Ecology

All Alteromonas strains support long-term survival of Prochlorococcus under N starvationPrevious research showed that Prochlorococcus, and to some extent Synechococcus depend on co-occurring heterotrophic bacteria to survive various types of stress, including nitrogen starvation [33, 34, 42, 43]. At the first encounter between previously axenic Prochlorococcus and Alteromonas (E1), all co-cultures and axenic controls grew exponentially (Fig. 1B, C). However, all axenic cultures showed a rapid and mostly monotonic decrease in fluorescence starting shortly after the cultures stopped growing, reaching levels below the limit of detection after ~20–30 days. None of the axenic Prochlorococcus cultures were able to re-grow when transferred into fresh media after 60 days (Fig. 1C). In contrast, the decline of co-cultures rapidly slowed, and in some cases was interrupted by an extended “plateau” or second growth stage (Fig. 1B). Across multiple experiments, 92% of the co-cultures contained living Prochlorococcus cells for at least 140 days, meaning that they could be revived by transfer into fresh media. Thus, the ability of Alteromonas to support long-term N starvation in Prochlorococcus was conserved in all analyzed strains.Fig. 1: Experimental designs and overview of the dynamics of Prochlorococcus-Alteromonas co-cultures from first encounter and over multiple transfers.A Schematic illustration of the experimental design. One ml from Experiment E1 was transferred into 20 ml fresh media after 100 days, starting experiment E2. Experiment E2 was similarly transferred into fresh media after 140 days, starting experiment E3. Additional experiments replicating these transfers are described in Supplementary Fig. S1. B Overview of the growth curves of the 25 Prochlorococcus-Alteromonas co-cultures over three transfers spanning ~1.2 years (E1, E2 and E3). Results show mean + standard error from biological triplicates, colored by Prochlorococcus strain as in panel D. C Axenic Prochlorococcus grew exponentially in E1 but failed to grow when transferred into fresh media after 60, 100, or 140 days. Axenic Alteromonas cultures were counted after 60 and 100 days, as their growth cannot be monitored sensitively and non-invasively using fluorescence (optical density is low at these cell numbers). D High reproducibility and strain-specific dynamics of the initial contact between Prochlorococcus and Alteromonas strains (E1). Three biological replicates for each mono-culture and co-culture are shown. Note that the Y axis is linear in panels B, C and logarithmic in panel D. Au: arbitrary units.Full size imageIt has previously been shown that Prochlorococcus MIT9313 is initially inhibited by co-culture with Alteromonas HOT1A3, while Prochlorococcus MED4 is not [12, 32]. This “delayed growth” phenotype was observed here too, was specific to MIT9313 (not observed in other Prochlorococcus strains) and occurred with all Alteromonas strains tested (Fig. 1D). MIT9313 belongs to the low-light adapted clade IV (LLIV), which are relatively distant from other Prochlorococcus strains and differ from them in multiple physiological aspects including the structure of their cell wall [44], the use of different (and nitrogen-containing) compatible solutes [45], and the production of multiple peptide secondary metabolites (lanthipeptides, [46, 47]). LLIV cells also have larger genomes, and are predicted to take up a higher diversity of organic compounds such as sugars and amino acids [48]. It is intriguing that specifically this strain, which has higher predicted metabolic and regulatory flexibilities [49], is the only one initially inhibited in co-culture with Alteromonas.Differences in co-culture phenotype are related to Prochlorococcus and not Alteromonas strains and occur primarily during the decline stageWhile co-culture with all Alteromonas strains had a major effect on Prochlorococcus viability after long-term starvation, there was no significant effect of co-culture on traditional metrics of growth such as maximal growth rate, maximal fluorescence, and lag phase (with the exception of the previously described inhibition of MIT9313; Fig. 2A–C). However, a visual inspection of the growth curves suggested subtle yet consistent differences in the shape of the growth curve, and especially the decline phase, between the different Prochlorococcus strains in the co-cultures (Fig. 1D). To test this, we used the growth curves as input for a principal component analysis (PCA), revealing that the growth curves from each Prochlorococcus strain clustered together, regardless of which Alteromonas strain they were co-cultured with (Fig. 2D). The growth curves of all high-light adapted strains (MED4, MIT9312, and MIT0604) were relatively similar, the low-light I strain NATL2A was somewhat distinct, and the low-light IV strain MIT9313 was a clear outlier (Fig. 2D), consistent with this strain being the only one initially inhibited in all co-cultures. Random forest classification supported the observation that the growth curve shapes were affected more by the Prochlorococcus rather than Alteromonas strains, and also confirmed the visual observation that most of the features differentiating between Prochlorococcus strains occurred during culture decline (random forest is a supervised machine learning algorithm explained in more detail in Supplementary Text S2; see also Supplementary Fig. S2). Thus, co-culture with Alteromonas affects the decline stage of Prochlorococcus in co-culture in a way that differs between Prochlorococcus but not Alteromonas strains.Fig. 2: Growth analysis and principal component analysis (PCA) of the growth curves from all co-cultures during 140 days of E1.A Growth rate, B Maximum fluorescence, and C duration of lag phase during experiment E1. Box-plots represent mean and 75th percentile of co-cultures, circles represent measurements of individual cultures of the axenic controls. The only significant difference between axenic and co-cultures is in the length of the lag phase for MIT9313 (Bonferroni corrected ANOVA, p  More

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Chromatographic separation and mass spectrometric optimizationTo obtain the best monitoring conditions for each compound, a 0.5 mg/L mixed standard solution of 11 pesticides was mixed with the mobile phase through a syringe pump and then injected into the mass spectrometer for tuning. The precursor ion of the compound to be tested was determined by the primary mass spectrometry scan under ESI+ and ESI- modes, and then the product ion was scanned by the secondary mass spectrometry. Two groups of ion pairs with the best sensitivity were selected for detection; one group was used for quantification, and another, for qualitative analysis. The optimization results showed high sensitivity of all the 11 pesticides under the ESI+ mode. Among them, abamectin (B1a), β-cypermethrin, deltamethrin, fenpropathrin, and bifenthrin were [M + NH4]+, and other compounds were [M + H]+. MS parameters of 11 pesticides are mentioned in Table S2.Formic acid and ammonium acetate are commonly used reagents to enhance the ionization of target compounds [M + H]+ and [M + NH4]+ under the ESI+ mode, and they can effectively improve the peak pattern, making the peak sharper and more symmetrical; therefore, they need to be added during gradient elution38. To improve work efficiency, it is necessary to separate and complete the monitoring of 11 pesticides in the shortest possible time; therefore, we selected two different types of chromatographic columns (ACQUITY UPLC HSS C18 and ACQUITY UPLC HSS T3) and three different mobile phases (Ι: 0.1% formic acid aqueous solution—ACN, II: 0.05% formic acid aqueous solution—ACN, and III: 0.1% formic acid/5 mmol/L ammonium acetate aqueous solution—ACN) for optimization experiments. We observed that when using the HSS T3 chromatographic column, β-cypermethrin, deltamethrin, fenpropathrin, and bifenthrin did not show a good retention effect under the three mobile phase systems, and there was substantial tailing of the chromatographic peak. The shape of the chromatographic peak and sensitivity of the target compound were used as evaluation indicators. Compared with Ι and II, mobile phase III produced better sensitivity for all target compounds (Fig. 1), with sharper and more symmetrical peaks of β-cypermethrin, deltamethrin, fenpropathrin, and bifenthrin. This may be because the addition of 5 mmol/L ammonium acetate improved the retention performance of the HSS C18 chromatography columns without affecting the ionization efficiency of all target compounds. In summary, we selected the HSS C18 column for chromatographic separation and used 0.1% formic acid/5 mmol/L ammonium acetate aqueous solution—ACN as the mobile phase to further optimize the gradient elution procedure and effectively separate and detect all the target compounds within 8 min.Figure 1When using HSS C18, the peak areas of 11 pesticides in three different mobile phases.Full size imageOptimization of purification materialsThe flesh of apricot contains sugar, protein, calcium, phosphorus, carotene, thiamine, riboflavin, niacin, and vitamin C. Due to these diverse impurities, the analysis of the sample matrix becomes highly complex. Therefore, these impurities need to be removed from the matrix samples before analysis. Currently, PSA, C18, and MWCNTs are widely used to adsorb to the fruit substrate39. PSA has a strong adsorption capacity for metal ions, fatty acids, sugars, and fat-soluble pigments, C18 has a strong adsorption capacity for non-polar impurities (such as fat, sterol, and volatile oil), while MWCNTs have a strong adsorption capacity for pigments, which can effectively remove chlorophyll, lutein, and carotene. However, C18 and MWCNTs can also simultaneously adsorb pesticides, resulting in poor recovery. Nano-ZrO2 has a large specific surface area and good adsorption stability and has recently been used to purify substrates. It can selectively remove fats and pigments from samples compared to conventional C18 fillers.In the current study, different purification materials were combined for the analysis of 11 pesticide residues and to propose the best purification strategy in the pretreatment of apricot samples. As displayed in Fig. 2, the average recovery of 11 pesticides in the apricot was higher using the C18/nano-ZrO2/MWCNTs than other combinations. Nano-ZrO2 showed better adsorption than PSA in purifying fatty acids, organic acids, polar pigments, and sugars in apricot, owing to its larger specific surface area, better adsorption capacity, and stability. To conclude, the combination of 10 mg C18, 30 mg nano-ZrO2, and 5 mg MWCNTs demonstrated the best recovery for 11 pesticides, with recovery in the range of 72% to 114%, at a pesticide spiking level of 0.01 mg/kg. In summary, we finally determined that among the tested combinations, C18/nano-ZrO2/MWCNTs (10 mg/ 30 mg/5 mg) is the best purification combination for the pre-treatment of apricot samples.Figure 2The recoveries of 11 pesticides in apricot matrix under different scavenger combinations (2–1 C18/nano-ZrO2/MWCNTs, 2–2 PSA/C18/MWCNTs, 2–3 nano-ZrO2/PSA/MWCNTs; 0.01 mg/kg, n = 3).Full size imageLinearity, matrix effects, limit of detection and limit of quantificationThe standard curve obtained from the standard working solutions of 11 pesticides and the calibration curve from blank apricot matrix spiked with 11 pesticides showed good linearity (0.001, 0.005, 0.01, 0.05, 0.1, and 0.5 mg/L), with R2 ≥ 0.9959 for all tested samples (Table 1).Table 1 The standard curves, R2 and MEs of 11 pesticides in apricot.Full size tableTo evaluate MEs, the slopes of matching 11 pesticide standards with solvent and apricot matrix were calculated at the same concentration. According to the derived slope of the matrix-matched calibration curve, MEs of 11 pesticides in apricot were between 89 and 113% (Table 1), well within the range of 80% to 120%, indicating that the MEs could be ignored. It also suggests that the current pre-treatment method has a good purification effect and eliminates the matrix effect very well, laying a robust foundation for the subsequent step of quantitative analysis of samples. We next used the standard solution curve to quantify the 11 pesticide residues in apricot.The LOD refers to the minimum concentration or minimum amount of a component to be tested that can be detected from a test sample under a given confidence level by an analytical method. Its physical meaning is the amount of the measured component when the signal is 3 times the standard deviation (S = 3σ) of the reagent blank signal (background signal). Sometimes it also refers to the amount of the measured component corresponding to when the signal is three times the background signal generated by the reagent blank (S = 3 N). The LOQ refers to the minimum amount of the analyte in the sample that can be quantitatively determined, and the determination result should have a certain accuracy40. The LOQ reflects whether the analytical method has the sensitive quantitative detection ability. The LOQ is the lowest validated level with sufficient recovery and precision, which was estimated to be 0.001 mg/L, while the LOD is the lowest calibration level, which was 2 µg/kg, according to SANTE/12,682/2020.Accuracy and precisionIn the matrix, 11 pesticides were spiked at four levels (0.002, 0.02, 0.1, and 1 mg/kg), and for each spiked sample, there were six replicates. The recoveries of 11 pesticides in apricot at all levels ranged between 72 and 119%. The inter- and intra-level relative standard deviations (RSDs, %) of 11 pesticides in apricot were  More

Each of the 23 key variables can be used for analysis. To validate the dataset, we used five plant-related variables (diversity of order, family, genus, species and species endemic to China) to demonstrate the process of using the dataset for analysis as follows:(1) For the four variables of plant taxa “order”, “family”, “genus” and “species”, the similarity and difference in spatial distribution pattern of diversity of different taxa in the Qinling-Daba Mountains climate transition zone were analyzed. The spatial distribution pattern of the diversity of the four taxa is shown in Fig. 3, which is increasingly lower from south (low latitude) to north (high latitude). This result is consistent with the classical latitudinal gradient model of plant diversity. The boundary between higher diversity in the south and lower diversity in the north is roughly located in the area of Funiu Mountains in the eastern Qinling-Daba Mountains, Taibai Mountains in the central Qinling-Daba Mountains and Baishui River in the western Qinling-Daba Mountains. However, with the reduction in taxon scale, the spatial distribution pattern of diversity tends to be complex. Orders (Fig. 3a) and families (Fig. 3b) can be divided by lines, while genera (Fig. 3c) need thicker lines, and species (Fig. 3d) can only be divided by polygons. Figure 3 shows that the taxonomic groups of families are more clearly divided, while species can only be divided by staggered bands. Therefore, when dividing the north–south boundary, the family taxon scale is appropriate, whereas the species scale is more appropriate when studying the north–south transition zone.Fig. 3Spatial distribution of diversity of orders, families, genera and species. (a) The blue dotted line is basically the dividing line of the order diversity of 50 species. The order diversity to the north of the blue dotted line is lower than 50 species, and the order diversity to the south of the blue dotted line is higher than 50 species. (b) The blue dotted line is basically the dividing line of the family diversity of 150 species. The family diversity to the north of the blue dotted line is lower than 150 species, and the family diversity to the south of the blue dotted line is higher than 150 species. (c) The thicker blue dotted line is basically the dividing line of genus diversity of 578–681 species. The genus diversity to the north of the blue dotted line is lower than 578 species, and the genus diversity to the south of the blue dotted line is higher than 681 species. (d) The blue area is basically the dividing line of species diversity of 1385–1618 species. The species diversity to the north of the blue dotted line is lower than 1385 species, and the species diversity to the south of the blue dotted line is higher than 1618 species.Full size imageThe dataset can also count the orders, families and genera that appear in 58 nature reserves, indicating that these orders, families and genera are widely distributed in this area, while the orders, families and genera that only appear in a single nature reserve indicate that these taxa are unique to this nature reserve in this area, reflecting their locality and uniqueness, which is helpful to understanding the specific distribution of plants in detail. The relevant statistics are as follows:
There are 28 orders present in every nature reserve:
Liliales, Dipsacales, Lamiales, Fabales, Ericales, Poales, Saxifragales, Malpighiales, Malvales, Asterales, Fagales, Gentianales, Geraniales, Ranunculales, Rosales, Solanales, Apiales, Cornales, Brassicales, Caryophyllales, Dioscoreales, Santalales, Myrtales, Asparagales, Celastrales, Sapindales, Alismatales, and Boraginales.The order that only appears in one nature reserve is Petrosaviales, which appears in the Dabashan Nature Reserve in Chongqing.
There are 51 families present in every nature reserve:
Liliaceae, Primulaceae, Plantaginaceae, Lamiaceae, Euphorbiaceae, Cannabaceae, Juncaceae, Fabaceae, Poaceae, Elaeagnaceae, Betulaceae, Apocynaceae, Violaceae, Malvaceae, Crassulaceae, Campanulaceae, Asteraceae, Orchidaceae, Polygonaceae, Orobanchaceae, Onagraceae, Gentianaceae, Geraniaceae, Ranunculaceae, Rubiaceae, Rosaceae, Caprifoliaceae, Thymelaeaceae, Apiaceae, Cyperaceae, Cornaceae, Paeoniaceae, Brassicaceae, Amaryllidaceae, Caryophyllaceae, Rhamnaceae, Santalaceae, Asparagaceae, Celastraceae, Sapindaceae, Adoxaceae, Araliaceae, Berberidaceae, Hydrangeaceae, Scrophulariaceae, Convolvulaceae, Urticaceae, Salicaceae, Papaveraceae, Iridaceae, and Boraginaceae.There are 15 families that only appear in one nature reserve, as shown in Table 2.Table 2 Endemic families of the nature reserves in the Qinling-Daba Mountains and surrounding areas.Full size table
There are 54 genera present in every nature reserve:
Patrinia, Polygonum, Sanicula, Plantago, Allium, Delphinium, Euphorbia, Juncus, Cynanchum, Trigonotis, Artemisia, Sorbus, Polygonatum, Scutellaria, Cirsium, Viburnum, Ajuga, Viola, Galium, Geranium, Salix, Epilobium, Gentiana, Ranunculus, Malus, Acer, Rubia, Rosa, Torilis, Lonicera, Adenophora, Philadelphus, Cornus, Paeonia, Rhamnus, Rumex, Carex, Thalictrum, Asparagus, Carpesium, Clematis, Potentilla, Euonymus, Eleutherococcus, Berberis, Spiraea, Rubus, Populus, Vicia, Silene, Iris, Poa, Aster, and Buddleja.There were 225 genera that only appeared in one nature reserve, as shown in Figshare file 269.(2) For the “species endemic to China” variable of plants, we can see from the diversity distribution pattern of species endemic to China in this region (Fig. 4) that the number of endemic species in the Qinling-Daba Mountains is higher than that of species outside of the region, which reflects the strong transition zone in the Qinling-Daba Mountains. The variables of species endemic to China obtained from the Qinling-Daba Mountains and their surroundings were clustered by the Bray–Curtis dissimilarity measure70 and Ward’s minimum variance (the clustering method recommended for plant cluster analysis). The clustering results are shown in Fig. 5a. At the same time, the clustering results are displayed in space. Figure 5b shows that category 3 extends from the east outside the Qinling-Daba Mountains to the Baishuijiang Nature Reserve inside the western Qinling-Daba Mountains, which is consistent with the fact that the Qinling-Daba Mountains are an important ecogeographical “corridor” connecting the east and the west.Fig. 4Spatial distribution of diversity of species endemic to China in the Qinling-Daba Mountains and adjacent areas.Full size imageFig. 5(a) Clustering results of Ward’s connection aggregation of species endemic to China in 58 nature reserves. (b) Spatial distribution of clustering results of species endemic to China; the larger the dot and the darker the color, the earlier it is merged into this category, and the smaller the dot and the lighter the color, the later it is merged into this category.Full size image More

SamplesThe intestine and muscle samples from 22 wild boars were collected between September 4 and March 2, 2020, in Namie town in Fukushima prefecture. Furthermore, control intestine samples were collected from three wild boars in Hyogo prefecture. Each location is depicted in Fig. 1. In each case, after the licensed hunters slaughtered the wild boar to be exterminated, only the tissue was transferred to the study.Measurement of radioactivityRadioactivity in the muscle samples was determined by gamma-ray spectrometry using high-purity germanium (HPGe) detectors (Ortec Co., Oak Ridge, TN, USA), as described in our previous report3. Gamma rays from 137Cs were observed.Exposure dose estimationIn order to estimate internal and external dose rates of the wild boars according to the ICRP publication 10826, we supposed the shapes of wild boars as prolate spheroids whose long axis was to be their body lengths. The short axis was given from their weight assuming their specific gravities were the same
as water. The dose rates were calculated from the contribution of 137Cs, not including
natural radionuclides. The energy deposition to the spheroids by beta and gamma rays from radionuclides were calculated by the numerical simulation with the use of the Particle and Heavy Ion Transport code System (PHITS)27. For the sake of simplicity, we supposed the spheroids consisted of only muscle, which would give overestimated values because muscle contains more radio cesium than other organs. The external exposure dose was calculated from the air dose rates which were observed from the monitoring post near the boars captured place. The average values of the air dose rates were obtained from fitting observed data of two years with decay curve. The background due to the natural radionuclides was estimated to be 0.05 µGy/h which was observed before the Fukushima Daiichi accident, and was removed before the fittings. The half-lives of the air dose rates were 2000–3000 days depending on the environment. Assuming the external exposure dose was ascribed to the 137Cs included in the surface of the ground. The amount of the 137Cs was calculated so as to reproduce the observed air does rates. Since the maximum range of the beta ray from 137Cs is a few millimeters, almost all of the beta ray from inside the body should be absorbed in the boar’s body, but the beta ray from outside the body would stop in its fur. The beta rays contribute 100% to internal exposure dose but 0% to external one. Since the linear attenuation coefficient for gamma rays from 137Cs is 0.084 cm−1 = (12 cm)−1, some of the gamma rays cannot stop in the body depending on the size of the body. The numerical simulation suggested that 65–90 percent of the gamma rays from 137Cs inside the body would go out, and 40–65 percent of the gamma rays from 137Cs outside would go through the body.Pathological analysisA piece of the small intestine was fixed in 10% neutral formalin at 4 °C for 24–48 h. Then, paraffin blocks were prepared for pathomorphological examination using hematoxylin and eosin (HE) staining.Gene expression analysisTotal RNA was extracted from the whole tissue of the intestine using TRIzol Reagent (Life Technologies, Inc., Frederic, MD, USA) according to the manufacturer’s instructions. RNA concentration was measured using a NanoDrop spectrophotometer (Thermo Scientific, Wilmington, DE, USA), and cDNA was synthesized using random primers and SuperScript II (Life Technologies, Inc.). Real-time PCR for IFN-γ, TLR3, and CyclinG1 was performed using Brilliant SYBR Green QPCR Master Mix III (Stratagene, La Jolla, CA, USA) with an AriaMx system (Agilent Technologies, Santa Clara, CA, USA). Primer sequences were designed using Primer-BLAST with sequences obtained from GenBank as described in the previous report4. Amplification conditions were 95 °C for 3 min, 40 cycles at 95 °C for 5 s, and 60 °C for 20 s. Fluorescence signals measured during the amplification were analyzed. Ribosomal RNA primers were used as an internal control, and all data were normalized to constitutive rRNA values. Quantitative differences between the groups were analyzed using the AriaMx software (Agilent Technologies).Statistical analysisAll data are presented as mean ± standard error (SE) for each treatment group. Differences in mRNA expression among the groups were determined using the unpaired t-test with Welch’s correction. (Prism: GraphPad Software Inc., La Jolla, CA, USA). Differences were considered to be statistically significant at a P value of  More

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