Ecology

All fungal specimens were collected on Crown Lands for which no permission is required. Specimens of fresh, field-collected mushroom fruiting bodies were either air-dried at a temperature of 30–35 °C or frozen at − 80 °C until processing, and air-dried voucher specimens have been deposited at the Dr. Laurie L. Consaul Herbarium, London, Canada (UWO) and the National Mycological Herbarium of Canada, Ottawa (DAOM) (Table 1).DNA extraction, PCR amplification and sequencingGenomic DNA was extracted from air-dried specimens following Thorn et al.9. Primers ITS1 and ITS6R were used to amplify the ITS region, LS1 and LR3 to amplify ~ 650 bases of the 5’-LSU region and Canth-ef1a983-F and Canth-ef1a-1567-R to amplify Tef-19,41,42,43,44. The PCR products were checked using gel electrophoresis and successful products were cleaned using Bio Basic EZ-10 Spin Column PCR Products Purification Kit. Cleaned PCR products were submitted to the sequencing facility of Robarts Institute (University of Western Ontario) to obtain sequences through Sanger sequencing with amplification primers, and internal sequencing primers CanthITS1_Internal-R, 5.8S-R-Canth, and ITS86R-Canth for the ITS region9. New sequences produced for this study were deposited in GenBank as accessions MN181445–MN181461 and MN206911–MN206945.Phylogenetic analysesSequences of the ITS, LSU and Tef-1 regions were cleaned and assembled with SeqEd v.1.03, then, together with sequences of related species downloaded from GenBank, each region was aligned separately with MAFFT v.745 under the G-INS-i strategy, with “leave gappy regions” selected. The draft Cantharellus cibarius genome (QOWL00000000.1)18 was searched by BLASTn for ribosomal and Tef-1 sequences. The full-length match to our Tef-1 sequences was found in a single scaffold (QOWL01010594_RC), but no ITS sequences and only partial LSU sequences were found (in QOWL01010930_RC, QOWL01012415, QOWL01007530_RC, and QOWL01005980_RC). Alignments were imported into MEGA X46,47, trimmed and concatenated into a single ITS-LSU-Tef-1 dataset, then optimized manually. Phylogenetic trees were constructed with maximum likelihood (ML), with 1000 bootstrapping replicates in MEGA X. Analyses were repeated with Bayesian inference using MrBayes 3.2.6 with 4 chains and 5 million generations, discarding the first 25% of trees, when the average standard deviation of split frequencies had stabilized below 0.0148. Tree topologies were compared, and Bayesian prior probabilities transferred to the ML bootstrap tree in Adobe Acrobat.Genetic analysis of carotenoid synthesis genesIn order to design primers to amplify portions of the Al-1 and Al-2 genes in white and golden chanterelles, these genes were first located in three published Cantharellus genome sequences18 using tBLASTn49 to query the genomes with protein sequences from N. crassa (PRJNA132; Al-1 XM_959620.2 and Al-2 XM_960632.3)19. Candidate gene sequences from Cantharellus appalachiensis (QLPK00000000.1; Al-1 Scaffold 4647: QLPK01003932.1 and Al-2 Scaffold 1419: QLPK01001208.1), C. cibarius (QOWL00000000.1; Al-1 Scaffold 15338: QOWL01009792.1 and Al-2 Scaffold 1560: QOWL01001169.1), and C. cinnabarinus (QLPJ00000000.1; Al-1 Scaffold 1057: QLPJ01000880.1 and Al-2 Scaffold 2474: QLPJ01001998.1) were annotated in Geneious using a discontiguous megablast against GenBank to search for homologous motifs50. Based on these alignments, putative ORFs were annotated and aligned, and an overlapping set of PCR primers for AL-1 and AL-2 were designed in Geneious (Table 5). Designed primers were tested for specificity using the BLAST algorithm against the three Cantharellus genomes18. PCR amplified products were assessed for quality, cleaned, sent for sequencing, and assembled as above. Assembled sequences of white and gold samples were compared, along with their putative amino acid products determined using ExPASy51, guided by the translations of the Neurospora crassa Al-1 (XM_959620.2) and Al-2 (XM_960632.3) genes. Partial sequences of the Al-1 and Al-2 genes of gold and white variants have been deposited in GenBank as MW442833–MW442836.Table 5 PCR primers designed to amplify portions of the phytoene desaturase gene (Al-1) and phytoene synthase gene (Al-2) from Cantharellus species, based on genomic sequences from Cantharellus appalachiensis, C. cibarius, and C. cinnabarinus18, listed in “Materials and methods” section.Full size tablePigment analysisField-collected mushroom fruiting bodies were weighed while fresh, wrapped in aluminum foil, and frozen at − 80 °C for pigment analyses, and other samples were weighed fresh and then dried to obtain a conversion for fresh to dry weight. Pigments were extracted with ice-cold 100% acetone at 4 °C and dim light. The supernatant was filtered through a 0.22 µm syringe filter and samples were stored at − 80 °C until analysed. Pigments were separated and quantified by high-performance liquid chromatography (HPLC) as described previously20, with some modifications. The system consisted of a Beckman System Gold programmable solvent module 126, diode array detector module 168 (Beckman Instruments, San Ramon, California, USA), CSC-Spherisorb ODS-1 reverse-phase column (5 mm particle size, 25 × 0.46 cm I.D.) with an Upchurch Perisorb A guard column (both columns from Chromatographic Specialties Inc., Concord, Ontario, Canada). Samples were injected using a Beckman 210A sample injection valve with a 20 μL sample loop. Pigments were eluted isocratically for 6 min with a solvent system of acetonitrile:methanol:0.1 M Tris–HCl (pH 8.0), (72:8:3.5, v/v/v), followed by a 2 min linear gradient to 100% methanol:hexane (75:25, v/v) which continued isocratically for 4 min. Total run time was 12 min. Flow rate was 2 mL min−1. Absorbance was detected at 440 nm and peak areas were integrated by Beckman System Gold software. Retention times and response factors of Chl a, Chl b, lutein and ß-carotene were determined by injection of known amounts of pure standards purchased from Sigma (St. Louis, MO, USA). The retention times of zeaxanthin, antheraxanthin, violaxanthin and neoxanthin were determined by using pigments purified by thin-layer chromatography as described by Diaz et al.52.Extraction and analysis of chanterelle lipidsSamples of each chanterelle species were homogenized to fine powder in a cryomill (Reitch, Germany) and 100 mg of the homogenized powder mixed with 1 mL methanol (MeOH), 1 mL chloroform (CHCl3) and 0.8 mL water following Pham et al.53. The sample mixture was thoroughly vortexed, then centrifuged (Sorvall Legend XT/XF centrifuge; ThermoFisher Scientific, Mississauga, Ontario) at 2500 rpm for 15 min. The organic layer was transferred to new vials, dried under nitrogen and then reconstituted in 1 mL chloroform:methanol (1:1 v/v). Aliquots were then used for either gas chromatography with mass spectrometric and flame ionization detection (GC–MS/FID) or ultra-high-performance liquid chromatography with heated electrospray ionization high resolution accurate mass tandem mass spectrometric analysis (UHPLC-HESI-HRAM/MS–MS) for fatty acids and intact lipids analysis, respectively.For GC–MS/FID analysis, chanterelle fatty acids were converted to fatty acid methyl esters (FAMEs) as follows: To 300 µL aliquot of the lipid extract, 50 μL of C18:0 alkane (1 mg mL−1 in chloroform: methanol 1:1 v/v) was added as internal standards and the samples dried under nitrogen and the fatty acids esterified by adding 400 µL methanolic HCl (1.5 N). The samples were then incubated in a pre-heated oven at 60 °C for 30 min. After incubation, 0.8 mL of distilled water was added to the cooled samples and the FAMEs extracted with 3 aliquots each of 500 μL of hexane. The fractions were combined, dried under N2, re-suspended in 50 μL hexane, and the FAMEs analyzed using a Trace 1300 gas chromatograph coupled to a Flame Ionization Detector and TSQ 8000 mass spectrometer (Thermo Fisher Scientific). The FAMEs were separated on a BPX70 high-resolution column (10 m × 0.1 mm ID × 0.2 μm, Canadian Life Science, Peterborough, Ontario) using helium as the carrier gas at a flow rate of 1 mL min−1. One μL of each sample was injected in split mode (1:15) using a Tri-plus auto-sampler (Thermo Fisher Scientific). The operation conditions were as follows: initial oven temperature set at 50 °C for 0.75 min, increased to 155 °C at 4 °C min−1, ramped to 210 °C at 6 °C min−1, then 240 °C at 15 °C min−1 and final temperature held for 2 min. Methylated fatty acids were determined by comparison with retention times and mass spectra obtained from commercial standards (Supelco 37 component mix, Supelco PUFA No. 3, Sigma Aldrich, Oakville, Ontario) and the NIST database (Thermo Fisher Scientific). Standard curves were employed to determine the amount of individual fatty acids, and values are presented as nmole%.For the UHPLC-HESI-HRAM/MS–MS analysis, a Q-Exactive Orbitrap mass spectrometer (Thermo Fisher Scientific) coupled to an automated Dionex UltiMate 3000 UHPLC system was used to analyze the intact chanterelle lipids according to our previously published method53. Briefly, the intact lipids were resolved using an Accucore C30 column (150 mm × 2 mm I.D., particle size: 2.6 µm, pore diameter: 150 Å) and the following solvent systems: (i) Solvent A consisted of acetonitrile:water (60:40 v/v) containing 10 mM ammonium formate and 0.1% formic acid and (ii) Solvent B consisted of isopropanol:acetonitrile:water (90:10:1 v/v/v) with 10 mM ammonium formate and 0.1% formic acid. The conditions used for separation were 30 °C (column oven temperature), flow rate of 0.2 mL min−1, and 10 µL of sample injected. The gradient system used was as follow: solvent B increased to 30% in 3 min; 43% in 5 min, 50% in 1 min, 90% in 9 min, 99% in 8 min, and finally maintained at 99% for 4 min. The column was re-equilibrated for 5 min before each new injection. Full scans and tandem MS acquisitions were performed in both negative and positive modes using the following parameters: sheath gas: 40, auxiliary gas: 2, ion spray voltage: 3.2 kV, capillary temperature: 300 °C; S-lens RF: 30 V; mass range: 200–2000 m/z; full scan at 70,000 m/z resolution; top-20 data-dependent MS/MS resolution at 35,000 m/z, collision energy of 35 (arbitrary unit); injection time of 35 min for C30RP chromatography; isolation window: 1 m/z; automatic gain control target: 1e5 with dynamic exclusion setting of 5.0 s. The instrument was externally calibrated to 1 ppm using electrospray ionization (ESI); negative and positive calibration solutions (Thermo Fisher Scientific) were used to calibrate the instrument at 1 ppm. Tune parameters were optimized using PC 18:1(9Z)/18:1(9Z), Cer d18:1/18:1(9Z), PG 18:1(9Z)/18:1(9Z), sulfoquinovosyl diacylglycerols [SQDG] 18:3(9Z,12Z,15Z)/16:0, monogalactosyl diglyceride [MGDG] 18:3(9Z,12Z,15Z)/16:3(7Z,10Z,13Z), and digalactosyldiacylglycerol [DGDG] 18:3(9Z,12Z,15Z)/18:3(9Z,12Z,15Z) lipid standards (Avanti Polar Lipids, Alabaster, AL, USA) in both negative and positive ion modes. The data were processed using either X-Calibur 4.0 (Thermo Fisher Scientific) or LipidSearch version 4.1 (Mitsui Knowledge Industry, Tokyo, Japan) software packages.Phenolics analysis by GC–MSReagent grade phenolic acid standards including benzoic acids, p-hydroxybenzoic acid, vanillic acid, gallic acid, 3,4-dihydroxybenzoic acid, syringic acid, gentisic acid, veratric acid, salicylic acid, cinnamic acid, o-coumaric acid, m-coumaric acid, p-coumaric acid, ferulic acid, sinapic acid, caffeic acid, sodium hydroxide, N,O-Bis(trimethylsilyl)trifluoroacetamide (BSTFA-TCMS) were purchased from Sigma Aldrich. Methanol, ethyl acetate, and hydrochloric acid (36% w/v) were purchased from VWR (Mississauga, Ontario, Canada). For alkaline hydrolysis of powdered chanterelles, 100 µL of aqueous 3,4-dihydroxybenzoic acid solution (0.2 mg mL−1) was added to a mixture containing 4 g of sample in 8 mL 1 M sodium hydroxide. The resultant mixture was incubated in the dark for 24 h at 25 °C on an orbital shaker (50 rpm). The pH of the reaction mixture was adjusted to 2.0–2.5 using concentrated HCl then vortexed. The organic components were extracted four times with 4 mL methanol: ethyl acetate (1:3 ratio) into pre-weighed vials. The solvent was evaporated under nitrogen at 35 °C to determine the crude extraction yield. The extracts were resuspended in 1 mL ethyl acetate, vortexed, then 300 µL of extract transferred into a pre-weighed vial, dried under nitrogen, and 50 µL of BSTFA-TCMS and 50 µL of pyridine added. The resultant mixture was incubated at 70 °C in darkness for 30 min then transferred to GC vials for GC–MS analysis. Standard solutions were derivatized in a similar manner.A Thermo Scientific Trace 1300 gas chromatograph coupled to a Triple Quad mass spectrometer (Thermo Fisher Scientific) was used for the analysis and the compounds resolved on a ZB-5MS non-polar stationary phase column (30 m × 0.25 mm I.D., 0.25 μm film thickness, Phenomenex, Torrance, CA, USA) with helium as the carrier gas (flow rate of 0.6 mL min−1). One microliter of the standard or sample was injected in basic mode (15:0) using a Tri-plus auto-sampler. The oven temperature program was as follows: the initial oven temperature was 70 °C (held for 1 min), was increased at 12 °C min−1 to 220 °C (held for 3 min), 15 °C min−1 to reach 250 °C and held for 1 min. Identification of the phenolic acids (as trimethylsilyl ether, TMS) was based on the comparison of their retention times and mass spectra with that of the NIST library and commercial standards, with quantities calculated and expressed as nmole%.
Analysis of the volatile profile of chanterelles by SPME-GC/MSVolatile metabolites were extracted and analysed by Solid-Phase Microextraction and Gas Chromatography/Mass Spectrometry (SPME-GC/MS) following Vidal et al.54. Briefly, 100 mg of sample powder obtained after cryo homogenization was placed in 10 mL headspace glass vials and kept at 50 °C for 5 min (sample equilibration) before volatile metabolites extraction and analysis began. A divinylbenzene/carboxen/polydimethylsyloxane (DVB/CAR/ PDMS) coated fibre (1 cm long, 50/30 μm film thickness; Supelco, Sigma-Aldrich), was inserted into the headspace of the sample vial and held there for 60 min55,56. Chanterelle volatile composition was analyzed using a Trace 1300 gas chromatograph coupled to a TSQ 8000 Triple Quadrupole mass spectrometer (Thermo Fisher Scientific). The extracted volatile compounds were separated using a ZB-5MS non-polar stationary phase column (30 m × 0.25 mm I.D., 0.25 μm film thickness; Phenomenex) with He used as the carrier at a flow rate of 1 mL min−1. After extraction the fibre was desorbed for 10 min in the injection port and the instrument operated as follows: splitless mode with a purge time of 5 min, initial oven temperature set at 50 °C (5 min hold) and increased to 290 °C at 4 °C min−1 (2 min hold). Ion source and quadrupole mass analyzer temperatures were set at 230 and 150 °C respectively, injector and detector temperatures held at 250 and 290 °C respectively, mass spectra ionization energy set at 70 eV, and data acquisition done in scan mode. After each sample desorption, the fiber was cleaned for 10 min at 250 °C in the conditioning station. Volatile compounds were identified by matching the obtained mass spectra with those of available standards, and mass spectra from commercial libraries NIST/EPA/NIH (version 2.2, Thermo Fisher Scientific) or the scientific literature55,56. Volatile compounds in the chanterelle samples were semi-quantified based on the area counts × 10−6 of the base peak. Compounds with lower abundances than 10−6 area counts were considered as traces. Although the chromatographic response factor of each compound is different, the area counts determined are useful for comparison of the relative abundance of each compound in the different samples analysed55,56.Statistics and reproducibilityResults of analyses of lipids and phenolics are presented as means and standard errors of 4 replicates and those of head-space analyses of volatiles are based on 2 replicates (Tables 2, 3, 4). The values of all dependent variables were tested for normal distribution and homoscedasticity by Shapiro–Wilk and Levene tests, respectively. For variables with homogeneous variance, parametric one-way analysis of variance (ANOVA) was used to determine if there were significant differences between chanterelle samples. Where significance was detected, the means were compared with Fisher’s Least Significant Difference (LSD), α = 0.05. Where the assumption of normality was not met (pentanal, 3-octen-2-one, 2-nonanone, 2-undecanone, (E)-α-Ionone, 3-hydroxy-α-ionene, all phenolic acids, and some minor fatty acids 10:0, 15:0, 14:1, 16:1n5, 20:1n9 and 24:1n9), no significance was detected after the data were treated with a non-parametric Kruskal–Wallis test and significance (p  More

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Bacterial strains isolation and identificationFifty six bacterial cultures were isolated from both management systems (G1 and G2) of mango orchards (rhizosphere) at CISH, Lucknow, India. Isolation of microorganisms using spread plate methods revealed that the Nutrient agar medium had the highest number of colony appearances compared to the Rose Bengal Agar medium. Microbial enumeration showed organic system enriched with higher bacterial and fungal population than conventional system (Fig. 1). From organic system, thirty seven bacteria were isolated out of which, twenty-three isolates were (G+), and fourteen were (G−). While, in the conventional system, nineteen bacteria were isolated, out of which fifteen were (G+) and four were (G−) isolates.Figure 1Comparative microbial enumeration of organic and conventional treated mango rhizosphere soil the CFU mL−1 of selected samples showing growth of fungus and bacterial populations under two different treatments i.e. organic and conventional. The results are the average of five replicates (n = 5), with bars representing standard error. Significant differences based on the analysis variance (ANOVA) are shown by different letters above the error bars, followed by the post hoc DMRT test (p ≤ 0.05) using the software SPSS.Full size imagePlant growth promotion propertiesFor plant growth promotory properties out of fifty-six bacterial isolates total, ten bacterial cultures (2, 3, 4, 8, 15, 23 and 31) from the organic system showed positive results for phosphate solubilization. In contrast, three bacterial cultures (I1, I8 and I9) from the inorganic system (conventional system) showed positive phosphate solubilization in Pikovaskya’s agar medium. For siderophore production, bacterial cultures (2, 3, 4, 8, 12 and 26) from the organic system showed positive results, while four bacterial cultures (I1, I6, I8 and I9) inorganic system showed positive results. Bacterial cultures (2, 3, 4 and 8) from the organic system showed positive results for K-solubilization, while five bacterial cultures (I1, I2, I7, I8 and I9) from the inorganic system showed positive K-solubilization. A total of ten isolates (7 from organic and 3 from the inorganic system) possessed Zn-solubilizing activity. The test isolated from the organic system showed better Zn (ZnO), Zn3 (PO4)2, and (ZnCO3) solubilization as compared to test culture isolated from the inorganic system (Supplementary S1.8).Acetylene reduction assay (ARA)Results from acetylene reduction assay showed in aerophilic condition, bacterial isolates 1, 3, 4 (from organic treated soil) and I1, I8 and I9 (conventional system) showed 134.8, 37.70, 36.73, 13.15, 16.70 and 12.87 ppm of ethylene tube−1 h−1, respectively. In case of microaerophilic condition, bacterial isolates 4, 9, I9 showed 24.17, 19.14, and 12.71 ppm ethylene, respectively. Results indicate possible use of these bacterial isolates as a bioinoculant agent for horticultural crops, especially mango and other subtropical climate fruit crops.Soil enzymatic studyThe soil enzymatic activity in the organic system (G1) showed better dehydrogenase activity than the conventional system (G2). For both methods, alkaline phosphatase almost showed similar activity (at pH 11), while in the case of acid phosphatase showed better activity in the inorganic system (G2) as compared to the organic system (G1) at pH level 6.5 (Fig. 2). The dehydrogenase enzyme oxidizes the organic matter, and it belongs to the oxidoreductase type of enzyme. In the process of respiration of soil microorganisms, the dehydrogenase enzyme facilitates the transfer of protons and electrons from the substrate to the acceptor. It was significant to observe that the dehydrogenase activity was higher in organic treated soils (0.784 µg TPF g−1 h−1) than in conventional system (0.053 µg TPF g−1 h−1).Figure 2Comparative soil enzymes activities of conventional and organic treated mango rhizosphere soil the dehydrogenase, acid phosphatase and alkaline phosphatase activities were showing in µg TPF formed g−1 of soil h−1 and µg PNP g−1 soil h−1 respectively. The results are the average of five replicates (n = 5), with bars representing standard error. Significant differences based on the analysis variance (ANOVA) are shown by different letters above the error bars, followed by the post hoc DMRT test (p ≤ 0.05) using the software SPSS.Full size imageAlpha biodiversity with samples and rarefaction curvesIn this segment, by measuring Shannon, Chao1, and observed species metrics, we analyze the microbial diversity within the samples. The chao1 metric measures the richness of the ecosystem, while the Shannon metric is the formula for calculating reported OTU abundances and accounts for both prosperity and equality. The rarefaction curve is provided in Fig. 3 for each metric. Using QIIME software, the metric measurement was done. The impact of both treatments on the microbial complexity and abundance in the sample was also revealed using the Shannon diversity Index (depicting richness and evenness) and Chao 1 representing only richness. Shannon’s diversity index of the bacterial community in the treatment (G1 and G2) was 8.06 and 8.12. The Simpson index in ecology is used to quantify biological diversity in a region, which was also nearly similar in both the treatments. Chao 1 richness estimator showed an increase in species richness. Rarefaction analysis conducted to confirm species richness revealed a difference in the number of reads and OTUs between the samples. The Rare fraction curve had a similar pattern for both samples and showed an impact on the bacterial population in the experiment (Fig. 3a–c).Figure 3Shanon (a), Chao1 (b) curves and observed species (c) obtained for the samples (G1 and G2).Full size imageBacterial diversity analysis at phyla levelTaxonomic study of the 16S rRNA gene amplicon reads yielded seven classifiable bacterial phyla. Six phyla, namely Acidobacteria, Actinobacteria, Bacteroides, Proteobacteria, Firmicutes, and Chloroflexi were dominant in both the systems. The Organically treated soil (G1) sample harbored a higher percentage of Bacteroidetes (14.55%), Actinobacteria (7.45%), and Proteobacteria (10.82%) as compared to conventional treatment (G2) 8.98%, 5.71%, and 6.64%, respectively. However, phylum Acidobacteria(13.6%), Firmicutes(4.84%), and Chloroflexi (2.56) were higher abundance in conventional treatment as compared to the organic treatment, which showed the same phyla with lesser quantity, i.e., 5.63%, 0.91%, and 0.79% respectively (Fig. 4a).Figure 4Comparative microbiome (a-phylum and b-order) analysis of organic (G1) and conventional (G2) treated mango orchards soil by using metagenomic (V3 and V4 region) approach.Full size imageDistribution of bacterial community at order levelThe bacterial orders in both systems were diversified. The most abundant orders in organic and conventional systems were Chitinophagales (Organic-11.32%, Conventional-43%), Elev-16S-573 (Organic-3.09%, Conventional-8.69%), Pedosphaerales (Organic-1.56%, Conventional-3.55%), Opitutales (Organic-2.46%, Conventional-0.27%), Chthoniobacterales (Organic-1.35%, Conventional-2.84%), Bacillales (Organic-0.91%, Conventional-4.84%) and Solibacterales (Organic-1.39%, Conventional-2.26%) (Fig. 4b).Bacterial community distribution at family levelBacterial family members were identified and enriched including Pedosphaeraceae (O-1.56%, C-3.55%), Opitutaceae (O-2.46%, C-0.27%), Chthoniobacteraceae (O-1.03%, C-2.68%), Steroidobacteraceae (O-2.05%, C-0.73%), Bacillaceae (O-0.77%, C-4.55%), Chitinophagaceae (O-10.99%, C-5.06%), and Xanthomonadaceae (O-1.39%, C-0.06%) and other families (Fig. 5a).Figure 5Comparative microbiome (a-family and b-genus) analysis of organic (G1) and conventional (G2) treated mango orchards soil by using metagenomic (V3 and V4 region) approach.Full size imageBacterial community distribution at the genus levelComparative abundance of unidentified genus in organic system were uncultured soil bacterium, Glycomyces, Chitinophaga, Lysobacter, Udaeobacter, Bacillus (not detected, 1.85%, 4.77%, 1.19%,1.03% and 0.75% respectively) whereas same genus-group were observed in conventional system with different percentage i.e., 0.11%, not detected, 0.56%, 0.04%, 2.67%, 4.54% respectively (Fig. 5b).Bacterial communities at species levelBecause most of the species were unidentified and uncultured bacterium based on relative abundance, they could not be assigned a species name in either sample. Few species are identified in both systems, like Sphingomonas sp. (O-1.57%, C-1.05%), Bacillus drentensis (O-0.25%, C-2.65%), and Chitinophaga sp. (O-4.64%, C-0.11%) (Fig. 6).Figure 6Comparative microbiome (Species) analysis of organic (G1) and conventional (G2) treated mango orchards soil by using metagenomic (V3 and V4 regions) approach.Full size imageHeat map and PCA analysisUnder long-term exposure of organic and conventional treatments, a microbial shift was observed in the rhizosphere microbiome of mango orchards. Based on percent abundance, nine different microbial genera Acidobacteria, Actinobacteria, Bacteroidetes and Proteobacteria formed Cluster I. While, Firmicutes, Chloroflexi and Opitutales were abundances in cluster II. Cluster III includes Chitinobacterales, Bacillales, Chitinophagarales and Otherales genera. Whereas cluster IV (Elev7-16S-573, Otherales, Solibacterales and Pedobacteriaceae), cluster V (Opitutaceae, Chitnobacteraceae, Bacillaceae, Chitinophagaceae and Otherales), cluster VI (Xanthomonadaceae, Uncultured soil bacterium, Candidatus-Udaeobacter, Lysobacter and Bacillus), cluster VII (Chitinophaga, Glycomyces and Other), cluster VIII (Uncultured bacterium and Others) and cluster IX (Bacillus drentensis and Others) (Fig. 7). The cluster I observed with the highest abundance was closely related to clusters II and III. Cluster IV to IX created large groups and is distantly related to cluster I to III of the microbial groups in organic and conventional systems (Fig. 7). In the organic system (G1), microbial groups like Proteobacteria, Actinobacteria, Bacteroidetes, and Opitutaceae were largely dominated and provided benefits to the mango rhizosphere in terms of nutrient availability, plant growth promotion, and protection against biotic and abiotic stress. Phylum Proteobacteria and Actinobacteria are closely linked with the rhizosphere and identified as potential PGPR. Acidobacteria and firmicutes, on the other hand, were dominated primarily by conventional systems and serve as a bio-indicator of anthropogenic stress caused by excessive chemical fertilizer application. Undefined Acidobacteria is oligotrophic in nature and considered as an indicator of low organic carbon and acidic environment. To desire higher productivity, the indiscriminate use of chemical fertilizers or pesticides in conventional systems leads to low nutrient availability, microbial shift, less PGPR, and developing the environment for Acidobacteria, Firmicutes and Chloroflexia group of microorganisms. Principal component analysis (PCA) was performed for both systems (organic-component 1; conventional-component 2). The total variables of principal component analysis were the percentage of different parameters such as alkaline phosphatase, acid phosphatase, DHA, Acetylene reduction assay (ARA1, ARA2, ARA3), and CFU mL−1 (bacteria and fungi). The results of PCA yielded two components that explained 100% of the total variance in the data and had an Eigen value of 6.1 for component 1. In contrast, 1.8 for component 2 and together they described 100% of the total variance in the data (Fig. 8). In the organic system, the loading factor with score plot indicates that component-1 is positively associated with DHA, ARA1, ARA2, alkaline phosphatase, acid phosphatase while negatively correlated with CFU ARA3 activity. Component-1 explains the 76.42% variance of the experimental data, while component-2 explains 23.58%. The second component (PC2) represents the positive association with DHA, ARA1, ARA2, ARA3 activity, and CFU while negatively correlated with alkaline phosphatase and acid phosphatase. In the conventional system, the loading factor with score plot indicates that component-1 is positively associated with single variable acid phosphatise while negatively correlated with DHA, ARA1, ARA2, ARA3, CFU, and alkaline phosphatase activity. The second component (PC2) of the conventional system showed positive association with DHA, ARA1, ARA2, ARA3 activity, and CFU, while the negative association with alkaline phosphatase and acid phosphatase.Figure 7Comparative (G1 organic and G2 conventional) heat map of dominant microbial diversity and their clusters in terms of T1 (phylum), T2 (order), T3 (family), T4 (Genus) and T5 (Species).Full size imageFigure 8PCA analysis of different parameters for organic and conventional systems.Full size image More

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Influence of soil organic matter on zeta potentialIn this study, the zeta potential of a clayey red soil was compared among 4 types of long-term treatments including manure, NPK + straw, NPK and CK in a subtropical climate. Generally, the manure treatment which also had the greatest concentration of SOC resulted in the highest clay zeta potential (less intense charge imbalance), while NPK + straw did not result in the second highest zeta potential as expected compared to the NPK and CK treatments. Variation in clay zeta potential among types of fertilization might be related with their different SOM content, because SOM had an influence on the zeta potentials via affecting the negative charges of soils19. The zeta potential of manure and NPK + straw treatments having high SOC agreed with earlier studies in Marchuk et al.9 that decreases of SOC via NaOH treatments decreased the negative zeta potential value9, where Claremont soil originally having high SOC (2.2%) displayed a greater degree of decline in negative zeta potential (from − 29 to − 34.9 mV) than Urrbrae having lower SOC (1.4%) (− 66.3 to − 68 mV). However, zeta potential in water dispersible clay responded to SOC contrastly in the study of Melo et al.12 , where Londrina soil with high SOC (5–20 g kg−1) displayed lower negative zeta potential values in water dispersible clay than that in Rondon soil (SOC 5 to 12 g kg−1) in subtropical Brazil.Differences of SOC effect on zeta potential in our study and other studies were probably because ionic strength in bulk solution also affected the intensity of soil charge imbalance. Generally, in tropical and subtropical Ferralsols, high amounts of SOM that was released following the breakdown of macroaggregate provided an excess of negative charges and intensified the imbalance in charge, resulting in more negative in zeta potential of clay12. In contrast to Ferralsols in Brazil, red soil (highly-weathered) in our study showed higher negative zeta potential in manure soils with higher SOM. This was because high ionic strength in bulk solution might counterbalance the negative charges from SOM, and attenuated the imbalance in charges. Hence, manure treatment which provided greater EC and Ca2+, Mg2+ concentration and possibly higher ionic strength was reasonable to allow for more charge balance and greater negative zeta potential values than other treatment.In this study, NPK + straw treatment exhibited similar negative zeta potential values as that in NPK but slightly lower than manure, probably due to the effect of SOM functional group from straw and soil solution concentration. Straw can increase the humin content as reported in the study of Sheng et al.11, and then a decrease of negative zeta potential can be induced as addition of humic acid on a Luvisol20. But the negative humic effect from straw on zeta potential was probably stronger than the positive effect from the increased bulk soil solution concentration in NPK + straw relative to NPK in Fig. 3 where increase of bulk solution concentration was found to increase the negative charge numbers and the negative zeta potential in Ultisol and Oxisol15. Therefore, our hypothesis that organic treatments decreased negative zeta potential value of soil was not supported for manure treatment, but was for NPK + straw treatment.NPK + straw’s similar effect on negative zeta potential as NPK treatment was probably also related with their similar pH values. The effect of pH on the potential of clay surfaces can be related to the amount of variable charge on the external surface of the clay particles. Negative zeta potential decreased with rising pH of the solution due to deprotonation of the functional groups on the surface of the organic matter and Fe/Aloxides in NPK + straw treated soils. An increase of soil pH (from 3.5 to 7.5) influenced zeta potential through production of more negative net surface charges on soils in subtropical Australia21,22. Therefore, the pH in our study after KCl adjustment that showed a first increase and then decrease pattern with the increase of concentration, can help to explain the bell shape pattern of negative zeta potential (first decrease and then increase). However, in our study, the pH pattern with increment of KCl concentration was different from the results in study of Yu et al.8 where a continuous decline pattern in pH of two soils (Vertisol and Ultisol) was reported when the KCl concentration increased from 10–5 to 10–1 mol L−1. This is probably because the Ultisol possessed high amount of variable charges from Fe or Al oxides, which resulted in the diffusion layer attracted more positive charged cations (i.e. K+) from bulk solution to balance the increased negative charge on the surface of colloidal particles in order to maintain the electrical neutrality of the system15. This indicated that when KCl concentration was low, between 0 and 10–2 mol L−1, part of K+ was attracted to the diffuse double layer and the remaining K+ hydration allowed for raising in soil pH. When KCl concentration was beyond 10–2 mol L−1, many Al3+ions on soil exchange site were released into solution (0.03 to 0.12 mg L−1) through K+ exchange and probably dropped soil pH (data not shown).Studies also found that the effect of SOM on zeta potential of clay also varied for soils in different climate. Yu et al.8 compared rice straw incorporation effect on two soils (Ultisol and Vertisol) and found that similar SOC content resulted in contrasting effects on surface potential of two types of soils, where surface potential of Ultisol continuously increased while firstly increased and became stable for Vertisol with increase of treated solution concentration. Different SOM effect on soil potential properties of two soils were probably associated with presence of soil variable charges in Ultisol23. SOM and Fe/Al (hydro)oxides in Ultisol carried a larger number of variable surface charges, and resulted in a strong overlapping of oppositely charged electric double layers (EDLs) between SOM and Fe/Al (hydro)oxides at low concentration8. The overlapping of oppositely charged EDLs between SOM and Fe/Al probably yielded in an increase in negative surface charge for Ultisols compared to Vertisol.Effect of SOM and zeta potential on soil aggregationIncrement in content of SOM after additions of straw or other organic treatments can improve aggregate stability6,24,25. The hydrophobic organic compounds that coated around soil particle can act as nucleus of aggregate formation and reduce the destruction effect from water infiltration26,27. The hydrophobic-C/hydrophilic-C increased from 1.04 to 1.07, from 1.22 to 1.27 for chicken manure and maize residues treatments, respectively, when soil water conditions changed from water deficiency to natural rainfall treatment28. This indicated that a small change of hydrophobic-C/hydrophilic-C might result in substantial change in soil water, which was a critical factor of aggregate development28. Xue et al.24 also reported that a small difference of aromatic percentage between tillage + straw and no tillage + straw treatments resulted in significant differences for aggregate ( > 0.25 mm). Hence, small variation in soil hydrophobic-C groups can yield in soil aggregate variation. In our study, the manure treatment, which had higher SOM and hydrophobic-C (aromatic C) while lower hydrophilic-C than other treatments, was probably reasonable to yield in its higher stability than others. In these previous studies, the positive effect of SOM on soil aggregate development was attributed to the increment in van der Waals force between soil particles. However, different from our study, Melo et al.12 reported that Londrina soil with high SOC released greater water dispersible clay (60–80%) than that in Rondon with low SOC (50–70%) after mechanical breakdown of macroaggregate. This was probably due to the repulsive force prevailing attractive force between soil particles as affected by more negative zeta potential or surface potential8.Clay zeta potential influenced the powerful electrostatic fields, soil internal forces and aggregate stability9. Decrease in negative clay zeta potential mainly yielded an increase in the soil microaggregate portion ( More

Device design and fabricationVectorchips with integrated PDMS elastomer membranes were fabricated using a combination of laser patterning and soft lithography. We selected PDMS as the material of choice due to its low cost, biocompatibility, optical transparency, and chemical stability. PDMS-based devices can be fabricated using reproducible and scalable fabrication techniques26. Such devices have routinely been used for molecular analysis of small sample volumes using microfluidics27, and are compatible with molecular assays and cell cultures21,22. We used laser patterning to create open-ended arrayed compartments (3−4 mm deep) to capture saliva droplets from mosquito bites in PDMS micro-wells (Fig. 1a–d). In order to obtain ultra-thin PDMS films that the mosquito mouth parts can pierce, we utilized spin-coating of uncured PDMS over a flat silicon surface. We obtained membrane-integrated devices after plasma-induced PDMS-PDMS bonding of the laser-patterned array with the PDMS film (Fig. 1e). Detailed steps describing chip fabrication are included in the methods section below. The versatile fabrication process can provide user-defined variation in the size and density of the individual compartments. We were able to fabricate chips with hole diameters ranging from 150 μm to 1 cm (Supplementary Fig. 1) showing the scales of operation for the laser-ablation process while maintaining membrane stability. Additionally, the membrane can be easily integrated with fluidic networks for direct interfacing with mosquito bites, enabling assays involving on-chip fluid exchange (Fig. 1f). The microfluidic compartments on the chips can hold feeding media such as blood or sugar water (Fig. 1g), collect saliva during biting events, and act as isolated reaction chambers for molecular assays.Fig. 1: Fabrication of Vectorchip for collection of mosquito saliva and molecular assays.a Schematic showing the fabrication process of Vectorchip. Laser patterning was used to obtain through-holes in blocks of PDMS. A thin sacrificial layer of photoresist (Microposit™ S1813 G2, DOW Inc., USA) was spread onto a silicon wafer followed by spin-coating a thin film of PDMS on the wafer surface. The laser-patterned body of the chip was then bonded to the thin film of PDMS using plasma-activated PDMS-PDMS bonding. Membrane-bonded chips were obtained by placing the wafer in an acetone bath, where the sacrificial layer of resist was dissolved. b A PDMS block with laser-generated through-holes (diameter 250 μm). c A PDMS chip bonded to a 1.6 μm thin PDMS membrane on a 4-inch silcon wafer. d A Vectorchip with around 3000 wells (dia: 250 μm) is shown. e Chip with well diameter 1.75 mm and a visible PDMS membrane (thickness 1.6 μm). f Microfluidic channel-integrated chips with a PDMS membrane on top. g These wells can be loaded with feeding solution, reaction reagents, and can store mosquito saliva after biting assays. Wells loaded with feeding media (10% sucrose laced with blue food color) shown in the image. Scale bars represent 1 mm in (b), 5 mm in (e), and 2 mm in (f, g).Full size imageMosquito-chip interactionsNext, we examined the ability of mosquitoes to pierce through the PDMS membranes. Mosquitoes were attracted to the chips using heat as a guiding cue although several other baiting methods could be used. We placed a camera above the chip and observed stylet insertion through 1.6 μm thick PDMS membranes (Fig. 2a, Supplementary Movies 1 and 2). Abdominal engorgement in mosquitoes was observed after biting, indicating that mosquitoes can successfully feed through these membranes. Four species of mosquitoes were tested (Aedes aegypti, Aedes albopictus, Culex tarsalis, Culex quinquefasciatus) and all demonstrated successful probing and feeding through 1.6 μm thick PDMS membranes.Fig. 2: Mosquito biting on Vectorchip.a Stylet entry through a PDMS membrane for an Aedes aegypti female. b Mosquito feeding success as a function of membrane thickness for two mosquito species—Aedes aegypti and Culex tarsalis. We demonstrate that by changing the membrane thickness we can turn biting on or off. Biting off is defined as when no mosquito in the cage was able to feed from the chip in a duration of 30 min. The feeding assays were repeated at least three times (n = 3) at every given membrane thickness to confirm the ability of mosquitoes to feed through the membrane. c A Culex tarsalis female mosquito biting through a 20 μm thick PDMS membrane. d Bending of the proboscis observed for an Aedes aegypti female, indicating failure in biting through the 20 μm membrane. e Fluorescent salivary droplets expectorated by an Aedes aegypti mosquito while probing. These mosquitoes were fed on rhodamine-laced sugar water resulting in fluorescent saliva. Three salivary droplets (1, 2, and 3) are encircled. f Timelapse images show a magnified view of fluorescent salivary droplet deposition (droplets numbered 1, 2, and 3 as shown in panel (e) over a period of 4 s). Scale bars represent 500 μm in (a) and 2 mm in (c, d).Full size imageWe also examined whether the membrane thicknesses can be engineered to selectively prevent the biting of some species while letting other species feed through the barrier. We loaded the laser-patterned microfluidic compartments with blood and warmed the chips to attract mosquitoes (Supplementary Fig. 2 and Supplementary Movie 3). We considered biting to be off when none of the mosquitoes in the cage demonstrated abdominal engorgement within 30 min since the start of the experiment. Tests were performed with Aedes aegypti (~45 mosquitoes per cage) or Culex tarsalis (~25 mosquitoes per cage) while varying the thickness of the PDMS membrane from 900 nm to 100 μm. We observed significant differences in the biting ability of the two species, where complete inhibition of feeding by Aedes aegypti was observed with membrane thickness at or greater than 15 μm (Supplementary Movies 4 and 5). In contrast, complete inhibition of feeding by Culex tarsalis was only observed for membranes that were 100 μm thick (Fig. 2b). This remarkable difference in the biting capacity of these two species has not been reported earlier. While some studies have focused on understanding the biting mechanics of a single mosquito species (Aedes aegypti or Aedes albopictus)28,29, we are not aware of any study which quantifies the differences in biting strength between different species and the evolutionary or biomechanical differences involved in this variation. Evolutionary pressure on mosquitoes biting different hosts could generate great variability of biting strength in mosquito species, which may be needed to probe different skin thickness. This intriguing biomechanical phenomenon can be advantageous for deploying devices where membranes of desired thickness can act as species-selective filters, providing a means to better track highly relevant species and pathogens in the field.In order to extract molecular information from mosquito bites, we exploited the release of saliva during probing and biting events. The volume of saliva expectorated by mosquitoes in oil has been reported previously to be around 0.03 nL/min for Culex pipiens30, and 6.8 nL in approximately an hour for Aedes aegypti31 using a forced salivation method. Aedes aegypti have been estimated to salivate around 4.7 nL during blood feeding events32. We demonstrate that feeding rhodamine-labeled sugar water to mosquitoes renders their saliva fluorescent such that the release of saliva droplets can be directly visualized using fluorescence imaging (Fig. 2e and Supplementary Movie 6). Time sequences (Fig. 2f) show magnified images of three locations on a membrane surface where short probing events result in the deposition of fluorescent salivary droplets. We estimate that the volume of salivary droplets expectorated during this process is approximately 0.66 nL (Supplementary Fig. 3). This calculated mean volume is considerably lower compared to the volume associated with blood-feeding events reported earlier32; however, this discrepancy likely reflects differences between probing events such as observed in Fig. 2e, f and full feeding events as reported by Devine et al.32. The deposited salivary droplets harbor several biomarkers in the form of mosquito salivary proteins33, active pathogens, mosquito cells, and/or nucleic acids that can be used for their identification14,20,34.Distribution of bites on chipAn important consideration for molecular surveillance of mosquitoes is the granularity at which the population is sampled. Methods can range from pooling strategies with low coverage where samples are pooled together at the population scale, to the individual collection of saliva from mosquitoes while recording species identity. Vectorchip potentially provides an adjustable method between these two strategies by increasing or decreasing the density of bite wells presented to a population of mosquitoes.We first analyzed the distribution of bites on Vectorchip by tracking mosquito activity during biting assays. In order to perform sucrose biting assays for molecular diagnostics, we filled the wells with 10% sucrose, and covered the open side of the chip with a glass slide. A resistor that acts as a heat source was placed atop the glass slide and the chip-resistor assembly was placed on top of the mesh ceiling of a mosquito cage. A part of the chip (50 percent of the total chip area) was protected by paper tape such that mosquitoes could not bite into that section of the device. The area of the chip protected by tape served as negative control for the downstream molecular assays. A camera (Raspberry Pi 3 camera module V2.1) was placed on the bottom of the cage to track and record individual mosquito activity on chip (Supplementary Fig. 4). Image recognition and mosquito tracking algorithms have been described by us previously25, where we used computer vision to analyze image sequences and robustly track the presence, movement, and biting behavior of individual mosquitoes at high resolution. In this study, we have used an imaging setup where the camera is attached to the bottom of the cage (Supplementary Fig. 4a). The obtained image datasets reveal mosquito trajectories on the chip as well as areas with prolonged interactions (Fig. 3a–d, Supplementary Fig. 4, and Supplementary Movie 7). Unique trajectories are identified as movements performed by an individual mosquito while in the field of view (e.g., landing, exploration, biting, and take-off by a single mosquito would constitute a trajectory). The movement of mosquitoes on a chip is shown in Fig. 3e–g and Supplementary Fig. 4. Depending on the duration of mosquito-chip interactions, multiple trajectories do not overlap (Fig. 3e), partially overlap (Fig. 3f), or completely overlap (Fig. 3g). Trajectory plots can indicate the likelihood that a set of wells has been probed by more than one mosquito. These results indicate that by modulating the time of interaction between the mosquito population and the chip, the user can control the degree of probing experienced by the chip. As an example, a chip with no overlap between trajectories should exclusively contain wells probed by single mosquitoes. Tracking mosquito movement on chip was used to locate trajectories and thus regions with unique mosquito presence.Fig. 3: Tracking mosquito activity on chip.a Image recognition software was used to identify and track mosquitoes on the chip. Wells are highlighted in yellow. b Trajectories of mosquito movement can be plotted and indicate the overlap between different movement tracks. c Location plot of mosquitoes on the chip are indicated by red circles. Regions with overlapping red circles are darker in color. d Trajectory and location plot can be used to identify regions with individual mosquito locomotion activity as compared to regions with more crowding. e−g Varying experimental parameters (symbols indicate the duration of experiments and number of mosquitoes) can be used to obtain either individual mosquito plots or population data. Three cases are discussed with e no overlap between multiple probing trajectories, f partial overlap, and g complete overlap. h Representative image for a chip where the presence of fluorescent droplets on wells has been indicated by a red dot. i Zoomed-in fluorescent image of the same chip showing wells with single, or multiple droplets. j Number of bites per well were calculated by counting fluorescent droplets deposited on the wells after biting events. k We defined the number of mosquitoes = Nm, the number of wells = Nw, probing frequency = η, and time = t. The probability of obtaining ‘k’ bites on a well for the parameters (Nm, Nw, η, t) utilized in (h−j) has been shown. Scale bars represent 5 mm in (a−c, e−g), 2 mm in (d), and 1 mm in (i).Full size imageWhile tracking provided macro-scale information about mosquito position and activity, we also utilized the release of fluorescent salivary droplets deposited by biting mosquitoes to resolve mosquito probing events at higher resolution. We quantified the number of fluorescent spots seen in every well of the PDMS chip by using fluorescent scans of the chip before and after biting (Fig. 3h). No spots were observed in the negative control region where access was blocked to mosquitoes, while several fluorescent spots observed on the open areas of the chip indicate the wells which have been probed. The images show the presence of wells with multiple bite marks, as well as single bite marks.We further examined the design parameters used to build Vectorchip that can dictate the resolution of this sampling strategy. The primary determinants of how often a single mosquito will bite a specific well are dependent on multiple factors, including the number of mosquitoes in the cage, the time mosquitoes have access to the chip and biting behavior (e.g., frequency), and the size and number of wells on the chip. Other additional factors like the feeding media may affect the number of biting events performed by a female mosquito. In this study, we focused on sucrose as the feeding media since it is a diet source that does not inhibit polymerase chain reaction (PCR) assays. We considered a simple mathematical model to arrive at a first approximation for the distribution of bites on a chip. In this model, we made a simple assumption that mosquitoes probe the chip at a fixed frequency (η). We also assumed that mosquito bites are random and independent events, since no spatial gradients have been created on the chip. Detailed analysis of this model can be found in Supplementary Section 1.We defined the number of mosquitoes = Nm, the number of wells = Nw, and probing frequency = η, and estimated the probability (P) that a well receives k bites in given time t as (Supplementary Note 1):$$P[k]=({N}_{m}eta t)!/(k!times ({N}_{m}eta t-k)!)times 1/{N}_{w}^{k}times {(1-1/{N}_{w})}^{{N}_{m}eta t-k}$$
(1)
This was approximated by the form (Supplementary Note 1):$$P[k]approx {{{{{{mathrm{e}}}}}}}^{-{N}_{m}eta t/{N}_{w}}times {({N}_{m}eta t/{N}_{w})}^{k}/k!$$
(2)
and represents a Poisson distribution of the form,$$P[k]={{{{{{mathrm{e}}}}}}}^{-lambda }times {(lambda )}^{k}/k!$$
(3)
with the expected rate of events (i.e., bites per well), λ = Nmηt/Nw. Probability distributions for the expected number of bites on a well were plotted while varying the number of mosquitoes, wells, and probing frequencies (Supplementary Fig. 5). The probing frequency of Aedes aegypti mosquitoes on a parafilm membrane has been estimated previously to be in the range of 0–1 bites per min35. Supplementary Fig. 5 shows a diverse phase space where a single bite per well is the most dominant outcome, i.e., λ was less than or equal to 1, when up to 50 mosquitoes interact with a 150-well Vectorchip for 15 min—in line with our experiments. In the context of a larger number of mosquitoes interacting with the chip, increasing the number of wells can ensure that the majority of wells will not get bitten more than once. We quantified bites on wells as indicated from fluorescent droplets (Supplementary Table 1), and compared it to the analytical model (Fig. 3j, k). The fitting parameter was η = 0.1 bites per minute, which is close to the range of probing frequencies reported recently35. With water as the feeding media and parafilm as the probing membrane, Jove et al. report that the majority of tested mosquitoes showed a probing frequency below 0.5 bites per minute35. While we observe and model slightly lower probing frequencies, this difference is possible due to the change in the membrane material from parafilm to PDMS, and other differences in the setup. The methods discussed above provide multiple ways to understand mosquito−chip interactions as well as help us define parameters to obtain data at single-bite resolution.PCR diagnostics on-chipHaving established that mosquitoes can bite through the membrane and deposit salivary droplets in microwells while biting on a Vectorchip, we analyzed these bites for their DNA and RNA content. Running PCR-based nucleic acid amplification directly on individual wells establishes a low-cost assay to detect bite content in a high-throughput manner. We utilized devices with a PDMS membrane thickness of 1.6 μm and well diameter of 1.75 mm to perform molecular analysis of salivary samples; providing 145 independent reactions per chip (within a chip area of 5 cm × 2.5 cm).Detection of mosquito mitochondrial DNA (mtDNA) and RNA from viruses in saliva was performed using on-chip PCR with end-point fluorescence readout. A detailed protocol for performing PCR in Vectorchip is provided in the Methods section and Supporting Information file (Supplementary Fig. 6). We tested the efficiency of PCR amplification in Vectorchip by manually loading a known concentration of DNA and RNA into the reaction wells (Fig. 4a–c). We performed the PCR for 42 cycles and detect DNA amplification from approximately 5 DNA copies in 4 μL of reaction mix (~1 copy/μL) (Fig. 4b). Simultaneously detection of Zika virus RNA using reverse transcription-PCR (RT-PCR), was successfully demonstrated from as low as 15 RNA copies in 4 μL of reaction mix (Fig. 4c). PCR reactions were also performed in 96-well plates to test if amplification accuracy and sensitivity were similar as compared to Vectorchips. Both qPCR amplification curves and end point fluorescence information was obtained from the well plates. The 96-well plate reactions were performed in DI water and also in the presence of dried sucrose (10%) to ensure that the amplification response provided a good comparison to the Vectorchip-based reactions using manually spiked concentrations, and to test if the presence of sucrose would inhibit the reactions (see Supplementary Fig. 7a, b).Fig. 4: On-chip PCR for detection of mosquito and pathogen.The symbols indicate manually spiked assays, uninfected mosquito assays, and Zika infected mosquito assays from top to bottom. a–c A spiked assay with successful amplification of Aedes aegypti DNA and Zika RNA on chip. a Indicates wells filled with PCR mix, with ROX dye providing the background fluorescence. Fluorescent wells indicate amplification of b mosquito DNA, and c Zika RNA on chip. Assays were performed with uninfected mosquitoes where d tracking patterns were collected and demonstrate extensive translocation activity on the chip. e PCR shows detection of mosquito DNA on the chip directly from biting. f Percentage of wells available for biting on Vectorchip that display a positive PCR outcome. The PCR assay was repeated 6 times on different chips (n = 6). The false positive rate (amplification in wells that were covered by tape) was close to zero (0.23%). Assays performed with Zika infected mosquitoes indicate the presence of both g Aedes aegypti DNA and h Zika virus RNA after bites on chip. i Percentage of wells available for biting that show a positive signature for Zika RNA, mosquito DNA or both. The PCR assay was repeated 3 times on different chips (n = 3). Scale bars represent 5 mm in (a−e) and 3 mm in (g, h). Source data are provided as a Source Data file.Full size imageVectorchip provides an opportunity for tracking both pathogens in salivary bites, as well as identifying host species by detecting DNA from the host biting the well. Mosquito DNA has previously been reported in salivary droplets deposited by feeding Culex mosquitoes20. Utilizing uninfected Aedes aegypti mosquitoes, we performed PCR assays on-chip to detect the presence of mosquito DNA in deposited saliva droplets. Figure 4d, e shows the tracking data and PCR detection of mtDNA for a chip, which was placed on a cage with 75 uninfected Aedes aegypti females for 45 min. Areas highlighted in green were protected by paper tape such that mosquitoes could not probe them and serve as negative controls. The tracking data obtained from the chip indicate significant activity of the mosquitoes on-chip as represented by several overlapping trajectories. PCR data confirms that mosquito DNA indeed can be detected in probed wells. Multiple experiments indicate that a subset (3−12%, n = 6) of the wells where mosquitoes are active test positive for the presence of mosquito DNA (Fig. 4f). The rate of false positives obtained from wells not accessible to mosquitoes was very close to zero (0.23%, n = 6). In order to further validate the rate of detection of mosquito DNA on Vectorchips, we performed a biting assay using a 96 well plate loaded with agarose and covered with a parafilm membrane. Agarose gel was used as feeding media during this test, to minimize the risk of cross-contamination between wells during the peeling off of the parafilm membrane (details in “Methods” section). We recorded the tracking and DNA amplification response from uninfected mosquito bites on the well plate (Supplementary Fig. 7c, d). Approximately 15% of the wells where mosquitoes were active tested positive for mosquito DNA, which is close to the fractions observed on Vectorchips. Since mosquito DNA can only be detected in a subset of wells showing mosquito activity, this suggests that the presence of detectable levels of mosquito DNA in saliva is a noisy physiological process, likely dependent on multiple variables such as duration of feeding and time of last bite. It is furthermore important to note that our tracking algorithm only detects the presence of mosquitoes, but does not provide information regarding if a well was bitten or not.Next, we utilized the device to perform assays on infected mosquitoes, for the detection of both mosquito species and deposited pathogens. Figure 4h shows results obtained when Aedes aegypti infected with Zika virus (ZIKV)36 interacted with the chip for 20 min. We subsequently performed RT-PCR on the chip and observed that Aedes aegypti mtDNA and ZIKV RNA could be detected in 24/118 and 37/118 wells, respectively. We summarize the detection rates of mosquito DNA and ZIKV RNA for three assays performed with ZIKV-infected mosquitoes in Fig. 4i. Interestingly, not all wells positive for ZIKV RNA showed positive for mosquito DNA and vice versa, highlighting the stochastic genetic composition of bite-derived nanoliter saliva droplets. A higher fraction of wells (mean ~ 22%, n = 3) showed positive for ZIKV RNA as compared to mosquito mtDNA (mean ~ 17%, n = 3), indicating a higher prevalence of viral genomic material in mosquito saliva as compared to mosquito mtDNA. Detection of mosquito DNA and virus RNA in these assays demonstrates the capacity of this tool to identify mosquito and pathogen species directly from mosquito bites on-chip.Detection of infectious viral particles directly from bitesWhile PCR-based end-point assays are an important strategy for multiplexed detection of vectors and pathogens in various settings, they cannot determine the presence or concentration of infectious virus particles in bites. Detection of infectious pathogen load in mosquito bites is important to understand the vector competence (including potential and efficiency of pathogen transmission through bites) of mosquito-pathogen systems and its dependency on factors such as local climate37, mosquito species38, physiology39, and presence of endosymbionts (e.g., Wolbachia)40. Typical methods to measure vector competence rely on manual “forced salivation” of individual mosquitoes12, where the viral loads may differ from biting events and mosquitoes are sacrificed preventing time-course measurements. Sugar feeding on filter papers has also been utilized recently, and provides promising results towards longitudinal sampling of viral transmission by individual mosquitoes20. As compared to filter paper-based approaches, the Vectorchip uses an integrated PDMS membrane which can enable purely bite-based collection and avoid possible contamination from e.g., excretion events. Furthermore, our method improves the throughput of the assay as mosquitoes do not need to be individually housed and sampled. Finally, filter paper assays are reliant on sampling nucleic acids, and cannot perform live cell culture assays to detect infectious pathogens.We tested the potential of Vectorchip to perform focus forming assays (FFA) and detect infectious viral particles directly as a result of mosquito bites on chip (Fig. 5). Vectorchip wells were filled with cell culture media (Dulbecco’s modified eagle medium (DMEM) supplemented with 10% fetal bovine serum (FBS). The efficiency of FFA on-chip was tested using manual spiking of viral particles into Vectorchip (Supplementary Fig. 8). In order to perform biting assays, Aedes aegypti mosquitoes were infected with Mayaro virus, an emerging zoonotic pathogen that causes a dengue-like disease41. The mosquitoes were able to bite through the membrane into the wells and transfer live Mayaro viral particles directly into the cell culture medium (Fig. 5a). These viral particles then infect a monolayer of vero cells cultured on the PDMS membrane. These infections can be identified via fluorescent antibodies specific to viral envelope proteins (Fig. 5c). Each fluorescent patch (focus) is attributed to an infection resulting from a single viral particle. Foci counted on two sample chips can be seen in Fig. 5d. No foci were visible from negative control samples. The distribution of viral foci on the chips indicates the heterogeneity of infectious particle dose in mosquito bites. Figure 5 shows that the Vectorchip supports the growth of a cell monolayer on the chip and can enable the direct collection of mosquito saliva through bites in cell culture media. Antibody assays can be used to directly quantify infectious viral particles in the wells. These results demonstrate the suitability of using the Vectorchip to probe the transmission of infectious viral particles.Fig. 5: Quantifying active viral particles using focus-forming assays.Focus forming assays on Vectorchip can directly quantify the number of active viral particles in mosquito bites, without the need for isolation of individual mosquitoes and manual salivary extraction. a Image shows Aedes aegypti mosquitoes infected with Mayaro virus biting on Vectorchips filled with DMEM cell culture media. b A monolayer of vero cells was cultured on Vectorchip membrane. The formation of vero cell monolayers on membranes was verified on 5 independent chips (n = 5). c FFA performed on a Vectorchip. Symbols indicate wells in the top row which were bitten by mosquitoes with no infection (negative control), and rows of wells bitten by mosquitoes infected with Mayaro virus. The green channel shows fluorescence from an antibody against a viral envelope protein. Every green island represents infected cells likely to have resulted from an active viral particle. No active viral particles were seen in the control wells. FFA on Vectorchips was verified on 3 chips (n = 3) for both uninfected and Mayaro infected mosquitoes. d FFA formed on two chips and viral foci were counted using the antibody fluorescence. Scale bars represent 2 mm in (a), 30 μm in (b), and 100 μm in (c).Full size imageFinally, we examined if the feeding media provided in Vectorchip can enable blood meal-mimic biting behavior through the addition of phagostimulants such as ATP, while maintaining accurate molecular analysis (Supplementary Note 2). We observed that the addition of 100 μM ATP, (which is a phagostimulant and used by mosquito sensory neurons to identify blood35), to the feeding media did not impact nucleic acid amplification reactions (Supplementary Fig. 9a) (n = 3). Interestingly, mosquitoes feeding on DMEM supplemented with 10% FBS and ATP (1 mM) led to a similar or higher number of abdominal engorgements in 45 min as compared to blood meals (n = 3) (Supplementary Fig. 9b). This is reasonable as DMEM (supplemented with FBS and ATP) contains a variety of the components used by mosquito sensory neurons to identify blood at relevant concentrations (Supplementary Note 2)35). In summary, our data indicate that Vectorchip can provide physiologically relevant mosquito feeding behavior, enabling accurate assessment of vector and pathogen identity through nucleic acid amplification reactions for field surveillance, as well as on-chip assessment of transmission of infectious viral particles in bites through focus forming assays. More