# Dynamic Relocalization and Divergent Expression of a Major Facilitator Carrier Subfamily in Diatoms

Shun Liu, Victoria Powell, Shun-min Yang, France Lam, Chris Bowler, Miroslav Obornik, Richard G Dorrell

► To cite this version:

Shun Liu, Victoria Powell, Shun-min Yang, France Lam, Chris Bowler, et al. Dynamic Relocalization and Divergent Expression of a Major Facilitator Carrier Subfamily in Diatoms. Physiologia Plantarum, 2025, 177 (3), pp.e70355. 10.1111/ppl.70355. hal-05134059

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Submitted on 28 Jun 2025

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# Dynamic relocalization and divergent expression of a major facilitator carrier subfamily in diatoms

Shun Liu<fnref n="1" />,<fnref n="2" />, Victoria Powell<fnref n="3" />⁺, Shun-Min Yang<fnref n="4" />,<fnref n="5" />⁺, France Lam<fnref n="6" />, Chris Bowler<fnref n="1" />,<fnref n="7" />, Miroslav Oborník<fnref n="3" />,<fnref n="4" />, and Richard G. Dorrell<fnref n="1" />,<fnref n="3" />

+contributed equally to this article

*to whom correspondence should be addressed: richard.dorrell@sorbonne-universite.fr

## Funding Statement

SL acknowledges a China Scholarship Council doctoral studentship grant awarded via the Université Paris-Saclay, and SMY acknowledges a visiting student exchange grant, anchoring the Biology Centre CAS in the European Research Area (grant number: CZ.02.2.69/0.0/0.0/18_054/0014649) and GA JU travel award from the University of South Bohemia. CB acknowledges 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), as well as funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Diatomic; grant agreement No. 835067), and the Agence Nationale de la Recherche (BrownCut project; ANR-19-CE20-0020). MO acknowledges funding from the Czech Science Foundation, grant number 23-06203S. RGD acknowledges a CNRS Momentum Fellowship, awarded 2019-2021, and an ERC Starting Grant ("ChloroMosaic", grant number 101039760, awarded 2023-2027).

## Abstract

Eukaryotic organisms, including microbial members such as protists and green algae, utilise suites of transporter proteins to move essential metabolites across cell organelle membranes. Amongst these different transporter families, the MCF (Mitochondrial Carrier

<footnote n="1">¹ Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris 75005, France</footnote>

<footnote n="2">² Guangzhou Marine Geological Survey, China Geological Survey, Guangzhou 510075, China</footnote>

<footnote n="3">³ Department of Computational, Quantitative and Synthetic Biology (CQSB), UMR7238, Institut de Biologie Paris-Seine (IBPS), CNRS, INSERM, Université, Paris, Sorbonne, 75005, France</footnote>

<footnote n="4">⁴ Faculty of Science, University of South Bohemia, Branišovská 31, 37005 České Budějovice, Czech Republic</footnote>

<footnote n="5">⁵ Institute of Parasitology, Biology Centre, Czech Academy of Sciences, Branišovská 31, 37005 České Budějovice, Czech Republic</footnote>

<footnote n="6">⁶ Imagerie Photonique Paris Seine (I2PS) facility, Institut de Biologie Paris Seine (IBPS)</footnote>

<footnote n="7">⁷ CNRS Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/ Tara Oceans GOSEE, 75016 Paris, France</footnote>
Family) is one of the most diverse, encompassing essential NAD+ and ADP/ATP translocators, as well as amino acid, sugar and cofactor transporters. They are typically associated with the mitochondrial inner membrane but some display more dynamic localization. Here, we perform a census of MCF domains in the genome of the model diatom alga *Phaeodactylum tricornutum*, identifying a new family of three proteins (termed here and elsewhere "MCFc") with strong internal sequence conservation but limited similarity to other MCF proteins encoded in its genome. Considering both phylogenetic data and experimental localization, we posit that MCFc is widespread across algae with complex red chloroplasts alongside some primary green algae, and contains multiple sub-families targeted to diatom mitochondria, plastids, and endomembranes. Finally, using data from *Tara Oceans*, we identify putative roles for MCFc in diatom cells, including a possible association of the plastid-targeted Phatr3_J46742 sub-family in cellular nitrate assimilation. Our data provide insights into the evolutionary diversification of the membrane transport mechanisms associated with diatoms and other eukaryotic algae.

**Keywords:** Solute carrier family 25; plastid proteomes; gene co-regulation; environmental sequencing; GFP-confocal microscopy; spectral deconvolution

## Introduction

Eukaryotic cells are defined by the presence of discrete organelles (i.e., nuclei, mitochondria, chloroplasts) that direct diverse and essential cellular activities. Many of these organelles date back to the last common eukaryotic ancestor, alongside more recent and lineage-specific organelles acquired through endosymbiosis and through duplication events (Hammond et al., 2022; More et al., 2024; Novák Vanclová and Dorrell, 2024; Richards et al., 2024). As each of these organelles is separated by others by one or multiple hydrophobic membrane bilayers, it is essential that metabolites, proteins and cofactors required for their function, and both metabolic and waste products of their function, are actively transferred into and out of organelles by a diverse range of transporter proteins (Saier, Tran and Barabote, 2006). Protein transporters are highly diverse in terms of their structural evolution, metabolic functions and distribution across the eukaryotic tree of life (Saier, Tran and Barabote, 2006). For example, of nearly 80 membrane transporters associated with the chloroplasts of the model plant *Arabidopsis* only 11 are conserved with the model diatom *Phaeodactylum tricornutum*, and it is likely that even the protein homologues shared between both species have different substrates and kinetic activities despite their underlying structural similarity (Moog et al., 2020; Liu et al., 2022).

One of the more diverse and enigmatic membrane transporters are the mitochondrial carrier family (MCF) proteins. The MCF proteins (also known as solute carrier family, SLC25; PFAM domain PF00153) are highly diverse, forming the single most expanded protein carrier family in the human genome (48 known genes), and include essential ADP/ATP transporters, phosphate carriers, and essential transporters for amino acids (aspartate, glutamate), cofactors (thiamine) and sugars (ketoglutarate, citrate) (Kunji, 2004; Ruprecht and Kunji, 2020). MCFs are typically defined by six transmembrane domains, although other conformations exist (Miyake et al., 2002; Ito, Matsukawa and Kato, 2006), and contain three successive regions each
containing two linked hydrophobic domains, with a consensus sequence each of
P-X-[D/E]-X-X-[R/K]₂₀ [D/E]GXXXX[W/Y/F][K/R]G (Kuan and Saier, 1993; Kunji, 2004; Monné
and Palmieri, 2014). While the majority of annotated MCFs are believed to localize to the
mitochondrial inner membrane, some do not, including NAD+ transporters localized to
peroxisomes in humans, yeast and plants (Palmieri et al., 2001; Agrimi, Russo, Pierri, et al.,
2012; Toleco et al., 2020) and to plant chloroplasts (Palmieri et al., 2009) and ATP and other
nucleotide triphosphate transporters localized to the chloroplasts of both plants (Ast et al., 2009;
Nunes-Nesi, Cavalcanti and Fernie, 2020). Beyond different localizations, substantial internal
differences exist in the structure and functions of MCF, with many described examples showing
less than 10% reciprocal protein sequence similarity to one another (Kuan and Saier, 1993).

Beyond established cell biology models in animals, fungi, and plants, eukaryotes have
diverged into a wide range of lifestyle and cell forms (More et al., 2024; Penot-Raquin et al.,
2024). These include diatoms, which are typically unicellular and chain-forming algae abundant
both in marine and freshwater habitats, separated from plants by hundreds of millions of years
of evolutionary distance, who perform nearly one-quarter of planetary primary production
(Serôdio and Lavaud, 2020). Diatoms differ from plants in having chloroplasts surrounded by
four (not two) membranes, the outermost of which is contiguous with the endoplasmic reticulum
(Ast et al., 2009; Moog et al., 2020), derived from the secondary or more complex
endosymbiosis of a eukaryotic red alga itself with a nucleus and chloroplast (Novák Vanclová
and Dorrell, 2024). Diatom biology is progressively being understood through cellular models
such as *P. tricornutum*, for which extensive synthetic biology techniques and high-throughput
gene expression and proteomics data are available (Siaut et al., 2007; Liu et al., 2022; Huang et
al., 2024; Villar et al., 2025). In addition, reflecting their abundance in marine communities,
diatoms are well-represented in environmental sequencing expeditions such as *Tara Oceans*,
giving us unprecedented opportunities for identifying the spatial occurrence, abundance and
relative expression of the dark matter of their genomes in the wild (Liu et al., 2022;
Penot-Raquin et al., 2024).

Here, concomitant with a mutant study of the same protein (Giustini et al., 2025), we
identify a novel *P. tricornutum* MCF protein (Phatr3_J46742) with apparent chloroplast
localization, based on GFP confocal microscopy and spectral deconvolution. We further present
an overview of MCF protein diversity, function, and expression trends in diatoms. We note that
Phatr3_J46742 forms a novel MCF subfamily including two other members (Phatr3_J42874,
Phatr3_J46612) that we localize respectively to the endomembrane system and to the
mitochondria. Considering a manually curated phylogeny of these proteins, including inferred
localization, from over 250 algal genomes and transcriptomes, we infer this protein likely derives
from green algae, with recent paralogy events underpinning the progressive recruitment of
Phatr3_J46742 from the mitochondria to the chloroplast (Liu et al., 2022). We further consider
both cellular (Villar et al., 2024) and environmental expression data from diatoms (Penot-Raquin
et al., 2024) to suggest that these proteins may have diverged in function, alongside changing
localization, and play different physiological roles in the diatom cell.

**Materials and Methods**
*Phylogenetics*. MCF domains (PF00153) were identified in peptide models from the version 3 annotation of the *Phaeodactylum tricornutum* genome using hmmsearch with e-value 10⁻⁵, per (Kuan and Saier, 1993; Villar *et al.*, 2024). *In silico* localizations, KEGG functions, and transcriptional coregulation data were obtained from the DiatomicBase database of *P. tricornutum* genome annotations, per (Liu *et al.*, 2022; Villar *et al.*, 2024) Fold-enrichments and P-values of enrichment of each protein in *P. tricornutum* plastid-enriched proteome fractions were taken from (Huang *et al.*, 2024). Equivalences between MCF proteins identified in purified *Thalassiosira pseudonana* plastid and mitochondrial extracts were identified by BLASTp with the *T. pseudonana* protein as a query and -max-target_seqs 1 condition (Schober *et al.*, 2019).

To identify MCFc/m/e homologues, the complete sequence of Phatr3_J46742, Phatr3_J42874 and Phatr3_J46612 in the *P. tricornutum* v3 genome annotation (Villar *et al.*, 2024) were searched against a composite library of sequences across the tree of life (Liu *et al.*, 2022; Penot-Raquin *et al.*, 2024). This library consisted of a complete version of uniprot (accessed 2018), along with decontaminated versions of the MMETSP transcriptome project, and JGI PhycoCosm algal genomes, as previously described (Keeling *et al.*, 2014; Suzek *et al.*, 2015; Grigoriev *et al.*, 2021). Possible homologues were extracted by reciprocal BLASTp best hit, with threshold e-value for the initial BLAST search 1x10⁻⁵, and best hits identified by reciprocal search against the entire *P. tricornutum* genome (Liu *et al.*, 2022).

Probable localisations of sequences derived from lineages with four membrane plastid structures (cryptomonads, haptophytes and ochrophytes) were inferred using the consensus predictions of SignalP v 3.0, ASAFind, HECTAR, and MitoFates (Bendtsen *et al.*, 2004; Gschloessl, Guermeur and Cock, 2008; Fukasawa *et al.*, 2015; Gruber *et al.*, 2015); as previously described (Liu *et al.*, 2022). A taxonomically representative subset of the sequences obtained were aligned using MAFFT v 5.0, MUSCLE v 8.0, and GenELOUS v 4.76 (Edgar, 2004; Kearse *et al.*, 2012; Katoh, Rozewicki and Yamada, 2019); trimmed manually and with trimAl in -gt 0.5 setting (Capella-Gutiérrez, Silla-Martínez and Gabaldón, 2009); and consensus phylogenies were inferred using MrBayes and RAxML programmes integrated into the CIPRES server, using three substitution matrices (GTR, Jones/JTT, and WAG), as previously described (Huelsenbeck and Ronquist, 2001; Miller, Pfeiffer and Schwartz, 2010; Stamatakis, 2014).

*Experimental localization.* The complete sequences of Phatr3_J42874 and Phatr3_J46612, as amplified from cDNA and using *P. tricornutum* version 3 genome annotation gene models, were cloned into a pPhat-Fcp::eGFP-SHBLE vector by Gibson cloning, using previously defined methodology(Ast *et al.*, 2009; Gile *et al.*, 2015), yielding constitutively overexpressed constructs with a C-terminal GFP tag. For Phatr3_J46742, the complete coding sequence was found to be unable to produce GFP fluorescence, so a truncated construct, consisting of the last 209 bp of the predicted 5' UTR and first encoded 261 bp (87 aa) of the coding sequence was similarly cloned. This construct was designed both to cover several potential alternative initiator codons (in-frame methionines) in the CDS 5' end, but to avoid out-of-frame initiator codons that could interfere with the correct translation of the C-terminal GFP fusion. Vector sequences confirmed by PCR and Sanger sequencing were introduced into wild-type *P. tricornutum* strain Pt1 by biolistic transformation, following the methodology of (Falciatore *et al.*, 1999).
Transformant colonies were selected first on f/2-agar plates supplemented with 100µg ml⁻¹ zeocin. Resistant colonies were transferred to liquid media, and GFP expression was first assessed by Western blot using a mouse anti-GFP antibody (ThermoFisher) (Ast et al., 2009). Constructs found to express GFP were visualised by confocal microscopy. Mitochondria in cell lines were stained with MitoTracker Orange (Sigma) (1µg ml⁻¹) for 30 minutes prior to visualization, following (Tanaka et al., 2015).

Fluorescence was first assessed using a Leica SP8 confocal microscopy (IBENS imaging platform), with a 485 nm excitation laser and detection window 500-525 nm to identify GFP fluorescence, and 548 nm excitation laser and detection window 565-590 nm to identify MitoTracker fluorescence, chlorophyll autofluorescence detected over a 650-700 nm window and whole cells visualized with bright-field microscopy. Negative controls (either MitoTracker-stained wild-type cells, or unstained GFP expressing cells) were used to confirm specificity of each detection window and set exposure thresholds, and fluorescence was confirmed via sequential excitation of each laser to exclude the possibility of crosstalk between GFP and Mitotracker fluorescence. Composite fluorescence images were prepared with ImageJ.

To acquire specific fluorescence images for GFP and chlorophyll, we used a spectral deconvolution technique implemented on a LSM 710 Zeiss confocal equipped with a 63x water immersion objective via the IBPS imaging facility, following previous studies (Lam et al., 2017). In order to separate properly chlorophyll autofluorescence from the specific emission of GFP, a spectral detector was designed with 32 detection cells, and used to acquire simultaneously emission profiles for *P. tricornutum* after a single 488 nm excitation. The emission spectrum of cytoplasmic regions of *P. tricornutum* expressing unlinked GFP was used to obtain a specific GFP emission signature, while the emission spectrum of chloroplast regions wild-type cells was used to construct a specific spectral profile for chlorophyll. Each cell line was then revisualised with a 488 nm laser, using an « online fingerprinting » mode that evaluates the relative emission intensities at each pixel using the previously acquired characteristic spectra for GFP and chlorophyll. Their relative contribution of both emissions are then converted in two different channelscolors to separate GFP signals from chlorophyll autofluorescence.

*Gene expression analysis.* Gene coregulation analysis in *P. tricornutum* was performed using methodology previously described in (Liu et al., 2022; Penot-Raquin et al., 2024), using two gene expression databases (DiatomPortal- microarray; PhaeoNet- RNAseq) converted into ranked values to allow calculation of Spearman correlation coefficients. Core organelle metabolic pathways for the plastid and mitochondria were constructed from previously available models (Kroth et al., 2008; Ast et al., 2009; Smith et al., 2019).

*Tara* Oceans calculations were performed following previously defined methodology (Liu *et al.*, 2022) based on phylogenetic reconciliation of environmental sequences to annotated cultured species homologues. Briefly, potential meta-transcriptome ("metaT") and meta-genome ("metaG") homologues of MCFc proteins were identified by BLASTp with threshold e-value 10⁻⁰⁵ in *Tara* Oceans meta-gene data (Busseni *et al.*, 2019; Penot-Raquin *et al.*, 2024). These were searched by BLASTp against the complete version 3 *P. tricornutum* genome and against the previously generated cultured species alignment, with only sequences that retrieved (i) an MCFc family gene (Phatr3_J42874, Phatr3_J46612, Phatr3_J46742) and (ii) a cultured diatom as best hit retained for further analysis. Identified putative homologues were aligned against the
cultured species MCFc alignment using MAFFT as above, and inspected using RAxML using
350 bootstrap replicates and the PROTGAMMAJTT substitution model. Sequences that were
reconciled as monophyletic groups with each diatom MCFc subfamily, to the exclusion of other
proteins and organisms, were assigned as such. Relative metaT and metaG abundances
expressed as a fraction of all meta-gene coverage at each station, in both 5-20 and 20-180 µm
size fractions at surface and deep chlorophyll maximum ("DCM") depths; were compared to one
another and to different environmental variables via a matrix determinant test, following (Liu *et al.*, 2022).

**Results and Discussion**

*Diversity of MCF proteins in the Phaeodactylum tricornutum genome*

First, we constructed a census of MCF domains encoded in the version 3 annotation of
the *P. tricornutum* genome, identified using hmmsearch of the associated PFAM (PF00153)
using hmmsearch with e-value 10⁻⁰⁵ **(Fig. 1)** (Liu *et al.*, 2022; Villar *et al.*, 2024). A total of 65
genes encoding potential MCF domains were identified, slightly more than those found in the
genomes of *Arabidopsis thaliana* (59) or the model green alga *Chlamydomonas reinhardtii* (46)
(Fernie, Cavalcanti and Nunes-Nesi, 2020). We note that amongst published diatom genomes
(e.g., housed in JGI Phycocosm) and transcriptomes (MMETSP) the number of annotated
MCFs ranges from 22 (*Chaetoceros brevis* CCMP164 MMETSP transcriptome) to 113
(*Corethron pennatum* MMETSP transcriptome), which broadly corresponds to the range
(39-141) associated with sequenced plant genomes (Keeling *et al.*, 2014; Grigoriev *et al.*, 2021).
Considering percentage abundance, the number of MCF domains observed in the *P. tricornutum*
genome (0.63% of all PFAMs) is likewise within the range observed for other algal genomes and
transcriptomes **(Table S1, sheet 1; Fig. S1A)**.

Through gene coregulation module, *in silico* localization prediction, PFAM and Kofam
annotations (Villar *et al.*, 2024) and also pairwise reciprocal BLASTp similarities, we attempt a
preliminary classification of *P. tricornutum* MCF proteins based purely on primary sequence in
**Fig. 1**. A searchable version of this figure is provided as **Table S1**, sheet 2, and a consensus
RAxML (GTR, JTT, WAG substitution matrices) phylogenetic representation of these sequences
provided in **Fig. S1B**. Considering KEGG annotations, the *P. tricornutum* genome encodes
multiple probable adenine nucleotide transporters with homology both to human mitochondrial
(KEGG ID: K05863; Phatr3_J22873, Phatr3_J15797, PhatrJ_16286, Phatr3_J21789) and
peroxisomal proteins (KEGG ID: K13354, Phatr3_J3755) (Neckelmann *et al.*, 1987; Agrimi,
Russo, Scarcia, *et al.*, 2012), although we note that the *in silico* targeting predictions of the *P.
tricornutum* homologues were unclear. Further MCF transporters were annotated by KEGG to
be homologues of known aspartate/ glutamate, carnitine/ acylcarnitine, folate, iron, ornithine,
oxoglutarate, phosphate, S-adenosylmethionine and thiamine pyrophosphate transporters
(Palmieri, 2004) (**Fig. 1**). Several of these gene families were highly expanded (e.g., twelve
protein homologues to carnitine/ acylcarnitine transporters) and in many cases showed strong
homology (reciprocal BLASTp evalue < 10⁻²⁰) to one another (**Fig. 1**) alongside monophyly in
the *P. tricornutum* MCF phylogeny (**Fig. S1B**). Analysis of the peptide sequences with tmhmm
(Krogh *et al.*, 2001) suggested that many contained six transmembrane domains, consistent
with what is known for MCF proteins (Palmieri *et al.*, 2011). Others contained fewer (e.g., 4 to
5), which has been documented rarely amongst mitochondrial carrier proteins (Miyake et al., 2002; Ito, Matsukawa and Kato, 2006), or more (7 to 9) which may imply protein fusions or additional N-terminal targeting sequences, although we note that *Phaeodactylum* transmembrane domains are typically difficult to predict with conventional hydrophobicity based structural predictors (Gruber et al., 2023).

Surprisingly, only two (Phatr3_J36259, a putative mitochondrial carnitine transporter; and Phatr3_J49674, an oxoglutarate transporter) had N-terminal mitochondrial presequences detectable by targeting predictors (HECTAR, which incorporates MitoProt2 iPSort, Predotar, PredSL, and TargetP; and MitoFates) typically able to detect diatom mitoproteins with high sensitivity (Gile et al., 2015; Hammond et al., 2022). Two putative phosphate transporters, Phatr3_J11414 and Phatr3_J37916, and one S-adenosylmethionine transporter, Phatr3_J35894 were inferred to possess dual-targeting sequences (i.e., presequences hydrophobic enough to be recognised by SignalP containing a post-cleavage ASAFAP motif; but hydrophilic enough to also function as a cleavable transit peptide) that might direct them both to the plastid and mitochondria (Fig. 1). While dual-targeting is known in diatom organelles it is typically associated with proteins involved in translation (e.g., aminoacyl-tRNA synthetases) and the degree to which it is associated with transporter proteins awaits experimental study (Gile et al., 2015; Liu et al., 2022). To increase our identification of mitochondria-targeted MCF proteins, we additionally considered homology to MCF proteins identified by mass spectrometry from purified mitochondrial fractions from the distantly related diatom *Thalassiosira pseudonana* (Schober et al., 2019), revealing further probable mitochondrial carnitine (Phatr3_J18927), citrate (Phatr3_J18489), oxoglutarate (Phatr3_J23908, Phatr3_J8990, Phatr3_J35949) and phosphate transporters (Phatr3_J47954) that did not pass *in silico* targeting prediction (Fig. 1; Table S1). It remains to be determined if these proteins are targeted to *P. tricornutum* mitochondria by alternative means not detectable via HECTAR or MitoFates, e.g. internal C-terminal targeting sequences (Diekert et al., 1999; Lee et al., 1999) that are typically associated with inner mitochondrial membrane proteins (Palmieri et al., 2011; Hammond et al., 2022); or whether their underlining gene models include cryptic exons in the 5' UTR encoding N-terminal targeting sequences (Russo et al., 2015).

More surprisingly, we detected 14 MCF proteins with identifiable signal peptides (e.g., using SignalP 5.0), suggesting endomembrane localization. Five of these, including Phatr3_J46742, contained bipartite signal and transit peptides (inferred either using ASAFind or HECTAR prediction) associated with plastid-targeting presequences in diatoms (Moog et al., 2020; Liu et al., 2022). Amongst these putatively plastid-targeted MCFs were an inferred carnitine transporter (Phatr3_J12788), a phosphate transporter (Phatr3_J11866) and an S-adenosylmethionine transporter (Phatr3_J10896) detected in previous analyses of the *P. tricornutum* plastid transporter proteome (Liu et al., 2022). Of note, all three of these transporters appear to be co-expressed in published *P. tricornutum* gene expression data with chloroplast light harvesting complex proteins (Phatr3_J12788) and photosynthesis and Calvin cycle metabolism (Phatr3_J10896; Phatr3_J11866), supporting likely plastidial functions (Villar et al., 2024).

Only one of these proteins (Phatr3_J47954) is predicted to be a direct homologue of a protein experimentally identified in *T. pseudonana* plastid fractions (Thaps3a_21531, also detected in mitochondrial fractions (Schober et al., 2019)), so to validate the plastidial
localization of some of them, we used a secondary experimental proteomic dataset, comparing
protein content in plastid-enriched and whole-cell fractions from *P. tricornutum* (Huang *et al.*,
2024) (Dataset S1). Within this dataset, all of the four putatively plastid-targeted MCF
transporters found at detectable levels (Phatr3_J10896, Phatr3_J11886, Phatr3_J35625,
Phatr3_J46742), were enriched in plastid over whole-cell fractions, coherent with the *in silico*
targeting predictions (Fig. S2). That said, the majority of the MCF domain proteins, irrespective
of localization prediction, were enriched in plastidial fractions, with two putatively
endomembrane-targeted transporters (Phatr3_J13538, Phatr3_J47954) and four that lack any
recognizable targeting presequences (Phatr3_J19030, Phatr3_J23709, Phatr3_J23908,
Phatr3_J35949) deemed to be significantly enriched (P < 0.01; Fig. S2) (Huang *et al.*, 2024).
The occurrence of these presumably non-plastidial transporters in the plastid-enriched fraction
may relate to the close association of the *Phaeodactylum* plastids, endomembrane, and indeed
mitochondria in vivo, which may impede separation of their associated proteomes by organelle
enrichment approaches (Schober *et al.*, 2019; Uwizeye *et al.*, 2021).

Identification of a novel MCF protein subfamily

We were interested to further explore the evolutionary diversity of MCF proteins related to Phatr3_J46742, as a likely plastid-localized MCF transporter (considering both *in silico* prediction and experimental proteomic data) of unknown function. Alongside Phatr3_J46742, we note that two additional MCF transporters (Phatr3_J42874, Phatr3_J46612) showed high reciprocal BLASTp similarity (e-values of < 10<sup>-60</sup> in each pairwise combination) but limited similarity (P > 10<sup>-05</sup>) to any other described MCF in the *P. tricornutum* genome (Fig. 1). These MCFs further lacked any apparent KEGG annotations, considering combined BLASTkoala, GHOSTkoala, and KofamKoala annotations (Jin *et al.*, 2023; Kanehisa *et al.*, 2024) and may represent a new functional subfamily of MCFs. Analysis with AlphaFold (Jumper *et al.*, 2021) revealed similar structures between all three proteins, with Phatr3_J42874 containing six clearly identifiable transmembrane sequences; and Phatr3_J46612 and Phatr3_J46742 four each (Fig. S1C). Phatr3_J42874 was predicted to encode a signal peptide but lacked a downstream ASAFAP site or transit peptide, consistent with endomembrane localization, and Phatr3_J46612 lacked any apparent targeting presequences. Phylogenetic comparisons between these proteins and all other *P. tricornutum* MCF domains (Fig. S1B) confirmed they resolve as a robustly supported (bootstrap = 100%, three substitution matrices) monophyletic subfamily, and are distantly related to all MCF domain proteins with identifiable substrates based on KEGG annotation. Consistent with nomenclature used in a simultaneous mutant study of Phatr3_J46742 (Giustini *et al.*, 2025) we herefore refer to this new MCF family as "MCFc", i.e. "mitochondrial carrier family chloroplastic".

Phylogeny of MCFc reveals homologues in other complex red plastid-bearing and primary green
algae

To reconstruct the deeper evolutionary history of MCFc, we searched for homologues of each
complete *P. tricornutum* peptide sequence across a composite genome and transcriptome
library of the entire tree of life, following previously defined methodology (Liu *et al.*, 2022).
Briefly, this involved performing a primary BLASTp of each MCFc subunits, with threshold
evalue of 10<sup>-05</sup> against a composite library of over 250 diatom and related algal genomes and
transcriptomes, alongside complete copies of uniref (Suzek *et al.*, 2015), MMETSP transcriptomes (Keeling *et al.*, 2014) and JGI Phycocosm genomes (Grigoriev *et al.*, 2021), followed by a reciprocal BLASTp search against the version 3 annotation of the *P. tricornutum* genome (Villar *et al.*, 2024). Only sequences that yielded an MCFc family protein as a BLAST best hit were retained for downstream analysis. From this approach, we identified nearly 2000 potential homologous sequences (**Fig. S3; Table S1**). These homologues were distributed across a wide range of eukaryotic groups, although were primarily found in lineages with a history of complex endosymbiosis (i.e., cryptomonads, haptophytes, ochrophytes and dinoflagellates) (Novák Vančlová and Dorrell, 2024; Penot-Raquin *et al.*, 2024); and were noticeably absent from prokaryotes (**Fig. S3A**). Considering probable localisations, the vast majority of chloroplast-targeted sequences in species with four membrane-bound plastids were found to either be homologous to Phatr3_J46742 (in diatoms and cryptomonads), or to Phatr3_J42874 (in other ochrophytes and in haptophytes), with Phatr3_J46612 homologues largely lacking clear localisations (**Fig. S3B**).

Next, we constructed a 78 taxa x 262 aa phylogeny of a representative subset of the homologues identified, which was manually trimmed to remove poorly aligned sequences and uninformative regions such as N-terminal presequences (**Table S1**, sheets 2-6). This phylogeny revealed a complex evolutionary history of MCFc family proteins (**Fig. 2**). Phatr3_J46742 formed a robustly supported (MrBayes PP = 1.0 ; RAxML bootstrap support > 80%; 3 substitution matrices), monophyletic group with other diatom chloroplast-targeted proteins, consistent with its apparent proteomic localisation. This formed a sister-clade to the predominantly endomembrane-targeted proteins related to Phatr3_J42874, and a large clade of dinoflagellate, cryptomonad and haptophyte sequences, which might be consistent with endosymbiotic or other transfers of genes encoding plastid-targeted proteins from ochrophytes into these lineages (Liu *et al.*, 2022; Novák Vančlová and Dorrell, 2024). Notably, the closest sister-group of the combined clade of ochrophyte, cryptomonad, haptophyte and dinoflagellate sequences was a monophyletic clade of prasinophyte green algae, including members of the Mamiellophyceae, Pyramimonadales, Prasinococcales and Chlorodendrales (**Fig. 2**). This may indicate that the MCFc gene family is of green origin, although this hypothesis requires further testing with more deeply-positioned sister-groups.

*MCFc homologues show different subcellular localizations suggestive of evolutionary history*

Next, we localized all three MCFc family proteins in *P. tricornutum* cells using C-terminal GFP fusion constructs and confocal microscopy (**Fig. 3, Table S2**). Phatr3_J42874 and Phatr3_J46612 were localized using full-length constructs, while Phatr3_J46742 was localized using a short N-terminal region consisting of the final 209 bp of the 5' UTR and the first encoded 87 aa, which was found to produce strong GFP expression. The Phatr3_J46742 N-terminal region localizes within the chloroplast, and does not colocalize with the mitochondria, as assessed by staining with MitoTracker Orange (**Fig. 3A**).

The remaining two MCFc family proteins had non-plastidial localizations. Phatr3_J42874 was found in contrast to have a filamentous extra-plastidial localization that did not overlap with the mitochondria, which may suggest endomembrane targeting in agreement with *in silico* localization predictions. In contrast, Phatr3_J46612 clearly overlapped with the MitoTracker signal, confirming it as a bona fide mitochondrial transporter (**Fig. 3B, Fig. 3C**). Mitotracker- and
GFP-negative control lines did not suggest any crosstalk between the two detection windows used (Fig. S4).

Overall, our data are consistent with the MCFc family having both plastidial and non-plastidial members. Considering the phylogenetic proximity of Phatr3_J46742 to Phatr3_J42874, and comparative distance to Phatr3_J46612, we propose it is likely to have been an ancestrally mitochondria targeted protein (similar to Phatr3_J46612) that was subsequently recruited to the plastid, potentially via an endomembrane-targeted intermediate (Phatr3_J42874) (Fig. 2).

Spectral deconvolution indicates potential sub-chloroplastic localizations for Phatr3_J46742

To gain a more detailed view of MCFc family protein localization, we additionally visualised GFP-expressing lines via spectral deconvolution of GFP and chlorophyll emission, following previous studies (Lam *et al.*, 2017) (Fig. 4). For Phatr3_J46742, in approximately half of the cell lines visualised, we observed a single monolayer of GFP in the chloroplast interior, reflecting a localization obtained in an independent study(Giustini *et al.*, 2025) (Fig. 4A). In an equivalent number, however, we observed a bilayer of GFP in the interior of the chloroplast and coincident with chlorophyll, which may reflect either the thylakoids (principally identified as an annular structure reflecting the diatom girdle lamella) (Dawson, 1973) or the plastid inner membrane (Fig. 4B). The remaining cells presented intermediate (e.g., reticulate) fluorescence or spots in the chloroplast interior (Fig. 4C). In contrast, Phatr3_J42874 was localized adjacent to but separate from chlorophyll (Fig. S5A), and Phatr3_J46612 was extra-plastidial but formed a filamentous structure distinct from the localization observed for cytoplasmic GFP (Fig. S5B, Fig. S5C), supportive of endomembrane and mitochondrial localizations respectively.

We note that the 87 aa region used for the N-terminal localization contains both the signal peptide and the first transmembrane domain of the mature protein sequence, as inferred from AlphaFold and tmhmm prediction (Fig. S1C), and we therefore feel that the membrane-like localization observed in **Fig. 4B** is likely to be the most biologically accurate, although we cannot exclude mislocalization of a partial protein sequence to the stroma, reflecting that the construct was expressed under a constitutive and strong promoter. Nonetheless, a general plastidial localization for MCFc is supported by experimental proteomic data above, where Phatr3_46742 shows a strong enrichment (14.7-fold, P = 0.12) in plastid-enriched to whole-cell fractions (Fig. S2) (Huang *et al.*, 2024). We also identified a perfect coincidence of GFP and chlorophyll signals in individual chloroplastic pixels in Phatr3_J46742 lines (Fig. S6), confirming plastid colocalization, although the specific sublocalization will be best determined by more precise visualization techniques such as immuno-EM (Balamurugan *et al.*, 2017) or self-assembling GFP (Liu *et al.*, 2016).

Gene expression analysis of MCFc genes in P. tricornutum indicate distinct roles for different MCFc transporters

Given the distinct localizations of different MCFc family proteins in *P. tricornutum*, we considered whether they are likely to perform linked or separate transport functions. First, we used a previously published approach in which we performed Spearman correlations of ranked transcript relative abundance between each MCF family protein gene (Phatr3_J46742, Phatr3_J42874, Phatr3_J46612) across a large dataset of over 300 normalized and previously
published microarray and RNAseq libraries (Ashworth et al., 2016; Liu et al., 2022; Penot-Raquin et al., 2024; Villar et al., 2024). The data from these analyses are provided as a pivot table (Table S3).

Briefly, the plastid localized MCFc Phatr3_J46742 showed typically weak or negative correlation to most genes encoding core *P. tricornutum* organelle metabolic processes, including both other MCFc transporters (Spearman correlation < -0.2; **Fig. S7A; Table S3**, sheets 1-3). This may indicate that this gene does not form a core component of organelle metabolism, and may be inducibly expressed under certain conditions only. Phatr3_J46742 nonetheless shows strong positive coregulation (Spearman correlation > 0.5; **Fig. S7B**) with a small number (17) of genes encoding mitochondria- and/or plastid-targeted proteins with identifiable (KEGG, PFAM functions). These notably include a mitochondria-targeted major facilitator superfamily transporter (geneID: Phatr3_J27361) which is strongly positively coexpressed (correlation 0.706). Across a broader search of core organelle metabolic pathways (**Table S3**, sheets 4-5) we also identified strong positive coregulation within the plastid (correlation > 0.45) to both phosphofructokinase 1 (PFK) and fructose-1,6-bisphosphatase (FBP) involved in the Calvin cycle, and a strong negative coregulation (correlation <-0.5) to glutamate synthase (GOGAT) implicated in nitrogen assimilation, which may point to further interacting partners in plastid metabolism (Kroth et al., 2008; Smith et al., 2019). Phatr3_J42874 and Phatr3_J46612 showed in contrast strong positive coregulation with a wide range of core plastid- and mitochondria-targeted proteins, with in particular (one-way ANOVA, *P* < 0.01) larger positive coregulation values observed for mitochondria-targeted glycolytic and TCA cycle proteins (Phatr3_J42874) and plastidial ribosome and carbon concentrating mechanism proteins, and mitochondrial complex I (Phatr3_J46612) than other proteins targeted to the same organelle (Table S3, sheets 6-9).

Next, we considered under what discrete conditions different MCFc family genes were differentially expressed in *P. tricornutum*, leveraging normalized RNAseq data integrated into the DiatomicBase server (Villar et al., 2024). We noted that each MCFc gene showed responses to different environmental stimuli, potentially suggesting different physiological roles (**Fig. S8**). Phatr3_J46742 was found to be upregulated in nitrate reductase knockout lines compared to control lines, as well as in knockout lines for the blue light receptor aureochrome after 60 minutes illumination with red or blue light, suggestive of roles both in nitrate metabolism and photoperception (McCarthy et al., 2017; Mann et al., 2020). It was conversely downregulated both under CO₂ and phosphate depletion conditions, which might either relate to linked functions to these nutrients or changes in nutrient stoichiometry (e.g., N to P ratio) in these cells (Li et al., 2022; Shu et al., 2022). In contrast, Phatr3_J42874 was found to be upregulated under phosphate depletion, and in aureochrome knockout lines only under very short-term (10 minute) illumination conditions; while Phatr3_J46612 was downregulated both in aureochrome knockouts, and knockdown lines for the mitochondrial alternative oxidase (Murik et al., 2019) (**Fig. S8**).

Exploring the nitrate reductase knockout phenotype of Phatr3_J46742 in more detail, we note that it undergoes sustained induction over the full 228h time-course in which knockout and control lines were tested, suggesting both short-term and long-term roles in tolerating nitrogen starvation (**Fig. 5A**). This expression pattern was not observed either in Phatr3_J42874 or Phatr3_J46612, indicating that these proteins play different physiological roles in *P. tricornutum*
(Fig. 5A). The sustained expression phenotype of Phatr3_J46742 also contrasted with highly nitrogen-sensitive proteins identified in a secondary nitrate enrichment and metabolomic study (Smith et al., 2019) to be involved in nitrogen uptake, urea transport, or ornithine: urea cycling, all of which were upregulated uniquely in the first 100 hours post-induction (Fig. 5B). We therefore tentatively suggest that Phatr3_J46742 does not directly transport nitrate or a metabolite implicated in nitrate uptake or recycling (e.g., ornithine, urea), but is likely to transport a metabolite necessary for physiological acclimation to chronic nitrogen limitation conditions. We note that a coherent conclusion is obtained by Giustini et al from structural data, who suggest a role for this protein in shuttling amino acids (arginine, aspartate/glutamate) or sugars (citrate, oxoglutarate) that may be involved in equilibrating cellular N: C pool ratios (Giustini et al., 2025) under nitrate starvation conditions in *P. tricornutum* (Alipanah et al., 2015; Remmers et al., 2018).

Tara Oceans data indicate Phatr3_J46742 expression is sensitive to environmental nitrate concentrations

Finally, we searched meta-gene data from *Tara Oceans* for genes encoding each MCF transporter subfamily. This analysis, adapted from (Liu *et al.*, 2022), involved retrieving all *Tara Oceans* meta-genes with known MCF annotations, and then identifying those that corresponded to each diatom MCF sub-family by reciprocal BLASTp against the *P. tricornutum* genome and against the curated MCF homologue alignment used for the single-gene tree. The meta-genes that were identified as encoding MCFc sequences from the reciprocal BLASTp search to *P. tricornutum*, and of diatom origin by BLASTp search against the cultured species alignment, were added to the cultured species alignment and used to build a single-gene RAxML tree. Finally, meta-genes that resolved in monophyletic clades with each *P. tricornutum* MCFc protein (Phatr3_J46742, Phatr3_J46612, or Phatr3_J42874) to the exclusion of all non-diatom proteins were annotated as such. Identified meta-genes, their relative abundances in meta-transcriptome (metaT) and meta-genome (metaG) data, and their correlations to measured *Tara* environmental variables are shown in **Table S4**.

Using these data, we could see ubiquitous expression of Phatr3_J46742 meta-genes in metaT data, with particular concentrations in Southern Ocean, Southern Atlantic and coastal mid-Pacific (e.g., Marquesas Islands) sites (Fig. 6A). While the same sequences were also abundant in Southern Ocean stations in metaG data, suggesting underlying high abundance of the diatoms encoding these genes, the remaining stations in which we observed high metaT abundances did not have corresponding high metaG abundances (Fig. 6B), suggesting transcriptional regulation of Phatr3_J46742 genes in the wild. Taking into account that the stations in which Phatr3_J46742 shows high metaT abundances are typically from Fe-limited but N-replete regions of the global ocean (Busseni *et al.*, 2019; Ustick *et al.*, 2021), we considered the possibility that nitrogen availability informs Phatr3_J46742 expression levels in *Tara Oceans* data. Strikingly, Phatr3_J46742 metaT abundances show strong positive correlations ($r^2$ 0.34-0.90, depending on depth and size fraction) to nitrate ($NO_3^-$) concentrations, both considering direct measurements and simulated concentrations based on the Darwin model at 5m depth (Fig. 6) (Pesant *et al.*, 2015). In contrast, only weak correlations were found between nitrate concentration and metaG relative abundance, with $r^2$ < 0.2 in all depth and size fractions except the 5-20 µm surface fraction (Fig. S9). Moreover,
Phatr3_J46742 metaT abundances showed nil ($r^2$ < 0.05) or even moderate negative correlations to simulated ammonium concentrations in *Tara* Oceans data, which suggests specific nitrate regulation of its expression (**Fig. S10**). A global search of all *Tara* Oceans environmental variables, comparing the strength of correlation to Phatr3_J46742 metaT versus metaG relative abundance via matrix determinant testing, further revealed significant positive relationships between phosphate and silicate abundance on Phatr3_J46742 relative metaT abundance, and negative relationships to PIC (particulate inorganic carbon), suggesting other environmental factors may determine its expression (**Fig. 6**).

Meta-genes corresponding to both Phatr3_J42874 and Phatr3_J46612 were both more abundant (in metaG) and more highly expressed (in metaT) in nutrient-rich but iron-limited stations from the Southern Ocean, as typical for diatom transporter proteins (Liu *et al.*, 2022). Consistent with roles in primary production and carbon fixation, both Phatr3_J42874 and Phatr3_J46612 showed positive associations between metaT relative abundance and pigment (chlorophyll c3, fucoxanthin) and carbon (carbon total, HCO3-) concentrations, but only in the 5-20 µm size fraction from surface samples (**Table S4**, sheet 12). Of note, for the 20-180 µm size fraction, Phatr3_J46612 like Phatr3_J46742 showed a significantly lower (less positive, or more negative) correlation of metaT abundance compared to metaG abundance against ammonium concentration, whereas the converse (i.e., a significantly more positive, or less negative difference between metaT and metaG correlations) was observed for Phatr3_J42874, which may suggest again separate biological functions (**Fig. 6; Table S4**, sheet 12).

## Concluding Remarks

In this study, we have used evolutionary (BLASTp, phylogeny), experimental (sub-cellular localization, using confocal microscopy and spectral deconvolution), and gene expression approaches (in *Phaeodactylum tricornutum* and *Tara* Oceans) to better understand a novel family of MCF proteins, termed MCFc, in diatom algae. These data broadly suggest that MCFc stands apart from other MCF protein families with known functions, is predominantly restricted to primary green algae and algae with complex red chloroplasts, and contains distinct sub-families with probable mitochondrial, endomembrane, and plastid localizations. These sub-families do not however have similar transcriptional patterns to one another, showing co-regulation to different components of *P. tricornutum* core mitochondrial and plastid metabolism, and showing different expression responses to nitrogen and carbon in *Tara* Oceans. The consistent response of Phatr3_J46742, indicative of the chloroplast-targeted sub-family to nitrate starvation in cellular transcriptome data and to nitrate concentrations in *Tara* Oceans metaT but not metaG data, despite nil or negative correlations to ammonium concentrations, may suggest associative roles in nitrate uptake, reduction and assimilation (Busseni *et al.*, 2019; Smith *et al.*, 2019). These roles, and indeed the functions of other MCFc family transporters, will be best explored via the interrogation of complemented knockout mutants in model species such as *P. tricornutum*, as explored in an independent study of this gene (Giustini *et al.*, 2025), alongside *in vitro* identification of substrates and kinetic profiling for each transporter (Palmieri *et al.*, 2009; Moog *et al.*, 2020).

## Author Contributions
RGD coordinated the study, performed phylogenetic and gene co-regulation analyses, synthesized gene constructs for biolistic transformation. SL performed biolistic transformations, visualized transformant lines of Phatr3_J42874 and Phatr3_J46612 with confocal microscopy, and performed all *Tara* Oceans analyses. SMY performed confocal microscopy visualization of Phatr3_J46742 and aided with Mitotracker staining. VP performed AlphaFold modelling, and with the supervision of FL performed spectral deconvolution microscopy. MO and CB acquired funding for the study, and provided essential and unpublished genetic resources for the gene co-regulation and *Tara* Oceans analyses performed. RGD wrote the manuscript with input from SMY, FL and CB. All co-authors read, provided critical feedback and approved the manuscript prior to submission.

**Data availability statement**

Raw confocal microscopy images, retrieved homologue sequences, and *Tara* Oceans distributions are provided in the linked osf.io repository (doi: 10.17605/OSF.IO/89VM3), in the subfolder "Transporters > MCF proteins". All remaining supporting data, including alignments and phylogenies of cultured species sequences, are provided in **Tables S1-S4**. Pre-analyzed RNAseq data described in this study is available from the DiatomicBase server: https://www.diatomicsbase.bio.ens.psl.eu/. Cryopreserved plasmids and transformant lines used for confocal visualization of MCFc family proteins are available from the corresponding author on reasonable request.

**Figure Legends**
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Diversity of MCF domain proteins in *Phaeodactylum tricornutum*. This figure shows all 65 genes in the *P. tricornutum* genome version 3 annotation inferred to encode a Mitochondrial Carrier Family (PF00153.30) PFAM (Palmieri *et al.*, 2011), alongside a heatmap of reciprocal BLASTp x BLASTp -log10 evalue. WGCNA Transcriptional modules (from (Ait-Mohamed *et al.*, 2020)), consensus localizations based on *in silico* predictions (Gschloessl, Guermeur and Cock, 2008; Fukasawa *et al.*, 2015; Gruber *et al.*, 2015) and experimental data (Schober *et al.*, 2019), and KEGG annotations are provided per (Villar *et al.*, 2024). Genes are grouped by inferred function, with a novel MCF sub-family (MCFc: Phatr3_J42874, Phatr3_J46612, Phatr3_J46742) with high (e< 10-50) reciprocal BLASTp similarity to one another, but no described KEGG function, highlighted by a blue box.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2. Phylogeny of subselected MCFc proteins across the tree of life.** The topology shown is the consensus of MrBayes consensus trees and RAxML best-scoring trees, each realised with three substitution matrices (GTR, Jones/JTT, WAG). Branches are shaded by taxonomy, and leaf nodes by predicted in silico localization based on consensus targeting predictions (HECTAR, ASAFind, SignalP, MitoFates) and experimental localization with GFP (*P. tricornutum* proteins only). *P. tricornutum* proteins are highlighted with the appellation “MCFc*”. Nodes are labelled with coloured circles that define minimal MrBayes posterior probabilities and RAxML bootstrap values across all substitution matrices studied. Broadly, three clades of proteins are visible in algae with chloroplasts of secondary or higher red origin, each exemplified by *P. tricornutum* proteins, with respectively plastid (Phatr3_J46742), endomembrane (Phatr3_J42874) or mitochondrial (Phatr3_J46612) localization, with a green algal sister-group.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Differential localization of *P. tricornutum* MCFc homologues. This figure shows confocal microscopy images of transgenic *P. tricornutum* expressing C-terminal linked GFP</figcaption></figure>
constructs for three closely-related MCFc transporters, Phatr3_J46742 (A), Phatr3_J46612 (B), and Phatr3_J42874 (C). Phatr3_J46612 and Phatr3_J42874 are full-length constructs, and Phatr3_J46742 is an N-terminal construct avoiding out-of-frame internal ORFs that may otherwise impede in-frame GFP translation. All cell lines are stained with MitoTracker Orange (false-stained blue) per Tanaka et al., 2015. Control images (stained and unstained cell lines expressing unlinked cytoplasmic GFP, and Mitotracker-stained wild-type cell lines) are shown in Fig. S4. Scale bar for all images = 10 µm.

<figure><img src="image_5.png" /></figure>

**Fig. 4.** Confocal localization of Phatr3_J46742/ MCFc as determined by spectral deconvolution of GFP and chlorophyll signals. Three distinct localization patterns are observed. **A**: monolayer of GFP in chloroplast centre, suggestive of plastid stroma or pyrenoid lumen (c. 40% cells). **B**: bilayer of GFP overlying chlorophyll, suggestive of thylakoid or pyrenoid exterior (c. 40% cells). **C**: alternative and ambiguous localizations, including scattered fluorescence and spots overlaying chlorophyll (c. 20% cells). Scale bar for all images = 10 µm.
A

Phatr3_J46742

Phatr3_J42874

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

B

N assimilation genes (Smith et al, 2019)

Highly nitrogen sensitive genes

Urea transport genes

OUC genes

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Expression dynamics of MCFc family proteins under nitrate starvation conditions in *P. tricornutum*. Graphs show the log₂ ratio of relative expression in induced nitrate reductase knockout compared to wild-type *P. tricornutum* cells, following (McCarthy *et al.*, 2017), over a 228h time-course. **A:** mean ratio (coloured lines) and standard deviations of Phatr3_J46742, Phatr3_J42874 and Phatr3_J46612 expression. Uniquely MCFc shows an induction of expression in nitrate reductase knockouts compared to controls. **B:** comparison of MCFc expression dynamics to proteins implicated in rapid nitrogen uptake, urea import, or ornithine/ urea cycling, annotated following data from (Smith *et al.*, 2019). MCFc contrasts with all three protein groups by sustained induction in NR knockouts throughout the full time course, as opposed to within the first 50-100 hours post-induction uniquely, suggesting that it does not directly function in cellular nitrogen uptake but may transport metabolites necessary for cell physiology during prolonged nitrogen shortage.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>B) Phatr3_J46742 metaG relative abundances</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>C) Environmental correlations</figcaption></figure>

<table><thead><tr><th rowspan="2">Environmental variable</th><th colspan="4">Phatr3_J46742</th><th colspan="4">Phatr3_J46612</th><th colspan="4">Phatr3_J42874</th></tr><tr><th>DCM, 5-20</th><th>DCM, 20-180</th><th>SRF, 5-20</th><th>SRF, 20-180</th><th>DCM, 5-20</th><th>DCM, 20-180</th><th>SRF, 5-20</th><th>SRF, 20-180</th><th>DCM, 5-20</th><th>DCM, 20-180</th><th>SRF, 5-20</th><th>SRF, 20-180</th></tr></thead><tbody><tr><td>NO3 (µmol/l)</td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td></tr><tr><td>Nitrate_5m* (µmol/l)</td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td></tr><tr><td>NO2 (µmol/l)</td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td></tr><tr><td>Nitrite_5m* (µmol/l)</td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td></tr><tr><td>Ammonium_5m* (µmol/l)</td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td></tr><tr><td>PO4 (µmol/l)</td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td></tr><tr><td>Si (µmol/l)</td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td></tr><tr><td>PIC* (mol/m3)</td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td><td style="background-color: #d9531e;"></td><td style="background-color: #d9531e;"></td><td></td><td></td></tr></tbody></table>

Key: **metaT** correl more positive than **metaG** correl, P < 0.00001

**metaT** correl more positive than **metaG** correl, P < 0.05

**metaT** correl more negative than **metaG** correl, P < 0.00001

**metaT** correl more negative than **metaG** correl, P < 0.05

**Fig. 6.** Tara Oceans maps of Phatr3_J46742 relative distributions. This figure shows maps of the relative abundance of MATOUs phylogenetically reconciled to belong to the Phatr3_J46742/ MCFc subfamily of MCF proteins and of diatom origin, based on metaT (meta-transcriptome, **A**) and metaG (meta-genome, **B**) relative mapped abundances, for two depths (DCM- Deep Chlorophyll Maximum; SRF- surface) and size fractions (5-20 µm, 20-180 µm) in which diatoms predominate. Phatr3_J46742 meta-transcripts show cosmopolitan distributions but with particular occurrence in Southern Ocean sites in which their corresponding meta-genes are also abundant, alongside coastal South Atlantic and Pacific sites in which they are not. **C) Heatmap** of P-values of parametric correlation tests performed between the metaT and metaG total relative abundances of three closely related diatom MCFc transporter families (Phatr3_J46742- chloroplastic; Phatr3_J46612- mitochondrial, Phatr3_J42874- endomembrane) for 5-20 µm and 20-180 µm size fractions and surface and DCM depths. Briefly, these plots show if for each depth and size combination if meta-transcriptome relative abundances show significantly more positive (stronger positive, or weaker negative) correlation to the variable than meta-genome abundances do, accounting for the dependent relationships between metaT and metaG data. Complete data are shown in **Table S4**, sheet 12. Phatr3_J46742 shows significantly stronger positive correlations to measured nitrate, phosphate and silicate concentrations in metaT than metaG data, no clear correlations to nitrite, and significantly more negative concentrations to ammonium and to PIC (particulate organic carbon). The same trends are observed much more weakly, or not at all, for the other two MCFc subfamilies.
<figure><img src="image_10.png" /><figcaption>**Fig. S1.** Evolutionary diversity and transmembrane structure of *Phaeodactylum tricornutum* MCF-domain containing proteins. **A:** scatterplot of total number (horizontal axis) and % of total PFAMs (vertical) of MCF domains (PF00153) in JGI Phycocosm algal genomes (Grigoriev et al., 2021) and MMETSP transcriptomes (Keeling et al., 2014), shaded by taxonomy. **B:** consensus RAXML topology (GTR, JTT, WAG) of a 77 branch x 313 aa alignment of all version 3 gene models identified by hmhmsearch to contain an MCF domain. Sequences are annotated by function (KEGG), consensus localization (HECTAR, ASAFind, MitoFates; comparison to experimental proteomic data from Schober et al., 2019) and number of inferred TMDs (transmembrane domains). MCFc proteins form a distinct sub-family apart from all MCFs with inferable substrates. **C:** consensus AlphaFold structures of MCFc sub-family proteins, showing either four (Phatr3_j46612/Phatr3_j46742) or 6 TMDs (Phatr3_j42874), inclusive of N-terminal targeting sequences. **D:** detailed AlphaFold and trhmm annotation of the plastid-localized Phatr3_j46742, showing that the N-terminal 87aa used for GFP localization likely includes the plastid-targeting sequence (i.e., including signal peptide) and first transmembrane domain of the mature protein.</figcaption></figure>
Peptide ratio: plastid/ whole-cell (Huang et al., Plant J 2024)

<figure><img src="image_11.png" /><figcaption>**Fig. S2. Occurrence of MCF domains in plastid-enriched versus total cellular experimental proteomic fractions, shown as per Huang et al. (2014).** Proteins are shaded per encoded genome and predicted *in silico* localization, based on consensus ASAFind and HECTAR targeting prediction. Four MCF protein proteins with predicted plastid localization detectable both in plastid-enriched and total cell fractions are labelled, in each case showing enrichment in plastid fractions.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_13.png" /></figure>

**Fig. S3.** Distribution and localisation of homologues to Phatr3_J46742. Panel A shows the distribution of reciprocal BLAST best-hit matches (with threshold e-value 10⁻⁵) to MCFc/Phatr3_J46742 and two related proteins (Phatr3_J42874, Phatr3_J46612) encoded in the *Phaeodactylum tricornutum* genome (Villar et al., 2024). Panel B shows the proportion of these proteins for species with four-membrane bound chloroplasts (i.e., ochrophytes, cryptomonads and haptophytes) inferred to localise to the chloroplast (using ASAFind or HECTAR in silico prediction; Gschloessl et al., 2008; Gruber et al., 2015), predicted to contain an N-terminal signal peptide (using SignalP v 3.0; Bendtsen et al., 2005), predicted to encode a mitochondrial presequence (using HECTAR or MitoFates, Fukusawa et al. 2015) or with no predicted localization. Homologues of MCFc show a notable propensity towards chloroplast localizations in diatoms, whereas homologues of Phatr3_J42874 and Phatr3_J46612 show more frequent endomembrane, mitochondrial or untargeted localization predictions.
<figure><img src="image_14.png" /><figcaption>**Fig. S4.** Control images for localization of *Phaeodactylum MCFc* homologues. This figure shows confocal microscopy images of transgenic *P. tricornutum* expressing C-terminal linked GFP constructs stained and unstained cell lines expressing unlinked cytoplasmic GFP, and Mitotracker-stained wild-type cell lines, as per Fig. 3.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. S5.** Confocal localization of Phatr3_J42874/ MCFe, Phatr3_J46612/ MCFm and cytoplasmic (unlinked) GFP as determined by spectral convolution of GFP and chlorophyll signals. Data are presented as per Fig. 4.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>b) cytoplasmic GFP</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>c) WT cells</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Fig. S6. Spectral profiles for GFP (green) and chlorophyll (magenta) in selected regions (circled) of different *P. tricornutum* cell lines.</figcaption></figure>

A) Phatr3_J46742-GFP, which show strongest emission in both green and red regions, suggesting colocalization of GFP and chlorophyll signals. Equivalent spectra are shown for B) cytoplasmic GFP, showing spatially separated GFP and chlorophyll signals, and C) wild-type cells, showing chlorophyll emission only.
<figure><img src="image_19.png" /></figure>

B

<table><thead><tr><th>GenelD</th><th>MFCc coefficient</th><th>Localisation</th><th>Biological Process</th><th>PFAM Domains</th><th>Kofam ID</th></tr></thead><tbody><tr><td>Phatr3_J46657</td><td>0.616</td><td>Chloroplast</td><td>oxidation-reduction</td><td>Rieske [2Fe-2S] domain; Cytochrome B6-F Fe-S subunit</td><td>cytochrome b6-f complex iron-sulfur subunit</td></tr><tr><td>Phatr3_J12330</td><td>0.624</td><td>Chloroplast</td><td>isoprenoid biosynthesis</td><td>YgbB family</td><td>2-C-methyl-D-erythritol 2 4-cyclodiphosphate synthase</td></tr><tr><td>Phatr3_EG00329</td><td>0.670</td><td>Chloroplast</td><td>regulation of transcription</td><td>'Cold-shock' DNA-binding domain; Acid Phosphatase</td><td>magnesium-dependent phosphatase</td></tr><tr><td>Phatr3_J11459</td><td>0.517</td><td>Chloroplast</td><td>metabolic process</td><td>WW domain; Stage II sporulation protein E (SpolIE)</td><td>protein phosphatase PTC7</td></tr><tr><td>Phatr3_J19831</td><td>0.605</td><td>Chloroplast</td><td>NA</td><td>Carbohydrate kinase</td><td>ATP-dependent NAD(P)H-hydrate dehydratase</td></tr><tr><td>Phatr3_J40048</td><td>0.532</td><td>Chloroplast</td><td>translation</td><td>Ribosome recycling factor</td><td>ribosome recycling factor</td></tr><tr><td>Phatr3_J41282</td><td>0.607</td><td>Chloroplast</td><td>translation</td><td>Ribosome recycling factor</td><td>ribosome recycling factor</td></tr><tr><td>Phatr3_J44979</td><td>0.589</td><td>Chloroplast</td><td>rhamnose metabolism</td><td>Domain of unknown function (DUF718)</td><td>L-rhamnose mutarotase</td></tr><tr><td>Phatr3_J45684</td><td>0.526</td><td>Chloroplast</td><td>NA</td><td>4Fe-4S single cluster domain</td><td>ferredoxin</td></tr><tr><td>Phatr3_EG02115</td><td>0.588</td><td>Mitochondria</td><td>protein complex assembly</td><td>ATP11 protein</td><td>ATP synthase mitochondrial F1 complex assembly factor 1</td></tr><tr><td>Phatr3_J21296</td><td>0.517</td><td>Mitochondria</td><td>biotin biosynthesis</td><td>Radical SAM superfamily; Biotin and Thiamin Synthesis domain</td><td>biotin synthase</td></tr><tr><td>Phatr3_J23168</td><td>0.658</td><td>Mitochondria</td><td>metabolic process</td><td>Fumarylacetoacetate (FAA) hydrolase family</td><td>fumarylpyruvate hydrolase</td></tr><tr><td>Phatr3_J23365</td><td>0.515</td><td>Mitochondria</td><td>ketone body catabolism</td><td>Coenzyme A transferase</td><td>3-oxoacid CoA-transferase</td></tr><tr><td>Phatr3_J25572</td><td>0.543</td><td>Mitochondria</td><td>oxidation-reduction</td><td>Acyl-CoA dehydrogenase</td><td>glutaryl-CoA dehydrogenase</td></tr><tr><td>Phatr3_J27361</td><td>0.706</td><td>Mitochondria</td><td>transmembrane transport</td><td>Sugar (and other) transporter</td><td>MFS transporter SP family solute carrier family 2 (myo-inositol transporter) member 13</td></tr><tr><td>Phatr3_J45141</td><td>0.596</td><td>Mitochondria</td><td>oxidation-reduction</td><td>6-phosphogluconate dehydrogenase/ hydroxyisobutyrate dehydrogenase</td><td>3-hydroxyisobutyrate dehydrogenase</td></tr><tr><td>Phatr3_J9211</td><td>0.531</td><td>Mitochondria</td><td>metabolic process</td><td>pfkB family carbohydrate kinase; Indigoidine synthase A like protein</td><td>pseudouridylate synthase / pseudouridine kinase</td></tr></tbody></table>

**Fig. S7. Identification of *P. tricornutum* genes coexpressed with Phatr3_J46742 A:** boxplots of mean Spearman correlations of co-regulation of proteins associated with *P. tricornutum* organelle proteomes to Phatr3_J46742 in a meta-analysis of published microarray and transcriptome data (Ashworth et al., 2016; Villar et al., 2024) following the methodology of Liu et al. 2022. Phatr3_J46742 is typically found to be anticorrelated to most core organelle processes, with coregulation coefficients typically lower than -0.25 to most organelle-targeted proteins of annotated function. **B:** details of 17 *P. tricornutum* genes coding for organelle-targeted proteins with (i) functional annotations from combined PFAM and Kofam annotation, and (ii) co-regulation coefficients > 0.5 to MFCc. These notable encode a putative mitochondria-targeted major facilitator superfamily transporter (genelD: Phatr3_J27361) with co-regulation coefficient >0.7.
<figure><img src="image_20.png" /><figcaption>**Fig. S8.** Transcriptional regulation of three related MCFc genes. This figure plots relative fold-changes deemed to be statistically significant (p < 0.05) in a meta-study of published *P. tricomutum* RNAseq data (Villar et al., 2024). Transcriptomic data are grouped by conditions. Briefly, PhatrJ_46612 is downregulated in aureochrome knockout, high light, and nitrate starvation conditions; Phatr3_J42784 is upregulated under short-term illumination in aureochrome knockouts, high light conditions, and phosphate depletion; and Phatr3_J46742 is upregulated under long-term illumination in aureochrome knockouts and nitrate starvation, but downregulated under low CO₂ and phosphate depletion conditions.</figcaption></figure>
<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /><figcaption>Fig. S9. Scatterplots of Phatr3_J46742 metaT/G abundances and simulated NO₃⁻, 5m depth. Phatr3_J46742 meta-transcripts show strong positive correlations to nitrate abundance, where meta-genes show no clear correlation.</figcaption></figure>
<figure><img src="image_29.png" /><figcaption>Fig. S10. Scatterplots of Phatr3_J46742 metaT/G abundances and simulated NH₄⁺, 5m depth. No significant correlations are observed between ammonium and metaT, despite weak positive correlations to metaG abundance.</figcaption></figure>
Acknowledgments

This paper is contribution XXX to Tara Oceans. The authors acknowledge Priscillia Pierre-Elies
(Institut de Biologie de l'Ecole Normale Supérieure) and Pauline Clémente (Lycée ENCPB-
Gilles de Gennes) for aid with the biolistic transformation and the confocal microscopy shown in
**Fig. 3**, and Alban Varona (Lycée ENCPB- Gilles de Gennes) and the staff of the Imagerie
Photonique Paris Seine (I2PS) facility, Institut de Biologie Paris Seine (IBPS), for aid with
spectral deconvolution analyses shown in **Fig. 4**.

References

Agrimi, G., Russo, A., Scarcia, P., et al. (2012) ‘The human gene SLC25A17 encodes a peroxisomal transporter of coenzyme A, FAD and NAD+’, *The Biochemical journal*, 443(1), pp. 241–247.

Agrimi, G., Russo, A., Pierri, C.L., et al. (2012) ‘The peroxisomal NAD+ carrier of Arabidopsis thaliana transports coenzyme A and its derivatives’, *Journal of bioenergetics and biomembranes*, 44(3), pp. 333–340.

Ait-Mohamed, O. et al. (2020) ‘PhaeoNet: A Holistic RNAseq-Based Portrait of Transcriptional Coordination in the Model Diatom’, *Frontiers in plant science*, 11, p. 590949.

Alipanah, L. et al. (2015) ‘Whole-cell response to nitrogen deprivation in the diatom Phaeodactylum tricornutum’, *Journal of experimental botany*, 66(20), pp. 6281–6296.

Ashworth, J. et al. (2016) ‘Pan-transcriptomic analysis identifies coordinated and orthologous functional modules in the diatoms Thalassiosira pseudonana and Phaeodactylum tricornutum’, *Marine genomics*, 26, pp. 21–28.

Ast, M. et al. (2009) ‘Diatom plastids depend on nucleotide import from the cytosol’, *Proceedings of the National Academy of Sciences of the United States of America*, 106(9), pp. 3621–3626.

Balamurugan, S. et al. (2017) ‘Occurrence of plastidial triacylglycerol synthesis and the potential regulatory role of AGPAT in the model diatom’, *Biotechnology for biofuels*, 10, p. 97.

Bendtsen, J.D. et al. (2004) ‘Improved prediction of signal peptides: SignalP 3.0’, *Journal of molecular biology*, 340(4), pp. 783–795.

Busseni, G. et al. (2019) ‘Meta-Omics Reveals Genetic Flexibility of Diatom Nitrogen Transporters in Response to Environmental Changes’, *Molecular biology and evolution*, 36(11), pp. 2522–2535.

Capella-Gutiérrez, S., Silla-Martínez, J.M. and Gabaldón, T. (2009) ‘trimAI: a tool for automated alignment trimming in large-scale phylogenetic analyses’, *Bioinformatics* (*Oxford, England*), 25(15), pp. 1972–1973.

Dawson, P.A. (1973) ‘OBSERVATIONS ON THE STRUCTURE OF SOME FORMS OF
GOMPHONEMA PARVULUM KÜTZ. III. FRUSTULE FORMATION", *Journal of phycology*, 9(4), pp. 353–365.

Diekert, K. et al. (1999) ‘An internal targeting signal directing proteins into the mitochondrial intermembrane space’, *Proceedings of the National Academy of Sciences of the United States of America*, 96(21), pp. 11752–11757.

Edgar, R.C. (2004) ‘MUSCLE: multiple sequence alignment with high accuracy and high throughput’, *Nucleic acids research*, 32(5), pp. 1792–1797.

Falciatore, A. et al. (1999) ‘Transformation of Nonselectable Reporter Genes in Marine Diatoms’, *Marine biotechnology* (New York, N.Y.), 1(3), pp. 239–251.

Fernie, A.R., Cavalcanti, J.H.F. and Nunes-Nesi, A. (2020) ‘Metabolic Roles of Plant Mitochondrial Carriers’, *Biomolecules*, 10(7). Available at: https://doi.org/10.3390/biom10071013.

Fukasawa, Y. et al. (2015) ‘MitoFates: improved prediction of mitochondrial targeting sequences and their cleavage sites’, *Molecular & cellular proteomics : MCP*, 14(4), pp. 1113–1126.

Gile, G.H. et al. (2015) ‘Dual Organellar Targeting of Aminoacyl-tRNA Synthetases in Diatoms and Cryptophytes’, *Genome biology and evolution*, 7(6), pp. 1728–1742.

Giustini, C. et al. (2025) ‘A Mitochondrially Derived Plastidial Transporter Regulates Photosynthesis in the Diatom Phaeodactylum tricornutum’, *bioRxiv*. Available at: https://doi.org/10.1101/2025.02.14.638225.

Grigoriev, I.V. et al. (2021) ‘PhycoCosm, a comparative algal genomics resource’, *Nucleic acids research*, 49(D1), pp. D1004–D1011.

Gruber, A. et al. (2015) ‘Plastid proteome prediction for diatoms and other algae with secondary plastids of the red lineage’, *The Plant journal : for cell and molecular biology*, 81(3), pp. 519–528.

Gruber, A. et al. (2023) ‘Comparison of different versions of SignalP and TargetP for diatom plastid protein predictions with ASAFind’, *arXiv [q-bio.SC]* [Preprint]. Available at: https://doi.org/10.48550/ARXIV.2303.02509.

Gschloessl, B., Guermeur, Y. and Cock, J.M. (2008) ‘HECTAR: a method to predict subcellular targeting in heterokonts’, *BMC bioinformatics*, 9, p. 393.

Hammond, M. et al. (2022) ‘Eukaryotic cellular intricacies shape mitochondrial proteomic complexity’, *BioEssays : news and reviews in molecular, cellular and developmental biology*, 44(5), p. e2100258.

Huang, T. et al. (2024) ‘Proteomes reveal the lipid metabolic network in the complex plastid of Phaeodactylum tricornutum’, *The Plant journal : for cell and molecular biology*, 117(2), pp. 385–403.

Huelsenbeck, J.P. and Ronquist, F. (2001) ‘MRBAYES: Bayesian inference of phylogenetic trees’, *Bioinformatics* (Oxford, England), 17(8), pp. 754–755.

Ito, K., Matsukawa, K. and Kato, Y. (2006) ‘Functional analysis of skunk cabbage SfUCPB, a
unique uncoupling protein lacking the fifth transmembrane domain, in yeast cells', *Biochemical and biophysical research communications*, 349(1), pp. 383-390.

Jin, Z. et al. (2023) ‘KEGG tools for classification and analysis of viral proteins’, *Protein science : a publication of the Protein Society*, 32(12), p. e4820.

Jumper, J. et al. (2021) ‘Highly accurate protein structure prediction with AlphaFold’, *Nature*, 596(7873), pp. 583–589.

Kanehisa, M. et al. (2024) ‘KEGG: biological systems database as a model of the real world’, *Nucleic acids research* [Preprint]. Available at: https://doi.org/10.1093/nar/gkae909.

Katoh, K., Rozewicki, J. and Yamada, K.D. (2019) ‘MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization’, *Briefings in bioinformatics*, 20(4), pp. 1160–1166.

Kearse, M. et al. (2012) ‘Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data’, *Bioinformatics* (Oxford, England), 28(12), pp. 1647–1649.

Keeling, P.J. et al. (2014) ‘The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing’, *PLoS biology*, 12(6), p. e1001889.

Krogh, A. et al. (2001) ‘Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes’, *Journal of molecular biology*, 305(3), pp. 567–580.

Kroth, P.G. et al. (2008) ‘A model for carbohydrate metabolism in the diatom Phaeodactylum tricornutum deduced from comparative whole genome analysis’, *PloS one*, 3(1), p. e1426.

Kuan, J. and Saier, M.H., Jr (1993) ‘The mitochondrial carrier family of transport proteins: structural, functional, and evolutionary relationships’, *Critical reviews in biochemistry and molecular biology*, 28(3), pp. 209–233.

Kunji, E.R.S. (2004) ‘The role and structure of mitochondrial carriers’, *FEBS letters*, 564(3), pp. 239–244.

Lam, F. et al. (2017) ‘Super-resolution for everybody: An image processing workflow to obtain high-resolution images with a standard confocal microscope’, *Methods (San Diego, Calif.)*, 115, pp. 17–27.

Lee, C.M. et al. (1999) ‘The DNA helicase, Hmi1p, is transported into mitochondria by a C-terminal cleavable targeting signal’, *The Journal of biological chemistry*, 274(30), pp. 20937–20942.

Li, J. et al. (2022) ‘Unsuspected functions of alkaline phosphatase PhoD in the diatom Phaeodactylum tricornutum’, *Algal research*, 68(102873), p. 102873.

Liu, S. et al. (2022) ‘A metabolic, phylogenomic and environmental atlas of diatom plastid transporters from the model species’, *Frontiers in plant science*, 13, p. 950467.

Liu, X. et al. (2016) ‘Addressing various compartments of the diatom model organism Phaeodactylum tricornutum via sub-cellular marker proteins’, *Algal research*, 20, pp. 249–257.
Mann, M. et al. (2020) ‘The Aureochrome Photoreceptor PtAUREO1a Is a Highly Effective Blue Light Switch in Diatoms’, *iScience*, 23(11), p. 101730.

McCarthy, J.K. et al. (2017) ‘Nitrate Reductase Knockout Uncouples Nitrate Transport from Nitrate Assimilation and Drives Repartitioning of Carbon Flux in a Model Pennate Diatom’, *The Plant cell*, 29(8), pp. 2047–2070.

Miller, M.A., Pfeiffer, W. and Schwartz, T. (2010) ‘Creating the CIPRES Science Gateway for inference of large phylogenetic trees’, in *2010 Gateway Computing Environments Workshop (GCE). 2010 Gateway Computing Environments Workshop (GCE)*, IEEE. Available at: https://doi.org/10.1109/gce.2010.5676129.

Miyake, S.-I. et al. (2002) ‘Identification and characterization of a novel mitochondrial tricarboxylate carrier’, *Biochemical and biophysical research communications*, 295(2), pp. 463–468.

Monné, M. and Palmieri, F. (2014) ‘Antiporters of the mitochondrial carrier family’, *Current topics in membranes*, 73, pp. 289–320.

Moog, D. et al. (2020) ‘Substrate specificity of plastid phosphate transporters in a non-photosynthetic diatom and its implication in evolution of red alga-derived complex plastids’, *Scientific reports*, 10(1), p. 1167.

More, K.J. et al. (2024) ‘Evolutionary origins of the lysosome-related organelle sorting machinery reveal ancient homology in post-endosome trafficking pathways’, *Proceedings of the National Academy of Sciences of the United States of America*, 121(43), p. e2403601121.

Murik, O. et al. (2019) ‘Downregulation of mitochondrial alternative oxidase affects chloroplast function, redox status and stress response in a marine diatom’, *The New phytologist*, 221(3), pp. 1303–1316.

Neckelmann, N. et al. (1987) ‘cDNA sequence of a human skeletal muscle ADP/ATP translocator: lack of a leader peptide, divergence from a fibroblast translocator cDNA, and coevolution with mitochondrial DNA genes’, *Proceedings of the National Academy of Sciences of the United States of America*, 84(21), pp. 7580–7584.

Novák Vanclová, A.M.G. and Dorrell, R.G. (2024) ‘Complex plastids across the eukaryotes: An overview of inherited and convergently evolved characters’, in *Endosymbiotic Organelle Acquisition*. Cham: Springer International Publishing, pp. 39–88.

Nunes-Nesi, A., Cavalcanti, J.H.F. and Fernie, A.R. (2020) ‘Characterization of in vivo function(s) of members of the plant mitochondrial carrier family’, *Biomolecules*, 10(9), p. 1226.

Palmieri, F. (2004) ‘The mitochondrial transporter family (SLC25): physiological and pathological implications’, *Pflugers Archiv : European journal of physiology*, 447(5), pp. 689–709.

Palmieri, F. et al. (2009) ‘Molecular identification and functional characterization of Arabidopsis thaliana mitochondrial and chloroplastic NAD+ carrier proteins’, *The Journal of biological chemistry*, 284(45), pp. 31249–31259.

Palmieri, F. et al. (2011) ‘Evolution, structure and function of mitochondrial carriers: a review with new insights’, *The Plant journal : for cell and molecular biology*, 66(1), pp. 161–181.
Palmieri, L. et al. (2001) ‘Identification and functional reconstitution of the yeast peroxisomal
adenine nucleotide transporter’, *The EMBO journal*, 20(18), pp. 5049–5059.

Penot-Raquin, M. et al. (2024) ‘Prevalence and environmental abundance of the elusive membrane trafficking complex TSET in five cosmopolitan eukaryotic groups’, *bioRxiv*. Available at: https://doi.org/10.1101/2024.06.15.599112.

Pesant, S. et al. (2015) ‘Open science resources for the discovery and analysis of Tara Oceans data’, *Scientific data*, 2, p. 150023.

Remmers, I.M. et al. (2018) ‘Orchestration of transcriptome, proteome and metabolome in the diatom Phaeodactylum tricornutum during nitrogen limitation’, *Algal research*, 35, pp. 33–49.

Richards, T.A. et al. (2024) ‘Reconstructing the last common ancestor of all eukaryotes’, *PLoS biology*, 22(11), p. e3002917.

Ruprecht, J.J. and Kunji, E.R.S. (2020) ‘The SLC25 Mitochondrial Carrier Family: Structure and Mechanism’, *Trends in biochemical sciences*, 45(3), pp. 244–258.

Russo, M.T. et al. (2015) ‘The upstream regulatory sequence of the light harvesting complex Lhcf2 gene of the marine diatom Phaeodactylum tricornutum enhances transcription in an orientation- and distance-independent fashion’, *Marine genomics*, 24 Pt 1, pp. 69–79.

Saier, M.H., Jr, Tran, C.V. and Barabote, R.D. (2006) ‘TCDB: the Transporter Classification Database for membrane transport protein analyses and information’, *Nucleic acids research*, 34 part 6 (Database issue), pp. D181–6.

Schober, A.F. et al. (2019) ‘Organelle Studies and Proteome Analyses of Mitochondria and Plastids Fractions from the Diatom Thalassiosira pseudonana’, *Plant & cell physiology*, 60(8), pp. 1811–1828.

Serôdio, J. and Lavaud, J. (2020) ‘Diatoms and their ecological importance’, in *Encyclopedia of the UN Sustainable Development Goals*. Cham: Springer International Publishing, pp. 1–9.

Shu, H. et al. (2022) ‘Transcriptomic-Guided Phosphonate Utilization Analysis Unveils Evidence of Clathrin-Mediated Endocytosis and Phospholipid Synthesis in the Model Diatom’, *mSystems*, 7(6), p. e0056322.

Siaut, M. et al. (2007) ‘Molecular toolbox for studying diatom biology in Phaeodactylum tricornutum’, *Gene*, 406(1-2), pp. 23–35.

Smith, S.R. et al. (2019) ‘Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom’, *Nature communications*, 10(1), p. 4552.

Stamatakis, A. (2014) ‘RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies’, *Bioinformatics* (Oxford, England), 30(9), pp. 1312–1313.

Suzek, B.E. et al. (2015) ‘UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches’, *Bioinformatics* (Oxford, England), 31(6), pp. 926–932.

Tanaka, A. et al. (2015) ‘Ultrastructure and Membrane Traffic During Cell Division in the Marine Pennate Diatom Phaeodactylum tricornutum’, *Protist*, 166(5), pp. 506–521.
Toleco, M.R. et al. (2020) ‘Plant Mitochondrial Carriers: Molecular Gatekeepers That Help to Regulate Plant Central Carbon Metabolism’, *Plants* (Basel, Switzerland), 9(1). Available at: https://doi.org/10.3390/plants9010117.

Ustick, L.J. et al. (2021) ‘Metagenomic analysis reveals global-scale patterns of ocean nutrient limitation’, *Science* (New York, N.Y.), 372(6539), pp. 287–291.

Uwizeye, C. et al. (2021) ‘Morphological bases of phytoplankton energy management and physiological responses unveiled by 3D subcellular imaging’, *Nature communications*, 12(1), p. 1049.

Villar, E. et al. (2024) ‘DiatOmicBase, a gene-centered platform to mine functional omics data across diatom genomes’, *bioRxiv*. Available at: https://doi.org/10.1101/2024.09.12.612655.

Villar, E. et al. (2025) ‘DiatOmicBase: a versatile gene-centered platform for mining functional omics data in diatom research’, *The Plant journal : for cell and molecular biology*, 121(6), p. e70061.