Positive transformant colonies, as verified by immunoblot analysis with a mouse anti-GFP antibody (Thermo Fisher), were visualised using a SP8 inverted spinning disc confocal microscopy (Leica) under 400 x magnification, with excitation wavelength 485 nm and emission wavelength filters 500-550 nm. GFP-negative colonies were used to confirm detection specificity, and empty-vector GFP (with cytoplasmic localizations) were used as fluorescence positive controls. A minimum of 12 GFP expressing clones were visualised for each construct with consistent localization.

## CRISPR mutagenesis

CRISPR target sequences for cpEnolase and cpPGAM1A genes were identified using PhytoCRISP-Ex (Rastogi, Murik et al. 2016), privileging positions in the N-terminal region of the conserved domain to minimize the probability of enzyme functionality in knockout lines, and uniqueness across the entire *P. tricornutum* genome within the final 11 bp of the target sequence to minimize off-target effects. Primers were designed for each target sequence, and introduced into a pu6:SG1 CRISPR target sequence plasmid by PCR, as previously described (Nymark, Sharma et al. 2016). 2 µg insertion-positive pu6:SG1 plasmids, as confirmed by Sanger sequencing were co-introduced into wild type *P. tricornutum* Pt186 cells by bombardment along with 2 µg HA-tagged Cas9 and pPhat vectors, as described above. Mutant colonies were genotyped using a DNA lysis buffer containing 0.14 M NaCl, 5 mM KCl, 10 mM Tris-HCl pH 7.5, 1% v/v NP40 to generate crude DNA extracts, followed by PCR amplification across the CRISPR target sequences with DreamTaq PCR reagent (Promega) and Sanger sequencing (Eurofins genomics). Mixed mutant: wild-type colonies were segregated via repeated dilution on ESAW: zeocin plates until only homozygous mutant genotypes were detected (Nymark, Sharma et al. 2016, McCarthy, Smith et al. 2017). Empty vector control lines were generated using the same protocol, except with only HA-Cas9 and pPhat plasmids, cotransformed without a CRISPR target sequence.

Tabulated cleaned knockout mutants, their associated genotypes and the expression levels of mutated gene copies are shown in **Supplementary Dataset S4**, sheets 1-2. Mutant colony genotypes were periodically confirmed (approx. once per month) by PCR and Sanger sequencing throughout the duration of all subsequent experiments,
and the CRISPR-induced gene modifications were found to remain stable. *P. tricornutum* Enolase proteins were determined by immunoblot to be crossreactive to an anti-*Arabidopsis thaliana* Enolase-2 antibody (Agrisera), and thus knockout line protein expression was confirmed by RT-qPCR, as described below.

## Complementation of knockout lines

Knockout lines were complemented with pPhat:GFP vectors carrying overexpressing copies (under an FcpA promoter) of cpEnolase and cpPGAM1A synthetic constructs, with all CRISPR target sequences replaced with silent mutations (Eurofins). Genes were fused to C-terminal GFP, allowing the verification of protein expression and localization. Vectors were identical to those previously used for localization, but with a blasticidin S-deaminase gene in lieu of SHBLE (Buck, Río Bártulos et al. 2018) introduced by NEB Hi-Fi kit as before. Complementation constructs were transformed via bombardment, and cotransformed colonies were selected on ½ ESAW- 1% agarose plates supplemented with 100 µg ml⁻¹ ampicillin, 100 µg ml⁻¹ streptomycin, 30 µg ml⁻¹ chloramphenicol, 100 µg ml⁻¹ zeocin, 4 µg ml⁻¹ blasticidin.

For each complementation, three cpEnolase and cpPGAM1A knockout lines (including at least one for each CRISPR target sequence) were complemented both with the conjugate construct, and an empty blasticidin resistance vector as a placebo; and two empty vector lines were further complemented with both cpEnolase and cpPGAM1A overexpressor constructs, plus an empty blasticidin resistance vector, to exclude possible effects from ectopic overexpression of each gene on cell physiology. A total of 47 colonies, with a minimum of 6 colonies for each knockout: complementation combination, including lines complemented from at least two distinct primary knockout mutant genotypes, were selected for subsequent study (Supplementary Dataset S4, sheet 7). The retention of the primary knockout mutant genotype in each complemented line was verified by colony PCR and sequencing as above, and the overexpression and correct localization of the complementing protein sequence (i.e., to the chloroplast for cpEnolase:GFP and cpPGAM1:GFP, or the cytoplasm for ectopic GFP) was verified by immunoblot with an anti-GFP antibody (Thermo Fisher) (Erdene-Ochir, Shin et al. 2019) and confocal microscopy.
and the CRISPR-induced gene modifications were found to remain stable. *P. tricornutum* Enolase proteins were determined by immunoblot to be crossreactive to an anti-*Arabidopsis thaliana* Enolase-2 antibody (Agrisera), and thus knockout line protein expression was confirmed by RT-qPCR, as described below.

## Complementation of knockout lines

Knockout lines were complemented with pPhat:GFP vectors carrying overexpressing copies (under an FcpA promoter) of cpEnolase and cpPGAM1A synthetic constructs, with all CRISPR target sequences replaced with silent mutations (Eurofins). Genes were fused to C-terminal GFP, allowing the verification of protein expression and localization. Vectors were identical to those previously used for localization, but with a blasticidin S-deaminase gene in lieu of SHBLE (Buck, Río Bártulos et al. 2018) introduced by NEB Hi-Fi kit as before. Complementation constructs were transformed via bombardment, and cotransformed colonies were selected on ½ ESAW- 1% agarose plates supplemented with 100 µg ml⁻¹ ampicillin, 100 µg ml⁻¹ streptomycin, 30 µg ml⁻¹ chloramphenicol, 100 µg ml⁻¹ zeocin, 4 µg ml⁻¹ blasticidin.

For each complementation, three cpEnolase and cpPGAM1A knockout lines (including at least one for each CRISPR target sequence) were complemented both with the conjugate construct, and an empty blasticidin resistance vector as a placebo; and two empty vector lines were further complemented with both cpEnolase and cpPGAM1A overexpressor constructs, plus an empty blasticidin resistance vector, to exclude possible effects from ectopic overexpression of each gene on cell physiology. A total of 47 colonies, with a minimum of 6 colonies for each knockout: complementation combination, including lines complemented from at least two distinct primary knockout mutant genotypes, were selected for subsequent study (Supplementary Dataset S4, sheet 7). The retention of the primary knockout mutant genotype in each complemented line was verified by colony PCR and sequencing as above, and the overexpression and correct localization of the complementing protein sequence (i.e., to the chloroplast for cpEnolase:GFP and cpPGAM1:GFP, or the cytoplasm for ectopic GFP) was verified by immunoblot with an anti-GFP antibody (Thermo Fisher) (Erdene-Ochir, Shin et al. 2019) and confocal microscopy.
Growth rate measurements

A starting density of 10⁴ ml⁻¹ stationary phase *P. tricornutum* cells of a given culture line, as verified with a Malassez haemocytometer, were inoculated into a 15 ml volume antibiotic-free ESAW within a sterile, ventilated cap plastic culture flask (Celltreat) and grown under LD, CL, or 8C culture conditions as described. Cell densities were recorded: every day from one day post-inoculation (CL); every day from two days post-inoculation (LD); or every two days from five days post-inoculation (8C) at the mid-point of the LD light induction phase using a counting CyFlow Cube 8 cytometer (ParTec).

Typically, 15 µl cell culture, passed at 0.5 µl s⁻¹, were used for each measurement, with three technical replicates performed for each culture of which the first (enriched in non-cellular particles) was excluded from downstream calculations. Cytometer particle counts were correlated to actual cell densities using a calibration curve realised from haemocytometer counted densities of wild-type cell culture, and cultures with observed densities > 2 x 10⁶ cells ml⁻¹ were diluted ten-fold in blank growth media to avoid saturation of the cytometer.

Cell densities were measured until cell lines were confirmed to have exited log phase (i.e., reached a stationary phase plateau). Primary knockout mutant growth curves were repeated a minimum of six times (three biological replicates per-inoculation, with two independent repetitions) for each mutant line. Growth curves were tested for seven cpEnolase knockout, five cpPGAM1A knockout and four empty vector control lines, providing a minimum of 24 measurements (i.e., four distinct mutant lines) per each genotype studied (cpEnolase knockout, cpPGAM1A knockout and empty vector control lines).

To avoid artifacts based on the proximity of the seed cell culture to exponential phase at the time of inoculation (which may impact on lag phase length) or the relative diameter of each cell in culture (which may impact on carrying capacity), cell growth rates were measured exclusively from the log-phase relative division rate. This was calculated via considering Δlog₂ (cell density) / Δlog₂ (time) for a time-period corresponding to 5 x 10⁴ to 4 x 10⁶ cells/ ml, covering in most cases six
Growth rate measurements

A starting density of 10⁴ ml⁻¹ stationary phase *P. tricornutum* cells of a given culture line, as verified with a Malassez haemocytometer, were inoculated into a 15 ml volume antibiotic-free ESAW within a sterile, ventilated cap plastic culture flask (Celltreat) and grown under LD, CL, or 8C culture conditions as described. Cell densities were recorded: every day from one day post-inoculation (CL); every day from two days post-inoculation (LD); or every two days from five days post-inoculation (8C) at the mid-point of the LD light induction phase using a counting CyFlow Cube 8 cytometer (ParTec).

Typically, 15 µl cell culture, passed at 0.5 µl s⁻¹, were used for each measurement, with three technical replicates performed for each culture of which the first (enriched in non-cellular particles) was excluded from downstream calculations. Cytometer particle counts were correlated to actual cell densities using a calibration curve realised from haemocytometer counted densities of wild-type cell culture, and cultures with observed densities > 2 x 10⁶ cells ml⁻¹ were diluted ten-fold in blank growth media to avoid saturation of the cytometer.

Cell densities were measured until cell lines were confirmed to have exited log phase (i.e., reached a stationary phase plateau). Primary knockout mutant growth curves were repeated a minimum of six times (three biological replicates per-inoculation, with two independent repetitions) for each mutant line. Growth curves were tested for seven cpEnolase knockout, five cpPGAM1A knockout and four empty vector control lines, providing a minimum of 24 measurements (i.e., four distinct mutant lines) per each genotype studied (cpEnolase knockout, cpPGAM1A knockout and empty vector control lines).

To avoid artifacts based on the proximity of the seed cell culture to exponential phase at the time of inoculation (which may impact on lag phase length) or the relative diameter of each cell in culture (which may impact on carrying capacity), cell growth rates were measured exclusively from the log-phase relative division rate. This was calculated via considering Δlog₂ (cell density) / Δlog₂ (time) for a time-period corresponding to 5 x 10⁴ to 4 x 10⁶ cells/ ml, covering in most cases six
successive measurements of each individual growth curve. To confirm that the cells
were measured in exponential phase and were neither influenced by particle
contamination of the cytometer or cell exhaustion of the growth medium, the linear
correlation was calculated between the log value, with most calculated correlations
(129/ 132) showing linearity (r> 0.95). Three exemplar growth curve outputs are
provided in **Supplementary Dataset S4**, sheets 3-5; and an overview of relative
growth rates expressed as a function of mean empty vector control growth rates are
provided in **Supplementary Dataset S4**, sheet 6.

Complementation growth curves were repeated with at least two independent
repetitions for each cell line, with five timepoints taken to project growth rates, and
therefore a minimum of sixty independent measurements for each mutant:
complementation genotype under each growth condition, with the average of the two
fastest growth rates of each culture calculated as estimates for the growth rate. A
heatmap of all estimated complementation line growth rates is provided in
**Supplementary Dataset S4**, sheet 7.

Photophysiology

Cultures for photophysiological analysis were grown in 10ml ventilated plastic flasks, without shaking, under 19C CL and 19C LD as described above. Cultures were grown from a starting inoculum of 10⁵ cells ml⁻¹ as measured with a Malassez haemocytometer. Cell cultures that had reached a measured density of 10⁶ cells ml⁻¹ were then refreshed into fresh media at the initial starting concentration of 10⁵ cells ml⁻¹ to allow a prolonged adaptation to each photophysiological condition under a continuous exponential phase. Cells from refreshed culture lines were harvested in exponential phase (between 1 and 3 × 10⁶ cells ml⁻¹, and good physiology was verified by Fv/Fm measurements > 0.6 across all measured lines **(Supplementary Dataset S4, sheet 8)**.

Steady-state light curves (SSLC) were conducted with a fluorescence CCD camera
recorder (SpeedZen, jBeamBio, France) in a selected set of control lines (n=2),
cpPGAM (n=3) and cpEnolase knockouts (n=6), as well in complemented cpEnolase
(n=2) and cpPGAM1A (n=3) knockout lines in which we observed a suppression of
successive measurements of each individual growth curve. To confirm that the cells
were measured in exponential phase and were neither influenced by particle
contamination of the cytometer or cell exhaustion of the growth medium, the linear
correlation was calculated between the log value, with most calculated correlations
(129/ 132) showing linearity (r> 0.95). Three exemplar growth curve outputs are
provided in **Supplementary Dataset S4**, sheets 3-5; and an overview of relative
growth rates expressed as a function of mean empty vector control growth rates are
provided in **Supplementary Dataset S4**, sheet 6.

Complementation growth curves were repeated with at least two independent
repetitions for each cell line, with five timepoints taken to project growth rates, and
therefore a minimum of sixty independent measurements for each mutant:
complementation genotype under each growth condition, with the average of the two
fastest growth rates of each culture calculated as estimates for the growth rate. A
heatmap of all estimated complementation line growth rates is provided in
**Supplementary Dataset S4**, sheet 7.

Photophysiology

Cultures for photophysiological analysis were grown in 10ml ventilated plastic flasks, without shaking, under 19C CL and 19C LD as described above. Cultures were grown from a starting inoculum of 10⁵ cells ml⁻¹ as measured with a Malassez haemocytometer. Cell cultures that had reached a measured density of 10⁶ cells ml⁻¹ were then refreshed into fresh media at the initial starting concentration of 10⁵ cells ml⁻¹ to allow a prolonged adaptation to each photophysiological condition under a continuous exponential phase. Cells from refreshed culture lines were harvested in exponential phase (between 1 and 3 × 10⁶ cells ml⁻¹, and good physiology was verified by Fv/Fm measurements > 0.6 across all measured lines **(Supplementary Dataset S4, sheet 8)**).

Steady-state light curves (SSLC) were conducted with a fluorescence CCD camera
recorder (SpeedZen, jBeamBio, France) in a selected set of control lines (n=2),
cpPGAM (n=3) and cpEnolase knockouts (n=6), as well in complemented cpEnolase
(n=2) and cpPGAM1A (n=3) knockout lines in which we observed a suppression of
the knockout growth defect compared to complemented control lines. Measurements were repeated a minimum of two and in most cases four times per line and treatment condition, with a minimum of six unique measurements performed for each genotype and treatment. Curves were measured on cell cultures concentrated to between 2 and 5 × 10⁷ cells ml⁻¹. Samples were exposed to an initial 5 min illumination of 35 µmol photons m⁻² s⁻¹ green actinic light (532 nm), followed by a 6 steps of 3 min each of increasing intensity to 750 µmol photons m⁻² s⁻¹.

Minimum ($F_0$) and maximum ($F_M$) fluorescence were measured in dark-adapted (at least 1 min) samples, before and at the end of a 250 ms saturating (multiple turnover) pulse of light (532 nm, 5000 µmol photons m⁻² s⁻¹) and the maximum quantum yield of PSII in the dark was calculated as $F_v/F_M = (F_M - F_0)/F_M$. Every minute of light exposure, steady-state fluorescence ($F_s$) and maximum fluorescence under Light ($F_M$) were measured. PSII quantum yield ($\phi$PSII) and nonphotochemical quenching (NPQ) were calculated on the last time point of each light step as $\phi$PSII = ($F_M'-F_s)/F_M'$ and NPQ = $F_M/F_M' - 1$, and rETR at PSII as rETR = $\phi$PSII·E.

The whole rETR vs E curve was fitted as $rETR = rETR_M \cdot (1 - \exp(-\alpha \cdot E/rETR_M))$ where $rETR_M$ is the maximum rETR and $\alpha$ is the light-limited slope of rETR vs E (Jassby and Platt 1976). Only rETR values from 0 to 450 µmol photons m⁻² were used for the fit because values from 600 and 750 µmol photons m⁻² were too noisy. The light saturation parameter $E_K$ was calculated as $rETR_M/\alpha$ and the fitted values of the parameters were used to estimate $\phi$PSII under the growth light intensity of 50 µmol photons m⁻² s⁻¹ as $\phi$PSII₅₀µE = (rETR_M·(1 - exp(-α·50/rETR_M)))/50. The NPQ vs E curve was fitted as $\text{NPQ} = \text{NPQ}_M \cdot E^n / (E_{50}\text{NPQ}^n + E^n)$, where $\text{NPQ}_M$ is the maximal NPQ, $E_{50}\text{NPQ}$ is the half-saturation intensity for NPQ and $n$ is the sigmoidicity coefficient (Serôdio and Lavaud 2011).

The PSII functional absorption cross-section, $\sigma$PSII, was calculated from the fluorescence induction upon a single turnover flash of blue light (100 µs, 455 nm, 60 nm bandwidth) on non-concentrated cell culture. The induction curve was measured on 20 min dark-acclimated samples before centrifugation (average of 2-4 independent replicates) with a Fluorescence Induction and Relaxation (miniFIR) fluorometer (Gorbunov, Shirsin et al. 2020), which also measures single turnover
the knockout growth defect compared to complemented control lines. Measurements were repeated a minimum of two and in most cases four times per line and treatment condition, with a minimum of six unique measurements performed for each genotype and treatment. Curves were measured on cell cultures concentrated to between 2 and 5 × 10⁷ cells ml⁻¹. Samples were exposed to an initial 5 min illumination of 35 µmol photons m⁻² s⁻¹ green actinic light (532 nm), followed by a 6 steps of 3 min each of increasing intensity to 750 µmol photons m⁻² s⁻¹.

Minimum ($F_0$) and maximum ($F_M$) fluorescence were measured in dark-adapted (at least 1 min) samples, before and at the end of a 250 ms saturating (multiple turnover) pulse of light (532 nm, 5000 µmol photons m⁻² s⁻¹) and the maximum quantum yield of PSII in the dark was calculated as $F_V/F_M = (F_M - F_0)/F_M$. Every minute of light exposure, steady-state fluorescence ($F_s$) and maximum fluorescence under Light ($F_M$) were measured. PSII quantum yield ($\phi$PSII) and nonphotochemical quenching (NPQ) were calculated on the last time point of each light step as $\phi$PSII = ($F_M'-F_s)/F_M'$ and NPQ = $F_M/F_M' - 1$, and rETR at PSII as rETR = $\phi$PSII·E.

The whole rETR vs E curve was fitted as $rETR = rETR_M \cdot (1 - \exp(-\alpha \cdot E/rETR_M))$ where $rETR_M$ is the maximum rETR and $\alpha$ is the light-limited slope of rETR vs E (Jassby and Platt 1976). Only rETR values from 0 to 450 µmol photons m⁻² were used for the fit because values from 600 and 750 µmol photons m⁻² were too noisy. The light saturation parameter $E_K$ was calculated as $rETR_M/\alpha$ and the fitted values of the parameters were used to estimate $\phi$PSII under the growth light intensity of 50 µmol photons m⁻² s⁻¹ as $\phi$PSII₅₀μE = (rETR_M \cdot (1 - \exp(-\alpha \cdot 50/rETR_M))) / 50$. The NPQ vs E curve was fitted as $\text{NPQ} = \text{NPQ}_M \cdot E^n / (E_{50}\text{NPQ}^n + E^n)$, where $\text{NPQ}_M$ is the maximal NPQ, $E_{50}\text{NPQ}$ is the half-saturation intensity for NPQ and $n$ is the sigmoidicity coefficient (Serôdio and Lavaud 2011).

The PSII functional absorption cross-section, $\sigma$PSII, was calculated from the fluorescence induction upon a single turnover flash of blue light (100 µs, 455 nm, 60 nm bandwidth) on non-concentrated cell culture. The induction curve was measured on 20 min dark-acclimated samples before centrifugation (average of 2-4 independent replicates) with a Fluorescence Induction and Relaxation (miniFIR) fluorometer (Gorbunov, Shirsin et al. 2020), which also measures single turnover
$Fv/F_M$ and PSII connectivity. Parameters measured with the miniFIR fluorometer (as defined below) were also quantified for cultures grown under 8C CL, as the measurements were sufficiently rapid to allow the culture to be maintained at growth temperatures (Gorbunov, Shirsin et al. 2020). Measured photophysiological values are tabulated in **Supplementary Dataset S4**, sheet 8. Violin plots of photophysiological parameters were generated with BoxPlotR (Spitzer, Wildenhain et al. 2014).

## Gene expression analysis

Libraries were prepared from 200 ng DNAse-treated RNA for each mutant line and treatment condition, with at least three replicates per sample. Sequencing was performed by Fasteris (Plan-les-Ouates, Switzerland). After initial quality control checks, stranded Illumina mRNA libraries were prepared with a Novaseq V1.5 kit and sequenced with an SP-flow cell with 2x 100 bp over 200 cycles, yielding circa 130-160 Gb sequence data per sample with ≥85% of bases higher than Q30.

FastQ files were mapped using Nextflow's RNA sequencing assembly pipeline https://nf-co.re/rnaseq/usage, to gff3 annotations of the *P. tricornutum* version 3 genome (Rastogi, Maheswari et al. 2018, Lataretu and Hölzer 2020). Total mapped read counts were then compared between all biological and technical replicates for (i) each mutant line sequenced, (ii) each genotype (cpEnolase knockout, cpPGAM1A knockout, control), and (iii) each treatment condition performed (LD, CL, 8C) by principal component analysis (PCA) using the R package factoextra, with highly variant libraries removed (Kassambara and Mundt 2017). The final dataset included 63 RNAseq libraries, including five cpEnolase and five cpPGAM1A knockout lines and four empty vector controls, and a minimum of four RNA libraries prepared from at least two genetically distinct mutant constructs for each genotype (cpEnolase, cpPGAM1A and control) considered (**Supplementary Dataset S5**, sheets 1-2), Differentially expressed genes (DEGs) were then calculated between each genotype for each treatment condition using DESeq2 with cutoff fold-change 2 and P-value 0.05 (Liu, Wang et al. 2021) (**Supplementary Dataset S5**, sheets 2-3).
$Fv/F_M$ and PSII connectivity. Parameters measured with the miniFlRe fluorometer (as defined below) were also quantified for cultures grown under 8C CL, as the measurements were sufficiently rapid to allow the culture to be maintained at growth temperatures (Gorbunov, Shirsin et al. 2020). Measured photophysiological values are tabulated in **Supplementary Dataset S4**, sheet 8. Violin plots of photophysiological parameters were generated with BoxPlotR (Spitzer, Wildenhain et al. 2014).

## Gene expression analysis

Libraries were prepared from 200 ng DNAse-treated RNA for each mutant line and treatment condition, with at least three replicates per sample. Sequencing was performed by Fasteris (Plan-les-Ouates, Switzerland). After initial quality control checks, stranded Illumina mRNA libraries were prepared with a Novaseq V1.5 kit and sequenced with an SP-flow cell with 2x 100 bp over 200 cycles, yielding circa 130-160 Gb sequence data per sample with ≥85% of bases higher than Q30.

FastQ files were mapped using Nextflow's RNA sequencing assembly pipeline https://nf-co.re/rnaseq/usage, to gff3 annotations of the *P. tricornutum* version 3 genome (Rastogi, Maheswari et al. 2018, Lataretu and Hölzer 2020). Total mapped read counts were then compared between all biological and technical replicates for (i) each mutant line sequenced, (ii) each genotype (cpEnolase knockout, cpPGAM1A knockout, control), and (iii) each treatment condition performed (LD, CL, 8C) by principal component analysis (PCA) using the R package factoextra, with highly variant libraries removed (Kassambara and Mundt 2017). The final dataset included 63 RNAseq libraries, including five cpEnolase and five cpPGAM1A knockout lines and four empty vector controls, and a minimum of four RNA libraries prepared from at least two genetically distinct mutant constructs for each genotype (cpEnolase, cpPGAM1A and control) considered (**Supplementary Dataset S5**, sheets 1-2), Differentially expressed genes (DEGs) were then calculated between each genotype for each treatment condition using DESeq2 with cutoff fold-change 2 and P-value 0.05 (Liu, Wang et al. 2021) (**Supplementary Dataset S5**, sheets 2-3).
The mean transcript abundances of DEGs in knockout compared to control lines were first assessed in RNAseq data of N and P-limited *P. tricornutum* cell lines under two and nine time-points respectively (**Supplementary Dataset S5**, sheet 4) (Cruz de Carvalho, Sun et al. 2016, McCarthy, Smith et al. 2017). No significant differences were found between DEGs and other genes in the *P. tricornutum* genome (one-way ANOVA, P > 0.05; **Supplementary Dataset S5**, sheet 5), confirming that the RNAseq samples were not generated from N- or P-limited cultures. Next, functional enrichments in DEGs from previously tabulated values for the entire *P. tricornutum* genome (**Supplementary Dataset S5**, sheets 6-10) (Rastogi, Maheswari et al. 2018, Ait-Mohamed, Novák Vanclová et al. 2020). Functional enrichments were tested by two-tailed chi-square (P < 0.05) of a differentially expressed gene occurring in either one (cpEnolase v control; cpPGAM1A v control) knockout-versus-control line tests, or in both tests realised under each physiological condition. Finally, the distribution of DEGs across *P. tricornutum* core plastid and mitochondrial metabolism pathways were mapped onto a previously defined proteomic model of each organelle (Ait-Mohamed, Novák Vanclová et al. 2020); with the strongest DEG enrichment taken in the case of enzymes with multiple homologues (**Supplementary Dataset S5**, sheet 11).

Quantitative RT-PCR (RT-qPCR) validations were performed using cDNA synthesised from 5 ng dNase-treated RNA (per RT-qPCR reaction) and a RT Maxima First Strand synthesis kit (Thermo Fisher), following the manufacturer's instruction; using a 384-well Lightcycler (Roche) and Takyon™ No ROX SYBR 2X MasterMix (Eurogentec), following the manufacturers' protocols. Typical amplification conditions were: 10 minutes at 95°C, followed by 40 cycles of 30 seconds at 95°C, 30 seconds at 55°C, and 30 seconds at 72°C. Primer pairs for RT-qPCR amplifications were designed using NCBI Primer-BLAST (Ye, Coulouris et al. 2012), privileging unique amplification targets within the genomic sequence, an amplicon size of 100 to 150 base pairs, primer positions at different regions of the gene studied, and a 3' terminal G or C on each primer. Primer efficiencies were tested by qPCR with serial dilutions of *P. tricornutum* gDNA, with only primer pairs that yielded a Cp increment of between 0.9 and 1.1 per half dilution of DNA retained for RT-qPCR analysis. RT-qPCRs were at least three times for each amplicon: sample pair. RT-equivalents were performed to subtract residual genomic DNA from each Cp
The mean transcript abundances of DEGs in knockout compared to control lines were first assessed in RNAseq data of N and P-limited *P. tricornutum* cell lines under two and nine time-points respectively (**Supplementary Dataset S5**, sheet 4) (Cruz de Carvalho, Sun et al. 2016, McCarthy, Smith et al. 2017). No significant differences were found between DEGs and other genes in the *P. tricornutum* genome (one-way ANOVA, P > 0.05; **Supplementary Dataset S5**, sheet 5), confirming that the RNAseq samples were not generated from N- or P-limited cultures. Next, functional enrichments in DEGs from previously tabulated values for the entire *P. tricornutum* genome (**Supplementary Dataset S5**, sheets 6-10) (Rastogi, Maheswari et al. 2018, Ait-Mohamed, Novák Vanclová et al. 2020). Functional enrichments were tested by two-tailed chi-square (P < 0.05) of a differentially expressed gene occurring in either one (cpEnolase v control; cpPGAM1A v control) knockout-versus-control line tests, or in both tests realised under each physiological condition. Finally, the distribution of DEGs across *P. tricornutum* core plastid and mitochondrial metabolism pathways were mapped onto a previously defined proteomic model of each organelle (Ait-Mohamed, Novák Vanclová et al. 2020); with the strongest DEG enrichment taken in the case of enzymes with multiple homologues (**Supplementary Dataset S5**, sheet 11).

Quantitative RT-PCR (RT-qPCR) validations were performed using cDNA synthesised from 5 ng dNase-treated RNA (per RT-qPCR reaction) and a RT Maxima First Strand synthesis kit (Thermo Fisher), following the manufacturer's instruction; using a 384-well Lightcycler (Roche) and Takyon™ No ROX SYBR 2X MasterMix (Eurogentec), following the manufacturers' protocols. Typical amplification conditions were: 10 minutes at 95°C, followed by 40 cycles of 30 seconds at 95°C, 30 seconds at 55°C, and 30 seconds at 72°C. Primer pairs for RT-qPCR amplifications were designed using NCBI Primer-BLAST (Ye, Coulouris et al. 2012), privileging unique amplification targets within the genomic sequence, an amplicon size of 100 to 150 base pairs, primer positions at different regions of the gene studied, and a 3' terminal G or C on each primer. Primer efficiencies were tested by qPCR with serial dilutions of *P. tricornutum* gDNA, with only primer pairs that yielded a Cp increment of between 0.9 and 1.1 per half dilution of DNA retained for RT-qPCR analysis. RT-qPCRs were at least three times for each amplicon: sample pair. RT-equivalents were performed to subtract residual genomic DNA from each Cp
value obtained, and two housekeeping genes (Ribosomal protein S1, RPS; and
TATA binding protein, TBP) previously shown to have conditionally invariant
expression patterns in *P. tricornutum* were used as quantification references
(Sachse, Sturm et al. 2013). Tabulated RT-qPCR outputs are shown in
**Supplementary Dataset S5**, sheet 13; and sample information and reaction
conditions per MIQE guidelines (Bustin, Benes et al. 2009) are tabulated in
**Supplementary Dataset S5**, sheet 14.

Metabolite analysis

Cell pellets were taken from exponential-phase *P. tricornutum* culture (counted density 1-2 x 10⁶ cells ml⁻¹, 1.5 x 10⁸ cells per sample) for metabolite and lipid analysis. Cell pellets were collected without washing to minimise impacts on metabolite turnover, then transferred to a pre-weighed, double-pierced and double-autoclaved 1.5 ml Eppendorf tube for lyophilization. Cell pellet masses were recorded, and samples were immediately snap-frozen in liquid nitrogen and stored at -80 °C for subsequent analysis.

Metabolite profiling was carried out by gas chromatography-mass spectrometry (ChromaTOF software, Pegasus driver 1.61; LECO) as described previously (Lisec, Schauer et al. 2006). The chromatograms and mass spectra were evaluated using TagFinder software (Luedemann, von Malotky et al. 2012). Metabolite identification was manually checked by the mass spectral and retention index collection of the Golm Metabolome Database (Kopka, Schauer et al. 2005). Peak heights of the mass fragments were normalized successively on the basis of the fresh weight of the sample, the added amount of an internal standard (ribitol) and values obtained for loading column controls obtained from the same experiment.

Glycerolipid analysis

Glycerolipids were extracted by suspending cell pellets in 4 mL of boiling ethanol for 5 minutes to prevent lipid degradation. Lipids were extracted by addition of 2 mL methanol and 8 mL chloroform at room temperature (Folch, Lees et al. 1957). The mixture was then saturated with argon and stirred for 1 hour at room temperature.
value obtained, and two housekeeping genes (Ribosomal protein S1, RPS; and
TATA binding protein, TBP) previously shown to have conditionally invariant
expression patterns in *P. tricornutum* were used as quantification references
(Sachse, Sturm et al. 2013). Tabulated RT-qPCR outputs are shown in
**Supplementary Dataset S5**, sheet 13; and sample information and reaction
conditions per MIQE guidelines (Bustin, Benes et al. 2009) are tabulated in
**Supplementary Dataset S5**, sheet 14.

Metabolite analysis

Cell pellets were taken from exponential-phase *P. tricornutum* culture (counted density 1-2 x 10⁶ cells ml⁻¹, 1.5 x 10⁸ cells per sample) for metabolite and lipid analysis. Cell pellets were collected without washing to minimise impacts on metabolite turnover, then transferred to a pre-weighed, double-pierced and double-autoclaved 1.5 ml Eppendorf tube for lyophilization. Cell pellet masses were recorded, and samples were immediately snap-frozen in liquid nitrogen and stored at -80 °C for subsequent analysis.

Metabolite profiling was carried out by gas chromatography-mass spectrometry (ChromaTOF software, Pegasus driver 1.61; LECO) as described previously (Lisec, Schauer et al. 2006). The chromatograms and mass spectra were evaluated using TagFinder software (Luedemann, von Malotky et al. 2012). Metabolite identification was manually checked by the mass spectral and retention index collection of the Golm Metabolome Database (Kopka, Schauer et al. 2005). Peak heights of the mass fragments were normalized successively on the basis of the fresh weight of the sample, the added amount of an internal standard (ribitol) and values obtained for loading column controls obtained from the same experiment.

Glycerolipid analysis

Glycerolipids were extracted by suspending cell pellets in 4 mL of boiling ethanol for 5 minutes to prevent lipid degradation. Lipids were extracted by addition of 2 mL methanol and 8 mL chloroform at room temperature (Folch, Lees et al. 1957). The mixture was then saturated with argon and stirred for 1 hour at room temperature.
After filtration through glass wool, cell remains were rinsed with 3 mL chloroform/methanol 2:1, v/v and 5 mL of NaCl 1% was added to the filtrate to initiate biphase formation. The chloroform phase was dried under argon and stored at -20 °C. The lipid extract was resuspended in pure chloroform when needed.

Total glycerolipids were quantified from their fatty acids: in an aliquot fraction, 5 µg of 15:0 was added and the fatty acids present were converted to methyl esters (FAME) by a 1-hour incubation in 3 mL 2.5% H₂SO₄ in pure methanol at 100 °C (Jouhet, Maréchal et al. 2003). The reaction was stopped by addition of 3 mL water and 3 mL hexane. The hexane phase was analyzed by a gas chromatography-flame ionization detector (GC-FID) (Perkin Elmer) on a BPX70 (SGE) column. FAMEs were identified by comparison of their retention times with those of standards (Sigma) and quantified using 15:0 for calibration.

Glycerolipids were further analyzed by high pressure liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), based on a previously described procedure (Rainteau, Humbert et al. 2012). The lipid extracts corresponding to 25 nmol of total fatty acids were dissolved in 100 µL of chloroform/methanol [2/1, (v/v)] containing 125 pmol of each internal standard. Internal standards used were phosphatidylethanolamine (PE) 18:0-18:0 and diacylglycerol (DAG) 18:0-22:6 from Avanti Polar Lipid, and sulfoquinovosyldiacylglycerol (SQDG) 16:0-18:0 extracted from spinach thylakoids (Demé, Cataye et al. 2014) and hydrogenated (Buseman, Tamura et al. 2006). Lipid classes were separated using an Agilent 1200 HPLC system using a 150 mm × 3 mm (length × internal diameter) 5 µm diol column (Macherey-Nagel), at 40 °C. The mobile phases consisted of hexane/ isopropanol/water/ 1 M ammonium acetate, pH 5.3 [625/350/24/1, (v/v/v/v)] (A) and isopropanol/water/ 1 M ammonium acetate, pH 5.3 [850/149/1, (v/v/v)] (B). The injection volume was 20 µL. After 5 min, the percentage of B was increased linearly from 0% to 100% in 30 min and kept at 100% for 15 min. This elution sequence was followed by a return to 100% A in 5 min and an equilibration for 20 min with 100% A before the next injection, leading to a total runtime of 70 min. The flow rate of the mobile phase was 200 µL min⁻¹. The distinct glycerophospholipid classes were eluted successively as a function of the polar head group. Mass spectrometric analysis was performed on a 6460 triple quadrupole mass spectrometer (Agilent) equipped with a Jet stream
After filtration through glass wool, cell remains were rinsed with 3 mL chloroform/methanol 2:1, v/v and 5 mL of NaCl 1% was added to the filtrate to initiate biphase formation. The chloroform phase was dried under argon and stored at -20 °C. The lipid extract was resuspended in pure chloroform when needed.

Total glycerolipids were quantified from their fatty acids: in an aliquot fraction, 5 µg of 15:0 was added and the fatty acids present were converted to methyl esters (FAME) by a 1-hour incubation in 3 mL 2.5% H₂SO₄ in pure methanol at 100 °C (Jouhet, Maréchal et al. 2003). The reaction was stopped by addition of 3 mL water and 3 mL hexane. The hexane phase was analyzed by a gas chromatography-flame ionization detector (GC-FID) (Perkin Elmer) on a BPX70 (SGE) column. FAMEs were identified by comparison of their retention times with those of standards (Sigma) and quantified using 15:0 for calibration.

Glycerolipids were further analyzed by high pressure liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), based on a previously described procedure (Rainteau, Humbert et al. 2012). The lipid extracts corresponding to 25 nmol of total fatty acids were dissolved in 100 µL of chloroform/methanol [2/1, (v/v)] containing 125 pmol of each internal standard. Internal standards used were phosphatidylethanolamine (PE) 18:0-18:0 and diacylglycerol (DAG) 18:0-22:6 from Avanti Polar Lipid, and sulfoquinovosyldiacylglycerol (SQDG) 16:0-18:0 extracted from spinach thylakoids (Demé, Cataye et al. 2014) and hydrogenated (Buseman, Tamura et al. 2006). Lipid classes were separated using an Agilent 1200 HPLC system using a 150 mm × 3 mm (length × internal diameter) 5 µm diol column (Macherey-Nagel), at 40 °C. The mobile phases consisted of hexane/ isopropanol/water/ 1 M ammonium acetate, pH 5.3 [625/350/24/1, (v/v/v/v)] (A) and isopropanol/water/ 1 M ammonium acetate, pH 5.3 [850/149/1, (v/v/v)] (B). The injection volume was 20 µL. After 5 min, the percentage of B was increased linearly from 0% to 100% in 30 min and kept at 100% for 15 min. This elution sequence was followed by a return to 100% A in 5 min and an equilibration for 20 min with 100% A before the next injection, leading to a total runtime of 70 min. The flow rate of the mobile phase was 200 µL min⁻¹. The distinct glycerophospholipid classes were eluted successively as a function of the polar head group. Mass spectrometric analysis was performed on a 6460 triple quadrupole mass spectrometer (Agilent) equipped with a Jet stream
electrospray ion source under following settings: drying gas heater at 260 °C, drying gas flow at 13 L·min⁻¹, sheath gas heater at 300 °C, sheath gas flow at 11 L·min⁻¹, nebulizer pressure at 25 psi, capillary voltage at ± 5000 V and nozzle voltage at ± 1,000 V. Nitrogen was used as the collision gas. The quadrupoles Q1 and Q3 were operated at widest and unit resolution, respectively.

Phosphatidylcholine (PC) and diacylglycerol hydroxymethyltrimethyl-β-alanine (DGTA) analyses were carried out in positive ion modes by scanning for precursors of m/z 184 and 236 respectively at a collision energy (CE) of 34 and 52 eV. SQDG analysis was carried out in negative ion mode by scanning for precursors of m/z -225 at a CE of -56eV. PE, phosphatidylinositol (PI), phosphatidylglycerol (PG), monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG) measurements were performed in positive ion modes by scanning for neutral losses of 141 Da, 277 Da, 189 Da, 179 Da and 341 Da at cEs of 20 eV, 12 eV, 16 eV, 8 eV and 8 eV, respectively. DAG and triacylglycerol (TAG) species were identified and quantified by multiple reaction monitoring (MRM) as singly charged ions [M+NH4]⁺ at a CE of 16 and 22 eV respectively. Quantification was done for each lipid species by MRM with 50 ms dwell time with the various transitions previously recorded (Abida, Dolch et al. 2015). Mass spectra were processed using the MassHunter Workstation software (Agilent) for lipid identification and quantification. Lipid amounts (pmol) were corrected for response differences between internal standards and endogenous lipids as described previously (Jouhet, Lupette et al. 2017).

Normalised metabolite and lipid abundances were screened by PCA, as per the RNAseq analysis above, and outliers and biologically non-representative samples were removed. The final datasets consist of 139 libraries (metabolite GC-MS), 55 libraries (lipid GC-MS) and 49 libraries (lipid LC-MS), with a minimum of three libraries prepared from at least two genetically distinct mutant constructs for each genotype considered (Supplementary Dataset S6, sheet 1). Violin plots of differentially accumulated lipids were generated with BoxPlotR (Spitzer, Wildenhain et al. 2014).
electrospray ion source under following settings: drying gas heater at 260 °C, drying gas flow at 13 L·min⁻¹, sheath gas heater at 300 °C, sheath gas flow at 11 L·min⁻¹, nebulizer pressure at 25 psi, capillary voltage at ± 5000 V and nozzle voltage at ± 1,000 V. Nitrogen was used as the collision gas. The quadrupoles Q1 and Q3 were operated at widest and unit resolution, respectively.

Phosphatidylcholine (PC) and diacylglycerol hydroxymethyltrimethyl-β-alanine (DGTA) analyses were carried out in positive ion modes by scanning for precursors of m/z 184 and 236 respectively at a collision energy (CE) of 34 and 52 eV. SQDG analysis was carried out in negative ion mode by scanning for precursors of m/z -225 at a CE of -56eV. PE, phosphatidylinositol (PI), phosphatidylglycerol (PG), monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG) measurements were performed in positive ion modes by scanning for neutral losses of 141 Da, 277 Da, 189 Da, 179 Da and 341 Da at cEs of 20 eV, 12 eV, 16 eV, 8 eV and 8 eV, respectively. DAG and triacylglycerol (TAG) species were identified and quantified by multiple reaction monitoring (MRM) as singly charged ions [M+NH4]⁺ at a CE of 16 and 22 eV respectively. Quantification was done for each lipid species by MRM with 50 ms dwell time with the various transitions previously recorded (Abida, Dolch et al. 2015). Mass spectra were processed using the MassHunter Workstation software (Agilent) for lipid identification and quantification. Lipid amounts (pmol) were corrected for response differences between internal standards and endogenous lipids as described previously (Jouhet, Lupette et al. 2017).

Normalised metabolite and lipid abundances were screened by PCA, as per the RNAseq analysis above, and outliers and biologically non-representative samples were removed. The final datasets consist of 139 libraries (metabolite GC-MS), 55 libraries (lipid GC-MS) and 49 libraries (lipid LC-MS), with a minimum of three libraries prepared from at least two genetically distinct mutant constructs for each genotype considered (Supplementary Dataset S6, sheet 1). Violin plots of differentially accumulated lipids were generated with BoxPlotR (Spitzer, Wildenhain et al. 2014).
Measurements of cpEnolase and cpPGAM1A reaction rates were performed following a previously defined protocol (Zhang, Sampathkumar et al. 2020) (Supplementary Figure 20). First, codon-optimised constructs for *E. coli* expression were synthesized (Eurofins) using full-length cpEnolase and cpPGAM1A mRNA sequences as template. Constructs were cloned into a Gateway pDest-CTD-His vector via a pDONR intermediate and BP/LR clonase (all reagents Thermo Fisher) following the manufacturer's instructions (Hartley, Temple et al. 2000). To enable optimal expression in *E. coli*, multiple N-terminal length variants were synthesized from each gene, with those corresponding to the full gene length minus the predicted N-terminal signal peptide domain as inferred with SignalP (Almagro Armenteros, Tsirigos et al. 2019). Complete constructs and primers tested are provided in **Supplementary Dataset S6**, sheet 7.

cpEnolase and cpPGAM1A-CTDHis vectors were cloned into Rosetta (DE3) strain *E. coli* (Novagen) and coselected on ampicillin (100 µg/ml) and chloramphenicol (34 µg/ml). Proteins were induced in overnight cultures at 28°C, purified on a His-Trap column (GE Healthcare) following the manufacturers' instructions, and eluted in a buffer consisting of 125 mM NaCl, 250 mM Imidiazol (Sigma) and protease inhibitors. Eluted proteins were desalted using a Q10/ PD10 column (GE Healthcare) and quantified using a Bradford. Protein integrity and quantity were assessed routinely throughout the purification using SDS-PAGE.

Reaction rates were measured on purified 100 µg cpPGAM1A and 100 µg cpEnolase, as quantified with a nanodrop spectrometer. Rates were measured separately for glycolytic and gluconeogenic activity. Briefly, to measure glycolytic reaction rates, both enzymes were combined with 10 units pyruvate kinase and 10 units lactate dehydrogenase (both Sigma-Aldrich) at 25°C, alongside varying concentrations 9 mM D(-)3-Phosphoglyceric Acid, 25 mM Adenosine "-Diphosphate, and 25 mM reduced β-Nicotinamide Adenine Dinucleotide (NADH). Enzymatic activity was measured by considering 340 nm colorimetry as a proxy for NADH consumption following a previously defined protocol (Sigma protocols EC 5.4.2.1) (Sutherland, Posternak et al. 1949). To measure gluconeogenic reaction rates, a similar reaction was performed with both enzymes combined with 10 units phosphoglycerate kinase and 10 units glyceraldehyde-3-phosphate dehydrogenase
(both Sigma-Aldrich), alongside 9 mM phospho-enol-pyruvate, 25 mM Adenosine 5'-Diphosphate, and 25 mM reduced β-Nicotinamide Adenine Dinucleotide (NADH). Enzymatic activity was similarly measured by 340 nm colorimetry. A schematic of the measured reactions is provided in **Supplementary Figure 20**. Complete measured reaction rates over all technical replicates are provided in **Supplementary Dataset S6**, sheet 8.

**Statistical analysis.**

Expression levels of cpEnolase and cpPGAM, mutant growth rates, and mutant GC-MS / LC-MS metabolite distributions compared to control lines were assessed by one-way ANOVA. Correlations to environmental variations in *Tara* Oceans data were assessed by Pearson and Spearman correlations, with significance identified via one-way *t*-test. Differentially enriched genes were assessed using iDEP.91, with cut-off fold-change 2 and P-value 0.1. Functional enrichments in differentially expressed gene categories were assessed by two-tailed *chi*-squared tests. All statistical analyses, including mean values, standard deviations and sample sizes related to each calculation, are provided in the relevant tabs of **Supplementary Datasets S1-6**. P-values are signed as follows : for ANOVAs, negative values indicate the first category in the comparison has a smaller mean value than the second, and positive values that the first category in the comparison has a larger mean value than the second ; for correlations, negative values indicate a negative correlation coefficient, and positive values a positive correlation coefficient ; and for *chi*-squared tests, negative values indicate the observed frequency of a particular character is less than the expected frequency, and positive values that the observed frequency of a particular character is greater than the expected frequency.

**Accession Numbers**

RNAseq data associated with this project have been deposited with NCBI BioProject with project number PRJNA788211.

**Materials Distribution Statement**
(both Sigma-Aldrich), alongside 9 mM phospho-enol-pyruvate, 25 mM Adenosine 5'-Diphosphate, and 25 mM reduced β-Nicotinamide Adenine Dinucleotide (NADH). Enzymatic activity was similarly measured by 340 nm colorimetry. A schematic of the measured reactions is provided in **Supplementary Figure 20**. Complete measured reaction rates over all technical replicates are provided in **Supplementary Dataset S6**, sheet 8.

**Statistical analysis.**

Expression levels of cpEnolase and cpPGAM, mutant growth rates, and mutant GC-MS / LC-MS metabolite distributions compared to control lines were assessed by one-way ANOVA. Correlations to environmental variations in *Tara* Oceans data were assessed by Pearson and Spearman correlations, with significance identified via one-way *t*-test. Differentially enriched genes were assessed using iDEP.91, with cut-off fold-change 2 and P-value 0.1. Functional enrichments in differentially expressed gene categories were assessed by two-tailed *chi*-squared tests. All statistical analyses, including mean values, standard deviations and sample sizes related to each calculation, are provided in the relevant tabs of **Supplementary Datasets S1-6**. P-values are signed as follows : for ANOVAs, negative values indicate the first category in the comparison has a smaller mean value than the second, and positive values that the first category in the comparison has a larger mean value than the second ; for correlations, negative values indicate a negative correlation coefficient, and positive values a positive correlation coefficient ; and for *chi*-squared tests, negative values indicate the observed frequency of a particular character is less than the expected frequency, and positive values that the observed frequency of a particular character is greater than the expected frequency.

**Accession Numbers**

RNAseq data associated with this project have been deposited with NCBI BioProject with project number PRJNA788211.

**Materials Distribution Statement**
The author(s) responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plcell/pages/General-Instructions) are: Richard G. Dorrell richard.dorrell@sorbonne-universite.fr) and Chris Bowler (cbowler@bio.ens.psl.eu).

All remaining supporting data not provided directly in paper supporting tables are provided in the linked Open Science Foundation Supporting database https://osf.io/89vm3/ (Dorrell, Novak Vanclova et al. 2022). Project contents are ordered hierarchically by theme, with an overview of all contents provided on the site wiki page. A dedicated README file in each project folder explains the data presented and provides detailed methodology for each analysis.

**Supplementary Data**

**Supplementary Figure S1.** Distribution of lower plastid glycolysis-glucneogenesis across photosynthetic eukaryotes.

**Supplementary Figure 2.** Consensus topology of a 380 taxa x 413 aa alignment of Enolase sequences.

**Supplementary Figure 3.** Consensus topology of a 220 aa x 560 taxa alignment of PGAM isoform 1 sequences.

**Supplementary Figure 4.** Consensus phylogeny of a 235 aa x 66 taxa alignment of PGAM isoform 2 sequences.

**Supplementary Figure 6.** Relative abundances and transcriptional coordination of plastidial enolase, PGAM1 and PGAM2.

**Supplementary Figure 7.** Relative transcriptional regulation of cpEnolase and cpPGAM under nutrient, light and temperature stress conditions.
The author(s) responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plcell/pages/General-Instructions) are: Richard G. Dorrell richard.dorrell@sorbonne-universite.fr) and Chris Bowler (cbowler@bio.ens.psl.eu).

All remaining supporting data not provided directly in paper supporting tables are provided in the linked Open Science Foundation Supporting database https://osf.io/89vm3/ (Dorrell, Novak Vanclova et al. 2022). Project contents are ordered hierarchically by theme, with an overview of all contents provided on the site wiki page. A dedicated README file in each project folder explains the data presented and provides detailed methodology for each analysis.

**Supplementary Data**

**Supplementary Figure S1.** Distribution of lower plastid glycolysis-glucneogenesis across photosynthetic eukaryotes.

**Supplementary Figure 2.** Consensus topology of a 380 taxa x 413 aa alignment of Enolase sequences.

**Supplementary Figure 3.** Consensus topology of a 220 aa x 560 taxa alignment of PGAM isoform 1 sequences.

**Supplementary Figure 4.** Consensus phylogeny of a 235 aa x 66 taxa alignment of PGAM isoform 2 sequences.

**Supplementary Figure 6.** Relative abundances and transcriptional coordination of plastidial enolase, PGAM1 and PGAM2.

**Supplementary Figure 7.** Relative transcriptional regulation of cpEnolase and cpPGAM under nutrient, light and temperature stress conditions.
**Supplementary Figure 8.** Identification of *Tara* Oceans homologs of diatom plastid-targeted enolase and PGAM enzymes.

**Supplementary Figure 9.** Relative abundances of *Tara* Oceans diatom plastid glycolysis meta-genes.

**Supplementary Figure 10.** Normalized latitudinal regressions of *Tara* cpEnolase and cpPGAM1 sequences.

**Supplementary Figure 11.** Total relative abundances of meta-genes phylogenetically reconciled to diatom PGAM2 in 0.8-2000 µm filtered surface samples.

**Supplementary Figure 12.** Occurrence and mean coverage depth of *Tara* Oceans MAGs divided by chloroplast glycolysis state.

**Supplementary Figure 13.** Genotypes of *P. tricornutum* glycolysis knockout lines.

**Supplementary Figure 14.** Absolute and individual growth phenotypes of cpEnolase and cpPGAM1A CRISPR-Cas9 knockout mutant lines.

**Supplementary Figure 15.** Measured photo-physiology of glycolysis knockout lines.

**Supplementary Figure 16.** Bar plots of the mean and standard deviation of the ratios of 39 metabolites assessed by GC-MS in plastid glycolysis mutant lines under the three tested experimental conditions.

**Supplementary Figure 17.** Lipid accumulation profiles under 19C LD conditions.

**Supplementary Figure 18.** Lipid accumulation profiles under 19C CL conditions.

**Supplementary Figure 19.** Lipid accumulation profiles under 8C CL conditions.
**Supplementary Figure 8.** Identification of *Tara* Oceans homologs of diatom plastid-targeted enolase and PGAM enzymes.

**Supplementary Figure 9.** Relative abundances of *Tara* Oceans diatom plastid glycolysis meta-genes.

**Supplementary Figure 10.** Normalized latitudinal regressions of *Tara* cpEnolase and cpPGAM1 sequences.

**Supplementary Figure 11.** Total relative abundances of meta-genes phylogenetically reconciled to diatom PGAM2 in 0.8-2000 µm filtered surface samples.

**Supplementary Figure 12.** Occurrence and mean coverage depth of *Tara* Oceans MAGs divided by chloroplast glycolysis state.

**Supplementary Figure 13.** Genotypes of *P. tricornutum* glycolysis knockout lines.

**Supplementary Figure 14.** Absolute and individual growth phenotypes of cpEnolase and cpPGAM1A CRISPR-Cas9 knockout mutant lines.

**Supplementary Figure 15.** Measured photo-physiology of glycolysis knockout lines.

**Supplementary Figure 16.** Bar plots of the mean and standard deviation of the ratios of 39 metabolites assessed by GC-MS in plastid glycolysis mutant lines under the three tested experimental conditions.

**Supplementary Figure 17.** Lipid accumulation profiles under 19C LD conditions.

**Supplementary Figure 18.** Lipid accumulation profiles under 19C CL conditions.

**Supplementary Figure 19.** Lipid accumulation profiles under 8C CL conditions.
**Supplementary Figure 20.** Schematic diagram of the reaction kinetics measured for *P. tricornutum* cpEnolase and cPGAM1A enzymes.

**Supplementary Dataset S1.** Phylogenetic diversity of Enolase and PGAM sequences from across the tree of life.

**Supplementary Dataset S2.** Transcriptional and localization patterns of cpPGAM and cpEnolase genes in *Phaeodactylum tricornutum*.

**Supplementary Dataset S3.** Tara Oceans analysis of diatom plastid glycolysis.

**Supplementary Dataset S4.** Genotyping, growth dynamics and photophysiology in *P. tricornutum* plastid glycolysis mutant lines.

**Supplementary Dataset S5.** Differentially and conditionally expressed genes in *P. tricornutum* plastid glycolysis mutants.

**Supplementary Dataset S6.** Lipid and metabolite profiles of *P. tricornutum* plastid glycolysis mutant lines revealed by GC- and LC-mass spectrometry, and measured reaction kinetics of expressed enzymes.

## Author Contributions

RGD designed the research, with critical input from YZ, YZ, DC, BB, ARF, JJ, EM and CB. RGD, YZ, NG, TN, DC, MP, and VG performed the research. RGD, YZ, YL, DC and MP analysed the data. SA provided new analytical tools for cell growth measurements, and JJPK and NZ provided new computational tools for meta-genomic and RNA-seq analysis. RGD wrote the paper, with critical input from YL, TN, MP, SA, BB, JJPK, JJ, EM and CB. All coauthors read and approved the manuscript draft prior to final submission.

## Funding
**Supplementary Figure 20.** Schematic diagram of the reaction kinetics measured for *P. tricornutum* cpEnolase and cPGAM1A enzymes.

**Supplementary Dataset S1.** Phylogenetic diversity of Enolase and PGAM sequences from across the tree of life.

**Supplementary Dataset S2.** Transcriptional and localization patterns of cpPGAM and cpEnolase genes in *Phaeodactylum tricornutum*.

**Supplementary Dataset S3.** Tara Oceans analysis of diatom plastid glycolysis.

**Supplementary Dataset S4.** Genotyping, growth dynamics and photophysiology in *P. tricornutum* plastid glycolysis mutant lines.

**Supplementary Dataset S5.** Differentially and conditionally expressed genes in *P. tricornutum* plastid glycolysis mutants.

**Supplementary Dataset S6.** Lipid and metabolite profiles of *P. tricornutum* plastid glycolysis mutant lines revealed by GC- and LC-mass spectrometry, and measured reaction kinetics of expressed enzymes.

## Author Contributions

RGD designed the research, with critical input from YZ, YZ, DC, BB, ARF, JJ, EM and CB. RGD, YZ, NG, TN, DC, MP, and VG performed the research. RGD, YZ, YL, DC and MP analysed the data. SA provided new analytical tools for cell growth measurements, and JJPK and NZ provided new computational tools for meta-genomic and RNA-seq analysis. RGD wrote the paper, with critical input from YL, TN, MP, SA, BB, JJPK, JJ, EM and CB. All coauthors read and approved the manuscript draft prior to final submission.

## Funding
RGD acknowledges a CNRS Momentum Fellowship, awarded from 2019-2021, an ANR JCJC Grant (« PanArctica » ANR-21-CE02-0014-01) awarded 2021-2022 and an ERC Starting Grant Grant (ChloroMosaic, 101039760), awarded from 2023-2027. CB acknowledges support from FFEM - French Facility for Global Environment, French Government 'Investissements d'Avenir' programs OCEANOMICS (ANR-11-BTBR-0008), FRANCE GENOMIQUE (ANR-10-INBS-09-08), MEMO LIFE (ANR-10-LABX-54), and PSL Research University (ANR-11-IDEX-0001-02), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Diatomic; grant agreement No. 835067), and from the ANR 'BrownCut' project (ANR-19-CE20-0020). CB, EM and JJ were supported by ANR 'DIM' (ANR-21-CE02-0021) and PEPR AlgAdvance (22-PEBB-0002). The lipid analyses were performed at the LIPANG (Lipid analysis in Grenoble) platform hosted by the LPCV (UMR 5168 CNRS-CEA-INRAE-UGA) and supported by the Rhône-Alpes Region, the European Regional Development Fund (ERDF), Institut Carnot 3BCAR and Labex GRAL (10-LABX-0049), financed within the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS (ANR-17-EURE-0003). YZ and ARF acknowledge funding from the European Union's Horizon 2020 research and innovation program, project PlantaSYST (SGA-CSA No. 739582 under FPA No. 66462; and the BG05M2OP001-1.003-001-C01 project, financed by the European Regional Development Fund through the Bulgarian 'Science and Education for Smart Growth' Operational Programme. D.C and B.B acknowledge the support of the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (PhotoPHYTOMIX project, grant agreement No. 715579).

## Acknowledgments

The authors acknowledge Giselle McCallum, Elena Kazamia and Xia Gao (IBENS) for assistance with the optimization of cell cytometry protocols; Amandine Baylet (Lycée ENCPB-Pierre Gilles de Gennes), Quentin Caris (Lycée de la Vallée de Chevreuse) and Yonna Lauruol (Ecole Sup'BioTech) for assistance with growth measurements, Frédy Barneche and Clara Richet-Bourbousse (IBENS) for the use of culture facilities; Nathalie Joli (IBENS) for aid with RNAseq library production; Catherine Cantrel and Priscillia Pierre-Elies (IBENS), Pauline Clémente (Lycée
RGD acknowledges a CNRS Momentum Fellowship, awarded from 2019-2021, an ANR JCJC Grant (« PanArctica » ANR-21-CE02-0014-01) awarded 2021-2022 and an ERC Starting Grant Grant (ChloroMosaic, 101039760), awarded from 2023-2027. CB acknowledges support from FFEM - French Facility for Global Environment, French Government 'Investissements d'Avenir' programs OCEANOMICS (ANR-11-BTBR-0008), FRANCE GENOMIQUE (ANR-10-INBS-09-08), MEMO LIFE (ANR-10-LABX-54), and PSL Research University (ANR-11-IDEX-0001-02), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Diatomic; grant agreement No. 835067), and from the ANR 'BrownCut' project (ANR-19-CE20-0020). CB, EM and JJ were supported by ANR 'DIM' (ANR-21-CE02-0021) and PEPR AlgAdvance (22-PEBB-0002). The lipid analyses were performed at the LIPANG (Lipid analysis in Grenoble) platform hosted by the LPCV (UMR 5168 CNRS-CEA-INRAE-UGA) and supported by the Rhône-Alpes Region, the European Regional Development Fund (ERDF), Institut Carnot 3BCAR and Labex GRAL (10-LABX-0049), financed within the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS (ANR-17-EURE-0003). YZ and ARF acknowledge funding from the European Union's Horizon 2020 research and innovation program, project PlantaSYST (SGA-CSA No. 739582 under FPA No. 66462; and the BG05M2OP001-1.003-001-C01 project, financed by the European Regional Development Fund through the Bulgarian 'Science and Education for Smart Growth' Operational Programme. D.C and B.B acknowledge the support of the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (PhotoPHYTOMIX project, grant agreement No. 715579).

## Acknowledgments

The authors acknowledge Giselle McCallum, Elena Kazamia and Xia Gao (IBENS) for assistance with the optimization of cell cytometry protocols; Amandine Baylet (Lycée ENCPB-Pierre Gilles de Gennes), Quentin Caris (Lycée de la Vallée de Chevreuse) and Yonna Lauruol (Ecole Sup'BioTech) for assistance with growth measurements, Frédy Barneche and Clara Richet-Bourbousse (IBENS) for the use of culture facilities; Nathalie Joli (IBENS) for aid with RNAseq library production; Catherine Cantrel and Priscillia Pierre-Elies (IBENS), Pauline Clémente (Lycée
ENCPB-Pierre Gilles de Gennes) and Shun-Min Yang (University of South Bohemia) for the preparation of media substrates and aid with biolistic transformations; Ansgar Gruber (University of South Bohemia) for advice concerning the experimental localisation of chloroplast glycolysis proteins; Max Gorbunov (Rutgers University, NJ, USA) for the provision of the miniFIRE fluorometer used for photophysiological assays; and Mattia Storti (CEA Grenoble) for cell outlines used in the production of **Fig. 1A**. This paper is contribution 152 of *Tara Oceans*.

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Almagro Armenteros, J. J., K. D. Tsirigos, C. K. Sønderby, T. N. Petersen, O. Winther, S. Brunak, G. von Heijne and H. Nielsen (2019). SignalP 5.0 improves signal peptide predictions using deep neural networks. *Nat Biotechnol* **37**(4): 420-423.

Andriotis, V. M., N. J. Kruger, M. J. Pike and A. M. Smith (2010). Plastidial glycolysis in developing *Arabidopsis* embryos. *New Phytol* **185**(3): 649-662.

Anoman, A. D., M. Flores-Tornero, S. Rosa-Telléz, J. Muñoz-Bertomeu, J. Segura and R. Ros (2016). The specific role of plastidial glycolysis in photosynthetic and heterotrophic cells under scrutiny through the study of glyceraldehyde-3-phosphate dehydrogenase. Plant Signal Behav **11**(3): e1128614.

Ashworth, J., S. Turkarslan, M. Harris, M. V. Orellana and N. S. Baliga (2016). Pan-transcriptomic analysis identifies coordinated and orthologous functional modules in the diatoms *Thalassiosira pseudonana* and *Phaeodactylum tricornutum*. *Mar Genomics* **26**: 21-28.

Bai, Y., T. Cao, O. Dautermann, P. Buschbeck, M. B. Cantrell, Y. Chen, C. D. Lein, X. Shi, M. A. Ware, F. Yang, H. Zhang, L. Zhang, G. Peers, X. Li and M. Lohr (2022). Green diatom mutants reveal an intricate biosynthetic pathway of fucoxanthin. Proc Natl Acad Sci USA **119**(38): e2203708119.

Bailleul, B., N. Berne, O. Murik, D. Petroutsos, J. Prihoda, A. Tanaka, V. Villanova, R. Bligny, S. Flori, D. Falconet, A. Krieger-Liszkay, S. Santabarbara, F. Rappaport, P. Joliot, L. Tirichine, P. G. Falkowski, P. Cardol, C. Bowler and G. Finazzi (2015). Energetic coupling between plastids and mitochondria drives CO2 assimilation in diatoms. *Nature* **524**(7565): 366-3267.
ENCPB-Pierre Gilles de Gennes) and Shun-Min Yang (University of South Bohemia) for the preparation of media substrates and aid with biolistic transformations; Ansgar Gruber (University of South Bohemia) for advice concerning the experimental localisation of chloroplast glycolysis proteins; Max Gorbunov (Rutgers University, NJ, USA) for the provision of the miniFIRE fluorometer used for photophysiological assays; and Mattia Storti (CEA Grenoble) for cell outlines used in the production of **Fig. 1A**. This paper is contribution 152 of *Tara Oceans*.

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Kroth, P. G., A. Chiovitti, A. Gruber, V. Martin-Jezequel, T. Mock, M. S. Parker, M. S.

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Krebs, H. A. (1963). Renal gluconeogenesis. *Adv Enzyme Regul* 1: 385-400.

Kroth, P. G., A. Chiovitti, A. Gruber, V. Martin-Jezequel, T. Mock, M. S. Parker, M. S.

Stanley, A. Kaplan, L. Caron, T. Weber, U. Maheswari, E. V. Armbrust and C. Bowler (2008). A model for carbohydrate metabolism in the diatom *Phaeodactylum tricornutum* deduced from comparative whole genome analysis. *PLoS One* 3(1): e1426.

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Lisec, J., N. Schauer, J. Kopka, L. Willmitzer and A. R. Fernie (2006). Gas chromatography mass spectrometry-based metabolite profiling in plants. *Nat Protocol* 1(1): 387-396.

Liu, S., M. Storti, C. Bowler, G. Finazzi and R. G. Dorrell (2022). A metabolic, phylogenomic and environmental atlas of diatom plastid transporters from the model species *Phaeodactylum*. *Front Plant Sci* 13, 950467.

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Mitochondrial Glycolysis in a Major Lineage of Eukaryotes. *Genom Biol Evol* 10(9): 2310-2325.

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Paoli, C. Tara Oceans, S. Chaffron, D. Eveillard, E. Karsenti, S. Sunagawa, P. Wincker, L.

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Yang, L., Z. Wang, A. Zhang, R. Bhawal, C. Li, S. Zhang, L. Cheng and J. Hua (2022). Reduction of the canonical function of a glycolytic enzyme enolase triggers immune responses that further affect metabolism and growth in *Arabidopsis*. *Plant Cell* **34**(5): 1745-1767.

Ye, J., G. Coulouris, I. Zaretskaya, I. Cutcutache, S. Rozen and T. L. Madden (2012). Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. *BMC Bioinform.* **13**: 134.

Yin, W. and H. Hu (2023). CRISPR/Cas9-Mediated Genome Editing via Homologous Recombination in a Centric Diatom Chaetoceros muelleri. *ACS Synth Biol* **12**(4): 1287-1296.

Young, E. F. and J. T. Holt (2007). Prediction and analysis of long-term variability of temperature and salinity in the Irish Sea. *J Geophys Res* **112**(C1).

Yu, G., K. Nakajima, A. Gruber, C. Rio Bartulos, A. F. Schober, B. Lepetit, E. Yohannes, Y. Matsuda and P. G. Kroth (2022). Mitochondrial phosphoenolpyruvate carboxylase contributes to carbon fixation in the diatom *Phaeodactylum tricornutum* at low inorganic carbon concentrations. *New Phytol* **235**: 1379-1393.

Zhang, Y., A. Sampathkumar, S. M.-L. Kerber, C. Swart, C. Hille, K. Seerangan, A. Graf, L. Sweetlove and A. R. Fernie (2020). A moonlighting role for enzymes of glycolysis in the co-localization of mitochondria and chloroplasts. *Nat Commun* **11**(1): 4509.

Zhao, Z. and S. M. Assmann (2011). The glycolytic enzyme, phosphoglycerate mutase, has critical roles in stomatal movement, vegetative growth, and pollen production in *Arabidopsis thaliana*. *J Exp Bot* **62**(14): 5179-5189.

Zheng, Y. T., A. H. Quinn and G. Sriram (2013). Experimental evidence and isotopomer analysis of mixotrophic glucose metabolism in the marine diatom *Phaeodactylum tricornutum*. *Microbial Cell Factories* **12**: 16.

Zhu, B.-H., H.-P. Shi, G.-P. Yang, N.-N. Lv, M. Yang and K.-H. Pan (2016). Silencing UDP-glucose pyrophosphorylase gene in *Phaeodactylum tricornutum* affects carbon allocation. *New Biotechnol* **33**(1): 237-244.

Zulu, N. N., K. Zienkiewicz, K. Vollheyde and I. Feussner (2018). Current trends to comprehend lipid metabolism in diatoms. *Progr Lipid Res* **70**: 1-16.
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Metabolic context and evolution of the lower half of the diatom plastid glycolysis-gluconeogenesis pathway. **A:** Schematic comparison of diatom and plant core carbon metabolism, highlighting the localization and functions of two</figcaption></figure>
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Metabolic context and evolution of the lower half of the diatom plastid glycolysis-gluconeogenesis pathway. **A:** Schematic comparison of diatom and plant core carbon metabolism, highlighting the localization and functions of two</figcaption></figure>
enzymes in the lower half of glycolysis-gluconeogenesis (phospho-glycerate mutase, and enolase) whose localization to the chloroplast can connect endogenous enzymes in the Calvin-Benson-Bassham Cycle and pyruvate hub to create a complete glycolytic-gluconeogenic-gluconeogenic pathway. Abbreviations: GA3P-glyceraldehyde-3-phosphate; 1,3-PGA and 2,3-PGA- 1,3 and 2,3 bis-phosphoglycerate; Glu-6-P- glucose-6-phosphate; PEP- phospho-enol-pyruvate; RuBP- ribulose *bis*-phosphate; PGAM- phospho-glycerate mutase; cER- chloroplast: endoplasmic reticulum. **B, C**: consensus MrBayes topologies realised with three substitution matrices (GTR, Jones, WAG) of a 163 taxa x 413 aa alignment of organelle-targeted enolase (**B**) and 105 taxa x 220 aa alignment of selected organelle-targeted PGAM1 enzymes from diatoms and their closest relatives (**C**). For clarity, green, red, mitochondria and chloroplast-targeted diatom sequences are indicated with labelled boxes. These topologies identify recent duplications and recruitments of respiratory glycolytic-gluconeogenic enzymes from the mitochondria to plastid in diatoms and their closest relatives. Branch lines correspond to the frequency with which a given branching relationship was recovered, with thick branches identified by all three substitution matrices. For ease of viewing, trees are rooted between bacterial and eukaryotic sequences.
enzymes in the lower half of glycolysis-gluconeogenesis (phospho-glycerate mutase, and enolase) whose localization to the chloroplast can connect endogenous enzymes in the Calvin-Benson-Bassham Cycle and pyruvate hub to create a complete glycolytic-gluconeogenic-gluconeogenic pathway. Abbreviations: GA3P-glyceraldehyde-3-phosphate; 1,3-PGA and 2,3-PGA- 1,3 and 2,3 bis-phosphoglycerate; Glu-6-P- glucose-6-phosphate; PEP- phospho-enol-pyruvate; RuBP- ribulose *bis*-phosphate; PGAM- phospho-glycerate mutase; cER- chloroplast: endoplasmic reticulum. **B, C**: consensus MrBayes topologies realised with three substitution matrices (GTR, Jones, WAG) of a 163 taxa x 413 aa alignment of organelle-targeted enolase (**B**) and 105 taxa x 220 aa alignment of selected organelle-targeted PGAM1 enzymes from diatoms and their closest relatives (**C**). For clarity, green, red, mitochondria and chloroplast-targeted diatom sequences are indicated with labelled boxes. These topologies identify recent duplications and recruitments of respiratory glycolytic-gluconeogenic enzymes from the mitochondria to plastid in diatoms and their closest relatives. Branch lines correspond to the frequency with which a given branching relationship was recovered, with thick branches identified by all three substitution matrices. For ease of viewing, trees are rooted between bacterial and eukaryotic sequences.
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 2. Localisation and expression of *Phaeodactylum tricornutum* cpEnolase and cpPGAM1A.** A: individual channel and overlay images of GFP-tagged full-length cpEnolase (Phatr3_J41515) and cpPGAM1A (Phatr3_J17086) constructs (green), chlorophyll (red) and bright-field images of transformant *P. tricornutum* lines. Scale bar: 10 µm. GFP (green) and chlorophyll (red) overlay one another, indicating chloroplast localisation. B: quantitative RT-PCR (RT-qPCR) of *cpenolase* and *cppgam1A* expression in mid-exponential phase cells harvested at the mid-point of a</figcaption></figure>
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 2. Localisation and expression of *Phaeodactylum tricornutum* cpEnolase and cpPGAM1A.** A: individual channel and overlay images of GFP-tagged full-length cpEnolase (Phatr3_J41515) and cpPGAM1A (Phatr3_J17086) constructs (green), chlorophyll (red) and bright-field images of transformant *P. tricornutum* lines. Scale bar: 10 µm. GFP (green) and chlorophyll (red) overlay one another, indicating chloroplast localisation. B: quantitative RT-PCR (RT-qPCR) of *cpenolase* and *cppgam1A* expression in mid-exponential phase cells harvested at the mid-point of a</figcaption></figure>
19C 12h light: 12h dark cycle (19LD); at the same timepoint but under 24h continuous light (19CL); and under 8C and continuous light (8CL). Relative expression levels were normalised against two housekeeping genes (rps, RNA polymerase subunit 1; tbp, Tata binding protein) that show invariant expression in response to light cycles in *Phaeodactylum* per (Sachse, Sturm et al. 2013). Error bars correspond to one standard deviation. *: significantly different expression levels, one-way ANOVA, P < 0.05; **, the same, P < 0.001.
19C 12h light: 12h dark cycle (19LD); at the same timepoint but under 24h continuous light (19CL); and under 8C and continuous light (8CL). Relative expression levels were normalised against two housekeeping genes (*rps*, RNA polymerase subunit 1; *tbp*, Tata binding protein) that show invariant expression in response to light cycles in *Phaeodactylum* per (Sachse, Sturm et al. 2013). Error bars correspond to one standard deviation. *: significantly different expression levels, one-way ANOVA, P < 0.05; **, the same, P < 0.001.
<figure><img src="image_5.png" /><figcaption>**Fig. 3. Environmental distributions of diatom plastidial lower glycolysis-gluconeogenesis meta-genes.** Total transcriptome (A) and genome (B) relative abundances, sampled from all (0.8-2000µm) size fractions and surface layer (i,iii) or DCM (ii,iv) stations for *Tara* Oceans meta-genes phylogenetically resolved to diatom cpEnolase (i,ii) and cpPGAM1 (iii,iv). These data provide a global overview of *Tara* Oceans meta-gene abundances, in complement to data from individual size fractions shown in **Supplementary Figure 9**. These data demonstrate higher meta-transcript abundance without commensurate increases in meta-gene abundance at high northern and southern latitudes.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3. Environmental distributions of diatom plastidial lower glycolysis-gluconeogenesis meta-genes.** Total transcriptome (A) and genome (B) relative abundances, sampled from all (0.8-2000µm) size fractions and surface layer (i,iii) or DCM (ii,iv) stations for *Tara* Oceans meta-genes phylogenetically resolved to diatom cpEnolase (i,ii) and cpPGAM1 (iii,iv). These data provide a global overview of *Tara* Oceans meta-gene abundances, in complement to data from individual size fractions shown in **Supplementary Figure 9**. These data demonstrate higher meta-transcript abundance without commensurate increases in meta-gene abundance at high northern and southern latitudes.</figcaption></figure>
A

(i) 19C and LD

<figure><img src="image_6.png" /></figure>

(ii) 19C and CL

<figure><img src="image_7.png" /></figure>

Cells/ml (log₁₀)

Days post-inoculation

<figure><img src="image_8.png" /></figure>

B

Days post-inoculation

<figure><img src="image_9.png" /></figure>

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

(i) 19C and LD

<figure><img src="image_6.png" /></figure>

(ii) 19C and CL

<figure><img src="image_7.png" /></figure>

Days post-inoculation

Cells/ml (log₁₀)

(iii) 8C and CL

<figure><img src="image_8.png" /></figure>

Days post-inoculation

B

<figure><img src="image_9.png" /></figure>

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

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

* P < 0.05

** P < 0.01
**Fig. 4.** Growth phenotypes of cpEnolase and cpPGAM1A CRISPR-Cas9 knockout mutant and zeocin-resistant empty vector control *P. tricornutum* lines. **A:** exemplar growth curves from single experiments realised for *P. tricornutum* lines in 50 µE m⁻² s⁻¹ illumination, non-shaken cultures and replete ESAW media, under three conditions- (i) 19°C and 12h light: 12h dark Circadian cycles (« 19C LD »); (ii) 19°C and 24h continuous light (« 19C CL »); and (iii) 8°C and 24h continuous light (« 8C CL »). Hashed black lines show the approximative concentrations (between 5 x10⁴ and 4 x 10⁶ cells ml⁻¹) over which growth rates were calculated). **B:** mean relative log phase growth rates of each genotype under each condition, measured through a minimum of three biological replicates and two technical repetitions (six measurements per line, minimum 24 measurements per genotype), over five time-points with linear (r² > 0.95) relationship between log cell density and time). Asterisks indicate significant differences as inferred by one-way ANOVA. An alternative version of this figure showing absolute growth rates of individual cell lines is provided in **Supplementary Figure 14**.
**Fig. 4.** Growth phenotypes of cpEnolase and cpPGAM1A CRISPR-Cas9 knockout mutant and zeocin-resistant empty vector control *P. tricornutum* lines. **A:** exemplar growth curves from single experiments realised for *P. tricornutum* lines in 50 µE m⁻² s⁻¹ illumination, non-shaken cultures and replete ESAW media, under three conditions- (i) 19°C and 12h light: 12h dark Circadian cycles (« 19C LD »); (ii) 19°C and 24h continuous light (« 19C CL »); and (iii) 8°C and 24h continuous light (« 8C CL »). Hashed black lines show the approximative concentrations (between 5 x10⁴ and 4 x 10⁶ cells ml⁻¹) over which growth rates were calculated). **B:** mean relative log phase growth rates of each genotype under each condition, measured through a minimum of three biological replicates and two technical repetitions (six measurements per line, minimum 24 measurements per genotype), over five time-points with linear (r² > 0.95) relationship between log cell density and time). Asterisks indicate significant differences as inferred by one-way ANOVA. An alternative version of this figure showing absolute growth rates of individual cell lines is provided in **Supplementary Figure 14**.
<figure><img src="image_11.png" /><figcaption>**Fig. 5.** Changes in plastid and mitochondrial metabolic architecture inferred from gene expression analyses. **A:** predicted consensus localizations (either: chloroplast, or non-chloroplast) from ASAFind (Gruber, Rocab et al. 2015) and HECTAR (Gschloessl, Guermeur et al. 2008) of all genes inferred (P < 0.05, fold-change expression >2) to be up- or down-regulated in both cpEnolase and</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Fig. 5.** Changes in plastid and mitochondrial metabolic architecture inferred from gene expression analyses. A: predicted consensus localizations (either: chloroplast, or non-chloroplast) from ASAFind (Gruber, Rocab et al. 2015) and HECTAR (Gschloessl, Guermeur et al. 2008) of all genes inferred (P < 0.05, fold-change expression >2) to be up- or down-regulated in both cpEnolase and</figcaption></figure>
cpPGAM1A knockout compared to control lines under 19C LD, 19C CL and 8C CL.
Significantly enriched localizations (two-tailed chi-squared test) are asterisked. B:
relative mRNA abundances of eleven genes encoding exemplar chloroplast- and
mitochondria-targeted proteins, verified by RT-qPCR. Genes differentially expressed
(two-tailed t-test, P < 0.05) in each condition are asterisked.
cpPGAM1A knockout compared to control lines under 19C LD, 19C CL and 8C CL.
Significantly enriched localizations (two-tailed chi-squared test) are asterisked. B:
relative mRNA abundances of eleven genes encoding exemplar chloroplast- and
mitochondria-targeted proteins, verified by RT-qPCR. Genes differentially expressed
(two-tailed t-test, P < 0.05) in each condition are asterisked.
<figure><img src="image_12.png" /><figcaption>**Fig. 6.** Volcano plots of differentially accumulated metabolites assessed by GC-MS. Scatterplots of the log₂ accumulation ratios and –log₁₀ P-values of difference in the mass, ribitol and quality-control-normalised abundances of 39 sugars and amino acid metabolites in cpEnolase and cpPGAM1A knockout compared to empty vector control lines, measured by GC-MS in all three experimental conditions tested. Metabolites that show a differential accumulation in each plot (P < 10⁻⁵) are labelled,</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>**Fig. 6.** Volcano plots of differentially accumulated metabolites assessed by GC-MS. Scatterplots of the log₂ accumulation ratios and –log₁₀ P-values of difference in the mass, ribitol and quality-control-normalised abundances of 39 sugars and amino acid metabolites in cpEnolase and cpPGAM1A knockout compared to empty vector control lines, measured by GC-MS in all three experimental conditions tested. Metabolites that show a differential accumulation in each plot (P < 10⁻⁵) are labelled,</figcaption></figure>
with metabolites that show a differential accumulation in both knockout lines in each condition shown in black text, and five metabolites that are uniquely over-accumulated in cpEnolase knockout lines under all three conditions shown in dark red text.

<figure><img src="image_13.png" /><figcaption>**Fig. 7.** LC-MS lipid distributions in glycolysis-gluconeogenesis mutant lines. **A:** scatterplots of relative proportions of MGDG and TAG in total lipid LC-MS samples in cpEnolase and cpPGAM1A knockout lines and empty vector controls under each growth condition, showing increased MGDG: TAG in glycolysis knockout lines under 19C, and the inverse relationship in cpEnolase knockout lines only under 8C. **B:** violin plots of relative abundances of three further lipid categories inferred to differentially accumulate in glycolysis knockout lines under different growth conditions. Significant differences between knockout and control lines (one-way ANOVA) are asterisked.</figcaption></figure>
with metabolites that show a differential accumulation in both knockout lines in each condition shown in black text, and five metabolites that are uniquely over-accumulated in cpEnolase knockout lines under all three conditions shown in dark red text.

<figure><img src="image_15.png" /><figcaption>**Fig. 7.** LC-MS lipid distributions in glycolysis-gluconeogenesis mutant lines. **A:** scatterplots of relative proportions of MGDG and TAG in total lipid LC-MS samples in cpEnolase and cpPGAM1A knockout lines and empty vector controls under each growth condition, showing increased MGDG: TAG in glycolysis knockout lines under 19C, and the inverse relationship in cpEnolase knockout lines only under 8C. **B:** violin plots of relative abundances of three further lipid categories inferred to differentially accumulate in glycolysis knockout lines under different growth conditions. Significant differences between knockout and control lines (one-way ANOVA) are asterisked.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>**Fig. 8. Proposed activities of *P. tricornutum* plastid lower glycolysis-gluconeogenesis.** Schematic diagram showing potential inferred roles of lower half diatom plastid glycolysis-gluconeogenesis in each environmental condition tested. The measured Vmax of purified cpEnolase and cPGAM1A supplemented with 3-PGA (glycolytic direction) or PEP (gluconeogenic direction) are provided for 9mM substrate in each case.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>**Fig. 8. Proposed activities of *P. tricornutum* plastid lower glycolysis-gluconeogenesis.** Schematic diagram showing potential inferred roles of lower half diatom plastid glycolysis-gluconeogenesis in each environmental condition tested. The measured Vmax of purified cpEnolase and cPGAM1A supplemented with 3-PGA (glycolytic direction) or PEP (gluconeogenic direction) are provided for 9mM substrate in each case.</figcaption></figure>