Termite mounds contain distinct methanotroph communities that are kinetically adapted to elevated methane concentrations

Termite mounds have recently been confirmed to mitigate approximately half of termite methane (CH4) emissions, but the aerobic methane-oxidizing bacteria (methanotrophs) responsible for this consumption have not been resolved. Here we describe the abundance, composition, and kinetics of the methanotroph communities in the mounds of three distinct termite species. We show that methanotrophs are rare members of the termite mound biosphere and have a comparable abundance, but distinct composition, to those of adjoining soil samples. Across all mounds, the most abundant and prevalent particulate methane monooxygenase sequences detected were affiliated with Upland Soil Cluster α (USCα), with sequences homologous to Methylocystis and Tropical Upland Soil Cluster also detected. The Michaelis-Menten kinetics of CH4 oxidation in mounds were estimated from in situ reaction rates. The apparent CH4 affinities of the communities were in the low micromolar range, which is one to two orders of magnitude higher than those of upland soils, but significantly lower than those measured in soils with a large CH4 source such as landfill-cover soils. The rate constant of CH4 oxidation, as well as the porosity of the mound material, were significantly positively correlated with the abundance of methanotroph communities of termite mounds. We conclude that termite-derived CH4 emissions have selected for unique methanotroph communities that are kinetically adapted to elevated CH4 concentrations. However, factors other than substrate concentration appear to limit methanotroph abundance and hence these bacteria only partially mitigate termite-derived CH4 emissions. Our results also highlight the predominant role of USCα in an environment with elevated CH4 concentrations and suggest a higher functional diversity within this group than previously recognised.


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Termites are mound-building eusocial insects that live in colonies throughout the tropics 45 and subtropics. These organisms completely degrade lignocellulose in a process 46 primarily mediated by anaerobic symbiotic microorganisms in their hindgut [1]. During this 47 process, hydrogenotrophic methanogens produce substantial amounts of methane (CH4) 48 that is emitted from the termite into the atmosphere [2-4]. Production rates vary by three 49 to four orders of magnitude depending on the termite species and their dietary 50 preferences (i.e. wood-, grass-, soil-or fungus-feeding) [1,4,5]. Current models suggest 51 that termites are responsible for between 1 to 3 % of global CH4 emissions to the 52 atmosphere [6]. 53 Aerobic methane-oxidizing bacteria (methanotrophs) significantly mitigate emissions of 54 CH4 from termites [7]. Methanotrophs gain carbon and energy by oxidising CH4 to carbon 55 dioxide, with the first step in this reaction being catalysed by particulate and soluble 56 methane-monooxygenases [8]. It is controversial whether termite hindguts harbor such 57 organisms; while Methylocystis spp. were recently isolated from termites [9], other studies 58 could not detect methanotroph functional gene markers or measurable amounts of 14 CO2 59 during 14 CH4 incubation experiments [10]. We also observed that the addition of inhibitors 60 of CH4 oxidation did not increase direct termite CH4 emissions [7]. However, many termite 61 colonies construct large mounds built from soil material or build their nest in soil, which is 62 generally a sink for atmospheric CH4 [11]. While results from incubation experiments of 63 mound material were conflicting [12][13][14], we recently presented clear evidence of 64 widespread CH4 oxidation in North Australian termite mounds [7]: results from three 65 different in situ methods to measure CH4 oxidation in mounds confirmed that 66 methanotrophs mitigate between 20 to 80 % of termite-derived CH4 before emission to 67 the atmosphere. However, the community composition and kinetic behaviour of the 68 methanotrophs responsible remain largely unknown. 69 Compared to soils, methanotrophs inhabiting termite mounds have received little 70 attention. Ho et al. [14] investigated mound material of the African fungus-feeding termite 71 Macrotermes falciger using a pmoA-based diagnostic microarray approach. Community 72 composition differed between mound and soil, and at some locations within the large 73 and low CH4 concentrations. The mound community was reportedly dominated by 75 gammaproteobacterial methanotrophs of the JR3 cluster, while the functional gene of the 76 soluble methane-monooxygenase could not be detected, nor could methanotrophs of the 77 Verrucomicrobia and Methylomirabilota (NC10) phyla. Beside this pioneering work, the 78 mound methanotroph communities of no other termite species has been investigated. 79 However, large differences might exist between termite species and particularly their 80 dietary preferences, as well as the mounds' impressive variety of sizes, shapes and 81 internal structures [15,16]. Reflecting this, in situ studies have shown that there is a large 82 variation in methanotroph activity in mounds both within and between species; for 83 example, Tumulitermes pastinator mounds appear to be largely inactive and still a high 84 fraction of termite-derived CH4 can be oxidised in soil beneath mounds, due to facilitation 85 of CH4 transport within the mound [7]. It remains unclear whether differences in 86 methanotroph community abundance or composition account for these activity 87

differences. 88
In this work, we aimed to resolve these discrepancies by conducting a comprehensive 89 analysis of the composition and kinetics of the methanotroph communities within termite 90 mounds of Australian termite species. Three mound-building termite species were 91 selected, the wood-feeding Microcerotermes nervosus (Mn), soil-interface-feeding 92 Macrognathotermes sunteri (Ms), and grass-feeding Tumulitermes pastinator (Tp), which 93 represent the three main feeding groups present in Australia [17]. Mounds of these 94 species were previously confirmed to oxidise a high fraction of termite-produced CH4 [7]. 95 We used the pmoA gene, encoding a subunit of the particulate methane monooxygenase 96 present in most methanotrophs [18], as a molecular marker to study the abundance, 97 diversity, and composition of the methanotrophs within 17 mounds and a subset of 98 adjoining soils. In parallel, we performed in situ studies using gas push-pull tests (GPPTs) 99 to derive the kinetic parameters of CH4 oxidation. We demonstrate that methanotrophic 100 communities in the core and periphery of termite mounds are compositionally and 101 kinetically distinct from those of surrounding soil, and primarily comprise methanotrophs 102 affiliated with the Upland Soil Cluster α (USCα) with an apparent medium affinity for CH4. 103

Field sites and sampling 105
Field tests and sampling were performed in April and May 2016 in a coastal savanna 106 woodland on the campus of Charles Darwin University in Darwin, Northern Territory, 107 Australia (12.370° S, 130.867° E). The site is described in detail in Nauer et al. [7]. For 108 this study, 29 mounds were first subject to in situ methane kinetic measurements using 109 gas push-pull tests (described below). For further investigations following field 110 measurements, we selected 17 termite mounds of an appropriate size for processing in 111 the laboratory (initially 18, but one was damaged during transport and had to be 112 discarded). These mounds were first excavated but kept intact to measure internal 113 structure, volume, densities, and porosities as previously described [19]. They were then 114 deconstructed to (i) sample termites for species identification, (ii) collect mound material 115 for gravimetric water content measurements, and (iii) collect mound material for molecular 116 analyses of methanotrophic community. For species determination, soldiers were 117 individually picked and stored in pure ethanol for species confirmation as previously 118 described [19]. For gravimetric water content measurements, approximately 200 g of 119 mound material from both core and periphery locations were subsampled and oven dried 120 at 105 °C for >72 hours; subsamples were measured before and after drying and the 121 water content calculated based on mass loss. Subsamples for physicochemical 122 parameters were oven-dried at 60 °C for 72 h, carefully homogenised into a composite 123 sample for each termite species and location, and sent to an external laboratory for 124 analyses according to standard protocols (CSBP laboratories, Bibra Lake WA, Australia). 125 For community analysis, mound and soil material was collected under sterile conditions 126 using bleach-and heat-sterilised spatulas, and immediately stored in autoclaved 2 mL 127 centrifuge tubes at -20 °C. For each sampling location (mound core and periphery, soil 128 beneath and surrounding the mound), we collected triplicates of pooled materials deriving 129 from three different spots. Mound cores were sampled from within 20 to 30 cm from the 130 approximate centroid of mound, whereas mound periphery was collected from the outer 131 5 to 10 cm of the mound. For a subset of the investigated mounds, soil was collected from 132 were adapted from those used for previous PCRs  Each sample was analyzed in triplicate, and a total of three assays were required for each 168 gene to include all the samples. Amplification efficiencies calculated from the slopes of 169 calibration curves were >70% and R 2 values were >0.98. 170

Methanotroph community analysis 171
The structure of the methanotroph community within each sample was inferred from Plymouth, United Kingdom). Negative binomial models were performed on the non-211 rarefied OTU dataset to assess the differential abundance of bacterial OTUs between 212 sample groups, and the false discovery rate approach was used to account for multiple 213 testing. 214

Gas push-pull tests 215
The gas push-pull test was used to estimate in situ activity coefficients as described 216 previously [19,31]. Michaelis-Menten parameters estimated from in situ methods are 217 integrated measures across a large mass of substrate and are thus better suited to 218 characterise the kinetic potential of whole microbial communities in heterogeneous 219 systems than laboratory microcosms, which suffer from inevitable sampling bias [32]. In 220 (Ar) was injected at a rate of ~0.5 L min -1 into the lower center of the termite mounds and 222 then immediately extracted from the same location at the same flow rate. During 223 extraction, the injected gas mixture was gradually diluted with termite-mound air down to 224 background levels; the tracer Ar accounted for this dilution due to its similar transport 225 behavior to CH4. A timeseries of CH4 and Ar concentrations was collected during the 24 226 min injection phase, and the 36 min extraction phase. Concentrations of CH4 were 227 measured quasi-continuously (frequency of 1 Hz) using a field-portable spectrometer 228 Quantitative PCR was used to estimate the abundance of the methanotroph community 261 (pmoA copy number) and total bacterial community (16S gene copy number) in each 262 mound and soil sample. Bacterial abundance was consistently high (av. 2.7 × 10 10 16S 263 copy numbers per gram of dry soil; range 2.5 × 10 8 to 3.4 × 10 11 ) and did not significantly 264 differ between sample locations (Fig. 1b & Fig. S1); an earlier study found higher 265 microbial biomass in the mound compared to soil [37], but this may reflect different 266 methodologies applied to each substrate. In contrast, pmoA copy number was relatively 267 low across the samples (av. 1.5 × 10 6 copies per gram of dry sample material; range: 2.0 268 × 10 4 to 1.8 × 10 7 ) and just 0.0076% that of 16S copy number (range: 0.00018% to 269 0.048%) (Figure 1a). Such values are comparable to those previously reported for the 270 abundance of pmoA genes in upland soils that mediate atmospheric CH4 oxidation (~10 6 271 copy number, ~0.01% relative abundance [38]). 272 Some differences in methanotroph abundance were observed between sample locations 273 and termite species. Overall, pmoA copy number was 3.5-fold higher in mound core and 274 1.5-fold higher in soil beneath than in surrounding soil, though differences were below the 275 threshold of significance (Fig. S1). In contrast, pmoA copy numbers were significantly 276 lower in mound periphery samples of all species (p = 0.028) (Fig. S1) and in mound 277 samples of T. pastinator compared to the other two species tested (p = 0.028) (Fig. S2); 278 the latter observation is in line with the finding that CH4 oxidation occurs at low rates in T. were detected (Figure 2). Observed and estimated richness of these OTUs was higher 293 in soil samples compared to mound samples (av. Chao1 of 9.0 for mound core, 5.9 for 294 mound periphery, 12.3 for soil samples; p < 0.001) (Figure 1c); however, these 295 differences were driven primarily by rare OTUs in soil samples, with Shannon and inverse 296 Simpson indices similar between samples ( Figure S3). Beta diversity of the samples was 297 analysed by weighted Unifrac and visualised on an nMDS ordination plot (Figure 3a). 298 PERMANOVA analysis confirmed communities significantly differed between sample 299 locations (p = 0.001) and termite species (p = 0.022). With respect to sample location, 300 communities within mound core and periphery samples were similar and were 301 compositionally distinct from soil communities; in addition, methanotroph communities in 302 soils beneath mounds were more similar to those within mounds than those in soils 303 surrounding mounds. This confirms previous inferences that mound and soil communities 304 are different and shaped by termite activity [14]. In addition, core and peripheral mound 305 communities significantly clustered by termite species, while soil samples did not; mound 306 communities of M. nervosus and T. pastinator were more closely related than those of M. 307 sunteri (Figure 3a). 308 sequences from a curated pmoA gene database [29]. Phylogenetic analysis indicates that 310 all OTUs were affiliated with proteobacterial methanotroph sequences (Figure 2). Across 311 all samples, over 80% of the sequences were affiliated with USCα, a recently cultivated 312 lineage of alphaproteobacterial methanotrophs known to mediate atmospheric CH4 313 oxidation [41, 42] (Figure 2 & Figure 3b). The second most dominant taxonomic groups 314 were affiliated with the alphaproteobacterial lineages Methylocystis in mound samples 315 (<10% relative abundance) and the gammaproteobacterial lineage TUSC in soil samples 316 (<10% relative abundance). There was a large proportion of shared taxa across the 317 samples, with the three most abundant OTUs (USCα-affiliated) present in all samples, 318 regardless of type (mound vs soil), location and termite species (Figure 3b). However, 319 differential abundance analysis supported the observed differences between sample type 320 and termite species observed by Unifrac analysis (Figure 3a). Overall, USCα and 321 Methylocystis OTUs were more abundant in mound core, mound periphery, and soils 322 beneath, whereas TUSC OTUs were more abundant in surrounding soils. Significant 323 differential abundance was also observed for certain OTUs between termite species 324 ( Figure 3b). 325 It should be noted that community composition of the mounds from the three Australian  (Table S1). In contrast, USCγ and associates 333 lineages are commonly found in upland soils of neutral to basic pH [44,45], which 334 corresponds well to pH values of 7 to 8 in Macrotermes falciger mounds [14]. 335

Methanotroph communities are kinetically adapted to elevated CH 4 concentrations 336
We determined the kinetics of CH4 oxidation in the mounds by performing in situ GPPTs. 337 Methane oxidation rate was high across the 29 mounds from all three species 338 best fitted a Michaelis-Menten model for 18 mounds and a linear model for 11 mounds 340 based on AIC values (Figure 4a). For the former group of mounds, apparent Michaelis-341 Menten coefficients (Km, Vmax) were calculated. Estimated Km values for the 18 mounds 342 ranged from 0.32 to 47 µmol (L air) -1 , and Vmax from 8.4 to 280 µmol (L air) -1 h -1 . These 343 parameters did not significantly differ between termite species. The overall mean values 344 for Km and Vmax were 17.5 ± 3.5 µmol (L air) -1 and 78.3 ± 17 µmol (L air) -1 h -1 , respectively 345 (standard error of the mean); such values were close to the optimal parameters when 346 fitting a Michaelis-Menten model to combined GPPT data (excluding mounds with linear 347 behavior): Km = 13.2 ± 3.5 µmol (L air) -1 and Vmax = 55.4 ± 8.5 µmol (L air) -1 h -1 ( Figure  348   4a and 4b). Thus, the methanotroph communities within termite mounds have an 349 apparent medium (µM) affinity for CH4. The apparent Km is approximately one to two 350 orders of magnitude higher than high-affinity (nM) uptake observed in upland soils [46-351 48], but one to two orders of magnitude lower than the low-affinity (mM) uptake measured 352 in landfill-cover soils [49]. Similar Michaelis-Menten values were estimated from GPPTs 353 in the vadose zone above a contaminated aquifer (~1 to 40 µL L -1 ), which featured CH4 354 concentrations in a similar range to termite mounds [35]. 355 It is noteworthy that 11 mounds showed an apparent linear increase of reaction rates with 356 substrate concentrations (Figure 4a). This could indicate that Vmax has not been reached 357 during GPPTs with a maximum injected CH4 concentration of ~900 µL L -1 (~40 µM); 358 indeed, the injection concentration is in the range of our highest Km, thus the capacity of 359 some mounds to oxidise CH4 can be substantially higher. A linear increase could also 360 indicate a shift in kinetics during the course of the GPPT. It is in the nature of the GPPT 361 that different areas of the mound are exposed to different concentration ranges, 362 depending on their distance to the gas injection/extraction point [34]. Hence, gas 363 extracted at different times may have had a "history" of exposure to methanotroph 364 communities with different kinetics. It is even conceivable that the 1 h long exposure to CH4 cell -1 h -1 , one to two orders of magnitude higher than observed in upland soils [38]. 378 Though differences between species were not significant, highest cell-specific rates were 379 calculated for T. pastinator, with some values close to a value determined from a landfill-380 cover biofilter [51]. This would imply that these methanotroph communities operate close 381 to their maximum potential of CH4 oxidation. However, it is likely that values for T. 382 pastinator are an overestimate given previous studies indicate that most CH4 for this 383 species is oxidised in the soil beneath rather than mound itself [7]; thus, for this species 384 but not the two others tested, core pmoA numbers underestimate the active methanotroph 385 community involved in mitigating termite CH4 emissions. 386

Conclusions and perspectives 387
Overall, our results imply that local environmental concentrations of CH4 shape the 388 composition and kinetics of the methanotroph community. Elevated CH4 production from 389 termites appears to have selected for a specialised medium-affinity methanotroph