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Climatic conditions and phenologyThe growth and development of T. durum plants was moderately differentiated by weather conditions in the analyzed years (Table 1).Table 1 The duration of growing seasons (Days), sum of temperatures (Temp.) and sum of precipitation (Prec.) during the growth and development of T. durum Desf. in the analyzed years.Full size tableThe growing seasons of 2015, 2016 and 2017 lasted 136, 132 and 145 days, respectively; the sum of temperatures was determined at 2011.3, 1895.6 and 2069.9 °C, respectively, and the sum of precipitation was determined at 366.7, 360.1 and 350.5 mm, respectively. However, a comparison of cumulative temperatures and precipitation in the phenological phases of T. durum in each year of the study indicates that temperature and precipitation could have influenced the duration of the examined phases and plant growth indicators (Fig. 1). Weather conditions were generally favorable for the growth and development of T. durum in 2015 and 2016. Cumulative temperatures and precipitation were quite similar in 2015 and 2016 up to the booting stage, but precipitation levels in successive stages were higher in 2016 than in 2015. The growing season was shortest in 2016 and longest in 2017, mainly due to low temperatures during sowing and seed germination, and high precipitation during tillering, grain formation and ripening.Figure 1Cumulative temperatures and precipitation in the phenological phases of T. durum in 2015–2017.Full size imageBiophysical parameters: LAI and SPADThe LAI denotes the area of photosynthetic tissue per unit ground surface area (m2 m−2). The LAI is directly associated with plant canopy, and it is an indicator of net primary production, water and nutrient use, and the carbon balance. SPAD is a measure of leaf greenness that is directly associated with chlorophyll content and nitrogen sufficiency.The main effects of LAI and SPAD were analyzed separately in the framework of the Zadoks scale to reveal the significant effects of years, nitrogen rates and sowing density, and an absence of significant effects associated with the application of the growth regulator (see Tables 1.1–1.6 in the Supplementary Information). In the analyzed years, LAI and SPAD were similar in the 2nd node detectable stage (Z32), but they differed in the stem elongation stage (Z45) and the ear emergence and heading stage (Z59), when LAI values were higher and leaf greenness values were lower in 2016 and 2017 than in 2015. These findings can be attributed to moderate temperatures and precipitation in 2015, and high precipitation in the critical growth stages in the remaining years. The general trend associated with the nitrogen rate was similar across the examined growth stages, i.e. a significant increase in LAI and SPAD values with nearly identical effects were noted in treatments with nitrogen rates of 80 and 120 kg ha−1. A similar trend was observed in sowing density. In treatments with a sowing density of 450 and 550 germinating seeds per m2, LAI values continued to increase, whereas SPAD values were below those noted in the treatment with a sowing density of 350 germinating seeds per m2. The only significant interaction was observed between years and nitrogen rates.The average values of LAI continued to increase in successive growth stages and were determined at 1.30 at Z32, 1.75 at Z45, and 1.99 at Z59. In turn, leaf greenness was significantly lower in the stem elongation stage (Z45) than in the preceding (Z32) and subsequent (Z59) stages.The significant effect of the years × growth stages interaction for LAI and SPAD values resulted from similar means in stage Z32 in all years, as well as higher LAI values and lower SPAD values in subsequent growth stages in 2016 and 2017 than in 2015. In 2015, the increase in the nitrogen rate induced only a rising trend in LAI and SPAD values, whereas significant differences were observed in 2016 and 2017. To summarize, it should be noted that in successive Zadoks growth stages, the interactions between years, nitrogen rates and sowing density exerted significant effects on LAI and SPAD values, whereas the effects of year × nitrogen rate interactions were significant only in selected growth stages.Contribution of different sources of variation to physiological and biophysical parameters of plant growthThe calculated eta-squares η2 provide information about the contribution of different sources of variation to physiological variables (Table 2). The experimental years and agronomic factors (33.1% and 38.6%), growth stages, and interactions with other factors (32.5% and 39.3%) and random factors (34.4% and 22.1%) made similar contributions to the variation in the LAI and chlorophyll content. The variation in the net photosynthetic rate was related mostly to variations across years (32.6%) and the interactions between growth stages and other factors (24.3%). The variation in the transpiration rate was attributed mostly to the main effects of growth stages (45.8%) and the year × growth stage interaction (16.1%). Instantaneous WUE was strongly determined by variation in agronomic factors and growth stages (22.3% and 21.1%, respectively).Table 2 Eta-square (η2) values for the sources of variation in the leaf area index (LAI), chlorophyll content (SPAD), net photosynthetic ratio (Pn), transpiration rate (E) and instantaneous water use efficiency (WUE).Full size tableIt is worth noting that the variation in agronomic factors made a considerable contribution to the total variation in LAI (22.3%) and SPAD (11.8%), but only a marginal contribution to the net photosynthetic rate (0.4%) and transpiration (2.0%).Photosynthetic indicators— net photosynthetic rate, transpiration rate, and instantaneous water use efficiencyThe effects of the net photosynthetic rate (Pn), transpiration rate (E) and instantaneous WUE were highly differentiated in successive growth stages, and relatively small differences were noted for agronomic factors (see Tables 2.1–2.9 in the Supplementary Information). At the same time, the analyzed photosynthetic indicators differed in successive stages of growth. The net photosynthetic rate was similar in the 2nd node detectable stage (Z32) and the stem elongation stage (Z45) at 29.7 μmol CO2 m–2 s–1, and it was 15% higher at the end of the heading stage (Z59) than in the preceding stages. The transpiration rate continued to increase by 60% on average in successive stages of growth and development, from 1.59 H2O m–2 s–1 in stage Z32, to 2.52 mmol H2O m–2 s–1 in stage Z45, and 4.06 mmol H2O m–2 s–1 in stage Z59.An analysis of the results noted in different growth stages across years revealed significant year × growth stage and growth regulator × growth stage interactions (Fig. 2).Figure 2Mean values and standard error of photosynthesis indicators for year × growth stage (upper) and growth regulator × growth stage interactions (GR 0—without growth regulator, GR 1—with growth regulator).Full size imageThe year × growth stage interaction resulted from differences in the rates of photosynthesis and transpiration in the analyzed growth stages across years. In 2015, the net photosynthetic rate was similar in the first two growth stages, and it increased by around 30% at the end of the heading stage (Z59). In 2016, the photosynthetic rate continued to increase in successive growth stages. In 2017, the net photosynthetic rate was around 10% higher in the 2nd node detectable stage (Z32) than in the stem elongation stage (Z45) and at the end of the heading stage (Z59). The transpiration rate increased significantly in successive stages of plant growth and development, and the only exception was noted in 2015, when the analyzed parameter was similar in stages Z32 and Z45. The WUE index was highest in stage Z32, and a significant interaction was noted due to the correlation between the net photosynthetic rate and the transpiration rate in the remaining stages. Water use efficiency was similar in stages Z32 and Z45 in 2015, and in stages Z45 and Z59 in 2016, whereas significantly lower values in successive stages of plant growth were noted in 2017.The growth regulator was the only agronomic factor that induced significant differences in the net photosynthetic rate across the examined growth stages. Photosynthesis indicators were similar regardless of the application of the growth regulator, and significant interactions resulted mainly from varied disproportions between the end of heading and the stem elongation stage in treatments with and without the application of the growth regulator.It should be noted that the interactions between growth stages and nitrogen rates and sowing density were not significant, which implies that the effects of the interactions between increasing nitrogen rates and sowing density on photosynthetic indicators in successive growth stages were similar to the average values of photosynthetic indicators in the corresponding growth stages (Supplementary Information).Agronomic traitsThe means for yield components and yield are presented in Tables 3.1–3.8 of the Supplementary Information. Stem length was differentiated by the nitrogen rate and nitrogen rate × year interaction. Nitrogen rates of 80 and 120 kg N per ha increased stem length by 11% and 13%, respectively, relative to the unfertilized control. The significant year × nitrogen rate interaction resulted from the fact that the nitrogen-induced increase in stem length was smaller in 2015 (0.07 cm per 1 kg of nitrogen) than in 2016 and 2017 (0.09 cm per 1 kg of nitrogen). In 2015, ear length was similar to that noted in the remaining years, and only in 2017, ear length was 7% higher than in 2016. Ear length and the number of kernels per ear increased with a rise in nitrogen rate and decreased with a rise in sowing density.Grain weight per ear and 1000 kernel weight were highest in 2015 and significantly lower in the following years. Grain weight per ear increased only in response to the nitrogen rate of 120 kg ha−1, but 1000 kernel weight was not affected. Both traits decreased with a rise in sowing density. The significant year × nitrogen rate and year × sowing density interactions for both traits can be largely attributed to the magnitude of differences between years, rather than an increase or a decrease in this trend.The biological yield (grain and straw) differed across years and nitrogen rates. In 2016, the biological yield was similar to that noted in 2015 and significantly higher (by 30%) than that noted in 2017. The biological yield increased by 28% and 35% in response to nitrogen rates of 80 and 120 kg ha−1, respectively, relative to the unfertilized control. The significant year × nitrogen rate interaction was associated with variations in nitrogen use efficiency, and the difference between maximal biological yield was determined at 0.5 t ha−1 in 2015, 2.3 t ha−1 in 2016, and 2.8 t ha−1 in 2017.Grain yield was similar in 2015 (4.94 t ha−1) and 2016 (5.38 t ha−1), and it was significantly lowest in 2017 (3.87 t ha−1). Straw yield was highest in 2016 (2.86 t ha−1), and it exceeded the values noted in the remaining years by 16%. The harvest index was similar in 2015 and 2016, and it was 9% lower in 2017. Grain yield increased by 30% and 36%, whereas straw yield increased by 20% and 35% in response to the nitrogen rates of 80 and 120 kg ha−1, respectively. A minor increase in grain yield (3%) was observed in treatments with a sowing density of 550 seeds m−2 relative to the remaining sowing densities.Path modellingA simple correlation analysis of manifest variables in all phenological stages revealed significant correlations between the LAI and leaf greenness (SPAD) only in stage Z32, as well as a very strong correlation between the net photosynthetic rate and the transpiration rate, which was positive in stages Z32 and Z45 and negative in stage Z59. No simple correlations were noted between the indicators of physiological processes (Pn, E, WUE) and biophysical parameters (LAI, SPAD). AAll correlations between the manifest variables of yield components and biological yield were statistically significant, excluding the correlation between stem length and ear length (Supplementary information).All correlations between the manifest variables of yield components and biological yield were statistically significant, excluding the correlation between stem length and ear length (Supplementary Information). The outer and inner PLS-PM models well fit the data, and their goodness of fit was determined at 0.973 and 0.786, respectively. The outer weights provide information about the relative importance of a manifest variable for the corresponding latent variable (for details please see the Supplementary Information). Outer weights that exceed 0.3 are considered meaningful. By the same token, loading estimates represent the correlations between a latent variable and the corresponding manifest variables. Loadings higher than 0.7 capture more than 50% of the variability contributed by a latent variable to the corresponding manifest variable. In general, both indicators in the outer model, i.e. outer weights and loadings, exceeded the thresholds, which indicates that manifest variables were strongly related with latent variables. Growth regulators (({w}_{GR}) = − 0.007) and the length of the growing season (({w}_{DAYS})=0.197) provided the only evidence for the low explanatory value of latent variable A (agronomic factors).In the inner model, all equations that regressed latent variables well fit the data and were statistically significant (Table 3). The latent variables expressed by the value of R2 increased in successive stages of T. durum growth and development, from 0.218 in physiological processes in stage Z32 (Table 3, Eq. 1) to 0.698 and 0.708 in yield components and Biological Yield, respectively (Table 3, Eqs. 7 and 8). It is worth noting that in successive stages of growth, the value of physiological processes was relatively lower in comparison with biophysical parameters.Table 3 Parameters of regression models for latent variables.Full size tableThe analysis of path coefficients (βi) revealed that agronomic factors (A) and climate conditions (CC) in stages Z32, Z45 and Z59 exerted a specific influence on physiological processes (PP) and biophysical parameters (BP) of T. durum plants. Agronomic factors directly determined physiological processes in all stages and biophysical parameters in stages Z32 and Z59. At the same time, climate conditions did not exert a direct influence on physiological processes in any stage, but directly affected biophysical parameters in all stages. All of the modeled parameters, i.e. agronomic factors, climate conditions and physiological processes, significantly influenced biophysical parameters in stages Z32 and Z59, but not Z45. Consequently, it can be stated that agronomic factors were the main determinant of variability in physiological processes (photosynthesis, transpiration) in a model evaluating the impact of agricultural practices on yield and the manifest variables associated with T. durum growth and development. At the same time, physiological processes made a significant but negative contribution to biophysical parameters. A one unit increase in photosynthesis processes with constant values of agronomic factors and climate conditions implies a decrease of − 0.382, − 0.065 and − 0.395 in biophysical parameters in stages Z32, Z45 and Z59, respectively.The performance of every preceding latent variable in terms of its total impact on the target latent variable, i.e. the biological yield of T. durum (IPMA – Importance-Performance Map Analysis), was analyzed to highlight latent variables associated with agricultural practices that improve biological yield. The total effect (importance) of preceding latent variables (A, CC32, PP32, BP32, CC45, PP45, BP45, CC59, PP59, BP59, YC and CC) on the anticipated performance of the specific target (Biological Yield) is presented in Fig. 3.Figure 3Importance-Performance Map Analysis presenting the impact of latent variables on biological yield (A—agronomic factors, YC—yield components, CC32, CC45, CC59—climate conditions in growth stages, PP32, PP45, PP59—physiological processes, BP32, BP45, BP59—biophysical parameters in the phenological stages of plant growth and development Z32, Z45 and Z59, CC—climate conditions for the entire growing season).Full size imageThe importance and performance of latent variables that influenced the biological yield of T. durum varied. The biological yield of T. durum was affected mostly by agronomic factors (A), followed by yield components (YC) and biophysical parameters (BP) in growth stages Z59 (BP59) and Z32 (BP32), climate conditions in stage Z59 (CC59), and climate conditions in stage Z32 (CC32). A one unit increase in the above latent variables led to an increase of 0.575, 0.422, 0.234, 0.203 and 0.109 units in biological yield, respectively. At the same time, the performance scores of these latent variables were determined at 53.1, 53.5, 67.1, 59.6 and 61.8, respectively (scores closer to 100 denote higher performance). The remaining latent variables, in particular climate conditions for the entire growing season and physiological processes in stage Z32, were characterized by low importance and exerted a relatively small effect on biological yield performance.The results of the importance-performance analysis clearly indicate that latent variables have considerable potential to optimize the agricultural conditions for the growth and development of T. durum plants. More

Direct nucleotide sequencingReadable electropherograms were obtained from both direction in COI fragments of all three individuals of the Japanese spiny lobster. COI sequences determined by direct nucleotide sequencing ranged from 807 to 864 bp and have been deposited in International Nucleotide Sequence Database Collection (INSDC) under accession numbers of LC571524‒LC571526. No stop codon was observed in these sequences (designated by PJK1-direct, PJK2-direct, and PJK3-direct). No indel was observed between these sequences. All nucleotide substitutions at 19 variable sites observed between these sequences were transition at the 3rd position of a codon, and all substitutions were synonymous. The mean Kimura two parameter (K2P) distance between these three haplotypes was 1.510 ± 0.352% SE and that between these sequences and a reference sequence of P. japonicus (NC_004251) was 1.087 ± 0.270%, which were all well within the range reported for Japanese spiny lobster samples collected in Japan and Taiwan9,10.Electropherograms obtained by forward primer for 12S fragments were not readable, while those by reverse primer were readable in all individuals. 12S sequences determined by direct nucleotide sequencing using reverse primer alone ranged from 551 to 570 bp and have been deposited in INSDC under accession numbers of LC605705‒LC605707. Of nine variable sites, eight were transition and one was indel. The mean K2P distance between these three haplotypes (designated by PJK1-12Sdirect, PJK2-12Sdirect, and PJK3-12Sdirect) was 0.970 ± 0.338%, and that between these sequences and a reference sequence of P. japonicus was 0.835 ± 0.282%.Electropherograms obtained by both primers for Dloop fragments were readable only in one individual (PJK2). This Dloop sequence determined by direct nucleotide sequencing was 762 bp and deposited in INSDC under accession number of LC605749. K2P distance between this haplotype (designated by PJK2-Dloopdirect) and a reference sequence of P. japonicus was 3.666%. No indel was observed between the two sequences, and 25 of 27 variable sites were transition.Phylogenetic analysis of clones, heteroplasmy and NUMTsAmong the 36–42 positive COI clones examined per individual, sequences (809–892 bp) of 22–31 clones per individual (75 clones in total) were successfully determined. After alignment, both ends of all sequences were trimmed to fit the shortest sequence obtained by direct nucleotide sequencing, yielding 774–810 bp sequences. Eleven clones of PJK1 were identical to PJK1-direct, as well as seven of PJK2 to PJK2-direct and three of PJK3 to PJK3-direct. These dominant haplotypes (807 bp) were determined to be genuine COI haplotypes of each individual, and representative sequences of these three genuine haplotypes were deposited in INSDC (LC 571527, LC571533 and LC571538). Nucleotide sequences of the remaining 54 clones were all different one another, in which 20 haplotypes were observed in PJK1, 14 in PJK2, and 20 in PJK3 (LC571541–LC571577, OK429332–OK429343, LC654683-LC654687).Phylogenetic tree constructed using three genuine COI haplotypes, 57 unique haplotypes and eight sequences of reference lobster species is shown in Fig. 1. Haplotypes detected from P. japonicus were segregated into four groups (designated by A, B, C and D). Among the outgroup species used, Australian rock lobster (P. cygnus) that morphologically and genetically belongs to the P. japonicus group11,12, appeared to be the closest kin to all haplotypes detected from P. japonicus. All haplotypes in group A were of the same length (807 bp), and no indel was observed. Three distinct clades (designated by c-I to c-III) were observed in group A, in which 14 haplotypes from PJK1, 11 from PJK2 and 11 from PJK3 were cohesively clustered together with their corresponding genuine haplotypes (bold italic). PJK1-C25 was outlier, having 10 nucleotide differences from the genuine COI sequence. The numbers of variable nucleotide sites between haplotypes within c-I, c-II and c-III were 20, 15 and 26, respectively, of which nonsynonymous nucleotide substitutions were observed at 11, 13 and 10 sites. Stop codon was observed only in one haplotype (PJK3-C1). The mean K2P distance between different haplotypes within these clades ranged from 0.320 ± 0.075 to 0.561 ± 0.103%. The mean K2P distances between three clades ranged from 1.343 ± 0.339 to 2.178 ± 0.464%. Although group A must be composed of sequences containing those caused by Taq polymerase error or true heteroplasmic sequences as well as genuine haplotypes, it is difficult to determine the former two categories. All of the non-genuine haplotypes in group A had singleton difference one another, supporting the occurrence of Taq polymerase error. We determined haplotypes (marked with dagger in Fig. 1) differed by less than two substitutions from the genuine haplotype to be due to Taq polymerase error. This criterion may be reasonable, since Taq polymerase-mediated errors were estimated to occur approximately at a frequency of 7.2 × 10−5 per bp per cycle13 to one mutation per 10,000 nucleotides per cycle14. When Taq polymerase error is taken into account, these K2P distances within and between clades and number of haplotypes are likely to be somewhat overestimated. PJK1-C25, two (PJK1-C5 and PJK1-C60) in c-I clade, one (PJK2-C26) in c-II, and five (PJK3-C1, PJK3-C5, PJK3-C26, PJK3-C31, PJK3-C34) in c-III differed by 3 to 10 nucleotides from their genuine haplotypes, which were determined to be heteroplasmic haplotypes.Figure 1Neighbor-joining phylogenetic (NJ) tree showing relationships among 57 different haplotypes of cytochrome oxidase subunit I (COI) or COI-like sequences obtained from the Japanese spiny lobster (Panulirus japonicus), and COI sequences of eight congeneric species derived from the GenBank database. Haplotypes detected from the same individual of the Japanese spiny lobster share the same color. Genuine mtDNA haplotype is shown in bold italic and number of clones examined is shown in parenthesis. Stop codons were observed in haplotypes carrying asterisk. Haplotypes carrying dagger differ from the corresponding genuine mtDNA haplotype by less than two nucleotides (including indel). The bootstrap values greater than 60% (out of 1000 replicates) are shown at the nodes.Full size imageSequence size of haplotypes in groups B to D ranged from 774 to 810 bp. K2P distance between haplotypes of groups A and B ranged from 7.169 to 8.177% with a mean of 7.754 ± 0.973%, that between A and C ranged from 12.073 to 17.392% with a mean of 14.521 ± 1.151%, and that between A and D ranged from 17.472 to 23.880% with a mean of 21.042 ± 1.600%. Multiple stop codons were observed in a haplotype of group B, in five of eight haplotypes of group C, and all haplotypes of group D. Three haplotypes in group C had no stop codon but differed in four to 10 deduced amino acids from the genuine haplotypes. BLAST homology search revealed no identical sequence for haplotypes in groups B to D but indicated that the closest species were P. japonicus or P. cygnus with moderate similarity (83–89% homology). Therefore, all haplotypes of groups B to D (LC571565–LC571570, LC571572–LC571577, LC654683-LC654687) were determined to be NUMTs.Among the 30–35 positive 12S clones examined per individual, sequences (772–806 bp) of 25–27 clones per individual (77 clones in total) were successfully determined. After alignment, primer sequences were trimmed, yielding 731–765 bp sequences. Thirteen clones of PJK1 were identical one another, as well as 12 of PJK2 and three of PJK3, and these were identical to PJK1-12Sdirect, PJK2-12Sdirect and PJK3-12Sdirect, respectively. These dominant haplotypes ranging from 761 to 762 bp in size were determined to be genuine 12S haplotypes of the individual, and representative sequences of these three genuine haplotypes were deposited in INSDC (LC605708‒LC605710). Nucleotide sequences of the remaining 49 clones were all different one another, in which 12 haplotypes were observed in PJK1, 23 in PJK2, and 14 in PJK3 (LC605711‒LC605748, OK429126–OK429131, LC654678-LC654682).Since incorporation of all eight Panulirus species sequences made sequence alignment ambiguous because of multiple indels, reference sequences of P. japonicus and of closely related P. cygnus were used for constructing phylogenetic tree (Fig. 2). Haplotypes detected from P. japonicus were segregated into three groups (designated by A to C). Sequence size of haplotypes in group A ranged from 760 to 762 bp. Three distinct clades (s-I to s-III) were observed in group A, in which 12 haplotypes each from PJK1, PJK2 and PJK3 were cohesively clustered together with their corresponding genuine haplotypes (bold italic). The numbers of variable nucleotide sites between haplotypes within s-I, s-II and s-III were 24, 17 and 16, respectively. Of these variable sites, transversion was observed at five, one and three sites, and indel was observed at one, zero and one sites, respectively. The mean K2P distances between different haplotypes within these clades ranged from 0.345 ± 0.081 to 0.519 ± 0.101%. The mean K2P distances between three clades ranged from 0.936 ± 0.275 to 1.371 ± 0.359%. Haplotypes differed by less than two substitutions (including indel) from the genuine haplotypes are marked with dagger. Five haplotypes in s-I clade and two haplotypes in s-III clade differed by three to six nucleotides from their genuine haplotypes, which were determined to be heteroplasmic copies.Figure 2Neighbor-joining phylogenetic (NJ) tree showing relationships among 52 different haplotypes of clones of 12S rDNA (12S) or 12S-like sequences obtained from the Japanese spiny lobster (Panulirus japonicus), and 12S rDNA sequences of P. japonicus and P. cygnus derived from the GenBank database. Haplotypes detected from the same lobster individual share the same color. Genuine mtDNA haplotype is shown in bold italic and number of clones examined is shown in parenthesis. Haplotypes carrying dagger differ from corresponding genuine mtDNA haplotype by less than two nucleotides (including indel). The bootstrap values greater than 60% (out of 1000 replicates) are shown at the nodes.Full size imageSequence size of haplotypes in group B varied from 731 to 762 bp. K2P distance between groups A and B ranged from 1.336 to 7.445% with a mean of 3.449 ± 0.398%, and those between a reference sequence of P. japonicus and groups A and B were 0.864 ± 0.236% and 3.189 ± 0.410%, respectively. Sequence size of haplotypes in group C varied from 744 to 765 bp. K2P distance between groups A and C ranged from 3.104 to 22.434% with a mean of 12.049 ± 0.901%, and those between a reference sequence of P. japonicus and group C ranged from 3.951 to 21.287% with a mean of 11.764 ± 0.901%. BLAST homology search indicated that the closest species for haplotypes in groups B and C was P. japonicus or P. cygnus with moderate to high similarity (84–98% homology). Therefore, all 13 haplotypes (LC605741‒LC605748, LC654678-LC654682) in groups B and C were determined to be NUMTs.Among the 36–49 positive Dloop clones examined per individual, sequences (777–893 bp) of 26–38 clones per individual (92 clones in total) were successfully determined. After alignment, primer sequences were trimmed, yielding 736–853 bp sequences. Three clones (821 bp) of PJK1 were identical one another and determined to be genuine haplotype of this individual. Nine clones (813 bp) of PJK2 were identical to PJK2-Dloopdirect and determined to be genuine haplotype of this individual. Three clones (821 bp) of PJK3 were identical one another and determined to be genuine haplotype of this individual. Representative sequences of these three genuine haplotypes were deposited in INSDC (LC605750‒LC605752). Nucleotide sequences of the remaining 78 clones were all different one another, in which 25 haplotypes were observed in PJK1, 17 in PJK2, and 36 in PJK3 (LC605753‒LC605815, LC654419-LC654430, LC654675-LC654677).Incorporation of all eight Panulirus species sequences made sequence alignment considerably unreliable because of multiple indels, reference sequences of P. japonicus and of closely related P. cygnus were used for constructing phylogenetic tree (Fig. 3). Haplotypes detected from P. japonicus were segregated into four groups (designated by A to D). Sequence size of haplotypes in group A ranged from 812 to 822 bp. Three distinct clades (d-I to d-III) were observed in group A, in which 17 haplotypes from PJK1, 13 from PJK2 and 15 from PJK3 were cohesively clustered together with their corresponding genuine haplotypes (bold italic). The numbers of variable nucleotide sites between haplotypes within d-I, d-II and d-III were 27, 61 and 28, respectively, of which indels were observed at five, two and four sites and transversion was observed at 0, six and six sites. The mean K2P distance between different haplotypes within these clades ranged from 0.340 ± 0.067 to 1.097 ± 0.139%. The mean K2P distance between these three clades ranged from 7.577 ± 0.951 to 8.770 ± 0.984%. Haplotypes differed by less than two substitutions (including indel) from the genuine haplotypes are marked with dagger. Eight haplotypes in d-I clade, three in d-II clade, and four in d-III clade differed by three to five nucleotides from the genuine haplotype were determined to be heteroplasmic copies.Figure 3Neighbor-joining phylogenetic (NJ) tree showing relationships among 80 different haplotypes of control region (Dloop) or Dloop-like sequences obtained from the Japanese spiny lobster (Panulirus japonicus), and control region sequences of P. japonicus and P. cygnus derived from the GenBank database. Haplotypes detected from the same lobster individual share the same color. Genuine mtDNA haplotype is shown in bold italic and number of clones examined is shown in parenthesis. Haplotypes carrying dagger differ from corresponding genuine mtDNA haplotype by less than two nucleotides (including indel). The bootstrap values greater than 60% (out of 1000 replicates) are shown at the nodes.Full size imageSequence size of haplotypes in groups B to D largely varied from 736 to 853 bp. K2P distances between group A and others ranged from 14.748 ± 1.030% (A vs B) to 61.619 ± 3.045% (A vs D), whereas that between haplotypes of group A and a reference sequence of P. japonicus was much smaller (6.333 ± 0.663%). BLAST homology search revealed no identical sequence for haplotypes in groups B to D and indicated that the closest species for haplotypes in groups B and C was P. japonicus with low to moderate similarity (74–88% homology). On the other hand, no significantly similar sequence was found for haplotypes in group D. Therefore, all 31 haplotypes (LC605788‒LC605815, LC654675-LC654677) in groups B to D were determined to be NUMTs.Impact of heteroplasmy and NUMTs for direct nucleotide sequencingPartial electropherogram obtained by direct nucleotide sequencing for COI amplicon of PJK3 is shown in Fig. 4 (top). Peak signals of this electropherogram are readable, but there are a number of sites where two (asterisk) or three (dagger) signals overlap. Alignment of a genuin haplotype (PJK-C7) and nine NUMTs sequences, corresponding to this partial electropherogram, is shown in Fig. 4 (bottom). At the sites where plural peaks overlap, different NUMT haplotypes were observed to share the same nucleotide different from the PJK3-direct. Heteroplasmic copies in COI determined in this study may have little negative impact on direct nucleotide sequencing, since nucleotides different from the genuine haplotypes were all unique to each heteroplasmic haplotype. Thus, the plural peaks at a site were composed of signals from genuine plus NUMT haplotypes, and the intensity of each peak was positively related to the copy numbers of these haplotypes. Frequent failure to obtain readable electropherograms in 12S and Dloop regions by direct sequencing may be due to extensive indels observed in the NUMT haplotypes.Figure 4A part of electropherogram obtained by direct nucleotide sequencing for COI region of PJK3 (top), and corresponding sequences from genuine haplotype (PJK3-C7) and nine NUMT haplotypes (see Fig. 1) are aligned (bottom). Apparent double (asterisk) and triple (dagger) peaks are observed at seven and five sites, respectively, which are comprised of signals from genuine and NUMT haplotypes.Full size image More

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Insects and bacteriaBean bugs were reared in petri dishes (90 mm in diameter and 20-mm high) at 25 °C under a long-day regimen (16-h light, 8-h dark) and fed with soybean seeds and distilled water containing 0.05% ascorbic acid (DWA). Burkholderia symbiont strain SFA119, a MEP-degrading strain conferring MEP resistant in the bean bug, and its GFP-(green fluorescent protein) labeled derivative, strain SJ586, were used in this study. The symbiont was cultured at 30 °C on YG medium (0.5% yeast extract, 0.4% glucose, and 0.1% NaCl). The GFP-labeled strain was constructed by the Tn7 mini-transposon system, as previously described31.Genome sequencingDNA was extracted from cultured cells of strain SFA1 by the phenol–chloroform extraction as previously described32. The DNA library for Illumina short reads (the mean insert size: 500 bp) was constructed by using the Covaris S2 ultrasonicator (Covaris) and the KAPA HyperPrep Kit (Kapa Biosystems). For the library construction for Nanopore long reads, Native Barcoding Expansion (EXP-NBD104, Oxford Nanopore Technologies) and the Ligation Sequencing Kit (SQK-LSK109, Oxford Nanopore Technologies) were used. The genome sequencing was performed with NextSeq using the 2 × 151-bp protocol (Illumina) and GridION using an R9.4.1 flow cell (Oxford Nanopore Technologies). The Illumina short reads were processed by using Sickle Ver 1.33 (available at https://github.com/najoshi/sickle) for removing the low-quality and shorter reads. After processing the Nanopore long-reads with Porechop Ver 0.2.3 (available at https://github.com/rrwick/Porechop) and Filtlong Ver 0.2.0 (available at https://github.com/rrwick/Filtlong), error correction was performed by using Canu Ver 1.833. These processed short- and long reads were assembled by using Unicycler Ver 0.4.734, resulting in the eight circular replicons (Supplementary Fig. 1). The assembled genome was annotated by DFAST Ver 1.1.035. After the homology searches of the protein sequences by blastp 2.5.0 + 36 against the COG database (PMID: 25428365), circular replicons were visualized with circos v 0.69-837. The chromosomes and plasmids were assigned according to the genome of Caballeronia (Burkholderia) cordobensis strain YI2338.Phylogenetic analysisNucleotide sequences of 16 S rRNA gene of representative Burkholderia spp. and outgroup species were aligned by using SINA v1.2.1139. Protein sequences of MEP-degrading genes (mpd, pnpB, and mhqA) and a plasmid-transfer gene (traH) on plasmid 2 were subjected to the blastp search against the nr database (downloaded in Jul. 2019) and top ~30 hit sequences were retrieved for each gene. Multiple sequencing alignments of each gene were constructed with L-INS-I of mafft v7.40740. Gap-including and ambiguous sites in the alignments were then removed. Unrooted maximum-likelihood (ML) phylogenetic trees were reconstructed with RAxML v8.2.341 using the GTR + Γ model (for 16 S rRNA gene) or the LG + Γ model42 (for other genes). The bootstrap values of 1000 replicates for all internal branches were calculated with a rapid bootstrapping algorithm43.Preparation of SFA1 cultures for RNA-seqBurkholderia symbiont SFA1 was precultured in minimal medium (20 mM phosphate buffer [pH 7.0], 0.01% yeast, 0.1% (NH4)2SO4, 0.02% NaCl, 0.01% MgSO4⋅7H2O, 0.005% CaCl2⋅2H2O, 0.00025% FeSO4⋅7H2O, and 0.00033% EDTA⋅2Na) containing 1.0 mM of MEP on a gyratory shaker (210 rpm) at 30 °C overnight, and subcultured in newly prepared MEP-containing minimal medium under the same conditions for 5 h. As a control, SFA1 was precultured in minimal medium containing 0.1% citrate overnight, and then the overnighter was subcultured in a newly prepared citrate-containing minimal medium under the same conditions for 10 h. The culture was mixed with an equal amount of RNAprotect Bacteria Regent (Qiagen, Valencia, CA, USA), then centrifuged to harvest the cells for the RNA-seq analysis.Preparation of midgut symbiont cells for RNA-seqThe oral administration of the symbiont strain SFA1 was performed as described19,44. The symbiont was inoculated to 2nd instar nymphs, and three days after molting to the 3rd instar, nymphs were transdermally administered with 1 µl of 0.2 µM or 20 µM of MEP (dissolved in acetone). One- or three days after the treatment, insects were dissected and the crypt-bearing symbiotic gut region was subjected to the RNA extraction and RNA-seq analysis. As a control, untreated insects were analyzed.RNA-seq analysisTotal RNA was extracted from triplicate samples from cultures by the hot-phenol method as previously described45 or from the midgut symbiont cells by using RNAiso Plus (Takara Bi, Kusatsu, Shiga, Japan) and the RNeasy mini kit (Qiagen). The extracted total RNA was purified by phenol–chloroform extraction and digestion by DNase (RQ1 RNase-Free DNase, Promega, Fitchburg, WI, USA) and repurified by using a RNeasy Mini Kit. The mRNA in the samples was further enriched by the RiboMinus Transcriptome Isolation Kit bacteria (Thermo Fisher Scientific, Waltham, MA, USA) and the RiboMinus Eukaryote Kit for RNA-Seq (Thermo Fisher Scientific), and purified by using an AMPure XP kit (Beckman Coulter, Brea, CA, USA). The cDNA libraries were constructed from approximately 100 ng of rRNA-depleted RNA samples by the use of a NextUltraRNA library prep kit (New England Biolabs, Ipswich, MA, USA). Size selection of cDNA (200–300 bp) and determination of the size distribution and concentration of the purified cDNA samples were performed as described previously46. In total, 21 cDNA libraries were constructed and sequenced by MiSeq (Illumina, Inc., San Diego, CA, USA). To ensure high sequence quality, the remaining sequencing adapters and the reads with a cutoff Phred score of 15 (for leading and tailing sequences, Phred score of >20) and a length of less than 80 bp in the obtained RNA-seq data were removed by the program Trimmomatic v0.30 using Illumina TruSeq3 adapter sequences for the clipping47. The remaining paired reads were analyzed by FastQC version 0.11.9 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) for quality control, and Bowtie2 ver. 2.2.248 for mapping on the symbiont genome (DDBJ/EMBL/GenBank accession: AP022305–AP022312). After the conversion of the output BAM files to BED files using the bamtobed program in BEDTools ver. 2.14.349, gene expression levels were calculated in TPM (transcripts per kilobase million) values by using in-house scripts46.Gene deletion and complementationMEP-degrading genes (mpd, pnpA1, and pnpA2) were deleted by the homologous-recombination-based deletion method using pK18mobsacB or pUC18, as previously described50,51. Primers used for the mutagenesis are listed in Supplementary Table 1. For mpd gene deletion, pK18mobsacB was used to construct a markerless mutant. For single deletion of pnpA1 and pnpA2 genes, pUC18 was used to substitute each gene locus with a kanamycin-resistance gene cassette. The double deletion of pnpA1 and pnpA2 genes was performed by substituting pnpA2 gene locus with a tetracycline-resistance gene cassette in the pnpA1-deletion mutant. Gene complementation of mpd was also performed by homologous recombination using plasmid pUC18 with primers listed in Supplementary Table 1. To investigate growth profiles of the wild-type SFA1, the gene-deletion mutants (Δmpd, ΔpnpA1, ΔpnpA2, and ΔpnpA1/ΔpnpA2), and the mph-complement mutant (Δmpd/mpd+) in the MEP-containing minimal medium, the strains were precultured in minimal medium containing 1.0 mM MEP on a gyratory shaker (210 rpm) at 30 °C overnight, and then cultured in newly prepared MEP-containing minimal medium under the same condition. The growth of cultures was estimated by OD600 measurements. To confirm the basic growth abilities of the mutants, these bacterial strains were pre- and subcultured in minimal medium containing 0.1% glucose under the same conditions. These symbiont strains and mutants were inoculated to the bean bug as described above.Quantitative PCRSymbiont titers in the midgut crypts were evaluated by quantitative PCR (qPCR) of bacterial dnaA gene copies. The qPCR was performed by using a KAPA SYBR Fast qPCR Master Mix (Kapa Biosystems) and the LightCycler 96 System (Roche Applied Science) with the following primers: BSdnaA–F (5′-AGC GCG AGA TCA GAC GGT CGT CGA T-3′) and BSdnaA–R (5′-TCC GGC AAG TCG CGC ACG CA-3′).MEP treatment of insectsMEP treatment of R. pedestris was performed as previously described19. Soybean seeds were dipped in 0.2 mM MEP for 5 s and dried at room temperature. In each clean plastic container, 15 individuals of 3rd-instar nymphs were reared on three seeds of the MEP-treated soybean and DWA at 25 °C under the long-day regime, and the number of dead insects was counted 24 h after the treatments. The survival rate of the insects was analyzed under Fisher’s exact test by use of the program R ver. 3.6.3 (available at https://www.R-project.org/). Multiple comparisons were corrected by the Bonferroni method.Bactericidal activities of MEP and its degradation product 3M4NTo measure bactericidal activities of MEP and 3M4N on cultured cells of SFA1, 104 cells of log-phase growing bacteria were mixed with a defined concentration of MEP or 3M4N, and spotted on a YG agar plate. To measure the bactericidal activity against midgut crypt-colonizing cells, the symbiotic organs infected with SFA1 were dissected from 3rd-instar insects, homogenized in PBS, and purified by a 5-µm-size pore Syringe filter to harvest colonizing symbiont cells50. MEP or 3M4N was added to approximately 104 cells of the harvested cells and spotted on a YG agar plate. Bactericidal activities of the chemical compounds were then checked in 24 h after incubation at 30 °C.HPLC detection of in vitro and in vivo MEP-degrading activities of the symbiontTo determine in vitro MEP-degradation activity, cultured cells of SFA1 were prepared as above, and 106 cells were incubated at 25 °C in 200 µl of MEP solution (2 mM MEP in Tris-Hcl [pH 8.5] with 0.1% Triton X-100) in a 1.5-ml microtube. To determine in vivo MEP-degradation activity, the midgut of a 5th-instar insect infected with SFA1 was dissected, the posterior and anterior parts of the crypt-bearing symbiotic region were closed with 0.2-mm polyethylene fishline (Supplementary Fig. 6a), and incubated at 25 °C in 200 µl of the MEP solution. For the in vivo determination, 250 mM of trehalose, known as a major sugar source of insects’ hemolymph52, was added to the MEP solution to keep the tissue fresh. After incubation for different times, the reaction was stopped by adding 400 µl of methanol. After centrifugation, supernatants were subjected to high-performance liquid chromatography (HPLC) analyses to detect MEP and 3M4N, as previously reported21, and precipitated cells and tissues were subjected to DNA extraction and qPCR to estimate symbiont-cell numbers of each reaction.LC–ESI–MS detection of 3M4N in feces from 3M4N-fed insectsAn insect-rearing system for feeding 3M4N and collecting feces is shown in Supplementary Fig. 7. Insects were fed with DW or DW containing 10 mM 3M4N in a plastic container, in which the solution supplier was covered by 0.5-mm mesh, so that insects were able to drink the solution by probing with their proboscis, but did not directly touch the solution by their legs or body. Twenty insects were reared per container and their feces were accumulated on the bottom of the container for five days. The collected feces (DW- or 3M4N-treated) were suspended in 1 ml of MilliQ water, and the water-soluble fractions were extracted by thorough vortexing. Solids and insoluble fractions were removed from the suspension by centrifugation and subsequent filtration using a cellulose-acetate membrane (Φ, 0.20 μm, ADVANTEC, Tokyo, Japan). The resultant fraction was diluted 10-fold by MilliQ water and analyzed by liquid chromatography–electrospray-ionization mass spectrometry (LC–ESI–MS) according to a previous report53,54,55. HPLC was performed using the Nexera X2 system (Shimadzu, Kyoto, Japan) composed of LC-30AD pump, SPD-M30A photodiode-array detector, and SIL-30AC autosampler. Develosil HB ODS-UG column (ID 2.0 mm × L 75 mm, Nomura Chemical Co., Ltd, Aichi, Japan) was employed with a flow rate of 0.2 mL/min. The following gradient system was used for analysis of metabolites: MilliQ water (solvent A) and methanol (solvent B), 90% A and 10% B at 0–5 min, linear gradient from 90% A and 10% B to 20% A and 80% B at 5–15 min, 20% A and 80% B at 15–20 min, and 90% A and 10% B at 20–25 min. Retention time of 3M4N standard reagent was 14.2 min. Electrospray-ionization mass spectrometry (ESI–MS) in positive and negative ion modes was simultaneously performed using amaZon SL (Bruker, Billerica, MA, USA). 3M4N (MW = 153.14) standard showed a clear peak in negative mode at m/z of 151.53.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Nature Video
05 November 2021

Whale-cams reveal how much they really eat

Baleen whales consume twice as much krill as previously estimated.

Sara Reardon

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Sara Reardon

Sara Reardon is a freelance writer in Bozeman, Montana.

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Tagging whales with cameras and sensors has allowed researchers to calculate how much food these huge creatures are consuming. It’s the most accurate estimate yet and reveals an even more significant impact of whales on ocean ecosystems than was previously known.Read the paper here.

doi: https://doi.org/10.1038/d41586-021-03026-z

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