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Study designInitially, we assessed life-history traits of both Ag(QFS1) males and females as well as of the wild-type strain G3 of An. gambiae and assessed their longevity under large-cage conditions (4.7 m3) in order to emulate more natural population dynamics16 (see Fig. 2, Supplementary Material). Considering the initial Kaplan–Meier Survival estimate of wild-type G3 adult mosquitoes in 4.7 m3 cages of 2 m × 1 m × 2.35 m size and the establishment of overlapping generations with bi-weekly introductions of 400 G3 pupae with a start-up population of 800 mosquitoes, we then analysed ASL populations with an expected mean size of ~570 adult mosquitoes as ‘receiving’ populations for gene drive release experiments (Source Data). To mimic field-like conditions absent in small cage conditions, the climate chambers were maintained under near-natural environmental conditions including simulated dusk, dawn and daylight, and each cage was equipped with proven swarming stimuli and a resting shelter14 (Fig. 1). Under these conditions male swarming, an important component of successful mating behaviour, was frequently observed. To mimic a hypothetical field gene drive release, we seeded Ag(QFS1) mosquitoes over a single week (two releases) into the established ‘receiving’ wild-type populations at two different starting frequencies, low (12.5% initial allele frequency) and medium (25% allele frequency), as well as control cages (0% gene drive release), all in duplicate (6 cages total). The ASL population dynamics and the potential selection of drive-resistant alleles were monitored in treated and control cages until wild-type populations were fully suppressed by the gene drive in the treatments. Finally, we constructed an individual-based stochastic simulation model of the experiment to better understand the observed dynamics of the gene drive frequency and population suppression.Mosquito strainsTwo An. gambiae mosquito strains were used, the wild-type G3 strain (MRA-112) and Female Sterile Gene Drive strain, Ag(QFS)1, previously known as dsxFCRISPRh9.This strain contains a Cas9-based homing cassette within the coding sequence of the female-specific exon 5 of the dsx gene (Supplementary Fig. 1). The cassette includes a human codon-optimised Streptococcus pyogenes Cas9 (hSpCas9)29 gene under the regulation of the zero population growth (zpg) promoter and terminator30 of An. gambiae and a gRNA against exon 5 under the control of the An. gambiae U6 snRNA promoter. The cassette also carries a dsRed fluorescent protein marker under the expression of the 3xP3 promoter.Mosquito containment and maintenanceAnopheles gambiae mosquito strains were contained in a purpose-built Arthropod Containment Level 2 plus facility at Polo d’Innovazione di Genomica, Genetica e Biologia, Genetics & Ecology Research Centre, Terni, Italy. Mosquitoes were reared in cubical cages of 17.5 cm × 17.5 cm × 17.5 cm (BugDorm-4) as described in Valerio et al.31 at 28 °C and 80% relative humidity (Supplementary Fig. 2). Larvae were maintained in trays (253 × 353 × 81 mm) at a density of 200 larvae per tray using 400 mL deionized water with sea salt at a concentration of 0.3 g/L and 5 mL of 2% w/v larval diet32 and screened for fluorescent markers en masse using a Complex Object Parametric Analyzer and Sorter (COPAS, Union Biometrica, Boston, USA).Large-cage environmentFor experimental purposes, mosquitoes were housed in a large-cage environment as described in Pollegioni et al.16 A single large climatic chamber was equipped with six 4.7 m3 cages of 2 m × 1 m × 2.35 m (length, width and height) (Fig. 1) and maintained at 28 °C ± 0.5 °C and 80 ± 5% relative humidity (Fig. 1, Supplementary Fig. 2). The climatic chamber was illuminated by three sets of three LEDs (3000, 4000 and 6500 K correlated colour temperatures) controlled by Winkratos software (ANGELANTONI Industries S.p.A, Massa Martana, Italy), allowing a gentle transition between light and dark sufficient to emulate dawn, and dusk. For the purpose of the current study, full light conditions (800 lux) were simulated using all LEDs and adjusted to last 11 h and 15 min. Cages were additionally equipped with ambient lighting (3000 K) designed to stimulate swarming14, and a terracotta resting shelter moistened with a soaked sponge. Mosquitoes were fed on 10% sucrose and 0.1% methylparaben solution and blood fed bi-weekly using defibrinated and heparinized sterile cow blood via the Hemotek membrane feeder (Discovery Workshops, Accrington, 34 UK). Oviposition sites consisted of a 12 cm diameter Petri dish with a wet filter paper strip introduced 2 days after the blood meal. Mosquito pupae, food, blood and water were introduced or removed through two openings, 12 cm in diameter, at the front of each cage with no operators entering the cage. Blood meal was administered by the introduction of two Hemotek feeders in each cage through one of the two openings at the front, leaving the power unit outside. No adult mosquitoes were removed from the large cages throughout the cage trials.Measuring the life-history parametersTo assess life-history parameters of wild-type G3 and Ag(QFS)1 strains, standardised phenotypic assays were performed as described in Pollegioni et al.16. In brief, clutch size, hatching rate, larval, pupal and adult mortality rates, as well as the bias in transgenics among the offspring of heterozygous Ag(QFS)1 were measured in wild-type G3 and Ag(QFS)1 strains in triplicate in standard small laboratory cages (BugDorm-4). Ag(QFS)1 heterozygotes used in these assays had inherited the drive allele paternally and were therefore subject to paternal, but not maternal, effects of embryonic nuclease deposition that can lead to a mosaicism of somatic mutations at the doublesex locus and a resultant effect on fitness9. 150 females and 150 males were mated to wild-type mosquitoes for 4 days, blood fed and their progeny counted as eggs using EggCounter v1.0 software33. Hatching rate was evaluated 3 days post oviposition by visually inspecting 200 eggs under a stereomicroscope (Stereo Microscope M60, Leica Microsystems, Germany). Sex-specific larval mortality was calculated by rearing 200 larvae/tray and counting/sexing the number of surviving pupae.Sex-specific adult survival was assessed in triplicate for each genotype separately by introducing and sexing 100 male and 100 female pupae of G3 and heterozygous Ag(QFS)1 into either small (0.0049 m³) or large cages (4.7 m³) (Supplementary Fig. 3). In the small cages, we tested 100 individuals in each cage divided by genotype and sex. In each large cage, 100 male and 100 female pupae following sexing and counting were tested together. Because homozygous Ag(QFS)1 do not show clear sex-specific phenotypes as pupae9, 100 Ag(QFS)1 total homozygotes (males and intersex females) were introduced into the small and large cages unsexed (Supplementary Fig. 3a). Sex-specific survival of emerged adults was calculated from daily collections of dead adult mosquitoes from the respective cages and their sexing. The adult survival assays in large cages were performed twice, one before the large-cage Ag(QFS)1 release experiment started and one after the large-cage Ag(QFS)1 release experiment finished. For the latter adult survival assay, around 400 individual mosquitoes were collected from large-cage populations at larval stage (before the cage populations declined, day 231 and 311 post-release for Ag(QFS)1 and G3 wild type, respectively), and kept in small cages until the start of the assay (Supplementary Fig. 3b).Establishment, maintenance and monitoring of age-structured large cage (ASL) populationsTo test the suppressive potential of Ag(QFS)1, we first established stable ASL populations of An. gambiae (G3 strain) housed in a purpose-built climatic chamber. Each population was initiated and maintained at the maximum rearing capacity through twice-weekly introductions of 400 G3 pupae (200 males and 200 females) over a period of 21 days (establishment), estimated to sustain a mean adult population of 574 mosquitoes based on the initial Kaplan–Meier estimate (Supplementary Fig. 3a). After this initial period only progeny of these populations were used to repopulate the cages twice-weekly (re-stocking) for a period of 53 days (pre-release, 74 days total), or supplemented with wild type reared separately when progeny numbers were too low. Each ASL population was considered stabilised after retrieving a sufficiently large and stable number of eggs to restock the population over four consecutive weeks. In detail, the receiving populations in all six cages were stabilised to produce a similar number of eggs in the 31 days before Ag(QFS)1 release, with an average egg production per cage ranging from 2262 to 5334. Twice-weekly blood meals were initiated at dusk and extended for a period of 5 h, and oviposition sites were illuminated with blue light for egg collection 2 days later. Eggs were removed from the cages, counted, and allowed to hatch in a single tray within the climatic test chamber. For re-stocking the cage populations with wild-type pupae, a maximum of 400 randomly selected pupae were collected at the peak of pupation, manually sexed and screened and introduced to their respective cage twice per week.Ag(QFS)1 release experiments in large cagesTo assess invasion dynamics of the Ag(QFS)1 strain in ASL populations of Anopheles gambiae, we performed duplicate releases designed to randomly seed ASL populations at low (12.5%, cages 2 and 5) or medium (25%, cages 3 and 6) allelic frequencies. After 74 days pre-release initiation period, heterozygous Ag(QFS)1 males were released into duplicate cages in addition to the regular re-stocking of the ASL populations with wild-type pupae. Releases took place on two consecutive re-stocking occasions, representing 15.2% (71 and 72) or 26.3% (142 and 143) of pupae introduced that week (943 and 1085, respectively), equivalent to 25 or 50% of the estimated mean pre-released adult population (on average 574 mosquitoes were present in large cages). No further releases were carried out and indoor ASL populations were maintained through re-stocking of 400 pupae twice per week. From then, the ASL populations were maintained in the same way we established the receiving population, with the same constant re-stocking rate from offspring. No adult mosquitoes were removed from the cages. Duplicate control cages were similarly maintained, but without release of Ag(QFS)1.While not statistically significant (Kruskal–Wallis Test P = 0.06 ns), there was some variation in reproductive output amongst the six cages due to random effects (cage 1: mean egg number = 4265.77, CI 95% = 1550.36; cage 2: mean egg number = 2691.73, CI 95% = 790.41; cage 3: mean egg number = 2517.46, CI 95% = 889.66; cage 4: mean egg number = 1799.18, CI 95% = 573.18; cage 5: mean egg number = 2350.82, CI 95% = 745.44; cage 6: mean egg number = 2060.05, CI 95% = 767.77). To control for random effects that could affect reproductive capacity of the population independently of the effect of the gene drive, we chose as control populations those cages with reproductive output at the upper and lower end of the distribution (cages 1 and 4). Replicate gene-drive release cages were distributed to cages 2 and 5 (12.5% allelic frequency) and cages 3 and 6 (25% allelic frequency) to mitigate against potential local environmental position effects (Fig. 2).Key indicators of population fitness and drive invasion were monitored for the duration of the experiment, including total egg output, hatching rate, pupal mortality, and the frequency of transgenics amongst L1 offspring and the pupal cohorts used for re-stocking. Total larvae were counted and screened for RFP fluorescence linked to Ag(QFS)1 using the COPAS larval sorter, and 1000 randomly selected to rear at a density of 200 per tray. Pupae positive for the gene drive element could be identified by expression of the RFP marker gene that is contained within the genetic element. Triplicate samples of up to 400 L1 larvae were stored in absolute ethanol at −80 °C for subsequent analysis.ModellingA stochastic model was set up to replicate the experimental design with respect to twice-weekly egg laying, the initiation phase, the transgene introductions, and the subsequent monitoring phase (Supplementary Methods). In brief, daily changes to the population result from egg laying, deaths, and matings, and are assumed to occur with probabilities that may be genotype specific. Adult longevity parameters were estimated from the large-cage survival assays that were performed before the gene-drive release experiments began, and after the gene-drive dynamics had run their course. The ASL caged populations showed a similar trend of increasing egg output over time prior to the suppressive effect of the drive (Fig. 2a–c) that may be explained by a general increase in adult survival that was observed between the start and end of the population experiment (Supplementary Fig. 3). To account for these changes in the stochastic model, we assumed a small increase in adult survival over time, irrespective of genotype, based on experimental data (Supplementary Fig. 3).We were particularly interested in the drive allele fertility costs, because these are potentially important to drive allele dynamics in natural populations22,23. Fertility costs may arise from paternal and maternal effects of Cas9 deposition into the sperm or egg, or from ectopic activity of Cas9 in the soma9. It is therefore possible that female offspring of transgenic fathers differ, in terms of fertility, from female offspring of transgenic mothers, and to investigate this possibility we fitted a separate parameter for the fertility of each type of female.We compared the data to model simulations using a suite of summary statistics34 (Supplementary Methods) to infer the fertility of females with a transgenic father or mother. In addition, we inferred two parameters that determined the egg production of unaffected (wild-type) females, and one parameter that determined the rate of R2 allele creation. We obtained a posterior distribution for all five parameters by retaining the 200 best fitting parameter combinations from 50,000 parameter samples generated by a Monte-Carlo algorithm (Supplementary Table 1). The simulation codes are available from Github: https://github.com/AceRNorth/TerniLargeCage.Pooled amplicon sequencing and analysisWe previously developed a strategy to detect and quantify target-site resistance based upon targeted amplicon sequencing using pooled samples of larvae6, and found no evidence for resistance to Ag(QFS)1 in small caged release populations9. To further investigate resistance in the large-caged release experiment, we analysed mutations found at the genomic target of Ag(QFS)1 in samples collected at early and late timepoints. Genomic DNA (gDNA) was extracted en masse from triplicate samples of 400 L1 larvae, or 50–300 larvae where larval numbers were limiting, that were collected after blood meals given on days 4 and 193 from all 6 cages, and on day 235 where sufficient larvae were available.gDNA extractions were performed using the DNeasy Blood & Tissue kit (Qiagen). 100 ng of extracted gDNA was used to amplify a 291 bp region spanning the target site of Ag(QFS)1 in doublesex, using the KAPA HiFi HotStart Ready Mix PCR kit (Kapa Biosystems) and primers containing Illumina Genewiz AmpEZ partial adaptors (underlined): Illumina-AmpEZ-4050-F1 ACACTCTTTCCCTACACGACGCTCTTCCGATCTACTTATCGGCATCAGTTGCG and Illumina-AmpEZ-4050-R1 GACTGGAGTTCAGACGTGTGCTCTTCCGATCTGTGAATTCCGTCAGCCAGC. PCR reactions were performed under non-saturating conditions and run for 25 cycles, as in Hammond et al.6 to maintain proportional representation of alleles from the extracted gDNA in the PCR products.Pooled amplicon sequencing reads, averaging ~1.5 million per condition, were analysed using CRISPResso235, using an average read quality threshold of 30. Insertions and deletions were included if they altered a window of 20 bp surrounding the cleavage site that was chosen on the basis of previously observed mutations at this locus9. Individual allele frequencies were calculated based upon their total frequency in triplicate samples. A threshold frequency of 0.25% per mutant allele was set to distinguish putative resistant alleles from sequencing error20.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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Marine microbiome is considered as the largest environment on earth which has many secrets concealed into it1,2. Many marine microbes play a key role in biogeochemical cycles. However, high proportions of microbes remain uncultured in vitro3 and so instead of analysing the microbes individually, cultivation-independent genome-level characterization methods notably single-cell genomics and metagenomics are frequently being applied for microbiome analysis4. Amplicon sequencing based cultivation-independent studies are enriching the microbial diversity knowledge of various hitherto less studied environmental niche, specifically within the marine resources. However, amplicon analysis is just a preliminary step in metagenomics as it focuses only on one gene for the community diversity assessment.With the view of studying the marine microbial community for determination of its composition in terms of diversity as well as function, whole metagenomics has become the preferred approach. Recently, it has been realized that the actual understanding of metagenomics data can be obtained by individual genome binning, which eventually also enhances the microbial genome database5. This requires use of various complex computational algorithms including those relying on previous data findings viz., the supervised classifiers and the unsupervised classifiers that rely on sequence specific features like the GC content, k-mer frequency and coverage estimation for binning the genomes. Most of the recently developed tools for binning include a combined approach of both the algorithms6. Binning aids in revealing the link between the potential functional genes in a given microbiome to its taxonomy.The unique properties of the Gulfs of Kathiawar Peninsula like extreme tidal variations, different sediment texture and physicochemical variations make them an ideal place for studying the microbial diversity. Varied onshore anthropogenic activities may have imparted unique features to the microflora of the Gulfs. Study of microbial diversity and functions in the mentioned Gulfs have largely been focused on cultivation based approaches and very few molecular studies have been conducted on the shore sediments. Additionally, the presence of several on-shore industries like fertilizer, chemicals, oil refineries, power plants and ASSBRY (Alang Ship Breaking Yard) may have also influenced the deeper sediment microbiome leading to their variable gene profile7. Our previous insights into the pelagic sediment resistome profile by metagenomics approach have shown that the deeper sediments, earlier thought to be primeval are actually hosting microbes with a concerning number of resistance genes7,8. This acted as a propeller to the present study wherein we tried to look deeper into the metagenomics data of the samples collected from the Gulfs of Kathiawar Peninsula and a sample from the Arabian Sea by sorting individual prokaryoplankton genomes from the data using the binning approach.We successfully reconstructed 309 Metagenome Assembled Genomes (MAGs) from the nine sediment metagenomics sequences (Table 1) from Gulf of Khambhat (GOC), Gulf of Kutch (GOK) and Arabian Sea (A) by differential coverage approach and considering the GC percent and tetranucleotide frequencies. Out of the 309 MAGs, 39 were archaeal genomes (Online-only Table 1) and 270 were bacterial genomes (Online-only Table 2). Seventy-one were high quality drafts with a completeness of ≥90% and contamination More

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