cannot rule out starvation related immunosuppression masking underlying processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Cellular stress in oxygen minimum zone\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGills are the primary organ for physiological exchanges and first response to stressors like hypoxia [\u003cspan class=\"CitationRef\"\u003e106\u003c/span\u003e]. Low DO acclimatization involves optimizing uptake and distribution, reflected in enhanced erythrocyte and hemoglobin concentrations [\u003cspan class=\"CitationRef\"\u003e107\u003c/span\u003e]. Identified genes, including hemoglobin subunit beta-2-like \u003cem\u003ehbb2\u003c/em\u003e, linked to oxygen transfer and upregulated in OMZ, mirror findings in other percid species like Elbe estuarine ruffe and yellow croaker [\u003cspan class=\"CitationRef\"\u003e108\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e109\u003c/span\u003e]. Hypoxic conditions also induce epigenetic modulation by KMT2 family genes [\u003cspan class=\"CitationRef\"\u003e110\u003c/span\u003e], proposed as adaptive coping mechanism in rainbow trout [\u003cspan class=\"CitationRef\"\u003e111\u003c/span\u003e] and identified here as module hubs (\u003cem\u003ekmt2d\u003c/em\u003e and \u003cem\u003ekmt2bb\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eThe upregulation of carbon and amino acid metabolism pathways together with TCA and OXPHOS in juvenile zander liver from the estuarine OMZ indicates a locally enhanced energy demand. Although there is no significant correlation with body condition and HSI, the latter is especially low in several animals from the oxygen minimum zone, which could indicate the first signs of depletion of energy reserves. Remodeling of energy metabolism is an effective strategy in fish to compensate increased demand during hypoxic conditions [\u003cspan class=\"CitationRef\"\u003e112\u003c/span\u003e] or infection [\u003cspan class=\"CitationRef\"\u003e113\u003c/span\u003e]. The liver plays a pivotal role controlling energy reserves utilizing amino acids as primary fuel [\u003cspan class=\"CitationRef\"\u003e114\u003c/span\u003e] with upregulated metabolism [\u003cspan class=\"CitationRef\"\u003e115\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e116\u003c/span\u003e] observed during hypoxic episodes. Prolonged hypoxia increased dominance of aerobic TCA and OXPHOS [\u003cspan class=\"CitationRef\"\u003e112\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eLocal response was further marked by different aspects of cellular stress response[\u003cspan class=\"CitationRef\"\u003e117\u003c/span\u003e] including cell cycle arrest and DNA repair accompanied by affected proteostasis in both tissues. Halting energy-intensive genome duplication processes leverages time for macromolecular repair and allows for energy redirection [\u003cspan class=\"CitationRef\"\u003e118\u003c/span\u003e]. Recovery from stressors on the other hand is accompanied by sustained repair processes [\u003cspan class=\"CitationRef\"\u003e119\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e120\u003c/span\u003e]. The \u003cem\u003eppp5c\u003c/em\u003e is a cross-tissue hub gene in our study involved in cell cycle arrest and DNA damage repair[\u003cspan class=\"CitationRef\"\u003e121\u003c/span\u003e] shown dysregulated in fish under severe stress [\u003cspan class=\"CitationRef\"\u003e122\u003c/span\u003e]. The ATP-dependent ubiquitin-proteasome system (UPS) mitigates proteotoxicity from damaged proteins[\u003cspan class=\"CitationRef\"\u003e123\u003c/span\u003e] resulting i.a. from disrupted endoplasmic reticulum (ER) [\u003cspan class=\"CitationRef\"\u003e124\u003c/span\u003e]. In both tissues we found moderate hypoxia correlated with UPS and endoplasmic reticulum (ER) stress response[\u003cspan class=\"CitationRef\"\u003e125\u003c/span\u003e] characterized by luminal chaperone and ER-associated degradation (ERAD) activation. Besides ubiquitin-proteasome subunits, deubiquitinase genes (DUBs) \u003cem\u003eusp5\u003c/em\u003e \u0026amp; \u003cem\u003eusp14\u003c/em\u003e showed highest intramodular connectivity linking enhanced protein turnover with DNA repair [\u003cspan class=\"CitationRef\"\u003e126\u003c/span\u003e], immune response[\u003cspan class=\"CitationRef\"\u003e127\u003c/span\u003e] and autophagic processes [\u003cspan class=\"CitationRef\"\u003e128\u003c/span\u003e]. Enrichment in apoptosis and necrosis pathways specific in gill indicates stress exceeding cellular tolerance levels[\u003cspan class=\"CitationRef\"\u003e117\u003c/span\u003e] in the exposed tissue.\u003c/p\u003e\n\u003cp\u003eAdaptive modification of the ubiquitin-proteasome composition by disassembly into the 20S unit increases recognition capacity for oxidatively damaged proteins [\u003cspan class=\"CitationRef\"\u003e129\u003c/span\u003e]. Inflammation as well as oxidative stress induce transcriptional upregulation of \u003cem\u003ePSMB8\u003c/em\u003e, \u003cem\u003ePSMB9\u003c/em\u003e, \u003cem\u003ePSMB10\u003c/em\u003e, encoding specialized beta subunits in the 20S proteasome core creating the so-called immunoproteasome [\u003cspan class=\"CitationRef\"\u003e130\u003c/span\u003e]. Immunoproteasomes are central to processing antigenic peptides presented by major histocompatibility complex class (MHC) I molecules, clearance of oxidized proteins and protection of cells from inflammation induced damage [\u003cspan class=\"CitationRef\"\u003e131\u003c/span\u003e]. In mammals, transcription of the inducible B-type subunits is suspected to be controlled by the Interferon regulation factor-1 (IRF-1) signal transduction pathway [\u003cspan class=\"CitationRef\"\u003e132\u003c/span\u003e]. In line, we identified B-type subunits (\u003cem\u003epsmb8a\u003c/em\u003e \u0026amp; \u003cem\u003eb\u003c/em\u003e, \u003cem\u003epsmb9a\u003c/em\u003e, \u003cem\u003epsmb10\u003c/em\u003e) together with \u003cem\u003eirf1b\u003c/em\u003e as hub genes in the largest oxygen correlated gill module. So far there is relatively little research focusing in depth on proteasomal gene expression in fish. Enhanced expression of \u003cem\u003epsmb\u003c/em\u003e (8, 9, 9-L, 10) was shown during antibacterial and antiviral response [\u003cspan class=\"CitationRef\"\u003e133\u003c/span\u003e] used as discriminative markers for infection monitoring [\u003cspan class=\"CitationRef\"\u003e134\u003c/span\u003e]. The expression and high intramodular connectivity of a full set of 20S proteasome, proteasome activator and inducible immunoproteasome indicate elevation of oxidized proteins due to oxygen stress and elevated ROS-levels and increased activity of immunologically active cells in the gill tissue of Hamburg harbor fish coordinated by \u003cem\u003eirf1b\u003c/em\u003e signaling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3.1 Gill immune response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth the co-expression networks and the differential gene expression analyses identified specific marker genes for T and B cell signaling upregulated in Hamburg area. Adaptive immune module hubs \u003cem\u003eptprc\u003c/em\u003e as essential regulator of T and B cell antigen receptor [\u003cspan class=\"CitationRef\"\u003e135\u003c/span\u003e] and T cell signaling related LCP2 (\u003cem\u003elcp2a\u003c/em\u003e) were also identified as immune hubs in bacteria challenged flounder [\u003cspan class=\"CitationRef\"\u003e136\u003c/span\u003e]. The latter was also found regulated upon infection with eukaryotic parasites [\u003cspan class=\"CitationRef\"\u003e137\u003c/span\u003e] and bacteria [\u003cspan class=\"CitationRef\"\u003e138\u003c/span\u003e] indicating cross-species and cross-tissue importance in teleost immune response. Immune cells use reactive oxygen species (ROS) for destruction of pathogen cells involving CYBA and NOX2 encoded phagocyte NADPH oxidase multiprotein complex [\u003cspan class=\"CitationRef\"\u003e139\u003c/span\u003e]. In mucosal immunity NOX1 is hypothesized to replace NOX2 [\u003cspan class=\"CitationRef\"\u003e140\u003c/span\u003e], activity of which requires CYBA, NOXO1 and NOXA1 stimulated by IFN-\u0026gamma; [\u003cspan class=\"CitationRef\"\u003e141\u003c/span\u003e]. Phagocyte NADPH oxidase has key regulatory function in innate immune response via ROS mediated signaling in mammals [\u003cspan class=\"CitationRef\"\u003e142\u003c/span\u003e], matching identification of this gene set as innate immune module hubs in juvenile zander. The highly upregulated eosinophil peroxidase variants in Hamburg area indicate involvement of eosinophils in anti-microbial immune response [\u003cspan class=\"CitationRef\"\u003e143\u003c/span\u003e]. Eosinophils are associated with parasitic infections, controlling inflammation and maintaining epithelial barrier [\u003cspan class=\"CitationRef\"\u003e144\u003c/span\u003e] but have only been studied in a limited number of teleosts including zebrafish [\u003cspan class=\"CitationRef\"\u003e145\u003c/span\u003e], turbot [\u003cspan class=\"CitationRef\"\u003e146\u003c/span\u003e] and flounder [\u003cspan class=\"CitationRef\"\u003e147\u003c/span\u003e]. All variants show a strong negative correlation with DO, indicative for eukaryotic infestation in the OMZ. Strong upregulation of Mucin-2 like isoforms involved in GIALT physical barrier in Hamburg area further support the local immune response. These variants have already been found enhanced in skin mucus of salmonids under physiological stress going along with immune suppression and overgrowth in bacteria [\u003cspan class=\"CitationRef\"\u003e148\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 The zander gill microbiome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJuvenile zander host a complex gill microbiome distinct from that of the surrounding water column which was clearly impacted by the sampling location along the estuary. Although influenced by the bacterioplankton [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e], gill structure and function support a unique and highly diverse bacterial composition in wild fish [\u003cspan class=\"CitationRef\"\u003e149\u003c/span\u003e]. Differing tolerances of inhabiting taxa to physiochemical gradients influence the composition of fish mucus communities [\u003cspan class=\"CitationRef\"\u003e150\u003c/span\u003e]. The strong variation in bacterial composition along the estuary identified in this study is accompanied by only a slight increase in observed taxa with decreasing salinity. The significant differentiation between sampling sites combined with relatively high similarity of bacterial composition on individuals from the same section might be indicative for low movement patterns of juvenile zander within the course of the estuary. Overall, the oxygen minimum zone and freshwater transition in the Hamburg harbor area were found most influential for variable as well as core bacterial composition, where Proteobacteria show a large increase in relative abundance from 50 to 80 percent. This was on one hand driven by the decline in large parts of the core microbiome identified in this study. Core species are assumed to serve beneficial roles in the host, disruption in composition could theoretically be used to identify diseased animals [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. In line, \u003cem\u003eAsinibacterium\u003c/em\u003e and \u003cem\u003eEnterobacter\u003c/em\u003e taxa are considered to inhibit pathogen growth [\u003cspan class=\"CitationRef\"\u003e151\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e152\u003c/span\u003e], \u003cem\u003eLelliottia\u003c/em\u003e is described as critical compartment in juvenile percides gut microbiome [\u003cspan class=\"CitationRef\"\u003e153\u003c/span\u003e]. \u003cem\u003eElizabethkingia\u003c/em\u003e is a well-known component in mucus communities in freshwater and marine fish [\u003cspan class=\"CitationRef\"\u003e154\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e155\u003c/span\u003e] and the most abundant taxon overall in this study (rel. abundance 31\u0026ndash;4%, WF: 0\u0026ndash;1 %). On the other hand, known freshwater taxa lke \u003cem\u003ePolynucleobacter\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e156\u003c/span\u003e] and \u003cem\u003eVerticella\u003c/em\u003e emerge together with many opportunistic pathogenic taxa in the OMZ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4.1 Holobiont interaction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHost-microbe and microbe-microbe interaction are gaining more and more attention due to their tremendous importance for fish health [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. A few recent studies focused on the bacterial composition on gills in wild fish[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e] assessing pathogen load from the presence of specific genera and species [\u003cspan class=\"CitationRef\"\u003e149\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e157\u003c/span\u003e]. Here, we aimed to describe the holobiont incorporating bacterial abundance with tissue specific gene expression patterns in the host. Stressful conditions are expected to affect the interplay [\u003cspan class=\"CitationRef\"\u003e158\u003c/span\u003e], oxygen deficiency in aquaculture for example has been linked to immunosuppressive effects[\u003cspan class=\"CitationRef\"\u003e159\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e160\u003c/span\u003e] and increased abundance of potentially pathogenic taxa [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. As such, a substantial number of suspected or confirmed fish pathogens on zander aligned with an intensified host immune response in the oxygen minimum zone. Taxa with suspected opportunistic pathogenic functions comprise \u003cem\u003ePseudomonas\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e161\u003c/span\u003e], \u003cem\u003eChryseobacterium\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e162\u003c/span\u003e], \u003cem\u003eAcinetobacter\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e163\u003c/span\u003e] and \u003cem\u003ePsychrobacter\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e164\u003c/span\u003e] and \u003cem\u003eAeromonas\u003c/em\u003e (8 ASVs), the latter known for causing multi-tissue damage in freshwater fish [\u003cspan class=\"CitationRef\"\u003e165\u003c/span\u003e]. Kidney specific transcriptome studies indicated OXPHOS and proteasome to be strongly activated upon Aeromonas infection followed by cellular senescence and apoptosis pathways[\u003cspan class=\"CitationRef\"\u003e113\u003c/span\u003e] matching our results. \u003cem\u003eShewanella\u003c/em\u003e strains (16 ASVs), including the species \u003cem\u003eS. putrefaciens\u003c/em\u003e and \u003cem\u003eS. baltica\u003c/em\u003e, showed highest correlation with abundances of up to 3.5% in fish while being scarce in bacterioplankton. \u003cem\u003eShewanella\u003c/em\u003e is recognized for its role in organic matter turnover under hypoxic conditions[\u003cspan class=\"CitationRef\"\u003e166\u003c/span\u003e] and polyunsaturated fatty acids (PUFA) production [\u003cspan class=\"CitationRef\"\u003e167\u003c/span\u003e]. Different strains were isolated from freshwater walleye gastrointestinal tracts[\u003cspan class=\"CitationRef\"\u003e168\u003c/span\u003e] and gill mucus communities of different marine fish species [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e157\u003c/span\u003e], with substantial numbers of ASVs resembling potentially pathogenic species [\u003cspan class=\"CitationRef\"\u003e149\u003c/span\u003e]. The pathogenic role of \u003cem\u003eShewanella\u003c/em\u003e remains unclear[\u003cspan class=\"CitationRef\"\u003e169\u003c/span\u003e] despite repeated recovery from diseased fish [\u003cspan class=\"CitationRef\"\u003e170\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e171\u003c/span\u003e]. Notably, Esteve et al. recovered \u003cem\u003eS. putrefaciens\u003c/em\u003e strains from diseased eel in a freshwater lake system where infection rates increased from zero to 64% morbidity over a period of ten years especially when DO values felt below 5 mg/L. Isolates exhibited pathogenicity capable of killing healthy fish at doses similar to well-known pathogens [\u003cspan class=\"CitationRef\"\u003e172\u003c/span\u003e]. The activated immune response, the bacterial load and the cellular stress response in the OMZ fish are not matched compromised physiological end point markers in our study. However, the overall analysis constitutes only a snapshot and a continuous monitoring is required to understand the relationships in ongoing processes.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis is the first network-based study co-analyzing matching host and microbial data in relation to physiological and abiotic factors in an estuarine wild-fish population. We show local adaptations of zander to salinity gradients and moderate hypoxia and confirm regulatory key genes in signal transduction pathways. Implications for the health situation by starvation in turbid waters and cellular stress and immune response combined with potential pathogenic bacteria in freshwater transition and low oxygen areas are identified. Responses show strong tissue specificity. As expected, metabolic responses correlated with physiological measurements were more prevalent in the liver. The changes in the gill transcriptome and its associated microbiome provide deep insights into the holobiont and can be monitored non-invasively. With expected increases in temperature and related decline in DO in estuarine habitats, diseases like shewanellosis might become prominent in inhabiting fish species. Meta-data analyses indicate lower tolerance to fluctuating conditions in embryos and breeding adults [\u003cspan citationid=\"CR173\" class=\"CitationRef\"\u003e173\u003c/span\u003e]. In general, it is largely unknown how species in dynamic systems such as estuaries respond to changes in the abiotic environment [\u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e174\u003c/span\u003e]. We propose a continuous monitoring of particularly pathogenic bacteria and a future inclusion of further chemical, hydrological, geographical data in a time series analysis of the holobiont over different life stages to understand ongoing processes and the decline of the total fish biomass in estuaries like the tidal Elbe.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDEG differentially expressed genes,\u003c/p\u003e\n\u003cp\u003eDEA differential expression analysis,\u003c/p\u003e\n\u003cp\u003eSSU Small subunit rRNA\u003cstrong\u003e, \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWGCNA Weighted gene correlation network analysis,\u003c/p\u003e\n\u003cp\u003eDO dissolved oxygen,\u003c/p\u003e\n\u003cp\u003eER endoplasmatic reticulum,\u003c/p\u003e\n\u003cp\u003eTCA tricarboxylic acid cycle,\u003c/p\u003e\n\u003cp\u003eTPM million mapped reads\u003c/p\u003e\n\u003cp\u003eFDR false discovery rate\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the Research Training Group 2530: \u0026ldquo;Biota-mediated effects on Carbon cycling in Estuaries\u0026rdquo; (project number 407270017; contribution to Universit\u0026auml;t Hamburg and Leibniz-Institut f\u0026uuml;r Gew\u0026auml;sser\u0026ouml;kologie und Binnenfischerei im Forschungsverbund Berlin e.V.). This work was also supported by the DFG research grand \u0026ldquo;Large Scale Sequencing to Unravel Carbon Cycling in the Elbe estuary (Micro)biota\u0026rdquo; Project number: 496691966 / FA 1568. \u0026nbsp;and by the project \u0026ldquo;Blue Estuaries\u0026rdquo; funded by the Federal Ministry for Education and Research under funding code 03F0864F.\u003c/p\u003e\n\u003ch2\u003ePermits\u003c/h2\u003e\n\u003cp\u003eSampling procedures were according to the standards described in the German Animal Welfare Act (\u0026sect;4 TierSchG). In detail, after being brought on board, the fish are stunned with a blow to the head, before being killed with a heart stab. Samples were cooled to 4\u0026deg; until sampling immediately afterwards to ensure best RNA quality. The implementation of the stow-net fishing for scientific purposes is approved in accordance with \u0026sect; 10 Regulation for the implementation of the Hamburg Fisheries Act in the Elbe estuary by the Authority for the Environment Climate, Energy and Agarwirtschaft (A132-Supreme Fisheries Authority, Stadthausbr\u0026uuml;cke 12, 20355 Hamburg), by the State Fisheries Office Bremerhaven (Fischkai 31, 27572 Bremerhaven) according to \u0026sect; 10 of the Lower Saxony Coastal Fisheries Ordinance and by the State Office for Agriculture, Environment and Rural Areas of Schleswig-Holstein (Department 3, Fisheries, Hamburger Chaussee 25, 24200 Flintbek). Exemptions to the ordinances on nature reserves M\u0026uuml;hlenberger Loch/Ne\u0026szlig;sand (Amt f\u0026uuml;r Naturschutz, Gr\u0026uuml;nplanung und Bodenschutz, Abteilung Naturschutz, Neuenfelder Strasse 19, 21109 Hamburg) as well as a nature conservation permit to conduct research fishing in protected areas in the NSG \u0026quot;Rhinplate und Elbufer s\u0026uuml;dlich Gl\u0026uuml;ckstadt\u0026quot;/FHH area DE 2393-393 \u0026quot;Schleswig-Holsteinisches Elb\u0026auml;stuar mit angrenzenden Fl\u0026auml;chen\u0026quot; from the Office of Environmental Protection (Department of Nature Conservation, Langer Peter 27a, 25506 Itzehoe).\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eRK: Conceptualization, Methodology, Investigation, Formal analysis, Data curation, Validation, Writing- original draft, Visualization, Project administration. JT: Investigation, Permit acquisition, Review \u0026amp; Editing. EH: Investigation, Visualization, Review \u0026amp; Editing. JW: Supervision, Validation, Review \u0026amp; Editing. AF: Funding acquisition, Conceptualization, Supervision, Validation, Review \u0026amp; Editing, Project administration. RT, CM: Conceptualization, Funding acquisition, Resources, Review \u0026amp; Editing.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe thank Claus \u0026amp; Harald Zeeck and Dirk Stumpe for their help with sample collection and Prof. Dr. Kathrin Dausmann for mentoring.\u003c/p\u003e\n\u003ch2\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/h2\u003e\n\u003cp\u003eDuring the preparation of this work RK used DeepL/translate/write in order to improve language of the manuscript. After using this tool, RK reviewed and edited the content as needed and takes full responsibility for the content of the publication.\u003c/p\u003e\n\u003ch2\u003eDeclarations of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing or financial interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eF. Wei, K. Sakata, T. Asakura, Y. Date, and J. Kikuchi, \u0026ldquo;Systemic Homeostasis in Metabolome, Ionome, and Microbiome of Wild Yellowfin Goby in Estuarine Ecosystem,\u0026rdquo; \u003cem\u003eSci Rep\u003c/em\u003e, 2018, doi: 10.1038/s41598-018-20120-x.\u003c/li\u003e\n\u003cli\u003eH. K. Lotze \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Depletion degradation, and recovery potential of estuaries and coastal seas,\u0026rdquo; \u003cem\u003eScience (1979)\u003c/em\u003e, 2006, doi: 10.1126/science.1128035.\u003c/li\u003e\n\u003cli\u003eM. Elliott and V. 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P\u0026ouml;rtner, \u0026ldquo;Thermal bottlenecks in the life cycle define climate vulnerability of fish,\u0026rdquo; \u003cem\u003eScience (1979)\u003c/em\u003e, vol. 369, no. 6499, pp. 65\u0026ndash;70, Jul. 2020, doi: 10.1126/SCIENCE.AAZ3658/SUPPL_FILE/AAZ3658_DAHLKE_SM.PDF.\u003c/li\u003e\n\u003cli\u003eS. S. Lauchlan and I. Nagelkerken, \u0026ldquo;Species range shifts along multistressor mosaics in estuarine environments under future climate,\u0026rdquo; \u003cem\u003eFish and Fisheries\u003c/em\u003e, vol. 21, no. 1, pp. 32\u0026ndash;46, Jan. 2020, doi: 10.1111/faf.12412.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Materials","content":"\u003cp\u003eFigure S1 and List S1 to S5 are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"RNAseq, Metabarcoding, Network analysis, Hypoxia, Holobiont","lastPublishedDoi":"10.21203/rs.3.rs-3990815/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3990815/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoastal and estuarine environments are under endogenic and exogenic pressures jeopardizing survival and diversity of inhabiting biota. Information of possible synergistic effects of multiple (a)biotic stressors and holobiont interaction are largely missing in the Elbe estuary but are of importance to estimate unforeseen effects on animals’ physiology. Here, we seek to leverage host-transcriptional RNA-seq and gill mucus microbial 16S rRNA metabarcoding data coupled with physiological and abiotic measurements in a network analysis approach to deconvolute the impact of multiple stressors on the health of juvenile \u003cem\u003eSander lucioperca\u003c/em\u003e along one of the largest European estuaries. We find mesohaline areas characterized by gill tissue specific transcriptional responses matching osmosensing and tissue remodeling. Liver transcriptomes instead emphasized that zander from highly turbid areas were undergoing starvation which was supported by compromised body condition. Potential pathogenic bacteria, including \u003cem\u003eShewanella\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eAeromonas \u003c/em\u003eand \u003cem\u003eChryseobacterium\u003c/em\u003e, dominated the gill microbiome along the freshwater transition and oxygen minimum zone. Their occurrence coincided with a strong adaptive and innate transcriptional immune response in host gill and enhanced energy demand in liver tissue supporting their potential pathogenicity. Overall, we demonstrate the information gain from integration of omics data into biomonitoring of fish and point out bacterial species with disease potential.\u003c/p\u003e","manuscriptTitle":"Network-based integration of omics, physiological and environmental data in real-world Elbe estuarine Zander","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 21:50:36","doi":"10.21203/rs.3.rs-3990815/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f97a1e44-a7f7-4f6e-a82f-68988caca6bf","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29058288,"name":"Biological sciences/Ecology/Conservation biology"},{"id":29058289,"name":"Biological sciences/Molecular biology/Transcriptomics"},{"id":29058290,"name":"Biological sciences/Microbiology/Bacteria/Bacterial host response"}],"tags":[],"updatedAt":"2024-06-02T14:05:13+00:00","versionOfRecord":{"articleIdentity":"rs-3990815","link":"https://doi.org/10.1016/j.scitotenv.2024.173656","journal":{"identity":"science-of-the-total-environment","isVorOnly":true,"title":"Science of The Total Environment"},"publishedOn":"2024-06-01 14:05:13","publishedOnDateReadable":"June 1st, 2024"},"versionCreatedAt":"2024-03-05 21:50:36","video":"","vorDoi":"10.1016/j.scitotenv.2024.173656","vorDoiUrl":"https://doi.org/10.1016/j.scitotenv.2024.173656","workflowStages":[]},"version":"v1","identity":"rs-3990815","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3990815","identity":"rs-3990815","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}