# Global characterization of Dictyostelium discoideum gene and protein expression changes under hypoxic conditions

Elisabet Gas-Pascual, Hanke van der Wel, Olivier Cochet-Escartin, Stephane

Joly, Jean-Paul Rieu, Christopher M West, Christophe Anjard, Julie Hesnard

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Elisabet Gas-Pascual, Hanke van der Wel, Olivier Cochet-Escartin, Stephane Joly, Jean-Paul Rieu, et al.. Global characterization of Dictyostelium discoideum gene and protein expression changes under hypoxic conditions. BMC Genomics, 2025, 26, pp.1143. 10.1186/s12864-025-12328-9. hal-05449135

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# Global characterization of *Dictyostelium discoideum* gene and protein expression changes under hypoxic conditions

Julie Hesnard¹*, Elisabet Gas-Pascual², Hanke van der Wel², Olivier Cochet-Escartin¹, Stephane Joly¹, Jean-Paul Rieu¹, Christopher M. West²* and Christophe Anjard¹*

## Abstract

**Background** Aerobic eukaryotes utilize O₂ to oxidize metabolites and generate ATP. The response to oxygen deprivation leads to migration to locations with more favorable concentrations (aerotaxis), differentiation, metabolic reprogramming, or more global stress responses. In mammals, changes in gene expression are particularly well studied, with the involvement of the Hypoxia-Inducible Factor (HIF-1), which regulates the transcription of hundreds of genes. However, the protist kingdom lacks the HIF-dependent transcriptional response network to adapt to low O₂ levels, and their hypoxic adaptation remains unexplored.

**Results** To address the scale and scope of hypoxic responses in protists, we characterized transcriptomic and proteomic changes when the social amoeba *Dictyostelium* is subjected to low (1%) O₂ under nutritive conditions over 24 h followed by reoxygenation. Remarkably, 32% of the transcripts quantified were differentially expressed during hypoxia, with greatest changes associated with early (1 h) and late phases (24 h). Protein changes were modestly correlated with and generally lagged behind transcriptional changes. Correlated changes were observed for transcripts and proteins associated with various metabolic, anabolic, and catabolic pathways, as well as chromosome organization, cell cycling, vesicular trafficking, and signaling. Interestingly, transcripts associated with the cAMP signaling pathway normally triggered by starvation were also up-regulated during chronic hypoxia. Additionally, we studied the molecular mechanisms driving the transcriptomic response. Analysis of 4 marker genes showed extremely rapid responses that were graded over a range of O₂ levels, with differential responses to inhibitors affecting protein synthesis and mitochondria, suggesting multiple induction mechanisms driving the transcriptomic response.

**Conclusion** Overall, the amoebal response to a low but non-toxic O₂-level resulted in massive and temporal remodeling of the transcriptome and proteome. This complex expression changes extend beyond simple metabolic adaptation and point toward a multiprong adaptation strategy.

Full list of author information is available at the end of the article

<footnote>*Correspondence:
Julie Hesnard
julie.hesnard@univ-lyon1.fr
Christopher M. West
westcm@uga.edu
Christophe Anjard
christophe.anjard@univ-lyon1.fr</footnote>
**Keywords** Dictyostelium discoideum, Hypoxia, Transcriptomic, Proteomic

**Background**

Most eukaryotes on Earth's surface are aerobic, using atmospheric oxygen (O₂) to generate the energy needed for their metabolism [1]. However, the concentration of oxygen dissolved in environmental, organismic and cellular waters varies dramatically from essentially zero to 21% or ~ 200–250 µM [2, 3]. Organisms and the cells within them have evolved their O₂-dependent enzymes and O₂-binding proteins to function within the variations that they normally encounter. The ability of cells to sense and utilize O₂ for energy production can provide information about their location within oxygen concentration gradients and/or enable them to adapt to changes. The responses may lead to migration to locations with more favorable concentrations (aerotaxis), differentiation, metabolic reprogramming, or more global stress responses. Changes in gene expression are particularly well-studied in metazoans using low O₂ environments [4–6]. It was shown that under normoxic conditions, prolyl hydroxylases (PHDs) use O₂ to hydroxylate the transcription co-factor HIF-1α [7]. This modification allows von Hippel-Lindau protein binding, poly-ubiquitination and degradation of the transcription co-factor [8–10]. Low O₂ inhibits hydroxylation, allowing HIF-1α to dimerize with HIF-1β, enter the nucleus and regulate the transcription of thousands of genes [11, 12]. Many target genes are up-regulated, being involved in angiogenesis, erythropoietin production, glycolysis enhancement and mitochondrial oxygen consumption reduction [13–15]. HIF-1 also induces transcriptional regulators such as O₂-dependent Jumonji-C lysine demethylases involved in epigenetic hypoxic regulation [16, 17]. Additionally, HIF-1 is often overexpressed in cancer cells and contributes to drug resistance, underscoring the importance of understanding hypoxic regulation for effective therapeutic treatments [18–20]. Recent studies revealed that some mammalian hypoxic responses are also HIF-independent, highlighting the complexity of this regulation. Transcription factors like Myc and SREBP2, which control respectively glutamine metabolism and cholesterol synthesis during hypoxia, are part of these alternative pathways [21, 22]. The mechanisms involved and the target genes in these regulations are not yet fully understood. Moreover, most studies are performed on whole organisms or tissues, but transcriptional responses to hypoxic conditions at the unicellular level is still poorly understood.

The social amoeba *Dictyostelium discoideum* is an obligate aerobic organism that lives in the soil, feeding on bacteria [23]. When starvation occurs, individual amoebae can aggregate to form multicellular slugs that migrate

to the soil surface to culminate into terminally differentiated fruiting bodies with dormant spores perched atop cellular stalks, allowing efficient spore dispersal [24]. O₂ is an important signal for *Dictyostelium*, by influencing single cell aerotaxis [25–27], slug migration [28], slug polarity [29], culmination [30], and terminal spore differentiation [31]. *Dictyostelium* lacks the HIF-1 transcription factor but has an O₂-sensing system based on PhyA [32], likely the protist ortholog of PHD2 [33]. The role of this O₂ sensor was mainly studied during multicellular development, revealing that PhyA controls the O₂ threshold for culmination leading to fruiting body formation, ensuring that fruiting bodies differentiate at the O₂-rich surface of the soil to facilitate future dispersal of spores [32, 34]. At higher O₂, PhyA hydroxylates a specific proline residue on Skp1, an adaptor subunit of the Skp1–Cul1–F-box (SCF) class of E3 ubiquitin ligases. This modification leads to Skp1 glycosylation, resulting in selective representation of certain F-box proteins (FBPs) with Skp1 and in SCF complexes [35]. This mechanism modulates the abundance of affected FBPs, which result from autoubiquitination in SCF complexes. Indeed, the absence of PhyA is partially rescued by treatment of cells with proteasome inhibitors. It is predicted that low O₂ modulates tagging of target proteins with polyubiquitin chains that mark them for recognition and degradation by the 26 S-proteasome. Below 10% O₂, PhyA activity is reduced, allowing differential remodeling of the proteome. However, the target proteins and their roles in O₂ adaptation remain unknown. This mechanism appears to be widely conserved among protists, including the pathogens *Toxoplasma gondii* and *Pythium ultimum* [36–38]. Independently, it was described in vegetative cells that hypoxia induces a switch from cytochrome C oxidase optional subunit CgxE to CxgS, resulting in a threefold increase in affinity for O₂ [39]. It was also shown that the two flavohemoglobins, FhbA and FhbB, are induced when cells are incubated at 0.4% O₂, presumably to detoxify nitric oxide produced during hypoxia [40, 41]. However, the global regulation and the biological processes involved in the hypoxic response of *Dictyostelium* and other protists remain largely unexplored.

The aim of this study is to identify new mediators of O₂ responses, by exploring the transcriptional and proteomic responses of vegetative stage (proliferating) *Dictyostelium discoideum* amoebae to 1% (12 µM) O₂. Given O₂ levels expected in its soil environment, this level will be referred to as hypoxic, in contrast to normoxic which will refer to the 21% (~ 240 µM) O₂ experienced at the soil surface. Transcriptomic and proteomic assays were performed over a time course of 24 h followed by 5 h
of reoxygenation. Bioinformatic analyses were used to investigate the biological functions associated with both transcriptomic and proteomic regulation. Additionally, RT-qPCR of select hypoxia marker genes was employed to examine the mechanisms driving the transcriptomic regulation by hypoxia. Several parameters were tested, including the immediate response and the dependence on O₂ levels. Furthermore, the effects of inhibitors targeting mitochondrial respiration, de novo protein synthesis, and the broader class of non-heme dioxygenases were evaluated.

# Materials and methods

## Cell culture and strains

*Dictyostelium discoideum* AX3 cells were provided by the National BioResource Project (NBRP) Nenkin, Japan) and cultured axenically with HL5 medium (HLF2; Formedium, UK) containing 100 mM glucose and 1/1000 penicillin-streptomycin (P/S) (15140-122; Gibco, USA). Cells were grown at 22 °C under agitation at 180 rpm.

## Experimental setup used for transcriptomic and proteomic time course

Exponentially growing cells were diluted to ~2.5 × 10⁵ cells/ml in flasks containing 150 ml (proteomic) or 30 ml (transcriptomic) degassed HL5 medium (Additional file 1: Fig. S1). Shaking flasks were continuously and serially purged with a humidified atmosphere of 1% O₂ in N₂ for 24 h, and then reoxygenated with 20% O₂ for 5 h. A flask was removed to collect cells at 1 h, 5 h, and 24 h under hypoxia, and at 1 h and 5 h during reoxygenation. The standard t0 was collected from the culture tube. After cell collection, cell density was evaluated using a hemocytometer at 5 h, 24 h, and 5 h reoxygenation. For the 2D proteomics analyses, a parallel set of samples maintained at 21% O₂ was collected. Three or more (as indicated) biological replicates were performed for both proteomic and transcriptomic analysis.

2 × 10⁶ or 10⁷ cells were collected for the transcriptomic or proteomic analyses, respectively, by rapid centrifugation at 1200 × g for 1 min at 4 °C, resuspension in ice-cold degassed 33 mM NaH₂PO₄, 10.6 mM Na₂HPO₄, 20 mM KCl, 6 mM MgSO₄, pH 5.8, and repeated centrifugation. After supernatant aspiration, cell pellets were snap-frozen in liquid N₂ and stored at -80 °C.

## Library preparation and RNA sequencing (Massive analyses of cDNA ends from GenXpro)

Total RNAs were extracted (RNeasy Plus Mini Kit, 74134, Qiagen, Germany) and quantified. 100 ng were of those RNA were used for cDNAs synthesis using oligo(dT) primers following the manufacturer's instructions (Rapid MACE-seq kit, GenXpro GmbH, Germany). The library was amplified by PCR, followed by a SPRI bead clean-up

(M1378-00 Mag-Bind TotalPure NGS Omega Bio-Tek, USA). Sequencing and bioinformatic analyses were performed by GenxPro. Samples were sequenced using the Illumina platform NextSeq 500 (5 M reads 1 × 75 bps). The reads were mapped to Ensembl *Dictyostelium discoideum* 2_7 as a reference genome. Data were analyzed using DESeq2 (version 1.38 [42]), and HTSeq (version 2.0.2) was used to quantify the transcripts. The mean transcripts per million (TPM) from the triplicates was used to calculate the log2 fold changes (log2FC) for each time point, using t0 (21% O₂) as the reference. The p-values were calculated using the Wald test and corrected with the Benjamini-Hochberg method to obtain the false discovery rate (FDR).

## Turnover calculation

For all detected transcripts, TPM values provided in Additional file 1: Table S1A, were used to calculate turnovers. For each gene, TPM values between two consecutive time points were subtracted to quantify the extent of degradation or synthesis. Then the absolute values of all TPM differences were summed. Finally, the sum was normalized to a ratio of 1 million and converted into a turnover percentage. For each turnover calculation, the mean value of the replicates and the standard deviation were calculated.

## Differential transcript expression analyses

To analyze differentially expressed transcripts, sequences assigned as non-coding RNAs, pseudogenes, mobile elements and tRNAs were removed manually, and the resulting coding RNAs are summarized in Additional file 1: Table S1B. Pearson correlation was used to assess continuity of total coding RNAs between time points. Then, an ad hoc code was applied to remove genes with at least one missing expression during the time course. The code was used to categorize genes into up-regulated (log2FC ≥ 1) and down-regulated (log2FC≤-1) groups. Only genes showing a significant expression change with an FDR < 0.05 were considered. Within each regulation group, differentially expressed transcripts were classified based on their GO annotations from DAVID, using dicty-Base_IDs [43, 44]. Only transcripts with significant direct GO term classification (p-value < 0.05) and fold enrichment (FDR < 0.05) values were included in the analysis. Interactions between encoded proteins from these enriched transcripts were visualized using STRING (version 12.0). Additional pathways were analyzed manually as indicated.

## Comparison with published transcriptomic data

Coding RNA from Additional file 1: Table S1B were compared to the transcriptomic data from Lamrabet et al. recent study [45]. Total identified RNA data were
extracted from the supplement (https://www.frontiersin.
org/journals/microbiology/articles/https://doi.org/10.
3389/fmicb.2020.00410/full) and converted in log2FC.
Differentially expressed transcripts (log2FC ≥ 1 or
log2FC≤-1; FDR < 0.05) after 1 h of hypoxia compared to
t0, and differentially expressed transcript (log2FC ≥ 1 or
log2FC≤-1; p.value adj < 0.05) compared to cells cultured
without bacteria, were analyzed.

Sample preparation for proteomic analyses

Whole cell protein lysates were obtained by SDS-lysis
followed by reduction, alkylation, and trypsin digestion
according to the S-Trap micro column manufacturer's
recommended digestion protocol (C02-micro-80, Pro-
tiFi, Fairport NY). Briefly, frozen pellets of 10⁷ cells were
thawed on ice in 100 µl 100 mM TEAB buffer (triethyl-
ammonium bicarbonate, natural pH 8.5; Supelco 18597)
and lysed by probe-sonication on ice-water, using a 1/16"
inch microtip and a Fisher Scientific Sonicator, model
FB505, with 20% amplitude and 10 cycles of 1 s on, 3 s
off. An equal volume of 10% SDS in water was mixed
and incubated for 55 min at room temperature, followed
by a 5 min additional incubation at 55 °C. Samples were
spun for 30 min at 21,000 x g at 4 °C and the supernatant
(S21) was transferred to a new microtube. 200 µl of each
S21 was reduced and alkylated by incubation in 5 mM
TCEP (GoldBio) for 15 min at 55 °C, then supplemented
with methyl methanethiosulfonate [46] (Sigma-Aldrich
208795) to 20 mM followed by a further 10 min incuba-
tion at room temperature in the dark. 10 µl, equivalent
to ~ 5 × 10⁵ cells or ~ 50 µg protein, was transferred to a
1.5 mL Protein LoBind tube (Eppendorf) and acidified by
vortexing for 30 s with 1 µl 27.5% H₃PO₄. 165 µl of Bind-
ing Buffer (BB: 100 mM TEAB, pH 7.55, 90% MeOH (LC/
MS-grade, Fisher A456-4)) was added and the sample
was loaded onto an S-Trap micro column (C02-micro-80,
ProtiFi) and spun for 30 s at 4000 x g. The column was
washed 8 times each with 150 µl BB, the final spin being
for 1 min. The columns were transferred to fresh LC/
MS-grade MeOH-rinsed tubes. 20 µl of 0.125 mg/ml
trypsin (sequencing grade, Promega V511A) in 50 mM
TEAB was applied to the column, which was capped and
incubated overnight at 37 °C in a moist chamber. The
S-Trap column was sequentially eluted with 40 µl 50 mM
TEAB, 0.2% formic acid (LC/MS-grade, Pierce PI28905)
and 0.2% formic acid in 50% acetonitrile (LC/MS-grade,
Fisher A955-4). The eluates were pooled, taken to dry-
ness by vacuum centrifugation, and resuspended in 100
µl 0.05% TFA, 5% acetonitrile. 50 µl were dried again,
reconstituted in 0.1% heptafluorobutyric acid (Fluka Bio-
chemika 77249), and purified on C18 Zip-tips (Agilent
Bond Elut Omix) as described in Boland et al. [47]. For
two 5 h samples, the other 50 µl was dried down, redis-
solved in 0.1% TFA, and fractionated using the Pierce

high pH Reversed-Phase Peptide Fractionation kit
(Thermo Scientific, #84868) into 9 pools according to the
manufacturer's instructions. Each were analyzed sepa-
rately below and together was referred to as a 2D analy-
sis, in contrast to the standard 1D analyses.

Mass spectrometry of peptides for proteomic analyzes

Peptides were separated on a C18 nano-column (Thermo
Acclaim PepMap 100 C18 series) using an Ultimate
3000 nano-HPLC, and directly infused into a Q-Exac-
tive Plus Orbitrap Mass Spectrometer (Thermo Fisher)
as described [47]. Raw files were processed in Proteome
Discoverer 2.5 using a *D. discoideum* protein database
essentially as described in Boland et al. [47] with the
following modifications: dynamic oxidation of Met,
dynamic loss of N-terminal Met with or without acety-
lation of N-terminal Met, plus static sulphenylation (+
45.988 Da) of Cys. Proteome Discoverer 2.5 calculated
protein abundances for proteins identified at high (FDR
< 0.01) and medium confidence (FDR < 0.05) were com-
pared with control (t0, 21% O₂), or same h control sam-
ples kept at 21% O₂ if indicated, using the MetaboAnalyst
5.0 data analysis tool [48]. The MS proteomics datasets,
which also include proteins identified with low confi-
dence (FDR < 0.10), are itemized in Additional file 1: S8
and are deposited in the ProteomeXchange Consortium
via the PRIDE [49] partner repository.

Proteomic data analyses

Three or more independent biological replicates, each
including 3 technical replicates, were initially performed
using the 1D method at each time point. A 0 h, 1 h and
5 h set (Series 1) was performed and analyzed separately
from a 0 h, 5 h, 24 h, 1 h reoxygenation and 5 h reoxygen-
ation set (Series 2) owing to differences in the quality and
depth of coverage between them. Pearson correlations
were calculated using the log2FC for the total proteins
identified at each time point in Series 1 and Series 2 with
≥2 peptides and an FDR≤0.05 (Additional file 1: Table
S4) in both series. The same correlation coefficient was
applied to compare total protein and total coding RNA
from Additional file 1: Table S1B. In addition, a 5 h sam-
ple from each series was subjected to 2D analysis, each
with 3 technical replicates. These were ratioed to parallel
5 h samples maintained at 21% O₂ and, owing to differ-
ential depth of coverage, were analyzed separately from a
combination of the 6 1D 5 h samples (pooled from Series
1 and 2) that was also analyzed relative to 5 h samples
maintained at 21% O₂. The following steps were taken to
classify proteins as upregulated at 5 h in Additional file
1: Table. S2. The data from each of the 2D analyses were
processed separately, and data from the six 1D biological
replicates were processed together, each in Metaboana-
lyst (Table S4). Proteins were selected if identified with
≥2 peptides in at least one of the 3 groups, and its level was significantly different from unity (p ≤ 0.05) in this group. Proteins were classified as up-regulated at 5 h if any of the following criteria were met: (i) levels were significantly (p ≤ 0.05) up in all 3 groups, (ii) levels were significantly up in 2 groups, and not significantly different from unity in the third group unless the peptides were of low quality, (iii) levels were significantly up (> 1.2x) in only 1 group, if powered by substantially higher quality hits (peptide number and quality) and/or number of hits, compared to the other two groups, and if neither of the other 2 groups had a significant down level, or (iv) levels were up ≥ 1.4x in all 3 groups, regardless of significance. A similar rule was used to classify proteins as down regulated, using reciprocal values for fold-change requirements, in Additional file 1: Table S2B.

The 24 h DEPs were identified based on ratios relative to t0 that were log2FC ≥ 0.26 or log2FC ≤ -0.26 (equivalent of FC ≥ 1.2 and FC ≤ 0.83) at *p* < 0.05, and are listed in Additional file 1: Table S3. Finally, the total proteins identified from Series 1 and Series 2, along with their relative expression to t0, were merged with the total identified coding RNAs in Additional file 1: Table S4. To assist in interpreting the potential functional significance of DEPs at either 5–24 h, proteins were manually classified into related functional groups based on experimentation or sequence homologies, with assistance from dictyBase curations. Traditional GO term analysis was not informative owing to the lack of statistical significance given the relatively small number of proteins in each group.

### RT-qPCR protocol

For each experiment, $2 \times 10^6$ cells resuspended in 100 µl of HL5 medium were inoculated into 2.9 ml of HL5, previously pre-equilibrated to the desired oxygen concentration using a flow of air/nitrogen mix and maintained at 22 °C. Cells were maintained in a controlled oxygen environment with agitation at 300 rpm. The oxygen concentration was continuously measured using an optical oxygen probe coupled to its oximeter (OXROB3 robust probe and Firesting oximeter, Pyroscience, Aachen, Germany). For the short time course experiment, the HL5 media was pre-equilibrated at 1% O₂ before inoculation, and cells were collected at 0, 5, 10, 20, 40 and 60 min (n = 4). For the drug treatment experiment, cells were exposed to either 1% or 21% O₂ for 1 h with the different chemicals (n = 3). General inhibitors of non-heme dioxygenases were tested at concentrations based on previous studies, including 500 µM cobalt(II) chloride hexahydrate (from a 500 mM stock solution in water, C8661, Sigma-Aldrich, USA), 200 µM deferoxamine mesylate salt (from a 200 mM stock in water, D9533, Sigma-Aldrich), and 1 mM dimethyloxalylglycine (DMOG, from a 100 mM stock in DMSO, 400091, Sigma-Aldrich) [34, 50]. Complex III of

the mitochondrial electron transport chain was inhibited with 37.5 µM antimycin A (from a 5 mM stock in ethanol, A8674, Sigma-Aldrich), and protein synthesis was inhibited using 400 µM cycloheximide (from a 200 mM stock in DMSO, C7698, Sigma-Aldrich) [40, 51]. Cells were treated just before the experiment, except for cycloheximide which was added 30 min in advance. To test the effect of the oxygen concentration on the hypoxic response, cells were exposed to 1%, 2%, 3%, 5%, 10%, 15% or 21% O₂ for 1 h (n = 3).

For each experiment, total RNAs were extracted from $2 \times 10^6$ cells (RNeasy Plus Mini Kit, 74134, Qiagen, Germany). Extracted RNAs were quantified and adjusted to 1 µg to synthesize cDNAs (First Strand cDNA Synthesis Kit for RT-PCR, 11483188001, Roche, Switzerland) using random primers (250ng/µl). cDNAs were diluted at 1/20 to perform qPCR (DyNAmo Flash SYBR Green qPCR Kit, F415L, Thermo Fisher Scientific, USA) (Thermocycler BIOER qPCR LINEGENE 9600 PLUS) using the following primers: *rnlA*-F (5'-GCACCTCGATGTCGGCTT AA-3'), *rnlA*-R (5'-CACCCCAACCCTTGGAAACT-3'), *cxgE*-F (5'-ATGTCCCACGCATTACCAG-3'), *cxgE*-R (5'-CATAGGCAGCGTAAAAATCTTCTC-3'), *cxgS*-F (5'-AAGTTGTTAAATCTCAACTC-3'), *cxgS*-R (5'-TTT ATCAACGCCATATTTAA-3'), *fhbB*-F (5'-GATGGT AAAGAGATAGCTAC-3'), and *fhbB*-R (5'-CTGATA AACTGTAATGTCTGAC-3'). For each qPCR, genes were tested in duplicate.

### RT-qPCRs analyses

For each RT-qPCR, the relative gene expression was determined using the $2^{-\Delta\Delta Ct}$ method with *rnlA* as a reference gene [52]. For the short time course, the relative expression was calculated using t0 (21% O₂) as a standard. In the experiment involving chemicals, the relative expression of cells exposed to 1% O₂ was calculated by using the 21% O₂ condition with the corresponding chemical as a reference, to assess the effect of hypoxia on gene expression. For cells cultured under 21% O₂, the condition without the drug was used as the standard to evaluate the effect of the drug under normoxic conditions. For each gene, the percentage of induction at both 21% and 1% O₂ were calculated using the hypoxic expression level without the drug as the 100% expression. Finally, for experiments across a range of O₂ levels, the relative expression was calculated using the 21% O₂ condition as the standard. Half-induction (50%) was determined by normalizing the maximum induction, which occurred at either 1% for *rabR*, *srsA* and *fhbB* or 2% O₂ for *cxgS*, to 100%. The percentage of expression for the other oxygen concentrations was then calculated accordingly. A curve fit using a logarithmic function was used to identify the oxygen concentration corresponding to 50% induction (Additional file 1: Fig. S2). For all RT-qPCR
experiments, the mean value of the replicates and the
standard deviation were calculated.

RT-qPCR variability analysis

Data from the 10 different experiments corresponding
to t0 (21% O₂ without chemical) and 1 h hypoxia (1%
O₂ without chemical) were combined to assess the main
source of variability (n = 10). The ratios Ct<sub>gene</sub>/Ct<sub>rnlA</sub> were
then calculated. In parallel, TPM at t0 and 1 h hypoxia
were also compared, using the triplicates from Additional
file 1: Table S1A (n = 3). For data visualization, TPM val-
ues were represented in log2.

Results

Hypoxia reduces cell division and triggers two distinct
temporal transcriptomic responses

The response of growth stage *Dictyostelium* amoebae residing in nutritive growth medium to acute (1 h), mid (5 h), and chronic (24 h) hypoxia, followed by reoxygenation (1 h reox and 5 h reox), was examined to cover a time period that at normoxia allows for ~3 population doublings. Cell density slightly increased between 0 and 5 h, then slowed down further between 5 and 24 h before stabilizing during reoxygenation, suggesting that hypoxia reduces cell cycling and that recovery takes time (Fig. 1A). In contrast, the total RNA content per cell, including polyA RNA and rRNA, strongly increased between 5 and 24 h, indicating a robust transcriptomic response (Fig. 1B). This was confirmed by the turnover rate calculated

<figure><img src="image_3.png" /><figcaption>**Fig. 1** Global transcriptomic response to hypoxia over the time course. **A:** Effect of hypoxia (1% O₂) on cell division. Cell density was estimated using a hemocytometer. The mean value of the replicates ± the standard deviation is shown on this graph. **B:** Total RNA quantity across the time course. This RNA measurement reflects the total RNA extracted from the collected cells, and was later used for library preparation. The average value of the replicates ± the standard deviation is displayed on this graph. **C:** RNA turnover across the time course. The turnover was calculated by subtracting TPM values between two successive time points for each gene, representing both newly synthesized and degraded RNA. Then the absolute values of all genes TPM differences were summed. Finally, the sum was normalized to a ratio of 1 million and converted into a turnover percentage. The average value of the replicates ± the standard deviation is displayed on this graph. **D:** Correlation between transcripts expression over the time course. Pearson correlation coefficients were calculated between all time points for all coding RNAs (12,265 transcripts). **E:** Number of regulated transcripts at each point of the time course compared to the t0 (21% O₂). An ad hoc code was applied to remove transcripts with at least one missing expression during the time course. From that list of 11,395 transcripts, only those with a significant differential expression (log2FC ≥ 1 or log2FC ≤ -1; FDR < 0.05) are represented. **F:** Venn diagram representing the overlap among the early, late and reoxygenation responses. Out of the 11,395 transcripts, only those with a significant differential expression (log2FC ≥ 1 or log2FC ≤ -1; FDR < 0.05) at 1 h, 24 h and both reoxygenation time point are shown</figcaption></figure>
on a per transcript analysis resulting from the RNA-seq data across time points. We calculated an overall turnover of approximately 20% between each time point, with a marked increase to 40% between 5 and 24 h (Fig. 1C).

Additionally, correlations between each time point of the RNA-seq data were calculated, based on the 12,265 detected transcripts associated with coding genes (summarized in Additional file 1: Table S1B). We found that the 1 h response was slightly correlated with the 5 h but not to the 24 h response (Fig. 1D). In contrast, the 24 h time point was poorly correlated with earlier time points, but displayed a strong correlation with both reoxygenation time points. The dynamics of only significantly regulated transcripts (log2FC≤1 or log2FC≥1; FDR<0.05) were analyzed to confirm these conclusions, by removing transcripts with at least one missing expression during the time course. Among the resulting 11,395 transcripts, we observed that 32% of the detected coding RNAs were significantly regulated during hypoxia. As shown in Figs. 1E and 560 genes were differentially expressed at 1 h compared to t0 (21% O₂). The number of regulated genes dramatically decreased to around 830 at 5 h, before sharply increasing to 2,531 genes at 24 h following hypoxia. This number remained stable during reoxygenation. These data suggest that the hypoxic response is mediated in two stages, at 1 h and 24 h. This conclusion was further validated by analyzing overlapping responses between those main time points and reoxygenation (Fig. 1F). Specifically, 8% of the genes regulated at 1 h were also regulated at 24 h (127 out of 1,559). In contrast, 61% of the genes regulated at 24 h were also regulated during reoxygenation (1,549 out of 2,521).

Together, these data showed a strong transcriptomic response to hypoxia that occurred in two steps: a rapid adaptation, during the first hour, followed by a later response that persisted during reoxygenation.

**Protein regulation also presents temporal phases with a moderate positive correlation with the transcriptomic data**

Parallel samples from the same time course were subjected to a mass spectrometry based proteomics workflow to identify proteins and determine their abundances (see Methods). We then evaluated the conservation of the ~1700 proteins that were detected at sufficient levels to be quantified of total protein regulation between time points. Changes recorded at 1 h were modestly conserved at 5 h, with a positive correlation of 0.67 (Fig. 2A). Given the limited time for protein levels to change and the improved statistics for affected proteins at 5 h, the 1 h data are not systematically summarized. A smaller positive correlation of 0.46 was calculated between 5 h and 24 h time points (Fig. 2B). The data suggest discrete early and late stages of adaptation that are, however, not as independent as suggested by the transcriptomic data.

In contrast, a strong positive correlation between 24 h and reoxygenation time points ranging from 0.84 to 0.93 was observed. This indicated delayed proteomic recovery during the 5 h of reoxygenation, as also observed transcriptomically. The strong similarities between the 24 h and 1 h of reoxygenation data validate the assignments of differentially expressed late phase proteins described below.

Additionally, the dynamics of significantly regulated proteins (log2FC≥0.26 or log2FC≤-0.26; FDR<0.05) were analyzed. Based on this threshold and out of the ~1700 detected proteins, 2.6, 5.0 and 25% of the total were differentially increased at 1, 5, and 24 h, respectively, whereas, 7.2, 5.1, and 17% were decreased (Fig. 2C). Thus by 24 h, 42% of the proteome quantitated was significantly affected by treatment at 1% O₂, indicative of major proteomic remodeling during hypoxia. Moreover, the approximate quadrupling of differentially expressed proteins at 24 h of adaptation corresponds to the burst differentially expressed transcripts.

To assess potential linked regulation, the correlation between the transcriptomic and proteomic datasets was calculated. While only a correlation coefficient of only ~0.34 was observed at 1 h (Fig. 2D), it improved to 0.33–0.66 (depending on the series) at 5 h and ~0.48–0.57 at 24 h and during reoxygenation (Fig. 2E). The correlations at the later time points indicated that a substantial fraction of the proteomic changes could be explained by changes in transcript levels, and lower correlation at 1 h is consistent with greater time required for proteomic responses. This is supported by the fact that the 1 h transcriptomic data show a stronger correlation with the 5 h proteomic data than with the 1 h proteomic data (0.58 vs. 0.34).

Taken together, the data indicate that the proteomic response is also temporally regulated, with a discrete early regulation phase that shows limited correlation with the transcriptomic response. In contrast, the later proteomic regulation, which persists during reoxygenation, is more closely aligned with the transcriptomic response. Therefore, we conducted further analyses to better understand the biological processes associated with these two distinct responses at both transcriptomic and proteomic level.

**The early transcriptome response (1 h) is associated with ribosome biogenesis**

We first investigated biological functions associated with the early response to hypoxia. 677 transcripts were exclusively regulated after 1 h of hypoxia, with a majority being weakly up-regulated compared to t0 (Additional file 1: Fig. S3A). No significant biological function was identified for the down-regulated transcripts. The proteins encoded by the up-regulated transcripts were identified
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Global proteomic response to hypoxia over the time course and correlation with the transcriptomic data. Correlations were calculated using the log2FC for the total proteins identified at each time point in each series with ≥ 2 peptides and an FDR ≤ 0.05. A: Correlation at the proteomic level across Series 1 (early response). Pearson correlation coefficients were calculated between the 1 h and 5 h hypoxia time points. B: Correlation at the proteomic level across Series 2 (late response). Pearson correlation coefficients were calculated between the 5 h, 24 h and both reoxygenation time points. C: Number of regulated proteins at each time point of the time course compared to the t0. Only proteins with significant differential expression (log2FC ≥ 0.26 or log2FC ≤ -0.26; *p* < 0.05) are shown. D: Correlation between transcriptomic and proteomic data across Series 1 (early response). Pearson correlation coefficients were calculated between the 1 h and 5 h hypoxia time points. E: Correlation between transcriptomic and proteomic data across Series 2 (late response). Coefficients were calculated between the 5 h, 24 h and both reoxygenation time points</figcaption></figure>

as forming a single cluster of strongly interacting proteins (Fig. 3A), suggesting participation in the same pathway. We showed that these transcripts were enriched relative to their representation in the total genome across multiple steps of the ribosome biogenesis pathway (Fig. 3B). These effects, which included both nuclear and cytoplasmic processes, showed an over representation of transcripts linked to both rRNA and tRNA methylation (Additional file 1: Table S5A). Uridine modifications were also enriched, with a notable up-regulation of transcripts associated with pseudouridine synthesis and wobble uridine modification in tRNAs. Furthermore, a particularly strong enrichment of genes involved in ribosomal subunit maturation and ribosome assembly was observed. Genes encoding enzymes for the histidine-to-diphthamide modification, a key modification required for translation, were also over-represented. Finally, genes involved in rRNA transcription were up-regulated, alongside positive regulation of RNA polymerase I. Interestingly, 16 ribosomal subunit proteins were significantly up-regulated in the proteome at 1 h but not to the 1.2 FC threshold, vs. 5

that were significantly down-regulated (Additional file 1: Table S4). This trend correlates with the transcriptional up-regulation observed for this class of genes.

Collectively, the transcriptomic data indicates the potential for rapidly induced ribosome biogenesis following hypoxia. Moreover, mid-hypoxia does not have a distinct biological function of its own but rather extends the acute hypoxic response (Additional file 1: Table S5B). These findings suggest that mid-hypoxia represents a transitional phase between rapid adaptation and the slower, long-term response.

**The early proteomic response (5 h) differentially favors select protein groups for up- or down-regulation**

We reasoned that the early transcriptome response would be most likely reflected in the 5 h proteome. To improve sensitivity, two of the six 5 h samples were also analyzed by the 2D method, each with three replicates. The combined analysis of all 5 h 1D and 2D data increased the number of quantified proteins by about 50% to 2,688 proteins. Of the 202 proteins that were
<figure><img src="image_5.png" /><figcaption>**Fig. 3** Acute hypoxia induces the up-regulation of transcripts related to ribosome biogenesis. **A:** Interactions between proteins encoded by transcripts up-regulated (log2FC ≥ 1; FDR < 0.05) after 1 h of hypoxia were evaluated using STRING analysis. Proteins involved in ribosome biogenesis are represented in green and those involved in total methylation, including rRNA methylation, are in yellow. **B:** Fold enrichment of transcripts up-regulated at 1 h after hypoxia. Only up-regulated transcripts at 1 h were analyzed (log2FC ≥ 1; FDR < 0.05), and only significant fold enrichments (FDR < 0.05) are shown (from Additional file 1: Table S5A). The fold enrichment was provided by DAVID comparing the representation of the GO category transcripts in the set of differentially expressed transcripts relative to the 7,539 genes from Dictyostelium reference genome in the DAVID database</figcaption></figure>

induced in this expanded list (Additional file 1: Table S2A), 22% were associated with O₂ as O₂-binding proteins, re-dox enzymes, or enzymes utilizing O₂ as a substrate (Fig. 4A). In contrast, only 9% of the down-regulated proteins were associated with this group. Similarly, proteins associated with lipid metabolism, central carbon and amino acid metabolism, signaling, and cytoskeleton were much more highly enriched in the up-regulated group compared to the down-regulated group. A notable example of a coordinated transcriptional and proteomic response was the dihydrophingosine desaturase DesA associated with lipid metabolism. This gene is induced at the transcriptomic level as early as 1 h after hypoxia and shows an increase in protein abundance after 5 h (Additional file 1: Table S4).

In comparison, the 157 proteins that were significantly down-regulated (Additional file 1: Table S2B) were more highly enriched with functions such as with cell replication, chromosomes, RNA expression, nuclear transport, and mitochondria (Fig. 4B). Notably, the steady state levels of the great majority of ribosome-related proteins were not affected. Together with effects noted in the 1-h proteome, the effects of up-regulation of ribosome-related transcripts at 1 h appear to be transient or supporting increased protein turnover rather than abundance.

**The long-term response is related to multiple processes that persist during reoxygenation**

We then analyzed the response to chronic hypoxia, which largely persists during reoxygenation, as identified in both transcriptomic and proteomic data. In the transcriptome, 986 transcripts were regulated at all these time points, and a significant portion of them were overexpressed (Additional file 1: Fig. S3B). In contrast to the short-term response, the proteins associated with these up-regulated transcripts are less interconnected, suggesting that distinct functions are involved (Fig. 5A). In comparison, about 700 of the ~1,700 proteins that were quantified were differentially expressed at 24 h. All transcripts and proteins that are not documented in the figures can be found in Additional file 1: Table S4. Additionally, the differentially expressed proteins are listed in Additional file 1: Table S3, where they are assigned to groups based on function or sequences suggestive of function. The expression trajectories from 5 h of hypoxia through 5 h of reoxygenation are plotted for some examples (see below).

An enrichment was observed for transcripts involved in protein transport, including several Vacuolar Protein Sorting genes (Fig. 5B, Additional file 1: Table S5C). Among them, proteins associated with COPII (rER to Golgi), endocytosis, vesicle fusion, and the ESCRT complex are similarly up-regulated at the protein level at 24 h, whereas rER proteins tended to be down-regulated at
<figure><img src="image_6.png" /><figcaption>**Fig. 4** Classification of differentially expressed proteins (DEP) after 5 h at low O₂. Within the outer circles, proteins related by sequence or functional annotations, and that were highly enriched in either up-regulated on down-regulated lists (p ≤ 0.05), are represented by circles scaled to group size. DEPs were identified based on their relative enrichment at 1% O₂ relative to 5 h at 21% O₂, in a set of 8 samples analyzed by 2D or 1D separation methods, which yielded greater depth of coverage (Additional file 1: Table S2). Under the group labels are the number of proteins in the up or down list, followed by the number of proteins in the opposite (down or up) list. Proteins assigned to multiple groups are represented in the overlap regions. Sums exceed the total value owing to classification in multiple categories. **A:** Classification of up-regulated proteins after 5 h at 1% O₂. Proteins that were increased (p ≤ 0.05) from the 2,688 proteins that were quantified with ≥ 2 peptides in the amalgamated 1D and 2D data (see Methods), are represented by the outer circle. **B:** Classification of down-regulated proteins after 5 h at 1% O₂. Same for proteins that were significantly down-regulated</figcaption></figure>

both transcript and protein levels (Additional file 1: Table S3F). Many transcripts encoding small GTPases associated with protein transport were strongly induced, such as *arrF*, *arrK*, *arrH*, and *arrJ*, with *arfA* showing more moderate up-regulation. As noted below, many transcripts and proteins associated with the cytoskeleton, and which might synergize with protein transport, were also up-regulated. Regarding potential regulation of these processes, many transcripts and 17 proteins associated with calcium (Fig. 5C; Additional file 1: Table S1D, Table S3K) were strongly over-expressed during chronic hypoxia. This includes calcium up-regulated proteins (CUPs), Ca²⁺-binding proteins, and calmodulin-related proteins. In the transcriptome, the CUPs exhibited a distinct pattern, with induction at 1 h and decreased expression at 5 h, followed by intense overexpression during chronic hypoxia and reoxygenation. This pattern was especially observed for *cupB*, *cupG*, *cupC* and *cupF*. Though the significance is not clear, dramatic transcript increases for the highly abundant discoidin proteins encoded by *dscA* and *dscD* were observed after 24 h hypoxia, while *dscE* increased only after 5 h reoxygenation, and *dscC* expression mostly remained constant

over the time course. These findings were confirmed at the protein level for *DscA*, *DscD*, and *DscE* (Additional file 1: Table S3D).

Regarding protein degradation, transcripts related to autophagy, including *atg8*, *atg12*, and *atg18*, were upregulated. The expression of *atg5* was also moderately induced, and the proteomic analysis showed that *Atg5* as well as *Atg8a* and *AreA* (autophagy regulator A) were strongly represented after 24 h of hypoxia and during reoxygenation (Additional file 1: Table S5C). Another enrichment was observed for transcripts involved in protein polyubiquitination, including *nosA* and *rbrA* E3 ubiquitin protein ligases. Even while they were not annotated in the GO biological process, an induction of several ubiquitin-related transcripts, including *ubqI* and *ubqH* (as well as *ubqK*, *ubqI*, *ubqG*, and *ubqA*) was also observed (Additional file 1: Table S1B). In the proteome, 15 of the 17 proteasome-associated, and all 12 of the ubiquitin-associated proteins were differentially up-regulated (Additional file 1: Fig. S4A and Table S3E). Among them, the proteins *UbcB* and *CulE*, were increased proteomically though did not meet the transcriptome criteria for change.
<figure><img src="image_7.png" /><figcaption>**Fig. 5** Chronic hypoxia regulates multiple processes that persist during reoxygenation. **A:** Interactions between proteins encoded by transcripts up-regulated at 24 h and during reoxygenation. Interactions were evaluated using STRING analysis for up-regulated transcripts (log2FC ≥ 1, FDR < 0.05). Proteins involved in protein transport are represented in green, those in the ubiquitin catabolic process in blue, those responding to cAMP in red, and those associated with autophagy organization in yellow. **B:** Fold enrichment of transcripts up-regulated after 24 h hypoxia and during reoxygenation. Only up-regulated transcripts were analyzed (log2FC ≥ 1; FDR < 0.05), and only significant fold enrichments (FDR < 0.05) are shown (from Additional file 1: Table S5C). **C:** Calcium associated proteins regulated at 24 h and during reoxygenation. All significantly regulated proteins (p-value < 0.05) are shown. **D:** Fold enrichment of down-regulated transcripts after 24 h hypoxia and during reoxygenation. Only down-regulated transcripts were analyzed (log2FC ≤ -1; FDR < 0.05), and only significant fold enrichments (FDR < 0.05) are shown (from Additional file 1: Table S5C). **E:** Regulation of histones and histone-related proteins during chronic hypoxia and reoxygenation. Note that H2Bv1 was a low-confidence hit</figcaption></figure>

Finally, many glycolytic enzymes were up-regulated at the proteomic level while remaining minimally changed in the transcriptome. The stronger up-regulation of FBPase and PEP-carboxykinase relative to no effect on rate-limiting steps of glycolysis suggested that gluconeogenesis was activated (Additional file 1: Fig. S4B and Table S3C). This is consistent with the strong up-regulation of the peroxisome-associated citrate synthase GltA likely involved in the glyoxylate cycle [53], and the up-regulation of acyl-CoA dehydrogenase, both of which might provide carbon for gluconeogenesis (Additional file 1: Fig. S4C). However, another enzyme of the glyoxylate cycle, malate synthase, was down-regulated. Glycogen is another potential source of carbon based on the strong up-regulation of Tf2, a putative transcription factor for GlpD [54]. Though an apparent increase in the Tf2 protein did not pass our FDR threshold, the *glpD* transcript was strongly increased, whereas the transcript

level of *glpV*, the vegetative isoform, was unaffected. Mixed effects on enzymes of the pentose phosphate and hexosamine biosynthetic pathways did not offer a clear destination for glucose. Effects on levels of enzymes of the TCA cycle were also mixed, and opposite effects were observed on two putative glutamine synthases. Finally, a substantial number of proteins related to oxygen or redox reactions, including some enzymes of oxidative phosphorylation, continued to be elevated in the proteome at 24 h, and outnumbered those downregulated by a 3.3:1 ratio (Additional file 1: Table S3B). However, they comprised only ~ 11% rather than 22% of total up-regulated proteins at 5 h of 1% O₂.

Among the down-regulated transcripts, a significant fold enrichment was observed for genes involved in the peptidoglycan catabolic process, including lysosome-related genes such as *alyC*, *alyB*, and *lyEh3* (Fig. 5D, Additional file 1: Table S5C). This enrichment is associated
with the defense response to bacterium and killing of cells against other organisms. In the proteomic data, 10 proteins associated with the membrane of lysosomes, including the vacuolar ATPase, were reduced in level (Additional file 1: Table S3G). Transcripts involved in vesicular proteolysis were also down-regulated, with moderate but statistically significant fold enrichment. Within this cluster, serine and cysteine proteases as well as peptidases like *tpp1C* and *tpp1B* were identified. The cluster also included lysosomal cathepsins, specifically *ctsZ* and *ctsD*, that were also strongly down regulated at the protein level (Additional file 1: Table S3G). Moreover, proteomic analyses highlighted that proteins associated with protein synthesis and lipidation were largely depressed during chronic hypoxia and reoxygenation (Additional file 1: Table S3D), though the lipidation enzyme changes were anticipated from the transcriptional analyses. Finally, an enrichment was observed for sexual reproduction for down-regulated transcripts (Fig. 5D). However, this annotation was based on the biological aspect of an ancestor (IBA) and lacks any additional characterization (Additional file 1: Table S5C).

At the protein level, 63 cytoplasmic and mitochondrial ribosomal proteins were reduced to a statistically significant extent (Additional file 1: Table S3D), against 4 that were increased. These effects were mirrored by the reduction in level of 7 of the 8 tRNA synthases/ligases that changed (Additional file 1: Fig. S4D and Table S3I), which roughly paralleled their transcriptome. These effects persisted through the 5 h reoxygenation period, implying continued reduced translational activity. Capacity for protein secretion appeared diminished based on decreased levels of all 6 rER enzymes classified as DEPs, and increased levels of all 4 COPII proteins associated with retrotranslocation back to the rER (Additional file 1: Table S3F). The majority (17/20) of other factors associated with protein translation were also decreased, with the exceptions potentially supporting selective translation of a subset of mRNAs (Additional file 1: Table S3D). As indicated above, reduced capacity for protein synthesis was matched by increased protein degradation capacity. Finally, proteomic data also showed that many histones (H1, H2Bv1 (below threshold), H2Bv3, H3a, H4b) and a putative nucleosome assembly protein were moderately repressed at 24 h, but the greatest effect was noted for H1 (log2FC=-1.6) (Fig. 5E, Additional file 1: Table S3H). An exception was H2AX, one of two H2a isomers, which was not affected. Interestingly, the transcripts for these histones, except for *H4b*, remained stable.

Taken together, those data imply that chronic hypoxia influences multiple processes, including decreased capacity for protein synthesis and up-regulation of proteasomal degradation and protein transport, and catalytic lysosomal activities that might also connect with

increased autophagy. Proteomic analyses revealed that chronic hypoxia was also associated with an increase in the activity of central carbon metabolism though the precise direction of metabolic flow was unclear. Interestingly, most changes in levels of metabolic enzymes and proteins involved in anabolic processes were not detected at the transcriptomic level. Thus, increased proteolysis might explain decreased protein synthesis capacity, but other mechanisms such as translational regulation may need to be invoked to explain effects on metabolic enzymes.

## Chronic hypoxia specifically down-regulates genes related to cell division

Among the transcripts regulated in the long-term response, some returned to their basal state immediately upon reoxygenation. Most were repressed (Additional file 1: Fig. S3C), with no functional clusters identified for the up-regulated transcripts. The down regulated transcripts were associated with cell division, with significant fold enrichments for DNA replication and mitotic division (Fig. 6A and B, Additional file 1: Table S5D). More specifically, the *orcD* transcripts, which encode a subunit of the origin recognition complex and *cdc45*, were downregulated. At the proteomic level, PCNA and Rfa1 were found to be repressed (Fig. 6C). In contrast, RcbA supposedly involved in DNA repair was shown to be induced. The expression of transcripts related to the mitotic spindle assembly checkpoint, including *anapc7* and *bub1*, were also down-regulated. A few repressed transcripts involved in chromosome separation were manually identified as proper annotation were lacking. These include the inner centrosome *icpA*, the kinetochore *spc25* and the kinesins *kif12* and *kif13* (also observed below threshold in the proteome). Consistently, two main proteins that contribute to cell cycle progression, Cdk1 and Gnl3, were down regulated at the protein level (Additional file 1: Table S3K). This best correlates with regulation via protein turnover rather than synthesis, as occurs for other regulators of cell cycle progression.

Collectively, these results indicate that hypoxia inhibits cell division, likely by limiting both DNA replication and the mitotic metaphase-to-anaphase transition. This response takes time to set up but disappears quickly when oxygen concentrations return to normoxia, especially at the transcriptomic level. These findings align with the near arrest of cell division between 5 and 24 h (Fig. 1A), despite an expected 8-fold increase in cell number after 24 h in normoxia.

## Chronic hypoxia stably induces genes linked to cAMP-dependent aggregation

We surprisingly found that transcripts related to the cAMP pathway were induced after 24 h of hypoxia and
<figure><img src="image_8.png" /><figcaption>**Fig. 6** Chronic hypoxia represses the expression of genes related to cell division. A: Interactions between proteins encoded by transcripts down-regulated (log2FC≤−1; FDR < 0.05) after 24 h of hypoxia. These direct and indirect interactions were evaluated using STRING. In purple are represented the proteins involved in DNA replication are represented in purple; proteins involved in the mitotic spindle are in red. B: Fold enrichment of transcripts down-regulated (log2FC≤−1; FDR < 0.05) after 24 h of hypoxia. Only significant fold enrichments (FDR < 0.05) are shown (from Additional file 1: Table S5D). C: Regulation of cell cycle-associated proteins. Only significantly regulated are shown</figcaption></figure>

during reoxygenation (Fig. 7) This well-studied pathway contributes at many levels to starvation-induced development, so further characterization was performed (Additional file 1: Table S1E). Manual analysis was required as many components associated with chemotaxis are known only in *Dictyostelium* and poorly included in global databases. Its 3 adenylate cyclases were up-regulated in the 24 h transcriptome, with *acaA* continuing to increase during reoxygenation (Fig. 7A). The cAMP receptors *carC* and *carD* were repressed during acute hypoxia, and then moderately up-regulated during chronic hypoxia and reoxygenation (Fig. 7B). *CarA* was strongly repressed at 1 h after hypoxia, before turning to be slightly induced at 24 h and beyond. The induction of the extracellular phosphodiesterase *pdeE* started during chronic hypoxia, while *pde4* and *pde7* expression were mainly correlated to reoxygenation (Fig. 7E). The phosphodiesterase inhibitor *pdiA* was strongly repressed during acute hypoxia, before being up-regulated during chronic hypoxia and reoxygenation. The intracellular cAMP phosphodiesterase RegA was induced at 5 h reoxygenation, while the

associated histidine kinase genes *dhkC* and *dhkB* were induced by 24 h (Fig. 7C). Finally, members of the cAMP dependent kinase pathway, including *pkaR* and *pkaC* as well as a downstream target, the transcription factor *bzpF*, were also induced (Fig. 7D). Proteins related to those transcripts were not detected, except RegA that showed a strong induction at 24 h but detected with an FDR < 0.1 by a single peptide. Overall, the transcriptomic profiles suggested initial hypoxic refractoriness to cAMP until persistent activation of the network at the chronic stage and during reoxygenation.

Moreover, many transcripts and proteins associated with the cytoskeleton were overexpressed during chronic hypoxia and reoxygenation (Additional file 1: Table S1D, Table S3D and Table S5C). Many actin-coding transcripts were strongly induced and, at the protein level, the Act21 family (17 different genes with identical protein sequences) and Act3 were also up-regulated. Transcripts encoding actin-binding proteins were significantly up-regulated, with a strong induction of ponticulin transcripts including *ponC* and *ponF*, alongside
<figure><img src="image_9.png" /><figcaption>**Fig. 7** The cAMP aggregation pathway is activated at the transcriptomic level during chronic hypoxia and reoxygenation. Only differentially up-regulated (log2FC ≥ 1; FDR < 0.05) transcripts after 24 h of hypoxia or for at least one time point during reoxygenation were plotted. **A:** Transcriptomic profiles of the cAMP receptors. CarA, CarC, and CarD, were differentially expressed. **B:** Regulation of the adenylate cyclases involved in cAMP production. **C:** Regulation of the RdeA/RegA two-component system and their regulatory kinases, DhkB and DhkC. RdeA expression is not represented as it was not differentially expressed. **D:** Transcriptomic profile of the regulatory (PkaR) and catalytic (PkaC) subunits of the cAMP dependent protein kinase A and its associated transcription factor BzpF. **E:** Transcriptomic profile of cAMP phosphodiesterases and an associated inhibitor involved in cAMP degradation</figcaption></figure>

a more moderate induction of fimbrin transcripts like *fimA*, which also showed increased expression at the protein level. Additionally, some regulators of the actin cytoskeleton, including the *arfGAPs*, were induced at the transcriptomic level. The myosin transcripts *myoC*, *myoK* and *myoB* were induced after 24 h in hypoxia and during the first hour of reoxygenation. At the protein level, MyoC and MyoK (below confidence threshold) and 6 other myosin heavy or light chains (but not MyoB) were induced (Table S3). In contrast, a few transcripts related to actin and its organization were down-regulated, including *ponI*, *ponD*, *act32* and *hatA* (also protein). Oxygen stress did not appear to substantially regulate microtubules, though the putative microtubule affinity-Regulating Kinase *mrkB* transcript was strongly induced

and proteomic data revealed an up-regulation of the tubulin *TubA*.

Finally, transcripts and proteins related to cell adhesion were also overexpressed after 24 h of hypoxia and during reoxygenation (Additional file 1: Table S1F and Table S3K). The contact site B transcripts, such as *csbC*, *csbB* and *csbA*, were strongly expressed. The calcium-dependent adhesion transcript *cad2* was also greatly induced, while *cadA* expression remained unchanged. *cad3* was only up-regulated at 5 h of reoxygenation, but with a high level of induction. At the protein level, *CsbC* and *Cad3* were also increased, as were *CsbB* and *Cad2* though they did not meet our cut-off filters. The *tiger* transcript *tgrB2* was also greatly induced by long hypoxic exposure. Moreover, *sibD* transcripts showed a strong induction
under chronic hypoxic conditions, while *sibB* expression increased progressively.

Taken together, these data suggest that chronic hypoxia induces the transcription of genes involved in the cAMP pathway used for chemotactic aggregation including those responsible for cAMP synthesis, reception, degradation, and signal transduction. Additionally, transcripts involved in later stages of aggregation, including those related to actin remodeling and cell adhesion, were up-regulated and identified in both transcriptomic and proteomic analyses. These inductions occur despite the fact that the cells are in an environment not conductive for aggregation as they are maintained in suspension at low density by shaking in growing media.

## Identification of marker genes and potential regulators of the hypoxic response

We surveyed individual transcripts to nominate potential regulators of the hypoxic response and predict marker genes for analysis of the dynamics and mechanisms of the response. The most substantially induced transcript under hypoxia was the small GTPase *rabR*, which exhibited more than a 250-fold increase in expression. This induction was specific to hypoxia, with a return to the basal expression level after 1 h in reoxygenation (Fig. 8A). The starvation-responsive small protein A, encoded by *srsA*, was also strongly and quickly induced by hypoxia, before being repressed during reoxygenation. Both SrsA and RabR were also strongly induced at the protein level. RabR protein expression tended to lag behind the increase in its transcript levels, whereas SrsA protein was already strongly expressed within the first hour of hypoxia. Interestingly, both proteins remained overexpressed during reoxygenation, while their transcript levels returned to baseline.

A similar pattern was found for genes already described as induced by hypoxia [39, 40]. Indeed, the 2 flavohemoglobins, *fhbA* and *fhbB*, were induced by 1 h in hypoxia, but with a lower fold change than *rabR* and *srsA* (Fig. 8B). Additionally, the two genes encoding subunit VII of the cytochrome C oxidase, *cxgS* and *cxgE*, were analyzed. *cxgS* was induced during hypoxia, before being repressed at 5 h of reoxygenation. In contrast, *cxgE* was repressed at 5 and 24 h, before returning to its basal level after the return to normoxia. FhbB, FhbA and CxgE, and CxgS (which was below our confidence cut-off), were similarly affected at the protein level with a delay as expected (Fig. 8A and B).

These findings highlight key marker genes and potential regulators that were further studied by RT-qPCR to better understand the hypoxic response and to elucidate regulatory mechanisms.

## Marker genes displays multiple dynamics mechanisms of expression

We further examined the dynamics and molecular mechanisms of transcript expression of the marker genes *srsA*, *rabR*, *fhbB*, and *cxgS*, which represent a diversity of biological functions. Using RT-qPCR, *srsA* and *rabR* exhibited stronger induction after 1 h at 1% O₂ than *cxgS* and *fhbB*, confirming the RNA-seq findings (Fig. 9A and B). Both *fhbB* and *cxgS* exhibited linear induction patterns, with *fhbB* expression plateauing at 40 min and *cxgS* induction continuing to increase past 60 min as indicated by RNA-seq data (Additional file 1: Table S1A). On the contrary, both *rabR* and *srsA* showed strong and almost complete induction within 10 min, while SrsA expression continued to slightly rise over the next 60 min.

Various inhibitors were tested to probe potential molecular mechanisms that regulate the early inductions

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 8** Identification of marker genes and potential regulators of the hypoxic response. Transcriptomic and proteomic regulation of genes in response to hypoxia and reoxygenation. Transcriptomic (from RNA-seq analyses) and proteomic profiles are represented by continuous and dashed lines, respectively. For the 5 h time point, the averages of series 1 and series 2 data are represented. **A:** Regulation pattern of two strongly hypoxia-induced genes used as markers. **B:** Regulation of genes previously described as hypoxia-regulated. CxgS was only detected as one peptide in the proteomic analyses, likely due to the small size of the protein</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 9** The transcriptomic response to hypoxia relies on multiple and diverse mechanisms. **A** and **B**: Relative expression of hypoxia marker genes over a short time period (n = 4). Cells were placed at 1% O₂ for 1 h, and collected at the indicated time points. Relative expressions were determined by RT-qPCR using t0 (21% O₂) as standard. Mean values from 4 trials are indicated with their associated standard deviations. **C-D**: Effect of the oxygen concentrations on hypoxia marker gene expression. Cells were exposed to the indicated concentration of O₂ for 1 h. Relative expressions were calculated relative to the 21% O₂ condition. Mean values from 3 trials with their standard deviations are indicated</figcaption></figure>

<table><caption>**Table 1** Effects of antimycin A and cycloheximide on genes expressed at 1% and 21% O₂. Cells were inoculated with the different chemicals for 1 h at either 1% or 21% O₂. For each gene, relative expression was determined using the 2⁻ΔΔCT method, using *RnlA* as a reference gene. For cells placed at 21% O₂, the relative expression was calculated using the condition without chemical treatment as the reference. For cells placed at 1% O₂, the relative expression was determined using the same chemical condition at 21% O₂ as the reference. Finally, the percentage of induction at both 21% and 1% O₂ was calculated using the hypoxic expression level without the drug as the 100% expression (% of induction 21% = 100 * (relative expression at 21% O₂ with drug normalized to the 21% condition without drug)/(relative expression at 1% O₂ no drug normalized to the conditions at 21% without drug)) and % of induction 1% = 100 * (relative expression at 1% O₂ with drug)/(relative expression at 1% O₂ no drug)). Mean values from 3 trials with their associated standard deviations are indicated</caption><thead><tr><th>Percentage of induction</th><th>srsA</th><th>fhbB</th><th>rabR</th><th>cxgS</th></tr></thead><tbody><tr><td>Antimycin A 21% O₂</td><td>0.1 ± 0.2</td><td>2.5 ± 0.8</td><td>0.1 ± 0.3</td><td>5 ± 5.7</td></tr><tr><td>Antimycin A 1% O₂</td><td>54 ± 40</td><td>70 ± 40</td><td>260 ± 265</td><td>9 ± 5</td></tr><tr><td>Cycloheximide 21% O₂</td><td>11 ± 6</td><td>3 ± 0.8</td><td>0.4 ± 0.3</td><td>2 ± 0.7</td></tr><tr><td>Cycloheximide 1% O₂</td><td>1 ± 0.3</td><td>116 ± 50</td><td>242 ± 128</td><td>57 ± 51</td></tr></tbody></table>

of *srsA*, *rabR*, *cxgS* and *fhbB*. As shown in Additional file 1: Table S6, DMOG, COCl₂ and deferoxamine, which inhibit non-heme dioxygenases and demethylases to mimic hypoxic transcriptional effects in other organisms,

failed to induce significant expression of these markers. This suggests that their expressions are not controlled by this class of O₂-dependent enzymes. At 1% O₂, neither deferoximine nor CoCl₂ affected gene expression (Additional file 1: Table S7). However, DMOG partially lowered *srsA* and *cxgS* expression, reducing their levels to approximately 35% of their normal expression. This suggests that *srsA* and *cxgS* expression might rely upon distinct 2-oxoglutarate dependent dioxygenases that are particularly sensitive to the combination of inhibitor and low O₂. The potential involvement of mitochondria was examined using Antimycin A (AA), an inhibitor of the mitochondrial cytochrome C oxidase subunit III (Table 1). At 21% O₂, AA did not induce gene expression, but it prevented *cxgS* induction under hypoxia. This finding implies a potential role of mitochondrial activity in the expression of the hypoxic cytochrome C subunit. Finally, the involvement of protein synthesis was tested using cycloheximide. This drug had no effect on gene expression at 21% O₂, but did clearly block *srsA* induction under hypoxia reducing its expression to 1% of the control level. Among the 4 tested genes, only *srsA* induction is dependent on *de novo* protein synthesis.

Finally, we investigated the dependence of marker gene expression levels on O₂ concentration. As shown in Fig. 9C-E, the expression levels of all the tested genes increased as the O₂ concentration decreased, with
maximum induction apparently occurring at 1–2% O₂. *srsA*, *fhbB* and *cxgS* showed a significant induction at even at 15% O₂, while *rabR* induction started at or below 10%. Half maximum induction was observed at 2.5–4.5% O₂ for *rabR* and *srsA* (Fig. C and D), and at 6% O₂ for *fhbB* and *cxgS* (Fig. 9E). The data suggest that gene expression is continuously and inversely proportional to O₂ concentration, rather than exhibiting switch-like dependence on a critical oxygen concentration threshold. Moreover, *srsA* and *rabR* require lower O₂ levels than *cxgS* and *fhbB* for maximum induction.

Taken together, the results show that the transcrip-
tomic response to hypoxia is driven by various factors,
including time after hypoxia and O₂ concentration, as
well as multiple mechanisms such as *de novo* protein syn-
thesis and mitochondrial activity.

**Discussion**

This study showed that *Dictyostelium discoideum* veg-
etative cells present a strong and complex transcriptomic
and proteomic response to hypoxia and reoxygenation.
32% of the detected transcripts were regulated ≥ 2-fold
by hypoxia and 42% of the detected proteins were sig-
nificantly regulated ≥ 1.2-fold. This proportion of regu-
lated transcripts is notably higher than what has been
reported in other eukaryotes. Studies on *Saccharomy-
ces cerevisiae* identified around 800 differentially regu-
lated transcripts out of total 6,200 genes, and human
cell lines show approximately 1,700 regulated transcripts
out of 24,000 genes in response to hypoxia [55, 56]. This
may be explained by the fact that we analyzed multiple
time points, which is uncommon, as most other studies
tend to focus on a single time point. The RNA turnover
between time points was estimated at 20%, which can be
compared with the 35% that occurs after 6 h of starva-
tion-induced development [57]. This study is focused on
mRNA, but TPM analysis also revealed that the non-cod-
ing RNA *dutA*, which is an inhibitor of late development
[58], was particularly induced between 5 and 24 h of
hypoxia, representing more than 1% of the total amount
of detected RNA within the cells. Thus, ncRNAs may also
play a role in the transcriptomic regulation of the hypoxic
response. This study also focused on genes with a FC ≥
2 and statistically significant differences. However, some
genes, particularly those involved in ribosome biogenesis
and the cAMP signalling pathway, exhibited more mod-
est expression changes, with FC values between 2 and 4.
Previous research has demonstrated that biologically rel-
evant functions can occur even at very low (FC < 2) in
RNA expression [59]. Furthermore, the enrichment of
hundreds of genes showing such modest but consistent
expression changes supports the notion that these altera-
tions are functionally meaningful and may reflect coordi-
nated biological responses. Finally, many of the strongly

regulated transcripts were annotated as hypothetical
proteins. We attempted to glean functional information
by searching for homologs using BLAST. Although some
genes appeared potentially involved in known functions
such as metabolism or hypoxia response, these analy-
ses did not substantially alter the overall interpretation,
as individual genes showed limited homology and no
clear metabolic pathways emerged beyond those already
described.

By identifying and analyzing a group of marker genes,
we observed extremely rapid (≤ 10 min) responses for
*srsA* and *rabR* at the transcriptional level that were also
quickly followed at the protein level. These results may
suggest an important role for these proteins in respond-
ing to hypoxic conditions. Their responses to hypoxia are
also graded with increasing induction over the range of
10 to 1% O₂. The halfway point varied modestly between
genes, suggesting multiple control pathways. The rapid
expression of *srsA* relies on *de novo* protein synthesis,
suggesting that it may be regulated by a HIF-like mech-
anism, where a protein is constantly synthesized but
degraded under normoxic conditions. By contrast, *RabR*
induction is as rapid but does not require *de novo* pro-
tein synthesis, pointing toward existence of another, fast-
acting, transcription regulation mechanism. Regarding
*cxgS* induction, a study in mammals showed that HIF-1
expression is also inhibited by AA and other mitochon-
drial chain inhibitors [51], so may be related. Consider-
ing the potential importance of non-heme dioxygenases
as O₂-sensors, we tested their potential involvement
using the pan-specific inhibitors cobalt (COCl₂), defer-
oxamine and DMOG. However, these revealed minimal
to no effect on early marker transcript expression, though
roles in the chronic phase are not excluded. Of specific
interest, the Skp1 prolyl hydroxylase PhyA that regulates
O₂-regulation of culmination [32] was down regulated
2–3-fold during both phases at the transcriptomic level,
supporting absence of its involvement in the response
studies here.

The RT-qPCR results for the data summarized above
showed high standard deviation values, indicating sub-
stantial variability, even though the same induction
trends were consistently observed. The source of this
variability was investigated by comparing the variabil-
ity of the Ct at t0 and 1 h after hypoxia (Additional file
1: Fig. S5). The results revealed that the major variabil-
ity occurred at t0. A similar pattern was observed with
RNA-seq data when comparing the TPM at t0 and 1 h
post-hypoxia. These data suggest that, despite controlling
cell culture conditions (density, temperature, agitation)
and harvesting procedures, minor experimental fluctua-
tions can lead to large expression changes. In our study,
the t0 samples often correspond to the culture condition
itself, which may also contribute to variability. Notably,
we demonstrated that small variations in oxygen con-
centration, such as exposure to approximately 15% O₂,
can induce genes like *fhbB* and *cxgS*, highlighting the
high sensitivity of the system. Moreover, we showed that
*srsA* and *rabR* are strongly expressed after just 5 min of
hypoxia, which could be influenced by centrifugation
stress. This variability likely reflects biological adaptation
to subtle environmental changes that naturally occur in
their habitat, as well as laboratory constraints that are
difficult to fully control.

The subsequent hypoxic response strongly depends on
time after hypoxia and can be summarized as occurring
in two major phases for both the transcriptome and the
proteome: acute (1–5 h) and chronic (24 h). A two-phase
response, including an early sensing phase and a later,
more stable adaptation, may provide a selective advan-
tage in such dynamic conditions. We found that the pro-
teomic data were only moderately positively correlated
with the transcriptomic data during chronic hypoxia,
while acute hypoxia showed poor correlation. Indeed, we
observed that the early transcriptomic adaptation (1 h)
was associated with an induction of transcripts involved
in ribosome biogenesis, which was only slightly regis-
tered at the protein level. Possible explanations are that
ribosome-related proteins experience increased turnover
necessitating increased synthesis during hypoxia, or that
mRNAs are sequestered in stress granules, which are
present in many eukaryotes [60]. The observed increase
in total mRNA upon hypoxia fits better with the second
hypothesis. The role of ribosomes under stressful con-
ditions remains unclear in most eukaryotes and might
involve complex regulation. In yeast, for example, ribo-
some biogenesis is halted, and global translation is inhib-
ited during stress [61]. In mammalian cells, a recent study
revealed that rRNAs can be produced and stored within
the nucleus before re-entering the ribosome biosynthe-
sis pathway once the stress is resolved [62]. In contrast,
histone proteins drop during chronic hypoxia, while their
mRNA levels remained stable. 90% of the histones are
typically synthesized during the S phase [63]. In our case,
hypoxia seems to arrest the cell cycle, which may offer a
potential explanation for this down-regulation. Another
hypothesis is that histone degradation could induce het-
erochromatin reorganization, which may explain the
large amount of regulated gene expression observed dur-
ing chronic hypoxia.

We showed that the early proteomic regulation at 5 h
was characterized by relative increases in protein groups
associated with O₂-binding and redox-reactions, metabo-
lism, signaling, cytoskeleton and lipid metabolism. These
effects potentially mobilize cell migration associated with
aerotaxis, though a clear mechanism is difficult to inter-
pret. Previous studies in mammalian cells have shown
that Des1 and Des2 are O₂-dependent enzymes, which

are rapidly and reversibly inhibit by hypoxia, indepen-
dently of HIF-1 [64]. It has thus been proposed that DesA
acts as an oxygen sensor, leading to increased dihydroc-
eramide levels under hypoxia, which in turn inhibit cell
proliferation. Further investigations in *Dictyostelium*
are required to determine whether DesA performs the
same function as Des1, and to elucidate the mecha-
nisms underlying its opposite regulation under hypoxia.
Moreover, four differentially expressed lipid-processing-
related proteins were found to potentially participate
in sterol biosynthesis (Table S3C). In some fungi and
yeast, certain enzymes involved in the generation of ste-
rols are rate limited by oxygen levels, and specific prod-
ucts regulate the membrane-bound sterol regulatory
element-binding protein (SREBP) transcription factor,
which in turn controls the majority of hypoxia response
genes [65]. It remains unclear whether these observed
responses suggest the existence of a similar hypoxia
response mechanism in *Dictyostelium* or if they repre-
sent a compensatory adaptation for sterol biosynthesis.

The later transcriptomic and proteomic responses
are linked to an activation of the cAMP pathway that is
reminiscent of early starvation induced development and
may enable a collective escape from stressful conditions
by promoting migration to a more oxygen-rich environ-
ment. SrsA, one of first gene induced by hypoxia, was
previously described to be involved in the early (1 h)
starvation response [66], suggesting a similarity between
both stresses that deserves to be explored in future stud-
ies. Hypoxia could also act as a trigger for *Dictyostelium*
sexual reproduction, which relies on cAMP signals and
occurs in submerged environments, typically character-
ized by low oxygen levels. Moreover, *tmcB*, which plays a
key role in macrocyst formation and regulates the cAMP
pathway during sexual reproduction [67], was slowly
induced after 24 h of hypoxia (Additional file 1: Table
S1B). In addition, *aco*, which produces ethylene essential
for sexual reproduction [68], was strongly induced after
24 h and during reoxygenation. However, this dioxygen-
ase has also been shown to be crucial for *Dictyostelium*
culmination [69], so is not specific to macrocyst forma-
tion. Although some of the corresponding proteins were
detected in our proteome screen, none met our crite-
ria for quantification owing presumably to low expres-
sion levels. Alternatively, the increased levels of proteins
involved in Ca²⁺-response, adhesion and motility might
support the previously described microphase separation
of *Dictyostelium* under hypoxia [70].

This study also emphasizes a reduction in cell division
after 5 h of hypoxia, followed by a recovery period of
more than 5 h of reoxygenation, which aligns with previ-
ous observations made in *Dictyostelium* [70]. This reduc-
tion was associated with inhibition of the expression of
cell cycle transcripts and proteins specifically during
chronic hypoxia, and includes transcripts associated with S phase and metaphase-to-anaphase transitions. Arrest also occurs in mammals, where HIF induces the cyclin-dependent kinases inhibitors p21 and p27 [71–73]. Other studies also described a HIF-independent mechanism, where PHD1 can modify Cep192 involved in centrosome duplication, leading to its proteasomal degradation [74, 75].

Interestingly, most transcripts and proteins regulated during reoxygenation showed continuity with their expression patterns observed during chronic hypoxia. This suggests that reoxygenation does not activate specific metabolic pathways on its own. This contrasts with what is observed in mammals, where reoxygenation induces a rapid increase in antioxidant and stress-related proteins to face the burst of O₂ [76]. Moreover, these data suggest that most transcript and protein levels require more than 5 h of reoxygenation to return to their basal state after exposure to chronic hypoxia. This could represent a preventive strategy to cope with another episode of hypoxia.

Finally, we compared our 1 h transcriptomic data with data from *Dictyostelium* cells exposed to different bacteria or folate (as proxy) for 4 h [45]. Comparison of differentially expressed transcripts showed that GPCRs involved in bacterial sensing displayed a similar pattern under hypoxic conditions, including the down-regulation of *grlA*, *grlB* and *grlG* (Additional file 1: Fig. S6A). The hypoxic response marker genes, *rabR*, *srsA*, *cxgS*, *cxgE*, *fhbA* and *fhbB*, were also induced or repressed with the same pattern in presence of bacteria (Additional file 1: Fig. S6B). Oxygen depletion by bacterial respiration might explain the common characteristics of the responses.

## Conclusion

Our findings revealed a temporal regulation of the transcriptome and proteome in response to hypoxia. Acute hypoxia was associated with the up-regulation of transcripts linked to ribosome biogenesis, while protein regulation involved an increase in O₂-related proteins and lipid metabolism. During chronic hypoxia, both transcripts and proteins related to various metabolic, anabolic, and catabolic pathways, as well as chromosome organization, cell cycling, vesicular trafficking, and signaling, were regulated. Notably, *Dictyostelium* cAMP aggregation related transcripts were also up-regulated during chronic hypoxia. Additionally, we investigated the molecular mechanisms driving the transcriptomic response by analyzing four marker genes. Our results showed that the response varied across different O₂ levels, with a particularly rapid response for *rabR* and *srsA* (< 10 min). Furthermore, differential responses to inhibitors affecting protein synthesis and mitochondrial

respiration suggested that multiple induction mechanisms are driving the transcriptomic response. Overall, the study reveals the complexity of the hypoxic response by highlighting the biological processes activated by hypoxia and investigating the multiple factors that drive this response. This study opens the door to further research on hypoxic regulation in *Dictyostelium discoideum*.

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12864-025-12328-9.

Supplementary Material 1.

Supplementary Material 2.

Supplementary Material 3.

Supplementary Material 4.

Supplementary Material 5.

Supplementary Material 6.

Supplementary Material 7.

## Acknowledgements

We thank A. Kafousi for help in RT-qPCR measurements, and S. Sawai for helpful discussions.

## Authors' contributions

CA, CMW, and JH conceptualized the study. JH and HvdW designed the experimental setups. HvdW performed the protein collection, and EGP conducted mass spectrometry and protein identification. JH carried out RNA extraction and library preparation. Formal analyses were performed by JH, EGP, and OCE. Investigations were conducted by JH, HvdW, CMW, and CA. JH conducted and analyzed the RT-qPCRs with methodological support from SJ. Data visualization was performed by JH, CMW, EGP, and CA. JH and CMW wrote the initial manuscript, which was reviewed by JH, CMW, and CA. Funding acquisition was handled by JPR and CMW.

## Funding

This work was supported by the International Human Frontier Science Program Organization, Grant Number RGP0051/2021 (to J.-P. Rieu, C.M. West) and ANR OCSYC N° ANR-24-CE30-5238-02 (to J.-P. Rieu).

## Data availability

The datasets supporting the conclusions of this article are available in the on NCBI repository, [GEO Accession numbers: GSE291941], DictyExpress project 13. Hypoxia [https://app.dictyexpress.org/bcm/] and in the ProteomeXchange Consortium via the PRIDE (49) [identifiers PXD061931, PXD062041, PXD061803, PXD061768, PXD061993, [https://www.ebi.ac.uk/pride/e/](https://www.ebi.ac.uk/pride)].

## Declarations

### Ethics approval and consent to participate

Not applicable.

### Consent for publication

Not applicable.

### Competing interests

The authors declare no competing interests.

### Author details

¹Institut Lumière Matière, Université Claude Bernard Lyon 1, UMR5306 CNRS, Villeurbanne, France
²Department of Biochemistry and Molecular Biology, Center for Molecular Medicine, University of Georgia, Athens, GA 30602, USA

Received: 14 April 2025 / Accepted: 11 November 2025

Published online: 29 December 2025

## References

1. Hsia CCW, Schmitz A, Lambertz M, Perry SF, Maina JN. Evolution of air breathing: oxygen homeostasis and the transitions from water to land and sky. Compr Physiol. 2013;3(2):849–915.

2. Vaupel K, Schlenger K, Knoop C, Höckel M. Oxygenation of human tumors: evaluation of tissue oxygen distribution in breast cancers by computerized O2 tension measurements. Cancer Res. 1991;51(12):3316–22.

3. Friedrich J, Janssen F, Aleynik D, Bange HW, Boltacheva N, Çagatay MN, et al. Investigating hypoxia in aquatic environments: diverse approaches to addressing a complex phenomenon. Biogeosciences. 2014;11(4):1215–59.

4. Triantafyllou A, Liakos P, Tsakalof A, Georgatsou E, Simos G, Bonanou S. Cobalt induces hypoxia-inducible factor-1alpha (HIF-1alpha) in HeLa cells via an iron-independent, but ROS-, PI-3K- and MAPK-dependent mechanism. Free Radic Res. 2006;40(8):847–56.

5. Selvaraju V, Parinandi NL, Adluri RS, Goldman JW, Hussain N, Sanchez JA, et al. Molecular mechanisms of action and therapeutic uses of Pharmacological inhibitors of HIF-prolyl 4-hydroxylases for treatment of ischemic diseases. Antioxid Redox Signal. 2014;20(16):2631–65.

6. Woo KJ, Lee TJ, Park JW, Kwon TK. Desferrioxamine, an iron chelator, enhances HIF-1alpha accumulation via cyclooxygenase-2 signaling pathway. Biochem Biophys Res Commun. 2006;343(1):8–14.

7. Semenza GL, Wang GL. A nuclear factor induced by hypoxia via de novo protein synthesis binds to the human erythropoietin gene enhancer at a site required for transcriptional activation. Mol Cell Biol. 1992;12(12):5447–54.

8. Maxwell PH, Wiesener MS, Chang GW, Clifford SC, Vaux EC, Cockman ME, et al. The tumour suppressor protein VHL targets hypoxia-inducible factors for oxygen-dependent proteolysis. Nature. 1999;399(6733):271–5.

9. Ivan M, Kondo K, Yang H, Kim W, Valiando J, Ohh M, et al. HIFalpha targeted for VHL-mediated destruction by proline hydroxylation: implications for O2 sensing. Science. 2001;292(5516):464–8.

10. Cockman ME, Masson N, Mole DR, Jaakkola P, Chang GW, Clifford SC, et al. Hypoxia inducible factor-alpha binding and ubiquitylation by the von Hippel-Lindau tumor suppressor protein. J Biol Chem. 2000;275(33):25733–41.

11. Jiang BH, Rue E, Wang GL, Roe R, Semenza GL, Dimerization. DNA binding, and transactivation properties of hypoxia-inducible factor 1. J Biol Chem. 1996;271(30):17771–8.

12. Semenza GL. The genomics and genetics of oxygen homeostasis. Annu Rev Genomics Hum Genet. 2020;21:183–204.

13. Haase VH. Regulation of erythropoiesis by hypoxia-inducible factors. Blood Rev. 2013;27(1):41–53.

14. Krock BL, Skuli N, Simon MC. Hypoxia-induced angiogenesis. Genes Cancer. 2011;2(12):1117–33.

15. Papandreou I, Cairns RA, Fontana L, Lim AL, Denko NC. HIF-1 mediates adaptation to hypoxia by actively downregulating mitochondrial oxygen consumption. Cell Metab. 2006;3(3):187–97.

16. Johansson C, Tumber A, Che K, Cain P, Nowak R, Gileadi C, et al. The roles of Jumonji-type oxygenases in human disease. Epigenomics. 2014;6(1):89–120.

17. Pollard PJ, Loenarz C, Mole DR, McDonough MA, Gleadle JM, Schofield CJ, et al. Regulation of Jumonji-domain-containing histone demethylases by hypoxia-inducible factor (HIF)-1alpha. Biochem J. 2008;416(3):387–94.

18. Semenza GL. Targeting HIF-1 for cancer therapy. Nat Rev Cancer. 2003;3(10):721–32.

19. Warfel NA, El-Deiry WS. HIF-1 signaling in drug resistance to chemotherapy. Curr Med Chem. 2014;21(26):3021–8.

20. Chen J, Ding Z, Peng Y, Pan F, Li J, Zou L, et al. HIF-1α inhibition reverses multidrug resistance in colon cancer cells via downregulation of MDR1/P-glycoprotein. PLoS One. 2014;9(6):e98882.

21. Dickson AS, Pauzaite T, Arnaiz E, Ortmann BM, West JA, Volkmar N, et al. A HIF independent oxygen-sensitive pathway for controlling cholesterol synthesis. Nat Commun. 2023;14(1):4816.

22. Munksgaard Thorén M, Vaapil M, Staaf J, Planck M, Johansson ME, Mohlin S, et al. Myc-induced glutaminolysis bypasses HIF-driven glycolysis in hypoxic small cell lung carcinoma cells. Oncotarget. 2017;8(30):48983–95.

23. Raper KB, Smith NR. The growth of dictyostelium discoideum upon pathogenic bacteria. J Bacteriol. 1939;38(4):431–45.

24. Cotter DA, Sands TW, Virdy KJ, North MJ, Klein G, Satre M. Patterning of development in dictyostelium discoideum: factors regulating growth, differentiation, spore dormancy, and germination. Biochem Cell Biol. 1992;70(10–11):892–919.

25. Cochet-Escartin O, Demircigil M, Hirose S, Allais B, Gonzalo P, Mikaelian I, et al. Hypoxia triggers collective aerotactic migration in Dictyostelium discoideum. Elife. 2021;10:e64731.

26. Hirose S, Rieu JP, Cochet-Escartin O, Anjard C, Funamoto K. The oxygen gradient in hypoxic conditions enhances and guides Dictyostelium discoideum migration. Processes. 2022;10(2):318.

27. Biondo M, Panuzzo C, Ali SM, Bozzaro S, Osella M, Bracco E, et al. The dynamics of aerotaxis in a simple eukaryotic model. Front Cell Dev Biol. 2021;9:720623.

28. Bonner JT, Lamont DS. Behavior of cellular slime molds in the soil. Mycologia. 2005;97(1):178–84.

29. Sternfeld J, David CN. Oxygen gradients cause pattern orientation in Dictyostelium cell clumps. J Cell Sci. 1981;50:9–17.

30. Xu Y, Wang ZA, Green RS, West CM. Role of the Skp1 prolyl-hydroxylation/glycosylation pathway in oxygen dependent submerged development of Dictyostelium. BMC Dev Biol. 2012;12(1):31.

31. Sternfeld J, Bonner JT. Cell differentiation in Dictyostelium under submerged conditions. Proc Natl Acad Sci U S A. 1977;74(1):268–71.

32. West CM, van der Wel H, Wang ZA. Prolyl 4-hydroxylase-1 mediates O2 signaling during development of dictyostelium. Development. 2007;134(18):3349–58.

33. Florimond C, Cordonnier C, Taujale R, van der Wel H, Kannan N, West CM, et al. A Toxoplasma Prolyl hydroxylase mediates oxygen stress responses by regulating translation elongation. mBio. 2019;10(2):e00234–19.

34. van der Wel H, Ercan A, West CM. The Skp1 Prolyl hydroxylase from dictyostelium is related to the Hypoxia-inducible Factor-α class of animal Prolyl 4-Hydroxylases*. J Biol Chem. 2005;280(15):14645–55.

35. Sheikh MO, Xu Y, van der Wel H, Walden P, Hartson SD, West CM. Glycosylation of Skp1 promotes formation of Skp1-cullin-1-F-box protein complexes in dictyostelium. Mol Cell Proteomics. 2015;14(1):66–80.

36. West CM, Blader IJ. Oxygen sensing by protozoans: how they catch their breath. Curr Opin Microbiol. 2015;26:41–7.

37. van der Wel H, Gas-Pascual E, West CM. Skp1 isoforms are differentially modified by a dual function prolyl 4-hydroxylase/N-acetyl Lglucosaminyltransferase in a plant pathogen. Glycobiology. 2019;29(10):705–14.

38. Mandalasi MN, Gas-Pascual E, Baptista CG, Deng B, van der Wel H, Kruijitzer JAW, et al. Oxygen-dependent regulation of F-box proteins in Toxoplasma gondii is mediated by Skp1 glycosylation. J Biol Chem. 2024;300(11):107801.

39. Bisson R, Vettore S, Aratri E, Sandona D. Subunit change in cytochrome c oxidase: identification of the oxygen switch in dictyostelium. EMBO J. 1997;16(4):739–49.

40. Hirose S, Hesnard J, Ghazi N, Roussel D, Voituron Y, Cochet-Escartin O, et al. The aerotaxis of dictyostelium discoideum is independent of mitochondria, nitric oxide and oxidative stress. Front Cell Dev Biol. 2023;11:1134011.

41. Castello PR, David PS, McClure T, Crook Z, Payton RO. Mitochondrial cytochrome oxidase produces nitric oxide under hypoxic conditions: implications for oxygen sensing and hypoxic signaling in eukaryotes. Cell Metab. 2006;3(4):277–87.

42. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.

43. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57.

44. Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022;50(W1):W216–21.

45. Lamrabet O, Melotti A, Burdet F, Hanna N, Perrin J, Nitschke J, et al. Transcriptional responses of dictyostelium discoideum exposed to different classes of bacteria. Front Microbiol. 2020;11:410.

46. Kuznetsova KG, Levitsky LI, Pyatnitskiy MA, Ilina IY, Bubis JA, Solovyeva EM, et al. Cysteine alkylation methods in shotgun proteomics and their possible effects on methionine residues. J Proteom. 2021;231:104022.

47. Boland AW, Gas-Pascual E, Nottingham BL, van der Wel H, Daniel NG, Sheikh MO, et al. Oxygen-dependent regulation of E3(SCF)ubiquitin ligases and a Skp1-associated JmjD6 homolog in development of the social amoeba Dictyostelium. J Biol Chem. 2022;298(9):102305.
48. Pang Z, Zhou G, Ewald J, Chang L, Hacariz O, Basu N, et al. Using metaboanaylist 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nat Protoc. 2022;17(8):1735-61.

49. Perez-Riverol Y, Csordas A, Bai J, Bernal-Linares M, Hewapathirana S, Kundu DJ, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 2019;47(D1):D442-50.

50. Nowak-Stępniowska A, Osuchowska PN, Fiedorowicz H, Trafny EA. Insight in hypoxia-mimetic agents as potential tools for mesenchymal stem cell priming in regenerative medicine. Stem Cells Int. 2022;2022:8775591.

51. Baby SM, Roy A, Lahiri S. Role of mitochondria in the regulation of hypoxia-inducible factor-1alpha in the rat carotid body glomus cells. Histochem Cell Biol. 2005;124(1):69-76.

52. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta delta C(T)) method. Methods. 2001;25(4):402-8.

53. Huang YC, Chen YH, Lo SR, Liu CI, Wang CW, Chang WT. Disruption of the peroxisomal citrate synthase CshA affects cell growth and multicellular development in dictyostelium discoideum. Mol Microbiol. 2004;53(1):81-91.

54. Warner N, Rutherford CL. Purification and cloning of TF2: a novel protein that binds a regulatory site of the gp2 promoter in dictyostelium. Arch Biochem Biophys. 2000;373(2):462-70.

55. Bendjilali N, MacLeon S, Kalra G, Willis SD, Hossian AKMN, Avery E, et al. Time-Course Analysis of Gene Expression During the Saccharomyces cerevisiae Hypoxic Response. G3 (Bethesda). 2017;G3(Bethesda):221-31.

56. Mata-Greenwood E, Goyal D, Goyal R. Comparative and experimental studies on the genes altered by chronic hypoxia in human brain microendothelial cells. Front Physiol. 2017;8:365.

57. Chung S, Landfear SM, Blumberg DD, Cohen NS, Lodish HF. Synthesis and stability of developmentally regulated dictyostelium mRNAs are affected by cell-cell contact and cAMP. Cell. 1981;24(3):785-97.

58. Shimada N, Kawata T. Evidence that noncoding RNA DutA is a multicopy suppressor of dictyostelium discoideum STAT protein Dd-STATa. Eukaryot Cell. 2007;6(6):1030-40.

59. St Laurent G, Shtokalo D, Tackett MR, Yang Z, Vyatkin Y, Milos PM, et al. On the importance of small changes in RNA expression. Methods. 2013;63(1):18-24.

60. Buchan JR, Parker R. Eukaryotic stress granules: the ins and outs of translation. Mol Cell. 2009;36(6):932-41.

61. Cheng-Guang H, Gualerzi CO. The ribosome as a switchboard for bacterial stress response. Front Microbiol. 2020;11:619038.

62. Szaflarski W, Sowiński M, Leśniczak M, Ojha S, Aulas A, Dave D et al. A novel stress response pathway regulates rRNA biogenesis [Internet]. bioRxiv; 2020 [cited 2024 Dec 9]. p. 2020.08.16.250183. Available from: https://www.biorxiv.org/content/10.1101/2020.08.16.250183v1.

63. Wu RS, Bonner WM. Separation of basal histone synthesis from S-phase histone synthesis in dividing cells. Cell. 1981;27(2 Pt 1):321-30.

64. Devlin CM, Lahm T, Hubbard WC, Van Demark M, Wang KC, Wu X, et al. Dihydroceramide-based response to hypoxia. J Biol Chem. 2011;286(44):38069-78.

65. Hughes AL, Lee CYS, Bien CM, Espenshade PJ. 4-methyl sterols regulate fission yeast SREBP-Scap under low oxygen and cell stress. J Biol Chem. 2007;282(33):24388-96.

66. Sasaki K, Chae SC, Loomis WF, Iranfar N, Amagai A, Maeda Y. An immediate- early gene, srsa: its involvement in the starvation response that initiates differentiation of dictyostelium cells. Differentiation. 2008;76(10):1093-103.

67. Muramoto T, Takeda S, Furuya Y, Urushihara H. Reverse genetic analyses of gamete-enriched genes revealed a novel regulator of the cAMP signaling pathway in Dictyostelium discoideum. Mech Dev. 2005;122(5):733-43.

68. Amagai A, Soramoto S, saku, Saito S, hei, Maeda Y. Ethylene induces zygote formation through an enhanced expression of *zyg1* in *Dictyostelium mucoroides*. Exp Cell Res. 2007;313(11):2493-503.

69. Heslop-Harrison G, Nakabayashi K, Espinosa-Ruiz A, Robertson F, Baines R, Thompson CRL, et al. Functional mechanism study of the allelochemical myrigalone A identifies a group of ultrapotent inhibitors of ethylene biosynthesis in plants. Plant Commun. 2024;5(6):100846.

70. Carrère A, d'Alessandro J, Cochet-Escartin O, Hesnard J, Ghazi N, Rivière C, et al. Microphase separation of living cells. Nat Commun. 2023;14(1):796.

71. Gardner LB, Li Q, Park MS, Flanagan WM, Semenza GL, Dang CV. Hypoxia inhibits G1/S transition through regulation of p27 expression. J Biol Chem. 2001;276(11):7919-26.

72. Goda N, Ryan HE, Khadivi B, McNulty W, Rickert RC, Johnson RS. Hypoxia-inducible factor 1alpha is essential for cell cycle arrest during hypoxia. Mol Cell Biol. 2003;23(1):359-69.

73. Hubbi ME, Kshitz null, Gilkes DM, Rey S, Wong CC, Luo W, et al. A nontranscriptional role for HIF-1α as a direct inhibitor of DNA replication. Sci Signal. 2013;6(262):ra10.

74. Moser SC, Bensaddek D, Ortmann B, Maure JF, Mudie S, Blow JJ, et al. PHD1 links Cell-Cycle progression to oxygen sensing through hydroxylation of the centrosomal protein Cep192. Dev Cell. 2013;26(4):381-92.

75. Henze AT, Garvalov BK, Seidel S, Cuesta AM, Ritter M, Filatova A, et al. Loss of PHD3 allows tumours to overcome hypoxic growth inhibition and sustain proliferation through EGFR. Nat Commun. 2014;5:5582.

76. Piacentini L, Karliner JS. Altered gene expression during hypoxia and reoxygenation of the heart. Pharmacol Ther. 1999;83(1):21-37.

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