{"paper_id":"2675e237-e38c-46a8-90e0-6ec181b92c11","body_text":"1 \nTitle: Transcriptional and metabolic stasis define desiccation-induced dormancy in soil bacteria 1 \nuntil water vapor initiates resuscitation. 2 \nAuthors:Paul Carini1,2,3, Adriana Gomez-Buckley*4, Christina R. Guerrero*1, Melanie R. 3 \nKridler*1, Isabella A. Viney*2, Roya AminiTabrizi1, Malak M. Tfaily1, Peter Moma5, Laura K. 4 \nMeredith3,5, Katherine B. Louie6, Benjamin P. Bowen6, Trent R. Northen6, Oona Snoeyenbos-5 \nWest1, and Ryan P. Bartelme1 6 \nAffiliations:  7 \n1University of Arizona, Department of Environmental Science  8 \n2University of Arizona, School of Animal and Comparative Biomedical Science  9 \n3 University of Arizona, BIO5 institute   10 \n4Molecular and Cellular Biology, University of Arizona 11 \n5School of Natural Resources and the Environment  12 \n6Joint Genome Institute, Lawrence Berkeley National Laboratory, One Cyclotron Road, 13 \nBerkeley CA, 94720 14 \n 15 \n*Authors contributed equally to this work  16 \nAll correspondence should be sent to: paulcarini@arizona.edu 17 \n 18 \nABSTRACT 19 \nMicrobes inhabiting soils experience periodic water deprivation. The effects of desiccation on 20 \nDNA, protein, and membrane integrity are well-described. However, the effects of drying and 21 \nrehydration on the composition of cellular RNA and metabolites are still poorly understood. 22 \nHere, we describe how slow drying and rehydration with water vapor influence the composition 23 \nof RNAs and metabolites in a soil Arthrobacter. While drying reduced cultivability relative to 24 \nhydrated controls, water vapor rehydration fully restored it. Ribosomal RNA proportions 25 \nremained constant throughout all treatments, and mRNA profiles showed stable composition 26 \nduring desiccation—changing only during transitions into and out of desiccation-induced 27 \ndormancy. Six transcriptional modules displayed distinct expression patterns in desiccated-28 \nrehydrated samples relative to hydrated controls, including desiccation-rehydration responsive 29 \nand rehydration-specific profiles. Targeted intracellular metabolomics revealed similarly static 30 \nprofiles during desiccation, with a cluster of ribonucleosides and nucleobases increasing in 31 \nresponse to desiccation and returning to baseline levels upon rehydration with water vapor. 32 \nThese findings demonstrate that both mRNA and metabolite profiles remain essentially frozen in 33 \ndesiccated Arthrobacter, with dynamics changes occurring only during state transitions. These 34 \nresults have important implications in environments with frequent drying cycles where stable 35 \nmRNA in dormant cells combined with intracellular RNA recycling may obscure interpretations 36 \nof RNA-based environmental analyses that use RNA as a marker of microbial activity. Our 37 \nresults suggest that RNA-based activity assessments in periodically dry environments require 38 \ncareful consideration of dormancy-associated molecular preservation. 39 \n 40 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n2 \nSIGNIFICANCE STATEMENT 41 \nMetabolic activity quickly ceases in drying bacteria as they enter desiccation-induced 42 \ndormancy. We show mRNA and metabolite profiles were variable during drying and rewetting 43 \nbut did not change while desiccated. Additionally, water vapor stimulated the shift from the 44 \nstatic to active state when exiting desiccation-induced dormancy. These shifts coincided with 45 \nincreased cultivability, indicating water vapor resuscitated dry cells. Because RNAs are transient, 46 \nlabile molecules that are turned over rapidly in growing bacteria, the presence of RNA in the 47 \nenvironment is used as a marker for microbial activity. Our research shows this assumption may 48 \nnot hold for desiccated cells, indicating reliance on RNA as a marker of activity in environments 49 \nthat experience drying may obscure estimates of in situ microbial activity.   50 \n 51 \nINTRODUCTION 52 \nWater is required for life as a substrate in essential biological reactions and as a solvent to 53 \ntransport nutrients and waste (1). As such, the lack of water stresses nearly all organisms. Future 54 \nclimate models predict changes in global precipitation patterns, further restricting precipitation in 55 \ndrylands and introducing drought to regions that have not previously experienced it (2–4). The 56 \nimpacts of drought can accelerate agricultural crop losses and food insecurity (5, 6), reduce 57 \nfreshwater connectivity and services (7), and alter soil biogeochemistry (8–11). Soil 58 \nmicrobiomes are restructured during drought (8, 12, 13) and can influence crop drought tolerance 59 \n(14–17). Despite the impacts of drying on soil microbes and their critical roles in global carbon 60 \ndynamics and food production, basic aspects of how soil microbes persist in the dry state remain 61 \nunknown.  62 \nAs soil dries, the solutes in pore water become concentrated, reducing water potential and 63 \nlimiting water availability. Microbes equilibrate rapidly to the water potential of their 64 \nsurroundings because of their small size (13, 18). Eventually, growth substrates precipitate, 65 \nrendering them unavailable for microbial metabolism. Thus, microbial activity slows with 66 \ndehydration until cellular respiration stops (13, 19). Macromolecules in dehydrated cells are 67 \nprone to structural damage. Proteins fold improperly, denature, or are oxidized (20–22). DNA 68 \ncan also be oxidized by reactive oxygen species (ROS) (18, 23). And membrane integrity can be 69 \ncompromised in the desiccated state or upon rehydration, where lysis may occur due to rapid 70 \nturgor pressure changes that come with the influx of water (24, 25).  71 \nSome bacteria endure water scarcity through dormancy, a reversible state of reduced metabolic 72 \nactivity (26). Spore-forming microbes like Bacillus and Streptomyces undergo dormancy via cell 73 \ndifferentiation to produce ultra-resistant spores in response to nutrient starvation (26–28). Yet, 74 \nmany—perhaps most—soil microbes cannot sporulate (29, 30), and the systems regulating their 75 \ndesiccation tolerance remain poorly understood. Non-spore formers respond to water loss by 76 \nmitigating cellular damage through multiple coordinated mechanisms. These include 77 \naccumulating organic osmolytes like trehalose that lower intracellular solute potential and may 78 \nstabilize membranes and macromolecules (13, 31–35); remodeling membrane lipids to resist 79 \ndisruption (36, 37); producing exopolysaccharides and biofilms that slow water loss (38, 39); 80 \nupregulating oxidative stress protection (40–42); and enriching transcripts for molecular 81 \nchaperones and DNA repair proteins (40, 41, 43). 82 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n3 \nYet, the fate of RNA and intracellular metabolites during desiccation-induced dormancy remain 83 \nunderstudied, particularly how the transcriptional and metabolic profiles change in dry cells and 84 \nupon rehydration. While endospores show a dramatic reduction in RNA content during 85 \ndormancy (44), the RNA dynamics in desiccation-tolerant non-spore-forming bacteria are 86 \nunderexplored. This question is important to resolve for environmental microbiology, where 87 \nRNA abundances—both ribosomal and messenger RNA—serve as a key indicator of microbial 88 \nactivity across diverse environments (45–48), including soils (49–51). If RNAs are detectable in 89 \ninactive cells, RNA may not reliably indicate microbial activity. Here, we demonstrate that 90 \ndesiccation induces a dormant state in a soil Arthrobacter characterized by: 1) reduced but 91 \nrecoverable cultivability, with water vapor alone sufficient for resuscitation; 2) stable mRNA and 92 \nintracellular metabolite profiles during desiccation, despite dynamic changes during transitions 93 \ninto and out of the desiccated state; and 3) ribosomal RNAs that occupy a constant fraction of the 94 \ntotal RNA pool irrespective of hydration (and hence, activity) status. By identifying distinct 95 \ntemporal patterns in mRNAs and metabolites across the desiccation-rehydration cycle, we reveal 96 \nimplications for how RNA-based approaches may misrepresent microbial activity in 97 \nenvironments that experience periodic drying. 98 \n 99 \nRESULTS & DISCUSSION 100 \nExperimental design. To investigate cellular responses to desiccation stress, we studied 101 \nArthrobacter sp. strain AZCC_0090, a soil actinobacterium isolated from semiarid soil in 102 \nSouthern Arizona that is closely related to the Arthrobacter phylotypes found across the United 103 \nStates (29, 52, 53). We developed a controlled desiccation system using two humidity chambers 104 \nin which the relative humidity (RH) of the atmosphere was controlled with saturated salts (Fig. 1 105 \na,b). The chambers consisted of a “control” chamber maintained at 100% RH and a “treatment” 106 \nchamber where RH was gradually reduced from 100% to 26% over 14 days to desiccate cells, 107 \ncorresponding to an atmospheric water potential change from 0 to -183 MPa (Fig. 1c). After 14 108 \ndays of desiccation, we restored the 100% RH atmosphere of the treatment chamber to rehydrate 109 \ndried cells for two additional days (Fig. 1c). The degree of desiccation extended far below the 110 \nwater potentials that inhibit growth in both E. coli (-4.6 MPa) and Arthrobacter spp. (-17 MPa) 111 \n(18). Cells were collected on non-hygroscopic polycarbonate filters and placed into these 112 \nchambers. Replicate filters were collected from the chambers at four timepoints to analyze cell 113 \ncultivability, intracellular metabolite profiles, and gene expression (Fig. 1c). 114 \nThis experimental design incorporated three unique aspects relative to previous studies (40, 41). 115 \nFirst, hydrated control cells were maintained alongside treatments, allowing us to specifically 116 \nidentify the responses to desiccation and rehydration. Second, dehydration occurred gradually 117 \nover 14 days rather than rapidly. Finally, rehydration was achieved through water vapor alone. 118 \nThis approach enabled control over cellular water status while minimizing confounding 119 \nvariables. 120 \nDry cells are dormant. We defined cultivability as the fraction of filtered cells that were 121 \nculturable on solid media. Initial cultivability immediately after filtering was 36.3 ± 24.5% 122 \n(mean ± SD, n=4; Fig. 2A), with variability reflecting the technical challenges of quantitatively 123 \nrecovering cells from filters. Cells maintained in hydrated control conditions showed stable 124 \nc\nultivability throughout the experiment, with no significant variation over time (Kruskal Wallis p 125 \n= 0.580; Fig. 2A). In contrast, cells subjected to desiccation showed significant temporal 126 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n4 \nvariation in cultivability (Kruskal Wallis p = 0.022), with mean values decreasing by an order of 127 \nmagnitude from 24.0% at day 2 (100% RH) to 2.46% at day 8 (65% RH; Fig. 2A). Desiccated 128 \ncells showed significantly reduced cultivability compared to hydrated controls at both day 8 and 129 \nday 14 (Mann-Whitney p = 0.028 for both timepoints; Fig. 2A), indicating that reduced 130 \ncultivability was due to desiccation. 131 \nWater vapor restored cultivability of desiccated cells, demonstrating dry cells were dormant, not 132 \ndead. When cells from day 14 (26% RH) were exposed to 100% RH for two days, their mean 133 \ncultivability increased to 14.4%—statistically indistinguishable from constantly hydrated 134 \ncontrols at day 16 (Mann-Whitney P = 0.49; Fig. 2A). Although the AZCC_0090 genome lacks 135 \ngenes associated with sporulation (53), we tested whether the observed recovery was due to 136 \nspore formation. No growth was observed after treating cells with ethanol, confirming 137 \nAZCC_0090 survives desiccation without sporulating. 138 \nWe further calculated the relative effects of desiccation and rehydration as the cultivability ratio 139 \nof treatment to control cells at each timepoint (Fig. 2B). This confirmed significant variation 140 \nacross the experiment (Kruskal-Wallis p = 1.76e-8), with days 8 and 14 showing significantly 141 \nlower relative cultivability compared to both pre-desiccation (day 2) and post-rehydration (day 142 \n16) timepoints (Fig. 2B; Dunn's test, Bonferroni-adjusted P ≤  0.05). These results demonstrate 143 \nthat desiccation caused reduced cultivability and water vapor restored cultivability in desiccated 144 \nArthrobacter. 145 \nEffect of desiccation and rehydration on RNA content and composition. Initial total RNA 146 \nyields from washed cells were 150 ± 29.1 ng µl-¹ (mean ± SD, n=3; Fig 3a). After 2 days at 147 \n100% RH, total RNA decreased in both the control and treatment groups relative to the washed 148 \ncells, with no significant difference between the conditions, likely due to nutrient starvation 149 \nleading to ribosome degradation (54, 55). Subsequently, RNA amounts in the control condition 150 \nvaried significantly over time (Kruskal-Wallis p = 0.033 between days 2 and 16; Fig. 3A), while 151 \ndesiccated and rehydrated cells showed no temporal variation (p = 0.305; Fig. 3A). Because 152 \nrRNA constitutes most of the total RNA, these temporal differences likely represent ribosomal 153 \nturnover in metabolically active control cells versus desiccated cells undergoing metabolic arrest. 154 \nHowever, no significant differences in total RNA content were detected between control and 155 \ntreatment groups at any individual timepoint (Wilcoxon rank-sum tests, all p ≥  0.08).  156 \nWe investigated whether the fraction of total RNA occupied by the main cellular rRNAs (16S 157 \nand 23S rRNA) varied during the experiment. To do this, we quantified 16S and 23S rRNA 158 \ntranscript abundances and normalized them to the amount of total extracted RNA (Fig. 3 B,C). 159 \nThe proportions of both rRNA species remained stable across all time points in both treatment 160 \nand control conditions (Kruskal-Wallis 16S rRNA treatment p=0.312 control p=0.541; 23S 161 \nrRNA treatment p=0.644, control p=0.384). Pairwise comparisons revealed no significant 162 \ndifferences between control and treatment groups at any individual timepoint for either 16S 163 \nrRNA or 23S rRNA (Wilcoxon rank-sum tests, p ≥  0.1). These results indicate that the 164 \ncontributions of 16S and 23S rRNAs to the total RNA pool were maintained despite changes in 165 \ntotal RNA content. 166 \nIn contrast to the effects on rRNA, drying and rehydrating reshaped mRNA transcription 167 \npatterns, but transcription profiles were stable in desiccated cells. Analysis of mRNA 168 \ntranscriptional responses revealed significant effects of both time point and treatment, with each 169 \nfactor explaining ~20% of the observed variation in gene expression (PERMANOVA time: 170 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n5 \n21.9%, p=0.001; treatment: 19.1%, p≤ 0.001; Supplemental Fig. 1). The interaction between time 171 \nand treatment explained an additional 16.8% variation (p=0.009), indicating that temporal gene 172 \nexpression patterns differed between control and treatment conditions. The control samples 173 \nshowed changes over time, though none of the consecutive timepoint shifts were significant 174 \n(PERMANOVA p >0.05 between consecutive timepoints). In contrast, the treatment samples 175 \ndisplayed a significant shift in gene expression upon desiccation (day 2 to 8: PERMANOVA 176 \np=0.023, R²=0.53). Upon rehydration, although the time variable explained a large proportion of 177 \nvariance in gene expression profiles (R²=0.56), this shift was not significant (day 14 to 16: 178 \nPERMANOVA p=0.1). Yet, the transcriptional profiles were indistinguishable in the desiccated 179 \nstate (day 8 to 14 PERMANOVA p=0.73, R2= 0.15). These findings show the composition of 180 \nmRNA profiles were variable during drying and rehydration but did not change over time in dry 181 \nArthrobacter cells. 182 \nAnalysis of the temporal patterns in gene expression supported our interpretation that gene 183 \nexpression profiles remained static during desiccation while hydrated cells were transcriptionally 184 \ndynamic. We detected 1,409 genes with significantly different temporal expression patterns 185 \nacross treatment and control conditions (ImpulseDE2 FDR-corrected p ≤ 0.05; Supplementary 186 \nTable 1). These genes clustered into six distinct gene co-expression modules based on their 187 \ncombined temporal patterns across both conditions (Fig. 4, Supplementary Table 1). Across all 188 \ntranscriptional modules, we observed further evidence of a frozen transcriptional state in 189 \ndesiccated cells, as no substantial change in mean gene expression patterns in the treatment 190 \nsamples between days 8 and 14 was apparent (bold brown lines, Fig. 4). However, transcriptional 191 \nprofiles were dynamic in treatment samples during drying and/or rehydration in Transcriptome 192 \nModules 2, 3, 4, 5 and 6 (Fig. 4 B-F). In contrast, these gene co-expression modules exhibited 193 \ndistinct variable responses throughout the experiment in control samples, including between days 194 \n8 and 14, suggesting hydrated cells were transcriptionally active and dynamically responded to 195 \nstarvation. 196 \nTwo main desiccation/rehydration-specific gene expression patterns emerged in the treatment 197 \nsamples. First, Transcriptome Modules 5 (143 genes) and 6 (80 genes) exhibited similar 198 \ntemporal responses where expression increased during drying, remained elevated throughout 199 \ndesiccation, and returned to baseline after rehydration (Fig. 4 E,F and Supplementary Table 1). 200 \nWhile these transcriptional modules showed similar expression patterns in treatment samples, 201 \ntheir expression in hydrated controls was distinct, suggesting that although their roles are similar 202 \nduring desiccation, they were expressed independently during starvation.  203 \nThe annotations of genes expressed in Transcriptome Modules 5 and 6 largely align with 204 \npreviously described responses to desiccation in non-spore forming microbes (Fig. 4 E,F; 205 \nSupplementary Table 1). For example, Transcriptome Module 5 included genes coding for fatty 206 \nacid metabolism, the osmolyte trimethyl glycine (betaine) synthesis and transport, and purine 207 \ncatabolism. The coordinated upregulation of choline-to-betaine conversion machinery alongside 208 \nosmolyte transporters suggests AZCC_0090 depends on the conversion of external choline to 209 \nbetaine for osmoregulation during drying. The upregulation of compatible solute synthesis and 210 \ntransport machinery is a common response to drying (42). Additionally, Transcriptome Module 5 211 \ncontained regulatory and repair systems including numerous transcriptional regulators, DNA 212 \nrepair proteins, and ROS protection enzymes—all hallmarks of desiccation stress responses in 213 \nmicrobes (42). Finally, a large-conductance mechanosensitive channel was also identified that 214 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n6 \nmay prime cells to cope with the stresses associated with rehydration (56). However, we were 215 \nsurprised by the presence of genes in Transcriptome Module 5 that code for protein synthesis 216 \nmachinery including several ribosomal proteins and translation factors. This was unexpected 217 \ngiven the apparent metabolic slowdown during drying, though similar findings have been 218 \npreviously reported (35, 57). Transcriptome Module 6 focused on distinct cellular processes 219 \nincluding carbohydrate processing, protein quality control through proteases, aromatic 220 \ncompound and lipid metabolism, and nucleotide processing via a putative NUDIX family 221 \npyrophosphohydrolase. The genes in Transcriptome Modules 5 and 6 largely align with 222 \npreviously reported bacterial desiccation responses, including expected mechanisms for ROS 223 \nmitigation, osmoregulation, putative membrane remodeling, and activation of DNA and protein 224 \nrepair systems to address oxidized biomolecules (35, 42, 58). Notably, 33% of Transcriptome 225 \nModule 5 and 38% of Transcriptome Module 6 genes lacked meaningful annotations, suggesting 226 \nnovel mechanisms and genes may contribute to desiccation tolerance. The expression patterns of 227 \nboth modules—increased relative abundance during drying and decreased relative abundance 228 \nupon rehydration—suggest these transcripts are likely recycled upon rewetting. 229 \nThe second desiccation-rehydration specific gene expression pattern was the dramatic increased 230 \nabundance of 290 genes across Transcriptome Modules 3 & 4 during rehydration with water 231 \nvapor, suggesting key roles in cellular resuscitation (Fig. 4 C,D). These genes were expressed at 232 \nlow levels in the treatment condition prior to rehydration though their expression was distinct in 233 \ncontrols (Fig. 4 C,D and Supplementary Table 1). Transcriptome Module 3 contained genes 234 \nencoding fatty acid beta-oxidation and aromatic compound degradation pathways, alongside 235 \nabundant transcriptional and translational machinery indicating active protein synthesis during 236 \nrehydration. Stress response signatures included multiple chaperones and catalase, suggesting 237 \nmitigation of oxidative damage, while the high density of transcriptional regulators indicates 238 \nextensive gene expression reprogramming during rehydration. Transcriptome Module 4 contains 239 \ngenes encoding osmoprotectant transport (distinct from Module 5), a xylose metabolism operon, 240 \nhistidine catabolism, DNA repair and replication machinery, and energy metabolism genes. The 241 \nxylose pathway and associated sugar phosphate enzymes may shuttle 5-carbon sugars toward 242 \nphosphoribosyl pyrophosphate (PRPP) for nucleic acid repair or synthesis. Histidine degradation 243 \nmay serve dual functions: chelating divalent cations during desiccation to reduce ROS 244 \ngeneration, then providing carbon and nitrogen upon rehydration (59). InterProScan analysis 245 \nrevealed seven 'hypothetical proteins' (HNP00_000350, HNP00_000836, HNP00_001555, 246 \nHNP00_001771, HNP00_002967, HNP00_002968, and HNP00_003348) containing disorder 247 \ndomains with polyampholyte subdomains—signatures of eukaryotic anhydrins with chaperone-248 \nlike roles during desiccation (60, 61). Two of these (HNP00_001555 and HNP00_001771) also 249 \ncontained HNH-nuclease-like domains, suggesting dual roles in nucleic acid metabolism and 250 \nprotein stability. Together, Transcriptome Modules 3 and 4 represent coordinated cellular 251 \nmachinery activated during rehydration: Module 3 mobilizes lipids for energy while reactivating 252 \ntranscription, translation, and stress management, while Module 4 manages osmotic stress, 253 \nalternative carbon utilization, DNA repair, and resource acquisition essential for growth 254 \nresumption.  255 \nDesiccacted cells are in metabolic stasis. To identify metabolic changes that correspond to 256 \nsurvival during drying and rehydration we also conducted targeted and untargeted intracellular 257 \nmetabolite analysis alongside the transcriptomes. Targeted metabolite analysis revealed that 258 \ntreatment was the dominant factor shaping metabolic composition (PERMANOVA R² = 24.3%, 259 \np < 0.001), followed by temporal dynamics (PERMANOVA R² = 17.8%, p < 0.001) and their 260 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n7 \ninteraction (PERMANOVA R² = 15.9%, p = 0.002). Collectively, these factors explained 58% of 261 \ntotal targeted metabolomic variance in the experiment. Like the results from the transcriptome 262 \nanalysis, we observed a treatment-specific response characterized by metabolic restructuring 263 \nduring drying (between days 2 and 8; PERMANOVA R² = 63.8%, p = 0.01) and rehydration 264 \n(between days 14 and 16; PERMANOVA R² = 33.3%, p = 0.01), with no change in the dry state 265 \n(between days 8 and 14; PERMANOVA R² = 7.3%, p = 0.71). In contrast, control samples 266 \ndisplayed a muted temporal drift (Day 2-8: PERMANOVA R² = 26.6%, p = 0.01; Day 8-14: 267 \nPERMANOVA R² = 21.5%, p = 0.05; Day 14-16: PERMANOVA R² = 10.8%, p = 0.43). These 268 \nresults demonstrate that like the observed compositional stasis in the dry state transcriptomes, 269 \nArthrobacter also undergo metabolic dormancy during desiccation with subsequent recovery 270 \ntoward profiles like the pre-desiccated state. 271 \nTo identify metabolites with correlated temporal profiles, we clustered targeted metabolite 272 \nprofiles based on the shape of their normalized peak height variation over time. Of the 105 273 \ntargeted metabolites analyzed, 46 (43%) showed significantly distinct temporal patterns across 274 \nthe treatment and control samples (modified ImpulseDE2 FDR-corrected p ≤ 0.05; 275 \nSupplementary Table 2). Clustering of significantly different metabolites across the treatment 276 \nand control conditions revealed four Targeted Metabolite Modules (Fig. 5; Supplementary Table 277 \n2). The largest Targeted Metabolite Module (module 1, 21 metabolites) showed a similar profile 278 \nduring desiccation to that observed for Transcriptome Modules 5 and 6 (Figs 4 E,F), 279 \ncharacterized by increasing peak area during drying, an elevated level when dry, and a return to 280 \nbaseline after rehydration (Fig. 5A). Of these metabolites, 12 (57%) were either directly related 281 \nto nucleic acid metabolism, including all ribonucleosides, purine and pyrimidine bases, or 282 \nnucleotide degradation products. These nucleotide degradation products likely accumulate 283 \nbecause transcription ceases in the transition to the dry state. We speculate that existing 284 \nnucleotide monophosphates are progressively dephosphorylated, and their N-glycosidic bonds 285 \ncleaved, releasing the ribonucleosides and nitrogenous bases we observed. This observation is 286 \nconsistent with internal mRNA recycling across the desiccation-rehydration continuum. Also 287 \nnotable were the presence of potential antioxidant compounds including B-vitamins (B1 & B3), 288 \nL-gulonolactone (an intermediate in the ascorbic acid biosynthetic pathway), and N-acetyl L-289 \nglutamic acid (62–65). Finally, both L-methionine and methylthioadenosine (MTA) were 290 \nelevated during desiccation. These metabolites may work as part of a methionine-methionine 291 \nsulfoxide antioxidant cycling system where methionine serves as a ROS scavenger, with MTA 292 \nindicating ongoing methionine recycling, SAM production, and/or polyamine synthesis during 293 \ndesiccation (66). 294 \nThe remaining Targeted Metabolite Modules showed metabolite depletion in treatment samples 295 \nfrom days 2-14, potentially due to active catabolism, abiotic degradation, non-enzymatic 296 \ntransformations, or residual enzymatic activity—possibilities our data cannot distinguish. 297 \nBetaine, a known osmolyte, decreased in both conditions (Targeted Metabolite Module 4; Fig. 298 \n5D) but more steeply in controls. In desiccated cells, betaine synthesis genes were more 299 \nabundant (Transcriptome Module 5; Fig 4E) despite declining betaine concentrations, suggesting 300 \nconsumption or export during osmotic adaptation. In hydrated controls, betaine catabolism genes 301 \n(Transcriptome Module 1; Fig 4A) increased in abundance alongside steeper betaine depletion, 302 \nconsistent with its use as a carbon or nitrogen source during starvation. Supporting betaine 303 \ncatabolism during combined stress, genes encoding sarcosine oxidase, glycine 304 \nhydroxymethyltransferase, and serine dehydratase (betaine-to-pyruvate pathway) were enriched 305 \nin Transcriptome Module 1 and more abundant in controls (Fig. 4A). Finally, Targeted 306 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n8 \nMetabolite Module 3 compounds (Fig. 5C) increased throughout the experiment in starving 307 \nhydrated cells and may represent critical survival compounds, including N-acetyl-alpha-D-308 \ngalactosamine, vanillic acid, 3-hydroxyphenylacetic acid, riboflavin, pyridoxine, thymine, 2,4-309 \ndihydroxypyrimidine-5-carboxylic acid, and kynurenic acid. 310 \nWe further analyzed 3,349 untargeted metabolite features from combined positive and negative 311 \nion modes (1,424 negative ion mode features and 1,925 positive ion mode features). Temporal 312 \nanalysis revealed that 133 features (4%) exhibited significant temporal trajectories across the 313 \nexperimental conditions (Supplementary Table 3). These features were clustered into three 314 \nmodules based on their combined treatment and control patterns, mirroring the approach used in 315 \nthe targeted metabolomics analysis. Untargeted Metabolite Module 1 (Supplementary Fig. 3A) 316 \nexhibited a temporal profile like Targeted Metabolome Module 1 (Fig. 5A): features increased 317 \nduring desiccation, returned to baseline upon rehydration, and remained stable under control 318 \nconditions. Untargeted Metabolite Module 2 is comprised of features that remained stable in the 319 \ntreatment condition across days 2, 8, and 14 but decreased in intensity upon rehydration 320 \n(Supplementary Fig. 3B). These same features were progressively depleted in controls, 321 \nconsistent with their loss in metabolically active hydrated cells while persisting in metabolically 322 \narrested desiccated cells. Untargeted Metabolite Module 3 features increased under control 323 \nconditions but remained low in treated cells on days 2, 8, and 14 (Supplementary Fig. 3C), then 324 \nincreased markedly upon rehydration—consistent with their production or accumulation in 325 \nmetabolically active hydrated cells. 326 \nMany untargeted features could not be assigned specific molecular formulas. Among those that 327 \nwere assignable, several showed consistent patterns across both targeted and untargeted analyses. 328 \nFor example, gluconic acid, uridine, and hypoxanthine exhibited similar temporal trajectories in 329 \nboth datasets (Fig 5A and Supplementary Fig. 3A). In contrast, several metabolites that showed 330 \nstrong temporal signals in the targeted analysis—with increased intensity upon desiccation and 331 \nreturn to baseline upon rehydration (Fig. 5A)—exhibited more muted responses in the untargeted 332 \nanalysis (Untargeted Metabolite Module 2; Supplementary Fig. 3B), characterized by stable 333 \nintensity during desiccation rather than increases, and reduced intensity in hydrated cells. These 334 \nmetabolites included adenosine, deoxyuridine, nicotinamide, adenine, xanthine, and 335 \nmethylthioadenosine. The differences between analytical approaches likely reflect 336 \nmethodological factors: targeted analyses use compound-specific optimization and isotope-337 \nlabeled internal standards for each metabolite, providing greater sensitivity and accuracy for 338 \nknown compounds, while untargeted approaches employ generic parameters optimized for 339 \ndetecting the broadest possible range of features, often at the expense of sensitivity for specific 340 \nmetabolites. The targeted data are therefore more reliable for quantifying these specific 341 \ncompounds, while the untargeted data provide complementary discovery of unanticipated 342 \nmetabolites. Importantly, despite quantitative differences, the directional trends—depletion in 343 \nactive cells and persistence in dry cells—were generally consistent between approaches. 344 \nCONCLUSION 345 \nDormancy is defined as a reversible state of low to no metabolic activity (67). Our results 346 \ndemonstrate that desiccation induces a dormant state in Arthrobacter sp. AZCC_0090, 347 \ncharacterized by compositionally stable mRNA and intracellular metabolite profiles in the 348 \ndesiccated state (days 8-14), despite dramatic restructuring during the transitions into and out of 349 \ndesiccation. Water vapor was sufficient to resuscitate dormant cells, inducing shifts in both 350 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n9 \nmRNA and metabolites that prime cells for regrowth. We identified the accumulation of 351 \nribonucleosides and nucleobases during desiccation that persisted when mRNA profiles were 352 \nstable. The source of these nucleotide degradation products during a period of apparent 353 \ntranscriptional stability remains unclear, but may represent degradation of damaged RNAs, 354 \nturnover of non-coding RNAs not captured in our analysis, or accumulation of RNA turnover 355 \nproducts generated during the drying transition. More focused experiments measuring RNA 356 \nturnover rates directly are needed to resolve this apparent paradox. 357 \nThe signatures we observed in desiccation-induced dormancy differ markedly from other 358 \ndormancy states such as endospores. While most RNAs in dormant endospores are unstable and 359 \ndegrade to serve as nucleotide reservoirs for germination (44, 68), Arthrobacter maintained 360 \nstable RNA profiles throughout desiccation. This pattern more closely resembles RNA dynamics 361 \nin the non-spore forming actinobacterium, Curtobacterium, and biocrust communities where 362 \nRNA profiles remained stable over extended dry periods (43, 69).  363 \nOur findings challenge fundamental assumptions underlying RNA-based assessments of 364 \nmicrobial activity. Though controversial (48, 70), the presence of RNA is thought to identify 365 \nactive microbes in situ. Methods like rRNA gene sequencing or rRNA:rDNA gene abundance 366 \nratios (51, 71) have been used as indicators of microbial activity and metatranscriptomic analyses 367 \nhave used transcript presence as evidence of ongoing transcription (72, 73). Our data 368 \ndemonstrate that: 1) RNA is extractable and sufficiently intact for quantification and sequencing 369 \nfrom desiccated cells, 2) some of these RNAs remain abundant during desiccation-induced 370 \ndormancy, and 3) proportional transcript changes occur primarily during transitions into or out of 371 \ndesiccation, but not during the dormant desiccated state. Consistent with this, recent studies show 372 \ncommunity-level transcription follows rewetting, suggesting that increased transcription is 373 \nassociated with the transition out of the dry state (35, 74). Collectively, the ability to extract 374 \ncompositionally stable RNAs from desiccated cells suggests that RNA-based activity 375 \nassessments cannot distinguish between metabolically active and dormant populations in 376 \nenvironments experiencing desiccation. This has important implications for interpreting RNA-377 \nbased studies in dryland soils, where the presence of RNA may reflect a mixture of active cells 378 \nand dormant cells retaining stable transcripts, potentially leading to overestimates of in situ 379 \nmicrobial activity. 380 \nThere are several important limitations of this work. First, we examined only one strain of 381 \nArthrobacter, a genus known for its desiccation tolerance. Thus, it is unclear whether RNA 382 \nstability is a specific feature of Arthrobacter's desiccation tolerance mechanisms or represents a 383 \nmore general response to desiccation stress. However, the similar findings in Curtobacterium  384 \nsuggest this phenomenon may be widespread among desiccation-tolerant bacteria (69). 385 \nAdditionally, these experiments were performed under controlled laboratory conditions, and their 386 \nrelevance field conditions remains yet to be determined. Unfortunately, methods for studying 387 \nRNA stability in soil microbial communities are currently lacking. Finally, while we 388 \ndemonstrated water vapor was sufficient for resuscitation, the mechanisms by which desiccated 389 \ncells sense and respond to water vapor remain unknown. Some metabolites accumulated during 390 \ndesiccation may exhibit humectant-like properties, but the biophysical and molecular basis of 391 \nvapor-phase rehydration requires further investigation. 392 \nThe persistence of RNA in desiccated cells has implications extending far beyond arid soil 393 \nm\nicrobiology, with relevance for understanding microbial survival and dispersal in extreme 394 \nenvironments, astrobiology applications, pathogen dispersal across climate regimes, and the 395 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n10 \nfunction of airborne microbiomes. The ability of desiccated cells to maintain RNA while 396 \nremaining dormant until water vapor triggers resuscitation suggests environmental microbes may 397 \nbe more resilient than previously recognized, potentially reshaping our understanding of 398 \nmicrobial biogeography and ecosystem function in water-limited systems on Earth and beyond. 399 \nMETHODS:  400 \nStrain source and propagation: Arthrobacter sp. strain AZCC_0090 was isolated as described 401 \nelsewhere (52) and its genome sequence is available (53). All growth experiments were 402 \nconducted on Yeast Mannitol media (YM), consisting of (per liter) 1.0 g yeast extract, 10.0 g 403 \nmannitol, 0.5 g dipotassium phosphate, 0.2 g magnesium sulfate, 0.1 g sodium chloride, and 1.0 404 \ng calcium carbonate. Solid media was prepared with 2% Noble agar. Cell washes and 405 \nresuspensions were conducted in YM salts (per liter): 0.5 dipotassium phosphate, 0.2 g 406 \nmagnesium sulfate, and 0.1 g sodium chloride. All incubations were at 25ºC.  407 \nCell preparation for desiccation: We pelleted a 1.0 L overnight (~18 h) culture of AZCC_0090 408 \n(27,500 rpm for 30 minutes) and washed once with YM salts before resuspending in 1.0 L YM 409 \nsalts. We incubated resuspended cells overnight at 25ºC with shaking. The following day, 10 ml 410 \naliquots of the cell suspension (~5-6 /i1108 cell ml-1) were vacuum-filtered through individual 0.2 411 \nµm pore-size hydrophilic non-hygroscopic polycarbonate membrane filters (GTTP-type; 25 mm 412 \ndiameter) to collect cells. Each filter was placed into a vented petri dish. 413 \nHumidity chamber construction and humidity control: Humidity chambers were constructed 414 \nfrom plastic storage containers. Several 1/4” diameter ports were drilled in each container to 415 \naccommodate humidity sensor wires and to suspend a small fan (SEPA 12V 0.03A). Access 416 \nports were sealed with tape or silicone. The relative humidity was controlled using a series of 417 \nsupersaturated salt solutions as described previously (75). The following saturated salts were 418 \nprepared with Nanopure water (nH2O): nH2O (no salt added), KCl, NaCl, NaBr, K2CO3, MgCl2, 419 \nand CH3CO2K. The salts were considered saturated by the appearance of precipitated salt at 420 \n25ºC. Saturated salt solutions were contained in an open beaker sitting underneath a fan blowing 421 \ntoward the surface of the solution. The fans were wired to a power supply (TekPower TP3005T) 422 \nset to 12V 0.08A. Temperature and relative humidity were continuously monitored with a 423 \nSensirion SEK-SHT35 digital humidity sensor placed inside an empty, closed vented petri dish. 424 \nThe Sensirion sensor was connected to a laptop via a Sensiron SEK-SensorBridge running the 425 \nSensiron Control Center. 426 \nExperimental Design: We randomized filtered cells to treatment or control humidity chambers. 427 \nThe RH in the control chamber was maintained at 99-100% for the duration of the 428 \nexperiment. The RH in the treatment chamber was slowly dehydrated from 100% RH to ~25% 429 \nRH over the course of two weeks (48 h at each humidity level). After 14 days the treatment 430 \nchamber was rehydrated with water vapor for 48 h. Replicate filters were selected randomly 431 \nfrom the treatment and control chambers for cultivability assays, metabolomics, or 432 \ntranscriptomics at days 2, 8, 14, and 16 after the start of the experiment. The chambers were only 433 \nopened to change the saturated salt solutions or for sampling.   434 \nCultivability and spore formation assays: Cells were resuspended from filters in YM salts by 435 \nvortexing for 10 minutes at maximum speed. 200 µl of resuspended cells were fixed with 5% 436 \nformaldehyde (vol/vol) and stained with SYBR green I (1:40 dilution of commercial stock in 437 \nTris-EDTA) for 24 hours before counting with an EMD-Millipore Guava Technologies flow 438 \ncytometer (Millipore, Billerica, MA, USA), as described previously (52). Culturable cells were 439 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n11 \nenumerated by plate counts by serially diluting resuspended cells in YM salts and plating on 440 \nsolid YM. We defined percent cultivability as the percent of resuspended cells enumerated with 441 \nflow cytometry that formed colonies on solid media. We used a 1 h treatment with 70% ethanol 442 \nto determine if Arthrobacter produced ethanol-resistant spores. In brief, filters containing dried 443 \ncells were floated on 70% ethanol for 1 h before spotting on solid YM and incubating. No 444 \ngrowth was observed after ethanol treatment.   445 \nRNA extraction, sequencing and mapping: Replicate filters were placed into microcentrifuge 446 \ntubes containing glass disruptor beads, and frozen at -80ºC until extraction. RNA was extracted 447 \nusing a Qiagen RNEasy mini kit, per the manufacturer’s instructions. DNA was removed with 448 \nAmbion TURBO DNase per the manufacturer’s instructions. RNA was quantified with a Qbit 449 \nusing hs or br kits, as appropriate based on RNA concentration. Samples were sequenced at 450 \nSeqCenter, LLC (Pittsburgh, PA), using their standard protocols. In brief, RNAs were DNAse 451 \ntreated a second time with Invitrogen DNAse (RNAse free). Library preparation was performed 452 \nusing Illumina’s Stranded Total RNA Prep Ligation with Ribo-Zero Plus kit and 10bp IDT for 453 \nIllumina indices. Samples were sequenced on a NextSeq2000 giving 2x51bp reads. 454 \nDemultiplexing, quality control, and adapter trimming was performed with BCL Convert v3.9.3. 455 \nThe quality of generated raw reads was assessed by FastQC v0.73 via Galaxy (galaxy0) (76, 77). 456 \nThe reads were mapped to Arthrobacter sp. AZCC_0090 genome using Bowtie2 v2.4.1 (78) with 457 \ndefault parameters in paired-end mode. Counts were generated with the featureCounts function 458 \nin the Rsubread v2.12.3 R Bioconductor package (79).  459 \nrRNA qPCR: RNA extracts were used as template for RT-qPCR using a SuperScript™ III One-460 \nStep RT-PCR System with Platinum™ Taq DNA Polymerase kit, per the manufacturer’s 461 \ninstructions. 16S rRNA RT-qPCR was performed using 515F 5′ -GTGCCA 462 \nGCMGCCGCGGTAA-3′  and 806R 5′ -GGACTACHVGGGTWTCTAAT-3′  16S rRNA primers 463 \n(80). 23s rRNA RT-qPCR was performed using 111F 5’-ATGTCCGAATGGGGAAACCC-3’ 464 \nand 557R 5’-CACGGTACTGGTCCGCTATC-3’ 23S rRNA primers. Cycle conditions for both 465 \n16S and 23S rRNAs: 48ºC 30 minutes, 95ºC for 10 minutes, followed by 40 cycles of 95ºC 15 466 \nseconds, and 60ºC for 1 minute. All qPCRs were run in triplicate and quantified against a 467 \nstandard curve prepared with Arthrobacter AZCC_0090 gDNA.   468 \nTemporal analysis of RNA abundances. Differential expression analysis was performed using 469 \nImpulseDE2 (81) to identify genes with distinct expression across the experimental conditions. 470 \nImpulseDE2 was selected for its ability to model complete temporal trajectories and detect 471 \nimpulse-like expression patterns where genes temporarily change expression before returning to 472 \nbaseline levels (81). The analysis was conducted in case-control mode to compare treatment 473 \n(case) and control conditions across all time points simultaneously, using count data for genes 474 \nthat had at least 10 reads in three or more samples (3709 of 4653 genes). Genes with adjusted p-475 \nvalues ≤  0.05 were considered significantly differentially expressed. 476 \nClustering for temporal transcriptome dynamics: We used DESeq2 size factor normalized 477 \nexpression values for significantly differentially expressed genes from the ImpulseDE2 analysis 478 \nfor clustering analysis. Size factor-normalized expression values were standardized using Z-479 \nscore normalization to ensure genes were grouped by expression patterns rather than magnitude. 480 \nMean expression values were calculated for each gene at each time point within treatment and 481 \ncontrol conditions. To determine the optimal number of gene expression clusters, we used 482 \nhierarchical clustering using correlation-based distances (1 - Pearson correlation) and Ward's 483 \nlinkage. The number of clusters was determined using the elbow method applied to within-484 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n12 \ncluster sum of squares, with a minimum threshold of 6 modules to ensure adequate resolution of 485 \nexpression patterns.  486 \nMetabolite extraction: Replicate filters were frozen at -80ºC until metabolite extraction. 487 \nIntracellular metabolites were extracted from filtered cells by direct sonication in methanol. 488 \nBriefly, we added 1 mL of cold methanol to tubes containing filtered cells on ice and sonicated 489 \nwith a probe-style sonicator (Fisher Scientific Model FB120 with CL-18 probe) for 5 minutes at 490 \nan amplitude of 60%. Filter debris was separated from supernatant by centrifugation. The 491 \nsupernatant was transferred to a new tube and dried to completeness in an Eppendorf Vaccufuge 492 \nplus at 45ºC for 40 min. We extracted metabolites from several filters without cells and methanol 493 \nonly as negative controls. 494 \nMetabolomics: Analyses were performed at the Joint Genome Institute using standard 495 \nprocedures. Dried extracts were resuspended in methanol containing internal standards (13C-15N 496 \nlabeled amino acids and 2-amino-3-bromo-5-methylbenzoic acid), filtered, and analyzed by LC-497 \nMS using an Agilent 1290 Infinity LC system (Agilent, Santa Clara, CA) coupled to a Thermo 498 \nQExactive HF orbitrap mass spectrometer (Thermo Scientific, San Jose, CA). Polar metabolites 499 \nwere separated using HILIC chromatography on a Poroshell 120 HILIC-Z column with a water-500 \nacetonitrile gradient. Full MS spectra (m/z 70-1050) and MS/MS fragmentation data were 501 \nacquired in both positive and negative ion modes. Sample injection order was randomized with 502 \nmethanol blanks between samples. Complete analytical conditions are provided in 503 \nSupplementary Methods. 504 \nFor targeted metabolite identification, experimental mass spectra were compared to compound 505 \nstandards using custom Python code (82). Spectral features were assigned confidence scores (0-506 \n3) according to Metabolomics Standards Initiative levels (83), with \"level 1\" identifications 507 \nrequiring mass accuracy and retention time matches to pure standards, and highest confidence 508 \nidentifications also requiring matching MS/MS fragmentation patterns. Complete analytical and 509 \nidentification parameters are provided in Supplementary Methods. 510 \nFor untargeted features, experimental mass spectra of unassigned features with MS/MS 511 \nfragmentation data were compared against the Global Natural Products Social Molecular 512 \nNetworking (GNPS) (84) that performs molecular networking and grouping partially and fully 513 \nidentified metabolites based on their MS/MS fragmentation tree, leading to the discovery of 514 \nrelated metabolites/features based on common/similar functional groups; and SIRIUS 4 (85) 515 \nwhich uses isotope patterns in MS spectra and fragmentation profiles in MS/MS spectra to 516 \nfurther assist in molecular formula assignment and potential structural elucidation. CSI:FingerID 517 \nwas used for molecular structure annotation through database searches and CANOPUS for de 518 \nnovo compound class prediction. This integrated workflow supports comprehensive 519 \nidentification of both known and novel compounds from tandem mass spectrometry data in 520 \ncomplex matrices. 521 \nVolume adjustment and Ion mode selection: We experienced sample volume loss during 522 \nmetabolite extraction for nine samples before drying: T0C rep 4 (100 μ L collected), T0C rep 1 523 \n(200 μ L), T0E rep 5 (200 μ L), T0E rep 4 (250 μ L), T0E rep 3 (300 μ L), T1C rep 3 (450 μ L), 524 \nT1E rep 3 (450 μ L), T1E rep 1 (300 μ L), and T2E rep 1 (400 μ L). To account for this volume 525 \nloss, we adjusted metabolite peak heights by multiplying by the ratio of expected volume (500 526 \nμ L) to actual volume collected: Adjusted peak height = Original peak height × (500/Actual 527 \nvolume collected). For targeted metabolites identified in both positive and negative ion modes, 528 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n13 \nwe selected the polarity with higher mean peak heights for subsequent analyses. For untargeted 529 \nfeatures, duplicate metabolites in the negative and positive ion mode were identified. Features 530 \nwith identical predicted molecular formulas and retention times within 5 seconds across positive 531 \nand negative ion modes were considered duplicates. For each duplicate pair, the metabolite 532 \nmeasured in the ion mode with higher mean intensity across all samples was retained. This 533 \ndeduplication process removed 32 duplicated features. For both targeted and untargeted 534 \nmetabolomics, a small value was added to missing and zero values (10% of the smallest positive 535 \npeak area) to facilitate Log transformation in downstream analyses. 536 \nTemporal analysis of metabolites: ImpulseDE2 was designed for RNA-seq count data analysis 537 \nbut was adapted for metabolomics peak intensity data. To do this, we Log2-transformed the peak 538 \nheights, multiplied them by 100, and rounded to create pseudo-count values. Results were 539 \nextracted and clustered using the same framework as transcriptomics analysis described above. 540 \nSignificantly different metabolites were defined as those with Impulse2DE adjusted p-values ≤  541 \n0.05. 542 \nMDS plots and statistics of transcriptome and metabolome data: We used multidimensional 543 \nscaling (MDS) plots and permutational multivariate analysis of variance (PERMANOVA) to 544 \ncompare transcriptome and metabolome profiles over time across conditions. Raw transcriptome 545 \ncount data were processed using DESeq2 (86) with variance stabilizing transformation for 546 \nvisualization and multivariate analyses. We Log10-transformed the metabolome data. For both 547 \ndatasets, multidimensional scaling was used to visualize sample relationships based on Euclidean 548 \ndistances. PERMANOVAs were implemented through the adonis2 function in the vegan R 549 \npackage (87), employing a factorial design (~day + treatment + day:treatment) with 999 550 \npermutations. To resolve temporal dynamics within each condition, we conducted separate 551 \npairwise PERMANOVA comparisons between time point combinations for control and 552 \ntreatment groups independently. 553 \n 554 \nDATA AVAILIBILITY:  555 \nProcessed RNA-seq data generated in this study have been deposited in the NCBI Gene 556 \nExpression Omnibus (GEO) under accession number GSE309279. Individual samples are found 557 \nin accessions GSM9264299-GSM9264323. Raw metabolome data is available in the massIVE 558 \nrepository at the Global Natural Product Social Molecular Networking (GNPS) site under the 559 \naccession MSV00009260. Code used in analyses and metabolite sample metadata are available 560 \nat 10.6084/m9.figshare.30370339. 561 \nACKNOWLEDGEMENTS:  562 \nThe authors would like to thank Bradley Schlottman, and Caitlin Tribelhorn for assistance with 563 \nequipment and experiments. Funding support is from the National Science Foundation in a 564 \nGraduate Research Fellowship Grant award to IAV DGE-2137419 and IOS-2141605 to PC. PC, 565 \nLM and PM were supported by the University of Arizona Research, Innovation & Impact (RII) 566 \nand Technology Research Initiative Fund/Water, Environmental, and Energy Solutions. The 567 \nwork (proposal:https://doi.org/10.46936/10.25585/60001177) conducted by the U.S. Department 568 \nof Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User 569 \nFacility, is supported by the Office of Science of the U.S. Department of Energy operated under 570 \nContract No. DE-AC02-05CH11231.  571 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n14 \nFIGURE LEGENDS: 572 \nFigure 1: Experimental system for controlled desiccation and rehydration. (A) Custom 573 \nhumidity chambers housed within a temperature-controlled incubator. (B) Interior chamber 574 \nconfiguration showing saturated salt solution for humidity control and real-time 575 \ntemperature/humidity sensor. (C) Relative humidity profiles over the 16-day experiment, with 576 \nsampling points (purple circles and vertical dashed lines) and water/salt solutions used at each 577 \nstage. Abbreviations: K-Ac: Potassium Acetate; dH2O: deionized water. 578 \nFigure 2: Desiccation-induced growth inhibition is reversible with water vapor. (A) Cell 579 \ncultivability throughout the 16-day experiment with points showing individual replicate filters 580 \n(n=4) for initial washed cells (no fill), controls (blue), and treatment samples (brown). Brackets 581 \nwith asterisk indicate significant differences between treatment and control (Mann-Whitney p ≤  582 \n0.05). (B) Relative cultivability (treatment/control ratio) showing desiccation-induced decreases 583 \nin cultivability and recovery post rehydration. Points are all-by-all pairwise cultivability ratios. 584 \nThe dashed line in (B) indicates 100% relative cultivability (treatment equals control). Box plots 585 \nwith shared letters are not significantly different (Dunn's test with Bonferroni correction, p > 586 \n0.05). C=Control, T=Treatment, given in terms of percent RH. 587 \nFigure 3: rRNA composition during desiccation and rehydration is constant. (A) Total RNA 588 \nyield from filtered cells over the 16-day experiment. Individual points represent RNA 589 \nconcentrations from replicate filters: initial samples (unfilled), hydrated controls (blue), and 590 \ndesiccation-rehydration-treated samples (brown). (B,C) Normalized abundance of rRNA 591 \ntranscripts (copies ng total RNA/i4 ¹) for (B) 16S rRNA and (C) 23S rRNA. Box plots show 592 \nmedian (horizontal line), interquartile range (box), and 1.5 × interquartile range (whiskers). 593 \nKruskal-Wallis test p-values shown for control (C) and treatment (T) groups across all 594 \ntimepoints (Days 2-16). 595 \nFigure 4: Temporal expression modules of differentially expressed genes. Six distinct co-596 \nexpression modules (A-F) identified through correlation-based hierarchical clustering of genes 597 \nwith significantly distinct temporal responses across treatment and control conditions 598 \n(ImpulseDE2, FDR-corrected p ≤  0.05). Each panel displays mean z-score normalized gene 599 \nexpression value (±SEM) for control (bold blue) and treatment (bold brown) conditions, with 600 \nindividual gene profiles shown as thin lines. Sample sizes (n) indicate the number of genes 601 \nwithin each module. 602 \nFigure 5: Temporal modules of differentially abundant targeted metabolites. Four 603 \nmetabolite modules (A-D) were identified through correlation-based hierarchical clustering of 604 \ntargeted metabolites with significantly distinct temporal responses across treatment and control 605 \nconditions (modified ImpulseDE2, FDR-corrected p ≤  0.05). Each panel displays mean z-score 606 \nnormalized targeted metabolite peak height value (±SEM) for control (bold blue) and treatment 607 \n(bold brown) conditions, with individual metabolite profiles shown as thin lines. Sample sizes 608 \n(n) indicate the number of targeted metabolites within each module. 609 \n 610 \nREFERENCES:  611 \n1.  R. Tecon, D. Or, Biophysical processes supporting the diversity of microbial life in soil. 612 \nFEMS Microbiol. Rev. 41, 599–623 (2017). 613 \n2.  S. Sherwood, Q. Fu, Climate change. A drier future? 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Comprehensive R Archive 811 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\n \n \n20 \nNetwork (CRAN) (2025). Available at: https://CRAN.R-project.org/package=vegan 812 \n[Accessed 11 September 2025]. 813 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\nRelative humidity (% at 25ºC)\n0\n10\n20\n30\n40\n50\n60\n70\n80\n90\n100\nTime elapsed (days)\n0 2 4 6 8 10 12 14 16\nTreatment \nControl \nCultivability\nTranscriptomics\nMetabolomics\nA B\nC dH2O\nKCl\nNaCl\nNaBr\nK2CO3\nMgCl2\nK-Ac\ndH2O\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\nWashed \nDay 2\nC: 100%\nT: 100%\nDay 8\nC: 100%\nT: 65%\nDay 14\nC: 100%\nT: 26%\nDay 16\nC: 100%\nT:100%\n* BA\nDay 2\nC: 100%\nT: 100%\nDay 8\nC: 100%\nT: 65%\nDay 14\nC: 100%\nT: 26%\nDay 16\nC: 100%\nT:100%\nCultivability (%)1\n10\n100 *\nRelative cultivability\n(% of hydrated control)\n0.01\n0.1\n1\n10\nKruskal-Wallis P= 1.76e-8\nba b a\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\nC: KW p = 0.033\nT: KW p = 0.305\nC: KW p = 0.384\nC: KW p = 0.541\nT: KW p = 0.644\nT: KW p = 0.312\nA\nB\nC\n23S rRNA copies ng RNA-1\n106\n2 106\n106\n107\nTotal RNA concentration (ng µl-1)\n0\n50\n100\n150\n200\n5 105\n2 105\nWashed Day 2 Day 8 Day 14 Day 16\n16S rRNA copies ng RNA-1\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\nCondition Control T reatment\nA\n−1\n0\n1\n2\n2 8 14 16\nDays\nZ−score Normalized Expression\nT ranscriptome module 1 (n=571)\nB\n−1\n0\n1\n2\n2 8 14 16\nDays\nZ−score Normalized Expression\nT ranscriptome module 2 (n=325)\nC\n−1\n0\n1\n2\n2 8 14 16\nDays\nZ−score Normalized Expression\nT ranscriptome module 3 (n=173)\nd\n−1\n0\n1\n2\n2 8 14 16\nDays\nZ−score Normalized Expression\nT ranscriptome module 4 (n=117)\nE\n−1\n0\n1\n2\n2 8 14 16\nDays\nZ−score Normalized Expression\nT ranscriptome module 5 (n=143)\nF\n−1\n0\n1\n2\n2 8 14 16\nDays\nZ−score Normalized Expression\nT ranscriptome module 6 (n=80)\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint \n\nA\n−1\n0\n1\n4 8 12 16\nTime\nZ−score Normalized Intensity\nMetabolite cluster 1 (n=21)\nB\n−1\n0\n1\n4 8 12 16\nTime\nZ−score Normalized Intensity\nMetabolite cluster 2 (n=14)\nC\n−1.5\n−1.0\n−0.5\n0.0\n0.5\n1.0\n4 8 12 16\nTime\nZ−score Normalized Intensity\nMetabolite cluster 3 (n=8)\nD\n−1.5\n−1.0\n−0.5\n0.0\n0.5\n1.0\n4 8 12 16\nTime\nZ−score Normalized Intensity\nMetabolite cluster 4 (n=3)\nCondition Control Treatment\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 16, 2025. ; https://doi.org/10.1101/2025.10.16.681527doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}