Philippa Kaur

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Our monetary compensation programme for poor rural communities in Mexico’s Sierra de Manantlán Biosphere Reserve encouraged them to forgo exploitation of their natural resources to provide ecosystem services for the city of Colima (see Nature 591, 178; 2021). But focusing solely on the monetary value of ecosystems isn’t enough.The National Forestry Commission of Mexico and the Fund for Natural Protected Areas have made compensatory payments of almost US$1 million to communities in the Cerro Grande region of the reserve since 2003, backed since 2013 by a local fiduciary fund of voluntary contributions from Colima’s citizens. However, the voluntary payments are minimal because most people don’t acknowledge the importance of the mountain forest that provides 90% of their water.As well as monetary schemes, the cutural traditions and the land-tenure rights of communal organizations must be recognized. They should be empowered to draw up contracts between owners of natural resources and urban beneficiaries that will promote their common social, economic and livelihood interests. To increase productivity and family income for impoverished small landowners, payment for ecosystem services could be implemented by using diverse marketing approaches that include sustainable agroforestry and livestock production. More

Insect culturePhyllotreta armoraciae beetles were reared on potted Brassica juncea cultivar “Bau Sin” plants or on potted Brassica rapa cultivar “Yo Tsai Sum” plants (Known-You Seed Co., Ltd) in mesh cages (Bugdorm, MegaView Science Co., Ltd) in a controlled environment chamber at 24 °C, 60% relative humidity, and a 16-h photoperiod. Food plants were cultivated in a growth chamber at 24 °C, 55% relative humidity, and a 14-h photoperiod. Beetles were provided with 3–4-week-old plants once per week, and plants with eggs were kept separately for larval development. Larvae were allowed to pupate in the soil, and after 3 weeks, the soil with pupae was transferred into plastic boxes (9 L, Lock&Lock) until the new generation of beetles emerged.RNA isolation, RNA sequencing, and de novo transcriptome assemblyTotal RNA was extracted from dissected foregut, midgut, hindgut, and Malpighian tubule tissue of newly emerged P. armoraciae beetles that were reared on B. juncea using the innuPREP RNA Mini Kit (Analytik Jena). Tissues from at least ten beetles were pooled per sample. RNA integrity was verified using an Agilent Technologies 2100 Bioanalyzer with the RNA 6000 Nano Kit (Agilent Technologies). RNA quantity was determined using a Nanodrop ND-1000 spectrophotometer (PEQlab Biotechnologie GmbH). One set of RNA samples was sequenced by GATC Biotech on the HiSeq 2500 System from Illumina in Rapid Run mode, using the paired-end (2 × 125 bp) read technology at a depth of 15–25 million reads for each sample. For a second set of samples consisting of four biological replicates per tissue, we additionally performed an on-column DNA digestion with the innuPREP DNase I Digest Kit (Analytik Jena) according to the manufacturer’s instructions. RNA samples were poly(A)-enriched, fragmented, and sequenced at the Max Planck Genome Centre Cologne on the HiSeq 3000 Sequencing System from Illumina, using the paired-end (2 × 150 bp) read technology at a depth of 22 million reads for each sample. Sequencing reads were filtered to remove bad-quality reads based on fastq file scores and trimmed based on read length using CLC Genomics Workbench software version 10.1. With a randomly sampled set of 420 million reads from the two sets of sequencing data, a transcriptome was assembled de novo with the following parameters: nucleotide mismatch cost = 1; insertion = deletion costs = 2; length fraction = 0.6; similarity = 0.9. Conflicts among the individual bases were resolved by voting for the base with the highest frequency. After removing contigs shorter than 250 bp, the final assembly contained 36,445 contigs with an N50 contig size of 2115 bp.Identification of coleopteran MFS transportersWe predicted a protein dataset for P. armoraciae by translating each contig of the gut and Malpighian tubule-specific transcriptome into all six reading frames. After removing sequences shorter than 50 amino acids, we submitted the protein dataset (267,568 sequences) to the TransAAP hosted at the TransportDB 2.0 web portal35. This initial annotation predicted a total of 1401 putative transporter sequences and revealed the MFS and the ABC transporters to be the largest transporter families (Supplementary Data 1). We focused on MFS transporters, which are classified into >80 families53. We used one protein sequence from each MFS family as a query to search candidate MFS transporters in the protein dataset from P. armoraciae using Blastp (E value threshold of 10−5), and assigned each candidate to an MFS family based on sequence similarity to transporter sequences deposited in TCDB. Additional candidates were identified by repeating the search procedure with an extended dataset including the candidate MFS transporters from P. armoraciae. The number of TMDs for each candidate was predicted using the TMHMM Server v.2.054. Partial sequences encoding less than six predicted TMDs were removed from the dataset. The same strategy was used to identify putative MFS transporters in protein datasets that were predicted from the genomes of Leptinotarsa decemlineata, genome annotations v.0.5.355, Anoplophora glabripennis, assembly Agla_1.056, and Tribolium castaneum, assembly Tcas3.057, respectively. The predicted protein sequences are provided in Supplementary Data 2.Digital gene expression analysisDigital gene expression analysis of putative MFS transporters identified in the P. armoraciae transcriptome was carried out using CLC Genomics Workbench v.10.1 by mapping the Illumina reads from the second set of samples onto the reference transcriptome, and counting the reads to estimate gene expression levels. For the cloned MFS genes, complete open-reading frames (ORFs) were used as reference sequences for mapping. For read alignment, we used the following parameters: nucleotide mismatch cost = 2; insertion = deletion costs = 3; length fraction = 0.6; similarity fraction = 0.9; maximum number of hits for a read = 15. Each pair of reads was counted as two. Biases in the sequence datasets and different transcript sizes were corrected using the TPM (transcripts per kilobase million) normalization method to obtain correct estimates for relative expression levels between samples.Phylogenetic analyses of coleopteran MFS transportersWe inferred the lineage-specific diversification patterns of putative MFS transporters from P. armoraciae, L. decemlineata, A. glabripennis, and T. castaneum in phylogenetic analyses with two different datasets, one containing all identified MFS transporters (867 sequences), the other containing a subset of putative sugar porters (120 sequences) from the above four species and 35 sugar porters from C. populi52. The corresponding protein sequences were aligned using the MUSCLE algorithm58 implemented in MEGA 7 with default parameters. The alignments were trimmed manually and the best substitution models were determined using ProtTest 3.4.259. Maximum-likelihood phylogenetic trees were constructed in IQ-TREE version 1.6.060 using the VT + G + F substitution model with 1000 ultrafast bootstrap replicates for the full dataset, and the LG + G + F substitution model with 1000 bootstrap replicates for the subset of putative sugar porters.Identification and sequencing of candidate transportersBased on our phylogenetic analysis, we selected the largest clade of putative MFS transporters that was specifically expanded in P. armoraciae for further studies. Transcriptome analyses revealed the presence of a pseudogene (PaMFS9-ps) that shares between 43 and 95% nucleotide sequence identity with members of the focal clade. The protein encoded by this pseudogene is predicted to possess only two transmembrane domains due to a premature stop codon caused by frameshift mutations in the coding sequence. To obtain the full-length ORFs of partial transcripts, we synthesized 5′- and 3′-rapid amplification of cDNA (complementary DNA) ends (RACE)–cDNA using the SMARTerRACE cDNA Amplification Kit (Clontech) and performed 5′- and 3′-RACE-PCR according to the manufacturer’s protocols (Clontech). All full-length ORFs were cloned into the pCR™4-TOPO® TA vector (Thermo Fisher Scientific) for sequence verification.Tissue-specific expression of candidate transportersWe used quantitative PCR (qPCR) to analyze the expression of candidate transporter genes in the foregut, midgut, hindgut, Malpighian tubules, and other tissues of 1-day-old adult P. armoraciae beetles (n = 4 biological replicates, each with two technical replicates), respectively. In addition, the expression of candidate transporter genes was analyzed in the proximal, central, and distal Malpighian tubule regions (Supplementary Fig. 2d) dissected from 4-day-old adult P. armoraciae beetles (n = 3 biological replicates, each with two technical replicates). Total RNA was extracted using the InnuPrep RNA Mini Kit (Analytik Jena), treated with TURBO DNase (Thermo Fisher Scientific), and purified using the RNeasy MinElute Cleanup Kit (Qiagen). First-strand cDNA was synthesized using the Verso cDNA Synthesis Kit (Thermo Fisher Scientific) using a 3:1 mixture (v/v) of random hexamer and oligo dT primers according to the manufacturer’s protocol. Quantitative PCR was performed in optical 96-well plates (Bio-Rad) on a Bio-Rad CFX Connect Real-Time System using the Absolute Blue qPCR SYBR Green Kit (Thermo Fisher Scientific). The PCR program was as follows: 95 °C for 15 min, then 40 cycles at 95 °C for 15 s, 57 °C for 30 s, and 72 °C for 30 s, followed by a melt cycle from 55 to 95 °C in 0.5 s increments. Primers (Supplementary Data 4) were designed using Primer3web version 4.1.0. We verified the amplification specificity by sequencing each PCR product after cloning into the pCR™4-TOPO® TA vector (Thermo Fisher Scientific) and by melting-curve analyses. Primer efficiencies were calculated using a cDNA template dilution series. Gene expression was normalized to the expression level of eukaryotic initiation factor 4A (eIF4A), which showed the lowest variability across tissues among four tested reference genes (Supplementary Table 4).Expression of candidate transporters in insect cellsFor protein expression, we cloned each ORF without stop codon into the pIEx-4 expression vector (Novagen) in frame with the vector-encoded carboxy-terminal 6× His-tag and sequenced the resulting constructs. Primer sequences are listed in Supplementary Data 4. One construct of each candidate gene was used for transfection of High Five™ insect cells (Gibco) cultured in Express Five® SFM medium (Gibco) supplemented with 20 mM glutamine (Gibco) and 50 μg/mL gentamicin (Gibco). Confluent insect cells were diluted 1:5, dispensed in 500 μL aliquots into 24-well plates, and incubated at 27 °C. The next day, we transfected the cells using FuGENE HD Transfection Reagent (Promega) according to the manufacturer’s protocol. Cells treated with transfection reagent only were used as a negative control. After 48 h, we harvested the cells for Western blotting and uptake assays.Western blottingTo confirm protein expression, transfected insect cells were washed twice with phosphate-buffered saline (PBS; pH 7.4), collected by centrifugation, and resuspended in hypotonic buffer (20 mM Tris-HCl (pH 7.5), 5 mM EDTA, 1 mM dithiothreitol, 0.1% benzonase nuclease (Merck Millipore) (v/v), and protease inhibitors (cOmplete Mini, ETDA-free, Roche Diagnostics GmbH)). After incubation on ice for 10 min, the samples were frozen in liquid nitrogen, thawed, and centrifuged (16,000 × g for 15 min at 4 °C). The resulting cell pellet was resuspended in hypotonic buffer and used for Western blotting using horseradish peroxidase-conjugated anti-His antibody (1:10,000; Novex, Life technologies).Glucoside uptake assays with transfected insect cellsCells were washed with PBS (pH 5.5) by pipetting and incubated with different glucoside substrates at 200 µM in PBS (pH 5.5) for 1 h at 27 °C. Assays were performed with substrate mixtures containing seven different glucosinolates (2-propenyl glucosinolate (Roth), 4-methylsulfinylbutyl glucosinolate (Phytoplan), 4-methylthiobutyl glucosinolate (Phytoplan), 2-phenylethyl glucosinolate (Phytoplan), benzyl glucosinolate (Phytoplan), 4-hydroxybenzyl glucosinolate (isolated from Sinapis alba seeds61), and I3M glucosinolate (Phytoplan)), or five other plant glucosides (salicin (Sigma-Aldrich), linamarin (BIOZOL), dhurrin (Roth), catalpol (Sigma-Aldrich), and aucubin (Sigma-Aldrich)). After incubation, cells were washed three times with ice-cold PBS (pH 5.5) by pipetting, collected in 300 μL 80% (v/v) methanol, frozen in liquid nitrogen, thawed, and centrifuged at 3220 × g for 10 min at 4 °C. The supernatant was dried by vacuum centrifugation, dissolved in ultrapure water, and analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). All glucoside uptake assays were performed in triplicates. The amount of each substrate in transporter-expressing cells was compared with that detected in control cells. Transporters were considered active when the average glucoside amounts detected in transporter-expressing cells were at least twofold higher than those detected in control cells.Cloning of PaGTR1 into the pNB1u vector and cRNA synthesisWe amplified the ORF of PaGTR1 without stop codon by PCR using uracil-containing primers (Supplementary Data 4). The 3′ primer was designed to encode a human influenza hemagglutinin tag to enable the detection of recombinant protein by Western blotting if necessary. The pNB1u vector was digested overnight at 37 °C with PacI and Nt.BbvCI (New England Biolabs) to generate 8-nt overhangs. One microliter gel-purified PCR product (100 ng/µL) was combined with 1 µL gel-purified vector (50 ng/µL), 1 U USER enzyme, 2 μL 5× PCR reaction buffer and 5 μL H2O, incubated at 37 °C for 25 min, followed by 25 min at room temperature. After the transformation of chemically competent E. coli cells, colonies containing the appropriate insert were identified by Sanger sequencing. The DNA template for complementary RNA (cRNA) synthesis was amplified by PCR from the X. laevis expression construct using pNB1uf/r primers (Supplementary Data 4) and cRNA was synthesized using the mMESSAGE mMACHINE™ T7 Transcription Kit (Invitrogen) according to the manufacturer’s manual.Biochemical characterization of PaGTR1 in Xenopus oocytesThe cRNA concentration was adjusted to 800 ng/μL with RNase-free water for oocyte injection. Xenopus laevis oocytes (Ecocyte Bioscience) were injected with 50 nL containing 40 ng cRNA or with 50 nL pure water as a control using a Drummond NANOJECT II (Drummond Scientific Company) or a Nanoliter 2010 Injector (World Precision Instruments).Injected oocytes were incubated in Kulori buffer (90 mM NaCl, 1 mM KCl, 1 mM MgCl2, 1 mM CaCl2, and 5 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid, pH 7.4) supplemented with 50 μg/mL gentamicin at 16 °C for 3 days until assaying. All transport assays were performed at room temperature. Injected oocytes were pre-incubated for 5 min in Kulori buffer (90 mM NaCl, 1 mM KCl, 1 mM MgCl2, 1 mM CaCl2, and 5 mM 2-(N-morpholino)ethanesulfonic acid (MES), pH 6.0) before they were transferred into the same buffer containing the substrate(s). To determine the substrate preference of PaGTR1, we incubated oocytes in Kulori buffer (pH 6.0) containing an equimolar mixture of 2-propenyl glucosinolate, 4-methylsulfinylbutyl glucosinolate, 4-methylthiobutyl glucosinolate, 2-phenylethyl glucosinolate, benzyl glucosinolate, 4-hydroxybenzyl glucosinolate, and I3M glucosinolate, each at 200 µM, for 1 h.The pH dependency of glucosinolate transport was determined by incubating the injected oocytes with 100 μM I3M glucosinolate for 10 min in Kulori buffer adjusted to different pH values. The effects of different ionophores on glucosinolate transport was studied by incubating the PaGTR1-expressing oocytes in Kulori buffer at pH 6.0 containing either 20 μM carbonyl cyanide m-chlorophenyl hydrazone (H+ ionophore, Sigma-Aldrich), 20 μM nigericin (K+/H+ exchanger, Abcam), or 20 μM valinomycin (K+ ionophore, Abcam) for 15 min. Afterwards, we incubated the oocytes in Kulori buffer containing 100 μM I3M glucosinolate and the corresponding ionophore for 10 min. Assays performed with oocytes incubated in Kulori buffer without any ionophore served as a control.The time course of I3M glucosinolate uptake was analyzed by incubating oocytes with 100 μM I3M glucosinolate in Kulori buffer (pH 6.0) for 5, 10, 20, 30, 45, 60, 120, 180, and 240 min, respectively. The apparent Km value of PaGTR1 for I3M glucosinolate was determined by incubating injected oocytes in Kulori buffer (pH 6.0) for 10 min with different substrate concentrations. The Km value was calculated by nonlinear regression analysis in SigmaPlot 14.0 (Systat Software Inc.).Each assay consisted of 14–15 oocytes and was stopped by washing oocytes four times with Kulori buffer. Afterwards, 12 of the washed oocytes were distributed into four Eppendorf tubes, with three oocytes per tube and immediately homogenized in 100 µL of 50% (v/v) methanol. After centrifugation (21,380 × g or 16,000 × g for 15 min), the supernatant was incubated at −20 °C for at least 1 h to precipitate proteins, which were pelleted by centrifugation (21,380 × g or 16,000 × g for 15 min). Finally, 60 μL sample was diluted with 120 μL ultrapure water, filtered through a 0.22 μm PVDF-based filter plate (Merck Millipore), and analyzed by LC-MS/MS. The glucosinolate concentration in oocytes was calculated by assuming an oocyte volume of 1 μL62.LC-MS/MSGlucosinolates were quantified by LC-MS/MS using an Agilent 1200 HPLC system connected to an API3200 tandem mass spectrometer (AB SCIEX). Separation was achieved on an EC 250/4.6 NUCLEODUR Sphinx RP column (250 mm × 4.6 mm, 5 μm; Macherey-Nagel) using a binary solvent system consisting of 0.2% (v/v) formic acid in water (A) and acetonitrile (B), with a flow rate of 1 mL/min at 25 °C. The elution gradient was as follows: 0–1 min, 1.5% B; 1–6 min, 1.5–5% B; 6–8 min, 5–7% B; 8–18 min, 7–21% B; 18–23 min, 21–29% B; 23–23.1 min, 29–100% B; 23.1–24 min, 100% B; 24–24.1 min, 100 to 1.5% B; 24.1–28 min, 1.5% B. Glucosinolates were detected in negative ionization mode. The ion spray voltage was set to −4500 V. Gas temperature was set to 700 °C, curtain gas to 20 psi, collision gas to 10, nebulizing gas to 70 psi, and drying gas to 60 psi. Nonhost glucosides were quantified using an Agilent 1200 HPLC system connected to an API5000 tandem mass spectrometer (AB SCIEX). Separation was achieved on an Agilent XDB-C18 column (5 cm × 4.6 mm, 1.8 μm) using a binary solvent system consisting of 0.05% (v/v) formic acid in water (A) and acetonitrile (B) with a flow rate of 1.1 mL/min at 25 °C. The elution gradient was as follows: 0–0.5 min, 5% B; 0.5–2.5 min, 5–31% B; 2.5–2.52 min, 31–100% B; 2.52–3.5 min, 100% B; 3.5–3.51 min, 100 to 5% B; 3.51–6 min, 5% B. Compounds were detected in negative ionization mode with ion spray voltage set to −4500 V. The gas temperature was set to 700 °C, curtain gas to 30 psi, collision gas to 6, and both nebulizing gas and drying gas to 60 psi. Multiple reaction monitoring (MRM) was used to monitor the transitions from precursor ion to product ion for each compound (Supplementary Table 5). Compounds were quantified using external standard curves. Analyst Software 1.6 Build 3773 (AB Sciex) was used for data acquisition and processing.Samples from the pH dependency experiment were analyzed by LC-MS/MS using an Advance UHPLC system (Bruker) connected to an EVOQ Elite TripleQuad mass spectrometer (Bruker) equipped with an electrospray ion source. Separation was achieved on a Kinetex 1.7u XB-C18 column (100 mm × 2.1 mm, 1.7 µm, 100 Å, Phenomenex) using a binary solvent system consisting of 0.05 % (v/v) formic acid in water (A) and acetonitrile with 0.05% (v/v) formic acid (B), with a flow rate of 0.4 mL/min at 40 °C. The elution gradient was as follows: 0–0.2 min, 2% B; 0.2–1.8 min, 2–30% B; 1.8–2.5 min, 30–100% B; 2.5–2.8 min, 100% B; 2.8–2.9 min, 100 to 2% B; and 2.9–4.0 min, 2% B. Glucosinolates were detected in negative ionization mode. The instrument parameters were optimized by infusion experiments with pure standards. The ion spray voltage was set to −4000 V. Cone temperature was set to 350 °C, cone gas to 20 psi, heated probe temperature to 200 °C, and probe gas flow to 50 psi. Nebulizing gas was set to 60 psi, and collision gas to 1.6 mTorr. MRM parameters are provided in Supplementary Table 5. Bruker MS Workstation software (Version 8.2.1, Bruker) was used for data acquisition and processing of glucosinolates. All other samples from experiments using the oocyte expression system were analyzed using the LC-MS/MS method described above for insect cell-based assays. The concentrations of all glucosinolates in the substrate preference assay were determined using external standard curves. Assays performed to characterize I3M glucosinolate transport were quantified using 2-propenyl glucosinolate as an internal standard.Double-stranded RNA synthesisWe synthesized seven different double-stranded RNAs (dsRNAs) between 120 and 298 bp in length, one specific for each PaGTR1/2/3/5/6/7/8, respectively, and a 223-bp fragment of the inducible metalloproteinase inhibitor (IMPI) from the greater wax moth Galleria mellonella (AY330624.1) (dsIMPI) using the T7 RiboMAX™ Express RNAi System (Promega). In silico off-target prediction was done by searches of all possible 21 mers of both RNA strands against the local P. armoraciae transcriptome database allowing for two mismatches. Except for PaGTR5, the prediction did not find any off-target towards putative transporter genes in the transcriptome (Supplementary Data 5).Function of PaGTR1 in vivoTo analyze the function of PaGTR1, we injected newly emerged adult P. armoraciae beetles (reared on B. juncea) with 100 nL ultrapure water containing 80 ng of dsPaGTR1 or 80 ng of dsIMPI, respectively, using a Nanoliter 2010 Injector (World Precision Instruments). Injected beetles were provided with detached leaves of 3–4-week-old B. juncea plants and kept in plastic containers with moistened tissue in the laboratory under ambient conditions. Four days after dsRNA injection, we collected dsIMPI- and dsPaGTR1-injected beetles for gene expression analysis (n = 5 replicates, three beetles per replicate) and glucosinolate analysis (n = 10 replicates, three beetles per replicate), respectively. The remaining beetles were used for a sequestration experiment with Arabidopsis thaliana Col-0 (Arabidopsis) plants that had been cultivated in a growth chamber at 21 °C, 55% relative humidity, and a 10-h photoperiod. To compare the accumulation and excretion of ingested glucosinolates in dsIMPI- and dsPaGTR1-injected beetles, we fed beetles with detached Arabidopsis leaves in Petri dishes (60 mm diameter) that contained 50 μL of ultrapure water and were sealed with parafilm to prevent leaf wilting (n = 10 replicates, five beetles per replicate). Feeding assays were performed in the laboratory under ambient conditions, and leaves were exchanged every day for five consecutive days. To estimate how much the beetles fed, we determined the weight of each leaf before and after feeding. Feces were collected every day using 100 µL of ultrapure water per replicate, combined with 300 μL of pure methanol in a 1.5 mL Eppendorf tube and dried by vacuum centrifugation. Feces samples were then homogenized in 200 μL of 80% (v/v) methanol using metal beads (2.4 mm diameter, Askubal) in a tissue lyzer (Qiagen) for 1 min at 30 Hz. After feeding, adults were starved for one day, weighed, frozen in liquid nitrogen, and stored at −20 °C until glucosinolate extraction. Beetle samples were homogenized using a plastic pestle in 200 μL 80% (v/v) methanol. All samples were then extracted with 1 mL 80% (v/v) methanol containing 25 μM 4-hydroxybenzyl glucosinolate as an internal standard. After centrifugation (16,000 × g for 10 min), glucosinolates were extracted from the supernatant, converted to desulfo-glucosinolates, and analyzed by high-performance liquid chromatography coupled with diode array detection (HPLC-DAD) as described below. The glucosinolate content in adults or feces was calculated in nanomole per adult, respectively.To confirm the specificity of PaGTR1 knockdown, we analyzed the effect of dsPaGTR1 injection on the expression of PaGTR2, PaGTR3, PaGTR9, and PaGTR10. PaGTR9 and PaGTR10 share the highest nucleotide sequence similarity (69% sequence identity) with PaGTR1. PaGTR2 and PaGTR3 expression was analyzed because the recombinant transporters also used I3M glucosinolate as a substrate. RNA extraction, purification, cDNA synthesis, and qPCR were performed as described above.Function of PaGTR5/6/7/8 in vivoTo analyze the function of PaGTR5/6/7/8, we injected newly emerged adult P. armoraciae beetles that had fed for 2 days on B. juncea leaves with 100 nL ultrapure water containing 100 ng of dsIMPI or each 100 ng of dsPaGTR5/6/7/8 using a Nanoliter 2010 Injector (World Precision Instruments). A subset of the dsRNA-injected beetles was fed with detached leaves of Arabidopsis plants in plastic containers with moistened tissue for gene expression analysis (n = 6 replicates, two beetles per replicate). The remaining dsRNA-injected beetles were used for a sequestration experiment with Arabidopsis plants to compare the accumulation and excretion of previously stored and ingested glucosinolates in dsIMPI- and dsPaGTR5/6/7/8-injected beetles. We fed the injected beetles with detached Arabidopsis leaves in Petri dishes (60 mm diameter) that contained 30 μL of ultrapure water and were sealed with parafilm to prevent leaf wilting (n = 10 replicates, six beetles per replicate). Feeding assays were performed in the laboratory under ambient conditions, and leaves were exchanged every day for six consecutive days. To estimate how much the beetles fed, we determined the weight of each leaf before and after feeding. Starting from the second day, feces were collected as above for 5 days. After drying by vacuum centrifugation, feces were homogenized in 1 mL of 80% (v/v) methanol containing 25 μM 4-hydroxybenzyl glucosinolate as an internal standard using metal beads (2.4 mm diameter, Askubal) in a tissue lyzer (Qiagen) for 1 min at 30 Hz. Fed adults were starved for 1 day, weighed, and frozen in liquid nitrogen until glucosinolate extraction. Beetle samples were homogenized using a plastic pestle in 200 μL of 80% (v/v) methanol and then extracted with 1 mL of 80% (v/v) methanol containing 25 μM 4-hydroxybenzyl glucosinolate as an internal standard. After centrifugation (16,000 × g for 10 min), glucosinolates were extracted from the supernatant, converted to desulfo-glucosinolates, and analyzed by HPLC-DAD as described below.To confirm the specificity of PaGTR5/6/7/8 knockdown, we analyzed the effect of dsPaGTR5/6/7/8 injection on the expression of PaGTR9 and PaGTR10, which share the highest nucleotide sequence similarity (67–69% sequence identity) with PaGTR5/6/7/8. RNA extraction, purification, cDNA synthesis, and qPCR were performed as described above.Function of PaGTR2/3 in vivoTo analyze the functions of PaGTR2 and PaGTR3, we injected third instar larvae of P. armoraciae (reared on B. rapa) with 100 nL ultrapure water containing 80 ng of dsPaGTR2 and 80 ng of dsPaGTR3 (dsPaGTR2/3) or 80 ng of dsIMPI, respectively, using a Nanoliter 2010 Injector (World Precision Instruments). Injected larvae were provided with detached B. rapa petioles and kept in plastic tubes with moistened tissue in the laboratory under ambient conditions until pupation. Newly emerged adults were again injected with dsRNAs and provided with detached B. rapa leaves. Three days after the second dsRNA injection, we collected dsIMPI- and dsPaGTR2/3-injected beetles for gene expression analysis (n = 6 replicates, two beetles per replicate) and glucosinolate analysis (n = 10 replicates, three beetles per replicate), respectively. The remaining beetles were used for a feeding experiment with Arabidopsis. Each replicate consisted of one Arabidopsis leaf and three beetles that were placed in a Petri dish (60 mm diameter) with 50 μL of ultrapure water (n = 12–13 replicates). Each leaf was photographed before and after feeding to determine the consumed leaf area. Fed leaves were frozen in liquid nitrogen, freeze-dried, and homogenized using metal beads (2.4 mm diameter, Askubal) in a tissue lyzer (Qiagen) for 2 min at 30 Hz. Fed beetles were starved for 1 day, weighed, frozen in liquid nitrogen, and stored at −20 °C until glucosinolate extraction. Beetle samples were homogenized using a plastic pestle in 200 μL 80% (v/v) methanol. All samples were extracted with 1 mL 80% (v/v) methanol containing 25 μM 1-methylethyl glucosinolate (extracted from Sisymbrium officinale seeds) as an internal standard. Extracts were applied to DEAE-Sephadex A-25 (Sigma-Aldrich) columns in 96-well filter plates (Nunc) that were preconditioned with 1 mL of ultrapure water and 2 × 500 μL of 80% (v/v) methanol. After loading 900 μL of extract, the columns were washed with 500 μL of 80% (v/v) methanol, followed by 2 × 1 mL of ultrapure water. To adjust the pH condition, 500 μL of 0.02 M MES buffer (pH 5.2) was added to each column. After adding 30 μL of Helix pomatia sulfatase solution to each column and overnight incubation at room temperature, desulfo-glucosinolates were eluted using 300 or 500 μL of ultrapure water into 96-deep well plates (Nunc). Samples were analyzed by desulfo-HPLC-DAD as described below. The glucosinolate content in adults and fed leaves was calculated in nanomole per adult and nanomole per cm2 leaf, respectively. The ingested glucosinolate amount was calculated based on the ingested leaf area and the corresponding leaf glucosinolate content. To elucidate which proportion of the ingested glucosinolates was sequestered, we expressed the glucosinolate amount detected in the beetles relative to the ingested glucosinolate amount from the leaves, which was set to 100%.HPLC-DADSamples were analyzed by HPLC on an Agilent Technologies HP1100 Series instrument equipped with a photodiode array detector. After injection of 100 μL of each sample, separation was achieved on an EC 250/4.6 NUCLEODUR Sphinx RP column (250 mm × 4.6 mm, 5 μm; Macherey-Nagel; samples of the RNA interference (RNAi) experiments of PaGTR1, and PaGTR2/3) or an EC 250/4.6 NUCLEODUR 100-5 C18ec column (250 mm × 4.6 mm, 5 μm; Macherey-Nagel; samples of the RNAi experiments of PaGTR5/6/7/8, and PaGTR2/3) using a binary solvent system consisting of ultrapure water (A) and acetonitrile (B), with a flow rate of 1 mL/min. The elution gradient was as follows: 0–1 min, 1.5% B; 1–6 min, 1.5–5% B; 6–8 min, 5–7% B; 8–18 min, 7–21% B; 18–23 min, 21–29% B; 23–23.5 min, 29–100% B; 23.5–26 min, 100% B; 26–26.1 min, 100 to 1.5% B; and 26.1–31 min, 1.5% B. The eluent was monitored by diode array detection between 190 and 360 nm. Desulfo-glucosinolates were identified based on a comparison of retention times and absorption spectra with those of known standards63. The glucosinolate content in each sample was calculated from the peak areas at 229 nm relative to the peak area of the internal standard using relative response factors64.Glucosinolate concentration in the hemolymph of adult P. armoraciae
Hemolymph was collected from 7-day-old adult P. armoraciae reared on B. juncea by cutting off an abdominal leg and collecting the extruding droplet using glass capillaries (0.5 µL, Hirschmann® minicaps®) (n = 6 replicates, 50 beetles per replicate). The capillaries were marked with 1 mm intervals (corresponding to 15.6 nL) to estimate the volume of collected hemolymph. The hemolymph was diluted in 500 µL of 90% (v/v) methanol, homogenized using metal beads (2.4 mm diameter, Askubal) in a tissue lyzer (Qiagen) for 1 min at 30 Hz, and boiled for 5 min at 95 °C. After two centrifugation steps (13,000 × g for 10 min each), the supernatant was dried by vacuum centrifugation, dissolved in 50 µL 50% (v/v) methanol, diluted in ultrapure water, and analyzed by LC-MS/MS.Morphology of the Malpighian tubule system of P. armoraciae
To investigate the structure of the Malpighian tubule system, we dissected the gut and Malpighian tubules of 4-day-old P. armoraciae adults in PBS (pH 6.0) under a stereomicroscope. The tracheae that attach Malpighian tubules to the gut were removed using fine forceps to release the tubules. Pictures were taken with a Canon EOS 600D camera.pH of hemolymph and excretion fluid of isolated Malpighian tubules of P. armoraciae
The pH of the hemolymph and Malpighian tubule excretion fluid of 5-day-old P. armoraciae adults was assessed using the pH indicator bromothymol blue (Alfa Aesar). Hemolymph was collected by cutting off an abdominal leg and collecting the extruding droplet using a pipette (n = 3 replicates, six to ten beetles per replicate). Excretion fluid was collected from dissected Malpighian tubules that were incubated in saline A as described for the Ramsay assay (n = 4 replicates, one tubule per replicate). Hemolymph and excretion fluid were immediately mixed with the same volume of 0.16% (w/v) bromothymol blue dissolved in 10% (v/v) ethanol, respectively, under water-saturated paraffin oil in a Sylgard-coated petri dish. The resulting color of the droplet was compared with those of citric acid–Na2HPO4 buffer solutions ranging from pH 5.2 to 6.6 in 0.2 increments mixed with 0.16% (w/v) bromothymol blue.Fate of plant glucosides injected in P. armoraciae beetlesTo analyze the fate of plant glucosides in vivo, we injected 100 nL of an equimolar mixture of 2-propenyl glucosinolate, 4-hydroxybenzyl glucosinolate, linamarin, salicin, and catalpol, each at 10 mM, and 0.15% (w/v) amaranth into the hemolymph of 2-day-old adult P. armoraciae (reared on B. rapa). One group of beetles was sampled 30 min after injection by freezing beetles in liquid nitrogen (n = 10 replicates, five beetles per replicate). The remaining beetles were fed with detached leaves of Arabidopsis in Petri dishes (60 mm diameter) in the laboratory under ambient conditions (n = 10 replicates, five beetles per replicate). We added 30 μL of ultrapure water to each Petri dish and sealed them with parafilm to prevent leaf wilting. After 1 day, we sampled the beetles as described above (n = 10 replicates, five beetles per replicate). Feces were collected using 100 µL of ultrapure water per replicate and combined with 300 μL of pure methanol in a 1.5 mL Eppendorf tube. All samples were stored at −20 °C until extraction. Beetle and feces samples were homogenized as described in the RNAi experiment. After centrifugation (16,000 × g for 10 min), the supernatant was dried by vacuum centrifugation, dissolved in 100 µL of ultrapure water, and analyzed by LC-MS/MS. The glucoside content in adults or feces was calculated as nanomole per adult, respectively.Ramsay assayTo analyze the excretion of plant glucosides in situ, we performed Ramsay assays65 with dissected Malpighian tubules from 4- to 5-day-old P. armoraciae adults reared on B. rapa (Supplementary Fig. 6). Malpighian tubules were dissected in saline A (100 mM NaCl, 8.6 mM KCl, 2 mM CaCl2, 8.5 mM MgCl2, 4 mM NaH2PO4, 4 mM NaHCO3, 24 mM glucose, 10 mM proline, 25 mM 3-(N-morpholino)propanesulfonic acid (MOPS), pH 6.0)66. Single tubules were transferred into a 10 μL droplet of saline B (60 mM NaCl, 10.3 mM KCl, 2.4 mM CaCl2, 10.2 mM MgCl2, 4.8 mM NaH2PO4, 4.8 mM NaHCO3, 28.8 mM glucose, 12 mM proline, 30 mM MOPS, 1 mM cyclic AMP, pH 6.0) under water-saturated paraffin oil in a Sylgard-coated petri dish. The proximal end of the tubule was pulled out of the droplet, attached to a metal pin, and cut using a glass capillary to allow the collection of excretion fluid. To start the assay, we added 2 μL of an equimolar glucoside mixture consisting of 2-propenyl glucosinolate, 4-methylsulfinylbutyl glucosinolate, 4-hydroxybenzyl glucosinolate, 2-phenylethyl glucosinolate, I3M glucosinolate, linamarin, salicin, and catalpol, each at 40 mM, and 0.6% (w/v) amaranth) to saline B. After 2–3 h, we collected the excretion fluid and 2 μL of the bathing droplet in 300 μL of 80% (v/v) methanol, respectively. The Malpighian tubule was washed three times in ~15 mL of saline A and afterwards transferred into 300 μL of 80% (v/v) methanol. All samples were stored at −20 °C until extraction. Malpighian tubule samples were homogenized using a plastic pestle. After centrifugation (16,000 × g for 10 min), the supernatant was dried by vacuum centrifugation, dissolved in 70 µL ultrapure water, and analyzed by LC-MS/MS. Out of 36 assays, 25 assays were excluded because no excretion fluid was visible within 2–3 h.Statistical analysisNo statistical methods were used to predetermine sample size. Statistical analyses were conducted in R 3.5.167 or in SigmaPlot 14.0 or 14.5 (Systat Software Inc.). Two groups were compared by two-tailed Student’s t test, Mann–Whitney U test, or the method of generalized least squares68, depending on the variance homogeneity and the normality of residuals. Data of the Ramsay assays were compared by paired two-tailed Student’s t test using FDR (false discovery rate)-corrected P values69. Three or more groups were compared by one-way analysis of variance, followed by post hoc multiple comparisons test, or the method of generalized least squares68. If necessary, data were transformed prior to analysis. For comparisons using the method of generalized least squares, we applied the varIdent variance structure, which allows each group to have a different variance. The P value was obtained by comparing models with and without explanatory variables using a likelihood ratio test70. Significant differences between groups were revealed with factor level reductions71. Information about data transformation, statistical methods, and results of the statistical analyses are summarized in Supplementary Tables 2 and 3 and Supplementary Data 6.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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Study areaChina is one of the countries with the poorest per capita water resources in the world while also having the largest water consumption in the world. In 2018, China’s total water consumption was 601.55 billion m3, with 369.31 billion m3 of water used in agriculture, accounting for 61.4% of the total water2. Agriculture is the most important industrial sector in water resource consumption. However, due to regional and climate differences, the distribution of agricultural water resources is uneven, and the shortage of water resources seriously affects agricultural development in water-deficient areas.Figure 1 shows the agricultural water consumption in China by province for 2007 and 2018. The agricultural water consumption includes farmland irrigation water consumption (classified as paddy field, irrigated land, vegetable field, groundwater exploitation), forest, animal husbandry, fishery, and livestock (classified as forest and fruit, grassland, fish pond, animal husbandry, groundwater exploitation), domestic water consumption of rural residents and rural ecological environment water consumption. Previous studies have mainly considered the irrigation water consumption of the planting industry as the research object at the provincial or regional levels (e.g., eastern, central, and western regions). Few were able to consider all 31 provinces in China and have comprehensively assessed water consumption and water use efficiency in the various types of agricultural production3,4,5,6,10,16,17,22,23,24,25,30. In this study, the agricultural water use efficiency and its influencing factors are assessed based on the agricultural water consumption of agriculture, forestry, animal husbandry, and fishery in China.Figure 1Agricultural water consumption in China by province for (a) 2007 and (b) 2018. Note: Map created using ArcGIS [10.2], (http://www.esri.com/software/arcgis).Full size imageResearch methodIn this study, the agricultural water use efficiency (under the common frontier and the group frontier) is calculated using the super-efficiency slacks-based measure (Super-SBM) model. The significant factors affecting water-use efficiency are then analyzed through the threshold regression model.Super-efficiency SBM modelData envelopment analysis (DEA) is an efficiency evaluation method proposed by Charnes31, a famous American operational research scientist. While traditional radial and angular DEA models do not require the specific form of the estimation function, they ignore the relaxation of variables and result in efficiency values in the range of 0 to 1. If there are multiple efficiency value of decision making units(DMUs) with an efficiency value of 1, these values cannot be compared. The efficiency of the super efficiency DEA model can be greater than 1, which means that the efficiency level of all decision-making units can be compared.To avoid the problem of slack variables, Tone (2001) proposed the SBM model, which is a non-radial and non-angular DEA analysis method based on the relaxation variable measure16,17,18,19,20,32. The SBM model of unexpected output solves the slack problem of input and output variables, minimizing deviations in the efficiency measurement. The super-efficiency SBM model combines the super-efficiency DEA model and the SBM model. It is also one of the methods based on data envelopment analysis, which can measure the efficiency of all decision-making units and the slack of input and output variables.Assume n to be the decision-making units, each of which has m inputs, expected output r1, and unexpected output r2. Let X (X ∈ Rm), Yd (Yd ∈ Rs1), and Yu (Yu ∈ Rs2) be matrices, such that (X=[{x}_{1},dots ,{x}_{n}]in {R}^{m*n}) and (Y=[{y}_{1}^{d}, dots ,{ y}_{n}^{d}in {R}^{{r}_{1}*n}). The form of the super-efficiency SBM model is as follows1,17,19,54:$$min=frac{frac{1}{m}sum_{i=1}^{m}(overline{x}/{x}_{ik})}{1/left({r}_{1}+{r}_{2}right)*(sum_{r=1}^{{r}_{1}}overline{{y}^{d}}/{y}_{rk}^{d}+sum_{q=1}^{{r}_{2}}overline{{y}^{u}}/{y}_{qk}^{u}}.$$
(1)
Among them,$$overline{x}ge sum_{j=1ne k}^{n}{x}_{ij}{lambda }_{j}, i=1,dots ,m;$$
(2)
$$overline{{y}^{d}}le sum_{j=1,ne k}^{n}{y}_{rj}^{d}{lambda }_{j}, r=1,dots ,{s}_{1};$$
(3)
$$overline{{y}^{d}}ge sum_{j=1,ne k}^{n}{y}_{qj}^{u}{lambda }_{j}, q=1,dots ,{s}_{2};$$
(4)
$${lambda }_{y}ge 0,j=1,dots ,n;jne 0;$$
(5)
$$overline{x}ge {x}_{k},k=1,dots ,m;$$
(6)
$$overline{{y}^{d}}le {y}_{k}^{d},d=1,dots ,{r}_{1};$$
(7)
$$overline{{y}^{u}}ge {y}_{k}^{u},b=1,dots ,{r}_{2}.$$
(8)
Based on the Super-SBM model (Eq. 1) and its constraint formula, the agricultural water use efficiency for the different provinces was calculated for the period 2007–2018 using Maxdea 8 ultra software.Threshold effectConsidering the differences in economic development and technical levels, the agricultural water use in different regions of China shows characteristics of time-series evolution, spatial heterogeneity, and unbalanced spatial distribution. There is a non-linear relationship between the influencing factors of agricultural water use efficiency, which suggests the existence of certain threshold characteristics33,34. This means that for a particular determinant, agricultural water use efficiency would be affected differently depending on whether the parameter has crossed the threshold. In this study, the threshold panel model proposed by Hansen is used. The threshold value of the threshold variable is taken as the critical point, and the regression equation is divided into different stage intervals to analyze the influence of threshold variables on the explained variables at different stages . Therefore, according to the relationship between agricultural water use efficiency and its influencing factors in different regions, the following single threshold regression model is set:$${Y}_{it}=alpha {X}_{it}+{beta }_{1}{T}_{it}Ileft({T}_{it}le {gamma }_{1}right)+{beta }_{2}{T}_{it}Ileft({T}_{it} >{gamma }_{1}right)+C+{varepsilon }_{it},$$
(9)
such that i is the province; t is the year; Yit and Tit are the explanatory variables and explained variables, respectively; Xit is the control variable that has a significant impact on the explained variables; Tit is threshold variable, which changes with the different explanatory variables; γ is a specific threshold value; α is the corresponding coefficient vector; β1 and β2 represent the influence coefficients of the threshold variable Tit on the explained variable Yit in the case of ({T}_{it}le {gamma }_{1}) and ({T}_{it} >{gamma }_{1}) , respectively; C is a constant; ε is random disturbance term, ({varepsilon }_{it}sim i.i.d.N(0,{sigma }^{2})); and, I (·) is an indicative function. After obtaining the estimated value of each parameter, two tests need to be carried out: (1) establish whether the threshold effect is significant; and (2) determine whether the estimated threshold value is equal to the true value. In addition, the above equation assumes that only one threshold exists. For two or more thresholds, the model would have to be adjusted according to the data.Based on the panel data of 31 provinces in China from 2007 to 201844,45,46, Stata15.0 software was used to perform threshold regression on seven variables: per capita water resources, rural labor force, disposable income, government’s attention, foreign trade dependence, industrial structure, and gross domestic product (GDP). The threshold effect of each factor can be analyzed, and the impact on agricultural water consumption can be assessed using the threshold value.Variable selection and data sourceThe super-efficiency SBM model was used in calculating the agricultural water use efficiency for the 31 provinces in China from 2007 to 2018. The input–output indicators were defined before the calculations, as shown in Extended Data Table 1.The selection of input–output factors to measure the utilization efficiency of agricultural water resources follows the principles of availability and operability. The input variables included: (1) agricultural water consumption, (2) the number of employees in agriculture, forestry, animal husbandry, and fishery, (3) the total power of agricultural machinery, and (4) the expenditure of local finance on agriculture, forestry, and water affairs. In terms of output, the added value in agriculture, forestry, animal husbandry, and fishery (based on 2007) was used as the expected output, while ammonia nitrogen emission, agricultural chemical oxygen demand emission, and agricultural carbon emission comprised the unexpected output.This study considered the scale of carbon emissions released by the agricultural system. According to existing research, agricultural carbon emissions are associated with rural environmental pollution35. The main consequence of agricultural pollutant emissions is soil pollution, which leads to rural groundwater pollution36,37,39,40,41,41. The deterioration of groundwater quality adversely affects the development of the agricultural economy and threatens the safety of the drinking water supply for rural residents.The threshold regression model was used to investigate the convergence of agricultural water use efficiency and observe the changes in agricultural water consumption under different influencing factors. The control variables include the following: water resource endowment, the number of agricultural labor, the income level of rural residents, industrial structure, the degree of government’s attention, the degree of dependence on foreign trade, and the level of economic development, as shown in Extended Data Table 2. For water resource endowment (WR), WR is expressed in per capita water resource (m3 / person). Zhang Lixiao45,46 and previous studies have shown a negative correlation between water resource endowment and water resource utilization. For agricultural labor (ah), the variable is expressed by the number of people engaged in agriculture, forestry, animal husbandry, and fishery (10,000 people). Past studies suggest rural population affects the consumption of agricultural water resources47,50,51,52,53,52. For income levels, rural residents’ income level is indicated by the per capita disposable income of rural households. Wang Xueyuan et al.3 and Han Qing et al.53 argue that the increase in the rural residents’ income would limit agricultural water consumption. For industrial structure (× 2), which is expressed by the proportion of industrial added value in GDP, research has shown water resource efficiency would vary under different industrial structures54,57,56. For the government’s attention degree (GA), the variable is expressed by the proportion of agriculture, water affairs, and forestry spending in the total financial expenditure. The government’s support for comprehensive agricultural development and infrastructure and technology upgrading for agricultural, forestry, and water conservation significantly affects water resource utilization efficiency16,56,59,58. For the degree of dependence on foreign trade (open), the parameter is indicated by the proportion of the total import and export of agricultural and sideline products in the GDP. Changes in import demand can reduce or increase the consumption and pollution of water resources. Likewise, export demand changes, especially in high water-consuming and high polluting products, can significantly improve or degrade water resource efficiency. And for the level of economic development, expressed in terms of GDP, the level of regional economic development plays a positive role in promoting the efficiency of water resource utilization59,62,61. More

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Isolation and characterisation of viruses infecting the picoeukaryote Bathycoccus Clade BIIBathycoccus BII isolates RCC716 and RCC715 used in our experiments were originally cultured from a nutrient-limited region in the Indian Ocean. Clade BII as a whole has been reported extensively in warm oligotrophic ocean gyres based on metagenome analyses [22,23,24]. Peak abundances occurr when well-developed deep chlorophyll maxima are present, or throughout the photic zone during mixing periods at Station ALOHA of the Hawaii Ocean Time-series [12]. We targeted BATS for viral isolation in springtime because Bathycoccus has been observed at relatively high abundance during this period using qPCR [74]. Here, three viruses were isolated against RCC716 [12] using seawater flown from BATS/Bermuda to the laboratory, obviating bringing this finicky strain into the field for use as a viral host. We then purified the viruses by serial dilutions and sequenced the partial PolB gene to determine whether they were evolutionarily different from other cultured viruses. BLASTn and preliminary phylogenetic analysis using GenBank nr sequences indicated they were distinct from described viruses with deposited sequences, with best BLASTn hits to Bathycoccus prasinos viruses (62–74% nucleotide identity). Transmission electron microscopy (TEM) revealed that all three viruses have similar morphology to other characterised prasinoviruses [75], with icosahedral capsids diameter ranging between 120 and 140 nm (Fig. 1A).Fig. 1: Morphology and evolutionary relationships of newly discovered Bathycoccus viruses.A Transmission electron micrographs of BII-V1, BII-V2 and BII-V3 (scale bar, 50 nm). The capsid diameters (n = 6 virions) measured 138 ± 2 nm (BII-V1), 150 ± 5 nm (BII-V2) and 152 ± 11 nm (BII-V3). B Maximum Likelihood (ML) phylogenetic reconstruction of green algal viruses inferred from a concatenated alignment of 22 core proteins shared among the viruses (7,001 positions) under the LG + G + F model. Node support was calculated from 1000 bootstrap (BS) replicates, with all branches acquired support values of 100% (white dots). Viruses infecting Chlorella were used as an outgroup and the branch connecting the prasinoviruses to the outgroup was truncated for display purpose. The new Bathycoccus viruses isolated against Bathycoccus Clade II (sensu [12]) isolate RCC716 (named as species Bathycoccus calidus herein, see below) are in bold. Colours reflect different host species within each genus. Letters alongside vertical lines (a and b) correspond to Bathycoccus viral clades. C Venn diagram of the shared and unique protein-encoding genes in the genome sequences of the new Bathycoccus viruses.Full size imageGenomic sequencing and multi-gene evolutionary analysesAssembly of DNA sequences from the viral isolates after deep sequencing by Illumina rendered one complete dsDNA genome sequence (BII-V3), and two others may still be partial (Table 1). The BII-V2 genome, which was in one contig, was similar in size (~208 kb) to that of BII-V3 (~212 kb). The BII-V1 genome assembly was ~174 kb and comprised of four linear dsDNA scaffolds. The viral concentrate was deeply sequenced ( >50x coverage) and minor fragmentation of the genome was partially related to repeats that were not resolved during assembly. The total number of putative open reading frames (ORFs) varied from 220 in BII-V1 to 235 in BII-V2 (Table 1). Gene synteny was globally well-conserved across the BII-Vs and the BpV1 and BpV2 viruses of B. prasinos (Fig. S1), with limited genomic rearrangements. Other genome characteristics such as the coding proportion (~90%) and G + C % (~36%) were similar to other described prasinoviruses infecting Mamiellophyceae [64, 75], for which the reported number of proteins range from 203 to 268 and G + C % from 37 to 45%. However, the full-length PolB gene from the genome assemblies differed for BII-V3 from the other two, in having a 329 amino acid intein (Supplementary information table S3). Likewise, inteins have been reported at the same PolB position in uncultivated prasinoviruses from the subtropical Pacific Ocean [76], where Bathycoccus BII is abundant [12].Table 1 Genomic characteristics of the three Bathycoccus viruses (BII-Vs) isolated against Clade BII isolate RCC716.Full size tableTo reconstruct a robust phylogeny for the new viruses, we employed 22 proteins previously identified as being shared across all available green algal virus genomes, including both prasinoviruses and chloroviruses [65]. We found all 22 in the predicted coding sequences of BII-V1; however, DNA helicase (SNF2) was not found in BII-V2 or -V3, FAD-dependent thymidylate synthase (thy1) and the topoisomerase IV were not found in BII-V2, nor was the prolyl 4-hydroxylase in the BII-V3 genome. Additional searches with tBLASTn did not recover these genes or fragments of them, suggesting they have been lost. Phylogenomic reconstruction grouped the three BII-Vs with the two BpVs [32], in a fully supported clade that branched adjacent to a large group of viruses that infect various species of Ostreococcus and Micromonas (Fig. 1B). The clade of Bathycoccus viruses was segregated in two subclades with BII-V2 and BII-V3 clustering together adjacent to BII-V1 and BpVs (Fig. 1B). While better resolution of the position of BII-V1 awaits greater taxonomic sampling, our results demonstrated that the three new viruses branch adjacent or basally to BpVs.Variation in prasinovirus gene content and functions encodedThe three Bathycoccus Clade BII viruses had 72–77% of their proteins held in common, and ~30 unique proteins as well as a few proteins shared by just two of the three viruses (Fig. 1C). The 170 shared proteins had higher amino acid identities between BII-V2 and BII-V3 (73% aa identity) than to BII-V1 (69% and 68%, respectively). Generally, only 19–21% of Bathycoccus viral genes could be assigned a functional category, based on EggNOG classification. Similar functional category distributions were observed across both prasinoviruses and chloroviruses, including lipid metabolism, RNA processing and modification, and nucleotide metabolism and transport (Fig. 2A). Other functional categories were more variable, such as cell wall/membrane/envelope biogenesis genes prevalent in chloroviruses (potentially related to their enveloped nature), as well as genes involved in modification of the capsid with compounds such as with chitin and hyaluronan [77, 78] that are absent from prasinoviruses sequenced to date (Fig. 2A). Within prasinoviruses, most of the unique proteins in the Bathycoccus viruses lack defined functional categories. Among those with functional assignments, all five Bathycoccus viruses encoded a P2X receptor in the intracellular trafficking and secretion category, and both BII-V2 and -V3 encode two proteins putatively involved in degrading the aromatic compound 4-hydroxy-2-oxopentanoate to acetyl-CoA (secondary metabolite category), that otherwise are only encoded by one other prasinovirus, MpV1 [32]. Similar to the phylogenetic relationships, the functional category distributions of BII-V1 were closer to those of BpVs than to BII-Vs. The primary difference was in carbohydrate metabolism, where BII-V2 and -V3 each encodes ribulose-phosphate 3-epimerase (involved in the pentose phosphate pathway and carbon fixation; not found in any other available virus genomes, but encoded by B. prasinos) and TDP-glucose 4,6-dehydratase (involved in biosynthesis of rhamnose and encoded by most other chloroviruses and prasinoviruses [79]). Notably, the putative high-affinity phosphate transporter (PHO4, also termed HAPT) was only present in BII-V1 and BpV1, as well as OtV2 (isolated against the Ostreococcus Clade OII ecotype), and most sequenced viruses of O. lucimarinus (Supplementary information table S3). This gene is hypothesised to enhance phosphate uptake during infection under phosphorus‐limited host growth [25], as observed for the PstS phosphate transport system expressed by cyanophages [80], mitigating limitation of this key component of viral genomes. However, most isolated prasinovirus genomes come from waters that are not considered phosphate-limited, hence presence of this gene may connect to poising the host for responding to sudden availability of other nutrients, such as nitrogen, which is often limiting in the ecosystems from which these viruses were isolated. Studies of virus-cell responses under various limiting nutrients are required to understand the retention of this host-derived HGT.Fig. 2: Distribution of functions and orthologous protein families across genome-sequenced prasinoviruses.A Functional category distributions across 21 genome-sequenced prasinoviruses and chloroviruses based on EggNOG categorisation. Viruses are clustered by similarity in their distribution of the functional categories on the y-axis and the frequency of each category across the viral genomes determines clustering along the x-axis ordering. Genes with homology to proteins in the EggNOG database but could not be assigned a function are in the “function unknown” category. B Orthogroups presence/absence patterns ordered along the x-axis by ranking according to the total number of genes in the orthogroup. For inclusion, the orthogroup was required to include protein sequences from at least two different viral genomes. Viruses are ordered along the vertical by their presence/absence pattern reconstructed by hierarchical clustering (topology on the left). Top histogram: frequency of each orthogroup in sequenced prasinoviruses. C Genes in each virus (number) not assigned to any orthogroup, with viruses in the same vertical order as B.Full size imageHierarchical clustering of orthologous proteins revealed patterns across prasinoviruses that generally corresponded with phylogenetic relationships. The BII- and Bp-viruses shared 130 orthologous proteins and hierarchical clustering (Fig. 2B) followed the clade structure of the phylogenomic reconstruction (Fig. 1B) with the exception of BII-V1 that grouped with BII-Vs, as well as OtV6, which grouped with Micromonas viruses. These orthologous proteins had on average 72% amino acid identity between BII-V2 and BII-V3, and 88% between the two B. prasinos viruses, but between 65 to 67% when comparing members of these two groups (Table 2). BII-V1 orthologs also had 67% and 66% amino acid identity to BII-V2 and BII-V3, respectively, while they had 83% and 80% identity to BpV1 and BpV2, respectively. Collectively, these results indicate that BII-V2 and -V3 diverged from BpVs prior to the divergence of BII-V1.Table 2 Average percent amino acid identity of the orthologous proteins between the five Bathycoccus viruses.Full size tableOf the 130 orthologous Bathycoccus virus proteins, 37% were assigned putative functions revealing core components of this viral group (Supplementary information table S3). These included genes involved in DNA replication and transcription, including PolB (type II), a DNA topoisomerase, a transcription factor S-II, mRNA capping enzymes, ribonucleases, a ribonucleotide reductase, and a dUTPase. Several others are necessary for viral particle synthesis, such as genes encoding structural elements for assembling the virion, including capsid proteins (5–6 copies per genome), as well as transcriptional regulators connected to the replication cycle. The BII viruses showed a number of differences among orthologous protein families. In addition to each having “unique” protein sets, there was a set of BII-V specific orthogroups, as well as some shared with BpVs, and/or other prasinoviruses (Fig. 1C and Supplementary information table S3). First, six predicted proteins showed orthologs across the three BII-Vs, but were not present in other prasinoviruses sequenced to date. Only one of these six was assigned putative function, belonging to the XRE family of transcriptional regulators. Additionally, all BII viruses harboured a tandem duplication of the FstH gene, while other sequenced prasinoviruses (including the two Clade BI viruses) have one copy (Supplementary information table S3). This ATP-dependent metalloprotease has been shown to be involved in photosystem II repair in cyanobacteria [81], and is present in genomes of photosynthetic eukaryotes, including all Mamiellophyceae [15, 16]. In Arabidopsis and Chlamydomonas it has been shown to be involved in protein quality control in the thylakoid membranes [82]. A gene of unknown function was also duplicated in the BII-virus genomes, that is a single copy in BpVs and absent from other sequenced prasinoviruses. Genes putatively encoding a glucose-1-phosphate adenylyltransferase, a glycosyltransferase and a thiamine pyrophosphate-requiring enzyme involved in amino acid biosynthesis were sporadically found in BII-viruses.Considering the two Bathycoccus virus subclades (Fig. 1B), there is one predicted protein of unknown function exclusive to BpV1, BpV2 and BII-V1 and six predicted proteins shared only by BII-V2 and BII-V3. Among the latter, one belonged to the Ribulose-5-Phosphate-3-Epimerase (RPE) family, which catalyses the interconversion of D-ribulose 5-phosphate (Ru5P) into d-xylulose 5-phosphate, as part of the Calvin cycle (although no transit peptide was detected using TargetP) and in the oxidative pentose phosphate pathway. The ortholog analyses further showed that among prasinoviruses, 9, 17 and 18 genes were unique to BII-V1, BII-V2 and BII-V3, respectively (Fig. 2B). Apart from one nucleotidyltransferase and one glycosyltransferase (group 1) in BII-V1, none of these unique genes had known functions.To study the evolutionary aspects of the shared prasinovirus proteins, we constructed and examined 130 phylogenies of orthogroups shared between Bathycoccus viruses. Nine showed a topology where all three BII-Vs grouped together with full support (100% bootstrap support), separate from the BpV orthologs, and in contrast to the multi-gene phylogeny where BII-V1 grouped with BpVs (Fig. 1B). The average amino acid similarities within each of these nine protein ortholog groups ranged from 85 to 88% between BII-Vs proteins, while they were 77 to 81% between BII-Vs and BpVs, different from overall amino acid similarity averages (Table 2). Interestingly, proteins from three of these nine ortholog groups, all lacking known functions, were adjacent to each other in the genome, or separated by only one gene. This synteny and co-location likely reflects the acquisition of these genes before co-infecting viruses diverged via viral recombination [83].Infection dynamics of Bathycoccus virusesGeneral host specificity of BII-viruses was assessed using two B. prasinos isolates (CCMP1898 and RCC4222, Clade BI), the two available Clade BII isolates (RCC715 and RCC716), four Ostreococcus species and one Micromonas species (Table 3). None were able to infect the B. prasinos, Ostreococcus or Micromonas isolates tested, suggesting BII-V specificity for Bathycoccus Clade BII. Similar host specificity has been observed in O. lucimarinus viruses, none of which infect O. tauri [64], and other viruses of eukaryotic and prokaryotic algae [84, 85]. Some other prasinoviruses appear to have broader host ranges [85,86,87], or their host species are less divergent than the two known Bathycoccus clades. For example, generalist viruses isolated against Micromonas commoda can infect M. bravo [85]. Further investigations are necessary to determine the extent to which the six shared proteins in BII-Vs (absent from BpVs), are responsible for the differences in host and virus specificity of interactions, versus variations in the shared Bathycoccus virus proteins (65–83% similarity). Importantly, host specificity tests for the new viruses described herein were limited by weak sampling of Bathycoccus diversity (in culture; all that we could acquire were tested).Table 3 Results of cross infectivity tests of BII-V1, BII-V2 and BII-V3 against isolates representing various picoprasinophyte species within the Class Mamiellophyceae.Full size tableAlthough specific for the BII clade, the three BII-Vs exhibited variations in infectivity of the two cultured BII strains available, despite their isolation from the same sample and having identical ITS1 and ITS2 sequences. BII-V1 lysed and cleared RCC715 and RCC716 cultures after four days (Table 3). The same was true for BII-V2 and BII-V3, when incubated with RCC716. Different from results for BII-V1, we found that while BII-V2 and -V3 initially lysed RCC715 cultures, resistant populations became evident at day 7 of infectivity tests, and measureable lysis of RCC715 could not be achieved thereafter. These results underscored the need to further examine host-virus interactions for the three new viruses.Infection dynamics over time course experiments further illuminated differences in BII-V impacts on hosts. In these experiments, growth rates of the uninfected (control) RCC715 and RCC716 cultures were 0.45 ± 0.04 day−1 and 0.49 ± 0.06 per day, respectively, similar to rates during the pre-experiment acclimation period (T-test, p  > 0.05). Host and virus dynamics were similar for RCC715 and RCC716 infected with BII-V1 (Fig. S2 and Fig. 3), with cell numbers starting to diverge from control abundances 10 h after inoculation (T-test, p  More