Isoprene

The approach to taxon sampling and analysis was in almost all respects the same as previously described (Hahnke et al., 2016 (link); Nouioui et al., 2018 (link)). A total of 1104 annotated type-strain genome sequences (Supplementary Table S1) for Alphaproteobacteria (ingroup) and Spirochaetes (outgroup) were collected. While some originated from GenBank the majority was obtained de novo in the course of the KMG projects phase II (Mukherjee et al., 2017 (link)) and phase IV and deposited in the Integrated Microbial Genomes platform (Chen et al., 2019 (link)) and in the Type-Strain Genome Server database (Meier-Kolthoff and Göker, 2019 (link)). Among Alphaproteobacteria KMG-II mainly targeted Rhodobacteraceae but also representatives of other families. All newly generated KMG sequences underwent standard quality control at DSMZ and JGI documented on the respective web pages and had < 100 contigs. All accepted genome sequences had < 500 contigs and matched the 16S rRNA gene reference database described below. Structural annotation at JGI and DSMZ was done using Prodigal v. 2.6.2 (Hyatt et al., 2010 (link)). The features of all genome sequences that entered these analyses are provided in Supplementary Table S1. These annotated genome sequences were processed further as in our previous study using the high-throughput version of the Genome BLAST Distance Phylogeny (GBDP) approach in conjunction with BLAST+ v2.2.30 in blastp mode (Auch et al., 2006 (link); Camacho et al., 2009 (link); Meier-Kolthoff et al., 2014a (link)) and FastME version 2.1.6.1 using the improved neighbor-joining algorithm BioNJ for obtaining starting trees followed by branch swapping under the balanced minimum evolution criterion (Desper and Gascuel, 2004 (link)) using the subtree-pruning-and-regrafting algorithm (Desper and Gascuel, 2006 (link); Lefort et al., 2015 (link)). One hundred pseudo-bootstrap replicates (Meier-Kolthoff et al., 2013a (link), 2014a (link)) were used to obtain branch-support values for these genome-scale phylogenies.
Trees were visualized using Interactive Tree Of Life (Letunic and Bork, 2019 (link)) in conjunction with the script deposited at https://github.com/mgoeker/table2itol. Outgroup-based rooting was compared with rooting using least-squares dating as implemented in LSD version 0.2 (To et al., 2016 (link)) after removing the outgroup taxa and inferring an accordingly reduced tree with FastME. Species and subspecies boundaries were investigated using digital DNA:DNA hybridization (dDDH) as implemented in the Genome-To-Genome Distance Calculator (GGDC) version 2.1 (Meier-Kolthoff et al., 2013a (link)) and in TYGS, the Type (Strain) Genome Server (Meier-Kolthoff and Göker, 2019 (link)).
In addition to GBDP formula d₅, which explores sequence (dis-)similarity and is the recommended one for phylogenetic inference (Auch et al., 2006 (link); Meier-Kolthoff et al., 2014a (link)) we here used formula d₃, which compares the gene content of the investigated genomes after correcting for reduction in genome size (Henz et al., 2005 (link)). While this analysis was also done using the GBDP software, for consistency with previous work we will refer to the d₅ phylogeny as GBDP tree and to the d₃ tree as gene-content analysis. There are various reasons why a gene-content phylogeny may fail to recover the true tree, as detailed below, hence the gene-content analysis is not intended to lend phylogenetic support. However, it may nevertheless be of taxonomic interest whether or not a certain branch is supported by gene-content data, particularly since the gene content conveys metabolic capabilities (Zhu et al., 2015 (link)) and yield independent evidence for conclusions from standard genome-scale phylogenies (Breider et al., 2014 (link)).
Full-length 16S rRNA gene sequences were extracted from the genomes using RNAmmer version 1.2 (Lagesen et al., 2007 (link)) and compared with the 16S rRNA gene reference database using BLAST and phylogenetic trees to verify the taxonomic affiliation of genomes. Non-matching genome sequences were discarded from further analyses. A comprehensive sequence alignment was generated with MAFFT version 7.271 with the “localpair” option (Katoh et al., 2005 ) using either the sequences extracted from the genome sequences or the previously published 16S rRNA gene sequences, depending on the length and number of ambiguous bases. Trees were inferred from the alignment with RAxML (Stamatakis, 2014 (link)) under the maximum-likelihood (ML) criterion and with TNT (Goloboff et al., 2008 (link)) under the maximum-parsimony (MP). In addition to unconstrained, comprehensive 16S rRNA gene trees (UCT), constrained comprehensive trees (CCT) were inferred with ML and MP using the bipartitions of the GBDP tree with ≥95% support as backbone constraint, as previously described (Hahnke et al., 2016 (link); Nouioui et al., 2018 (link)).
Taxa were analyzed to determine whether they were monophyletic, paraphyletic or polyphyletic (Farris, 1974 (link); Wood, 1994 (link)) Taxa non-monophyletic according to the GBDP tree were tested for evidence for their monophyly in the UCT and the 16S rRNA gene trees, if any, in the original publication. In the case of a significant conflict (i.e., high support values for contradicting bipartitions) between trees or low support in the GBDP tree, additional phylogenomic analyses of selected taxa were conducted. To this end, protein sequences of those taxa with the reciprocal best hits from GBDP/BLAST were clustered with MCL (Markov Chain Clustering) version 14-137 (Enright et al., 2002 (link)) under default settings and an e-value filter of 10^–5 in analogy to OrthoMCL (Li et al., 2003 (link)). The resulting sets of orthologous proteins were aligned with MAFFT and concatenated to form a supermatrix after discarding the few clusters that still contained more than a single protein for at least one genome. Comprehensive supermatrices were compiled from all the orthologs that occurred in at least four genomes, whereas core-genome supermatrices were constructed for the orthologs that occurred in all of the genomes. Supermatrices were analyzed with TNT, and with RAxML under the “PROTCATLGF” model, in conjunction with 100 partition bootstrap replicates (Siddall, 2010 (link); Simon et al., 2017 (link))
Additionally, selected phenotypic features relevant for the taxonomic classification of Alphaproteobacteria were as comprehensively as possible collected from the taxonomic literature: motility by flagella, absence or presence of carotenoids, absence or presence of bacteriochlorophyll α, absence or presence of sphingolipids, average number of isoprene residues of the major ubiquinones, and relationship to oxygen. To avoid circular reasoning, missing features of a species were only inferred from features of its genus when species and genus were described in the same publication or when the species description had explicitly been declared as adding to the features of the genus. For the binary chemotaxonomic characters an alternative coding was also investigated that treated all missing values as indicating absence. Ubiquinone percentages would be more informative than just statements about being “major” but mostly only the latter are provided in the literature. Oxygen conditions were coded as ordered multi-state character: (1) strictly anaerobic; (2) facultatively aerobic, facultatively anaerobic, or microaerophilic; (3) strictly aerobic. Among all nine coding options tested, this yielded the highest fit to the tree (Supplementary Table S1) but the differences between the coding options were not pronounced. Phylogenetic conservation of selected phenotypic and genomic characters with respect to the GBDP tree (reduced to represent each set of equivalent strains by only a single genome) was evaluated using a tip-permutation test in conjunction with the calculation of maximum-parsimony scores with TNT as previously described (Simon et al., 2017 (link); Carro et al., 2018 (link)) and 10,000 permutations. TNT input files were generated with opm (Vaas et al., 2013 (link)). The proportion of times the score of a permuted tree was at least as low as the score of the original tree yielded the p-value. Maximum-parsimony retention indices (Farris, 1989 (link); Wiley and Lieberman, 2011 ) were calculated to further differentiate between the fit of each character to the tree.
Taxa that were unambiguously non-monophyletic according to the genome-scale analyses were screened for published evidence of their monophyly. The published evidence was judged as inconclusive when based on unsupported branches in phylogenetic trees, based on probably homoplastic characters or on probable plesiomorphic character states. Plesiomorphies might well be “diagnostic” but just for paraphyletic groups (Hennig, 1965 ; Wiley and Lieberman, 2011 ; Montero-Calasanz et al., 2017 (link)) hence “diagnostic” features alone are insufficient in phylogenetic systematics.
For fixing the obviously non-monophyletic taxa taxonomic consequences were proposed if new taxon delineations could be determined that were sufficiently supported by the CCT. In these cases, the uncertain phylogenetic placement of taxa whose genome sequences were not available at the time of writing would not affect the new proposals. Where necessary taxa were tentatively place in newly delineated groups.

The atomic coordinates were taken from the X-ray structures; cyanobacterial PSI from Thermosynechococcus elongatus at 2.5 Å resolution (PDB code, ; 1JB0);2 (link) plant PSI from Pisum sativum at 2.8 Å resolution (PDB code, ; 4XK8); PbRC from Rhodobacter sphaeroides at 2.01 Å resolution (PDB code, ; 3I4D), 1.87 Å resolution (PDB code, ; 2J8C),4 (link) and 2.55 Å resolution (PDB code, ; 1M3X);3 (link) PbRC from Thermochromatium tepidum at 2.2 Å resolution (PDB code, ; 1EYS);30 (link) the PSII monomer unit (designated monomer A) of the PSII complexes from Thermosynechococcus vulcanus at 1.9 Å resolution (PDB code, ; 3ARC).5 (link) Hydrogen atoms were generated and energetically optimized with CHARMM.54 Atomic partial charges of the amino acids were adopted from the all-atom CHARMM22) parameter set.55 (link) For PSI, the atomic charges of cofactors were taken from previous studies (Chla, phylloquinone, β-carotene,56 (link) and the Fe₄S₄ cluster57 ). The atomic charges of the other cofactors ((B)Chla, including (B)Chla˙⁺ and (B)Chla˙^–, (B)Pheoa, ubiquinone, plastoquinone, spheroidene, sulfoquinovosyl diacylglycerol, heptyl 1-thiohexopyranoside, and the Fe complex) were determined by fitting the electrostatic potential in the neighborhood of these molecules using the RESP procedure58 (Tables S2–S11†). To obtain the atomic charges of the Mn₄CaO₅ cluster or the Fe complex, backbone atoms are not included in the RESP procedure (except for D1-Ala344) (Table S11†). The electronic wave functions were calculated after geometry optimization by the DFT method with the B3LYP functional and 6-31G** basis sets, using the JAGUAR program.59 For the atomic charges of the non-polar CH_n groups in cofactors (e.g., the phytol chains of (B)Chla and (B)Pheoa and the isoprene side-chains of quinones), the value of +0.09 was assigned for non-polar H atoms. We considered the Mn₄CaO₅ cluster to be fully deprotonated in S₁.
The protein inner spaces were represented implicitly with the dielectric constant ε_w = 80, whereas the following water molecules were represented explicitly; (i) for PSII, ligand water molecules of the Mn₄CaO₅ cluster (W1 to W4), a diamond-shaped cluster of water molecules near TyrZ (W5 to W7)60 (link), the water molecule distal to TyrD44 (link), ligand water molecules of Chl_D1 (A1003 and D424), Chl_D2 (A1009 and A359), and other Chla (B1001, B1007, B1027, C816, and C1004); (ii) for PSI, clusters of water molecules near A_1A (A5007, A5015, A5022, A5043, and A5049) and A_1B (B5018, B5019, B5030, B5055, B5056, and B5058), ligand water molecules of A_–1A (B5005), A_–1B (A5005), and other Chla (A5004, A5010, A5012, A5024, A5032, A5051, B5006, B5010, B5022, B5036, B5053, B5054, J127, L4023, and M155).

The chemical fate of ambient isoprene is primarily to undergo reaction with the hydroxyl radical (OH) and, to a lesser extent, with ozone and the nitrate radical (NO₃)^{53 (link)}. Abundances of all three oxidants are needed to establish the atmospheric lifetime of isoprene at a given location. Ozone concentrations were monitored throughout ACE, while those of OH and NO₃ were not measured. For the analysis presented here, modelled OH and NO₃ abundances were taken from two global models: the UM-UKCA model^{54 (link)} and the CAMS reanalysis of atmospheric composition⁵⁵ , which incorporates meteorological variables from the ERA-5 reanalysis. Isoprene lifetime with respect to atmospheric oxidation, τ_isop, was calculated as: ${\mathrm{\tau }}_{\mathrm{isop}}=1/\left\{k\left(\mathrm{OH}+\mathrm{isop}\right)\left[\mathrm{OH}\right]+k\left({\mathrm{O}}_3+\mathrm{isop}\right)\left[{\mathrm{O}}_3\right]+k\left({\mathrm{NO}}_3+\mathrm{isop}\right)\left[{\mathrm{NO}}_3\right]\right\}$ where k indicates the rate coefficient of the reactions of isoprene with OH, ozone and NO₃ (in units of cm³ molecule⁻¹ s⁻¹) and [OH], [O₃] and [NO₃] are the number densities of the hydroxyl radical, ozone and the nitrate radical, respectively (in units of molecules cm⁻³). Rate coefficients were taken from the IUPAC kinetic database^{56 (link)}. Air temperature from on-board measurements was used to calculate the temperature dependence of the rate coefficients for each reaction. τ_isop was used to adjust the length of each back-trajectory, so that effectively the back-trajectory for an air parcel at night-time (long τ_isop) will stretch further than one at day-time (short τ_isop). It is worth noting that using a fixed τ_isop of 2–3 h (typical at mid-latitudes) would limit all trajectories to the immediate vicinity of the ACE track, missing on the influences of different surface types (e.g., marginal ice), as illustrated in Fig. 2 and S4.

Isoprene emission and photosynthesis of a leaf of F. septica sapling were simultaneously analyzed by an online isoprene analyzer (KFCL-500, Anatec Yanako, Kyoto, Japan) connected with a CI-340 handheld photosynthesis system (CID Bio-Science, Inc., Washington, DC, USA, imported by Ogawa Seiki Co., Tokyo, Japan). Leaves were held in an LC-5 leaf chamber, and measurements of isoprene emission, photosynthesis, leaf temperature, and light intensity were monitored at the same time. Ambient fresh air was pumped into the leaf chamber at a flow rate of 400 mL/min, and the outlet flow from the LC-5 leaf chamber was introduced through a 3-way valve into the isoprene analyzer with a flow rate of 100 mL/min. Isoprene in the outlet flow was reacted with ozone to produce chemiluminescence, and the luminescence intensity was monitored online by a blue-sensitive photomultiplier tube [59 (link)].
Saplings were placed in a phytotron (Koito Manufacturing Co. Ltd., Tokyo, Japan), and the leaves were irradiated with LED light (CS Specialized Equipment Co., Sapporo, Japan) with 5 min stepwise variation in light intensity. The lighting program consisted of 13 steps of up and down phases, as shown in Supplementary Figure S1A. At each step, light intensity was held constant for 5 min. Leaf temperature was allowed to vary naturally with light intensity, as shown in Supplementary Figure S1B. The isoprene emission, leaf temperature, and light intensity acquired were averaged for 5 min and used for the optimization of G93 parameters, as described previously [44 (link)].
The isoprene analyzer was calibrated with standard isoprene gas (17.52 ppm) purchased from Tokyo Koatsu Co., Tokyo, Japan, as recommended by the supplier: first calibration on high-range mode using 17.52 ppm isoprene standard, followed by manual calibration of low-range mode (200 ppb max). Calibration by this procedure usually gives a minimum detection sensitivity (2 s) of 1.2 ppb for low-range measurements. Isoprene emission was analyzed using the low-range mode, and all measurements were corrected by subtracting the background emission level of isoprene (2–3 ppb) in ambient air. The changes in background level during the measurement were usually less than 1 ppb and remained within the range of minimum detection sensitivity (2 s = 1.2 ppb).